From c544fceea0a3cbbb3ee894e71131f44ad7602463 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 8 Aug 2022 11:34:53 +0200
Subject: [PATCH 001/410] Support lazy loading of annotation columns in
 differential assembly

---
 metagraph/integration_tests/test_assemble.py  |  9 +-
 .../column_compressed_lazy.hpp                | 51 +++++++++++
 metagraph/src/cli/assemble.cpp                | 24 +++++-
 metagraph/src/cli/config/config.cpp           |  1 +
 .../src/cli/load/load_annotated_graph.cpp     | 45 ++++++----
 .../src/graph/annotated_graph_algorithm.cpp   | 84 +++++++++++--------
 6 files changed, 161 insertions(+), 53 deletions(-)
 create mode 100644 metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp

diff --git a/metagraph/integration_tests/test_assemble.py b/metagraph/integration_tests/test_assemble.py
index a4a6ae4c30..0c9eb7f67f 100644
--- a/metagraph/integration_tests/test_assemble.py
+++ b/metagraph/integration_tests/test_assemble.py
@@ -42,6 +42,7 @@
 }
 
 GFAs = [name for name, _ in gfa_tests.items()]
+LOAD_TYPES = ['load', 'stream']
 
 NUM_THREADS = 4
 
@@ -266,11 +267,13 @@ def check_suffix(anno_repr, suffix):
             no_anchor_opt
         )
 
-    def test_diff_assembly(self):
+    @parameterized.expand(LOAD_TYPES)
+    def test_diff_assembly(self, load_type):
         assemble_command = f'{METAGRAPH} assemble -p {NUM_THREADS} \
                 -a {self.tempdir.name}/annotation{anno_file_extension[self.anno_repr]} \
                 -o {self.tempdir.name}/diff_contigs \
                 --diff-assembly-rules {TEST_DATA_DIR}/example.diff.json \
+                {"--separately" if load_type == "stream" else ""} \
                 {self.tempdir.name}/graph{graph_file_extension[self.graph_repr]}'
         res = subprocess.run([assemble_command], shell=True)
         self.assertEqual(res.returncode, 0)
@@ -296,11 +299,13 @@ def test_diff_assembly(self):
         self.assertEqual(results['>metasub_by_kmer'][0], 'CTTGGATCACACTCTTCTCAGAGCCCAGGCCAGGGGCCCCCAAGAAAGGCTCTGGTGGAGAACCTGTGCATGAAGGCTGTCAACCAGTCCATAGGCAGGGCCATCAGGCACCAAAGGGATTCTGCCAGCATAGTGCTCCTGGACCAGTGATACACCCGGCACCCTGTCCTGGACATGCTGTTGGCCTGGATCTGAGCCCTCGTGGAGGTCAAAGCCACCTTTGGTTCTGCCATTGCTGCTGTGTGGAAGTTCACTCAAGTAGGCCTCTTCCTG')
         self.assertEqual(results['>metasub_sym_diff'][0], 'TGGAAGTTCACTCAAGTAGGCCTCTTCCTGACAGGCAGCTGCACCACTGCCTGGCGCTGTGCCCTTCCTTTGCTCTGCCCGCTGGAGACGGTGTTTGTCATGGGCCTGGTCTGCAGG')
 
-    def test_diff_assembly_simple(self):
+    @parameterized.expand(LOAD_TYPES)
+    def test_diff_assembly_simple(self, load_type):
         assemble_command = f'{METAGRAPH} assemble -p {NUM_THREADS} \
                 -a {self.tempdir.name}/annotation{anno_file_extension[self.anno_repr]} \
                 -o {self.tempdir.name}/diff_contigs \
                 --diff-assembly-rules {TEST_DATA_DIR}/example_simple.diff.json \
+                {"--separately" if load_type == "stream" else ""} \
                 {self.tempdir.name}/graph{graph_file_extension[self.graph_repr]}'
         res = subprocess.run([assemble_command], shell=True)
         self.assertEqual(res.returncode, 0)
diff --git a/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp b/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
new file mode 100644
index 0000000000..9b8aa61e50
--- /dev/null
+++ b/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
@@ -0,0 +1,51 @@
+#ifndef __COLUMN_COMPRESSED_LAZY_HPP__
+#define __COLUMN_COMPRESSED_LAZY_HPP__
+
+
+#include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
+
+namespace mtg {
+namespace annot {
+
+template <typename Label = std::string>
+class ColumnCompressedLazy : public MultiLabelEncoded<Label> {
+  public:
+    typedef ColumnCompressed<Label> BaseAnnotator;
+    typedef typename BaseAnnotator::ColumnCallback ColumnCallback;
+    using Index = typename MultiLabelEncoded<Label>::Index;
+    using VLabels = typename MultiLabelEncoded<Label>::VLabels;
+
+    ColumnCompressedLazy(size_t num_objects, const std::vector<std::string> &files)
+          : num_objects_(num_objects), files_(files) {}
+
+    uint64_t num_objects() const override final { return num_objects_; }
+
+    void call_columns(const ColumnCallback &callback, size_t num_threads = 1) const {
+        BaseAnnotator::merge_load(files_, callback, num_threads);
+    }
+
+    const std::string& get_file(size_t i) const { return files_[i]; }
+
+    void set(Index, const VLabels &) override final { throw std::runtime_error("set not implemented"); }
+    void serialize(const std::string &) const override final { throw std::runtime_error("serialize not implemented"); }
+    bool load(const std::string &) override final { throw std::runtime_error("load not implemented"); }
+    void add_labels(const std::vector<Index> &, const VLabels &) override final { throw std::runtime_error("add_labels not implemented"); }
+    bool has_label(Index, const Label &) const override final { throw std::runtime_error("has_label not implemented"); }
+    bool has_labels(Index, const VLabels &) const override final { throw std::runtime_error("has_labels not implemented"); }
+    void insert_rows(const std::vector<Index> &) override final { throw std::runtime_error("insert_rows not implemented"); }
+    void rename_labels(const tsl::hopscotch_map<Label, Label> &) override final { throw std::runtime_error("rename_labels not implemented"); }
+    size_t num_labels() const override final { throw std::runtime_error("num_labels not implemented"); }
+    uint64_t num_relations() const override final { throw std::runtime_error("num_relations not implemented"); }
+    void call_objects(const Label &, std::function<void(Index)>) const override final { throw std::runtime_error("call_objects not implemented"); }
+    std::string file_extension() const override final { throw std::runtime_error("file_extension not implemented"); }
+    const binmat::BinaryMatrix& get_matrix() const override final { throw std::runtime_error("get_matrix not implemented"); }
+
+  private:
+    size_t num_objects_;
+    const std::vector<std::string> &files_;
+};
+
+} // namespace annot
+} // namespace mtg
+
+#endif // __COLUMN_COMPRESSED_LAZY_HPP__
diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 063ec950b5..e5cbfa3276 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -14,6 +14,7 @@
 #include "load/load_annotated_graph.hpp"
 #include "graph/annotated_graph_algorithm.hpp"
 #include "graph/representation/masked_graph.hpp"
+#include "annotation/representation/column_compressed/column_compressed_lazy.hpp"
 
 
 namespace mtg {
@@ -28,6 +29,11 @@ using mtg::graph::DifferentialAssemblyConfig;
 
 void check_labels(const tsl::hopscotch_set<std::string> &label_set,
                   const AnnotatedDBG &anno_graph) {
+    if (dynamic_cast<const annot::ColumnCompressedLazy<>*>(&anno_graph.get_annotator())) {
+        logger->trace("Skipping label check for streaming annotator");
+        return;
+    }
+
     bool detected_missing_labels = false;
     for (const std::string &label : label_set) {
         if (!anno_graph.label_exists(label)) {
@@ -76,6 +82,7 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
         throw std::iostream::failure("Failed to read assembly config JSON from " + config->assembly_config_file);
 
     size_t num_threads = std::max(1u, get_num_threads());
+    bool streaming = dynamic_cast<const annot::ColumnCompressedLazy<>*>(&anno_graph.get_annotator());
 
     Json::Value diff_json;
     fin >> diff_json;
@@ -110,6 +117,15 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
                 experiment, anno_graph.get_graph()
             );
 
+            std::string exp_name = experiment["name"].asString()
+                                    + (config->enumerate_out_sequences ? "." : "");
+
+            if (streaming && diff_config.label_mask_other_unitig_fraction != 1.0) {
+                logger->error("'unitig_other_max_fraction' not supported with --separately, skipping assembly: {}",
+                              exp_name);
+                continue;
+            }
+
             foreground_labels.clear();
             background_labels.clear();
 
@@ -124,8 +140,12 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
             check_labels(foreground_labels, anno_graph);
             check_labels(background_labels, anno_graph);
 
-            std::string exp_name = experiment["name"].asString()
-                                    + (config->enumerate_out_sequences ? "." : "");
+            if (streaming
+                    && (shared_foreground_labels.size() || shared_background_labels.size())) {
+                logger->error("'shared_labels' not supported with --separately, skipping assembly: {}",
+                              exp_name);
+                continue;
+            }
 
             logger->trace("Running assembly: {}", exp_name);
 
diff --git a/metagraph/src/cli/config/config.cpp b/metagraph/src/cli/config/config.cpp
index 684eb650d9..b7c56fbb26 100644
--- a/metagraph/src/cli/config/config.cpp
+++ b/metagraph/src/cli/config/config.cpp
@@ -1126,6 +1126,7 @@ if (advanced) {
             fprintf(stderr, "\t-a --annotator [STR] \t\tannotator to load []\n");
             fprintf(stderr, "\t   --diff-assembly-rules [STR] \tJSON file describing labels to mask in and out and their relative fractions []\n");
             fprintf(stderr, "\t                       \t\tSee the manual for the specification.\n");
+            fprintf(stderr, "\t   --separately \t\tstream annotator (only compatible with column annotator) [off]\n");
         } break;
         case STATS: {
             fprintf(stderr, "Usage: %s stats [options] GRAPH1 [[GRAPH2] ...]\n\n", prog_name.c_str());
diff --git a/metagraph/src/cli/load/load_annotated_graph.cpp b/metagraph/src/cli/load/load_annotated_graph.cpp
index 14163d8434..1563460ccc 100644
--- a/metagraph/src/cli/load/load_annotated_graph.cpp
+++ b/metagraph/src/cli/load/load_annotated_graph.cpp
@@ -5,6 +5,7 @@
 #include "annotation/binary_matrix/row_diff/row_diff.hpp"
 #include "annotation/binary_matrix/row_sparse/row_sparse.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
+#include "annotation/representation/column_compressed/column_compressed_lazy.hpp"
 #include "graph/representation/canonical_dbg.hpp"
 #include "graph/annotated_dbg.hpp"
 #include "common/logger.hpp"
@@ -37,8 +38,17 @@ std::unique_ptr<AnnotatedDBG> initialize_annotated_dbg(std::shared_ptr<DeBruijnG
 
     if (config.infbase_annotators.size()) {
         bool loaded = false;
+        bool streamed = false;
         if (auto *cc = dynamic_cast<annot::ColumnCompressed<>*>(annotation_temp.get())) {
-            loaded = cc->merge_load(config.infbase_annotators);
+            if (config.identity == Config::ASSEMBLE && config.separately) {
+                annotation_temp.reset(new annot::ColumnCompressedLazy<>(
+                    max_index, config.infbase_annotators
+                ));
+                loaded = true;
+                streamed = true;
+            } else {
+                loaded = cc->merge_load(config.infbase_annotators);
+            }
         } else {
             if (config.infbase_annotators.size() > 1) {
                 logger->warn("Cannot merge annotations of this type. Only the first"
@@ -53,20 +63,22 @@ std::unique_ptr<AnnotatedDBG> initialize_annotated_dbg(std::shared_ptr<DeBruijnG
         }
 
         // row_diff annotation is special, as it must know the graph structure
-        using namespace annot::binmat;
-        BinaryMatrix &matrix = const_cast<BinaryMatrix &>(annotation_temp->get_matrix());
-        if (IRowDiff *row_diff = dynamic_cast<IRowDiff*>(&matrix)) {
-            if (!dbg_graph) {
-                logger->error("Only succinct de Bruijn graph representations"
-                              " are supported for row-diff annotations");
-                std::exit(1);
-            }
-
-            row_diff->set_graph(dbg_graph);
-
-            if (auto *row_diff_column = dynamic_cast<RowDiff<ColumnMajor> *>(&matrix)) {
-                row_diff_column->load_anchor(config.infbase + kRowDiffAnchorExt);
-                row_diff_column->load_fork_succ(config.infbase + kRowDiffForkSuccExt);
+        if (!streamed) {
+            using namespace annot::binmat;
+            BinaryMatrix &matrix = const_cast<BinaryMatrix &>(annotation_temp->get_matrix());
+            if (IRowDiff *row_diff = dynamic_cast<IRowDiff*>(&matrix)) {
+                if (!dbg_graph) {
+                    logger->error("Only succinct de Bruijn graph representations"
+                                  " are supported for row-diff annotations");
+                    std::exit(1);
+                }
+
+                row_diff->set_graph(dbg_graph);
+
+                if (auto *row_diff_column = dynamic_cast<RowDiff<ColumnMajor> *>(&matrix)) {
+                    row_diff_column->load_anchor(config.infbase + kRowDiffAnchorExt);
+                    row_diff_column->load_fork_succ(config.infbase + kRowDiffForkSuccExt);
+                }
             }
         }
     }
@@ -75,7 +87,8 @@ std::unique_ptr<AnnotatedDBG> initialize_annotated_dbg(std::shared_ptr<DeBruijnG
     auto anno_graph
             = std::make_unique<AnnotatedDBG>(std::move(graph), std::move(annotation_temp));
 
-    if (!anno_graph->check_compatibility()) {
+    if (!dynamic_cast<const annot::ColumnCompressedLazy<>*>(&anno_graph->get_annotator())
+            && !anno_graph->check_compatibility()) {
         logger->error("Graph and annotation are not compatible");
         exit(1);
     }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 60dc814919..6959393836 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -4,6 +4,7 @@
 #include "common/vectors/vector_algorithm.hpp"
 #include "common/vectors/bitmap.hpp"
 #include "graph/representation/masked_graph.hpp"
+#include "annotation/representation/column_compressed/column_compressed_lazy.hpp"
 
 
 namespace mtg {
@@ -67,7 +68,6 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     const DifferentialAssemblyConfig &config,
                     size_t num_threads) {
     bool parallel = num_threads > 1;
-    size_t num_labels = anno_graph.get_annotator().num_labels();
     size_t num_in_labels = labels_in.size() + labels_in_round2.size();
     size_t num_out_labels = labels_out.size() + labels_out_round2.size();
     auto graph_ptr = std::dynamic_pointer_cast<const DeBruijnGraph>(
@@ -125,6 +125,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
         };
 
         logger->trace("Checking shared and other labels");
+        size_t num_labels = anno_graph.get_annotator().num_labels();
         masked_graph->call_sequences([&](const std::string &contig, const std::vector<node_index> &path) {
             for (const auto &[label, sig] : anno_graph.get_top_label_signatures(contig, num_labels)) {
                 bool found_in = labels_in.count(label);
@@ -309,42 +310,59 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
     // the in and out counts are stored interleaved
     sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16);
 
-    const auto &label_encoder = anno_graph.get_annotator().get_label_encoder();
-    const auto &binmat = anno_graph.get_annotator().get_matrix();
-
-    tsl::hopscotch_map<uint64_t, uint8_t> code_to_indicator;
-    for (const std::string &label_in : labels_in) {
-        code_to_indicator[label_encoder.encode(label_in)] = 1;
-    }
-    for (const std::string &label_out : labels_out) {
-        code_to_indicator[label_encoder.encode(label_out)] |= 2;
-    }
-
-    std::vector<uint64_t> label_codes;
-    label_codes.reserve(code_to_indicator.size());
-    for (const auto &[code, indicator] : code_to_indicator) {
-        label_codes.push_back(code);
-    }
-
+    const auto &annotator = anno_graph.get_annotator();
     std::mutex vector_backup_mutex;
     std::atomic_thread_fence(std::memory_order_release);
     bool parallel = num_threads > 1;
 
-    binmat.call_columns(label_codes,
-        [&](auto col_idx, const bitmap &rows) {
-            uint8_t col_indicator = code_to_indicator[label_codes[col_idx]];
-            rows.call_ones([&](auto r) {
-                node_index i = AnnotatedDBG::anno_to_graph_index(r);
-                set_bit(indicator.data(), i, parallel, MO_RELAXED);
-                if (col_indicator & 1)
-                    atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
-
-                if (col_indicator & 2)
-                    atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
-            });
-        },
-        num_threads
-    );
+    auto make_index_callback = [&](uint8_t col_indicator) {
+        return [&](auto r) {
+            node_index i = AnnotatedDBG::anno_to_graph_index(r);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED);
+            if (col_indicator & 1)
+                atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
+
+            if (col_indicator & 2)
+                atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+        };
+    };
+
+    if (const auto *ccl = dynamic_cast<const annot::ColumnCompressedLazy<>*>(&annotator)) {
+        ccl->call_columns([&](size_t, const std::string &label, auto&& column) {
+            uint8_t col_indicator = 0;
+            if (labels_in.count(label))
+                col_indicator = 1;
+
+            if (labels_out.count(label))
+                col_indicator |= 2;
+
+            column->call_ones(make_index_callback(col_indicator));
+        });
+    } else {
+        const auto &label_encoder = annotator.get_label_encoder();
+        const auto &binmat = annotator.get_matrix();
+
+        tsl::hopscotch_map<uint64_t, uint8_t> code_to_indicator;
+        for (const std::string &label_in : labels_in) {
+            code_to_indicator[label_encoder.encode(label_in)] = 1;
+        }
+        for (const std::string &label_out : labels_out) {
+            code_to_indicator[label_encoder.encode(label_out)] |= 2;
+        }
+
+        std::vector<uint64_t> label_codes;
+        label_codes.reserve(code_to_indicator.size());
+        for (const auto &[code, indicator] : code_to_indicator) {
+            label_codes.push_back(code);
+        }
+
+        binmat.call_columns(label_codes,
+            [&](auto col_idx, const bitmap &rows) {
+                rows.call_ones(make_index_callback(code_to_indicator[label_codes[col_idx]]));
+            },
+            num_threads
+        );
+    }
 
     std::atomic_thread_fence(std::memory_order_acquire);
 

From 2f0073c34365cd6aa3405e5632ad67c3ba8a2824 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 8 Aug 2022 11:50:47 +0200
Subject: [PATCH 002/410] fix

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6959393836..02121d5e1d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -316,6 +316,7 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
     bool parallel = num_threads > 1;
 
     auto make_index_callback = [&](uint8_t col_indicator) {
+        assert(col_indicator);
         return [&](auto r) {
             node_index i = AnnotatedDBG::anno_to_graph_index(r);
             set_bit(indicator.data(), i, parallel, MO_RELAXED);
@@ -336,7 +337,8 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
             if (labels_out.count(label))
                 col_indicator |= 2;
 
-            column->call_ones(make_index_callback(col_indicator));
+            if (col_indicator)
+                column->call_ones(make_index_callback(col_indicator));
         });
     } else {
         const auto &label_encoder = annotator.get_label_encoder();

From 6e62c3dcf753cd83d68e344d455252965eadf50c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 8 Aug 2022 12:11:07 +0200
Subject: [PATCH 003/410] minor fix

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 02121d5e1d..d6c028083b 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -317,7 +317,7 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
 
     auto make_index_callback = [&](uint8_t col_indicator) {
         assert(col_indicator);
-        return [&](auto r) {
+        return [&indicator,&counts,&vector_backup_mutex,parallel,col_indicator](auto r) {
             node_index i = AnnotatedDBG::anno_to_graph_index(r);
             set_bit(indicator.data(), i, parallel, MO_RELAXED);
             if (col_indicator & 1)

From a9571c410f8bf6e72fa38acb8c72cb866fbf7420 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 9 Aug 2022 16:33:27 +0200
Subject: [PATCH 004/410] patch

---
 metagraph/src/annotation/row_diff_builder.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/annotation/row_diff_builder.cpp b/metagraph/src/annotation/row_diff_builder.cpp
index f10328c8b9..c31bdc93ea 100644
--- a/metagraph/src/annotation/row_diff_builder.cpp
+++ b/metagraph/src/annotation/row_diff_builder.cpp
@@ -1015,7 +1015,7 @@ void convert_batch_to_row_diff(const std::string &pred_succ_fprefix,
                 uint64_t r = 0;
                 set_rows[l_idx][j].for_each([&](const auto &v) {
                     call(utils::get_first(v));
-                    if constexpr(with_values) {
+                    if constexpr(utils::is_pair_v<T>) {
                         assert(v.second && "zero diffs must have been skipped");
                         values[l_idx][j][r++] = matrix::encode_diff(v.second);
                     }

From 069dacaa0e56e4737222325efde97550d22e9b08 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 9 Aug 2022 16:51:45 +0200
Subject: [PATCH 005/410] Workaround for bug in g++-8.2

This reverts commit f2fb5beadd2150ee81b4424c67dcb1f0ec609571.
---
 Makefile                                      | 2 +-
 metagraph/src/annotation/row_diff_builder.cpp | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 2d776ae6a0..3bd8afdc6e 100644
--- a/Makefile
+++ b/Makefile
@@ -50,7 +50,7 @@ ifeq ($(env), docker)
  	$(shell mkdir -p $(BUILD_DIR_HOST_DOCKER) $(BUILD_DIR_STATIC_HOST_DOCKER) )
 	EXEC_CMD := $(DOCKER_BASE_CMD) bash -c
 else
-	EXEC_CMD := bash -l -c
+	EXEC_CMD := bash -c
 endif
 
 all: update build test
diff --git a/metagraph/src/annotation/row_diff_builder.cpp b/metagraph/src/annotation/row_diff_builder.cpp
index f10328c8b9..9a6979f0fe 100644
--- a/metagraph/src/annotation/row_diff_builder.cpp
+++ b/metagraph/src/annotation/row_diff_builder.cpp
@@ -1015,7 +1015,9 @@ void convert_batch_to_row_diff(const std::string &pred_succ_fprefix,
                 uint64_t r = 0;
                 set_rows[l_idx][j].for_each([&](const auto &v) {
                     call(utils::get_first(v));
-                    if constexpr(with_values) {
+                    // TODO: this should be constexpr(with_values), but due to a bug
+                    //       in g++-8.2, that doesn't compile
+                    if constexpr(utils::is_pair_v<T>) {
                         assert(v.second && "zero diffs must have been skipped");
                         values[l_idx][j][r++] = matrix::encode_diff(v.second);
                     }

From 1d14f5e185474acae5e7c8c82bc5587b4c33170f Mon Sep 17 00:00:00 2001
From: Harun Mustafa <hmusta@users.noreply.github.com>
Date: Tue, 9 Aug 2022 18:41:39 +0200
Subject: [PATCH 006/410] Support streaming of annotation columns in
 differential assembly (#424)

---
 .../representation/base/annotation.hpp        | 27 +++++-
 .../column_compressed_lazy.hpp                | 46 ++++++----
 metagraph/src/cli/assemble.cpp                | 21 ++---
 .../src/cli/load/load_annotated_graph.cpp     | 23 +++--
 metagraph/src/common/vectors/bitmap.cpp       |  7 ++
 metagraph/src/common/vectors/bitmap.hpp       |  5 +-
 metagraph/src/graph/annotated_dbg.cpp         | 31 ++++---
 metagraph/src/graph/annotated_dbg.hpp         | 23 +++--
 .../src/graph/annotated_graph_algorithm.cpp   | 91 ++++++++-----------
 .../tests/annotation/test_annotated_dbg.cpp   | 19 ++--
 10 files changed, 168 insertions(+), 125 deletions(-)

diff --git a/metagraph/src/annotation/representation/base/annotation.hpp b/metagraph/src/annotation/representation/base/annotation.hpp
index 7b555c4d16..a26d0717da 100644
--- a/metagraph/src/annotation/representation/base/annotation.hpp
+++ b/metagraph/src/annotation/representation/base/annotation.hpp
@@ -17,9 +17,21 @@
 namespace mtg {
 namespace annot {
 
+template <typename IndexType, typename LabelType>
+class Annotation {
+  public:
+    typedef std::function<void(size_t, const bitmap&)> LabelIndexCallback;
+    virtual ~Annotation() {}
+
+    virtual uint64_t num_objects() const = 0;
+
+    virtual void call_label_objects(const LabelType &labels,
+                                    const LabelIndexCallback &callback,
+                                    size_t num_threads = 1) const = 0;
+};
 
 template <typename IndexType, typename LabelType>
-class AnnotationCategory {
+class AnnotationCategory : public Annotation<IndexType, LabelType> {
   public:
     virtual ~AnnotationCategory() {}
 
@@ -71,7 +83,6 @@ class MultiLabelAnnotation
 
     /************************* Properties *************************/
 
-    virtual uint64_t num_objects() const = 0;
     virtual size_t num_labels() const = 0;
     virtual uint64_t num_relations() const = 0;
     virtual const std::vector<Label>& get_all_labels() const = 0;
@@ -134,6 +145,7 @@ class MultiLabelEncoded : public MultiLabelAnnotation<uint64_t, LabelType> {
     using Index = typename MultiLabelAnnotation<uint64_t, LabelType>::Index;
     using Label = typename MultiLabelAnnotation<uint64_t, LabelType>::Label;
     using VLabels = typename MultiLabelAnnotation<uint64_t, LabelType>::VLabels;
+    using LabelIndexCallback = typename Annotation<uint64_t, VLabels>::LabelIndexCallback;
 
     virtual ~MultiLabelEncoded() {}
 
@@ -170,6 +182,17 @@ class MultiLabelEncoded : public MultiLabelAnnotation<uint64_t, LabelType> {
 
     virtual const binmat::BinaryMatrix& get_matrix() const = 0;
 
+    virtual void call_label_objects(const VLabels &labels,
+                                    const LabelIndexCallback &callback,
+                                    size_t num_threads = 1) const override final {
+        std::vector<binmat::BinaryMatrix::Column> columns;
+        columns.reserve(labels.size());
+        for (const Label &label : labels) {
+            columns.emplace_back(label_encoder_.encode(label));
+        }
+        get_matrix().call_columns(columns, callback, num_threads);
+    }
+
   protected:
     LabelEncoder<Label> label_encoder_;
 };
diff --git a/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp b/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
index 9b8aa61e50..70ef084cb9 100644
--- a/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
+++ b/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
@@ -1,6 +1,7 @@
 #ifndef __COLUMN_COMPRESSED_LAZY_HPP__
 #define __COLUMN_COMPRESSED_LAZY_HPP__
 
+#include <tsl/hopscotch_map.h>
 
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 
@@ -8,38 +9,45 @@ namespace mtg {
 namespace annot {
 
 template <typename Label = std::string>
-class ColumnCompressedLazy : public MultiLabelEncoded<Label> {
+class ColumnCompressedLazy
+      : public Annotation<typename MultiLabelEncoded<Label>::Index,
+                          typename MultiLabelEncoded<Label>::VLabels> {
   public:
-    typedef ColumnCompressed<Label> BaseAnnotator;
-    typedef typename BaseAnnotator::ColumnCallback ColumnCallback;
     using Index = typename MultiLabelEncoded<Label>::Index;
     using VLabels = typename MultiLabelEncoded<Label>::VLabels;
+    using LabelIndexCallback = typename MultiLabelEncoded<Label>::LabelIndexCallback;
 
     ColumnCompressedLazy(size_t num_objects, const std::vector<std::string> &files)
           : num_objects_(num_objects), files_(files) {}
 
     uint64_t num_objects() const override final { return num_objects_; }
 
-    void call_columns(const ColumnCallback &callback, size_t num_threads = 1) const {
-        BaseAnnotator::merge_load(files_, callback, num_threads);
+    void call_label_objects(const VLabels &labels,
+                            const LabelIndexCallback &callback,
+                            size_t num_threads = 1) const override final {
+        tsl::hopscotch_map<Label, size_t> label_set;
+        for (size_t i = 0; i < labels.size(); ++i) {
+            label_set[labels[i]] = i;
+        }
+
+        ColumnCompressed<Label>::merge_load(files_,
+            [&](size_t, const Label &label, auto&& bitmap) {
+                if (bitmap->size() != num_objects_) {
+                    common::logger->error("Label {} has incorrect number of rows: {} != {}",
+                                          bitmap->size(), num_objects_);
+                    exit(1);
+                }
+
+                auto find = label_set.find(label);
+                if (find != label_set.end())
+                    callback(find->second, *bitmap);
+            },
+            num_threads
+        );
     }
 
     const std::string& get_file(size_t i) const { return files_[i]; }
 
-    void set(Index, const VLabels &) override final { throw std::runtime_error("set not implemented"); }
-    void serialize(const std::string &) const override final { throw std::runtime_error("serialize not implemented"); }
-    bool load(const std::string &) override final { throw std::runtime_error("load not implemented"); }
-    void add_labels(const std::vector<Index> &, const VLabels &) override final { throw std::runtime_error("add_labels not implemented"); }
-    bool has_label(Index, const Label &) const override final { throw std::runtime_error("has_label not implemented"); }
-    bool has_labels(Index, const VLabels &) const override final { throw std::runtime_error("has_labels not implemented"); }
-    void insert_rows(const std::vector<Index> &) override final { throw std::runtime_error("insert_rows not implemented"); }
-    void rename_labels(const tsl::hopscotch_map<Label, Label> &) override final { throw std::runtime_error("rename_labels not implemented"); }
-    size_t num_labels() const override final { throw std::runtime_error("num_labels not implemented"); }
-    uint64_t num_relations() const override final { throw std::runtime_error("num_relations not implemented"); }
-    void call_objects(const Label &, std::function<void(Index)>) const override final { throw std::runtime_error("call_objects not implemented"); }
-    std::string file_extension() const override final { throw std::runtime_error("file_extension not implemented"); }
-    const binmat::BinaryMatrix& get_matrix() const override final { throw std::runtime_error("get_matrix not implemented"); }
-
   private:
     size_t num_objects_;
     const std::vector<std::string> &files_;
diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index e5cbfa3276..086b47be00 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -29,7 +29,7 @@ using mtg::graph::DifferentialAssemblyConfig;
 
 void check_labels(const tsl::hopscotch_set<std::string> &label_set,
                   const AnnotatedDBG &anno_graph) {
-    if (dynamic_cast<const annot::ColumnCompressedLazy<>*>(&anno_graph.get_annotator())) {
+    if (dynamic_cast<const annot::ColumnCompressedLazy<>*>(&anno_graph.get_annotation())) {
         logger->trace("Skipping label check for streaming annotator");
         return;
     }
@@ -82,21 +82,16 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
         throw std::iostream::failure("Failed to read assembly config JSON from " + config->assembly_config_file);
 
     size_t num_threads = std::max(1u, get_num_threads());
-    bool streaming = dynamic_cast<const annot::ColumnCompressedLazy<>*>(&anno_graph.get_annotator());
+    bool streaming = dynamic_cast<const annot::ColumnCompressedLazy<>*>(&anno_graph.get_annotation());
 
     Json::Value diff_json;
     fin >> diff_json;
 
-    tsl::hopscotch_set<std::string> foreground_labels;
-    tsl::hopscotch_set<std::string> background_labels;
-    tsl::hopscotch_set<std::string> shared_foreground_labels;
-    tsl::hopscotch_set<std::string> shared_background_labels;
-
     for (const Json::Value &group : diff_json["groups"]) {
-        if (group["shared_labels"]) {
-            shared_foreground_labels.clear();
-            shared_background_labels.clear();
+        tsl::hopscotch_set<std::string> shared_foreground_labels;
+        tsl::hopscotch_set<std::string> shared_background_labels;
 
+        if (group["shared_labels"]) {
             for (const Json::Value &in_label : group["shared_labels"]["in"]) {
                 shared_foreground_labels.emplace(in_label.asString());
             }
@@ -113,6 +108,9 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
             throw std::runtime_error("Missing experiments in group");
 
         for (const Json::Value &experiment : group["experiments"]) {
+            tsl::hopscotch_set<std::string> foreground_labels;
+            tsl::hopscotch_set<std::string> background_labels;
+
             DifferentialAssemblyConfig diff_config = diff_assembly_config(
                 experiment, anno_graph.get_graph()
             );
@@ -126,9 +124,6 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
                 continue;
             }
 
-            foreground_labels.clear();
-            background_labels.clear();
-
             for (const Json::Value &in_label : experiment["in"]) {
                 foreground_labels.emplace(in_label.asString());
             }
diff --git a/metagraph/src/cli/load/load_annotated_graph.cpp b/metagraph/src/cli/load/load_annotated_graph.cpp
index 1563460ccc..6e1e219e18 100644
--- a/metagraph/src/cli/load/load_annotated_graph.cpp
+++ b/metagraph/src/cli/load/load_annotated_graph.cpp
@@ -32,29 +32,35 @@ std::unique_ptr<AnnotatedDBG> initialize_annotated_dbg(std::shared_ptr<DeBruijnG
         logger->trace("Primary graph wrapped into canonical");
     }
 
-    auto annotation_temp = config.infbase_annotators.size()
+    auto annotator_init = config.infbase_annotators.size()
             ? initialize_annotation(config.infbase_annotators.at(0), config, 0, max_chunks_open)
             : initialize_annotation(config.anno_type, config, max_index, max_chunks_open);
+    auto *annotator_temp = annotator_init.get();
+    std::unique_ptr<AnnotatedDBG::Annotation> annotation_temp(annotator_init.release());
+
+    bool try_stream = config.identity == Config::ASSEMBLE && config.separately;
 
     if (config.infbase_annotators.size()) {
         bool loaded = false;
-        bool streamed = false;
         if (auto *cc = dynamic_cast<annot::ColumnCompressed<>*>(annotation_temp.get())) {
-            if (config.identity == Config::ASSEMBLE && config.separately) {
+            if (try_stream) {
                 annotation_temp.reset(new annot::ColumnCompressedLazy<>(
                     max_index, config.infbase_annotators
                 ));
                 loaded = true;
-                streamed = true;
+                annotator_temp = nullptr;
             } else {
                 loaded = cc->merge_load(config.infbase_annotators);
             }
         } else {
+            if (try_stream)
+                logger->warn("--separately only supported with column annotator");
+
             if (config.infbase_annotators.size() > 1) {
                 logger->warn("Cannot merge annotations of this type. Only the first"
                              " file {} will be loaded.", config.infbase_annotators.at(0));
             }
-            loaded = annotation_temp->load(config.infbase_annotators.at(0));
+            loaded = annotator_temp->load(config.infbase_annotators.at(0));
         }
         if (!loaded) {
             logger->error("Cannot load annotations for graph {}, file corrupted",
@@ -63,9 +69,9 @@ std::unique_ptr<AnnotatedDBG> initialize_annotated_dbg(std::shared_ptr<DeBruijnG
         }
 
         // row_diff annotation is special, as it must know the graph structure
-        if (!streamed) {
+        if (annotator_temp) {
             using namespace annot::binmat;
-            BinaryMatrix &matrix = const_cast<BinaryMatrix &>(annotation_temp->get_matrix());
+            BinaryMatrix &matrix = const_cast<BinaryMatrix &>(annotator_temp->get_matrix());
             if (IRowDiff *row_diff = dynamic_cast<IRowDiff*>(&matrix)) {
                 if (!dbg_graph) {
                     logger->error("Only succinct de Bruijn graph representations"
@@ -87,8 +93,7 @@ std::unique_ptr<AnnotatedDBG> initialize_annotated_dbg(std::shared_ptr<DeBruijnG
     auto anno_graph
             = std::make_unique<AnnotatedDBG>(std::move(graph), std::move(annotation_temp));
 
-    if (!dynamic_cast<const annot::ColumnCompressedLazy<>*>(&anno_graph->get_annotator())
-            && !anno_graph->check_compatibility()) {
+    if (!anno_graph->check_compatibility()) {
         logger->error("Graph and annotation are not compatible");
         exit(1);
     }
diff --git a/metagraph/src/common/vectors/bitmap.cpp b/metagraph/src/common/vectors/bitmap.cpp
index f4888735e4..6d31c5f7c8 100644
--- a/metagraph/src/common/vectors/bitmap.cpp
+++ b/metagraph/src/common/vectors/bitmap.cpp
@@ -356,6 +356,13 @@ uint64_t bitmap_lazy::num_set_bits() const {
     return num_set_bits_;
 }
 
+std::optional<uint64_t> bitmap_lazy::try_get_num_set_bits() const {
+    if (num_set_bits_ == static_cast<size_t>(-1))
+        return std::nullopt;
+
+    return num_set_bits_;
+}
+
 void bitmap_lazy::add_to(sdsl::bit_vector *other) const {
     assert(other);
     assert(other->size() == size());
diff --git a/metagraph/src/common/vectors/bitmap.hpp b/metagraph/src/common/vectors/bitmap.hpp
index b5b855a5eb..5bba68af7d 100644
--- a/metagraph/src/common/vectors/bitmap.hpp
+++ b/metagraph/src/common/vectors/bitmap.hpp
@@ -176,6 +176,7 @@ class bitmap_lazy : public bitmap {
 
     uint64_t size() const { return size_; }
     uint64_t num_set_bits() const;
+    std::optional<uint64_t> try_get_num_set_bits() const;
 
     void add_to(sdsl::bit_vector *other) const;
     void call_ones_in_range(uint64_t begin, uint64_t end,
@@ -201,8 +202,8 @@ class bitmap_generator : public bitmap {
                      size_t size,
                      size_t num_set_bits = -1) noexcept;
 
-    bool operator[](uint64_t) const { throw std::runtime_error("Not implemented"); }
-    uint64_t get_int(uint64_t, uint32_t) const { throw std::runtime_error("Not implemented"); }
+    bool operator[](uint64_t) const { throw std::runtime_error("operator[] not implemented for bitmap_generator"); }
+    uint64_t get_int(uint64_t, uint32_t) const { throw std::runtime_error("get_int not implemented for bitmap_generator"); }
 
     uint64_t size() const { return size_; }
     uint64_t num_set_bits() const { return num_set_bits_; }
diff --git a/metagraph/src/graph/annotated_dbg.cpp b/metagraph/src/graph/annotated_dbg.cpp
index 625b151809..3bf8bbf41a 100644
--- a/metagraph/src/graph/annotated_dbg.cpp
+++ b/metagraph/src/graph/annotated_dbg.cpp
@@ -11,6 +11,7 @@
 #include "common/vectors/vector_algorithm.hpp"
 #include "common/vector_map.hpp"
 #include "common/logger.hpp"
+#include "common/vectors/bitmap.hpp"
 
 
 namespace mtg {
@@ -26,16 +27,17 @@ typedef std::pair<Label, size_t> StringCountPair;
 
 AnnotatedSequenceGraph
 ::AnnotatedSequenceGraph(std::shared_ptr<SequenceGraph> graph,
-                         std::unique_ptr<Annotator>&& annotation,
+                         std::unique_ptr<Annotation>&& annotation,
                          bool force_fast)
-      : graph_(graph), annotator_(std::move(annotation)), force_fast_(force_fast) {
+      : graph_(graph), annotation_(std::move(annotation)),
+        annotator_(std::shared_ptr<Annotator>{}, dynamic_cast<Annotator*>(annotation_.get())),
+        force_fast_(force_fast) {
     assert(graph_.get());
-    assert(annotator_.get());
     assert(check_compatibility());
 }
 
 AnnotatedDBG::AnnotatedDBG(std::shared_ptr<DeBruijnGraph> dbg,
-                           std::unique_ptr<Annotator>&& annotation,
+                           std::unique_ptr<Annotation>&& annotation,
                            bool force_fast)
       : AnnotatedSequenceGraph(dbg, std::move(annotation), force_fast), dbg_(*dbg) {}
 
@@ -147,6 +149,7 @@ void AnnotatedDBG::add_kmer_coord(std::string_view sequence,
         // only insert coordinates for matched k-mers and increment the coordinates
         if (i > 0)
             annotator_->add_label_coord(graph_to_anno_index(i), labels, coord);
+
         coord++;
     }
 }
@@ -172,6 +175,7 @@ void AnnotatedDBG::add_kmer_coords(
             // only insert coordinates for matched k-mers and increment the coordinates
             if (i > 0)
                 annotator_->add_label_coord(graph_to_anno_index(i), labels, coord);
+
             coord++;
         }
     }
@@ -826,6 +830,7 @@ AnnotatedDBG::get_top_labels(const std::vector<std::pair<row_index, size_t>> &in
 }
 
 bool AnnotatedSequenceGraph::label_exists(const Label &label) const {
+    assert(annotator_.get());
     return annotator_->label_exists(label);
 }
 
@@ -837,22 +842,24 @@ bool AnnotatedSequenceGraph::has_label(node_index index, const Label &label) con
 }
 
 void AnnotatedSequenceGraph
-::call_annotated_nodes(const Label &label,
-                       std::function<void(node_index)> callback) const {
+::call_annotated_nodes(const Annotator::VLabels &labels,
+                       const std::function<void(size_t, const bitmap&)> &callback,
+                       size_t num_threads) const {
     assert(check_compatibility());
 
-    annotator_->call_objects(
-        label,
-        [&](row_index index) { callback(anno_to_graph_index(index)); }
-    );
+    annotation_->call_label_objects(labels, [&](size_t j, const bitmap &bitmap) {
+        callback(j, bitmap_generator([&](const auto &call_one) {
+            bitmap.call_ones([&](uint64_t i) { call_one(anno_to_graph_index(i)); });
+        }, bitmap.size() + 1, bitmap.num_set_bits()));
+    }, num_threads);
 }
 
 bool AnnotatedSequenceGraph::check_compatibility() const {
     // TODO: what if CanonicalDBG is not the highest level? find a better way to do this
     if (const auto *canonical = dynamic_cast<const CanonicalDBG *>(graph_.get()))
-        return canonical->get_graph().max_index() == annotator_->num_objects();
+        return canonical->get_graph().max_index() == annotation_->num_objects();
 
-    return graph_->max_index() == annotator_->num_objects();
+    return graph_->max_index() == annotation_->num_objects();
 }
 
 
diff --git a/metagraph/src/graph/annotated_dbg.hpp b/metagraph/src/graph/annotated_dbg.hpp
index db5c2bd8e3..3129579347 100644
--- a/metagraph/src/graph/annotated_dbg.hpp
+++ b/metagraph/src/graph/annotated_dbg.hpp
@@ -19,11 +19,13 @@ class AnnotatedSequenceGraph {
   public:
     typedef std::string Label;
     typedef annot::MultiLabelEncoded<Label> Annotator;
+    typedef annot::Annotation<typename Annotator::Index,
+                              typename Annotator::VLabels> Annotation;
     using node_index = SequenceGraph::node_index;
     using row_index = Annotator::Index;
 
     AnnotatedSequenceGraph(std::shared_ptr<SequenceGraph> graph,
-                           std::unique_ptr<Annotator>&& annotation,
+                           std::unique_ptr<Annotation>&& annotation,
                            bool force_fast = false);
 
     virtual ~AnnotatedSequenceGraph() {}
@@ -39,9 +41,6 @@ class AnnotatedSequenceGraph {
     virtual void annotate_sequences(
         const std::vector<std::pair<std::string, std::vector<Label>>> &data);
 
-    virtual void call_annotated_nodes(const Label &label,
-                                      std::function<void(node_index)> callback) const;
-
     virtual bool label_exists(const Label &label) const;
 
     virtual bool check_compatibility() const;
@@ -49,7 +48,16 @@ class AnnotatedSequenceGraph {
     virtual const SequenceGraph& get_graph() const { return *graph_; }
     std::shared_ptr<const SequenceGraph> get_graph_ptr() const { return graph_; }
 
-    virtual const Annotator& get_annotator() const { return *annotator_; }
+    virtual const Annotator& get_annotator() const {
+        assert(annotator_.get());
+        return *annotator_;
+    }
+
+    virtual const Annotation& get_annotation() const { return *annotation_; }
+
+    virtual void call_annotated_nodes(const Annotator::VLabels &labels,
+                                      const std::function<void(size_t, const bitmap&)> &callback,
+                                      size_t num_threads = 1) const;
 
     static row_index graph_to_anno_index(node_index kmer_index) {
         assert(kmer_index);
@@ -61,7 +69,8 @@ class AnnotatedSequenceGraph {
 
   protected:
     std::shared_ptr<SequenceGraph> graph_;
-    std::unique_ptr<Annotator> annotator_;
+    std::unique_ptr<Annotation> annotation_;
+    const std::shared_ptr<Annotator> annotator_;
 
     std::mutex mutex_;
     bool force_fast_;
@@ -71,7 +80,7 @@ class AnnotatedSequenceGraph {
 class AnnotatedDBG : public AnnotatedSequenceGraph {
   public:
     AnnotatedDBG(std::shared_ptr<DeBruijnGraph> dbg,
-                 std::unique_ptr<Annotator>&& annotation,
+                 std::unique_ptr<Annotation>&& annotation,
                  bool force_fast = false);
 
     using AnnotatedSequenceGraph::get_labels;
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index d6c028083b..ac7d91b1c7 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -70,7 +70,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
     bool parallel = num_threads > 1;
     size_t num_in_labels = labels_in.size() + labels_in_round2.size();
     size_t num_out_labels = labels_out.size() + labels_out_round2.size();
-    auto graph_ptr = std::dynamic_pointer_cast<const DeBruijnGraph>(
+    auto graph_ptr = std::static_pointer_cast<const DeBruijnGraph>(
         anno_graph.get_graph_ptr()
     );
 
@@ -88,17 +88,15 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
     assert(counts.size() == init_mask.size() * 2);
 
     bool check_other = config.label_mask_other_unitig_fraction != 1.0;
-    sdsl::bit_vector union_mask;
 
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
                                                   config.add_complement, num_threads);
 
-    if (check_other || labels_in_round2.size() || labels_out_round2.size())
-        union_mask = dynamic_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
-
     // check all other labels and post labels
     if (check_other || labels_in_round2.size() || labels_out_round2.size()) {
+        sdsl::bit_vector union_mask
+            = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
         std::mutex vector_backup_mutex;
         std::atomic_thread_fence(std::memory_order_release);
 
@@ -161,7 +159,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
             && config.label_mask_other_unitig_fraction == 1.0) {
         logger->trace("Filtering by node");
         size_t total_nodes = masked_graph->num_nodes();
-        const auto &in_mask = dynamic_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
+        const auto &in_mask = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
         sdsl::bit_vector mask = in_mask;
 
         // TODO: make this part multithreaded
@@ -310,61 +308,50 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
     // the in and out counts are stored interleaved
     sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16);
 
-    const auto &annotator = anno_graph.get_annotator();
     std::mutex vector_backup_mutex;
     std::atomic_thread_fence(std::memory_order_release);
     bool parallel = num_threads > 1;
 
-    auto make_index_callback = [&](uint8_t col_indicator) {
-        assert(col_indicator);
-        return [&indicator,&counts,&vector_backup_mutex,parallel,col_indicator](auto r) {
-            node_index i = AnnotatedDBG::anno_to_graph_index(r);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            if (col_indicator & 1)
-                atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
+    AnnotatedDBG::Annotator::VLabels labels;
+    labels.reserve(labels_in.size() + labels_out.size());
+    for (const auto &label : labels_in) {
+        labels.emplace_back(label);
+    }
+    for (const auto &label : labels_out) {
+        if (!labels_in.count(label))
+            labels.emplace_back(label);
+    }
+    anno_graph.call_annotated_nodes(labels, [&](size_t j, const bitmap &column) {
+        uint8_t col_indicator = static_cast<bool>(labels_in.count(labels[j]));
+        if (labels_out.count(labels[j]))
+            col_indicator |= 2;
 
-            if (col_indicator & 2)
-                atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+        auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](node_index i) {
+            assert(i != DeBruijnGraph::npos);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
         };
-    };
 
-    if (const auto *ccl = dynamic_cast<const annot::ColumnCompressedLazy<>*>(&annotator)) {
-        ccl->call_columns([&](size_t, const std::string &label, auto&& column) {
-            uint8_t col_indicator = 0;
-            if (labels_in.count(label))
-                col_indicator = 1;
-
-            if (labels_out.count(label))
-                col_indicator |= 2;
-
-            if (col_indicator)
-                column->call_ones(make_index_callback(col_indicator));
-        });
-    } else {
-        const auto &label_encoder = annotator.get_label_encoder();
-        const auto &binmat = annotator.get_matrix();
+        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel](node_index i) {
+            assert(i != DeBruijnGraph::npos);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+        };
 
-        tsl::hopscotch_map<uint64_t, uint8_t> code_to_indicator;
-        for (const std::string &label_in : labels_in) {
-            code_to_indicator[label_encoder.encode(label_in)] = 1;
-        }
-        for (const std::string &label_out : labels_out) {
-            code_to_indicator[label_encoder.encode(label_out)] |= 2;
-        }
+        auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](node_index i) {
+            assert(i != DeBruijnGraph::npos);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+        };
 
-        std::vector<uint64_t> label_codes;
-        label_codes.reserve(code_to_indicator.size());
-        for (const auto &[code, indicator] : code_to_indicator) {
-            label_codes.push_back(code);
+        switch (col_indicator) {
+            case 1: { column.call_ones(add_in); } break;
+            case 2: { column.call_ones(add_out); } break;
+            case 3: { column.call_ones(add_both); } break;
+            default: {}
         }
-
-        binmat.call_columns(label_codes,
-            [&](auto col_idx, const bitmap &rows) {
-                rows.call_ones(make_index_callback(code_to_indicator[label_codes[col_idx]]));
-            },
-            num_threads
-        );
-    }
+    }, num_threads);
 
     std::atomic_thread_fence(std::memory_order_acquire);
 
@@ -380,7 +367,7 @@ void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
     std::atomic<uint64_t> num_kept_nodes(0);
     bool parallel = num_threads > 1;
 
-    sdsl::bit_vector mask = dynamic_cast<const bitmap_vector&>(masked_graph.get_mask()).data();
+    sdsl::bit_vector mask = static_cast<const bitmap_vector&>(masked_graph.get_mask()).data();
 
     std::atomic_thread_fence(std::memory_order_release);
 
diff --git a/metagraph/tests/annotation/test_annotated_dbg.cpp b/metagraph/tests/annotation/test_annotated_dbg.cpp
index fe8d84c49b..d3193703ab 100644
--- a/metagraph/tests/annotation/test_annotated_dbg.cpp
+++ b/metagraph/tests/annotation/test_annotated_dbg.cpp
@@ -52,18 +52,19 @@ void check_labels(const AnnotatedDBG &anno_graph,
 
     for (const auto &label : labels_present) {
         std::set<SequenceGraph::node_index> cur_indices;
-        anno_graph.call_annotated_nodes(
-            label,
-            [&](const auto &index) {
-                ASSERT_NE(SequenceGraph::npos, index);
-                cur_indices.insert(index);
-                EXPECT_TRUE(anno_graph.has_label(index, label));
-            }
-        );
+        anno_graph.call_annotated_nodes({ label }, [&](size_t, const bitmap &column) {
+            column.call_ones(
+                [&](const auto &index) {
+                    ASSERT_NE(SequenceGraph::npos, index);
+                    cur_indices.insert(index);
+                    EXPECT_TRUE(anno_graph.has_label(index, label));
+                }
+            );
+        });
         std::vector<SequenceGraph::node_index> diff;
         std::set_difference(indices.begin(), indices.end(),
                             cur_indices.begin(), cur_indices.end(),
-                            diff.begin());
+                            std::back_inserter(diff));
         EXPECT_EQ(0u, diff.size())
             << diff.front()
             << anno_graph.get_graph().get_node_sequence(diff.front());

From cecde7845b07ced678594df49de283d4681fe474 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 10 Aug 2022 08:52:05 +0200
Subject: [PATCH 007/410] better parallelism with multicolumn files

---
 .../binary_matrix/base/binary_matrix.cpp      |  4 ++
 .../annotate_column_compressed.cpp            | 40 +++++++++++++++----
 .../annotate_column_compressed.hpp            |  5 ++-
 .../column_compressed_lazy.hpp                | 13 +++++-
 4 files changed, 52 insertions(+), 10 deletions(-)

diff --git a/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp b/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp
index 8b7ec4ea06..580be8e401 100644
--- a/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp
+++ b/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp
@@ -1,6 +1,7 @@
 #include "binary_matrix.hpp"
 
 #include <ips4o.hpp>
+#include <progress_bar.hpp>
 
 #include "common/vectors/bitmap.hpp"
 #include "common/serialization.hpp"
@@ -40,9 +41,12 @@ BinaryMatrix::slice_rows(const std::vector<Row> &row_ids) const {
 void BinaryMatrix::call_columns(const std::vector<Column> &column_ids,
                                 const std::function<void(size_t, const bitmap&)> &callback,
                                 size_t num_threads) const {
+    ProgressBar progress_bar(column_ids.size(), "Parsing columns",
+                             std::cerr, !common::get_verbose());
     #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
     for (size_t k = 0; k < column_ids.size(); ++k) {
         callback(k, bitmap_generator(get_column(column_ids[k]), num_rows()));
+        ++progress_bar;
     }
 }
 
diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
index c46a0bddd1..d5c508070d 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
@@ -595,7 +595,9 @@ ColumnCompressed<Label>::load_label_encoder(const std::string &filename) {
 template <typename Label>
 bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenames,
                                          const ColumnCallback &callback,
-                                         size_t num_threads) {
+                                         size_t num_threads,
+                                         bool prefetch_total_column_count,
+                                         const std::function<void(size_t)> &post_num_columns) {
     std::atomic<bool> error_occurred = false;
 
     std::vector<uint64_t> offsets(filenames.size(), 0);
@@ -618,8 +620,24 @@ bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenam
     // compute global offsets (partial sums)
     std::partial_sum(offsets.begin(), offsets.end(), offsets.begin());
 
+    if (prefetch_total_column_count) {
+        size_t total_count = offsets.back();
+        auto fname = make_suffix(filenames.back(), kExtension);
+        try {
+            total_count += read_num_labels(fname);
+        } catch (...) {
+            logger->error("Can't load label encoder from {}", fname);
+            error_occurred = true;
+        }
+
+        if (error_occurred)
+            return false;
+
+        post_num_columns(total_count);
+    }
+
     // load annotations
-    #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+    #pragma omp taskloop num_tasks(num_threads) shared(error_occurred, filenames, offsets, logger)
     for (size_t i = 0; i < filenames.size(); ++i) {
         const auto &filename = make_suffix(filenames[i], kExtension);
         logger->trace("Loading annotations from file {}", filename);
@@ -639,17 +657,23 @@ bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenam
 
             // update the existing and add some new columns
             for (size_t c = 0; c < label_encoder_load.size(); ++c) {
-                auto new_column = std::make_unique<bit_vector_smart>();
+                auto *new_column = new bit_vector_smart();
 
-                if (!new_column->load(in))
+                if (!new_column->load(in)) {
+                    delete new_column;
                     throw std::ifstream::failure("can't load next column");
+                }
 
-                if (new_column->size() != num_rows)
+                if (new_column->size() != num_rows) {
+                    delete new_column;
                     throw std::ifstream::failure("inconsistent column size");
+                }
 
-                callback(offsets[i] + c,
-                         label_encoder_load.decode(c),
-                         std::move(new_column));
+                uint64_t offset = offsets[i] + c;
+                Label label = label_encoder_load.decode(c);
+
+                #pragma omp task firstprivate(new_column, label, offset)
+                callback(offset, label, std::unique_ptr<bit_vector_smart>(new_column));
             }
         } catch (const std::exception &e) {
             logger->error("Caught exception when loading columns from {}: {}", filename, e.what());
diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
index 8d14ffcd41..f54f138dbd 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
@@ -85,7 +85,10 @@ class ColumnCompressed : public MultiLabelEncoded<Label> {
                                               std::unique_ptr<bit_vector>&&)>;
     static bool merge_load(const std::vector<std::string> &filenames,
                            const ColumnCallback &callback,
-                           size_t num_threads = 1);
+                           size_t num_threads = 1,
+                           bool prefetch_total_column_count = false,
+                           const std::function<void(size_t)> &post_num_columns
+                               = [](size_t) {});
     static size_t read_num_labels(const std::string &filename);
     static LabelEncoder<Label> load_label_encoder(const std::string &filename);
 
diff --git a/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp b/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
index 70ef084cb9..cd90cbb514 100644
--- a/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
+++ b/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
@@ -2,6 +2,7 @@
 #define __COLUMN_COMPRESSED_LAZY_HPP__
 
 #include <tsl/hopscotch_map.h>
+#include <progress_bar.hpp>
 
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 
@@ -30,6 +31,7 @@ class ColumnCompressedLazy
             label_set[labels[i]] = i;
         }
 
+        std::unique_ptr<ProgressBar> progress_bar;
         ColumnCompressed<Label>::merge_load(files_,
             [&](size_t, const Label &label, auto&& bitmap) {
                 if (bitmap->size() != num_objects_) {
@@ -41,8 +43,17 @@ class ColumnCompressedLazy
                 auto find = label_set.find(label);
                 if (find != label_set.end())
                     callback(find->second, *bitmap);
+
+                ++(*progress_bar);
             },
-            num_threads
+            num_threads,
+            true,
+            [&](size_t total_column_count) {
+                progress_bar = std::make_unique<ProgressBar>(
+                    total_column_count, "Streaming columns",
+                    std::cerr, !common::get_verbose()
+                );
+            }
         );
     }
 

From 4c8faafcb5ba357a9f383f25c3d669783dd0f8be Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 10 Aug 2022 09:21:31 +0200
Subject: [PATCH 008/410] limit number of columns to read at once

---
 metagraph/src/cli/assemble.cpp                    | 3 ++-
 metagraph/src/graph/annotated_graph_algorithm.cpp | 6 ++++--
 metagraph/src/graph/annotated_graph_algorithm.hpp | 3 ++-
 3 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 086b47be00..f5563ec48f 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -149,7 +149,8 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
                                           background_labels,
                                           shared_foreground_labels,
                                           shared_background_labels,
-                                          diff_config, num_threads), exp_name);
+                                          diff_config, num_threads,
+                                          config->parallel_nodes), exp_name);
         }
     }
 }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ac7d91b1c7..3f5cafa51b 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -66,7 +66,9 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     const tsl::hopscotch_set<Label> &labels_in_round2,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
                     const DifferentialAssemblyConfig &config,
-                    size_t num_threads) {
+                    size_t num_threads,
+                    size_t num_parallel_files) {
+    num_parallel_files = std::min(num_threads, num_parallel_files);
     bool parallel = num_threads > 1;
     size_t num_in_labels = labels_in.size() + labels_in_round2.size();
     size_t num_out_labels = labels_out.size() + labels_out_round2.size();
@@ -80,7 +82,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
     auto count_vector = construct_diff_label_count_vector(
         anno_graph, labels_in, labels_out,
         std::max(num_in_labels, num_out_labels),
-        num_threads
+        num_parallel_files
     );
     auto &[counts, init_mask] = count_vector;
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 681caa864a..3deb560c19 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -57,7 +57,8 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in_round2,
                     const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out_round2,
                     const DifferentialAssemblyConfig &config,
-                    size_t num_threads = 1);
+                    size_t num_threads = 1,
+                    size_t num_parallel_files = std::numeric_limits<size_t>::max());
 
 } // namespace graph
 } // namespace mtg

From be77d393835b0d5f43832fc19666c91126c38d97 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 10 Aug 2022 09:34:36 +0200
Subject: [PATCH 009/410] check if config exists before loading graph

---
 metagraph/src/cli/assemble.cpp | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index f5563ec48f..cf5254b17c 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -158,6 +158,15 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
 int assemble(Config *config) {
     assert(config);
 
+    if (config->infbase_annotators.size()) {
+        assert(config->assembly_config_file.size());
+        if (!std::filesystem::exists(config->assembly_config_file)) {
+            logger->error("Differential assembly config does not exist\n{}",
+                          config->assembly_config_file);
+            exit(1);
+        }
+    }
+
     const auto &files = config->fnames;
 
     assert(files.size() == 1);
@@ -173,7 +182,6 @@ int assemble(Config *config) {
     if (config->infbase_annotators.size()) {
         config->infbase = files.at(0);
 
-        assert(config->assembly_config_file.size());
         auto anno_graph = initialize_annotated_dbg(graph, *config);
 
         logger->trace("Generating masked graphs...");

From 4eb1a274f3f06ebf99936b441344c14574056fce Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 10 Aug 2022 09:38:54 +0200
Subject: [PATCH 010/410] more log messages

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 3f5cafa51b..cc8bf54ea9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -304,14 +304,16 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
                                   const tsl::hopscotch_set<Label> &labels_out,
                                   size_t num_labels,
                                   size_t num_threads) {
+    logger->trace("Allocating mask vector");
     size_t width = sdsl::bits::hi(num_labels) + 1;
     sdsl::bit_vector indicator(anno_graph.get_graph().max_index() + 1, false);
 
+    logger->trace("Allocating count vector");
     // the in and out counts are stored interleaved
     sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16);
+    logger->trace("done");
 
     std::mutex vector_backup_mutex;
-    std::atomic_thread_fence(std::memory_order_release);
     bool parallel = num_threads > 1;
 
     AnnotatedDBG::Annotator::VLabels labels;
@@ -323,6 +325,9 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
         if (!labels_in.count(label))
             labels.emplace_back(label);
     }
+
+    logger->trace("Populating count vector");
+    std::atomic_thread_fence(std::memory_order_release);
     anno_graph.call_annotated_nodes(labels, [&](size_t j, const bitmap &column) {
         uint8_t col_indicator = static_cast<bool>(labels_in.count(labels[j]));
         if (labels_out.count(labels[j]))
@@ -356,6 +361,7 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
     }, num_threads);
 
     std::atomic_thread_fence(std::memory_order_acquire);
+    logger->trace("done");
 
     return std::make_pair(std::move(counts), std::move(indicator));
 }

From 44175dcf8e1b8a15b33390dd41c664870742ac10 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 10 Aug 2022 11:37:45 +0200
Subject: [PATCH 011/410] better parallel

---
 .../annotate_column_compressed.cpp            | 84 ++++++++++---------
 1 file changed, 45 insertions(+), 39 deletions(-)

diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
index d5c508070d..905cdb882a 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
@@ -598,6 +598,9 @@ bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenam
                                          size_t num_threads,
                                          bool prefetch_total_column_count,
                                          const std::function<void(size_t)> &post_num_columns) {
+    if (!num_threads)
+        num_threads = 1;
+
     std::atomic<bool> error_occurred = false;
 
     std::vector<uint64_t> offsets(filenames.size(), 0);
@@ -637,50 +640,53 @@ bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenam
     }
 
     // load annotations
-    #pragma omp taskloop num_tasks(num_threads) shared(error_occurred, filenames, offsets, logger)
-    for (size_t i = 0; i < filenames.size(); ++i) {
-        const auto &filename = make_suffix(filenames[i], kExtension);
-        logger->trace("Loading annotations from file {}", filename);
-        try {
-            std::ifstream in(filename, std::ios::binary);
-            if (!in.good())
-                throw std::ifstream::failure("can't open file");
-
-            const auto num_rows = load_number(in);
-
-            LabelEncoder<Label> label_encoder_load;
-            if (!label_encoder_load.load(in))
-                throw std::ifstream::failure("can't load label encoder");
-
-            if (!label_encoder_load.size())
-                logger->warn("No columns in {}", filename);
+    #pragma omp parallel num_threads(num_threads) shared(error_occurred, filenames, offsets, logger)
+    #pragma omp single
+    {
+        for (size_t i = 0; i < filenames.size(); ++i) {
+            const auto &filename = make_suffix(filenames[i], kExtension);
+            logger->trace("Loading annotations from file {}", filename);
+            try {
+                std::ifstream in(filename, std::ios::binary);
+                if (!in.good())
+                    throw std::ifstream::failure("can't open file");
+
+                const auto num_rows = load_number(in);
+
+                LabelEncoder<Label> label_encoder_load;
+                if (!label_encoder_load.load(in))
+                    throw std::ifstream::failure("can't load label encoder");
+
+                if (!label_encoder_load.size())
+                    logger->warn("No columns in {}", filename);
+
+                // update the existing and add some new columns
+                for (size_t c = 0; c < label_encoder_load.size(); ++c) {
+                    auto *new_column = new bit_vector_smart();
+
+                    if (!new_column->load(in)) {
+                        delete new_column;
+                        throw std::ifstream::failure("can't load next column");
+                    }
 
-            // update the existing and add some new columns
-            for (size_t c = 0; c < label_encoder_load.size(); ++c) {
-                auto *new_column = new bit_vector_smart();
+                    if (new_column->size() != num_rows) {
+                        delete new_column;
+                        throw std::ifstream::failure("inconsistent column size");
+                    }
 
-                if (!new_column->load(in)) {
-                    delete new_column;
-                    throw std::ifstream::failure("can't load next column");
-                }
+                    uint64_t offset = offsets[i] + c;
+                    Label label = label_encoder_load.decode(c);
 
-                if (new_column->size() != num_rows) {
-                    delete new_column;
-                    throw std::ifstream::failure("inconsistent column size");
+                    #pragma omp task firstprivate(new_column, label, offset)
+                    callback(offset, label, std::unique_ptr<bit_vector_smart>(new_column));
                 }
-
-                uint64_t offset = offsets[i] + c;
-                Label label = label_encoder_load.decode(c);
-
-                #pragma omp task firstprivate(new_column, label, offset)
-                callback(offset, label, std::unique_ptr<bit_vector_smart>(new_column));
+            } catch (const std::exception &e) {
+                logger->error("Caught exception when loading columns from {}: {}", filename, e.what());
+                error_occurred = true;
+            } catch (...) {
+                logger->error("Unknown exception when loading columns from {}", filename);
+                error_occurred = true;
             }
-        } catch (const std::exception &e) {
-            logger->error("Caught exception when loading columns from {}: {}", filename, e.what());
-            error_occurred = true;
-        } catch (...) {
-            logger->error("Unknown exception when loading columns from {}", filename);
-            error_occurred = true;
         }
     }
 

From 5a49464d7b3353ef906dde49157d626c02959478 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 10 Aug 2022 12:42:36 +0200
Subject: [PATCH 012/410] optimization

---
 .../src/graph/annotated_graph_algorithm.cpp   | 21 ++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index cc8bf54ea9..fb298af9f1 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -35,7 +35,8 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
                                   size_t num_labels,
-                                  size_t num_threads);
+                                  size_t num_threads,
+                                  bool add_out_labels_to_mask);
 
 // Regions of a graph mask which should be kept (i.e., masked in)
 typedef std::vector<std::pair<size_t, size_t>> Intervals;
@@ -76,20 +77,23 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
         anno_graph.get_graph_ptr()
     );
 
+    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
     logger->trace("Generating initial mask");
 
     // Construct initial masked graph from union of labels in labels_in
     auto count_vector = construct_diff_label_count_vector(
         anno_graph, labels_in, labels_out,
-        std::max(num_in_labels, num_out_labels),
-        num_parallel_files
+        std::max(num_in_labels, num_out_labels), num_parallel_files,
+        check_other || labels_in_round2.size() || labels_out_round2.size() || config.add_complement
+            || config.label_mask_in_unitig_fraction != 0.0
+            || config.label_mask_out_unitig_fraction != 1.0
+            || config.label_mask_other_unitig_fraction != 1.0
     );
     auto &[counts, init_mask] = count_vector;
 
     // in and out counts are stored interleaved in the counts vector
     assert(counts.size() == init_mask.size() * 2);
 
-    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
 
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
@@ -303,7 +307,8 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
                                   size_t num_labels,
-                                  size_t num_threads) {
+                                  size_t num_threads,
+                                  bool add_out_labels_to_mask) {
     logger->trace("Allocating mask vector");
     size_t width = sdsl::bits::hi(num_labels) + 1;
     sdsl::bit_vector indicator(anno_graph.get_graph().max_index() + 1, false);
@@ -339,9 +344,11 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
             atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
         };
 
-        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel](node_index i) {
+        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](node_index i) {
             assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED);
+            if (add_out_labels_to_mask)
+                set_bit(indicator.data(), i, parallel, MO_RELAXED);
+
             atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
         };
 

From 13713d41048e6d313373090e76d3dd689e2f717d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 10 Aug 2022 16:29:30 +0200
Subject: [PATCH 013/410] only take reverse complements if needed

---
 metagraph/src/cli/assemble.cpp                   |  8 ++------
 .../src/graph/annotated_graph_algorithm.cpp      | 16 ++++++++++------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index cf5254b17c..480a064136 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -46,10 +46,8 @@ void check_labels(const tsl::hopscotch_set<std::string> &label_set,
         exit(1);
 }
 
-DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment,
-                                                const DeBruijnGraph &graph) {
+DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     DifferentialAssemblyConfig diff_config;
-    diff_config.add_complement = graph.get_mode() == DeBruijnGraph::CANONICAL;
     diff_config.label_mask_in_kmer_fraction = experiment.get("in_min_fraction", 1.0).asDouble();
     diff_config.label_mask_in_unitig_fraction = experiment.get("unitig_in_min_fraction", 0.0).asDouble();
     diff_config.label_mask_out_kmer_fraction = experiment.get("out_max_fraction", 0.0).asDouble();
@@ -111,9 +109,7 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
             tsl::hopscotch_set<std::string> foreground_labels;
             tsl::hopscotch_set<std::string> background_labels;
 
-            DifferentialAssemblyConfig diff_config = diff_assembly_config(
-                experiment, anno_graph.get_graph()
-            );
+            DifferentialAssemblyConfig diff_config = diff_assembly_config(experiment);
 
             std::string exp_name = experiment["name"].asString()
                                     + (config->enumerate_out_sequences ? "." : "");
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fb298af9f1..1608ac670d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -78,16 +78,21 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
     );
 
     bool check_other = config.label_mask_other_unitig_fraction != 1.0;
+    bool unitig_mode = check_other || labels_in_round2.size() || labels_out_round2.size()
+            || config.label_mask_in_unitig_fraction != 0.0
+            || config.label_mask_out_unitig_fraction != 1.0
+            || config.label_mask_other_unitig_fraction != 1.0;
+
+    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
+        && (config.add_complement || unitig_mode);
+
     logger->trace("Generating initial mask");
 
     // Construct initial masked graph from union of labels in labels_in
     auto count_vector = construct_diff_label_count_vector(
         anno_graph, labels_in, labels_out,
         std::max(num_in_labels, num_out_labels), num_parallel_files,
-        check_other || labels_in_round2.size() || labels_out_round2.size() || config.add_complement
-            || config.label_mask_in_unitig_fraction != 0.0
-            || config.label_mask_out_unitig_fraction != 1.0
-            || config.label_mask_other_unitig_fraction != 1.0
+        add_complement
     );
     auto &[counts, init_mask] = count_vector;
 
@@ -97,7 +102,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
 
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
-                                                  config.add_complement, num_threads);
+                                                  add_complement, num_threads);
 
     // check all other labels and post labels
     if (check_other || labels_in_round2.size() || labels_out_round2.size()) {
@@ -259,7 +264,6 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     // counts interleaved
     assert(counts.size() == mask.size() * 2);
 
-    add_complement |= graph_ptr->get_mode() == DeBruijnGraph::CANONICAL;
     auto masked_graph = std::make_shared<MaskedDeBruijnGraph>(
         graph_ptr,
         add_complement

From 120f3ae4561b67edda24da77d18e5ab486428684 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 11 Aug 2022 11:06:37 +0200
Subject: [PATCH 014/410] refactor

---
 .../src/annotation/binary_matrix/base/binary_matrix.cpp    | 2 +-
 .../src/annotation/binary_matrix/base/binary_matrix.hpp    | 4 ++--
 .../binary_matrix/column_sparse/column_major.cpp           | 2 +-
 .../binary_matrix/column_sparse/column_major.hpp           | 2 +-
 .../src/annotation/representation/base/annotation.hpp      | 2 +-
 .../column_compressed/annotate_column_compressed.cpp       | 7 +++----
 .../column_compressed/annotate_column_compressed.hpp       | 5 ++---
 .../column_compressed/column_compressed_lazy.hpp           | 3 +--
 metagraph/src/common/vectors/bitmap.hpp                    | 6 ++++++
 metagraph/src/graph/annotated_dbg.cpp                      | 4 ++--
 metagraph/src/graph/annotated_dbg.hpp                      | 4 +++-
 metagraph/src/graph/annotated_graph_algorithm.cpp          | 2 +-
 metagraph/src/graph/annotated_graph_algorithm.hpp          | 2 --
 13 files changed, 24 insertions(+), 21 deletions(-)

diff --git a/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp b/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp
index 580be8e401..fb32ce927b 100644
--- a/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp
+++ b/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp
@@ -39,7 +39,7 @@ BinaryMatrix::slice_rows(const std::vector<Row> &row_ids) const {
 }
 
 void BinaryMatrix::call_columns(const std::vector<Column> &column_ids,
-                                const std::function<void(size_t, const bitmap&)> &callback,
+                                const std::function<void(size_t, const bitmap_generator&)> &callback,
                                 size_t num_threads) const {
     ProgressBar progress_bar(column_ids.size(), "Parsing columns",
                              std::cerr, !common::get_verbose());
diff --git a/metagraph/src/annotation/binary_matrix/base/binary_matrix.hpp b/metagraph/src/annotation/binary_matrix/base/binary_matrix.hpp
index b9d4815bc2..f5f8a166ce 100644
--- a/metagraph/src/annotation/binary_matrix/base/binary_matrix.hpp
+++ b/metagraph/src/annotation/binary_matrix/base/binary_matrix.hpp
@@ -9,7 +9,7 @@
 #include "common/vector.hpp"
 
 
-class bitmap;
+class bitmap_generator;
 
 namespace mtg {
 namespace annot {
@@ -41,7 +41,7 @@ class BinaryMatrix {
     // For each column id in columns, run callback on its respective index in columns
     // and a bitmap represnting the column
     virtual void call_columns(const std::vector<Column> &columns,
-                              const std::function<void(size_t, const bitmap&)> &callback,
+                              const std::function<void(size_t, const bitmap_generator&)> &callback,
                               size_t num_threads = 1) const;
 
     virtual bool load(std::istream &in) = 0;
diff --git a/metagraph/src/annotation/binary_matrix/column_sparse/column_major.cpp b/metagraph/src/annotation/binary_matrix/column_sparse/column_major.cpp
index acf930a484..30f4bf4a13 100644
--- a/metagraph/src/annotation/binary_matrix/column_sparse/column_major.cpp
+++ b/metagraph/src/annotation/binary_matrix/column_sparse/column_major.cpp
@@ -104,7 +104,7 @@ ColumnMajor::slice_rows(const std::vector<Row> &row_ids) const {
 }
 
 void ColumnMajor::call_columns(const std::vector<Column> &columns,
-                               const std::function<void(size_t, const bitmap&)> &callback,
+                               const std::function<void(size_t, const bitmap_generator&)> &callback,
                                size_t num_threads) const {
     #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
     for (size_t j = 0; j < columns.size(); ++j) {
diff --git a/metagraph/src/annotation/binary_matrix/column_sparse/column_major.hpp b/metagraph/src/annotation/binary_matrix/column_sparse/column_major.hpp
index a473e6f1d0..4d29932c2b 100644
--- a/metagraph/src/annotation/binary_matrix/column_sparse/column_major.hpp
+++ b/metagraph/src/annotation/binary_matrix/column_sparse/column_major.hpp
@@ -32,7 +32,7 @@ class ColumnMajor : public BinaryMatrix {
     std::vector<Column> slice_rows(const std::vector<Row> &rows) const override;
 
     void call_columns(const std::vector<Column> &columns,
-                      const std::function<void(size_t, const bitmap&)> &callback,
+                      const std::function<void(size_t, const bitmap_generator&)> &callback,
                       size_t num_threads = 1) const override;
 
     bool load(std::istream &in) override;
diff --git a/metagraph/src/annotation/representation/base/annotation.hpp b/metagraph/src/annotation/representation/base/annotation.hpp
index a26d0717da..95c9b89904 100644
--- a/metagraph/src/annotation/representation/base/annotation.hpp
+++ b/metagraph/src/annotation/representation/base/annotation.hpp
@@ -20,7 +20,7 @@ namespace annot {
 template <typename IndexType, typename LabelType>
 class Annotation {
   public:
-    typedef std::function<void(size_t, const bitmap&)> LabelIndexCallback;
+    typedef std::function<void(size_t, const bitmap_generator&)> LabelIndexCallback;
     virtual ~Annotation() {}
 
     virtual uint64_t num_objects() const = 0;
diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
index 905cdb882a..e25df442b9 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
@@ -596,8 +596,7 @@ template <typename Label>
 bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenames,
                                          const ColumnCallback &callback,
                                          size_t num_threads,
-                                         bool prefetch_total_column_count,
-                                         const std::function<void(size_t)> &post_num_columns) {
+                                         const std::optional<std::function<void(size_t)>> &num_columns_callback) {
     if (!num_threads)
         num_threads = 1;
 
@@ -623,7 +622,7 @@ bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenam
     // compute global offsets (partial sums)
     std::partial_sum(offsets.begin(), offsets.end(), offsets.begin());
 
-    if (prefetch_total_column_count) {
+    if (num_columns_callback) {
         size_t total_count = offsets.back();
         auto fname = make_suffix(filenames.back(), kExtension);
         try {
@@ -636,7 +635,7 @@ bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenam
         if (error_occurred)
             return false;
 
-        post_num_columns(total_count);
+        (*num_columns_callback)(total_count);
     }
 
     // load annotations
diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
index f54f138dbd..2ab62a78c9 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
@@ -86,9 +86,8 @@ class ColumnCompressed : public MultiLabelEncoded<Label> {
     static bool merge_load(const std::vector<std::string> &filenames,
                            const ColumnCallback &callback,
                            size_t num_threads = 1,
-                           bool prefetch_total_column_count = false,
-                           const std::function<void(size_t)> &post_num_columns
-                               = [](size_t) {});
+                           const std::optional<std::function<void(size_t)>> &num_columns_callback
+                               = std::nullopt);
     static size_t read_num_labels(const std::string &filename);
     static LabelEncoder<Label> load_label_encoder(const std::string &filename);
 
diff --git a/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp b/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
index cd90cbb514..cf7d9e59f1 100644
--- a/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
+++ b/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
@@ -47,8 +47,7 @@ class ColumnCompressedLazy
                 ++(*progress_bar);
             },
             num_threads,
-            true,
-            [&](size_t total_column_count) {
+            [&progress_bar](size_t total_column_count) {
                 progress_bar = std::make_unique<ProgressBar>(
                     total_column_count, "Streaming columns",
                     std::cerr, !common::get_verbose()
diff --git a/metagraph/src/common/vectors/bitmap.hpp b/metagraph/src/common/vectors/bitmap.hpp
index 5bba68af7d..9f8d68438a 100644
--- a/metagraph/src/common/vectors/bitmap.hpp
+++ b/metagraph/src/common/vectors/bitmap.hpp
@@ -198,6 +198,12 @@ class bitmap_generator : public bitmap {
                            std::for_each(s.begin(), s.end(), callback);
                        }) {}
 
+    bitmap_generator(const bitmap &bits) noexcept
+          : size_(bits.size()), num_set_bits_(bits.num_set_bits()),
+            generator_([&bits](const VoidCall<uint64_t> &callback) {
+                           bits.call_ones(callback);
+                       }) {}
+
     bitmap_generator(std::function<void(const VoidCall<uint64_t>&)>&& generator,
                      size_t size,
                      size_t num_set_bits = -1) noexcept;
diff --git a/metagraph/src/graph/annotated_dbg.cpp b/metagraph/src/graph/annotated_dbg.cpp
index 3bf8bbf41a..ef8a36be99 100644
--- a/metagraph/src/graph/annotated_dbg.cpp
+++ b/metagraph/src/graph/annotated_dbg.cpp
@@ -843,11 +843,11 @@ bool AnnotatedSequenceGraph::has_label(node_index index, const Label &label) con
 
 void AnnotatedSequenceGraph
 ::call_annotated_nodes(const Annotator::VLabels &labels,
-                       const std::function<void(size_t, const bitmap&)> &callback,
+                       const std::function<void(size_t, const bitmap_generator&)> &callback,
                        size_t num_threads) const {
     assert(check_compatibility());
 
-    annotation_->call_label_objects(labels, [&](size_t j, const bitmap &bitmap) {
+    annotation_->call_label_objects(labels, [&](size_t j, const bitmap_generator &bitmap) {
         callback(j, bitmap_generator([&](const auto &call_one) {
             bitmap.call_ones([&](uint64_t i) { call_one(anno_to_graph_index(i)); });
         }, bitmap.size() + 1, bitmap.num_set_bits()));
diff --git a/metagraph/src/graph/annotated_dbg.hpp b/metagraph/src/graph/annotated_dbg.hpp
index 3129579347..47eb014baa 100644
--- a/metagraph/src/graph/annotated_dbg.hpp
+++ b/metagraph/src/graph/annotated_dbg.hpp
@@ -12,6 +12,8 @@
 #include "common/vector.hpp"
 
 
+class bitmap_generator;
+
 namespace mtg {
 namespace graph {
 
@@ -56,7 +58,7 @@ class AnnotatedSequenceGraph {
     virtual const Annotation& get_annotation() const { return *annotation_; }
 
     virtual void call_annotated_nodes(const Annotator::VLabels &labels,
-                                      const std::function<void(size_t, const bitmap&)> &callback,
+                                      const std::function<void(size_t, const bitmap_generator&)> &callback,
                                       size_t num_threads = 1) const;
 
     static row_index graph_to_anno_index(node_index kmer_index) {
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 1608ac670d..8995c875b0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -337,7 +337,7 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
 
     logger->trace("Populating count vector");
     std::atomic_thread_fence(std::memory_order_release);
-    anno_graph.call_annotated_nodes(labels, [&](size_t j, const bitmap &column) {
+    anno_graph.call_annotated_nodes(labels, [&](size_t j, const bitmap_generator &column) {
         uint8_t col_indicator = static_cast<bool>(labels_in.count(labels[j]));
         if (labels_out.count(labels[j]))
             col_indicator |= 2;
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 3deb560c19..cb72eda8ef 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -1,8 +1,6 @@
 #ifndef __ANNOTATED_GRAPH_ALGORITHM_HPP__
 #define __ANNOTATED_GRAPH_ALGORITHM_HPP__
 
-#include <vector>
-
 #include <tsl/hopscotch_set.h>
 
 #include "graph/annotated_dbg.hpp"

From 8acf605441649e0eae3ab5a4ff5d109350d99bcf Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 11 Aug 2022 11:08:49 +0200
Subject: [PATCH 015/410] minor

---
 .../column_compressed/annotate_column_compressed.cpp     | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
index e25df442b9..832443882d 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
@@ -593,10 +593,11 @@ ColumnCompressed<Label>::load_label_encoder(const std::string &filename) {
 }
 
 template <typename Label>
-bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenames,
-                                         const ColumnCallback &callback,
-                                         size_t num_threads,
-                                         const std::optional<std::function<void(size_t)>> &num_columns_callback) {
+bool ColumnCompressed<Label>
+::merge_load(const std::vector<std::string> &filenames,
+             const ColumnCallback &callback,
+             size_t num_threads,
+             const std::optional<std::function<void(size_t)>> &num_columns_callback) {
     if (!num_threads)
         num_threads = 1;
 

From 44854ba9c384f6fd2911f9be6b11e9d575a3bb80 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <hmustafa@login-biomed-01>
Date: Mon, 30 Jan 2023 10:45:57 +0100
Subject: [PATCH 016/410] cleanup

---
 .../representation/base/annotation.hpp        |  27 +---
 .../annotate_column_compressed.cpp            | 134 ++++++++----------
 .../annotate_column_compressed.hpp            |   6 +-
 .../column_compressed_lazy.hpp                |  69 ---------
 metagraph/src/cli/config/config.cpp           |   4 +-
 .../src/cli/load/load_annotated_graph.cpp     |  52 +++----
 metagraph/src/graph/annotated_dbg.cpp         |  31 ++--
 metagraph/src/graph/annotated_dbg.hpp         |  25 +---
 .../tests/annotation/test_annotated_dbg.cpp   |  17 ++-
 9 files changed, 107 insertions(+), 258 deletions(-)
 delete mode 100644 metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp

diff --git a/metagraph/src/annotation/representation/base/annotation.hpp b/metagraph/src/annotation/representation/base/annotation.hpp
index 95c9b89904..7b555c4d16 100644
--- a/metagraph/src/annotation/representation/base/annotation.hpp
+++ b/metagraph/src/annotation/representation/base/annotation.hpp
@@ -17,21 +17,9 @@
 namespace mtg {
 namespace annot {
 
-template <typename IndexType, typename LabelType>
-class Annotation {
-  public:
-    typedef std::function<void(size_t, const bitmap_generator&)> LabelIndexCallback;
-    virtual ~Annotation() {}
-
-    virtual uint64_t num_objects() const = 0;
-
-    virtual void call_label_objects(const LabelType &labels,
-                                    const LabelIndexCallback &callback,
-                                    size_t num_threads = 1) const = 0;
-};
 
 template <typename IndexType, typename LabelType>
-class AnnotationCategory : public Annotation<IndexType, LabelType> {
+class AnnotationCategory {
   public:
     virtual ~AnnotationCategory() {}
 
@@ -83,6 +71,7 @@ class MultiLabelAnnotation
 
     /************************* Properties *************************/
 
+    virtual uint64_t num_objects() const = 0;
     virtual size_t num_labels() const = 0;
     virtual uint64_t num_relations() const = 0;
     virtual const std::vector<Label>& get_all_labels() const = 0;
@@ -145,7 +134,6 @@ class MultiLabelEncoded : public MultiLabelAnnotation<uint64_t, LabelType> {
     using Index = typename MultiLabelAnnotation<uint64_t, LabelType>::Index;
     using Label = typename MultiLabelAnnotation<uint64_t, LabelType>::Label;
     using VLabels = typename MultiLabelAnnotation<uint64_t, LabelType>::VLabels;
-    using LabelIndexCallback = typename Annotation<uint64_t, VLabels>::LabelIndexCallback;
 
     virtual ~MultiLabelEncoded() {}
 
@@ -182,17 +170,6 @@ class MultiLabelEncoded : public MultiLabelAnnotation<uint64_t, LabelType> {
 
     virtual const binmat::BinaryMatrix& get_matrix() const = 0;
 
-    virtual void call_label_objects(const VLabels &labels,
-                                    const LabelIndexCallback &callback,
-                                    size_t num_threads = 1) const override final {
-        std::vector<binmat::BinaryMatrix::Column> columns;
-        columns.reserve(labels.size());
-        for (const Label &label : labels) {
-            columns.emplace_back(label_encoder_.encode(label));
-        }
-        get_matrix().call_columns(columns, callback, num_threads);
-    }
-
   protected:
     LabelEncoder<Label> label_encoder_;
 };
diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
index 684e16cb68..fb7a2558dc 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
@@ -8,6 +8,7 @@
 #include <ips4o.hpp>
 
 #include "common/serialization.hpp"
+#include "common/utils/file_utils.hpp"
 #include "common/utils/string_utils.hpp"
 #include "common/logger.hpp"
 #include "common/unix_tools.hpp"
@@ -215,7 +216,7 @@ template <typename Label>
 void ColumnCompressed<Label>::serialize(const std::string &filename) const {
     flush();
 
-    std::ofstream out(make_suffix(filename, kExtension), std::ios::binary);
+    std::ofstream out = utils::open_new_ofstream(make_suffix(filename, kExtension));
     if (!out) {
         logger->error("Could not open file for writing: {}",
                       make_suffix(filename, kExtension));
@@ -243,7 +244,7 @@ void ColumnCompressed<Label>::serialize(const std::string &filename) const {
 template <typename Label>
 void ColumnCompressed<Label>::serialize_counts(const std::string &filename) const {
     auto counts_fname = remove_suffix(filename, kExtension) + kCountExtension;
-    std::ofstream out(counts_fname, std::ios::binary);
+    std::ofstream out = utils::open_new_ofstream(counts_fname);
     if (!out) {
         logger->error("Could not open file for writing: {}", counts_fname);
         throw std::ofstream::failure("Bad stream");
@@ -308,7 +309,7 @@ void ColumnCompressed<Label>::serialize_counts(const std::string &filename) cons
 template <typename Label>
 void ColumnCompressed<Label>::serialize_coordinates(const std::string &filename) const {
     auto coords_fname = remove_suffix(filename, kExtension) + kCoordExtension;
-    std::ofstream out(coords_fname, std::ios::binary);
+    std::ofstream out = utils::open_new_ofstream(coords_fname);
     if (!out) {
         logger->error("Could not open file for writing: {}", coords_fname);
         throw std::ofstream::failure("Bad stream");
@@ -448,8 +449,8 @@ void ColumnCompressed<Label>::serialize_coordinates(const std::string &filename)
 
             {
                 sdsl::bit_vector delim_bv;
-                std::ifstream in(tmp_path/"delim", std::ios::binary);
-                delim_bv.load(in);
+                std::unique_ptr<std::ifstream> in = utils::open_ifstream(tmp_path/"delim");
+                delim_bv.load(*in);
                 bit_vector_smart(std::move(delim_bv)).serialize(out);
             }
 
@@ -483,7 +484,8 @@ bool ColumnCompressed<Label>::load(const std::string &filename) {
     logger->trace("Loading annotations from file {}", f);
 
     try {
-        std::ifstream in(f, std::ios::binary);
+        std::unique_ptr<std::ifstream> in_ptr = utils::open_ifstream(f);
+        auto &in = *in_ptr;
         if (!in.good())
             throw std::ifstream::failure("can't open file");
 
@@ -576,12 +578,12 @@ bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenam
 
 template <typename Label>
 size_t ColumnCompressed<Label>::read_num_labels(const std::string &filename) {
-    return load_label_encoder(filename).size();
+    return read_label_encoder(filename).size();
 }
 
 template <typename Label>
 LabelEncoder<Label>
-ColumnCompressed<Label>::load_label_encoder(const std::string &filename) {
+ColumnCompressed<Label>::read_label_encoder(const std::string &filename) {
     auto fname = make_suffix(filename, kExtension);
     std::ifstream in(filename, std::ios::binary);
     std::ignore = load_number(in); // read num_rows
@@ -592,14 +594,9 @@ ColumnCompressed<Label>::load_label_encoder(const std::string &filename) {
 }
 
 template <typename Label>
-bool ColumnCompressed<Label>
-::merge_load(const std::vector<std::string> &filenames,
-             const ColumnCallback &callback,
-             size_t num_threads,
-             const std::optional<std::function<void(size_t)>> &num_columns_callback) {
-    if (!num_threads)
-        num_threads = 1;
-
+bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenames,
+                                         const ColumnCallback &callback,
+                                         size_t num_threads) {
     std::atomic<bool> error_occurred = false;
 
     std::vector<uint64_t> offsets(filenames.size(), 0);
@@ -622,70 +619,46 @@ ::merge_load(const std::vector<std::string> &filenames,
     // compute global offsets (partial sums)
     std::partial_sum(offsets.begin(), offsets.end(), offsets.begin());
 
-    if (num_columns_callback) {
-        size_t total_count = offsets.back();
-        auto fname = make_suffix(filenames.back(), kExtension);
+    // load annotations
+    #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+    for (size_t i = 0; i < filenames.size(); ++i) {
+        const auto &filename = make_suffix(filenames[i], kExtension);
+        logger->trace("Loading annotations from file {}", filename);
         try {
-            total_count += read_num_labels(fname);
-        } catch (...) {
-            logger->error("Can't load label encoder from {}", fname);
-            error_occurred = true;
-        }
+            std::unique_ptr<std::ifstream> in_ptr = utils::open_ifstream(filename);
+            auto &in = *in_ptr;
+            if (!in.good())
+                throw std::ifstream::failure("can't open file");
 
-        if (error_occurred)
-            return false;
+            const auto num_rows = load_number(in);
 
-        (*num_columns_callback)(total_count);
-    }
+            LabelEncoder<Label> label_encoder_load;
+            if (!label_encoder_load.load(in))
+                throw std::ifstream::failure("can't load label encoder");
 
-    // load annotations
-    #pragma omp parallel num_threads(num_threads) shared(error_occurred, filenames, offsets, logger)
-    #pragma omp single
-    {
-        for (size_t i = 0; i < filenames.size(); ++i) {
-            const auto &filename = make_suffix(filenames[i], kExtension);
-            logger->trace("Loading annotations from file {}", filename);
-            try {
-                std::ifstream in(filename, std::ios::binary);
-                if (!in.good())
-                    throw std::ifstream::failure("can't open file");
-
-                const auto num_rows = load_number(in);
-
-                LabelEncoder<Label> label_encoder_load;
-                if (!label_encoder_load.load(in))
-                    throw std::ifstream::failure("can't load label encoder");
-
-                if (!label_encoder_load.size())
-                    logger->warn("No columns in {}", filename);
-
-                // update the existing and add some new columns
-                for (size_t c = 0; c < label_encoder_load.size(); ++c) {
-                    auto *new_column = new bit_vector_smart();
-
-                    if (!new_column->load(in)) {
-                        delete new_column;
-                        throw std::ifstream::failure("can't load next column");
-                    }
+            if (!label_encoder_load.size())
+                logger->warn("No columns in {}", filename);
 
-                    if (new_column->size() != num_rows) {
-                        delete new_column;
-                        throw std::ifstream::failure("inconsistent column size");
-                    }
+            // update the existing and add some new columns
+            for (size_t c = 0; c < label_encoder_load.size(); ++c) {
+                auto new_column = std::make_unique<bit_vector_smart>();
 
-                    uint64_t offset = offsets[i] + c;
-                    Label label = label_encoder_load.decode(c);
+                if (!new_column->load(in))
+                    throw std::ifstream::failure("can't load next column");
 
-                    #pragma omp task firstprivate(new_column, label, offset)
-                    callback(offset, label, std::unique_ptr<bit_vector_smart>(new_column));
-                }
-            } catch (const std::exception &e) {
-                logger->error("Caught exception when loading columns from {}: {}", filename, e.what());
-                error_occurred = true;
-            } catch (...) {
-                logger->error("Unknown exception when loading columns from {}", filename);
-                error_occurred = true;
+                if (new_column->size() != num_rows)
+                    throw std::ifstream::failure("inconsistent column size");
+
+                callback(offsets[i] + c,
+                         label_encoder_load.decode(c),
+                         std::move(new_column));
             }
+        } catch (const std::exception &e) {
+            logger->error("Caught exception when loading columns from {}: {}", filename, e.what());
+            error_occurred = true;
+        } catch (...) {
+            logger->error("Unknown exception when loading columns from {}", filename);
+            error_occurred = true;
         }
     }
 
@@ -725,7 +698,7 @@ ::load_column_values(const std::vector<std::string> &filenames,
         const auto &filename = make_suffix(filenames[i], kExtension);
         logger->trace("Loading labels from {}", filename);
         try {
-            LabelEncoder<Label> label_encoder_load = load_label_encoder(filename);
+            LabelEncoder<Label> label_encoder_load = read_label_encoder(filename);
 
             if (!label_encoder_load.size()) {
                 logger->warn("No columns in {}", filename);
@@ -735,7 +708,8 @@ ::load_column_values(const std::vector<std::string> &filenames,
             const auto &values_fname
                 = remove_suffix(filename, kExtension) + kCountExtension;
 
-            std::ifstream values_in(values_fname, std::ios::binary);
+            std::unique_ptr<std::ifstream> in_ptr = utils::open_ifstream(values_fname);
+            auto &values_in = *in_ptr;
             if (!values_in)
                 throw std::ifstream::failure("can't open file " + values_fname);
 
@@ -799,7 +773,8 @@ ::load_columns_and_values(const std::vector<std::string> &filenames,
         const auto &filename = make_suffix(filenames[i], kExtension);
         logger->trace("Loading columns from {}", filename);
         try {
-            std::ifstream in(filename, std::ios::binary);
+            std::unique_ptr<std::ifstream> in_ptr = utils::open_ifstream(filename);
+            auto &in = *in_ptr;
             if (!in)
                 throw std::ifstream::failure("can't open file");
 
@@ -818,7 +793,8 @@ ::load_columns_and_values(const std::vector<std::string> &filenames,
                 = utils::remove_suffix(filename, ColumnCompressed<>::kExtension)
                                                 + ColumnCompressed<>::kCountExtension;
 
-            std::ifstream values_in(values_fname, std::ios::binary);
+            std::unique_ptr<std::ifstream> values_in_ptr = utils::open_ifstream(values_fname);
+            auto &values_in = *values_in_ptr;
             if (!values_in)
                 throw std::ifstream::failure("can't open file " + values_fname);
 
@@ -891,7 +867,8 @@ void ColumnCompressed<Label>::load_columns_delims_and_values(
         const auto &filename = make_suffix(filenames[i], kExtension);
         logger->trace("Loading columns from {}", filename);
         try {
-            std::ifstream in(filename, std::ios::binary);
+            std::unique_ptr<std::ifstream> in_ptr = utils::open_ifstream(filename);
+            auto &in = *in_ptr;
             if (!in)
                 throw std::ifstream::failure("can't open file");
 
@@ -910,7 +887,8 @@ void ColumnCompressed<Label>::load_columns_delims_and_values(
                     = utils::remove_suffix(filename, ColumnCompressed<>::kExtension)
                     + ColumnCompressed<>::kCoordExtension;
 
-            std::ifstream coord_in(coords_fname, std::ios::binary);
+            std::unique_ptr<std::ifstream> coords_in_ptr = utils::open_ifstream(coords_fname);
+            auto &coord_in = *coords_in_ptr;
             if (!coord_in)
                 throw std::ifstream::failure("can't open file " + coords_fname);
 
diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
index 2fe42ac713..1a01b73a1f 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
@@ -86,11 +86,9 @@ class ColumnCompressed : public MultiLabelEncoded<Label> {
                                               std::unique_ptr<bit_vector>&&)>;
     static bool merge_load(const std::vector<std::string> &filenames,
                            const ColumnCallback &callback,
-                           size_t num_threads = 1,
-                           const std::optional<std::function<void(size_t)>> &num_columns_callback
-                               = std::nullopt);
+                           size_t num_threads = 1);
     static size_t read_num_labels(const std::string &filename);
-    static LabelEncoder<Label> load_label_encoder(const std::string &filename);
+    static LabelEncoder<Label> read_label_encoder(const std::string &filename);
 
     using ValuesCallback = std::function<void(uint64_t offset,
                                               const Label &,
diff --git a/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp b/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
deleted file mode 100644
index cf7d9e59f1..0000000000
--- a/metagraph/src/annotation/representation/column_compressed/column_compressed_lazy.hpp
+++ /dev/null
@@ -1,69 +0,0 @@
-#ifndef __COLUMN_COMPRESSED_LAZY_HPP__
-#define __COLUMN_COMPRESSED_LAZY_HPP__
-
-#include <tsl/hopscotch_map.h>
-#include <progress_bar.hpp>
-
-#include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
-
-namespace mtg {
-namespace annot {
-
-template <typename Label = std::string>
-class ColumnCompressedLazy
-      : public Annotation<typename MultiLabelEncoded<Label>::Index,
-                          typename MultiLabelEncoded<Label>::VLabels> {
-  public:
-    using Index = typename MultiLabelEncoded<Label>::Index;
-    using VLabels = typename MultiLabelEncoded<Label>::VLabels;
-    using LabelIndexCallback = typename MultiLabelEncoded<Label>::LabelIndexCallback;
-
-    ColumnCompressedLazy(size_t num_objects, const std::vector<std::string> &files)
-          : num_objects_(num_objects), files_(files) {}
-
-    uint64_t num_objects() const override final { return num_objects_; }
-
-    void call_label_objects(const VLabels &labels,
-                            const LabelIndexCallback &callback,
-                            size_t num_threads = 1) const override final {
-        tsl::hopscotch_map<Label, size_t> label_set;
-        for (size_t i = 0; i < labels.size(); ++i) {
-            label_set[labels[i]] = i;
-        }
-
-        std::unique_ptr<ProgressBar> progress_bar;
-        ColumnCompressed<Label>::merge_load(files_,
-            [&](size_t, const Label &label, auto&& bitmap) {
-                if (bitmap->size() != num_objects_) {
-                    common::logger->error("Label {} has incorrect number of rows: {} != {}",
-                                          bitmap->size(), num_objects_);
-                    exit(1);
-                }
-
-                auto find = label_set.find(label);
-                if (find != label_set.end())
-                    callback(find->second, *bitmap);
-
-                ++(*progress_bar);
-            },
-            num_threads,
-            [&progress_bar](size_t total_column_count) {
-                progress_bar = std::make_unique<ProgressBar>(
-                    total_column_count, "Streaming columns",
-                    std::cerr, !common::get_verbose()
-                );
-            }
-        );
-    }
-
-    const std::string& get_file(size_t i) const { return files_[i]; }
-
-  private:
-    size_t num_objects_;
-    const std::vector<std::string> &files_;
-};
-
-} // namespace annot
-} // namespace mtg
-
-#endif // __COLUMN_COMPRESSED_LAZY_HPP__
diff --git a/metagraph/src/cli/config/config.cpp b/metagraph/src/cli/config/config.cpp
index f5395e8a5a..38c4940109 100644
--- a/metagraph/src/cli/config/config.cpp
+++ b/metagraph/src/cli/config/config.cpp
@@ -115,6 +115,8 @@ Config::Config(int argc, char *argv[]) {
     for (int i = 2; i < argc; ++i) {
         if (!strcmp(argv[i], "-v") || !strcmp(argv[i], "--verbose")) {
             common::set_verbose(true);
+        } else if (!strcmp(argv[i], "--mmap")) {
+            utils::with_mmap(true);
         } else if (!strcmp(argv[i], "--print")) {
             print_graph = true;
         } else if (!strcmp(argv[i], "--advanced")) {
@@ -1147,7 +1149,6 @@ if (advanced) {
             fprintf(stderr, "\t-a --annotator [STR] \t\tannotator to load []\n");
             fprintf(stderr, "\t   --diff-assembly-rules [STR] \tJSON file describing labels to mask in and out and their relative fractions []\n");
             fprintf(stderr, "\t                       \t\tSee the manual for the specification.\n");
-            fprintf(stderr, "\t   --separately \t\tstream annotator (only compatible with column annotator) [off]\n");
         } break;
         case STATS: {
             fprintf(stderr, "Usage: %s stats [options] GRAPH1 [[GRAPH2] ...]\n\n", prog_name.c_str());
@@ -1381,6 +1382,7 @@ if (advanced) {
     }
 
     fprintf(stderr, "\nGeneral options:\n");
+    fprintf(stderr, "\t   --mmap \t\tuse memory mapping when loading to reduce RAM [off]\n");
     fprintf(stderr, "\t-v --verbose \t\tswitch on verbose output [off]\n");
     fprintf(stderr, "\t   --advanced \t\tshow other advanced and legacy options [off]\n");
     fprintf(stderr, "\t-h --help \t\tprint usage info\n");
diff --git a/metagraph/src/cli/load/load_annotated_graph.cpp b/metagraph/src/cli/load/load_annotated_graph.cpp
index 6e1e219e18..14163d8434 100644
--- a/metagraph/src/cli/load/load_annotated_graph.cpp
+++ b/metagraph/src/cli/load/load_annotated_graph.cpp
@@ -5,7 +5,6 @@
 #include "annotation/binary_matrix/row_diff/row_diff.hpp"
 #include "annotation/binary_matrix/row_sparse/row_sparse.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
-#include "annotation/representation/column_compressed/column_compressed_lazy.hpp"
 #include "graph/representation/canonical_dbg.hpp"
 #include "graph/annotated_dbg.hpp"
 #include "common/logger.hpp"
@@ -32,35 +31,20 @@ std::unique_ptr<AnnotatedDBG> initialize_annotated_dbg(std::shared_ptr<DeBruijnG
         logger->trace("Primary graph wrapped into canonical");
     }
 
-    auto annotator_init = config.infbase_annotators.size()
+    auto annotation_temp = config.infbase_annotators.size()
             ? initialize_annotation(config.infbase_annotators.at(0), config, 0, max_chunks_open)
             : initialize_annotation(config.anno_type, config, max_index, max_chunks_open);
-    auto *annotator_temp = annotator_init.get();
-    std::unique_ptr<AnnotatedDBG::Annotation> annotation_temp(annotator_init.release());
-
-    bool try_stream = config.identity == Config::ASSEMBLE && config.separately;
 
     if (config.infbase_annotators.size()) {
         bool loaded = false;
         if (auto *cc = dynamic_cast<annot::ColumnCompressed<>*>(annotation_temp.get())) {
-            if (try_stream) {
-                annotation_temp.reset(new annot::ColumnCompressedLazy<>(
-                    max_index, config.infbase_annotators
-                ));
-                loaded = true;
-                annotator_temp = nullptr;
-            } else {
-                loaded = cc->merge_load(config.infbase_annotators);
-            }
+            loaded = cc->merge_load(config.infbase_annotators);
         } else {
-            if (try_stream)
-                logger->warn("--separately only supported with column annotator");
-
             if (config.infbase_annotators.size() > 1) {
                 logger->warn("Cannot merge annotations of this type. Only the first"
                              " file {} will be loaded.", config.infbase_annotators.at(0));
             }
-            loaded = annotator_temp->load(config.infbase_annotators.at(0));
+            loaded = annotation_temp->load(config.infbase_annotators.at(0));
         }
         if (!loaded) {
             logger->error("Cannot load annotations for graph {}, file corrupted",
@@ -69,22 +53,20 @@ std::unique_ptr<AnnotatedDBG> initialize_annotated_dbg(std::shared_ptr<DeBruijnG
         }
 
         // row_diff annotation is special, as it must know the graph structure
-        if (annotator_temp) {
-            using namespace annot::binmat;
-            BinaryMatrix &matrix = const_cast<BinaryMatrix &>(annotator_temp->get_matrix());
-            if (IRowDiff *row_diff = dynamic_cast<IRowDiff*>(&matrix)) {
-                if (!dbg_graph) {
-                    logger->error("Only succinct de Bruijn graph representations"
-                                  " are supported for row-diff annotations");
-                    std::exit(1);
-                }
-
-                row_diff->set_graph(dbg_graph);
-
-                if (auto *row_diff_column = dynamic_cast<RowDiff<ColumnMajor> *>(&matrix)) {
-                    row_diff_column->load_anchor(config.infbase + kRowDiffAnchorExt);
-                    row_diff_column->load_fork_succ(config.infbase + kRowDiffForkSuccExt);
-                }
+        using namespace annot::binmat;
+        BinaryMatrix &matrix = const_cast<BinaryMatrix &>(annotation_temp->get_matrix());
+        if (IRowDiff *row_diff = dynamic_cast<IRowDiff*>(&matrix)) {
+            if (!dbg_graph) {
+                logger->error("Only succinct de Bruijn graph representations"
+                              " are supported for row-diff annotations");
+                std::exit(1);
+            }
+
+            row_diff->set_graph(dbg_graph);
+
+            if (auto *row_diff_column = dynamic_cast<RowDiff<ColumnMajor> *>(&matrix)) {
+                row_diff_column->load_anchor(config.infbase + kRowDiffAnchorExt);
+                row_diff_column->load_fork_succ(config.infbase + kRowDiffForkSuccExt);
             }
         }
     }
diff --git a/metagraph/src/graph/annotated_dbg.cpp b/metagraph/src/graph/annotated_dbg.cpp
index aba404b518..e47c291a36 100644
--- a/metagraph/src/graph/annotated_dbg.cpp
+++ b/metagraph/src/graph/annotated_dbg.cpp
@@ -11,7 +11,6 @@
 #include "common/vectors/vector_algorithm.hpp"
 #include "common/vector_map.hpp"
 #include "common/logger.hpp"
-#include "common/vectors/bitmap.hpp"
 
 
 namespace mtg {
@@ -27,17 +26,16 @@ typedef std::pair<Label, size_t> StringCountPair;
 
 AnnotatedSequenceGraph
 ::AnnotatedSequenceGraph(std::shared_ptr<SequenceGraph> graph,
-                         std::unique_ptr<Annotation>&& annotation,
+                         std::unique_ptr<Annotator>&& annotation,
                          bool force_fast)
-      : graph_(graph), annotation_(std::move(annotation)),
-        annotator_(std::shared_ptr<Annotator>{}, dynamic_cast<Annotator*>(annotation_.get())),
-        force_fast_(force_fast) {
+      : graph_(graph), annotator_(std::move(annotation)), force_fast_(force_fast) {
     assert(graph_.get());
+    assert(annotator_.get());
     assert(check_compatibility());
 }
 
 AnnotatedDBG::AnnotatedDBG(std::shared_ptr<DeBruijnGraph> dbg,
-                           std::unique_ptr<Annotation>&& annotation,
+                           std::unique_ptr<Annotator>&& annotation,
                            bool force_fast)
       : AnnotatedSequenceGraph(dbg, std::move(annotation), force_fast), dbg_(*dbg) {}
 
@@ -149,7 +147,6 @@ void AnnotatedDBG::add_kmer_coord(std::string_view sequence,
         // only insert coordinates for matched k-mers and increment the coordinates
         if (i > 0)
             annotator_->add_label_coord(graph_to_anno_index(i), labels, coord);
-
         coord++;
     }
 }
@@ -175,7 +172,6 @@ void AnnotatedDBG::add_kmer_coords(
             // only insert coordinates for matched k-mers and increment the coordinates
             if (i > 0)
                 annotator_->add_label_coord(graph_to_anno_index(i), labels, coord);
-
             coord++;
         }
     }
@@ -824,7 +820,6 @@ AnnotatedDBG::get_top_labels(const std::vector<std::pair<row_index, size_t>> &in
 }
 
 bool AnnotatedSequenceGraph::label_exists(const Label &label) const {
-    assert(annotator_.get());
     return annotator_->label_exists(label);
 }
 
@@ -836,24 +831,22 @@ bool AnnotatedSequenceGraph::has_label(node_index index, const Label &label) con
 }
 
 void AnnotatedSequenceGraph
-::call_annotated_nodes(const Annotator::VLabels &labels,
-                       const std::function<void(size_t, const bitmap_generator&)> &callback,
-                       size_t num_threads) const {
+::call_annotated_nodes(const Label &label,
+                       std::function<void(node_index)> callback) const {
     assert(check_compatibility());
 
-    annotation_->call_label_objects(labels, [&](size_t j, const bitmap_generator &bitmap) {
-        callback(j, bitmap_generator([&](const auto &call_one) {
-            bitmap.call_ones([&](uint64_t i) { call_one(anno_to_graph_index(i)); });
-        }, bitmap.size() + 1, bitmap.num_set_bits()));
-    }, num_threads);
+    annotator_->call_objects(
+        label,
+        [&](row_index index) { callback(anno_to_graph_index(index)); }
+    );
 }
 
 bool AnnotatedSequenceGraph::check_compatibility() const {
     // TODO: what if CanonicalDBG is not the highest level? find a better way to do this
     if (const auto *canonical = dynamic_cast<const CanonicalDBG *>(graph_.get()))
-        return canonical->get_graph().max_index() == annotation_->num_objects();
+        return canonical->get_graph().max_index() == annotator_->num_objects();
 
-    return graph_->max_index() == annotation_->num_objects();
+    return graph_->max_index() == annotator_->num_objects();
 }
 
 
diff --git a/metagraph/src/graph/annotated_dbg.hpp b/metagraph/src/graph/annotated_dbg.hpp
index 47eb014baa..db5c2bd8e3 100644
--- a/metagraph/src/graph/annotated_dbg.hpp
+++ b/metagraph/src/graph/annotated_dbg.hpp
@@ -12,8 +12,6 @@
 #include "common/vector.hpp"
 
 
-class bitmap_generator;
-
 namespace mtg {
 namespace graph {
 
@@ -21,13 +19,11 @@ class AnnotatedSequenceGraph {
   public:
     typedef std::string Label;
     typedef annot::MultiLabelEncoded<Label> Annotator;
-    typedef annot::Annotation<typename Annotator::Index,
-                              typename Annotator::VLabels> Annotation;
     using node_index = SequenceGraph::node_index;
     using row_index = Annotator::Index;
 
     AnnotatedSequenceGraph(std::shared_ptr<SequenceGraph> graph,
-                           std::unique_ptr<Annotation>&& annotation,
+                           std::unique_ptr<Annotator>&& annotation,
                            bool force_fast = false);
 
     virtual ~AnnotatedSequenceGraph() {}
@@ -43,6 +39,9 @@ class AnnotatedSequenceGraph {
     virtual void annotate_sequences(
         const std::vector<std::pair<std::string, std::vector<Label>>> &data);
 
+    virtual void call_annotated_nodes(const Label &label,
+                                      std::function<void(node_index)> callback) const;
+
     virtual bool label_exists(const Label &label) const;
 
     virtual bool check_compatibility() const;
@@ -50,16 +49,7 @@ class AnnotatedSequenceGraph {
     virtual const SequenceGraph& get_graph() const { return *graph_; }
     std::shared_ptr<const SequenceGraph> get_graph_ptr() const { return graph_; }
 
-    virtual const Annotator& get_annotator() const {
-        assert(annotator_.get());
-        return *annotator_;
-    }
-
-    virtual const Annotation& get_annotation() const { return *annotation_; }
-
-    virtual void call_annotated_nodes(const Annotator::VLabels &labels,
-                                      const std::function<void(size_t, const bitmap_generator&)> &callback,
-                                      size_t num_threads = 1) const;
+    virtual const Annotator& get_annotator() const { return *annotator_; }
 
     static row_index graph_to_anno_index(node_index kmer_index) {
         assert(kmer_index);
@@ -71,8 +61,7 @@ class AnnotatedSequenceGraph {
 
   protected:
     std::shared_ptr<SequenceGraph> graph_;
-    std::unique_ptr<Annotation> annotation_;
-    const std::shared_ptr<Annotator> annotator_;
+    std::unique_ptr<Annotator> annotator_;
 
     std::mutex mutex_;
     bool force_fast_;
@@ -82,7 +71,7 @@ class AnnotatedSequenceGraph {
 class AnnotatedDBG : public AnnotatedSequenceGraph {
   public:
     AnnotatedDBG(std::shared_ptr<DeBruijnGraph> dbg,
-                 std::unique_ptr<Annotation>&& annotation,
+                 std::unique_ptr<Annotator>&& annotation,
                  bool force_fast = false);
 
     using AnnotatedSequenceGraph::get_labels;
diff --git a/metagraph/tests/annotation/test_annotated_dbg.cpp b/metagraph/tests/annotation/test_annotated_dbg.cpp
index a4d02931f5..7e3673123f 100644
--- a/metagraph/tests/annotation/test_annotated_dbg.cpp
+++ b/metagraph/tests/annotation/test_annotated_dbg.cpp
@@ -52,15 +52,14 @@ void check_labels(const AnnotatedDBG &anno_graph,
 
     for (const auto &label : labels_present) {
         std::set<SequenceGraph::node_index> cur_indices;
-        anno_graph.call_annotated_nodes({ label }, [&](size_t, const bitmap &column) {
-            column.call_ones(
-                [&](const auto &index) {
-                    ASSERT_NE(SequenceGraph::npos, index);
-                    cur_indices.insert(index);
-                    EXPECT_TRUE(anno_graph.has_label(index, label));
-                }
-            );
-        });
+        anno_graph.call_annotated_nodes(
+            label,
+            [&](const auto &index) {
+                ASSERT_NE(SequenceGraph::npos, index);
+                cur_indices.insert(index);
+                EXPECT_TRUE(anno_graph.has_label(index, label));
+            }
+        );
         std::vector<SequenceGraph::node_index> diff;
         std::set_difference(indices.begin(), indices.end(),
                             cur_indices.begin(), cur_indices.end(),

From f8cd051a7e3dae2be248598d011008589ac32425 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <hmustafa@login-biomed-01>
Date: Mon, 30 Jan 2023 11:21:34 +0100
Subject: [PATCH 017/410] cleanup

---
 metagraph/src/cli/assemble.cpp | 20 --------------------
 1 file changed, 20 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 1b2d766a13..ffa677f47a 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -14,7 +14,6 @@
 #include "load/load_annotated_graph.hpp"
 #include "graph/annotated_graph_algorithm.hpp"
 #include "graph/representation/masked_graph.hpp"
-#include "annotation/representation/column_compressed/column_compressed_lazy.hpp"
 #include "graph/representation/succinct/dbg_succinct.hpp"
 
 
@@ -30,11 +29,6 @@ using mtg::graph::DifferentialAssemblyConfig;
 
 void check_labels(const tsl::hopscotch_set<std::string> &label_set,
                   const AnnotatedDBG &anno_graph) {
-    if (dynamic_cast<const annot::ColumnCompressedLazy<>*>(&anno_graph.get_annotation())) {
-        logger->trace("Skipping label check for streaming annotator");
-        return;
-    }
-
     bool detected_missing_labels = false;
     for (const std::string &label : label_set) {
         if (!anno_graph.label_exists(label)) {
@@ -81,7 +75,6 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
         throw std::iostream::failure("Failed to read assembly config JSON from " + config->assembly_config_file);
 
     size_t num_threads = std::max(1u, get_num_threads());
-    bool streaming = dynamic_cast<const annot::ColumnCompressedLazy<>*>(&anno_graph.get_annotation());
 
     Json::Value diff_json;
     fin >> diff_json;
@@ -115,12 +108,6 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
             std::string exp_name = experiment["name"].asString()
                                     + (config->enumerate_out_sequences ? "." : "");
 
-            if (streaming && diff_config.label_mask_other_unitig_fraction != 1.0) {
-                logger->error("'unitig_other_max_fraction' not supported with --separately, skipping assembly: {}",
-                              exp_name);
-                continue;
-            }
-
             for (const Json::Value &in_label : experiment["in"]) {
                 foreground_labels.emplace(in_label.asString());
             }
@@ -132,13 +119,6 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
             check_labels(foreground_labels, anno_graph);
             check_labels(background_labels, anno_graph);
 
-            if (streaming
-                    && (shared_foreground_labels.size() || shared_background_labels.size())) {
-                logger->error("'shared_labels' not supported with --separately, skipping assembly: {}",
-                              exp_name);
-                continue;
-            }
-
             logger->trace("Running assembly: {}", exp_name);
 
             callback(*mask_nodes_by_label(anno_graph,

From 6c313124a20f89c98621b7dc3b0dee76b5990021 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <hmustafa@login-biomed-01>
Date: Mon, 30 Jan 2023 11:22:51 +0100
Subject: [PATCH 018/410] cleanup

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 8995c875b0..fe1fab67e6 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -4,7 +4,6 @@
 #include "common/vectors/vector_algorithm.hpp"
 #include "common/vectors/bitmap.hpp"
 #include "graph/representation/masked_graph.hpp"
-#include "annotation/representation/column_compressed/column_compressed_lazy.hpp"
 
 
 namespace mtg {

From f2c3d4e8fd87aa1d63ae05cbfaea7247c6629956 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <hmustafa@login-biomed-01>
Date: Mon, 30 Jan 2023 12:09:20 +0100
Subject: [PATCH 019/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 68 +++++++++++--------
 1 file changed, 38 insertions(+), 30 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fe1fab67e6..b55497368e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -324,51 +324,59 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
     std::mutex vector_backup_mutex;
     bool parallel = num_threads > 1;
 
+    std::vector<annot::binmat::BinaryMatrix::Column> columns;
+    const auto &encoder = anno_graph.get_annotator().get_label_encoder();
     AnnotatedDBG::Annotator::VLabels labels;
     labels.reserve(labels_in.size() + labels_out.size());
+    columns.reserve(labels_in.size() + labels_out.size());
     for (const auto &label : labels_in) {
         labels.emplace_back(label);
+        columns.emplace_back(encoder.encode(label));
     }
     for (const auto &label : labels_out) {
-        if (!labels_in.count(label))
+        if (!labels_in.count(label)) {
             labels.emplace_back(label);
+            columns.emplace_back(encoder.encode(label));
+        }
     }
 
     logger->trace("Populating count vector");
     std::atomic_thread_fence(std::memory_order_release);
-    anno_graph.call_annotated_nodes(labels, [&](size_t j, const bitmap_generator &column) {
-        uint8_t col_indicator = static_cast<bool>(labels_in.count(labels[j]));
-        if (labels_out.count(labels[j]))
-            col_indicator |= 2;
-
-        auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](node_index i) {
-            assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
-        };
-
-        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](node_index i) {
-            assert(i != DeBruijnGraph::npos);
-            if (add_out_labels_to_mask)
+    anno_graph.get_annotator().get_matrix().call_columns(columns,
+        [&](size_t j, const bitmap_generator &column) {
+            uint8_t col_indicator = static_cast<bool>(labels_in.count(labels[j]));
+            if (labels_out.count(labels[j]))
+                col_indicator |= 2;
+
+            auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](node_index i) {
+                assert(i != DeBruijnGraph::npos);
                 set_bit(indicator.data(), i, parallel, MO_RELAXED);
+                atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
+            };
 
-            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
-        };
+            auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](node_index i) {
+                assert(i != DeBruijnGraph::npos);
+                if (add_out_labels_to_mask)
+                    set_bit(indicator.data(), i, parallel, MO_RELAXED);
 
-        auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](node_index i) {
-            assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
-        };
+                atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+            };
 
-        switch (col_indicator) {
-            case 1: { column.call_ones(add_in); } break;
-            case 2: { column.call_ones(add_out); } break;
-            case 3: { column.call_ones(add_both); } break;
-            default: {}
-        }
-    }, num_threads);
+            auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](node_index i) {
+                assert(i != DeBruijnGraph::npos);
+                set_bit(indicator.data(), i, parallel, MO_RELAXED);
+                atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
+                atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+            };
+
+            switch (col_indicator) {
+                case 1: { column.call_ones(add_in); } break;
+                case 2: { column.call_ones(add_out); } break;
+                case 3: { column.call_ones(add_both); } break;
+                default: {}
+            }
+        }, num_threads
+    );
 
     std::atomic_thread_fence(std::memory_order_acquire);
     logger->trace("done");

From e34b9053c7f667979a47cb60b5c52a92d2f96738 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <hmustafa@login-biomed-01>
Date: Mon, 30 Jan 2023 12:54:33 +0100
Subject: [PATCH 020/410] fix

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b55497368e..067f964bc3 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -324,7 +324,9 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
     std::mutex vector_backup_mutex;
     bool parallel = num_threads > 1;
 
-    std::vector<annot::binmat::BinaryMatrix::Column> columns;
+    using Column = annot::binmat::BinaryMatrix::Column;
+
+    std::vector<Column> columns;
     const auto &encoder = anno_graph.get_annotator().get_label_encoder();
     AnnotatedDBG::Annotator::VLabels labels;
     labels.reserve(labels_in.size() + labels_out.size());
@@ -348,13 +350,15 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
             if (labels_out.count(labels[j]))
                 col_indicator |= 2;
 
-            auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](node_index i) {
+            auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
+                i = AnnotatedDBG::anno_to_graph_index(i);
                 assert(i != DeBruijnGraph::npos);
                 set_bit(indicator.data(), i, parallel, MO_RELAXED);
                 atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
             };
 
-            auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](node_index i) {
+            auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i) {
+                i = AnnotatedDBG::anno_to_graph_index(i);
                 assert(i != DeBruijnGraph::npos);
                 if (add_out_labels_to_mask)
                     set_bit(indicator.data(), i, parallel, MO_RELAXED);
@@ -362,7 +366,8 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
                 atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
             };
 
-            auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](node_index i) {
+            auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
+                i = AnnotatedDBG::anno_to_graph_index(i);
                 assert(i != DeBruijnGraph::npos);
                 set_bit(indicator.data(), i, parallel, MO_RELAXED);
                 atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);

From 04fff0271f3364d56a8492c0001572b42647818a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 27 Feb 2023 12:59:59 +0100
Subject: [PATCH 021/410] refactor to allow for streaming columns

---
 metagraph/src/cli/assemble.cpp                | 123 ++++++---
 .../src/graph/annotated_graph_algorithm.cpp   | 240 +++++++++++++-----
 .../src/graph/annotated_graph_algorithm.hpp   |  10 +
 3 files changed, 281 insertions(+), 92 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index ffa677f47a..eaf405c567 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -11,6 +11,7 @@
 #include "seq_io/sequence_io.hpp"
 #include "config/config.hpp"
 #include "load/load_graph.hpp"
+#include "load/load_annotation.hpp"
 #include "load/load_annotated_graph.hpp"
 #include "graph/annotated_graph_algorithm.hpp"
 #include "graph/representation/masked_graph.hpp"
@@ -132,6 +133,63 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
     }
 }
 
+void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                        Config *config,
+                        const CallMaskedGraphHeader &callback) {
+    assert(!config->assembly_config_file.empty());
+
+    std::ifstream fin(config->assembly_config_file);
+    if (!fin.good())
+        throw std::iostream::failure("Failed to read assembly config JSON from " + config->assembly_config_file);
+
+    for (const auto &name : config->fnames) {
+        if (parse_annotation_type(name) != Config::ColumnCompressed)
+            throw std::runtime_error("Multiple annotation files must be ColumnCompressed");
+    }
+
+    size_t num_threads = std::max(1u, get_num_threads());
+
+    Json::Value diff_json;
+    fin >> diff_json;
+
+    for (const Json::Value &group : diff_json["groups"]) {
+        if (group["shared_labels"]) {
+            throw std::runtime_error("Shared labels not supported with multiple annotation files");
+        }
+
+        if (!group["experiments"])
+            throw std::runtime_error("Missing experiments in group");
+
+        for (const Json::Value &experiment : group["experiments"]) {
+            tsl::hopscotch_set<std::string> foreground_labels;
+            tsl::hopscotch_set<std::string> background_labels;
+
+            DifferentialAssemblyConfig diff_config = diff_assembly_config(experiment);
+
+            std::string exp_name = experiment["name"].asString()
+                                    + (config->enumerate_out_sequences ? "." : "");
+
+            for (const Json::Value &in_label : experiment["in"]) {
+                foreground_labels.emplace(in_label.asString());
+            }
+
+            for (const Json::Value &out_label : experiment["out"]) {
+                background_labels.emplace(out_label.asString());
+            }
+
+            logger->trace("Running assembly: {}", exp_name);
+
+            callback(*mask_nodes_by_label(graph_ptr,
+                                          config->fnames,
+                                          config->fnames.size(),
+                                          foreground_labels,
+                                          background_labels,
+                                          diff_config, num_threads,
+                                          config->parallel_nodes), exp_name);
+        }
+    }
+}
+
 int assemble(Config *config) {
     assert(config);
 
@@ -146,7 +204,6 @@ int assemble(Config *config) {
 
     const auto &files = config->fnames;
 
-    assert(files.size() == 1);
     assert(config->outfbase.size());
 
     Timer timer;
@@ -157,47 +214,49 @@ int assemble(Config *config) {
     logger->trace("Graph loaded in {} sec", timer.elapsed());
 
     if (config->infbase_annotators.size()) {
-        config->infbase = files.at(0);
-
-        auto anno_graph = initialize_annotated_dbg(graph, *config);
-
         logger->trace("Generating masked graphs...");
+        std::mutex write_mutex;
+        size_t num_threads = std::max(1u, get_num_threads());
 
         std::filesystem::remove(
             utils::remove_suffix(config->outfbase, ".gz", ".fasta") + ".fasta.gz"
         );
 
-        std::mutex write_mutex;
-
-        size_t num_threads = std::max(1u, get_num_threads());
-
-        call_masked_graphs(*anno_graph, config,
-            [&](const graph::MaskedDeBruijnGraph &graph, const std::string &header) {
-                seq_io::FastaWriter writer(config->outfbase, header,
-                                           config->enumerate_out_sequences,
-                                           num_threads > 1, /* async write */
-                                           "a" /* append mode */);
-
-                if (config->unitigs || config->min_tip_size > 1) {
-                    graph.call_unitigs([&](const std::string &unitig, auto&&) {
-                                           std::lock_guard<std::mutex> lock(write_mutex);
-                                           writer.write(unitig);
-                                       },
-                                       num_threads, config->min_tip_size,
-                                       config->kmers_in_single_form);
-                } else {
-                    graph.call_sequences([&](const std::string &seq, auto&&) {
-                                             std::lock_guard<std::mutex> lock(write_mutex);
-                                             writer.write(seq);
-                                         },
-                                         num_threads, config->kmers_in_single_form);
-                }
+        auto graph_callback = [&](const graph::MaskedDeBruijnGraph &graph, const std::string &header) {
+            seq_io::FastaWriter writer(config->outfbase, header,
+                                       config->enumerate_out_sequences,
+                                       num_threads > 1, /* async write */
+                                       "a" /* append mode */);
+
+            if (config->unitigs || config->min_tip_size > 1) {
+                graph.call_unitigs([&](const std::string &unitig, auto&&) {
+                                       std::lock_guard<std::mutex> lock(write_mutex);
+                                       writer.write(unitig);
+                                   },
+                                   num_threads, config->min_tip_size,
+                                   config->kmers_in_single_form);
+            } else {
+                graph.call_sequences([&](const std::string &seq, auto&&) {
+                                         std::lock_guard<std::mutex> lock(write_mutex);
+                                         writer.write(seq);
+                                     },
+                                     num_threads, config->kmers_in_single_form);
             }
-        );
-
+        };
+
+        if (files.size() > 1) {
+            config->fnames.erase(config->fnames.begin(), config->fnames.begin() + 1);
+            call_masked_graphs(graph, config, graph_callback);
+        } else {
+            config->infbase = files.at(0);
+            auto anno_graph = initialize_annotated_dbg(graph, *config);
+            call_masked_graphs(*anno_graph, config, graph_callback);
+        }
         return 0;
     }
 
+    assert(files.size() == 1);
+
     logger->trace("Extracting sequences from graph...");
 
     timer.reset();
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 067f964bc3..37e041a80e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -4,6 +4,7 @@
 #include "common/vectors/vector_algorithm.hpp"
 #include "common/vectors/bitmap.hpp"
 #include "graph/representation/masked_graph.hpp"
+#include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 
 
 namespace mtg {
@@ -12,7 +13,9 @@ namespace graph {
 using mtg::common::logger;
 
 typedef AnnotatedDBG::node_index node_index;
+typedef AnnotatedDBG::Annotator Annotator;
 typedef AnnotatedDBG::Annotator::Label Label;
+using Column = annot::binmat::BinaryMatrix::Column;
 
 typedef std::function<size_t()> LabelCountCallback;
 
@@ -30,9 +33,19 @@ constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
  * with at least one in-label or out-label.
  */
 std::pair<sdsl::int_vector<>, sdsl::bit_vector>
-construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
+construct_diff_label_count_vector(const Annotator &annotator,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  size_t num_threads,
+                                  bool add_out_labels_to_mask);
+
+std::pair<sdsl::int_vector<>, sdsl::bit_vector>
+construct_diff_label_count_vector(const std::vector<std::string> &files,
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
                                   size_t num_labels,
                                   size_t num_threads,
                                   bool add_out_labels_to_mask);
@@ -59,6 +72,19 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                           size_t num_threads);
 
 
+std::shared_ptr<MaskedDeBruijnGraph>
+mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                    const AnnotatedDBG *anno_graph,
+                    sdsl::int_vector<>&& counts,
+                    sdsl::bit_vector&& mask,
+                    size_t num_labels,
+                    const tsl::hopscotch_set<Label> &labels_in,
+                    const tsl::hopscotch_set<Label> &labels_out,
+                    const tsl::hopscotch_set<Label> &labels_in_round2,
+                    const tsl::hopscotch_set<Label> &labels_out_round2,
+                    const DifferentialAssemblyConfig &config,
+                    size_t num_threads);
+
 std::shared_ptr<MaskedDeBruijnGraph>
 mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     const tsl::hopscotch_set<Label> &labels_in,
@@ -69,7 +95,6 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     size_t num_threads,
                     size_t num_parallel_files) {
     num_parallel_files = std::min(num_threads, num_parallel_files);
-    bool parallel = num_threads > 1;
     size_t num_in_labels = labels_in.size() + labels_in_round2.size();
     size_t num_out_labels = labels_out.size() + labels_out_round2.size();
     auto graph_ptr = std::static_pointer_cast<const DeBruijnGraph>(
@@ -89,22 +114,90 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
 
     // Construct initial masked graph from union of labels in labels_in
     auto count_vector = construct_diff_label_count_vector(
-        anno_graph, labels_in, labels_out,
+        anno_graph.get_annotator(), labels_in, labels_out, graph_ptr->max_index(),
         std::max(num_in_labels, num_out_labels), num_parallel_files,
         add_complement
     );
     auto &[counts, init_mask] = count_vector;
+    return mask_nodes_by_label(graph_ptr, &anno_graph,
+                               std::move(counts), std::move(init_mask),
+                               anno_graph.get_annotator().num_labels(),
+                               labels_in, labels_out,
+                               labels_in_round2, labels_out_round2,
+                               config, num_threads);
+}
+
+std::shared_ptr<MaskedDeBruijnGraph>
+mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                    const std::vector<std::string> &files,
+                    size_t num_labels,
+                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
+                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
+                    const DifferentialAssemblyConfig &config,
+                    size_t num_threads,
+                    size_t num_parallel_files) {
+    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
+    bool unitig_mode = check_other
+            || config.label_mask_in_unitig_fraction != 0.0
+            || config.label_mask_out_unitig_fraction != 1.0
+            || config.label_mask_other_unitig_fraction != 1.0;
+
+    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
+        && (config.add_complement || unitig_mode);
+
+    assert(!check_other);
+
+    logger->trace("Generating initial mask");
+
+    // Construct initial masked graph from union of labels in labels_in
+    auto count_vector = construct_diff_label_count_vector(
+        files, labels_in, labels_out, graph_ptr->max_index(),
+        std::max(labels_in.size(), labels_out.size()), num_parallel_files,
+        add_complement
+    );
+    auto &[counts, init_mask] = count_vector;
+    return mask_nodes_by_label(graph_ptr, nullptr,
+                               std::move(counts), std::move(init_mask),
+                               num_labels,
+                               labels_in, labels_out, {}, {},
+                               config, num_threads);
+}
+
+std::shared_ptr<MaskedDeBruijnGraph>
+mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                    const AnnotatedDBG *anno_graph,
+                    sdsl::int_vector<>&& counts,
+                    sdsl::bit_vector&& init_mask,
+                    size_t num_labels,
+                    const tsl::hopscotch_set<Label> &labels_in,
+                    const tsl::hopscotch_set<Label> &labels_out,
+                    const tsl::hopscotch_set<Label> &labels_in_round2,
+                    const tsl::hopscotch_set<Label> &labels_out_round2,
+                    const DifferentialAssemblyConfig &config,
+                    size_t num_threads) {
+    bool parallel = num_threads > 1;
+    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
+    bool unitig_mode = check_other || labels_in_round2.size() || labels_out_round2.size()
+            || config.label_mask_in_unitig_fraction != 0.0
+            || config.label_mask_out_unitig_fraction != 1.0
+            || config.label_mask_other_unitig_fraction != 1.0;
+
+    size_t num_in_labels = labels_in.size() + labels_in_round2.size();
+    size_t num_out_labels = labels_out.size() + labels_out_round2.size();
+
+    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
+        && (config.add_complement || unitig_mode);
 
     // in and out counts are stored interleaved in the counts vector
     assert(counts.size() == init_mask.size() * 2);
 
-
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
                                                   add_complement, num_threads);
 
     // check all other labels and post labels
     if (check_other || labels_in_round2.size() || labels_out_round2.size()) {
+        assert(anno_graph);
         sdsl::bit_vector union_mask
             = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
         std::mutex vector_backup_mutex;
@@ -126,16 +219,15 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
             call_ones(b, [&](size_t i) {
                 if (id_map[i]) {
                     set_bit(a.data(), id_map[i], parallel, MO_RELAXED);
-                    atomic_fetch_and_add(count_vector.first, id_map[i] * 2 + offset, 1,
+                    atomic_fetch_and_add(counts, id_map[i] * 2 + offset, 1,
                                          vector_backup_mutex, MO_RELAXED);
                 }
             });
         };
 
         logger->trace("Checking shared and other labels");
-        size_t num_labels = anno_graph.get_annotator().num_labels();
         masked_graph->call_sequences([&](const std::string &contig, const std::vector<node_index> &path) {
-            for (const auto &[label, sig] : anno_graph.get_top_label_signatures(contig, num_labels)) {
+            for (const auto &[label, sig] : anno_graph->get_top_label_signatures(contig, num_labels)) {
                 bool found_in = labels_in.count(label);
                 bool found_out = labels_out.count(label);
                 bool found_in_round2 = labels_in_round2.count(label);
@@ -175,8 +267,8 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
         // TODO: make this part multithreaded
         size_t kept_nodes = 0;
         call_ones(in_mask, [&](node_index node) {
-            uint64_t in_count = count_vector.first[node * 2];
-            uint64_t out_count = count_vector.first[node * 2 + 1];
+            uint64_t in_count = counts[node * 2];
+            uint64_t out_count = counts[node * 2 + 1];
 
             if (in_count >= min_label_in_count && out_count <= max_label_out_count) {
                 ++kept_nodes;
@@ -197,8 +289,6 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
     update_masked_graph_by_unitig(*masked_graph,
         [&](const std::string &, const std::vector<node_index> &path) -> Intervals {
             // return a set of intervals to keep in the graph
-            const auto &counts = count_vector.first;
-
             size_t in_kmer_count = 0;
 
             size_t begin = path.size();
@@ -306,15 +396,16 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 }
 
 std::pair<sdsl::int_vector<>, sdsl::bit_vector>
-construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
+construct_diff_label_count_vector(const std::function<void(const std::function<void(const Label &, const bitmap&)>)> &generate_columns,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
                                   size_t num_labels,
                                   size_t num_threads,
                                   bool add_out_labels_to_mask) {
     logger->trace("Allocating mask vector");
-    size_t width = sdsl::bits::hi(num_labels) + 1;
-    sdsl::bit_vector indicator(anno_graph.get_graph().max_index() + 1, false);
+    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
+    sdsl::bit_vector indicator(max_index + 1, false);
 
     logger->trace("Allocating count vector");
     // the in and out counts are stored interleaved
@@ -324,10 +415,62 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
     std::mutex vector_backup_mutex;
     bool parallel = num_threads > 1;
 
-    using Column = annot::binmat::BinaryMatrix::Column;
+    logger->trace("Populating count vector");
+    std::atomic_thread_fence(std::memory_order_release);
+    generate_columns([&](const Label &label, const bitmap &column) {
+        uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
+        if (labels_out.count(label))
+            col_indicator |= 2;
+
+        auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
+            i = AnnotatedDBG::anno_to_graph_index(i);
+            assert(i != DeBruijnGraph::npos);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
+        };
+
+        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i) {
+            i = AnnotatedDBG::anno_to_graph_index(i);
+            assert(i != DeBruijnGraph::npos);
+            if (add_out_labels_to_mask)
+                set_bit(indicator.data(), i, parallel, MO_RELAXED);
+
+            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+        };
+
+        auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
+            i = AnnotatedDBG::anno_to_graph_index(i);
+            assert(i != DeBruijnGraph::npos);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+        };
 
+        switch (col_indicator) {
+            case 1: { column.call_ones(add_in); } break;
+            case 2: { column.call_ones(add_out); } break;
+            case 3: { column.call_ones(add_both); } break;
+            default: {}
+        }
+    });
+
+    std::atomic_thread_fence(std::memory_order_acquire);
+    logger->trace("done");
+
+    return std::make_pair(std::move(counts), std::move(indicator));
+}
+
+
+std::pair<sdsl::int_vector<>, sdsl::bit_vector>
+construct_diff_label_count_vector(const Annotator &annotator,
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  size_t num_threads,
+                                  bool add_out_labels_to_mask) {
     std::vector<Column> columns;
-    const auto &encoder = anno_graph.get_annotator().get_label_encoder();
+    const auto &encoder = annotator.get_label_encoder();
     AnnotatedDBG::Annotator::VLabels labels;
     labels.reserve(labels_in.size() + labels_out.size());
     columns.reserve(labels_in.size() + labels_out.size());
@@ -342,51 +485,28 @@ construct_diff_label_count_vector(const AnnotatedDBG &anno_graph,
         }
     }
 
-    logger->trace("Populating count vector");
-    std::atomic_thread_fence(std::memory_order_release);
-    anno_graph.get_annotator().get_matrix().call_columns(columns,
-        [&](size_t j, const bitmap_generator &column) {
-            uint8_t col_indicator = static_cast<bool>(labels_in.count(labels[j]));
-            if (labels_out.count(labels[j]))
-                col_indicator |= 2;
-
-            auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
-                i = AnnotatedDBG::anno_to_graph_index(i);
-                assert(i != DeBruijnGraph::npos);
-                set_bit(indicator.data(), i, parallel, MO_RELAXED);
-                atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
-            };
-
-            auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i) {
-                i = AnnotatedDBG::anno_to_graph_index(i);
-                assert(i != DeBruijnGraph::npos);
-                if (add_out_labels_to_mask)
-                    set_bit(indicator.data(), i, parallel, MO_RELAXED);
-
-                atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
-            };
-
-            auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
-                i = AnnotatedDBG::anno_to_graph_index(i);
-                assert(i != DeBruijnGraph::npos);
-                set_bit(indicator.data(), i, parallel, MO_RELAXED);
-                atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
-                atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
-            };
-
-            switch (col_indicator) {
-                case 1: { column.call_ones(add_in); } break;
-                case 2: { column.call_ones(add_out); } break;
-                case 3: { column.call_ones(add_both); } break;
-                default: {}
-            }
-        }, num_threads
-    );
-
-    std::atomic_thread_fence(std::memory_order_acquire);
-    logger->trace("done");
+    return construct_diff_label_count_vector([&](const auto &callback) {
+        annotator.get_matrix().call_columns(columns,
+            [&](size_t j, const bitmap_generator &column) {
+                callback(labels[j], column);
+            }, num_threads
+        );
+    }, labels_in, labels_out, max_index, num_labels, num_threads, add_out_labels_to_mask);
+}
 
-    return std::make_pair(std::move(counts), std::move(indicator));
+std::pair<sdsl::int_vector<>, sdsl::bit_vector>
+construct_diff_label_count_vector(const std::vector<std::string> &files,
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  size_t num_threads,
+                                  bool add_out_labels_to_mask) {
+    return construct_diff_label_count_vector([&](const auto &callback) {
+        annot::ColumnCompressed<>::merge_load(files, [&](uint64_t, const auto &label, auto&& column) {
+            callback(label, *column);
+        }, num_threads);
+    }, labels_in, labels_out, max_index, num_labels, num_threads, add_out_labels_to_mask);
 }
 
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index cb72eda8ef..ae1413cca8 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -58,6 +58,16 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     size_t num_threads = 1,
                     size_t num_parallel_files = std::numeric_limits<size_t>::max());
 
+std::shared_ptr<MaskedDeBruijnGraph>
+mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                    const std::vector<std::string> &files,
+                    size_t num_labels,
+                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
+                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
+                    const DifferentialAssemblyConfig &config,
+                    size_t num_threads = 1,
+                    size_t num_parallel_files = std::numeric_limits<size_t>::max());
+
 } // namespace graph
 } // namespace mtg
 

From 4229b1b3c369378fcd6c5956463a7ab11de79cd8 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 27 Feb 2023 13:00:26 +0100
Subject: [PATCH 022/410] minor

---
 metagraph/src/cli/assemble.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index eaf405c567..9bcf277e58 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -143,8 +143,9 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         throw std::iostream::failure("Failed to read assembly config JSON from " + config->assembly_config_file);
 
     for (const auto &name : config->fnames) {
-        if (parse_annotation_type(name) != Config::ColumnCompressed)
+        if (parse_annotation_type(name) != Config::ColumnCompressed) {
             throw std::runtime_error("Multiple annotation files must be ColumnCompressed");
+        }
     }
 
     size_t num_threads = std::max(1u, get_num_threads());

From eee5a103b70b022f07a51e909424b4f1fbc3710d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 27 Feb 2023 13:05:25 +0100
Subject: [PATCH 023/410] cleanup

---
 metagraph/src/cli/assemble.cpp                    | 1 -
 metagraph/src/graph/annotated_graph_algorithm.cpp | 5 ++---
 metagraph/src/graph/annotated_graph_algorithm.hpp | 1 -
 3 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 9bcf277e58..6cc7deefc5 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -182,7 +182,6 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             callback(*mask_nodes_by_label(graph_ptr,
                                           config->fnames,
-                                          config->fnames.size(),
                                           foreground_labels,
                                           background_labels,
                                           diff_config, num_threads,
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 37e041a80e..e3b9c79df2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -71,7 +71,7 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                           bool add_complement,
                           size_t num_threads);
 
-
+// Helper function for calls in interface
 std::shared_ptr<MaskedDeBruijnGraph>
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const AnnotatedDBG *anno_graph,
@@ -130,7 +130,6 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
 std::shared_ptr<MaskedDeBruijnGraph>
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const std::vector<std::string> &files,
-                    size_t num_labels,
                     const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
                     const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
                     const DifferentialAssemblyConfig &config,
@@ -158,7 +157,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     auto &[counts, init_mask] = count_vector;
     return mask_nodes_by_label(graph_ptr, nullptr,
                                std::move(counts), std::move(init_mask),
-                               num_labels,
+                               files.size(),
                                labels_in, labels_out, {}, {},
                                config, num_threads);
 }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index ae1413cca8..52cad9ec03 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -61,7 +61,6 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
 std::shared_ptr<MaskedDeBruijnGraph>
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const std::vector<std::string> &files,
-                    size_t num_labels,
                     const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
                     const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
                     const DifferentialAssemblyConfig &config,

From 08c7ce530b9d810ced2648b4fa7197ee3e04ecbf Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 27 Feb 2023 17:15:55 +0100
Subject: [PATCH 024/410] fix other check

---
 .../src/graph/annotated_graph_algorithm.cpp   | 49 ++++++++++++-------
 1 file changed, 31 insertions(+), 18 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index e3b9c79df2..92139fae63 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -144,8 +144,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
         && (config.add_complement || unitig_mode);
 
-    assert(!check_other);
-
     logger->trace("Generating initial mask");
 
     // Construct initial masked graph from union of labels in labels_in
@@ -174,6 +172,9 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
                     const DifferentialAssemblyConfig &config,
                     size_t num_threads) {
+    // in and out counts are stored interleaved in the counts vector
+    assert(counts.size() == init_mask.size() * 2);
+
     bool parallel = num_threads > 1;
     bool check_other = config.label_mask_other_unitig_fraction != 1.0;
     bool unitig_mode = check_other || labels_in_round2.size() || labels_out_round2.size()
@@ -187,30 +188,26 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
         && (config.add_complement || unitig_mode);
 
-    // in and out counts are stored interleaved in the counts vector
-    assert(counts.size() == init_mask.size() * 2);
-
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
                                                   add_complement, num_threads);
 
-    // check all other labels and post labels
-    if (check_other || labels_in_round2.size() || labels_out_round2.size()) {
-        assert(anno_graph);
+    auto mask_or = [&](sdsl::bit_vector &a,
+                       const sdsl::bit_vector &b,
+                       const std::vector<node_index> &id_map) {
+        call_ones(b, [&](size_t i) {
+            if (id_map[i])
+                set_bit(a.data(), id_map[i], parallel, MO_RELAXED);
+        });
+    };
+
+    // check all other labels and round 2 labels
+    if (anno_graph && (check_other || labels_in_round2.size() || labels_out_round2.size())) {
         sdsl::bit_vector union_mask
             = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
         std::mutex vector_backup_mutex;
         std::atomic_thread_fence(std::memory_order_release);
 
-        auto mask_or = [&](sdsl::bit_vector &a,
-                           const sdsl::bit_vector &b,
-                           const std::vector<node_index> &id_map) {
-            call_ones(b, [&](size_t i) {
-                if (id_map[i])
-                    set_bit(a.data(), id_map[i], parallel, MO_RELAXED);
-            });
-        };
-
         auto count_merge = [&](sdsl::bit_vector &a,
                                const sdsl::bit_vector &b,
                                const std::vector<node_index> &id_map,
@@ -225,7 +222,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         };
 
         logger->trace("Checking shared and other labels");
-        masked_graph->call_sequences([&](const std::string &contig, const std::vector<node_index> &path) {
+        masked_graph->call_sequences([&](const std::string &contig,
+                                         const std::vector<node_index> &path) {
             for (const auto &[label, sig] : anno_graph->get_top_label_signatures(contig, num_labels)) {
                 bool found_in = labels_in.count(label);
                 bool found_out = labels_out.count(label);
@@ -247,6 +245,21 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::atomic_thread_fence(std::memory_order_acquire);
 
         masked_graph->set_mask(new bitmap_vector(std::move(union_mask)));
+    } else if (check_other && num_labels > num_in_labels + num_out_labels) {
+        assert(!anno_graph);
+        std::mutex vector_backup_mutex;
+        std::atomic_thread_fence(std::memory_order_release);
+
+        logger->trace("Checking other labels");
+        masked_graph->call_sequences([&](const std::string &contig,
+                                         const std::vector<node_index> &path) {
+            for (const auto &[label, sig] : anno_graph->get_top_label_signatures(contig, num_labels)) {
+                if (!labels_in.count(label) && !labels_out.count(label))
+                    mask_or(other_mask, sig, path);
+            }
+        }, num_threads);
+
+        std::atomic_thread_fence(std::memory_order_acquire);
     }
 
     // Filter unitigs from masked graph based on filtration criteria

From 453c0152a326f69b7ee0be441515b71ef6557367 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 27 Feb 2023 19:32:41 +0100
Subject: [PATCH 025/410] only load files that have in- or out-labels

---
 .../src/graph/annotated_graph_algorithm.cpp   | 67 +++++++++++++++----
 1 file changed, 53 insertions(+), 14 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 92139fae63..0607669895 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -23,16 +23,20 @@ constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
 
 
 /**
- * Return an int_vector<>, bit_vector pair, of lengths anno_graph.get_graph().max_index() * 2
- * and anno_graph.get_graph().max_index(), respectively.
+ * Return an int_vector<>, bit_vector of lengths anno_graph.get_graph().max_index() * 2
+ * and anno_graph.get_graph().max_index(), respectively, and a bit_vector of length
+ * equal to the total number of labels.
  * For an index i, the int_vector at indices 2*i and 2*i + 1 represent the
  * number of labels in labels_in and labels_out which the k-mer of index i is
  * annotated with, respectively. The width of the int_vector<> is computed to be
  * wide enough to contain counts up to num_labels.
  * The returned bit_vector is a k-mer mask indicating those k-mers annotated
- * with at least one in-label or out-label.
+ * with at least one in-label. If add_out_labels_to_mask is true, then it also indicates
+ * which those k-mers with at least one out-label.
+ * The second bit vector indicates which of the labels correspond to an "other"
+ * label (i.e., not in- or out-).
  */
-std::pair<sdsl::int_vector<>, sdsl::bit_vector>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
 construct_diff_label_count_vector(const Annotator &annotator,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
@@ -41,7 +45,7 @@ construct_diff_label_count_vector(const Annotator &annotator,
                                   size_t num_threads,
                                   bool add_out_labels_to_mask);
 
-std::pair<sdsl::int_vector<>, sdsl::bit_vector>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
 construct_diff_label_count_vector(const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
@@ -118,7 +122,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
         std::max(num_in_labels, num_out_labels), num_parallel_files,
         add_complement
     );
-    auto &[counts, init_mask] = count_vector;
+    auto &[counts, init_mask, other_labels] = count_vector;
     return mask_nodes_by_label(graph_ptr, &anno_graph,
                                std::move(counts), std::move(init_mask),
                                anno_graph.get_annotator().num_labels(),
@@ -152,7 +156,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::max(labels_in.size(), labels_out.size()), num_parallel_files,
         add_complement
     );
-    auto &[counts, init_mask] = count_vector;
+    auto &[counts, init_mask, other_labels] = count_vector;
     return mask_nodes_by_label(graph_ptr, nullptr,
                                std::move(counts), std::move(init_mask),
                                files.size(),
@@ -473,7 +477,7 @@ construct_diff_label_count_vector(const std::function<void(const std::function<v
 }
 
 
-std::pair<sdsl::int_vector<>, sdsl::bit_vector>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
 construct_diff_label_count_vector(const Annotator &annotator,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
@@ -497,16 +501,23 @@ construct_diff_label_count_vector(const Annotator &annotator,
         }
     }
 
-    return construct_diff_label_count_vector([&](const auto &callback) {
+    sdsl::bit_vector other_labels(encoder.size(), true);
+    for (Column column : columns) {
+        other_labels[column] = false;
+    }
+
+    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) {
         annotator.get_matrix().call_columns(columns,
             [&](size_t j, const bitmap_generator &column) {
                 callback(labels[j], column);
             }, num_threads
         );
     }, labels_in, labels_out, max_index, num_labels, num_threads, add_out_labels_to_mask);
+
+    return std::make_tuple(std::move(counts), std::move(init_mask), std::move(other_labels));
 }
 
-std::pair<sdsl::int_vector<>, sdsl::bit_vector>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
 construct_diff_label_count_vector(const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
@@ -514,11 +525,39 @@ construct_diff_label_count_vector(const std::vector<std::string> &files,
                                   size_t num_labels,
                                   size_t num_threads,
                                   bool add_out_labels_to_mask) {
-    return construct_diff_label_count_vector([&](const auto &callback) {
-        annot::ColumnCompressed<>::merge_load(files, [&](uint64_t, const auto &label, auto&& column) {
-            callback(label, *column);
-        }, num_threads);
+    std::vector<bool> other_labels;
+    other_labels.reserve(files.size());
+    std::vector<size_t> num_labels_per_file;
+    num_labels_per_file.reserve(files.size());
+    for (size_t i = 0; i < files.size(); ++i) {
+        auto label_encoder = annot::ColumnCompressed<>::read_label_encoder(files[i]);
+        num_labels_per_file.emplace_back(label_encoder.size());
+        for (const auto &label : label_encoder.get_labels()) {
+            other_labels.emplace_back(!labels_in.count(label) && !labels_out.count(label));
+        }
+    }
+
+    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) {
+        size_t j = 0;
+        for (size_t i = 0; i < files.size(); ++i) {
+            if (std::accumulate(other_labels.begin() + j,
+                                other_labels.begin() + j + num_labels_per_file[i],
+                                0ull)) {
+                annot::ColumnCompressed<>::merge_load({ files[i] },
+                    [&](size_t offset, const auto &label, auto&& column) {
+                        if (!other_labels[j + offset])
+                            callback(label, *column);
+                    },
+                    num_threads
+                );
+            }
+
+            j += num_labels_per_file[i];
+        }
     }, labels_in, labels_out, max_index, num_labels, num_threads, add_out_labels_to_mask);
+
+    return std::make_tuple(std::move(counts), std::move(init_mask),
+                           to_sdsl(std::move(other_labels)));
 }
 
 

From 0c175e9399676311d1859fe4b68cdf95eb542de5 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 27 Feb 2023 23:26:28 +0100
Subject: [PATCH 026/410] handle other labels when loading separate columns

---
 .../src/graph/annotated_graph_algorithm.cpp   | 61 ++++++++++++-------
 1 file changed, 38 insertions(+), 23 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0607669895..e619ce0b72 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -45,7 +45,7 @@ construct_diff_label_count_vector(const Annotator &annotator,
                                   size_t num_threads,
                                   bool add_out_labels_to_mask);
 
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::vector<size_t>>
 construct_diff_label_count_vector(const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
@@ -81,6 +81,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const AnnotatedDBG *anno_graph,
                     sdsl::int_vector<>&& counts,
                     sdsl::bit_vector&& mask,
+                    sdsl::bit_vector&& other_mask,
                     size_t num_labels,
                     const tsl::hopscotch_set<Label> &labels_in,
                     const tsl::hopscotch_set<Label> &labels_out,
@@ -123,8 +124,9 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
         add_complement
     );
     auto &[counts, init_mask, other_labels] = count_vector;
+    sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     return mask_nodes_by_label(graph_ptr, &anno_graph,
-                               std::move(counts), std::move(init_mask),
+                               std::move(counts), std::move(init_mask), std::move(other_mask),
                                anno_graph.get_annotator().num_labels(),
                                labels_in, labels_out,
                                labels_in_round2, labels_out_round2,
@@ -139,6 +141,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const DifferentialAssemblyConfig &config,
                     size_t num_threads,
                     size_t num_parallel_files) {
+    bool parallel = num_threads > 1;
     bool check_other = config.label_mask_other_unitig_fraction != 1.0;
     bool unitig_mode = check_other
             || config.label_mask_in_unitig_fraction != 0.0
@@ -156,9 +159,35 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::max(labels_in.size(), labels_out.size()), num_parallel_files,
         add_complement
     );
-    auto &[counts, init_mask, other_labels] = count_vector;
+    auto &[counts, init_mask, other_labels, num_labels_per_file] = count_vector;
+    sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
+    if (check_other && sdsl::util::cnt_one_bits(other_labels)) {
+        size_t j = 0;
+        std::atomic_thread_fence(std::memory_order_release);
+        for (size_t i = 0; i < files.size(); ++i) {
+            if (std::accumulate(other_labels.begin() + j,
+                                other_labels.begin() + j + num_labels_per_file[i],
+                                0ull)) {
+                annot::ColumnCompressed<>::merge_load({ files[i] },
+                    [&](size_t offset, const auto &, auto&& column) {
+                        if (other_labels[j + offset]) {
+                            call_ones(init_mask, [&](size_t i) {
+                                if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
+                                    set_bit(other_mask.data(), i, parallel, MO_RELAXED);
+                            });
+                        }
+                    },
+                    num_threads
+                );
+            }
+
+            j += num_labels_per_file[i];
+        }
+        std::atomic_thread_fence(std::memory_order_acquire);
+    }
+
     return mask_nodes_by_label(graph_ptr, nullptr,
-                               std::move(counts), std::move(init_mask),
+                               std::move(counts), std::move(init_mask), std::move(other_mask),
                                files.size(),
                                labels_in, labels_out, {}, {},
                                config, num_threads);
@@ -169,6 +198,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const AnnotatedDBG *anno_graph,
                     sdsl::int_vector<>&& counts,
                     sdsl::bit_vector&& init_mask,
+                    sdsl::bit_vector&& other_mask,
                     size_t num_labels,
                     const tsl::hopscotch_set<Label> &labels_in,
                     const tsl::hopscotch_set<Label> &labels_out,
@@ -192,7 +222,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
         && (config.add_complement || unitig_mode);
 
-    sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
                                                   add_complement, num_threads);
 
@@ -249,21 +278,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::atomic_thread_fence(std::memory_order_acquire);
 
         masked_graph->set_mask(new bitmap_vector(std::move(union_mask)));
-    } else if (check_other && num_labels > num_in_labels + num_out_labels) {
-        assert(!anno_graph);
-        std::mutex vector_backup_mutex;
-        std::atomic_thread_fence(std::memory_order_release);
-
-        logger->trace("Checking other labels");
-        masked_graph->call_sequences([&](const std::string &contig,
-                                         const std::vector<node_index> &path) {
-            for (const auto &[label, sig] : anno_graph->get_top_label_signatures(contig, num_labels)) {
-                if (!labels_in.count(label) && !labels_out.count(label))
-                    mask_or(other_mask, sig, path);
-            }
-        }, num_threads);
-
-        std::atomic_thread_fence(std::memory_order_acquire);
     }
 
     // Filter unitigs from masked graph based on filtration criteria
@@ -517,7 +531,7 @@ construct_diff_label_count_vector(const Annotator &annotator,
     return std::make_tuple(std::move(counts), std::move(init_mask), std::move(other_labels));
 }
 
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::vector<size_t>>
 construct_diff_label_count_vector(const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
@@ -542,7 +556,7 @@ construct_diff_label_count_vector(const std::vector<std::string> &files,
         for (size_t i = 0; i < files.size(); ++i) {
             if (std::accumulate(other_labels.begin() + j,
                                 other_labels.begin() + j + num_labels_per_file[i],
-                                0ull)) {
+                                0ull) != num_labels_per_file[i]) {
                 annot::ColumnCompressed<>::merge_load({ files[i] },
                     [&](size_t offset, const auto &label, auto&& column) {
                         if (!other_labels[j + offset])
@@ -557,7 +571,8 @@ construct_diff_label_count_vector(const std::vector<std::string> &files,
     }, labels_in, labels_out, max_index, num_labels, num_threads, add_out_labels_to_mask);
 
     return std::make_tuple(std::move(counts), std::move(init_mask),
-                           to_sdsl(std::move(other_labels)));
+                           to_sdsl(std::move(other_labels)),
+                           std::move(num_labels_per_file));
 }
 
 

From b300687f3fb1d1166bac4316202d46aa24fadfdd Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 27 Feb 2023 23:30:42 +0100
Subject: [PATCH 027/410] minor

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index e619ce0b72..135aef5e73 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -141,7 +141,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const DifferentialAssemblyConfig &config,
                     size_t num_threads,
                     size_t num_parallel_files) {
-    bool parallel = num_threads > 1;
     bool check_other = config.label_mask_other_unitig_fraction != 1.0;
     bool unitig_mode = check_other
             || config.label_mask_in_unitig_fraction != 0.0
@@ -162,6 +161,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     auto &[counts, init_mask, other_labels, num_labels_per_file] = count_vector;
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     if (check_other && sdsl::util::cnt_one_bits(other_labels)) {
+        bool parallel = num_parallel_files > 1;
         size_t j = 0;
         std::atomic_thread_fence(std::memory_order_release);
         for (size_t i = 0; i < files.size(); ++i) {
@@ -177,7 +177,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             });
                         }
                     },
-                    num_threads
+                    num_parallel_files
                 );
             }
 
@@ -431,7 +431,7 @@ construct_diff_label_count_vector(const std::function<void(const std::function<v
                                   const tsl::hopscotch_set<Label> &labels_out,
                                   size_t max_index,
                                   size_t num_labels,
-                                  size_t num_threads,
+                                  bool parallel,
                                   bool add_out_labels_to_mask) {
     logger->trace("Allocating mask vector");
     size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
@@ -443,7 +443,6 @@ construct_diff_label_count_vector(const std::function<void(const std::function<v
     logger->trace("done");
 
     std::mutex vector_backup_mutex;
-    bool parallel = num_threads > 1;
 
     logger->trace("Populating count vector");
     std::atomic_thread_fence(std::memory_order_release);
@@ -526,7 +525,7 @@ construct_diff_label_count_vector(const Annotator &annotator,
                 callback(labels[j], column);
             }, num_threads
         );
-    }, labels_in, labels_out, max_index, num_labels, num_threads, add_out_labels_to_mask);
+    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask);
 
     return std::make_tuple(std::move(counts), std::move(init_mask), std::move(other_labels));
 }
@@ -568,7 +567,7 @@ construct_diff_label_count_vector(const std::vector<std::string> &files,
 
             j += num_labels_per_file[i];
         }
-    }, labels_in, labels_out, max_index, num_labels, num_threads, add_out_labels_to_mask);
+    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask);
 
     return std::make_tuple(std::move(counts), std::move(init_mask),
                            to_sdsl(std::move(other_labels)),

From 14cb4787348d03779177bf5dfb7fe6663e89a782 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 28 Feb 2023 11:06:51 +0100
Subject: [PATCH 028/410] fix compile on clang

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 135aef5e73..46db947fe7 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -170,6 +170,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                 0ull)) {
                 annot::ColumnCompressed<>::merge_load({ files[i] },
                     [&](size_t offset, const auto &, auto&& column) {
+                        auto &[counts, init_mask, other_labels, num_labels_per_file]
+                            = count_vector;
                         if (other_labels[j + offset]) {
                             call_ones(init_mask, [&](size_t i) {
                                 if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])

From cf976235bc7813895ad12917a96bdb3b1a6c77c1 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 28 Feb 2023 16:07:51 +0100
Subject: [PATCH 029/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 44 +++++++++++--------
 1 file changed, 25 insertions(+), 19 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 46db947fe7..0a5ee8d1f7 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -126,7 +126,8 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
     auto &[counts, init_mask, other_labels] = count_vector;
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     return mask_nodes_by_label(graph_ptr, &anno_graph,
-                               std::move(counts), std::move(init_mask), std::move(other_mask),
+                               std::move(counts), std::move(init_mask),
+                               std::move(other_mask),
                                anno_graph.get_annotator().num_labels(),
                                labels_in, labels_out,
                                labels_in_round2, labels_out_round2,
@@ -136,8 +137,8 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
 std::shared_ptr<MaskedDeBruijnGraph>
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const std::vector<std::string> &files,
-                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
-                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
+                    const tsl::hopscotch_set<Label> &labels_in,
+                    const tsl::hopscotch_set<Label> &labels_out,
                     const DifferentialAssemblyConfig &config,
                     size_t num_threads,
                     size_t num_parallel_files) {
@@ -165,9 +166,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         size_t j = 0;
         std::atomic_thread_fence(std::memory_order_release);
         for (size_t i = 0; i < files.size(); ++i) {
-            if (std::accumulate(other_labels.begin() + j,
-                                other_labels.begin() + j + num_labels_per_file[i],
-                                0ull)) {
+            if (count_ones(other_labels, j, j + num_labels_per_file[i])) {
                 annot::ColumnCompressed<>::merge_load({ files[i] },
                     [&](size_t offset, const auto &, auto&& column) {
                         auto &[counts, init_mask, other_labels, num_labels_per_file]
@@ -189,7 +188,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
 
     return mask_nodes_by_label(graph_ptr, nullptr,
-                               std::move(counts), std::move(init_mask), std::move(other_mask),
+                               std::move(counts), std::move(init_mask),
+                               std::move(other_mask),
                                files.size(),
                                labels_in, labels_out, {}, {},
                                config, num_threads);
@@ -285,15 +285,18 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     // Filter unitigs from masked graph based on filtration criteria
     logger->trace("Filtering out background");
 
-    size_t min_label_in_count = std::ceil(config.label_mask_in_kmer_fraction * num_in_labels);
-    size_t max_label_out_count = std::floor(config.label_mask_out_kmer_fraction * num_out_labels);
+    size_t min_label_in_count = std::ceil(config.label_mask_in_kmer_fraction
+                                    * num_in_labels);
+    size_t max_label_out_count = std::floor(config.label_mask_out_kmer_fraction
+                                    * num_out_labels);
 
     if (config.label_mask_in_unitig_fraction == 0.0
             && config.label_mask_out_unitig_fraction == 1.0
             && config.label_mask_other_unitig_fraction == 1.0) {
         logger->trace("Filtering by node");
         size_t total_nodes = masked_graph->num_nodes();
-        const auto &in_mask = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
+        const auto &in_mask
+            = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
         sdsl::bit_vector mask = in_mask;
 
         // TODO: make this part multithreaded
@@ -540,24 +543,27 @@ construct_diff_label_count_vector(const std::vector<std::string> &files,
                                   size_t num_labels,
                                   size_t num_threads,
                                   bool add_out_labels_to_mask) {
-    std::vector<bool> other_labels;
-    other_labels.reserve(files.size());
+    std::vector<bool> other_labels_vec;
+    other_labels_vec.reserve(files.size());
+
     std::vector<size_t> num_labels_per_file;
     num_labels_per_file.reserve(files.size());
+
     for (size_t i = 0; i < files.size(); ++i) {
         auto label_encoder = annot::ColumnCompressed<>::read_label_encoder(files[i]);
         num_labels_per_file.emplace_back(label_encoder.size());
         for (const auto &label : label_encoder.get_labels()) {
-            other_labels.emplace_back(!labels_in.count(label) && !labels_out.count(label));
+            other_labels_vec.emplace_back(!labels_in.count(label) && !labels_out.count(label));
         }
     }
 
+    auto other_labels = to_sdsl(std::move(other_labels_vec));
+
     auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) {
         size_t j = 0;
         for (size_t i = 0; i < files.size(); ++i) {
-            if (std::accumulate(other_labels.begin() + j,
-                                other_labels.begin() + j + num_labels_per_file[i],
-                                0ull) != num_labels_per_file[i]) {
+            if (count_ones(other_labels, j, j + num_labels_per_file[i])
+                    != num_labels_per_file[i]) {
                 annot::ColumnCompressed<>::merge_load({ files[i] },
                     [&](size_t offset, const auto &label, auto&& column) {
                         if (!other_labels[j + offset])
@@ -572,8 +578,7 @@ construct_diff_label_count_vector(const std::vector<std::string> &files,
     }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask);
 
     return std::make_tuple(std::move(counts), std::move(init_mask),
-                           to_sdsl(std::move(other_labels)),
-                           std::move(num_labels_per_file));
+                           std::move(other_labels), std::move(num_labels_per_file));
 }
 
 
@@ -589,7 +594,8 @@ void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
 
     std::atomic_thread_fence(std::memory_order_release);
 
-    masked_graph.call_unitigs([&](const std::string &unitig, const std::vector<node_index> &path) {
+    masked_graph.call_unitigs([&](const std::string &unitig,
+                                  const std::vector<node_index> &path) {
         total_unitigs.fetch_add(1, MO_RELAXED);
 
         size_t last = 0;

From 3f0df76aeb3de5f6ff9b2e7d001b118f557f8e7b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 28 Feb 2023 17:38:53 +0100
Subject: [PATCH 030/410] remove redundant tests

---
 metagraph/integration_tests/test_assemble.py | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/metagraph/integration_tests/test_assemble.py b/metagraph/integration_tests/test_assemble.py
index 747676eefe..6f3846572b 100644
--- a/metagraph/integration_tests/test_assemble.py
+++ b/metagraph/integration_tests/test_assemble.py
@@ -40,7 +40,7 @@
 }
 
 GFAs = [name for name, _ in gfa_tests.items()]
-LOAD_TYPES = ['load', 'stream']
+MASKED = ['','--mask-dummy']
 
 
 class TestAnnotate(unittest.TestCase):
@@ -264,13 +264,11 @@ def check_suffix(anno_repr, suffix):
             no_anchor_opt
         )
 
-    @parameterized.expand(LOAD_TYPES)
-    def test_diff_assembly(self, load_type):
+    def test_diff_assembly(self):
         assemble_command = f'{METAGRAPH} assemble -p {NUM_THREADS} \
                 -a {self.tempdir.name}/annotation{anno_file_extension[self.anno_repr]} \
                 -o {self.tempdir.name}/diff_contigs \
                 --diff-assembly-rules {TEST_DATA_DIR}/example.diff.json \
-                {"--separately" if load_type == "stream" else ""} \
                 {self.tempdir.name}/graph{graph_file_extension[self.graph_repr]}'
         res = subprocess.run([assemble_command], shell=True)
         self.assertEqual(res.returncode, 0)
@@ -296,13 +294,11 @@ def test_diff_assembly(self, load_type):
         self.assertEqual(results['>metasub_by_kmer'][0], 'CTTGGATCACACTCTTCTCAGAGCCCAGGCCAGGGGCCCCCAAGAAAGGCTCTGGTGGAGAACCTGTGCATGAAGGCTGTCAACCAGTCCATAGGCAGGGCCATCAGGCACCAAAGGGATTCTGCCAGCATAGTGCTCCTGGACCAGTGATACACCCGGCACCCTGTCCTGGACATGCTGTTGGCCTGGATCTGAGCCCTCGTGGAGGTCAAAGCCACCTTTGGTTCTGCCATTGCTGCTGTGTGGAAGTTCACTCAAGTAGGCCTCTTCCTG')
         self.assertEqual(results['>metasub_sym_diff'][0], 'TGGAAGTTCACTCAAGTAGGCCTCTTCCTGACAGGCAGCTGCACCACTGCCTGGCGCTGTGCCCTTCCTTTGCTCTGCCCGCTGGAGACGGTGTTTGTCATGGGCCTGGTCTGCAGG')
 
-    @parameterized.expand(LOAD_TYPES)
-    def test_diff_assembly_simple(self, load_type):
+    def test_diff_assembly_simple(self):
         assemble_command = f'{METAGRAPH} assemble -p {NUM_THREADS} \
                 -a {self.tempdir.name}/annotation{anno_file_extension[self.anno_repr]} \
                 -o {self.tempdir.name}/diff_contigs \
                 --diff-assembly-rules {TEST_DATA_DIR}/example_simple.diff.json \
-                {"--separately" if load_type == "stream" else ""} \
                 {self.tempdir.name}/graph{graph_file_extension[self.graph_repr]}'
         res = subprocess.run([assemble_command], shell=True)
         self.assertEqual(res.returncode, 0)

From 52f7e2a638395e133eae442f0c35be0d8093da24 Mon Sep 17 00:00:00 2001
From: MyrtheW <willemsenmyrthe@gmail.com>
Date: Fri, 3 Mar 2023 16:47:19 +0100
Subject: [PATCH 031/410] Comments and todo items for differential assembly
 supporting count annotation.

---
 .../annotate_column_compressed.cpp            |   2 +-
 metagraph/src/cli/assemble.cpp                |   6 +
 .../src/common/vectors/vector_algorithm.hpp   |  22 +-
 metagraph/src/graph/annotated_dbg.cpp         |   2 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 301 ++++++++++++++++--
 .../src/graph/annotated_graph_algorithm.hpp   |   5 +-
 metagraph/src/graph/differential_tests.hpp    | 239 ++++++++++++++
 7 files changed, 538 insertions(+), 39 deletions(-)
 create mode 100644 metagraph/src/graph/differential_tests.hpp

diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
index fcd7e077e1..873bcca1cb 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
@@ -524,7 +524,7 @@ bool ColumnCompressed<Label>::load(const std::string &filename) {
 }
 
 template <typename Label>
-bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenames) {
+bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenames) {  // TODO Myrthe: QUESTION how does this partial streaming exactly work?
     // release the columns stored
     cached_columns_.Clear();
     bitmatrix_.clear();
diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 6cc7deefc5..fa26e669d7 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -49,6 +49,10 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.label_mask_out_kmer_fraction = experiment.get("out_max_fraction", 0.0).asDouble();
     diff_config.label_mask_out_unitig_fraction = experiment.get("unitig_out_max_fraction", 1.0).asDouble();
     diff_config.label_mask_other_unitig_fraction = experiment.get("unitig_other_max_fraction", 1.0).asDouble();
+    // diff_config.count_kmers = experiment.get("count-kmers", 1.0).asBool(); why is this not present for add_complement?
+    diff_config.family_wise_error_rate = experiment.get("family_wise_error_rate", 0.05).asDouble();
+
+
 
     logger->trace("Per-kmer mask in fraction:\t\t{}", diff_config.label_mask_in_kmer_fraction);
     logger->trace("Per-unitig mask in fraction:\t\t{}", diff_config.label_mask_in_unitig_fraction);
@@ -56,6 +60,8 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     logger->trace("Per-unitig mask out fraction:\t\t{}", diff_config.label_mask_out_unitig_fraction);
     logger->trace("Per-unitig other label fraction:\t{}", diff_config.label_mask_other_unitig_fraction);
     logger->trace("Include reverse complements:\t\t{}", diff_config.add_complement);
+    logger->trace("Include k-mer counts if present in the graph:\t\t{}", diff_config.count_kmers); // if node weights (k-mer count) are present in the graph and should be included in the differential assembly.
+    logger->trace("Family wise error rate for the Bonferroni test:\t{}", diff_config.family_wise_error_rate);
 
     return diff_config;
 }
diff --git a/metagraph/src/common/vectors/vector_algorithm.hpp b/metagraph/src/common/vectors/vector_algorithm.hpp
index 24dcef8e1e..7034ef71be 100644
--- a/metagraph/src/common/vectors/vector_algorithm.hpp
+++ b/metagraph/src/common/vectors/vector_algorithm.hpp
@@ -164,14 +164,14 @@ sdsl::bit_vector autocorrelate(const sdsl::bit_vector &vector, uint8_t offset);
 
 
 // Call (uint64_t index, uint64_t value) for each non-zero value in [begin, end)
-template <class Callback>
-inline void call_nonzeros(const sdsl::int_vector<> &vector,
+template <class Callback, class vector_type>
+inline void call_nonzeros(const vector_type &vector,
                           uint64_t begin, uint64_t end,
                           Callback callback);
 
 // Call (uint64_t index, uint64_t value) for each non-zero value in |vector|.
-template <class Callback>
-void call_nonzeros(const sdsl::int_vector<> &vector, Callback callback) {
+template <class Callback, class vector_type>
+void call_nonzeros(const vector_type &vector, Callback callback) {
     return call_nonzeros(vector, 0, vector.size(), callback);
 }
 
@@ -664,8 +664,8 @@ void call_zeros(const sdsl::bit_vector &vector,
     }
 }
 
-template <class Callback>
-void call_nonzeros(const sdsl::int_vector<> &vector,
+template <class Callback, class vector_type> //Myrthe: type for the vector bitmap or sdsl::int_vector<>, and later a sdsl::int_vector_buffer<>
+void call_nonzeros(const vector_type &vector,
                    uint64_t begin, uint64_t end,
                    Callback callback) {
     if (begin >= end)
@@ -721,6 +721,16 @@ void call_nonzeros(const sdsl::int_vector<> &vector,
     }
 }
 
+template void call_nonzeros<sdsl::int_vector<>>(const sdsl::int_vector<> &vector,
+              uint64_t begin, uint64_t end,
+              Callback callback);
+template void call_nonzeros<bitmap>(const bitmap &vector, // for bitmaps this should write a 1 to the column.
+              uint64_t begin, uint64_t end,
+              Callback callback){
+    vector.call_ones([&](uint64_t i){callback(i, 1);}     // creates a lambda function that write a custom argument for the callback
+}
+
+
 template <typename BitVector>
 inline uint64_t next1(const BitVector &v, uint64_t pos, size_t num_steps) {
     assert(pos < v.size());
diff --git a/metagraph/src/graph/annotated_dbg.cpp b/metagraph/src/graph/annotated_dbg.cpp
index b07bb61643..bc5a6f78ae 100644
--- a/metagraph/src/graph/annotated_dbg.cpp
+++ b/metagraph/src/graph/annotated_dbg.cpp
@@ -829,7 +829,7 @@ bool AnnotatedSequenceGraph::has_label(node_index index, const Label &label) con
     return annotator_->has_label(graph_to_anno_index(index), label);
 }
 
-void AnnotatedSequenceGraph
+void AnnotatedSequenceGraph //TODO Myrthe: Create such a function that supports count annotation?
 ::call_annotated_nodes(const Label &label,
                        std::function<void(node_index)> callback) const {
     assert(check_compatibility());
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0a5ee8d1f7..a1969225ae 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -167,19 +167,20 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::atomic_thread_fence(std::memory_order_release);
         for (size_t i = 0; i < files.size(); ++i) {
             if (count_ones(other_labels, j, j + num_labels_per_file[i])) {
-                annot::ColumnCompressed<>::merge_load({ files[i] },
-                    [&](size_t offset, const auto &, auto&& column) {
-                        auto &[counts, init_mask, other_labels, num_labels_per_file]
-                            = count_vector;
-                        if (other_labels[j + offset]) {
-                            call_ones(init_mask, [&](size_t i) {
-                                if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
-                                    set_bit(other_mask.data(), i, parallel, MO_RELAXED);
-                            });
-                        }
-                    },
-                    num_parallel_files
-                );
+                    annot::ColumnCompressed<>::merge_load({ files[i] },
+                        [&](size_t offset, const auto &, auto&& column) {
+                          auto &[counts, init_mask, other_labels, num_labels_per_file]
+                          = count_vector;
+                          if (other_labels[j + offset]) {
+                              call_ones(init_mask, [&](size_t i) {
+                                  if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
+                                      set_bit(other_mask.data(), i, parallel, MO_RELAXED);
+                              });
+                          }
+                        },
+                        num_parallel_files
+                    );
+
             }
 
             j += num_labels_per_file[i];
@@ -243,7 +244,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::mutex vector_backup_mutex;
         std::atomic_thread_fence(std::memory_order_release);
 
-        auto count_merge = [&](sdsl::bit_vector &a,
+        auto count_merge = [&](sdsl::bit_vector &a, // Myrthe ANSWER: what does count_merge do? Is a hack, I do not have to touch this now. Count_add would be better name. If you have a dense column, and include it in foreground or background. Wate time, Instead first create mask.  You only access dense columns, for instance a reference.
                                const sdsl::bit_vector &b,
                                const std::vector<node_index> &id_map,
                                size_t offset = 0) {
@@ -310,7 +311,18 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             } else {
                 mask[node] = false;
             }
-        });
+        }); // TODO Myrthe: implement statistical tests. parse in_count out_count to adapter version of 'process' in diff tests.
+        process(in_sum, out_sum, in_total_kmers, out_total_kmers);
+        // process(const Range<count_t>& controls, const Range<count_t>& cases)
+        // stastical test found here in kmdiff: https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/model.hpp
+        // in merge.hpp this code is applied. https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/merge.hpp
+        // statistical and multiple correction tests come together in https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/cmd/diff.hpp
+        // call
+        // std::tuple<double, Significance, double, double>
+        //    process(const Range<count_t>& controls, const Range<count_t>& cases)  --> per kmer.
+        // needs one parameter -> P-value. other input arguments are not needed.
+        //  why do they use bonferonni rather than benjamini (kmdiff says you can do both). https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/corrector.cpp
+
 
         masked_graph->set_mask(new bitmap_vector(std::move(mask)));
 
@@ -378,9 +390,16 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
  * Helpers
  */
 
+
+/**
+ * A version of `make_initial_masked_graph` with count annotation.
+ * This function reads from the column count vector and decides what rows to keep, i.e.
+ * k-mers labeled with at least one of the foreground and background labels are enumerated to determine the initial subgraph
+ * I think this function stays the same irregardless of the differential assembly method?
+ */
 std::shared_ptr<MaskedDeBruijnGraph>
-make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                          sdsl::int_vector<> &counts,
+        make_initial_annotated_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                          sdsl::int_vector<> &counts, //TODO Myrthe later: int_vector_buffer and make it a template class. Otherwise the function stays the same because initial mask should only check if a k-mer is present just once in either set.
                           sdsl::bit_vector&& mask,
                           bool add_complement,
                           size_t num_threads) {
@@ -413,7 +432,7 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     uint64_t in_count = atomic_fetch(counts, path[i] * 2, vector_backup_mutex, MO_RELAXED);
                     uint64_t out_count = atomic_fetch(counts, path[i] * 2 + 1, vector_backup_mutex, MO_RELAXED);
                     atomic_fetch_and_add(counts, *it * 2, in_count, vector_backup_mutex, MO_RELAXED);
-                    atomic_fetch_and_add(counts, *it * 2 + 1, out_count, vector_backup_mutex, MO_RELAXED);
+                    atomic_fetch_and_add(counts, *it * 2 + 1, out_count, vector_backup_mutex, MO_RELAXED); // Myrthe: for each kmer, counts is the total time this occurs in all for- resp. background samples?
 
                     set_bit(mask.data(), *it, in_count, MO_RELAXED);
                 }
@@ -430,6 +449,139 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     return masked_graph;
 }
 
+    std::shared_ptr<MaskedDeBruijnGraph>
+    make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                              sdsl::int_vector<> &counts,
+                              sdsl::bit_vector&& mask,
+                              bool add_complement,
+                              size_t num_threads) {
+        // counts is a double-length vector storing the in-label and out-label
+        // counts interleaved
+        assert(counts.size() == mask.size() * 2);
+
+        auto masked_graph = std::make_shared<MaskedDeBruijnGraph>(
+                graph_ptr,
+                add_complement
+                ? std::make_unique<bitmap_vector>(mask)
+                : std::make_unique<bitmap_vector>(std::move(mask)),
+                true
+        );
+
+        logger->trace("Constructed masked graph with {} nodes", masked_graph->num_nodes());
+
+        if (add_complement) {
+            logger->trace("Adding reverse complements");
+            std::mutex vector_backup_mutex;
+            std::atomic_thread_fence(std::memory_order_release);
+            masked_graph->call_sequences([&](const std::string &seq, const std::vector<node_index> &path) {
+                std::string rc_seq = seq;
+                std::vector<DeBruijnGraph::node_index> rc_path = path;
+                reverse_complement_seq_path(*graph_ptr, rc_seq, rc_path);
+
+                auto it = rc_path.rbegin();
+                for (size_t i = 0; i < path.size(); ++i, ++it) {
+                    if (*it) {
+                        uint64_t in_count = atomic_fetch(counts, path[i] * 2, vector_backup_mutex, MO_RELAXED);
+                        uint64_t out_count = atomic_fetch(counts, path[i] * 2 + 1, vector_backup_mutex, MO_RELAXED);
+                        atomic_fetch_and_add(counts, *it * 2, in_count, vector_backup_mutex, MO_RELAXED);
+                        atomic_fetch_and_add(counts, *it * 2 + 1, out_count, vector_backup_mutex, MO_RELAXED);
+
+                        set_bit(mask.data(), *it, in_count, MO_RELAXED);
+                    }
+                }
+            }, num_threads, true);
+
+            std::atomic_thread_fence(std::memory_order_acquire);
+
+            masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+
+            logger->trace("Updated masked graph has {} nodes", masked_graph->num_nodes());
+        }
+
+        return masked_graph;
+    }
+
+// TODO
+total_sum(labels, & columns){ // sum over all columns first, and then add the sum to calculate the total sum
+        sdsl::int_vector<> total_kmers_columns;
+        for (size_t i = 0; i < files.size(); ++i) {
+            total_kmers_columns[label] = std::accumulate(column.begin(), column.end());
+        }
+     size_t total_kmers{std::accumulate(in_total_kmers_columns.begin(), in_total_kmers_columns.end(), 0ULL)};
+     return total_kmers;
+}
+// ANSWER, where to best do this? -> after load column values. Note down how the output of loadcv works.
+
+template <class vector_type> // type for the vector bitmap or int_vector, later also buffered.
+std::pair<sdsl::int_vector<>, sdsl::bit_vector>
+construct_diff_label_count_annotation_vector(const std::function<void(const std::function<void(const Label &, const vector_type&)>)> &generate_columns, //Myrthe: ANSWER: this is a callable, why is it used, where is it exactly executed? Such that it can be used to handle different type of columns (e.g. in terms of compression) in different versions of the function. It is called/executed in call_ones.
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  bool parallel,
+                                  bool add_out_labels_to_mask) {
+    logger->trace("Allocating mask vector");
+    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask); // TODO Myrthe: this must be increased when including annotation counts. Think about how to. If we have disk buffering then 32 bit would work.
+    //Myrthe: otherwise we might estimate a good width based on the distributions / coverage. Maximum sum of in or outlabels < max(|inlabels|,|outlabels|) * max_coverage. An option could be to equalize the coverage.
+    //Myrthe: for the statistical tests we need for each k-mer the sum of its occurance over resp the in labels and out labels. Furthermore we need the sum of all kmers in inlabels resp outlabels. Extract the coverage.
+    sdsl::bit_vector indicator(max_index + 1, false);
+
+    logger->trace("Allocating count vector");
+    // the in and out counts are stored interleaved
+    sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16); //TODO Myrthe later: int_vector_buffer. In that case the function also to be a template too, or a template based argument, if vector_type== buffered_int_vector, then it should be a buffered (// if constexpr (std::is_same_v<vector_type, Bitmap>) { })
+    logger->trace("done");
+
+    std::mutex vector_backup_mutex;
+
+    logger->trace("Populating count vector");
+    std::atomic_thread_fence(std::memory_order_release);
+    generate_columns([&](const Label &label, const vector_type &column) { // ANSWERED: outer loop is over the columns, inner loop over the kmers.
+        // Myrthe: For the buffered_int_vector we would have to use operators [ ] [ ], but it might be problematic if it runs out of memory and push_back would need to be used instead.
+        // Myrthe: The second problem with buffered_int_vector might be that it is expensive, because we do random access on it. Instead a solution might be to use a piority queue of L columns, storing the smallest element as a triple (column , index, value), such that always k-mers in row x are processed before row x + 1.
+
+        uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
+        if (labels_out.count(label))
+            col_indicator |= 2;
+
+        auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i, uint64_t value) { // Myrthe: modify this to have a value, since call_nonzeros, also creates value
+            i = AnnotatedDBG::anno_to_graph_index(i); // Myrthe ANSWERED: The i indicates the row /k-mer
+            assert(i != DeBruijnGraph::npos);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED); //Myrthe: sets corresponding bit to true in mask.
+            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED); // Myrthe: This here increments counts by 1, for count annotation, increment by the count.
+        };
+
+        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i) {
+            i = AnnotatedDBG::anno_to_graph_index(i);
+            assert(i != DeBruijnGraph::npos);
+            if (add_out_labels_to_mask)
+                set_bit(indicator.data(), i, parallel, MO_RELAXED);
+
+            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
+        };
+
+        auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) { //ANSWER: theoretcially a label should not be both in the in and out labels, but this is just in case.
+            i = AnnotatedDBG::anno_to_graph_index(i); // Myrthe: indexes in annotation matrix are 1 offset off, so convert the index from annotation to graph, because the mask should be compatible with the graph.
+            assert(i != DeBruijnGraph::npos);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
+        };
+
+        switch (col_indicator) {
+            case 1: { column.call_nonzeros(add_in); } break; // Myrthe: outer loop over columns. call_ones is a inner loop . iterates over rows, (throubh bitmap). There is a call_nonzeros function that I'd need, but it also needs to be made compatible with int_vector_buffer.
+            case 2: { column.call_nonzeros(add_out); } break;
+            case 3: { column.call_nonzeros(add_both); } break;
+            default: {}
+        }
+    });
+
+    std::atomic_thread_fence(std::memory_order_acquire);
+    logger->trace("done");
+
+    return std::make_pair(std::move(counts), std::move(indicator)); // ANSWER: the indicator defines the initial mask?
+}
+
 std::pair<sdsl::int_vector<>, sdsl::bit_vector>
 construct_diff_label_count_vector(const std::function<void(const std::function<void(const Label &, const bitmap&)>)> &generate_columns,
                                   const tsl::hopscotch_set<Label> &labels_in,
@@ -495,15 +647,28 @@ construct_diff_label_count_vector(const std::function<void(const std::function<v
 }
 
 
+// SOLVED: what are the different versions of `construct_diff_label_count_vector` for? for different situations, only this one is useful for annotation, as it can take filenames
+// Myrthe
+/**
+ * A version of `construct_diff_label_count_vector` with count annotation, for streaming input columns.
+ * // the foreground and background label counts, and assembly parameters are taken as input, to create a count vector, are used to determine which k-mers are filtered out from the subgraph.
+
+ * This function reads from the column count vectors and decides what rows to keep, i.e.
+ * k-mers labeled with at least one of the foreground and background labels are enumerated to determine the initial subgraph
+ * TODO Myrthe
+ */
 std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
-construct_diff_label_count_vector(const Annotator &annotator,
+construct_diff_label_annotation_count_vector(const Annotator &annotator,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
                                   size_t max_index,
                                   size_t num_labels,
                                   size_t num_threads,
                                   bool add_out_labels_to_mask) {
-    std::vector<Column> columns;
+    std::vector<Column> columns; // TODO Myrthe later: Change to buffered vector int_vector_buffer(  ); Perhaps also based on the template type?
+    // see documentation in int_vector_buffer() {}, constructor.
+    // create a temporary filename, where? Will the file be created by the int_vector_buffer if it doesn't exist yet?
+    // sdsl::int_vector_buffer(  ) (const std::string filename, std::ios::openmode mode=std::ios::in, uint64_t buffer_size=1024*1024, const uint8_t int_width=t_width, const bool is_plain=false, const uint64_t file_offset=0)
     const auto &encoder = annotator.get_label_encoder();
     AnnotatedDBG::Annotator::VLabels labels;
     labels.reserve(labels_in.size() + labels_out.size());
@@ -524,12 +689,53 @@ construct_diff_label_count_vector(const Annotator &annotator,
         other_labels[column] = false;
     }
 
-    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) {
+    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) { // ANSWER: which function is called here? the above
         annotator.get_matrix().call_columns(columns,
             [&](size_t j, const bitmap_generator &column) {
                 callback(labels[j], column);
             }, num_threads
         );
+    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask); //Myrthe: this is generate columns call back. The function above above defines what you do with the generated objects.
+
+    return std::make_tuple(std::move(counts), std::move(init_mask), std::move(other_labels));
+}
+
+
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
+construct_diff_label_count_vector(const Annotator &annotator,
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  size_t num_threads,
+                                  bool add_out_labels_to_mask) {
+    std::vector<Column> columns;
+    const auto &encoder = annotator.get_label_encoder();
+    AnnotatedDBG::Annotator::VLabels labels;
+    labels.reserve(labels_in.size() + labels_out.size());
+    columns.reserve(labels_in.size() + labels_out.size());
+    for (const auto &label : labels_in) {
+        labels.emplace_back(label);
+        columns.emplace_back(encoder.encode(label));
+    }
+    for (const auto &label : labels_out) {
+        if (!labels_in.count(label)) {
+            labels.emplace_back(label);
+            columns.emplace_back(encoder.encode(label));
+        }
+    }
+
+    sdsl::bit_vector other_labels(encoder.size(), true);
+    for (Column column : columns) {
+        other_labels[column] = false;
+    }
+
+    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) {
+        annotator.get_matrix().call_columns(columns,
+                                            [&](size_t j, const bitmap_generator &column) {
+                                                callback(labels[j], column);
+                                            }, num_threads
+        );
     }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask);
 
     return std::make_tuple(std::move(counts), std::move(init_mask), std::move(other_labels));
@@ -559,18 +765,53 @@ construct_diff_label_count_vector(const std::vector<std::string> &files,
 
     auto other_labels = to_sdsl(std::move(other_labels_vec));
 
-    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) {
+    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) { // QUESTION merge_load --> callback as part of another callback, but where is it now exactly executed.
         size_t j = 0;
-        for (size_t i = 0; i < files.size(); ++i) {
+        for (size_t i = 0; i < files.size(); ++i) { // loop over the columns
             if (count_ones(other_labels, j, j + num_labels_per_file[i])
                     != num_labels_per_file[i]) {
-                annot::ColumnCompressed<>::merge_load({ files[i] },
-                    [&](size_t offset, const auto &label, auto&& column) {
-                        if (!other_labels[j + offset])
-                            callback(label, *column);
-                    },
-                    num_threads
-                );
+                if (not config.count_kmers){
+                    annot::ColumnCompressed<>::merge_load({ files[i] },
+                        [&](size_t offset, const auto &label, auto&& column) {
+                            if (!other_labels[j + offset])
+                                callback(label, *column); // ANSWER: is this the ^ callback above? yes.
+                        },
+                        num_threads
+                    );
+
+                }else{ // if count annotation is used
+                annot::ColumnCompressed<>::load_columns_and_values({ files[i] }, //Myrthe QUESTION: How to write this lambda. The following is from annotation_converters.cpp. However, there it passed all files, rather than files[i]
+                                                                                                               [&](size_t j,
+                                                                                                                   const std::string &label,
+                                                                                                                   std::unique_ptr<bit_vector> &&column,
+                                                                                                                   sdsl::int_vector<> &&values) {
+                                                                                                                   size_t local_max_val = 0;
+                                                                                                                   if (values.size())
+                                                                                                                       local_max_val = *std::max_element(values.begin(), values.end());
+
+                                                                                                                   std::lock_guard<std::mutex> lock(mu); //QUESTION
+
+                                                                                                                   if (local_max_val > max_val)
+                                                                                                                       max_val = local_max_val;
+
+                                                                                                                   num_set_bits += column->num_set_bits();
+
+                                                                                                                   while (columns.size() <= j) {
+                                                                                                                       columns.emplace_back();
+                                                                                                                       col_values.emplace_back();
+                                                                                                                       col_names.emplace_back();
+                                                                                                                   }
+                                                                                                                   columns[j] = std::move(column);
+                                                                                                                   col_values[j] = std::move(values); //QUESTION: Why does this happen?
+                                                                                                                   col_names[j] = label;
+                                                                                                               },
+                                                                                                               num_threads);
+            // Myrthe QUESTION: Now I'd have to convert column and values s.t. they can be passed to construct_diff_label_count_vector?
+            // We assumed there that the column would just be an int_vector, rather than two arrays.
+
+                    //total_sum( values)
+            }
+
             }
 
             j += num_labels_per_file[i];
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 52cad9ec03..3001681ed2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -33,6 +33,8 @@ struct DifferentialAssemblyConfig {
     double label_mask_out_kmer_fraction = 0.0;
     double label_mask_other_unitig_fraction = 1.0;
     bool add_complement = false;
+    bool count_kmers = false; // Myrthe
+    double family_wise_error_rate = 0.05; //Myrthe: differential assembly statistical tests, or the family wise rate for bonferonni or a p-value
 };
 
 /**
@@ -44,7 +46,8 @@ struct DifferentialAssemblyConfig {
  * respectively.
  * In round 2, if any of the sequences of the initial masked graph have the
  * labels in labels_in_round2 or labels_out_round2, or if they have other labels,
- * the count vector is updated accordingly.
+ * the count
+ * vector is updated accordingly.
  * In round 3, the config rules are used to discard (i.e., mask out) nodes from
  * the masked graph. The resulting graph is returned.
  */
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
new file mode 100644
index 0000000000..5ce8ddfda6
--- /dev/null
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -0,0 +1,239 @@
+#include "annotated_graph_algorithm.hpp"
+
+#include "common/logger.hpp"
+#include "common/vectors/vector_algorithm.hpp"
+#include "common/vectors/bitmap.hpp"
+#include "graph/representation/masked_graph.hpp"
+#include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
+
+// https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/model.hpp
+/*****************************************************************************
+ *   kmdiff
+ *   Authors: T. Lemane
+ *
+ *  This program is free software: you can redistribute it and/or modify
+ *  it under the terms of the GNU Affero General Public License as
+ *  published by the Free Software Foundation, either version 3 of the
+ *  License, or (at your option) any later version.
+ *
+ *  This program is distributed in the hope that it will be useful,
+ *  but WITHOUT ANY WARRANTY; without even the implied warranty of
+ *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ *  GNU Affero General Public License for more details.
+ *
+ *  You should have received a copy of the GNU Affero General Public License
+ *  along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ *****************************************************************************/
+
+#pragma once
+
+#include <algorithm>
+#include <cmath>
+#include <numeric>
+#include <random>
+#include <vector>
+
+#include <alglibinternal.h>
+#include <spdlog/spdlog.h>
+#include <specialfunctions.h>
+
+#define KMTRICKS_PUBLIC
+#include <kmtricks/utils.hpp>
+
+#include <kmdiff/range.hpp>
+#include <kmdiff/kmer.hpp>
+#include <kmdiff/log_factorial_table.hpp>
+#include <kmdiff/correction.hpp>
+#include <kmdiff/imodel.hpp>
+
+
+// Myrthe: the following code calculates the p-value for a single k-mer across the samples. All comes together in `process`.
+namespace kmdiff {
+
+    template <size_t MAX_C>
+    class Model
+    {
+        using count_t = typename km::selectC<MAX_C>::type;
+
+    public:
+        Model() {}
+
+        template <typename Iterable>
+        double compute_mean(Iterable&& v)
+        {
+            return std::accumulate(v.begin(), v.end(), 0.0) / v.size();
+        }
+
+        template <typename Iterable>
+        std::tuple<double, size_t> compute_mean_e(Iterable& v)
+        {
+            size_t sum = 0;
+            size_t positive = 0;
+            for (auto& e : v)
+            {
+                if (e > 0) positive++;
+                sum += e;
+            }
+            double mean = sum / static_cast<double>(v.size());
+            return std::make_tuple(mean, positive);
+        }
+
+        template <typename Iterable>
+        std::tuple<double, size_t> compute_sum_e(Iterable& v)
+        {
+            double sum = 0;
+            size_t positive = 0;
+            for (auto& e : v)
+            {
+                if (e > 0) positive++;
+                sum += e;
+            }
+            return std::make_tuple(sum, positive);
+        }
+
+        template <typename Iterable>
+        std::tuple<double, double> compute_mean_sd(Iterable& v)
+        {
+            double mean = compute_mean(v);
+            double sq_sum = std::inner_product(v.begin(), v.end(), v.begin(), 0.0);
+            double sd = std::sqrt(sq_sum / v.size() - mean * mean);
+            return std::make_tuple(mean, sd);
+        }
+
+    };
+
+    template <size_t MAX_C>
+    class PoissonLikelihood : public IModel<MAX_C>, public Model<MAX_C>
+    {
+        /*
+          Based on https://github.com/atifrahman/HAWK
+          https://doi.org/10.7554/eLife.32920.001
+          https://doi.org/10.1371/journal.pone.0245058
+        */
+
+        using count_t = typename km::selectC<MAX_C>::type;
+
+    public:
+        PoissonLikelihood(
+                size_t nb_controls,
+                size_t nb_cases,
+                const std::vector<size_t>& total_controls,
+                const std::vector<size_t>& total_cases,
+                size_t preload)
+                : m_nb_controls(nb_controls),
+                  m_nb_cases(nb_cases),
+                  m_total_kmer_controls(total_controls),
+                  m_total_kmer_cases(total_cases),
+                  m_preload(preload)
+        {
+        }
+
+        void configure(const std::string& config) override {}
+    private:
+        double log_factorial(int k)
+        {
+            double res = 0;
+            while (k > 1)
+            {
+                res += log(k);
+                k--;
+            }
+            return res;
+        }
+
+        double poisson_prob(int k, double lambda)
+        {
+            if (lambda <= 0) return 0;
+            if (k < 0) k = 0;
+            return (-lambda + (k * log(lambda) - m_lf_table[k]));
+        }
+
+    public:
+
+        std::tuple<double, Significance, double, double>
+        process(const Range<count_t>& controls, const Range<count_t>& cases) override
+        {
+            auto [mean_control, positive_controls] = this->compute_sum_e(controls);
+            auto [mean_case, positive_cases] = this->compute_sum_e(cases);
+
+            double mean = (mean_control + mean_case) / static_cast<double>(m_sum_controls + m_sum_cases);
+
+            double null_hypothesis = 0;
+            double alt_hypothesis = 0;
+
+            alt_hypothesis += poisson_prob(mean_control, mean_control);
+            alt_hypothesis += poisson_prob(mean_case, mean_case);
+
+            null_hypothesis += poisson_prob(mean_control, mean * m_sum_controls);
+            null_hypothesis += poisson_prob(mean_case, mean * m_sum_cases);
+
+            double likelihood_ratio = alt_hypothesis - null_hypothesis;
+
+            if (likelihood_ratio < 0) likelihood_ratio = 0;
+            double p_value = alglib::chisquarecdistribution(1, 2 * likelihood_ratio);
+
+            Significance sign;
+
+            mean_control = mean_control * m_sum_cases / m_sum_controls;
+
+            if (mean_control < mean_case)
+                sign = Significance::CASE;
+            else if (mean_control > mean_case)
+                sign = Significance::CONTROL;
+            else
+                sign = Significance::NO;
+
+            return std::make_tuple(p_value, sign, mean_control, mean_case);
+        }
+
+        std::tuple<double, Significance, double, double>
+        process_myrthe(double in_sum, double out_sum, double in_total_kmers, double out_total_kmers) override // previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
+        {
+            auto mean_control = out_sum; auto mean_case = in_sum;
+            double mean = (mean_control + mean_case) / static_cast<double>(m_sum_controls + m_sum_cases);
+
+            double null_hypothesis = 0;
+            double alt_hypothesis = 0;
+
+            alt_hypothesis += poisson_prob(mean_control, mean_control);
+            alt_hypothesis += poisson_prob(mean_case, mean_case);
+
+            null_hypothesis += poisson_prob(mean_control, mean * m_sum_controls);
+            null_hypothesis += poisson_prob(mean_case, mean * m_sum_cases);
+
+            double likelihood_ratio = alt_hypothesis - null_hypothesis;
+
+            if (likelihood_ratio < 0) likelihood_ratio = 0;
+            double p_value = alglib::chisquarecdistribution(1, 2 * likelihood_ratio);
+
+            Significance sign;
+
+            mean_control = mean_control * m_sum_cases / m_sum_controls;
+
+            if (mean_control < mean_case)
+                sign = Significance::CASE;
+            else if (mean_control > mean_case)
+                sign = Significance::CONTROL;
+            else
+                sign = Significance::NO;
+
+            return std::make_tuple(p_value, sign, mean_control, mean_case);
+        }
+
+    private:
+        size_t m_nb_controls{0};
+        size_t m_nb_cases{0};
+
+        const std::vector<size_t>& m_total_kmer_controls;
+        const std::vector<size_t>& m_total_kmer_cases;
+
+        size_t m_sum_controls{
+                std::accumulate(m_total_kmer_controls.begin(), m_total_kmer_controls.end(), 0ULL)};
+
+        size_t m_sum_cases{std::accumulate(m_total_kmer_cases.begin(), m_total_kmer_cases.end(), 0ULL)};
+
+        size_t m_preload;
+        LogFactorialTable m_lf_table{m_preload};
+    };
+
+} // end of namespace kmdiff

From 0187a87586d71d1b8d52f79f16c0e14cc45179f2 Mon Sep 17 00:00:00 2001
From: MyrtheW <willemsenmyrthe@gmail.com>
Date: Fri, 3 Mar 2023 17:01:53 +0100
Subject: [PATCH 032/410] Restructuring by Harun

---
 .../src/graph/annotated_graph_algorithm.cpp   | 476 ++--------
 .../annotated_graph_algorithm_myrthe.cpp      | 869 ++++++++++++++++++
 2 files changed, 971 insertions(+), 374 deletions(-)
 create mode 100644 metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a1969225ae..1bc08277d5 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -36,17 +36,15 @@ constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
  * The second bit vector indicates which of the labels correspond to an "other"
  * label (i.e., not in- or out-).
  */
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
-construct_diff_label_count_vector(const Annotator &annotator,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  size_t num_threads,
-                                  bool add_out_labels_to_mask);
+
+
+using ValueCallback = std::function<void(uint64_t /* row index */, uint64_t /* row value */)>;
+using ValueGenerator = std::function<void(const ValueCallback&)>;
+using ColumnCallback = std::function<void(uint64_t, const Label&, const ValueGenerator&)>>;
+using ColumnGenerator = std::function<void(const ColumnCallback&);
 
 std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::vector<size_t>>
-construct_diff_label_count_vector(const std::vector<std::string> &files,
+construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
                                   size_t max_index,
@@ -118,11 +116,43 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
     logger->trace("Generating initial mask");
 
     // Construct initial masked graph from union of labels in labels_in
+    // auto count_vector = construct_diff_label_count_vector(
+    //     anno_graph.get_annotator(), labels_in, labels_out, graph_ptr->max_index(),
+    //     std::max(num_in_labels, num_out_labels), num_parallel_files,
+    //     add_complement
+    // );
     auto count_vector = construct_diff_label_count_vector(
-        anno_graph.get_annotator(), labels_in, labels_out, graph_ptr->max_index(),
-        std::max(num_in_labels, num_out_labels), num_parallel_files,
+        [&](const ColumnCallback &column_callback) {
+            if (config.count_kmers) {
+                // TODO
+                throw std::runtime_error("AnnotatedDBG with counts not supported yet");
+            } else {
+                size_t offset = 0;
+                for (const auto &label : labels_in) {
+                    column_callback(label, offset, [&](const ValueCallback &value_callback) {
+                        anno_graph.call_annotated_nodes(label, [&](node_index i) {
+                            i = AnnotatedDBG::graph_to_anno_index(i);
+                            value_callback(i, 1);
+                        });
+                    });
+                    ++offset;
+                }
+                for (const auto &label : labels_out) {
+                    column_callback(label, offset, [&](const ValueCallback &value_callback) {
+                        anno_graph.call_annotated_nodes(label, [&](node_index i) {
+                            i = AnnotatedDBG::graph_to_anno_index(i);
+                            value_callback(i, 1);
+                        });
+                    });
+                    ++offset;
+                }
+            }
+        },
+        labels_in, labels_out, graph_ptr->max_index(),
+        std::max(labels_in.size(), labels_out.size()), num_parallel_files,
         add_complement
     );
+
     auto &[counts, init_mask, other_labels] = count_vector;
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     return mask_nodes_by_label(graph_ptr, &anno_graph,
@@ -135,7 +165,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
 }
 
 std::shared_ptr<MaskedDeBruijnGraph>
-mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this version i don't need.
                     const std::vector<std::string> &files,
                     const tsl::hopscotch_set<Label> &labels_in,
                     const tsl::hopscotch_set<Label> &labels_out,
@@ -153,12 +183,35 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     logger->trace("Generating initial mask");
 
-    // Construct initial masked graph from union of labels in labels_in
     auto count_vector = construct_diff_label_count_vector(
-        files, labels_in, labels_out, graph_ptr->max_index(),
+        [&](const ColumnCallback &column_callback) {
+            if (config.count_kmers) {
+                annot::ColumnCompressed<>::load_columns_and_values(files,
+                    [&](uint64_t offset, const Label &label, auto&& column, auto&& counts) {
+                        column_callback(label, offset, [&](const ValueCallback &value_callback) {
+                            column->call_nonzeros([&](uint64_t i) {
+                                value_callback(i, counts[i]);
+                            });
+                        });
+                    }
+                );
+            } else {
+                annot::ColumnCompressed<>::merge_load(files,
+                    [&](uint64_t offset, const Label &label, auto&& column) {
+                        column_callback(label, offset, [&](const ValueCallback &value_callback) {
+                            column->call_nonzeros([&](uint64_t i) {
+                                value_callback(i, 1);
+                            });
+                        });
+                    }
+                );
+            }
+        },
+        labels_in, labels_out, graph_ptr->max_index(),
         std::max(labels_in.size(), labels_out.size()), num_parallel_files,
         add_complement
     );
+
     auto &[counts, init_mask, other_labels, num_labels_per_file] = count_vector;
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     if (check_other && sdsl::util::cnt_one_bits(other_labels)) {
@@ -167,20 +220,19 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::atomic_thread_fence(std::memory_order_release);
         for (size_t i = 0; i < files.size(); ++i) {
             if (count_ones(other_labels, j, j + num_labels_per_file[i])) {
-                    annot::ColumnCompressed<>::merge_load({ files[i] },
-                        [&](size_t offset, const auto &, auto&& column) {
-                          auto &[counts, init_mask, other_labels, num_labels_per_file]
-                          = count_vector;
-                          if (other_labels[j + offset]) {
-                              call_ones(init_mask, [&](size_t i) {
-                                  if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
-                                      set_bit(other_mask.data(), i, parallel, MO_RELAXED);
-                              });
-                          }
-                        },
-                        num_parallel_files
-                    );
-
+                annot::ColumnCompressed<>::merge_load({ files[i] },
+                    [&](size_t offset, const auto &, auto&& column) {
+                        auto &[counts, init_mask, other_labels, num_labels_per_file]
+                            = count_vector;
+                        if (other_labels[j + offset]) {
+                            call_ones(init_mask, [&](size_t i) {
+                                if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
+                                    set_bit(other_mask.data(), i, parallel, MO_RELAXED);
+                            }); // QUESTION: change this herer to call_nonzeros?
+                        }
+                    },
+                    num_parallel_files
+                );
             }
 
             j += num_labels_per_file[i];
@@ -244,7 +296,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::mutex vector_backup_mutex;
         std::atomic_thread_fence(std::memory_order_release);
 
-        auto count_merge = [&](sdsl::bit_vector &a, // Myrthe ANSWER: what does count_merge do? Is a hack, I do not have to touch this now. Count_add would be better name. If you have a dense column, and include it in foreground or background. Wate time, Instead first create mask.  You only access dense columns, for instance a reference.
+        auto count_merge = [&](sdsl::bit_vector &a,
                                const sdsl::bit_vector &b,
                                const std::vector<node_index> &id_map,
                                size_t offset = 0) {
@@ -311,18 +363,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             } else {
                 mask[node] = false;
             }
-        }); // TODO Myrthe: implement statistical tests. parse in_count out_count to adapter version of 'process' in diff tests.
-        process(in_sum, out_sum, in_total_kmers, out_total_kmers);
-        // process(const Range<count_t>& controls, const Range<count_t>& cases)
-        // stastical test found here in kmdiff: https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/model.hpp
-        // in merge.hpp this code is applied. https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/merge.hpp
-        // statistical and multiple correction tests come together in https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/cmd/diff.hpp
-        // call
-        // std::tuple<double, Significance, double, double>
-        //    process(const Range<count_t>& controls, const Range<count_t>& cases)  --> per kmer.
-        // needs one parameter -> P-value. other input arguments are not needed.
-        //  why do they use bonferonni rather than benjamini (kmdiff says you can do both). https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/corrector.cpp
-
+        });
 
         masked_graph->set_mask(new bitmap_vector(std::move(mask)));
 
@@ -390,16 +431,9 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
  * Helpers
  */
 
-
-/**
- * A version of `make_initial_masked_graph` with count annotation.
- * This function reads from the column count vector and decides what rows to keep, i.e.
- * k-mers labeled with at least one of the foreground and background labels are enumerated to determine the initial subgraph
- * I think this function stays the same irregardless of the differential assembly method?
- */
 std::shared_ptr<MaskedDeBruijnGraph>
-        make_initial_annotated_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                          sdsl::int_vector<> &counts, //TODO Myrthe later: int_vector_buffer and make it a template class. Otherwise the function stays the same because initial mask should only check if a k-mer is present just once in either set.
+make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                          sdsl::int_vector<> &counts,
                           sdsl::bit_vector&& mask,
                           bool add_complement,
                           size_t num_threads) {
@@ -432,7 +466,7 @@ std::shared_ptr<MaskedDeBruijnGraph>
                     uint64_t in_count = atomic_fetch(counts, path[i] * 2, vector_backup_mutex, MO_RELAXED);
                     uint64_t out_count = atomic_fetch(counts, path[i] * 2 + 1, vector_backup_mutex, MO_RELAXED);
                     atomic_fetch_and_add(counts, *it * 2, in_count, vector_backup_mutex, MO_RELAXED);
-                    atomic_fetch_and_add(counts, *it * 2 + 1, out_count, vector_backup_mutex, MO_RELAXED); // Myrthe: for each kmer, counts is the total time this occurs in all for- resp. background samples?
+                    atomic_fetch_and_add(counts, *it * 2 + 1, out_count, vector_backup_mutex, MO_RELAXED);
 
                     set_bit(mask.data(), *it, in_count, MO_RELAXED);
                 }
@@ -449,141 +483,8 @@ std::shared_ptr<MaskedDeBruijnGraph>
     return masked_graph;
 }
 
-    std::shared_ptr<MaskedDeBruijnGraph>
-    make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                              sdsl::int_vector<> &counts,
-                              sdsl::bit_vector&& mask,
-                              bool add_complement,
-                              size_t num_threads) {
-        // counts is a double-length vector storing the in-label and out-label
-        // counts interleaved
-        assert(counts.size() == mask.size() * 2);
-
-        auto masked_graph = std::make_shared<MaskedDeBruijnGraph>(
-                graph_ptr,
-                add_complement
-                ? std::make_unique<bitmap_vector>(mask)
-                : std::make_unique<bitmap_vector>(std::move(mask)),
-                true
-        );
-
-        logger->trace("Constructed masked graph with {} nodes", masked_graph->num_nodes());
-
-        if (add_complement) {
-            logger->trace("Adding reverse complements");
-            std::mutex vector_backup_mutex;
-            std::atomic_thread_fence(std::memory_order_release);
-            masked_graph->call_sequences([&](const std::string &seq, const std::vector<node_index> &path) {
-                std::string rc_seq = seq;
-                std::vector<DeBruijnGraph::node_index> rc_path = path;
-                reverse_complement_seq_path(*graph_ptr, rc_seq, rc_path);
-
-                auto it = rc_path.rbegin();
-                for (size_t i = 0; i < path.size(); ++i, ++it) {
-                    if (*it) {
-                        uint64_t in_count = atomic_fetch(counts, path[i] * 2, vector_backup_mutex, MO_RELAXED);
-                        uint64_t out_count = atomic_fetch(counts, path[i] * 2 + 1, vector_backup_mutex, MO_RELAXED);
-                        atomic_fetch_and_add(counts, *it * 2, in_count, vector_backup_mutex, MO_RELAXED);
-                        atomic_fetch_and_add(counts, *it * 2 + 1, out_count, vector_backup_mutex, MO_RELAXED);
-
-                        set_bit(mask.data(), *it, in_count, MO_RELAXED);
-                    }
-                }
-            }, num_threads, true);
-
-            std::atomic_thread_fence(std::memory_order_acquire);
-
-            masked_graph->set_mask(new bitmap_vector(std::move(mask)));
-
-            logger->trace("Updated masked graph has {} nodes", masked_graph->num_nodes());
-        }
-
-        return masked_graph;
-    }
-
-// TODO
-total_sum(labels, & columns){ // sum over all columns first, and then add the sum to calculate the total sum
-        sdsl::int_vector<> total_kmers_columns;
-        for (size_t i = 0; i < files.size(); ++i) {
-            total_kmers_columns[label] = std::accumulate(column.begin(), column.end());
-        }
-     size_t total_kmers{std::accumulate(in_total_kmers_columns.begin(), in_total_kmers_columns.end(), 0ULL)};
-     return total_kmers;
-}
-// ANSWER, where to best do this? -> after load column values. Note down how the output of loadcv works.
-
-template <class vector_type> // type for the vector bitmap or int_vector, later also buffered.
-std::pair<sdsl::int_vector<>, sdsl::bit_vector>
-construct_diff_label_count_annotation_vector(const std::function<void(const std::function<void(const Label &, const vector_type&)>)> &generate_columns, //Myrthe: ANSWER: this is a callable, why is it used, where is it exactly executed? Such that it can be used to handle different type of columns (e.g. in terms of compression) in different versions of the function. It is called/executed in call_ones.
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  bool parallel,
-                                  bool add_out_labels_to_mask) {
-    logger->trace("Allocating mask vector");
-    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask); // TODO Myrthe: this must be increased when including annotation counts. Think about how to. If we have disk buffering then 32 bit would work.
-    //Myrthe: otherwise we might estimate a good width based on the distributions / coverage. Maximum sum of in or outlabels < max(|inlabels|,|outlabels|) * max_coverage. An option could be to equalize the coverage.
-    //Myrthe: for the statistical tests we need for each k-mer the sum of its occurance over resp the in labels and out labels. Furthermore we need the sum of all kmers in inlabels resp outlabels. Extract the coverage.
-    sdsl::bit_vector indicator(max_index + 1, false);
-
-    logger->trace("Allocating count vector");
-    // the in and out counts are stored interleaved
-    sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16); //TODO Myrthe later: int_vector_buffer. In that case the function also to be a template too, or a template based argument, if vector_type== buffered_int_vector, then it should be a buffered (// if constexpr (std::is_same_v<vector_type, Bitmap>) { })
-    logger->trace("done");
-
-    std::mutex vector_backup_mutex;
-
-    logger->trace("Populating count vector");
-    std::atomic_thread_fence(std::memory_order_release);
-    generate_columns([&](const Label &label, const vector_type &column) { // ANSWERED: outer loop is over the columns, inner loop over the kmers.
-        // Myrthe: For the buffered_int_vector we would have to use operators [ ] [ ], but it might be problematic if it runs out of memory and push_back would need to be used instead.
-        // Myrthe: The second problem with buffered_int_vector might be that it is expensive, because we do random access on it. Instead a solution might be to use a piority queue of L columns, storing the smallest element as a triple (column , index, value), such that always k-mers in row x are processed before row x + 1.
-
-        uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
-        if (labels_out.count(label))
-            col_indicator |= 2;
-
-        auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i, uint64_t value) { // Myrthe: modify this to have a value, since call_nonzeros, also creates value
-            i = AnnotatedDBG::anno_to_graph_index(i); // Myrthe ANSWERED: The i indicates the row /k-mer
-            assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED); //Myrthe: sets corresponding bit to true in mask.
-            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED); // Myrthe: This here increments counts by 1, for count annotation, increment by the count.
-        };
-
-        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i) {
-            i = AnnotatedDBG::anno_to_graph_index(i);
-            assert(i != DeBruijnGraph::npos);
-            if (add_out_labels_to_mask)
-                set_bit(indicator.data(), i, parallel, MO_RELAXED);
-
-            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
-        };
-
-        auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) { //ANSWER: theoretcially a label should not be both in the in and out labels, but this is just in case.
-            i = AnnotatedDBG::anno_to_graph_index(i); // Myrthe: indexes in annotation matrix are 1 offset off, so convert the index from annotation to graph, because the mask should be compatible with the graph.
-            assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
-        };
-
-        switch (col_indicator) {
-            case 1: { column.call_nonzeros(add_in); } break; // Myrthe: outer loop over columns. call_ones is a inner loop . iterates over rows, (throubh bitmap). There is a call_nonzeros function that I'd need, but it also needs to be made compatible with int_vector_buffer.
-            case 2: { column.call_nonzeros(add_out); } break;
-            case 3: { column.call_nonzeros(add_both); } break;
-            default: {}
-        }
-    });
-
-    std::atomic_thread_fence(std::memory_order_acquire);
-    logger->trace("done");
-
-    return std::make_pair(std::move(counts), std::move(indicator)); // ANSWER: the indicator defines the initial mask?
-}
-
 std::pair<sdsl::int_vector<>, sdsl::bit_vector>
-construct_diff_label_count_vector(const std::function<void(const std::function<void(const Label &, const bitmap&)>)> &generate_columns,
+construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
                                   size_t max_index,
@@ -599,43 +500,47 @@ construct_diff_label_count_vector(const std::function<void(const std::function<v
     sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16);
     logger->trace("done");
 
+    sdsl::bit_vector other_labels(num_labels, false);
     std::mutex vector_backup_mutex;
 
     logger->trace("Populating count vector");
     std::atomic_thread_fence(std::memory_order_release);
-    generate_columns([&](const Label &label, const bitmap &column) {
+    generate_columns([&](const Label &label, uint64_t offset, const ValueGenerator &value_generator) {
         uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
         if (labels_out.count(label))
             col_indicator |= 2;
 
-        auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
+        if (!col_indicator)
+            set_bit(other_labels.data(), offset, parallel, MO_RELAXED);
+
+        ValueCallback add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i, uint64_t v) {
             i = AnnotatedDBG::anno_to_graph_index(i);
             assert(i != DeBruijnGraph::npos);
             set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, v, vector_backup_mutex, MO_RELAXED);
         };
 
-        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i) {
+        ValueCallback add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i, uint64_t v) {
             i = AnnotatedDBG::anno_to_graph_index(i);
             assert(i != DeBruijnGraph::npos);
             if (add_out_labels_to_mask)
                 set_bit(indicator.data(), i, parallel, MO_RELAXED);
 
-            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2 + 1, v, vector_backup_mutex, MO_RELAXED);
         };
 
-        auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
+        ValueCallback add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i, uint64_t v) {
             i = AnnotatedDBG::anno_to_graph_index(i);
             assert(i != DeBruijnGraph::npos);
             set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, v, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2 + 1, v, vector_backup_mutex, MO_RELAXED);
         };
 
         switch (col_indicator) {
-            case 1: { column.call_ones(add_in); } break;
-            case 2: { column.call_ones(add_out); } break;
-            case 3: { column.call_ones(add_both); } break;
+            case 1: { value_generator(add_in); } break;
+            case 2: { value_generator(add_out); } break;
+            case 3: { value_generator(add_both); } break;
             default: {}
         }
     });
@@ -643,186 +548,9 @@ construct_diff_label_count_vector(const std::function<void(const std::function<v
     std::atomic_thread_fence(std::memory_order_acquire);
     logger->trace("done");
 
-    return std::make_pair(std::move(counts), std::move(indicator));
-}
-
-
-// SOLVED: what are the different versions of `construct_diff_label_count_vector` for? for different situations, only this one is useful for annotation, as it can take filenames
-// Myrthe
-/**
- * A version of `construct_diff_label_count_vector` with count annotation, for streaming input columns.
- * // the foreground and background label counts, and assembly parameters are taken as input, to create a count vector, are used to determine which k-mers are filtered out from the subgraph.
-
- * This function reads from the column count vectors and decides what rows to keep, i.e.
- * k-mers labeled with at least one of the foreground and background labels are enumerated to determine the initial subgraph
- * TODO Myrthe
- */
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
-construct_diff_label_annotation_count_vector(const Annotator &annotator,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  size_t num_threads,
-                                  bool add_out_labels_to_mask) {
-    std::vector<Column> columns; // TODO Myrthe later: Change to buffered vector int_vector_buffer(  ); Perhaps also based on the template type?
-    // see documentation in int_vector_buffer() {}, constructor.
-    // create a temporary filename, where? Will the file be created by the int_vector_buffer if it doesn't exist yet?
-    // sdsl::int_vector_buffer(  ) (const std::string filename, std::ios::openmode mode=std::ios::in, uint64_t buffer_size=1024*1024, const uint8_t int_width=t_width, const bool is_plain=false, const uint64_t file_offset=0)
-    const auto &encoder = annotator.get_label_encoder();
-    AnnotatedDBG::Annotator::VLabels labels;
-    labels.reserve(labels_in.size() + labels_out.size());
-    columns.reserve(labels_in.size() + labels_out.size());
-    for (const auto &label : labels_in) {
-        labels.emplace_back(label);
-        columns.emplace_back(encoder.encode(label));
-    }
-    for (const auto &label : labels_out) {
-        if (!labels_in.count(label)) {
-            labels.emplace_back(label);
-            columns.emplace_back(encoder.encode(label));
-        }
-    }
-
-    sdsl::bit_vector other_labels(encoder.size(), true);
-    for (Column column : columns) {
-        other_labels[column] = false;
-    }
-
-    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) { // ANSWER: which function is called here? the above
-        annotator.get_matrix().call_columns(columns,
-            [&](size_t j, const bitmap_generator &column) {
-                callback(labels[j], column);
-            }, num_threads
-        );
-    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask); //Myrthe: this is generate columns call back. The function above above defines what you do with the generated objects.
-
-    return std::make_tuple(std::move(counts), std::move(init_mask), std::move(other_labels));
-}
-
-
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
-construct_diff_label_count_vector(const Annotator &annotator,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  size_t num_threads,
-                                  bool add_out_labels_to_mask) {
-    std::vector<Column> columns;
-    const auto &encoder = annotator.get_label_encoder();
-    AnnotatedDBG::Annotator::VLabels labels;
-    labels.reserve(labels_in.size() + labels_out.size());
-    columns.reserve(labels_in.size() + labels_out.size());
-    for (const auto &label : labels_in) {
-        labels.emplace_back(label);
-        columns.emplace_back(encoder.encode(label));
-    }
-    for (const auto &label : labels_out) {
-        if (!labels_in.count(label)) {
-            labels.emplace_back(label);
-            columns.emplace_back(encoder.encode(label));
-        }
-    }
-
-    sdsl::bit_vector other_labels(encoder.size(), true);
-    for (Column column : columns) {
-        other_labels[column] = false;
-    }
-
-    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) {
-        annotator.get_matrix().call_columns(columns,
-                                            [&](size_t j, const bitmap_generator &column) {
-                                                callback(labels[j], column);
-                                            }, num_threads
-        );
-    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask);
-
-    return std::make_tuple(std::move(counts), std::move(init_mask), std::move(other_labels));
-}
-
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::vector<size_t>>
-construct_diff_label_count_vector(const std::vector<std::string> &files,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  size_t num_threads,
-                                  bool add_out_labels_to_mask) {
-    std::vector<bool> other_labels_vec;
-    other_labels_vec.reserve(files.size());
-
-    std::vector<size_t> num_labels_per_file;
-    num_labels_per_file.reserve(files.size());
-
-    for (size_t i = 0; i < files.size(); ++i) {
-        auto label_encoder = annot::ColumnCompressed<>::read_label_encoder(files[i]);
-        num_labels_per_file.emplace_back(label_encoder.size());
-        for (const auto &label : label_encoder.get_labels()) {
-            other_labels_vec.emplace_back(!labels_in.count(label) && !labels_out.count(label));
-        }
-    }
-
-    auto other_labels = to_sdsl(std::move(other_labels_vec));
-
-    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) { // QUESTION merge_load --> callback as part of another callback, but where is it now exactly executed.
-        size_t j = 0;
-        for (size_t i = 0; i < files.size(); ++i) { // loop over the columns
-            if (count_ones(other_labels, j, j + num_labels_per_file[i])
-                    != num_labels_per_file[i]) {
-                if (not config.count_kmers){
-                    annot::ColumnCompressed<>::merge_load({ files[i] },
-                        [&](size_t offset, const auto &label, auto&& column) {
-                            if (!other_labels[j + offset])
-                                callback(label, *column); // ANSWER: is this the ^ callback above? yes.
-                        },
-                        num_threads
-                    );
-
-                }else{ // if count annotation is used
-                annot::ColumnCompressed<>::load_columns_and_values({ files[i] }, //Myrthe QUESTION: How to write this lambda. The following is from annotation_converters.cpp. However, there it passed all files, rather than files[i]
-                                                                                                               [&](size_t j,
-                                                                                                                   const std::string &label,
-                                                                                                                   std::unique_ptr<bit_vector> &&column,
-                                                                                                                   sdsl::int_vector<> &&values) {
-                                                                                                                   size_t local_max_val = 0;
-                                                                                                                   if (values.size())
-                                                                                                                       local_max_val = *std::max_element(values.begin(), values.end());
-
-                                                                                                                   std::lock_guard<std::mutex> lock(mu); //QUESTION
-
-                                                                                                                   if (local_max_val > max_val)
-                                                                                                                       max_val = local_max_val;
-
-                                                                                                                   num_set_bits += column->num_set_bits();
-
-                                                                                                                   while (columns.size() <= j) {
-                                                                                                                       columns.emplace_back();
-                                                                                                                       col_values.emplace_back();
-                                                                                                                       col_names.emplace_back();
-                                                                                                                   }
-                                                                                                                   columns[j] = std::move(column);
-                                                                                                                   col_values[j] = std::move(values); //QUESTION: Why does this happen?
-                                                                                                                   col_names[j] = label;
-                                                                                                               },
-                                                                                                               num_threads);
-            // Myrthe QUESTION: Now I'd have to convert column and values s.t. they can be passed to construct_diff_label_count_vector?
-            // We assumed there that the column would just be an int_vector, rather than two arrays.
-
-                    //total_sum( values)
-            }
-
-            }
-
-            j += num_labels_per_file[i];
-        }
-    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask);
-
-    return std::make_tuple(std::move(counts), std::move(init_mask),
-                           std::move(other_labels), std::move(num_labels_per_file));
+    return std::make_pair(std::move(counts), std::move(indicator), std::move(other_labels));
 }
 
-
 void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
                                    const GetKeptIntervals &get_kept_intervals,
                                    size_t num_threads) {
diff --git a/metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp b/metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp
new file mode 100644
index 0000000000..af60ff05ea
--- /dev/null
+++ b/metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp
@@ -0,0 +1,869 @@
+#include "annotated_graph_algorithm.hpp"
+
+#include "common/logger.hpp"
+#include "common/vectors/vector_algorithm.hpp"
+#include "common/vectors/bitmap.hpp"
+#include "graph/representation/masked_graph.hpp"
+#include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
+
+
+namespace mtg {
+namespace graph {
+
+using mtg::common::logger;
+
+typedef AnnotatedDBG::node_index node_index;
+typedef AnnotatedDBG::Annotator Annotator;
+typedef AnnotatedDBG::Annotator::Label Label;
+using Column = annot::binmat::BinaryMatrix::Column;
+
+typedef std::function<size_t()> LabelCountCallback;
+
+constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
+
+
+/**
+ * Return an int_vector<>, bit_vector of lengths anno_graph.get_graph().max_index() * 2
+ * and anno_graph.get_graph().max_index(), respectively, and a bit_vector of length
+ * equal to the total number of labels.
+ * For an index i, the int_vector at indices 2*i and 2*i + 1 represent the
+ * number of labels in labels_in and labels_out which the k-mer of index i is
+ * annotated with, respectively. The width of the int_vector<> is computed to be
+ * wide enough to contain counts up to num_labels.
+ * The returned bit_vector is a k-mer mask indicating those k-mers annotated
+ * with at least one in-label. If add_out_labels_to_mask is true, then it also indicates
+ * which those k-mers with at least one out-label.
+ * The second bit vector indicates which of the labels correspond to an "other"
+ * label (i.e., not in- or out-).
+ */
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
+construct_diff_label_count_vector(const Annotator &annotator,
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  size_t num_threads,
+                                  bool add_out_labels_to_mask);
+
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::vector<size_t>>
+construct_diff_label_count_vector(const std::vector<std::string> &files,
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  size_t num_threads,
+                                  bool add_out_labels_to_mask);
+
+// Regions of a graph mask which should be kept (i.e., masked in)
+typedef std::vector<std::pair<size_t, size_t>> Intervals;
+
+// Returns a vector of kept regions given a unitig and its corresponding path
+typedef std::function<Intervals(const std::string&, const std::vector<node_index>&)> GetKeptIntervals;
+
+// Assemble unitigs from the masked graph, then use get_kept_intervals to generate
+// regions which should be masked in. Update the graph mask accordingly.
+void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
+                                   const GetKeptIntervals &get_kept_intervals,
+                                   size_t num_threads);
+
+// Given an initial mask and counts, generate a masked graph. If add_complement
+// is true, then add the reverse complements of all nodes to the graph as well.
+std::shared_ptr<MaskedDeBruijnGraph>
+make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                          sdsl::int_vector<> &counts,
+                          sdsl::bit_vector&& mask,
+                          bool add_complement,
+                          size_t num_threads);
+
+// Helper function for calls in interface
+std::shared_ptr<MaskedDeBruijnGraph>
+mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                    const AnnotatedDBG *anno_graph,
+                    sdsl::int_vector<>&& counts,
+                    sdsl::bit_vector&& mask,
+                    sdsl::bit_vector&& other_mask,
+                    size_t num_labels,
+                    const tsl::hopscotch_set<Label> &labels_in,
+                    const tsl::hopscotch_set<Label> &labels_out,
+                    const tsl::hopscotch_set<Label> &labels_in_round2,
+                    const tsl::hopscotch_set<Label> &labels_out_round2,
+                    const DifferentialAssemblyConfig &config,
+                    size_t num_threads);
+
+std::shared_ptr<MaskedDeBruijnGraph>
+mask_nodes_by_label(const AnnotatedDBG &anno_graph,
+                    const tsl::hopscotch_set<Label> &labels_in,
+                    const tsl::hopscotch_set<Label> &labels_out,
+                    const tsl::hopscotch_set<Label> &labels_in_round2,
+                    const tsl::hopscotch_set<Label> &labels_out_round2,
+                    const DifferentialAssemblyConfig &config,
+                    size_t num_threads,
+                    size_t num_parallel_files) {
+    num_parallel_files = std::min(num_threads, num_parallel_files);
+    size_t num_in_labels = labels_in.size() + labels_in_round2.size();
+    size_t num_out_labels = labels_out.size() + labels_out_round2.size();
+    auto graph_ptr = std::static_pointer_cast<const DeBruijnGraph>(
+        anno_graph.get_graph_ptr()
+    );
+
+    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
+    bool unitig_mode = check_other || labels_in_round2.size() || labels_out_round2.size()
+            || config.label_mask_in_unitig_fraction != 0.0
+            || config.label_mask_out_unitig_fraction != 1.0
+            || config.label_mask_other_unitig_fraction != 1.0;
+
+    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
+        && (config.add_complement || unitig_mode);
+
+    logger->trace("Generating initial mask");
+
+    // Construct initial masked graph from union of labels in labels_in
+    auto count_vector = construct_diff_label_count_vector(
+        anno_graph.get_annotator(), labels_in, labels_out, graph_ptr->max_index(),
+        std::max(num_in_labels, num_out_labels), num_parallel_files,
+        add_complement
+    );
+    auto &[counts, init_mask, other_labels] = count_vector;
+    sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
+    return mask_nodes_by_label(graph_ptr, &anno_graph,
+                               std::move(counts), std::move(init_mask),
+                               std::move(other_mask),
+                               anno_graph.get_annotator().num_labels(),
+                               labels_in, labels_out,
+                               labels_in_round2, labels_out_round2,
+                               config, num_threads);
+}
+
+std::shared_ptr<MaskedDeBruijnGraph>
+mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this function I don't need.
+                    const std::vector<std::string> &files,
+                    const tsl::hopscotch_set<Label> &labels_in,
+                    const tsl::hopscotch_set<Label> &labels_out,
+                    const DifferentialAssemblyConfig &config,
+                    size_t num_threads,
+                    size_t num_parallel_files) {
+    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
+    bool unitig_mode = check_other
+            || config.label_mask_in_unitig_fraction != 0.0
+            || config.label_mask_out_unitig_fraction != 1.0
+            || config.label_mask_other_unitig_fraction != 1.0;
+
+    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
+        && (config.add_complement || unitig_mode);
+
+    logger->trace("Generating initial mask");
+
+    // Construct initial masked graph from union of labels in labels_in
+    auto count_vector = construct_diff_label_count_vector(
+        files, labels_in, labels_out, graph_ptr->max_index(),
+        std::max(labels_in.size(), labels_out.size()), num_parallel_files,
+        add_complement
+    );
+    auto &[counts, init_mask, other_labels, num_labels_per_file] = count_vector;
+    sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
+    if (check_other && sdsl::util::cnt_one_bits(other_labels)) {
+        bool parallel = num_parallel_files > 1;
+        size_t j = 0;
+        std::atomic_thread_fence(std::memory_order_release);
+        for (size_t i = 0; i < files.size(); ++i) {
+            if (count_ones(other_labels, j, j + num_labels_per_file[i])) {
+                    annot::ColumnCompressed<>::merge_load({ files[i] },
+                        [&](size_t offset, const auto &, auto&& column) {
+                          auto &[counts, init_mask, other_labels, num_labels_per_file]
+                          = count_vector;
+                          if (other_labels[j + offset]) {
+                              call_ones(init_mask, [&](size_t i) {
+                                  if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
+                                      set_bit(other_mask.data(), i, parallel, MO_RELAXED);
+                              });
+                          }
+                        },
+                        num_parallel_files
+                    );
+
+            }
+
+            j += num_labels_per_file[i];
+        }
+        std::atomic_thread_fence(std::memory_order_acquire);
+    }
+
+    return mask_nodes_by_label(graph_ptr, nullptr,
+                               std::move(counts), std::move(init_mask),
+                               std::move(other_mask),
+                               files.size(),
+                               labels_in, labels_out, {}, {},
+                               config, num_threads);
+}
+
+std::shared_ptr<MaskedDeBruijnGraph>
+mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                    const AnnotatedDBG *anno_graph,
+                    sdsl::int_vector<>&& counts,
+                    sdsl::bit_vector&& init_mask,
+                    sdsl::bit_vector&& other_mask,
+                    size_t num_labels,
+                    const tsl::hopscotch_set<Label> &labels_in,
+                    const tsl::hopscotch_set<Label> &labels_out,
+                    const tsl::hopscotch_set<Label> &labels_in_round2,
+                    const tsl::hopscotch_set<Label> &labels_out_round2,
+                    const DifferentialAssemblyConfig &config,
+                    size_t num_threads) {
+    // in and out counts are stored interleaved in the counts vector
+    assert(counts.size() == init_mask.size() * 2);
+
+    bool parallel = num_threads > 1;
+    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
+    bool unitig_mode = check_other || labels_in_round2.size() || labels_out_round2.size()
+            || config.label_mask_in_unitig_fraction != 0.0
+            || config.label_mask_out_unitig_fraction != 1.0
+            || config.label_mask_other_unitig_fraction != 1.0;
+
+    size_t num_in_labels = labels_in.size() + labels_in_round2.size();
+    size_t num_out_labels = labels_out.size() + labels_out_round2.size();
+
+    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
+        && (config.add_complement || unitig_mode);
+
+    auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
+                                                  add_complement, num_threads);
+
+    auto mask_or = [&](sdsl::bit_vector &a,
+                       const sdsl::bit_vector &b,
+                       const std::vector<node_index> &id_map) {
+        call_ones(b, [&](size_t i) {
+            if (id_map[i])
+                set_bit(a.data(), id_map[i], parallel, MO_RELAXED);
+        });
+    };
+
+    // check all other labels and round 2 labels
+    if (anno_graph && (check_other || labels_in_round2.size() || labels_out_round2.size())) {
+        sdsl::bit_vector union_mask
+            = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
+        std::mutex vector_backup_mutex;
+        std::atomic_thread_fence(std::memory_order_release);
+
+        auto count_merge = [&](sdsl::bit_vector &a, // Myrthe ANSWER: what does count_merge do? Is a hack, I do not have to touch this now. Count_add would be better name. If you have a dense column, and include it in foreground or background. Wate time, Instead first create mask.  You only access dense columns, for instance a reference.
+                               const sdsl::bit_vector &b,
+                               const std::vector<node_index> &id_map,
+                               size_t offset = 0) {
+            call_ones(b, [&](size_t i) {
+                if (id_map[i]) {
+                    set_bit(a.data(), id_map[i], parallel, MO_RELAXED);
+                    atomic_fetch_and_add(counts, id_map[i] * 2 + offset, 1,
+                                         vector_backup_mutex, MO_RELAXED);
+                }
+            });
+        };
+
+        logger->trace("Checking shared and other labels");
+        masked_graph->call_sequences([&](const std::string &contig,
+                                         const std::vector<node_index> &path) {
+            for (const auto &[label, sig] : anno_graph->get_top_label_signatures(contig, num_labels)) {
+                bool found_in = labels_in.count(label);
+                bool found_out = labels_out.count(label);
+                bool found_in_round2 = labels_in_round2.count(label);
+                bool found_out_round2 = labels_out_round2.count(label);
+                if (!found_in && !found_out
+                        && !found_in_round2 && !found_out_round2 && check_other) {
+                    mask_or(other_mask, sig, path);
+                }
+
+                if (found_in_round2)
+                    count_merge(union_mask, sig, path);
+
+                if (found_out_round2)
+                    count_merge(union_mask, sig, path, 1);
+            }
+        }, num_threads);
+
+        std::atomic_thread_fence(std::memory_order_acquire);
+
+        masked_graph->set_mask(new bitmap_vector(std::move(union_mask)));
+    }
+
+    // Filter unitigs from masked graph based on filtration criteria
+    logger->trace("Filtering out background");
+
+    size_t min_label_in_count = std::ceil(config.label_mask_in_kmer_fraction
+                                    * num_in_labels);
+    size_t max_label_out_count = std::floor(config.label_mask_out_kmer_fraction
+                                    * num_out_labels);
+
+    if (config.label_mask_in_unitig_fraction == 0.0
+            && config.label_mask_out_unitig_fraction == 1.0
+            && config.label_mask_other_unitig_fraction == 1.0) {
+        logger->trace("Filtering by node");
+        size_t total_nodes = masked_graph->num_nodes();
+        const auto &in_mask
+            = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
+        sdsl::bit_vector mask = in_mask;
+
+        // TODO: make this part multithreaded
+        size_t kept_nodes = 0;
+        call_ones(in_mask, [&](node_index node) {
+            uint64_t in_count = counts[node * 2];
+            uint64_t out_count = counts[node * 2 + 1];
+
+            if (in_count >= min_label_in_count && out_count <= max_label_out_count) {
+                ++kept_nodes;
+            } else {
+                mask[node] = false;
+            }
+        }); // TODO Myrthe: implement statistical tests. parse in_count out_count to adapter version of 'process' in diff tests.
+        process(in_sum, out_sum, in_total_kmers, out_total_kmers);
+        // process(const Range<count_t>& controls, const Range<count_t>& cases)
+        // stastical test found here in kmdiff: https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/model.hpp
+        // in merge.hpp this code is applied. https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/merge.hpp
+        // statistical and multiple correction tests come together in https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/cmd/diff.hpp
+        // call
+        // std::tuple<double, Significance, double, double>
+        //    process(const Range<count_t>& controls, const Range<count_t>& cases)  --> per kmer.
+        // needs one parameter -> P-value. other input arguments are not needed.
+        //  why do they use bonferonni rather than benjamini (kmdiff says you can do both). https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/corrector.cpp
+
+
+        masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+
+        logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
+
+        return masked_graph;
+    }
+
+    logger->trace("Filtering by unitig");
+
+    update_masked_graph_by_unitig(*masked_graph,
+        [&](const std::string &, const std::vector<node_index> &path) -> Intervals {
+            // return a set of intervals to keep in the graph
+            size_t in_kmer_count = 0;
+
+            size_t begin = path.size();
+            size_t end = 0;
+            for (size_t i = 0; i < path.size(); ++i) {
+                if (counts[path[i] * 2] >= min_label_in_count) {
+                    if (begin == path.size())
+                        begin = i;
+
+                    end = std::max(end, i + 1);
+
+                    ++in_kmer_count;
+                }
+            }
+
+            if (begin >= end)
+                return {};
+
+            size_t size = end - begin;
+            size_t label_in_cutoff = std::ceil(config.label_mask_in_unitig_fraction * size);
+            if (in_kmer_count < label_in_cutoff)
+                return {};
+
+            size_t out_kmer_count = 0;
+            size_t other_kmer_count = 0;
+            size_t label_out_cutoff = std::floor(config.label_mask_out_unitig_fraction * size);
+            size_t other_cutoff = std::floor(config.label_mask_other_unitig_fraction * size);
+
+
+            for (size_t i = begin; i < end; ++i) {
+                if (counts[path[i] * 2 + 1] > max_label_out_count
+                        && ++out_kmer_count > label_out_cutoff) {
+                    return {};
+                }
+
+                if (check_other && other_mask[path[i]]
+                        && ++other_kmer_count > other_cutoff) {
+                    return {};
+                }
+            }
+
+            return { std::make_pair(begin, end) };
+        },
+        num_threads
+    );
+
+    return masked_graph;
+}
+
+
+/**
+ * Helpers
+ */
+
+
+/**
+ * A version of `make_initial_masked_graph` with count annotation.
+ * This function reads from the column count vector and decides what rows to keep, i.e.
+ * k-mers labeled with at least one of the foreground and background labels are enumerated to determine the initial subgraph
+ * I think this function stays the same irregardless of the differential assembly method?
+ */
+std::shared_ptr<MaskedDeBruijnGraph>
+        make_initial_annotated_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                          sdsl::int_vector<> &counts, //TODO Myrthe later: int_vector_buffer and make it a template class. Otherwise the function stays the same because initial mask should only check if a k-mer is present just once in either set.
+                          sdsl::bit_vector&& mask,
+                          bool add_complement,
+                          size_t num_threads) {
+    // counts is a double-length vector storing the in-label and out-label
+    // counts interleaved
+    assert(counts.size() == mask.size() * 2);
+
+    auto masked_graph = std::make_shared<MaskedDeBruijnGraph>(
+        graph_ptr,
+        add_complement
+            ? std::make_unique<bitmap_vector>(mask)
+            : std::make_unique<bitmap_vector>(std::move(mask)),
+        true
+    );
+
+    logger->trace("Constructed masked graph with {} nodes", masked_graph->num_nodes());
+
+    if (add_complement) {
+        logger->trace("Adding reverse complements");
+        std::mutex vector_backup_mutex;
+        std::atomic_thread_fence(std::memory_order_release);
+        masked_graph->call_sequences([&](const std::string &seq, const std::vector<node_index> &path) {
+            std::string rc_seq = seq;
+            std::vector<DeBruijnGraph::node_index> rc_path = path;
+            reverse_complement_seq_path(*graph_ptr, rc_seq, rc_path);
+
+            auto it = rc_path.rbegin();
+            for (size_t i = 0; i < path.size(); ++i, ++it) {
+                if (*it) {
+                    uint64_t in_count = atomic_fetch(counts, path[i] * 2, vector_backup_mutex, MO_RELAXED);
+                    uint64_t out_count = atomic_fetch(counts, path[i] * 2 + 1, vector_backup_mutex, MO_RELAXED);
+                    atomic_fetch_and_add(counts, *it * 2, in_count, vector_backup_mutex, MO_RELAXED);
+                    atomic_fetch_and_add(counts, *it * 2 + 1, out_count, vector_backup_mutex, MO_RELAXED); // Myrthe: for each kmer, counts is the total time this occurs in all for- resp. background samples?
+
+                    set_bit(mask.data(), *it, in_count, MO_RELAXED);
+                }
+            }
+        }, num_threads, true);
+
+        std::atomic_thread_fence(std::memory_order_acquire);
+
+        masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+
+        logger->trace("Updated masked graph has {} nodes", masked_graph->num_nodes());
+    }
+
+    return masked_graph;
+}
+
+    std::shared_ptr<MaskedDeBruijnGraph>
+    make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                              sdsl::int_vector<> &counts,
+                              sdsl::bit_vector&& mask,
+                              bool add_complement,
+                              size_t num_threads) {
+        // counts is a double-length vector storing the in-label and out-label
+        // counts interleaved
+        assert(counts.size() == mask.size() * 2);
+
+        auto masked_graph = std::make_shared<MaskedDeBruijnGraph>(
+                graph_ptr,
+                add_complement
+                ? std::make_unique<bitmap_vector>(mask)
+                : std::make_unique<bitmap_vector>(std::move(mask)),
+                true
+        );
+
+        logger->trace("Constructed masked graph with {} nodes", masked_graph->num_nodes());
+
+        if (add_complement) {
+            logger->trace("Adding reverse complements");
+            std::mutex vector_backup_mutex;
+            std::atomic_thread_fence(std::memory_order_release);
+            masked_graph->call_sequences([&](const std::string &seq, const std::vector<node_index> &path) {
+                std::string rc_seq = seq;
+                std::vector<DeBruijnGraph::node_index> rc_path = path;
+                reverse_complement_seq_path(*graph_ptr, rc_seq, rc_path);
+
+                auto it = rc_path.rbegin();
+                for (size_t i = 0; i < path.size(); ++i, ++it) {
+                    if (*it) {
+                        uint64_t in_count = atomic_fetch(counts, path[i] * 2, vector_backup_mutex, MO_RELAXED);
+                        uint64_t out_count = atomic_fetch(counts, path[i] * 2 + 1, vector_backup_mutex, MO_RELAXED);
+                        atomic_fetch_and_add(counts, *it * 2, in_count, vector_backup_mutex, MO_RELAXED);
+                        atomic_fetch_and_add(counts, *it * 2 + 1, out_count, vector_backup_mutex, MO_RELAXED);
+
+                        set_bit(mask.data(), *it, in_count, MO_RELAXED);
+                    }
+                }
+            }, num_threads, true);
+
+            std::atomic_thread_fence(std::memory_order_acquire);
+
+            masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+
+            logger->trace("Updated masked graph has {} nodes", masked_graph->num_nodes());
+        }
+
+        return masked_graph;
+    }
+
+// TODO
+total_sum(labels, & columns){ // sum over all columns first, and then add the sum to calculate the total sum
+        sdsl::int_vector<> total_kmers_columns;
+        for (size_t i = 0; i < files.size(); ++i) {
+            total_kmers_columns[label] = std::accumulate(column.begin(), column.end());
+        }
+     size_t total_kmers{std::accumulate(in_total_kmers_columns.begin(), in_total_kmers_columns.end(), 0ULL)};
+     return total_kmers;
+}
+// ANSWER, where to best do this? -> after load column values. Note down how the output of loadcv works.
+
+template <class vector_type> // type for the vector bitmap or int_vector, later also buffered.
+std::pair<sdsl::int_vector<>, sdsl::bit_vector>
+construct_diff_label_count_annotation_vector(const std::function<void(const std::function<void(const Label &, const vector_type&)>)> &generate_columns, //Myrthe: ANSWER: this is a callable, why is it used, where is it exactly executed? Such that it can be used to handle different type of columns (e.g. in terms of compression) in different versions of the function. It is called/executed in call_ones.
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  bool parallel,
+                                  bool add_out_labels_to_mask) {
+    logger->trace("Allocating mask vector");
+    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask); // TODO Myrthe: this must be increased when including annotation counts. Think about how to. If we have disk buffering then 32 bit would work.
+    //Myrthe: otherwise we might estimate a good width based on the distributions / coverage. Maximum sum of in or outlabels < max(|inlabels|,|outlabels|) * max_coverage. An option could be to equalize the coverage.
+    //Myrthe: for the statistical tests we need for each k-mer the sum of its occurance over resp the in labels and out labels. Furthermore we need the sum of all kmers in inlabels resp outlabels. Extract the coverage.
+    sdsl::bit_vector indicator(max_index + 1, false);
+
+    logger->trace("Allocating count vector");
+    // the in and out counts are stored interleaved
+    sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16); //TODO Myrthe later: int_vector_buffer. In that case the function also to be a template too, or a template based argument, if vector_type== buffered_int_vector, then it should be a buffered (// if constexpr (std::is_same_v<vector_type, Bitmap>) { })
+    logger->trace("done");
+
+    std::mutex vector_backup_mutex;
+
+    logger->trace("Populating count vector");
+    std::atomic_thread_fence(std::memory_order_release);
+    generate_columns([&](const Label &label, const vector_type &column) { // ANSWERED: outer loop is over the columns, inner loop over the kmers.
+        // Myrthe: For the buffered_int_vector we would have to use operators [ ] [ ], but it might be problematic if it runs out of memory and push_back would need to be used instead.
+        // Myrthe: The second problem with buffered_int_vector might be that it is expensive, because we do random access on it. Instead a solution might be to use a piority queue of L columns, storing the smallest element as a triple (column , index, value), such that always k-mers in row x are processed before row x + 1.
+
+        uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
+        if (labels_out.count(label))
+            col_indicator |= 2;
+
+        auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i, uint64_t value) { // Myrthe: modify this to have a value, since call_nonzeros, also creates value
+            i = AnnotatedDBG::anno_to_graph_index(i); // Myrthe ANSWERED: The i indicates the row /k-mer
+            assert(i != DeBruijnGraph::npos);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED); //Myrthe: sets corresponding bit to true in mask.
+            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED); // Myrthe: This here increments counts by 1, for count annotation, increment by the count.
+        };
+
+        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i) {
+            i = AnnotatedDBG::anno_to_graph_index(i);
+            assert(i != DeBruijnGraph::npos);
+            if (add_out_labels_to_mask)
+                set_bit(indicator.data(), i, parallel, MO_RELAXED);
+
+            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
+        };
+
+        auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) { //ANSWER: theoretcially a label should not be both in the in and out labels, but this is just in case.
+            i = AnnotatedDBG::anno_to_graph_index(i); // Myrthe: indexes in annotation matrix are 1 offset off, so convert the index from annotation to graph, because the mask should be compatible with the graph.
+            assert(i != DeBruijnGraph::npos);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
+        };
+
+        switch (col_indicator) {
+            case 1: { column.call_nonzeros(add_in); } break; // Myrthe: outer loop over columns. call_ones is a inner loop . iterates over rows, (throubh bitmap). There is a call_nonzeros function that I'd need, but it also needs to be made compatible with int_vector_buffer.
+            case 2: { column.call_nonzeros(add_out); } break;
+            case 3: { column.call_nonzeros(add_both); } break;
+            default: {}
+        }
+    });
+
+    std::atomic_thread_fence(std::memory_order_acquire);
+    logger->trace("done");
+
+    return std::make_pair(std::move(counts), std::move(indicator)); // ANSWER: the indicator defines the initial mask?
+}
+
+std::pair<sdsl::int_vector<>, sdsl::bit_vector>
+construct_diff_label_count_vector(const std::function<void(const std::function<void(const Label &, const bitmap&)>)> &generate_columns,
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  bool parallel,
+                                  bool add_out_labels_to_mask) {
+    logger->trace("Allocating mask vector");
+    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
+    sdsl::bit_vector indicator(max_index + 1, false);
+
+    logger->trace("Allocating count vector");
+    // the in and out counts are stored interleaved
+    sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16);
+    logger->trace("done");
+
+    std::mutex vector_backup_mutex;
+
+    logger->trace("Populating count vector");
+    std::atomic_thread_fence(std::memory_order_release);
+    generate_columns([&](const Label &label, const bitmap &column) {
+        uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
+        if (labels_out.count(label))
+            col_indicator |= 2;
+
+        auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
+            i = AnnotatedDBG::anno_to_graph_index(i);
+            assert(i != DeBruijnGraph::npos);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
+        };
+
+        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i) {
+            i = AnnotatedDBG::anno_to_graph_index(i);
+            assert(i != DeBruijnGraph::npos);
+            if (add_out_labels_to_mask)
+                set_bit(indicator.data(), i, parallel, MO_RELAXED);
+
+            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+        };
+
+        auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
+            i = AnnotatedDBG::anno_to_graph_index(i);
+            assert(i != DeBruijnGraph::npos);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
+        };
+
+        switch (col_indicator) {
+            case 1: { column.call_ones(add_in); } break;
+            case 2: { column.call_ones(add_out); } break;
+            case 3: { column.call_ones(add_both); } break;
+            default: {}
+        }
+    });
+
+    std::atomic_thread_fence(std::memory_order_acquire);
+    logger->trace("done");
+
+    return std::make_pair(std::move(counts), std::move(indicator));
+}
+
+
+// SOLVED: what are the different versions of `construct_diff_label_count_vector` for? for different situations, only this one is useful for annotation, as it can take filenames
+// Myrthe
+/**
+ * A version of `construct_diff_label_count_vector` with count annotation, for streaming input columns.
+ * // the foreground and background label counts, and assembly parameters are taken as input, to create a count vector, are used to determine which k-mers are filtered out from the subgraph.
+
+ * This function reads from the column count vectors and decides what rows to keep, i.e.
+ * k-mers labeled with at least one of the foreground and background labels are enumerated to determine the initial subgraph
+ * TODO Myrthe
+ */
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
+construct_diff_label_annotation_count_vector(const Annotator &annotator,
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  size_t num_threads,
+                                  bool add_out_labels_to_mask) {
+    std::vector<Column> columns; // TODO Myrthe later: Change to buffered vector int_vector_buffer(  ); Perhaps also based on the template type?
+    // see documentation in int_vector_buffer() {}, constructor.
+    // create a temporary filename, where? Will the file be created by the int_vector_buffer if it doesn't exist yet?
+    // sdsl::int_vector_buffer(  ) (const std::string filename, std::ios::openmode mode=std::ios::in, uint64_t buffer_size=1024*1024, const uint8_t int_width=t_width, const bool is_plain=false, const uint64_t file_offset=0)
+    const auto &encoder = annotator.get_label_encoder();
+    AnnotatedDBG::Annotator::VLabels labels;
+    labels.reserve(labels_in.size() + labels_out.size());
+    columns.reserve(labels_in.size() + labels_out.size());
+    for (const auto &label : labels_in) {
+        labels.emplace_back(label);
+        columns.emplace_back(encoder.encode(label));
+    }
+    for (const auto &label : labels_out) {
+        if (!labels_in.count(label)) {
+            labels.emplace_back(label);
+            columns.emplace_back(encoder.encode(label));
+        }
+    }
+
+    sdsl::bit_vector other_labels(encoder.size(), true);
+    for (Column column : columns) {
+        other_labels[column] = false;
+    }
+
+    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) { // ANSWER: which function is called here? the above
+        annotator.get_matrix().call_columns(columns,
+            [&](size_t j, const bitmap_generator &column) {
+                callback(labels[j], column);
+            }, num_threads
+        );
+    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask); //Myrthe: this is generate columns call back. The function above above defines what you do with the generated objects.
+
+    return std::make_tuple(std::move(counts), std::move(init_mask), std::move(other_labels));
+}
+
+
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
+construct_diff_label_count_vector(const Annotator &annotator,
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  size_t num_threads,
+                                  bool add_out_labels_to_mask) {
+    std::vector<Column> columns;
+    const auto &encoder = annotator.get_label_encoder();
+    AnnotatedDBG::Annotator::VLabels labels;
+    labels.reserve(labels_in.size() + labels_out.size());
+    columns.reserve(labels_in.size() + labels_out.size());
+    for (const auto &label : labels_in) {
+        labels.emplace_back(label);
+        columns.emplace_back(encoder.encode(label));
+    }
+    for (const auto &label : labels_out) {
+        if (!labels_in.count(label)) {
+            labels.emplace_back(label);
+            columns.emplace_back(encoder.encode(label));
+        }
+    }
+
+    sdsl::bit_vector other_labels(encoder.size(), true);
+    for (Column column : columns) {
+        other_labels[column] = false;
+    }
+
+    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) {
+        annotator.get_matrix().call_columns(columns,
+                                            [&](size_t j, const bitmap_generator &column) {
+                                                callback(labels[j], column);
+                                            }, num_threads
+        );
+    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask);
+
+    return std::make_tuple(std::move(counts), std::move(init_mask), std::move(other_labels));
+}
+
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::vector<size_t>>
+construct_diff_label_count_vector(const std::vector<std::string> &files,
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index,
+                                  size_t num_labels,
+                                  size_t num_threads,
+                                  bool add_out_labels_to_mask) {
+    std::vector<bool> other_labels_vec;
+    other_labels_vec.reserve(files.size());
+
+    std::vector<size_t> num_labels_per_file;
+    num_labels_per_file.reserve(files.size());
+
+    for (size_t i = 0; i < files.size(); ++i) {
+        auto label_encoder = annot::ColumnCompressed<>::read_label_encoder(files[i]);
+        num_labels_per_file.emplace_back(label_encoder.size());
+        for (const auto &label : label_encoder.get_labels()) {
+            other_labels_vec.emplace_back(!labels_in.count(label) && !labels_out.count(label));
+        }
+    }
+
+    auto other_labels = to_sdsl(std::move(other_labels_vec));
+
+    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) { // QUESTION merge_load --> callback as part of another callback, but where is it now exactly executed.
+        size_t j = 0;
+        for (size_t i = 0; i < files.size(); ++i) { // loop over the columns
+            if (count_ones(other_labels, j, j + num_labels_per_file[i])
+                    != num_labels_per_file[i]) {
+                if (not config.count_kmers){
+                    annot::ColumnCompressed<>::merge_load({ files[i] },
+                        [&](size_t offset, const auto &label, auto&& column) {
+                            if (!other_labels[j + offset])
+                                callback(label, *column); // ANSWER: is this the ^ callback above? yes.
+                        },
+                        num_threads
+                    );
+
+                }else{ // if count annotation is used
+                annot::ColumnCompressed<>::load_columns_and_values({ files[i] }, //Myrthe QUESTION: How to write this lambda. The following is from annotation_converters.cpp. However, there it passed all files, rather than files[i]
+                                                                                                               [&](size_t j,
+                                                                                                                   const std::string &label,
+                                                                                                                   std::unique_ptr<bit_vector> &&column,
+                                                                                                                   sdsl::int_vector<> &&values) {
+                                                                                                                   size_t local_max_val = 0;
+                                                                                                                   if (values.size())
+                                                                                                                       local_max_val = *std::max_element(values.begin(), values.end());
+
+                                                                                                                   std::lock_guard<std::mutex> lock(mu); //QUESTION
+
+                                                                                                                   if (local_max_val > max_val)
+                                                                                                                       max_val = local_max_val;
+
+                                                                                                                   num_set_bits += column->num_set_bits();
+
+                                                                                                                   while (columns.size() <= j) {
+                                                                                                                       columns.emplace_back();
+                                                                                                                       col_values.emplace_back();
+                                                                                                                       col_names.emplace_back();
+                                                                                                                   }
+                                                                                                                   columns[j] = std::move(column);
+                                                                                                                   col_values[j] = std::move(values); //QUESTION: Why does this happen?
+                                                                                                                   col_names[j] = label;
+                                                                                                               },
+                                                                                                               num_threads);
+            // Myrthe QUESTION: Now I'd have to convert column and values s.t. they can be passed to construct_diff_label_count_vector?
+            // We assumed there that the column would just be an int_vector, rather than two arrays.
+
+                    //total_sum( values)
+            }
+
+            }
+
+            j += num_labels_per_file[i];
+        }
+    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask);
+
+    return std::make_tuple(std::move(counts), std::move(init_mask),
+                           std::move(other_labels), std::move(num_labels_per_file));
+}
+
+
+void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
+                                   const GetKeptIntervals &get_kept_intervals,
+                                   size_t num_threads) {
+    std::atomic<uint64_t> kept_unitigs(0);
+    std::atomic<uint64_t> total_unitigs(0);
+    std::atomic<uint64_t> num_kept_nodes(0);
+    bool parallel = num_threads > 1;
+
+    sdsl::bit_vector mask = static_cast<const bitmap_vector&>(masked_graph.get_mask()).data();
+
+    std::atomic_thread_fence(std::memory_order_release);
+
+    masked_graph.call_unitigs([&](const std::string &unitig,
+                                  const std::vector<node_index> &path) {
+        total_unitigs.fetch_add(1, MO_RELAXED);
+
+        size_t last = 0;
+        for (const auto &pair : get_kept_intervals(unitig, path)) {
+            const auto &[begin, end] = pair;
+            kept_unitigs.fetch_add(1, MO_RELAXED);
+            num_kept_nodes.fetch_add(end - begin, MO_RELAXED);
+            for ( ; last < begin; ++last) {
+                unset_bit(mask.data(), path[last], parallel, MO_RELAXED);
+            }
+            last = end;
+        }
+
+        for ( ; last < path.size(); ++last) {
+            unset_bit(mask.data(), path[last], parallel, MO_RELAXED);
+        }
+
+    }, num_threads);
+    std::atomic_thread_fence(std::memory_order_acquire);
+
+    masked_graph.set_mask(new bitmap_vector(std::move(mask)));
+
+    logger->trace("Kept {} out of {} unitigs with average length {}",
+                  kept_unitigs, total_unitigs,
+                  static_cast<double>(num_kept_nodes + kept_unitigs * (masked_graph.get_k() - 1))
+                      / kept_unitigs);
+}
+
+} // namespace graph
+} // namespace mtg

From a3a2dee1eb2a598ca07e8a7fc39afc6fd7207e06 Mon Sep 17 00:00:00 2001
From: MyrtheW <willemsenmyrthe@gmail.com>
Date: Wed, 8 Mar 2023 09:32:17 +0100
Subject: [PATCH 033/410] Statistical tests. working on column width.

---
 .../src/common/vectors/vector_algorithm.hpp   |  58 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 153 +--
 .../annotated_graph_algorithm_myrthe.cpp      | 869 ------------------
 metagraph/src/graph/differential_tests.cpp    | 100 ++
 metagraph/src/graph/differential_tests.hpp    | 261 +-----
 5 files changed, 270 insertions(+), 1171 deletions(-)
 create mode 100644 metagraph/src/graph/differential_tests.cpp

diff --git a/metagraph/src/common/vectors/vector_algorithm.hpp b/metagraph/src/common/vectors/vector_algorithm.hpp
index 7034ef71be..7329805ba7 100644
--- a/metagraph/src/common/vectors/vector_algorithm.hpp
+++ b/metagraph/src/common/vectors/vector_algorithm.hpp
@@ -4,14 +4,16 @@
 #include <functional>
 #include <cassert>
 #include <cstdlib>
-
 #include <sdsl/int_vector.hpp>
 #include <sdsl/select_support_scan.hpp>
 
+#include "common/vectors/bitmap.hpp"
+
 class ThreadPool;
 class bit_vector;
 class bit_vector_adaptive;
 
+typedef std::function<void(uint64_t, uint64_t)> callback_template;
 
 sdsl::bit_vector to_sdsl(const std::vector<bool> &vector);
 sdsl::bit_vector to_sdsl(const std::vector<uint8_t> &vector);
@@ -163,15 +165,26 @@ sdsl::bit_vector generate_subindex(const bit_vector &column,
 sdsl::bit_vector autocorrelate(const sdsl::bit_vector &vector, uint8_t offset);
 
 
-// Call (uint64_t index, uint64_t value) for each non-zero value in [begin, end)
-template <class Callback, class vector_type>
-inline void call_nonzeros(const vector_type &vector,
-                          uint64_t begin, uint64_t end,
-                          Callback callback);
+
+
+void call_nonzeros(const sdsl::int_vector<> &vector,
+                   uint64_t begin, uint64_t end,
+                   callback_template callback);
+
+void call_nonzeros(const sdsl::bit_vector &vector,
+                   uint64_t begin, uint64_t end,
+                   callback_template callback);
+
+void call_nonzeros(const bitmap &vector,
+                   uint64_t begin, uint64_t end,
+                   callback_template callback);
 
 // Call (uint64_t index, uint64_t value) for each non-zero value in |vector|.
-template <class Callback, class vector_type>
-void call_nonzeros(const vector_type &vector, Callback callback) {
+template <class vector_type>
+void call_nonzeros(const vector_type &vector, callback_template callback);
+
+template <class vector_type>
+void call_nonzeros(const vector_type &vector, callback_template callback) {
     return call_nonzeros(vector, 0, vector.size(), callback);
 }
 
@@ -664,10 +677,9 @@ void call_zeros(const sdsl::bit_vector &vector,
     }
 }
 
-template <class Callback, class vector_type> //Myrthe: type for the vector bitmap or sdsl::int_vector<>, and later a sdsl::int_vector_buffer<>
-void call_nonzeros(const vector_type &vector,
+void call_nonzeros(const sdsl::int_vector<> & vector,
                    uint64_t begin, uint64_t end,
-                   Callback callback) {
+                   callback_template callback) {
     if (begin >= end)
         return;
 
@@ -676,25 +688,25 @@ void call_nonzeros(const vector_type &vector,
         case 64:
             std::for_each(reinterpret_cast<const uint64_t*>(vector.data()) + begin,
                           reinterpret_cast<const uint64_t*>(vector.data()) + end,
-                          [&](auto value) { if (value) callback(i, value); ++i; });
+                          [&](uint64_t value) { if (value) callback(i, value); ++i; });
             break;
 
         case 32:
             std::for_each(reinterpret_cast<const uint32_t*>(vector.data()) + begin,
                           reinterpret_cast<const uint32_t*>(vector.data()) + end,
-                          [&](auto value) { if (value) callback(i, value); ++i; });
+                          [&](uint64_t value) { if (value) callback(i, value); ++i; });
             break;
 
         case 16:
             std::for_each(reinterpret_cast<const uint16_t*>(vector.data()) + begin,
                           reinterpret_cast<const uint16_t*>(vector.data()) + end,
-                          [&](auto value) { if (value) callback(i, value); ++i; });
+                          [&](uint64_t value) { if (value) callback(i, value); ++i; });
             break;
 
         case 8:
             std::for_each(reinterpret_cast<const uint8_t*>(vector.data()) + begin,
                           reinterpret_cast<const uint8_t*>(vector.data()) + end,
-                          [&](auto value) { if (value) callback(i, value); ++i; });
+                          [&](uint64_t value) { if (value) callback(i, value); ++i; });
             break;
 
         default:
@@ -721,15 +733,17 @@ void call_nonzeros(const vector_type &vector,
     }
 }
 
-template void call_nonzeros<sdsl::int_vector<>>(const sdsl::int_vector<> &vector,
-              uint64_t begin, uint64_t end,
-              Callback callback);
-template void call_nonzeros<bitmap>(const bitmap &vector, // for bitmaps this should write a 1 to the column.
-              uint64_t begin, uint64_t end,
-              Callback callback){
-    vector.call_ones([&](uint64_t i){callback(i, 1);}     // creates a lambda function that write a custom argument for the callback
+void call_nonzeros(const sdsl::bit_vector &vector,
+                   uint64_t begin, uint64_t end,
+                   callback_template callback){
+    call_ones(vector, begin, end, [&](uint64_t i){callback(i, 1);});     // creates a lambda function that write a custom argument for the callback
 }
 
+void call_nonzeros(const bitmap &vector,
+                   uint64_t begin, uint64_t end,
+                   callback_template callback){
+    vector.call_ones_in_range(begin, end, [&](uint64_t i){callback(i, 1);});     // alternatively, use .call_ones() without begin or end.
+}
 
 template <typename BitVector>
 inline uint64_t next1(const BitVector &v, uint64_t pos, size_t num_steps) {
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 1bc08277d5..cc0c2b1e59 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -5,7 +5,7 @@
 #include "common/vectors/bitmap.hpp"
 #include "graph/representation/masked_graph.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
-
+#include "differential_tests.hpp"
 
 namespace mtg {
 namespace graph {
@@ -40,10 +40,10 @@ constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
 
 using ValueCallback = std::function<void(uint64_t /* row index */, uint64_t /* row value */)>;
 using ValueGenerator = std::function<void(const ValueCallback&)>;
-using ColumnCallback = std::function<void(uint64_t, const Label&, const ValueGenerator&)>>;
-using ColumnGenerator = std::function<void(const ColumnCallback&);
+using ColumnCallback = std::function<void(uint64_t /* offset */, const Label& /* annotation column */, const ValueGenerator&)>;
+using ColumnGenerator = std::function<void(const ColumnCallback&)>;
 
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::vector<size_t>>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>>
 construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
@@ -85,6 +85,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const tsl::hopscotch_set<Label> &labels_out,
                     const tsl::hopscotch_set<Label> &labels_in_round2,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
+                    std::tuple<size_t, size_t> total_kmers,
                     const DifferentialAssemblyConfig &config,
                     size_t num_threads);
 
@@ -98,8 +99,8 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     size_t num_threads,
                     size_t num_parallel_files) {
     num_parallel_files = std::min(num_threads, num_parallel_files);
-    size_t num_in_labels = labels_in.size() + labels_in_round2.size();
-    size_t num_out_labels = labels_out.size() + labels_out_round2.size();
+//    size_t num_in_labels = labels_in.size() + labels_in_round2.size();
+//    size_t num_out_labels = labels_out.size() + labels_out_round2.size();
     auto graph_ptr = std::static_pointer_cast<const DeBruijnGraph>(
         anno_graph.get_graph_ptr()
     );
@@ -115,21 +116,14 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
 
     logger->trace("Generating initial mask");
 
-    // Construct initial masked graph from union of labels in labels_in
-    // auto count_vector = construct_diff_label_count_vector(
-    //     anno_graph.get_annotator(), labels_in, labels_out, graph_ptr->max_index(),
-    //     std::max(num_in_labels, num_out_labels), num_parallel_files,
-    //     add_complement
-    // );
     auto count_vector = construct_diff_label_count_vector(
         [&](const ColumnCallback &column_callback) {
             if (config.count_kmers) {
-                // TODO
                 throw std::runtime_error("AnnotatedDBG with counts not supported yet");
             } else {
                 size_t offset = 0;
                 for (const auto &label : labels_in) {
-                    column_callback(label, offset, [&](const ValueCallback &value_callback) {
+                    column_callback(offset, label, [&](const ValueCallback &value_callback) {
                         anno_graph.call_annotated_nodes(label, [&](node_index i) {
                             i = AnnotatedDBG::graph_to_anno_index(i);
                             value_callback(i, 1);
@@ -138,7 +132,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     ++offset;
                 }
                 for (const auto &label : labels_out) {
-                    column_callback(label, offset, [&](const ValueCallback &value_callback) {
+                    column_callback(offset, label, [&](const ValueCallback &value_callback) {
                         anno_graph.call_annotated_nodes(label, [&](node_index i) {
                             i = AnnotatedDBG::graph_to_anno_index(i);
                             value_callback(i, 1);
@@ -146,26 +140,26 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     });
                     ++offset;
                 }
-            }
+            };
         },
-        labels_in, labels_out, graph_ptr->max_index(),
+        labels_in, labels_out, graph_ptr->max_index(), // Myrthe: error: ‘labels_in’ is not a type
         std::max(labels_in.size(), labels_out.size()), num_parallel_files,
         add_complement
     );
 
-    auto &[counts, init_mask, other_labels] = count_vector;
+    auto &[counts, init_mask, other_labels, total_kmers] = count_vector; //ERROR  only 3 names provided for structured binding, std::vector<long unsigned int, std::allocator<long unsigned int> > >’ decomposes into 4 elements
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     return mask_nodes_by_label(graph_ptr, &anno_graph,
                                std::move(counts), std::move(init_mask),
                                std::move(other_mask),
                                anno_graph.get_annotator().num_labels(),
                                labels_in, labels_out,
-                               labels_in_round2, labels_out_round2,
+                               labels_in_round2, labels_out_round2, total_kmers,
                                config, num_threads);
 }
 
 std::shared_ptr<MaskedDeBruijnGraph>
-mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this version i don't need.
+mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this version i don't need at first
                     const std::vector<std::string> &files,
                     const tsl::hopscotch_set<Label> &labels_in,
                     const tsl::hopscotch_set<Label> &labels_out,
@@ -183,23 +177,26 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
 
     logger->trace("Generating initial mask");
 
+    size_t sum_widths_in; size_t sum_widths_out; // calculate the sum of column widths to derive an upper bound for the required counts vector width.
     auto count_vector = construct_diff_label_count_vector(
-        [&](const ColumnCallback &column_callback) {
+            [&](const ColumnCallback &column_callback) {
             if (config.count_kmers) {
                 annot::ColumnCompressed<>::load_columns_and_values(files,
-                    [&](uint64_t offset, const Label &label, auto&& column, auto&& counts) {
-                        column_callback(label, offset, [&](const ValueCallback &value_callback) {
-                            column->call_nonzeros([&](uint64_t i) {
-                                value_callback(i, counts[i]);
+                    [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
+                        column_callback(offset, label, [&](const ValueCallback &value_callback) {
+                           if (labels_in.count(label)) sum_widths_in += std::pow(2, column_values.width());
+                           if (labels_out.count(label)) sum_widths_out += std::pow(2, column_values.width());
+                            call_ones(*column, [&](uint64_t i) {
+                                value_callback(i, column_values[column->rank1(i)]);
                             });
                         });
                     }
                 );
             } else {
                 annot::ColumnCompressed<>::merge_load(files,
-                    [&](uint64_t offset, const Label &label, auto&& column) {
-                        column_callback(label, offset, [&](const ValueCallback &value_callback) {
-                            column->call_nonzeros([&](uint64_t i) {
+                    [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column) {
+                        column_callback(offset, label, [&](const ValueCallback &value_callback) {
+                            call_ones(*column, [&](uint64_t i) {
                                 value_callback(i, 1);
                             });
                         });
@@ -212,30 +209,31 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         add_complement
     );
 
-    auto &[counts, init_mask, other_labels, num_labels_per_file] = count_vector;
+
+    auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     if (check_other && sdsl::util::cnt_one_bits(other_labels)) {
         bool parallel = num_parallel_files > 1;
         size_t j = 0;
         std::atomic_thread_fence(std::memory_order_release);
         for (size_t i = 0; i < files.size(); ++i) {
-            if (count_ones(other_labels, j, j + num_labels_per_file[i])) {
+            size_t num_labels_per_file = annot::ColumnCompressed<>::read_label_encoder(files[i]).size();
+            if (count_ones(other_labels, j, j + num_labels_per_file)) {
                 annot::ColumnCompressed<>::merge_load({ files[i] },
                     [&](size_t offset, const auto &, auto&& column) {
-                        auto &[counts, init_mask, other_labels, num_labels_per_file]
-                            = count_vector;
+                        auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
                         if (other_labels[j + offset]) {
-                            call_ones(init_mask, [&](size_t i) {
+                            call_ones(init_mask, [&](size_t i) { // check how many set bits are also in other labels.
                                 if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
                                     set_bit(other_mask.data(), i, parallel, MO_RELAXED);
-                            }); // QUESTION: change this herer to call_nonzeros?
+                            });
                         }
                     },
                     num_parallel_files
                 );
             }
 
-            j += num_labels_per_file[i];
+            j += num_labels_per_file;
         }
         std::atomic_thread_fence(std::memory_order_acquire);
     }
@@ -245,6 +243,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                std::move(other_mask),
                                files.size(),
                                labels_in, labels_out, {}, {},
+                               total_kmers,
                                config, num_threads);
 }
 
@@ -259,6 +258,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const tsl::hopscotch_set<Label> &labels_out,
                     const tsl::hopscotch_set<Label> &labels_in_round2,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
+                    std::tuple<size_t, size_t> total_kmers,
                     const DifferentialAssemblyConfig &config,
                     size_t num_threads) {
     // in and out counts are stored interleaved in the counts vector
@@ -296,7 +296,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::mutex vector_backup_mutex;
         std::atomic_thread_fence(std::memory_order_release);
 
-        auto count_merge = [&](sdsl::bit_vector &a,
+        auto count_merge = [&](sdsl::bit_vector &a,  // Myrthe ANSWER: what does count_merge do? Is a hack, I do not have to touch this now. Count_add would be better name. If you have a dense column, and include it in foreground or background. Wate time, Instead first create mask.  You only access dense columns, for instance a reference.
                                const sdsl::bit_vector &b,
                                const std::vector<node_index> &id_map,
                                size_t offset = 0) {
@@ -337,6 +337,27 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     // Filter unitigs from masked graph based on filtration criteria
     logger->trace("Filtering out background");
+    if (config.count_kmers){
+        const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
+        sdsl::bit_vector mask = in_mask;
+        auto &[in_total_kmers, out_total_kmers] = total_kmers;
+        auto total_hypotheses = counts.size()/2; // the total number of hypotheses tested.
+        auto test_model = DifferentialTest(config.family_wise_error_rate, total_hypotheses,
+                                           counts.width(), in_total_kmers, out_total_kmers); // similar to the width of the counts vector, the size size of the log-factorial table should be the maximum joint coverage over the in_labels resp. out_labels
+        size_t kept_nodes = 0;
+        call_ones(in_mask, [&](node_index node) {
+            uint64_t in_sum = counts[node * 2];
+            uint64_t out_sum = counts[node * 2 + 1];
+            auto [pvalue, sign] = test_model.likelihood_ratio_test(in_sum, out_sum);
+            if (test_model.bonferroni_correction(pvalue) and sign) {
+                ++kept_nodes;
+            } else {
+                mask[node] = false;
+            }
+        });
+
+    }
+
 
     size_t min_label_in_count = std::ceil(config.label_mask_in_kmer_fraction
                                     * num_in_labels);
@@ -348,8 +369,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             && config.label_mask_other_unitig_fraction == 1.0) {
         logger->trace("Filtering by node");
         size_t total_nodes = masked_graph->num_nodes();
-        const auto &in_mask
-            = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
+        const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
         sdsl::bit_vector mask = in_mask;
 
         // TODO: make this part multithreaded
@@ -365,6 +385,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         });
 
+
+
         masked_graph->set_mask(new bitmap_vector(std::move(mask)));
 
         logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
@@ -433,7 +455,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
 std::shared_ptr<MaskedDeBruijnGraph>
 make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                          sdsl::int_vector<> &counts,
+                          sdsl::int_vector<> &counts, // Myrthe later: int_vector_buffer and make it a template class. Otherwise the function stays the same because initial mask should only check if a k-mer is present just once in either set.
                           sdsl::bit_vector&& mask,
                           bool add_complement,
                           size_t num_threads) {
@@ -483,29 +505,42 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     return masked_graph;
 }
 
-std::pair<sdsl::int_vector<>, sdsl::bit_vector>
+
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>>
 construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
                                   size_t max_index,
                                   size_t num_labels,
-                                  bool parallel,
+                                  size_t num_threads,
                                   bool add_out_labels_to_mask) {
+    bool parallel = (num_threads > 1);
     logger->trace("Allocating mask vector");
-    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
+    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask); // TODO Myrthe: this must be increased when including annotation counts. Think about how to. If we have disk buffering then 32 bit would work.
+    //Myrthe: otherwise we might estimate a good width based on the distributions / coverage. Maximum sum of in or outlabels < max(|inlabels|,|outlabels|) * max_coverage. An option could be to equalize the coverage.
+    //Myrthe: for the statistical tests we need for each k-mer the sum of its occurance over resp the in labels and out labels. Furthermore we need the sum of all kmers in inlabels resp outlabels. Extract the coverage.
+
+//    const auto &values_fname = remove_suffix(files[i], ColumnCompressed<>::kExtension) + ColumnCompressed<>::kCountExtension;
+//    then open an ifstream to that file and read the info using int_vector<>::read_header
+//
+
     sdsl::bit_vector indicator(max_index + 1, false);
 
     logger->trace("Allocating count vector");
     // the in and out counts are stored interleaved
-    sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16);
+    sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16); //Myrthe later: int_vector_buffer. In that case the function also to be a template too, or a template based argument, if vector_type== buffered_int_vector, then it should be a buffered (// if constexpr (std::is_same_v<vector_type, Bitmap>) { })
     logger->trace("done");
 
     sdsl::bit_vector other_labels(num_labels, false);
     std::mutex vector_backup_mutex;
 
     logger->trace("Populating count vector");
-    std::atomic_thread_fence(std::memory_order_release);
-    generate_columns([&](const Label &label, uint64_t offset, const ValueGenerator &value_generator) {
+    std::atomic_thread_fence(std::memory_order_release); //Myrthe ANSWER: outer loop is over the columns, inner loop over the kmers (i indicating the rows)
+            // Myrthe later: For the buffered_int_vector we would have to use operators [ ] [ ], but it might be problematic if it runs out of memory and push_back would need to be used instead.
+            // Myrthe later: The second problem with buffered_int_vector might be that it is expensive, because we do random access on it. Instead a solution might be to use a piority queue of L columns, storing the smallest element as a triple (column , index, value), such that always k-mers in row x are processed before row x + 1.
+
+    size_t in_total_kmers; size_t out_total_kmers;
+    generate_columns([&](uint64_t offset, const Label &label, const ValueGenerator &value_generator) {
         uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
         if (labels_out.count(label))
             col_indicator |= 2;
@@ -513,28 +548,31 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
         if (!col_indicator)
             set_bit(other_labels.data(), offset, parallel, MO_RELAXED);
 
-        ValueCallback add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i, uint64_t v) {
-            i = AnnotatedDBG::anno_to_graph_index(i);
+        ValueCallback add_in = [&](Column i, uint64_t value) {
+            i = AnnotatedDBG::anno_to_graph_index(i); // indices in the annotation matrix have an offset of 1 compared to those in the graph. Since the mask should be compatible with the graph, we have to convert the index.
             assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2, v, vector_backup_mutex, MO_RELAXED);
+            set_bit(indicator.data(), i, parallel, MO_RELAXED); // set the corresponding bit to true in the mask, with the 'indicator' representing the mask.
+            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED);
+            in_total_kmers += value;
         };
 
-        ValueCallback add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i, uint64_t v) {
+        ValueCallback add_out = [&](Column i, uint64_t value) {
             i = AnnotatedDBG::anno_to_graph_index(i);
             assert(i != DeBruijnGraph::npos);
             if (add_out_labels_to_mask)
                 set_bit(indicator.data(), i, parallel, MO_RELAXED);
-
-            atomic_fetch_and_add(counts, i * 2 + 1, v, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
+            out_total_kmers += value;
         };
 
-        ValueCallback add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i, uint64_t v) {
+        ValueCallback add_both = [&](Column i, uint64_t value) { // theoretically a label should not be contained in both the in- and out-labels, but this is just in case.
             i = AnnotatedDBG::anno_to_graph_index(i);
             assert(i != DeBruijnGraph::npos);
             set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2, v, vector_backup_mutex, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2 + 1, v, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
+            out_total_kmers += value;
+            in_total_kmers += value;
         };
 
         switch (col_indicator) {
@@ -548,7 +586,8 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
     std::atomic_thread_fence(std::memory_order_acquire);
     logger->trace("done");
 
-    return std::make_pair(std::move(counts), std::move(indicator), std::move(other_labels));
+    return std::make_tuple(std::move(counts), std::move(indicator), std::move(other_labels),
+                           std::make_tuple(std::move(in_total_kmers), std::move(out_total_kmers)));
 }
 
 void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
@@ -593,5 +632,7 @@ void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
                       / kept_unitigs);
 }
 
+
+
 } // namespace graph
 } // namespace mtg
diff --git a/metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp b/metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp
index af60ff05ea..e69de29bb2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp
@@ -1,869 +0,0 @@
-#include "annotated_graph_algorithm.hpp"
-
-#include "common/logger.hpp"
-#include "common/vectors/vector_algorithm.hpp"
-#include "common/vectors/bitmap.hpp"
-#include "graph/representation/masked_graph.hpp"
-#include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
-
-
-namespace mtg {
-namespace graph {
-
-using mtg::common::logger;
-
-typedef AnnotatedDBG::node_index node_index;
-typedef AnnotatedDBG::Annotator Annotator;
-typedef AnnotatedDBG::Annotator::Label Label;
-using Column = annot::binmat::BinaryMatrix::Column;
-
-typedef std::function<size_t()> LabelCountCallback;
-
-constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
-
-
-/**
- * Return an int_vector<>, bit_vector of lengths anno_graph.get_graph().max_index() * 2
- * and anno_graph.get_graph().max_index(), respectively, and a bit_vector of length
- * equal to the total number of labels.
- * For an index i, the int_vector at indices 2*i and 2*i + 1 represent the
- * number of labels in labels_in and labels_out which the k-mer of index i is
- * annotated with, respectively. The width of the int_vector<> is computed to be
- * wide enough to contain counts up to num_labels.
- * The returned bit_vector is a k-mer mask indicating those k-mers annotated
- * with at least one in-label. If add_out_labels_to_mask is true, then it also indicates
- * which those k-mers with at least one out-label.
- * The second bit vector indicates which of the labels correspond to an "other"
- * label (i.e., not in- or out-).
- */
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
-construct_diff_label_count_vector(const Annotator &annotator,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  size_t num_threads,
-                                  bool add_out_labels_to_mask);
-
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::vector<size_t>>
-construct_diff_label_count_vector(const std::vector<std::string> &files,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  size_t num_threads,
-                                  bool add_out_labels_to_mask);
-
-// Regions of a graph mask which should be kept (i.e., masked in)
-typedef std::vector<std::pair<size_t, size_t>> Intervals;
-
-// Returns a vector of kept regions given a unitig and its corresponding path
-typedef std::function<Intervals(const std::string&, const std::vector<node_index>&)> GetKeptIntervals;
-
-// Assemble unitigs from the masked graph, then use get_kept_intervals to generate
-// regions which should be masked in. Update the graph mask accordingly.
-void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
-                                   const GetKeptIntervals &get_kept_intervals,
-                                   size_t num_threads);
-
-// Given an initial mask and counts, generate a masked graph. If add_complement
-// is true, then add the reverse complements of all nodes to the graph as well.
-std::shared_ptr<MaskedDeBruijnGraph>
-make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                          sdsl::int_vector<> &counts,
-                          sdsl::bit_vector&& mask,
-                          bool add_complement,
-                          size_t num_threads);
-
-// Helper function for calls in interface
-std::shared_ptr<MaskedDeBruijnGraph>
-mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                    const AnnotatedDBG *anno_graph,
-                    sdsl::int_vector<>&& counts,
-                    sdsl::bit_vector&& mask,
-                    sdsl::bit_vector&& other_mask,
-                    size_t num_labels,
-                    const tsl::hopscotch_set<Label> &labels_in,
-                    const tsl::hopscotch_set<Label> &labels_out,
-                    const tsl::hopscotch_set<Label> &labels_in_round2,
-                    const tsl::hopscotch_set<Label> &labels_out_round2,
-                    const DifferentialAssemblyConfig &config,
-                    size_t num_threads);
-
-std::shared_ptr<MaskedDeBruijnGraph>
-mask_nodes_by_label(const AnnotatedDBG &anno_graph,
-                    const tsl::hopscotch_set<Label> &labels_in,
-                    const tsl::hopscotch_set<Label> &labels_out,
-                    const tsl::hopscotch_set<Label> &labels_in_round2,
-                    const tsl::hopscotch_set<Label> &labels_out_round2,
-                    const DifferentialAssemblyConfig &config,
-                    size_t num_threads,
-                    size_t num_parallel_files) {
-    num_parallel_files = std::min(num_threads, num_parallel_files);
-    size_t num_in_labels = labels_in.size() + labels_in_round2.size();
-    size_t num_out_labels = labels_out.size() + labels_out_round2.size();
-    auto graph_ptr = std::static_pointer_cast<const DeBruijnGraph>(
-        anno_graph.get_graph_ptr()
-    );
-
-    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
-    bool unitig_mode = check_other || labels_in_round2.size() || labels_out_round2.size()
-            || config.label_mask_in_unitig_fraction != 0.0
-            || config.label_mask_out_unitig_fraction != 1.0
-            || config.label_mask_other_unitig_fraction != 1.0;
-
-    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
-        && (config.add_complement || unitig_mode);
-
-    logger->trace("Generating initial mask");
-
-    // Construct initial masked graph from union of labels in labels_in
-    auto count_vector = construct_diff_label_count_vector(
-        anno_graph.get_annotator(), labels_in, labels_out, graph_ptr->max_index(),
-        std::max(num_in_labels, num_out_labels), num_parallel_files,
-        add_complement
-    );
-    auto &[counts, init_mask, other_labels] = count_vector;
-    sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
-    return mask_nodes_by_label(graph_ptr, &anno_graph,
-                               std::move(counts), std::move(init_mask),
-                               std::move(other_mask),
-                               anno_graph.get_annotator().num_labels(),
-                               labels_in, labels_out,
-                               labels_in_round2, labels_out_round2,
-                               config, num_threads);
-}
-
-std::shared_ptr<MaskedDeBruijnGraph>
-mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this function I don't need.
-                    const std::vector<std::string> &files,
-                    const tsl::hopscotch_set<Label> &labels_in,
-                    const tsl::hopscotch_set<Label> &labels_out,
-                    const DifferentialAssemblyConfig &config,
-                    size_t num_threads,
-                    size_t num_parallel_files) {
-    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
-    bool unitig_mode = check_other
-            || config.label_mask_in_unitig_fraction != 0.0
-            || config.label_mask_out_unitig_fraction != 1.0
-            || config.label_mask_other_unitig_fraction != 1.0;
-
-    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
-        && (config.add_complement || unitig_mode);
-
-    logger->trace("Generating initial mask");
-
-    // Construct initial masked graph from union of labels in labels_in
-    auto count_vector = construct_diff_label_count_vector(
-        files, labels_in, labels_out, graph_ptr->max_index(),
-        std::max(labels_in.size(), labels_out.size()), num_parallel_files,
-        add_complement
-    );
-    auto &[counts, init_mask, other_labels, num_labels_per_file] = count_vector;
-    sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
-    if (check_other && sdsl::util::cnt_one_bits(other_labels)) {
-        bool parallel = num_parallel_files > 1;
-        size_t j = 0;
-        std::atomic_thread_fence(std::memory_order_release);
-        for (size_t i = 0; i < files.size(); ++i) {
-            if (count_ones(other_labels, j, j + num_labels_per_file[i])) {
-                    annot::ColumnCompressed<>::merge_load({ files[i] },
-                        [&](size_t offset, const auto &, auto&& column) {
-                          auto &[counts, init_mask, other_labels, num_labels_per_file]
-                          = count_vector;
-                          if (other_labels[j + offset]) {
-                              call_ones(init_mask, [&](size_t i) {
-                                  if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
-                                      set_bit(other_mask.data(), i, parallel, MO_RELAXED);
-                              });
-                          }
-                        },
-                        num_parallel_files
-                    );
-
-            }
-
-            j += num_labels_per_file[i];
-        }
-        std::atomic_thread_fence(std::memory_order_acquire);
-    }
-
-    return mask_nodes_by_label(graph_ptr, nullptr,
-                               std::move(counts), std::move(init_mask),
-                               std::move(other_mask),
-                               files.size(),
-                               labels_in, labels_out, {}, {},
-                               config, num_threads);
-}
-
-std::shared_ptr<MaskedDeBruijnGraph>
-mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                    const AnnotatedDBG *anno_graph,
-                    sdsl::int_vector<>&& counts,
-                    sdsl::bit_vector&& init_mask,
-                    sdsl::bit_vector&& other_mask,
-                    size_t num_labels,
-                    const tsl::hopscotch_set<Label> &labels_in,
-                    const tsl::hopscotch_set<Label> &labels_out,
-                    const tsl::hopscotch_set<Label> &labels_in_round2,
-                    const tsl::hopscotch_set<Label> &labels_out_round2,
-                    const DifferentialAssemblyConfig &config,
-                    size_t num_threads) {
-    // in and out counts are stored interleaved in the counts vector
-    assert(counts.size() == init_mask.size() * 2);
-
-    bool parallel = num_threads > 1;
-    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
-    bool unitig_mode = check_other || labels_in_round2.size() || labels_out_round2.size()
-            || config.label_mask_in_unitig_fraction != 0.0
-            || config.label_mask_out_unitig_fraction != 1.0
-            || config.label_mask_other_unitig_fraction != 1.0;
-
-    size_t num_in_labels = labels_in.size() + labels_in_round2.size();
-    size_t num_out_labels = labels_out.size() + labels_out_round2.size();
-
-    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
-        && (config.add_complement || unitig_mode);
-
-    auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
-                                                  add_complement, num_threads);
-
-    auto mask_or = [&](sdsl::bit_vector &a,
-                       const sdsl::bit_vector &b,
-                       const std::vector<node_index> &id_map) {
-        call_ones(b, [&](size_t i) {
-            if (id_map[i])
-                set_bit(a.data(), id_map[i], parallel, MO_RELAXED);
-        });
-    };
-
-    // check all other labels and round 2 labels
-    if (anno_graph && (check_other || labels_in_round2.size() || labels_out_round2.size())) {
-        sdsl::bit_vector union_mask
-            = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
-        std::mutex vector_backup_mutex;
-        std::atomic_thread_fence(std::memory_order_release);
-
-        auto count_merge = [&](sdsl::bit_vector &a, // Myrthe ANSWER: what does count_merge do? Is a hack, I do not have to touch this now. Count_add would be better name. If you have a dense column, and include it in foreground or background. Wate time, Instead first create mask.  You only access dense columns, for instance a reference.
-                               const sdsl::bit_vector &b,
-                               const std::vector<node_index> &id_map,
-                               size_t offset = 0) {
-            call_ones(b, [&](size_t i) {
-                if (id_map[i]) {
-                    set_bit(a.data(), id_map[i], parallel, MO_RELAXED);
-                    atomic_fetch_and_add(counts, id_map[i] * 2 + offset, 1,
-                                         vector_backup_mutex, MO_RELAXED);
-                }
-            });
-        };
-
-        logger->trace("Checking shared and other labels");
-        masked_graph->call_sequences([&](const std::string &contig,
-                                         const std::vector<node_index> &path) {
-            for (const auto &[label, sig] : anno_graph->get_top_label_signatures(contig, num_labels)) {
-                bool found_in = labels_in.count(label);
-                bool found_out = labels_out.count(label);
-                bool found_in_round2 = labels_in_round2.count(label);
-                bool found_out_round2 = labels_out_round2.count(label);
-                if (!found_in && !found_out
-                        && !found_in_round2 && !found_out_round2 && check_other) {
-                    mask_or(other_mask, sig, path);
-                }
-
-                if (found_in_round2)
-                    count_merge(union_mask, sig, path);
-
-                if (found_out_round2)
-                    count_merge(union_mask, sig, path, 1);
-            }
-        }, num_threads);
-
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-        masked_graph->set_mask(new bitmap_vector(std::move(union_mask)));
-    }
-
-    // Filter unitigs from masked graph based on filtration criteria
-    logger->trace("Filtering out background");
-
-    size_t min_label_in_count = std::ceil(config.label_mask_in_kmer_fraction
-                                    * num_in_labels);
-    size_t max_label_out_count = std::floor(config.label_mask_out_kmer_fraction
-                                    * num_out_labels);
-
-    if (config.label_mask_in_unitig_fraction == 0.0
-            && config.label_mask_out_unitig_fraction == 1.0
-            && config.label_mask_other_unitig_fraction == 1.0) {
-        logger->trace("Filtering by node");
-        size_t total_nodes = masked_graph->num_nodes();
-        const auto &in_mask
-            = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
-        sdsl::bit_vector mask = in_mask;
-
-        // TODO: make this part multithreaded
-        size_t kept_nodes = 0;
-        call_ones(in_mask, [&](node_index node) {
-            uint64_t in_count = counts[node * 2];
-            uint64_t out_count = counts[node * 2 + 1];
-
-            if (in_count >= min_label_in_count && out_count <= max_label_out_count) {
-                ++kept_nodes;
-            } else {
-                mask[node] = false;
-            }
-        }); // TODO Myrthe: implement statistical tests. parse in_count out_count to adapter version of 'process' in diff tests.
-        process(in_sum, out_sum, in_total_kmers, out_total_kmers);
-        // process(const Range<count_t>& controls, const Range<count_t>& cases)
-        // stastical test found here in kmdiff: https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/model.hpp
-        // in merge.hpp this code is applied. https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/merge.hpp
-        // statistical and multiple correction tests come together in https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/cmd/diff.hpp
-        // call
-        // std::tuple<double, Significance, double, double>
-        //    process(const Range<count_t>& controls, const Range<count_t>& cases)  --> per kmer.
-        // needs one parameter -> P-value. other input arguments are not needed.
-        //  why do they use bonferonni rather than benjamini (kmdiff says you can do both). https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/corrector.cpp
-
-
-        masked_graph->set_mask(new bitmap_vector(std::move(mask)));
-
-        logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
-
-        return masked_graph;
-    }
-
-    logger->trace("Filtering by unitig");
-
-    update_masked_graph_by_unitig(*masked_graph,
-        [&](const std::string &, const std::vector<node_index> &path) -> Intervals {
-            // return a set of intervals to keep in the graph
-            size_t in_kmer_count = 0;
-
-            size_t begin = path.size();
-            size_t end = 0;
-            for (size_t i = 0; i < path.size(); ++i) {
-                if (counts[path[i] * 2] >= min_label_in_count) {
-                    if (begin == path.size())
-                        begin = i;
-
-                    end = std::max(end, i + 1);
-
-                    ++in_kmer_count;
-                }
-            }
-
-            if (begin >= end)
-                return {};
-
-            size_t size = end - begin;
-            size_t label_in_cutoff = std::ceil(config.label_mask_in_unitig_fraction * size);
-            if (in_kmer_count < label_in_cutoff)
-                return {};
-
-            size_t out_kmer_count = 0;
-            size_t other_kmer_count = 0;
-            size_t label_out_cutoff = std::floor(config.label_mask_out_unitig_fraction * size);
-            size_t other_cutoff = std::floor(config.label_mask_other_unitig_fraction * size);
-
-
-            for (size_t i = begin; i < end; ++i) {
-                if (counts[path[i] * 2 + 1] > max_label_out_count
-                        && ++out_kmer_count > label_out_cutoff) {
-                    return {};
-                }
-
-                if (check_other && other_mask[path[i]]
-                        && ++other_kmer_count > other_cutoff) {
-                    return {};
-                }
-            }
-
-            return { std::make_pair(begin, end) };
-        },
-        num_threads
-    );
-
-    return masked_graph;
-}
-
-
-/**
- * Helpers
- */
-
-
-/**
- * A version of `make_initial_masked_graph` with count annotation.
- * This function reads from the column count vector and decides what rows to keep, i.e.
- * k-mers labeled with at least one of the foreground and background labels are enumerated to determine the initial subgraph
- * I think this function stays the same irregardless of the differential assembly method?
- */
-std::shared_ptr<MaskedDeBruijnGraph>
-        make_initial_annotated_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                          sdsl::int_vector<> &counts, //TODO Myrthe later: int_vector_buffer and make it a template class. Otherwise the function stays the same because initial mask should only check if a k-mer is present just once in either set.
-                          sdsl::bit_vector&& mask,
-                          bool add_complement,
-                          size_t num_threads) {
-    // counts is a double-length vector storing the in-label and out-label
-    // counts interleaved
-    assert(counts.size() == mask.size() * 2);
-
-    auto masked_graph = std::make_shared<MaskedDeBruijnGraph>(
-        graph_ptr,
-        add_complement
-            ? std::make_unique<bitmap_vector>(mask)
-            : std::make_unique<bitmap_vector>(std::move(mask)),
-        true
-    );
-
-    logger->trace("Constructed masked graph with {} nodes", masked_graph->num_nodes());
-
-    if (add_complement) {
-        logger->trace("Adding reverse complements");
-        std::mutex vector_backup_mutex;
-        std::atomic_thread_fence(std::memory_order_release);
-        masked_graph->call_sequences([&](const std::string &seq, const std::vector<node_index> &path) {
-            std::string rc_seq = seq;
-            std::vector<DeBruijnGraph::node_index> rc_path = path;
-            reverse_complement_seq_path(*graph_ptr, rc_seq, rc_path);
-
-            auto it = rc_path.rbegin();
-            for (size_t i = 0; i < path.size(); ++i, ++it) {
-                if (*it) {
-                    uint64_t in_count = atomic_fetch(counts, path[i] * 2, vector_backup_mutex, MO_RELAXED);
-                    uint64_t out_count = atomic_fetch(counts, path[i] * 2 + 1, vector_backup_mutex, MO_RELAXED);
-                    atomic_fetch_and_add(counts, *it * 2, in_count, vector_backup_mutex, MO_RELAXED);
-                    atomic_fetch_and_add(counts, *it * 2 + 1, out_count, vector_backup_mutex, MO_RELAXED); // Myrthe: for each kmer, counts is the total time this occurs in all for- resp. background samples?
-
-                    set_bit(mask.data(), *it, in_count, MO_RELAXED);
-                }
-            }
-        }, num_threads, true);
-
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-        masked_graph->set_mask(new bitmap_vector(std::move(mask)));
-
-        logger->trace("Updated masked graph has {} nodes", masked_graph->num_nodes());
-    }
-
-    return masked_graph;
-}
-
-    std::shared_ptr<MaskedDeBruijnGraph>
-    make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                              sdsl::int_vector<> &counts,
-                              sdsl::bit_vector&& mask,
-                              bool add_complement,
-                              size_t num_threads) {
-        // counts is a double-length vector storing the in-label and out-label
-        // counts interleaved
-        assert(counts.size() == mask.size() * 2);
-
-        auto masked_graph = std::make_shared<MaskedDeBruijnGraph>(
-                graph_ptr,
-                add_complement
-                ? std::make_unique<bitmap_vector>(mask)
-                : std::make_unique<bitmap_vector>(std::move(mask)),
-                true
-        );
-
-        logger->trace("Constructed masked graph with {} nodes", masked_graph->num_nodes());
-
-        if (add_complement) {
-            logger->trace("Adding reverse complements");
-            std::mutex vector_backup_mutex;
-            std::atomic_thread_fence(std::memory_order_release);
-            masked_graph->call_sequences([&](const std::string &seq, const std::vector<node_index> &path) {
-                std::string rc_seq = seq;
-                std::vector<DeBruijnGraph::node_index> rc_path = path;
-                reverse_complement_seq_path(*graph_ptr, rc_seq, rc_path);
-
-                auto it = rc_path.rbegin();
-                for (size_t i = 0; i < path.size(); ++i, ++it) {
-                    if (*it) {
-                        uint64_t in_count = atomic_fetch(counts, path[i] * 2, vector_backup_mutex, MO_RELAXED);
-                        uint64_t out_count = atomic_fetch(counts, path[i] * 2 + 1, vector_backup_mutex, MO_RELAXED);
-                        atomic_fetch_and_add(counts, *it * 2, in_count, vector_backup_mutex, MO_RELAXED);
-                        atomic_fetch_and_add(counts, *it * 2 + 1, out_count, vector_backup_mutex, MO_RELAXED);
-
-                        set_bit(mask.data(), *it, in_count, MO_RELAXED);
-                    }
-                }
-            }, num_threads, true);
-
-            std::atomic_thread_fence(std::memory_order_acquire);
-
-            masked_graph->set_mask(new bitmap_vector(std::move(mask)));
-
-            logger->trace("Updated masked graph has {} nodes", masked_graph->num_nodes());
-        }
-
-        return masked_graph;
-    }
-
-// TODO
-total_sum(labels, & columns){ // sum over all columns first, and then add the sum to calculate the total sum
-        sdsl::int_vector<> total_kmers_columns;
-        for (size_t i = 0; i < files.size(); ++i) {
-            total_kmers_columns[label] = std::accumulate(column.begin(), column.end());
-        }
-     size_t total_kmers{std::accumulate(in_total_kmers_columns.begin(), in_total_kmers_columns.end(), 0ULL)};
-     return total_kmers;
-}
-// ANSWER, where to best do this? -> after load column values. Note down how the output of loadcv works.
-
-template <class vector_type> // type for the vector bitmap or int_vector, later also buffered.
-std::pair<sdsl::int_vector<>, sdsl::bit_vector>
-construct_diff_label_count_annotation_vector(const std::function<void(const std::function<void(const Label &, const vector_type&)>)> &generate_columns, //Myrthe: ANSWER: this is a callable, why is it used, where is it exactly executed? Such that it can be used to handle different type of columns (e.g. in terms of compression) in different versions of the function. It is called/executed in call_ones.
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  bool parallel,
-                                  bool add_out_labels_to_mask) {
-    logger->trace("Allocating mask vector");
-    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask); // TODO Myrthe: this must be increased when including annotation counts. Think about how to. If we have disk buffering then 32 bit would work.
-    //Myrthe: otherwise we might estimate a good width based on the distributions / coverage. Maximum sum of in or outlabels < max(|inlabels|,|outlabels|) * max_coverage. An option could be to equalize the coverage.
-    //Myrthe: for the statistical tests we need for each k-mer the sum of its occurance over resp the in labels and out labels. Furthermore we need the sum of all kmers in inlabels resp outlabels. Extract the coverage.
-    sdsl::bit_vector indicator(max_index + 1, false);
-
-    logger->trace("Allocating count vector");
-    // the in and out counts are stored interleaved
-    sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16); //TODO Myrthe later: int_vector_buffer. In that case the function also to be a template too, or a template based argument, if vector_type== buffered_int_vector, then it should be a buffered (// if constexpr (std::is_same_v<vector_type, Bitmap>) { })
-    logger->trace("done");
-
-    std::mutex vector_backup_mutex;
-
-    logger->trace("Populating count vector");
-    std::atomic_thread_fence(std::memory_order_release);
-    generate_columns([&](const Label &label, const vector_type &column) { // ANSWERED: outer loop is over the columns, inner loop over the kmers.
-        // Myrthe: For the buffered_int_vector we would have to use operators [ ] [ ], but it might be problematic if it runs out of memory and push_back would need to be used instead.
-        // Myrthe: The second problem with buffered_int_vector might be that it is expensive, because we do random access on it. Instead a solution might be to use a piority queue of L columns, storing the smallest element as a triple (column , index, value), such that always k-mers in row x are processed before row x + 1.
-
-        uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
-        if (labels_out.count(label))
-            col_indicator |= 2;
-
-        auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i, uint64_t value) { // Myrthe: modify this to have a value, since call_nonzeros, also creates value
-            i = AnnotatedDBG::anno_to_graph_index(i); // Myrthe ANSWERED: The i indicates the row /k-mer
-            assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED); //Myrthe: sets corresponding bit to true in mask.
-            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED); // Myrthe: This here increments counts by 1, for count annotation, increment by the count.
-        };
-
-        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i) {
-            i = AnnotatedDBG::anno_to_graph_index(i);
-            assert(i != DeBruijnGraph::npos);
-            if (add_out_labels_to_mask)
-                set_bit(indicator.data(), i, parallel, MO_RELAXED);
-
-            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
-        };
-
-        auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) { //ANSWER: theoretcially a label should not be both in the in and out labels, but this is just in case.
-            i = AnnotatedDBG::anno_to_graph_index(i); // Myrthe: indexes in annotation matrix are 1 offset off, so convert the index from annotation to graph, because the mask should be compatible with the graph.
-            assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
-        };
-
-        switch (col_indicator) {
-            case 1: { column.call_nonzeros(add_in); } break; // Myrthe: outer loop over columns. call_ones is a inner loop . iterates over rows, (throubh bitmap). There is a call_nonzeros function that I'd need, but it also needs to be made compatible with int_vector_buffer.
-            case 2: { column.call_nonzeros(add_out); } break;
-            case 3: { column.call_nonzeros(add_both); } break;
-            default: {}
-        }
-    });
-
-    std::atomic_thread_fence(std::memory_order_acquire);
-    logger->trace("done");
-
-    return std::make_pair(std::move(counts), std::move(indicator)); // ANSWER: the indicator defines the initial mask?
-}
-
-std::pair<sdsl::int_vector<>, sdsl::bit_vector>
-construct_diff_label_count_vector(const std::function<void(const std::function<void(const Label &, const bitmap&)>)> &generate_columns,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  bool parallel,
-                                  bool add_out_labels_to_mask) {
-    logger->trace("Allocating mask vector");
-    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
-    sdsl::bit_vector indicator(max_index + 1, false);
-
-    logger->trace("Allocating count vector");
-    // the in and out counts are stored interleaved
-    sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16);
-    logger->trace("done");
-
-    std::mutex vector_backup_mutex;
-
-    logger->trace("Populating count vector");
-    std::atomic_thread_fence(std::memory_order_release);
-    generate_columns([&](const Label &label, const bitmap &column) {
-        uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
-        if (labels_out.count(label))
-            col_indicator |= 2;
-
-        auto add_in = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
-            i = AnnotatedDBG::anno_to_graph_index(i);
-            assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
-        };
-
-        auto add_out = [&indicator,&counts,&vector_backup_mutex,parallel,add_out_labels_to_mask](Column i) {
-            i = AnnotatedDBG::anno_to_graph_index(i);
-            assert(i != DeBruijnGraph::npos);
-            if (add_out_labels_to_mask)
-                set_bit(indicator.data(), i, parallel, MO_RELAXED);
-
-            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
-        };
-
-        auto add_both = [&indicator,&counts,&vector_backup_mutex,parallel](Column i) {
-            i = AnnotatedDBG::anno_to_graph_index(i);
-            assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2, 1, vector_backup_mutex, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2 + 1, 1, vector_backup_mutex, MO_RELAXED);
-        };
-
-        switch (col_indicator) {
-            case 1: { column.call_ones(add_in); } break;
-            case 2: { column.call_ones(add_out); } break;
-            case 3: { column.call_ones(add_both); } break;
-            default: {}
-        }
-    });
-
-    std::atomic_thread_fence(std::memory_order_acquire);
-    logger->trace("done");
-
-    return std::make_pair(std::move(counts), std::move(indicator));
-}
-
-
-// SOLVED: what are the different versions of `construct_diff_label_count_vector` for? for different situations, only this one is useful for annotation, as it can take filenames
-// Myrthe
-/**
- * A version of `construct_diff_label_count_vector` with count annotation, for streaming input columns.
- * // the foreground and background label counts, and assembly parameters are taken as input, to create a count vector, are used to determine which k-mers are filtered out from the subgraph.
-
- * This function reads from the column count vectors and decides what rows to keep, i.e.
- * k-mers labeled with at least one of the foreground and background labels are enumerated to determine the initial subgraph
- * TODO Myrthe
- */
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
-construct_diff_label_annotation_count_vector(const Annotator &annotator,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  size_t num_threads,
-                                  bool add_out_labels_to_mask) {
-    std::vector<Column> columns; // TODO Myrthe later: Change to buffered vector int_vector_buffer(  ); Perhaps also based on the template type?
-    // see documentation in int_vector_buffer() {}, constructor.
-    // create a temporary filename, where? Will the file be created by the int_vector_buffer if it doesn't exist yet?
-    // sdsl::int_vector_buffer(  ) (const std::string filename, std::ios::openmode mode=std::ios::in, uint64_t buffer_size=1024*1024, const uint8_t int_width=t_width, const bool is_plain=false, const uint64_t file_offset=0)
-    const auto &encoder = annotator.get_label_encoder();
-    AnnotatedDBG::Annotator::VLabels labels;
-    labels.reserve(labels_in.size() + labels_out.size());
-    columns.reserve(labels_in.size() + labels_out.size());
-    for (const auto &label : labels_in) {
-        labels.emplace_back(label);
-        columns.emplace_back(encoder.encode(label));
-    }
-    for (const auto &label : labels_out) {
-        if (!labels_in.count(label)) {
-            labels.emplace_back(label);
-            columns.emplace_back(encoder.encode(label));
-        }
-    }
-
-    sdsl::bit_vector other_labels(encoder.size(), true);
-    for (Column column : columns) {
-        other_labels[column] = false;
-    }
-
-    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) { // ANSWER: which function is called here? the above
-        annotator.get_matrix().call_columns(columns,
-            [&](size_t j, const bitmap_generator &column) {
-                callback(labels[j], column);
-            }, num_threads
-        );
-    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask); //Myrthe: this is generate columns call back. The function above above defines what you do with the generated objects.
-
-    return std::make_tuple(std::move(counts), std::move(init_mask), std::move(other_labels));
-}
-
-
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector>
-construct_diff_label_count_vector(const Annotator &annotator,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  size_t num_threads,
-                                  bool add_out_labels_to_mask) {
-    std::vector<Column> columns;
-    const auto &encoder = annotator.get_label_encoder();
-    AnnotatedDBG::Annotator::VLabels labels;
-    labels.reserve(labels_in.size() + labels_out.size());
-    columns.reserve(labels_in.size() + labels_out.size());
-    for (const auto &label : labels_in) {
-        labels.emplace_back(label);
-        columns.emplace_back(encoder.encode(label));
-    }
-    for (const auto &label : labels_out) {
-        if (!labels_in.count(label)) {
-            labels.emplace_back(label);
-            columns.emplace_back(encoder.encode(label));
-        }
-    }
-
-    sdsl::bit_vector other_labels(encoder.size(), true);
-    for (Column column : columns) {
-        other_labels[column] = false;
-    }
-
-    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) {
-        annotator.get_matrix().call_columns(columns,
-                                            [&](size_t j, const bitmap_generator &column) {
-                                                callback(labels[j], column);
-                                            }, num_threads
-        );
-    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask);
-
-    return std::make_tuple(std::move(counts), std::move(init_mask), std::move(other_labels));
-}
-
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::vector<size_t>>
-construct_diff_label_count_vector(const std::vector<std::string> &files,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  size_t num_threads,
-                                  bool add_out_labels_to_mask) {
-    std::vector<bool> other_labels_vec;
-    other_labels_vec.reserve(files.size());
-
-    std::vector<size_t> num_labels_per_file;
-    num_labels_per_file.reserve(files.size());
-
-    for (size_t i = 0; i < files.size(); ++i) {
-        auto label_encoder = annot::ColumnCompressed<>::read_label_encoder(files[i]);
-        num_labels_per_file.emplace_back(label_encoder.size());
-        for (const auto &label : label_encoder.get_labels()) {
-            other_labels_vec.emplace_back(!labels_in.count(label) && !labels_out.count(label));
-        }
-    }
-
-    auto other_labels = to_sdsl(std::move(other_labels_vec));
-
-    auto [counts, init_mask] = construct_diff_label_count_vector([&](const auto &callback) { // QUESTION merge_load --> callback as part of another callback, but where is it now exactly executed.
-        size_t j = 0;
-        for (size_t i = 0; i < files.size(); ++i) { // loop over the columns
-            if (count_ones(other_labels, j, j + num_labels_per_file[i])
-                    != num_labels_per_file[i]) {
-                if (not config.count_kmers){
-                    annot::ColumnCompressed<>::merge_load({ files[i] },
-                        [&](size_t offset, const auto &label, auto&& column) {
-                            if (!other_labels[j + offset])
-                                callback(label, *column); // ANSWER: is this the ^ callback above? yes.
-                        },
-                        num_threads
-                    );
-
-                }else{ // if count annotation is used
-                annot::ColumnCompressed<>::load_columns_and_values({ files[i] }, //Myrthe QUESTION: How to write this lambda. The following is from annotation_converters.cpp. However, there it passed all files, rather than files[i]
-                                                                                                               [&](size_t j,
-                                                                                                                   const std::string &label,
-                                                                                                                   std::unique_ptr<bit_vector> &&column,
-                                                                                                                   sdsl::int_vector<> &&values) {
-                                                                                                                   size_t local_max_val = 0;
-                                                                                                                   if (values.size())
-                                                                                                                       local_max_val = *std::max_element(values.begin(), values.end());
-
-                                                                                                                   std::lock_guard<std::mutex> lock(mu); //QUESTION
-
-                                                                                                                   if (local_max_val > max_val)
-                                                                                                                       max_val = local_max_val;
-
-                                                                                                                   num_set_bits += column->num_set_bits();
-
-                                                                                                                   while (columns.size() <= j) {
-                                                                                                                       columns.emplace_back();
-                                                                                                                       col_values.emplace_back();
-                                                                                                                       col_names.emplace_back();
-                                                                                                                   }
-                                                                                                                   columns[j] = std::move(column);
-                                                                                                                   col_values[j] = std::move(values); //QUESTION: Why does this happen?
-                                                                                                                   col_names[j] = label;
-                                                                                                               },
-                                                                                                               num_threads);
-            // Myrthe QUESTION: Now I'd have to convert column and values s.t. they can be passed to construct_diff_label_count_vector?
-            // We assumed there that the column would just be an int_vector, rather than two arrays.
-
-                    //total_sum( values)
-            }
-
-            }
-
-            j += num_labels_per_file[i];
-        }
-    }, labels_in, labels_out, max_index, num_labels, num_threads > 1, add_out_labels_to_mask);
-
-    return std::make_tuple(std::move(counts), std::move(init_mask),
-                           std::move(other_labels), std::move(num_labels_per_file));
-}
-
-
-void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
-                                   const GetKeptIntervals &get_kept_intervals,
-                                   size_t num_threads) {
-    std::atomic<uint64_t> kept_unitigs(0);
-    std::atomic<uint64_t> total_unitigs(0);
-    std::atomic<uint64_t> num_kept_nodes(0);
-    bool parallel = num_threads > 1;
-
-    sdsl::bit_vector mask = static_cast<const bitmap_vector&>(masked_graph.get_mask()).data();
-
-    std::atomic_thread_fence(std::memory_order_release);
-
-    masked_graph.call_unitigs([&](const std::string &unitig,
-                                  const std::vector<node_index> &path) {
-        total_unitigs.fetch_add(1, MO_RELAXED);
-
-        size_t last = 0;
-        for (const auto &pair : get_kept_intervals(unitig, path)) {
-            const auto &[begin, end] = pair;
-            kept_unitigs.fetch_add(1, MO_RELAXED);
-            num_kept_nodes.fetch_add(end - begin, MO_RELAXED);
-            for ( ; last < begin; ++last) {
-                unset_bit(mask.data(), path[last], parallel, MO_RELAXED);
-            }
-            last = end;
-        }
-
-        for ( ; last < path.size(); ++last) {
-            unset_bit(mask.data(), path[last], parallel, MO_RELAXED);
-        }
-
-    }, num_threads);
-    std::atomic_thread_fence(std::memory_order_acquire);
-
-    masked_graph.set_mask(new bitmap_vector(std::move(mask)));
-
-    logger->trace("Kept {} out of {} unitigs with average length {}",
-                  kept_unitigs, total_unitigs,
-                  static_cast<double>(num_kept_nodes + kept_unitigs * (masked_graph.get_k() - 1))
-                      / kept_unitigs);
-}
-
-} // namespace graph
-} // namespace mtg
diff --git a/metagraph/src/graph/differential_tests.cpp b/metagraph/src/graph/differential_tests.cpp
new file mode 100644
index 0000000000..4110f25f27
--- /dev/null
+++ b/metagraph/src/graph/differential_tests.cpp
@@ -0,0 +1,100 @@
+#include <cassert>
+#include <math.h>
+#include "differential_tests.hpp"
+
+// https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/log_factorial_table.hpp
+// cpp https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/log_factorial_table.cpp
+LogFactorialTable::LogFactorialTable(size_t size)
+        : m_size(size)
+{
+    m_table.reserve(m_size);
+    for (size_t i=0; i<size; i++)
+        m_table.push_back(log_factorial(i));
+}
+
+double LogFactorialTable::log_factorial(size_t k)
+{
+    double res = 0;
+    while (k > 1)
+    {
+        res += log(k);
+        k--;
+    }
+    return res;
+}
+
+
+DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t preload_table_size,
+                                   size_t in_total_kmers, size_t out_total_kmers) :
+        preload_table_size(preload_table_size), // similar to the width of the counts vector, this should be the maximum joint coverage over the in_labels resp. out_labels
+        family_wise_error_rate(family_wise_error_rate),
+        total_hypotheses(total_hypotheses),
+        out_total_kmers(out_total_kmers),
+        in_total_kmers(in_total_kmers)
+{}
+
+
+//double DifferentialTest::chi_sqrd_1_cdf(double x)// DOF = 1
+//{
+//    assert(x > 0); // assert that it is positive.
+//    double igf = lower_incomplete_gamma_function(x/0.5);
+//    if(std::isnan(igf) or std::isinf(igf) or igf <= 1e-8) return 1e-14;
+//    return 1.0 - igf / gamma;
+//}
+//
+//double DifferentialTest::lower_incomplete_gamma_function(double x) // lower incomplete_gamma_function lower incomplete. with s = 1/2
+//{ // https://en.wikipedia.org/wiki/Incomplete_gamma_function#Lower_incomplete_gamma_function
+//    double Sc = 2 * std::sqrt(x) * exp(-x);
+//    double s = 0.5; double sum = 1.0; double nominator = 1.0; double denominator = 1.0;
+//    for(int I = 0; I < 200; I++)
+//    {
+//        s++;
+//        nominator *= x;
+//        denominator *= s;
+//        sum += (nominator / denominator);
+//    }
+//    return sum * Sc;
+//}
+
+double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution
+{
+    if (lambda <= 0) return 0;
+    if (k < 0) k = 0;
+    return (-lambda + (k * std::log(lambda) - lf_table[k]));
+}
+
+//m be the total number of null hypotheses
+// family-wise error rate (FWER)
+bool DifferentialTest::bonferroni_correction(double& pvalue) //  &family_wise_error_rate, std::size_t & total_hypotheses, --> can also be class parameters.
+{
+    return pvalue < (family_wise_error_rate / total_hypotheses);
+}
+
+// adapted code from kmdiff.
+// previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
+std::tuple<double, bool> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
+{
+    double mean = (out_sum + in_sum) / static_cast<double>(out_total_kmers + in_total_kmers);
+
+    double null_hypothesis = 0;
+    double alt_hypothesis = 0;
+
+    alt_hypothesis += poisson_prob(out_sum, out_sum);
+    alt_hypothesis += poisson_prob(in_sum, in_sum);
+
+    null_hypothesis += poisson_prob(out_sum, mean * out_total_kmers);
+    null_hypothesis += poisson_prob(in_sum, mean * in_total_kmers);
+
+    double likelihood_ratio = alt_hypothesis - null_hypothesis;
+
+    if (likelihood_ratio < 0) likelihood_ratio = 0;
+    double p_value = Chi2PLookupTable.getPValue(2 * likelihood_ratio, 1);
+
+    auto out_sum_normalized = out_sum * in_total_kmers / out_total_kmers;
+
+    bool sign = false;
+    if (out_sum_normalized < in_sum) sign = true;
+
+    return std::make_tuple(p_value, sign);
+}
+
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index 5ce8ddfda6..e958430e0a 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -1,239 +1,52 @@
-#include "annotated_graph_algorithm.hpp"
-
-#include "common/logger.hpp"
-#include "common/vectors/vector_algorithm.hpp"
-#include "common/vectors/bitmap.hpp"
-#include "graph/representation/masked_graph.hpp"
-#include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
-
-// https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/model.hpp
-/*****************************************************************************
- *   kmdiff
- *   Authors: T. Lemane
- *
- *  This program is free software: you can redistribute it and/or modify
- *  it under the terms of the GNU Affero General Public License as
- *  published by the Free Software Foundation, either version 3 of the
- *  License, or (at your option) any later version.
- *
- *  This program is distributed in the hope that it will be useful,
- *  but WITHOUT ANY WARRANTY; without even the implied warranty of
- *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- *  GNU Affero General Public License for more details.
- *
- *  You should have received a copy of the GNU Affero General Public License
- *  along with this program.  If not, see <http://www.gnu.org/licenses/>.
- *****************************************************************************/
-
-#pragma once
-
-#include <algorithm>
 #include <cmath>
-#include <numeric>
-#include <random>
+#include <cstdint>
 #include <vector>
+#include <tuple>
 
-#include <alglibinternal.h>
-#include <spdlog/spdlog.h>
-#include <specialfunctions.h>
-
-#define KMTRICKS_PUBLIC
-#include <kmtricks/utils.hpp>
+#include "differential_tests_notes.hpp" // TODO rename file when running cmake
 
-#include <kmdiff/range.hpp>
-#include <kmdiff/kmer.hpp>
-#include <kmdiff/log_factorial_table.hpp>
-#include <kmdiff/correction.hpp>
-#include <kmdiff/imodel.hpp>
+class LogFactorialTable
+{
+public:
+    LogFactorialTable(size_t size);
 
-
-// Myrthe: the following code calculates the p-value for a single k-mer across the samples. All comes together in `process`.
-namespace kmdiff {
-
-    template <size_t MAX_C>
-    class Model
+    double operator[](size_t i)
     {
-        using count_t = typename km::selectC<MAX_C>::type;
-
-    public:
-        Model() {}
-
-        template <typename Iterable>
-        double compute_mean(Iterable&& v)
-        {
-            return std::accumulate(v.begin(), v.end(), 0.0) / v.size();
-        }
-
-        template <typename Iterable>
-        std::tuple<double, size_t> compute_mean_e(Iterable& v)
-        {
-            size_t sum = 0;
-            size_t positive = 0;
-            for (auto& e : v)
-            {
-                if (e > 0) positive++;
-                sum += e;
-            }
-            double mean = sum / static_cast<double>(v.size());
-            return std::make_tuple(mean, positive);
-        }
-
-        template <typename Iterable>
-        std::tuple<double, size_t> compute_sum_e(Iterable& v)
-        {
-            double sum = 0;
-            size_t positive = 0;
-            for (auto& e : v)
-            {
-                if (e > 0) positive++;
-                sum += e;
-            }
-            return std::make_tuple(sum, positive);
-        }
-
-        template <typename Iterable>
-        std::tuple<double, double> compute_mean_sd(Iterable& v)
-        {
-            double mean = compute_mean(v);
-            double sq_sum = std::inner_product(v.begin(), v.end(), v.begin(), 0.0);
-            double sd = std::sqrt(sq_sum / v.size() - mean * mean);
-            return std::make_tuple(mean, sd);
-        }
-
-    };
-
-    template <size_t MAX_C>
-    class PoissonLikelihood : public IModel<MAX_C>, public Model<MAX_C>
-    {
-        /*
-          Based on https://github.com/atifrahman/HAWK
-          https://doi.org/10.7554/eLife.32920.001
-          https://doi.org/10.1371/journal.pone.0245058
-        */
-
-        using count_t = typename km::selectC<MAX_C>::type;
-
-    public:
-        PoissonLikelihood(
-                size_t nb_controls,
-                size_t nb_cases,
-                const std::vector<size_t>& total_controls,
-                const std::vector<size_t>& total_cases,
-                size_t preload)
-                : m_nb_controls(nb_controls),
-                  m_nb_cases(nb_cases),
-                  m_total_kmer_controls(total_controls),
-                  m_total_kmer_cases(total_cases),
-                  m_preload(preload)
-        {
-        }
-
-        void configure(const std::string& config) override {}
-    private:
-        double log_factorial(int k)
-        {
-            double res = 0;
-            while (k > 1)
-            {
-                res += log(k);
-                k--;
-            }
-            return res;
-        }
-
-        double poisson_prob(int k, double lambda)
-        {
-            if (lambda <= 0) return 0;
-            if (k < 0) k = 0;
-            return (-lambda + (k * log(lambda) - m_lf_table[k]));
-        }
-
-    public:
-
-        std::tuple<double, Significance, double, double>
-        process(const Range<count_t>& controls, const Range<count_t>& cases) override
-        {
-            auto [mean_control, positive_controls] = this->compute_sum_e(controls);
-            auto [mean_case, positive_cases] = this->compute_sum_e(cases);
-
-            double mean = (mean_control + mean_case) / static_cast<double>(m_sum_controls + m_sum_cases);
-
-            double null_hypothesis = 0;
-            double alt_hypothesis = 0;
-
-            alt_hypothesis += poisson_prob(mean_control, mean_control);
-            alt_hypothesis += poisson_prob(mean_case, mean_case);
-
-            null_hypothesis += poisson_prob(mean_control, mean * m_sum_controls);
-            null_hypothesis += poisson_prob(mean_case, mean * m_sum_cases);
-
-            double likelihood_ratio = alt_hypothesis - null_hypothesis;
-
-            if (likelihood_ratio < 0) likelihood_ratio = 0;
-            double p_value = alglib::chisquarecdistribution(1, 2 * likelihood_ratio);
-
-            Significance sign;
-
-            mean_control = mean_control * m_sum_cases / m_sum_controls;
-
-            if (mean_control < mean_case)
-                sign = Significance::CASE;
-            else if (mean_control > mean_case)
-                sign = Significance::CONTROL;
-            else
-                sign = Significance::NO;
-
-            return std::make_tuple(p_value, sign, mean_control, mean_case);
-        }
-
-        std::tuple<double, Significance, double, double>
-        process_myrthe(double in_sum, double out_sum, double in_total_kmers, double out_total_kmers) override // previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
-        {
-            auto mean_control = out_sum; auto mean_case = in_sum;
-            double mean = (mean_control + mean_case) / static_cast<double>(m_sum_controls + m_sum_cases);
-
-            double null_hypothesis = 0;
-            double alt_hypothesis = 0;
-
-            alt_hypothesis += poisson_prob(mean_control, mean_control);
-            alt_hypothesis += poisson_prob(mean_case, mean_case);
-
-            null_hypothesis += poisson_prob(mean_control, mean * m_sum_controls);
-            null_hypothesis += poisson_prob(mean_case, mean * m_sum_cases);
-
-            double likelihood_ratio = alt_hypothesis - null_hypothesis;
-
-            if (likelihood_ratio < 0) likelihood_ratio = 0;
-            double p_value = alglib::chisquarecdistribution(1, 2 * likelihood_ratio);
-
-            Significance sign;
+        if (i < m_size) return m_table[i];
+        return log_factorial(i);
+    }
 
-            mean_control = mean_control * m_sum_cases / m_sum_controls;
+private:
+    double log_factorial(size_t k);
 
-            if (mean_control < mean_case)
-                sign = Significance::CASE;
-            else if (mean_control > mean_case)
-                sign = Significance::CONTROL;
-            else
-                sign = Significance::NO;
+private:
+    std::vector<double> m_table;
+    size_t m_size;
+};
 
-            return std::make_tuple(p_value, sign, mean_control, mean_case);
-        }
+class DifferentialTest
+{
+private:
+    size_t preload_table_size;
+    Chi2PLookup Chi2PLookupTable; // generated with https://github.com/MoseleyBioinformaticsLab/chi2plookup
+    LogFactorialTable lf_table{preload_table_size};
+    double family_wise_error_rate;
+    size_t total_hypotheses;
+    size_t out_total_kmers;
+    size_t in_total_kmers;
+    double gamma = std::sqrt(/* pi */ std::atan(1)*4);
 
-    private:
-        size_t m_nb_controls{0};
-        size_t m_nb_cases{0};
+public:
+    DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t preload_table_size,
+                     size_t in_total_kmers, size_t out_total_kmers);
 
-        const std::vector<size_t>& m_total_kmer_controls;
-        const std::vector<size_t>& m_total_kmer_cases;
+    double chi_sqrd_1_cdf(double x);
 
-        size_t m_sum_controls{
-                std::accumulate(m_total_kmer_controls.begin(), m_total_kmer_controls.end(), 0ULL)};
+    static double lower_incomplete_gamma_function(double x);
 
-        size_t m_sum_cases{std::accumulate(m_total_kmer_cases.begin(), m_total_kmer_cases.end(), 0ULL)};
+    double poisson_prob(int k, double lambda);
 
-        size_t m_preload;
-        LogFactorialTable m_lf_table{m_preload};
-    };
+    bool bonferroni_correction(double& pvalue);
 
-} // end of namespace kmdiff
+    std::tuple<double, bool> likelihood_ratio_test(double in_sum, double out_sum);
+};

From 2cbf1b3626d9d8f339f49ca4adddcff72d5fcfd6 Mon Sep 17 00:00:00 2001
From: MyrtheW <willemsenmyrthe@gmail.com>
Date: Thu, 9 Mar 2023 16:59:26 +0100
Subject: [PATCH 034/410] k-mer distributions, debugging.

---
 metagraph/src/cli/assemble.cpp                |  19 +--
 metagraph/src/cli/config/config.cpp           |   2 +-
 .../src/common/vectors/vector_algorithm.hpp   |  68 +++------
 .../src/graph/annotated_graph_algorithm.cpp   | 141 +++++++++++++++---
 metagraph/src/graph/differential_tests.cpp    |   9 +-
 metagraph/src/graph/differential_tests.hpp    |  46 +++---
 .../src/graph/lookup_table_chisqrd_cdf.cpp    |   9 ++
 .../src/graph/lookup_table_chisqrd_cdf.hpp    |  18 +++
 8 files changed, 213 insertions(+), 99 deletions(-)
 create mode 100644 metagraph/src/graph/lookup_table_chisqrd_cdf.cpp
 create mode 100644 metagraph/src/graph/lookup_table_chisqrd_cdf.hpp

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index fa26e669d7..d2150564da 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -49,11 +49,10 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.label_mask_out_kmer_fraction = experiment.get("out_max_fraction", 0.0).asDouble();
     diff_config.label_mask_out_unitig_fraction = experiment.get("unitig_out_max_fraction", 1.0).asDouble();
     diff_config.label_mask_other_unitig_fraction = experiment.get("unitig_other_max_fraction", 1.0).asDouble();
-    // diff_config.count_kmers = experiment.get("count-kmers", 1.0).asBool(); why is this not present for add_complement?
+    diff_config.add_complement = experiment.get("forward_and_reverse", false).asBool();
+    diff_config.count_kmers = experiment.get("count_kmers", false).asBool();
     diff_config.family_wise_error_rate = experiment.get("family_wise_error_rate", 0.05).asDouble();
 
-
-
     logger->trace("Per-kmer mask in fraction:\t\t{}", diff_config.label_mask_in_kmer_fraction);
     logger->trace("Per-unitig mask in fraction:\t\t{}", diff_config.label_mask_in_unitig_fraction);
     logger->trace("Per-kmer mask out fraction:\t\t{}", diff_config.label_mask_out_kmer_fraction);
@@ -62,7 +61,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     logger->trace("Include reverse complements:\t\t{}", diff_config.add_complement);
     logger->trace("Include k-mer counts if present in the graph:\t\t{}", diff_config.count_kmers); // if node weights (k-mer count) are present in the graph and should be included in the differential assembly.
     logger->trace("Family wise error rate for the Bonferroni test:\t{}", diff_config.family_wise_error_rate);
-
+    // TODO get the folder with the annotation columns from the .json or command, because it is redundant to have to give all of them as an input. How to? For all the in_labels and out_labels, collect the folder/<filename>.column.annodbg file and push them in the list of config->infbase_annotators.
     return diff_config;
 }
 
@@ -186,8 +185,10 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             logger->trace("Running assembly: {}", exp_name);
 
+            auto filenames = (config->infbase_annotators.size() > 1) ? config->infbase_annotators : config->fnames;
+
             callback(*mask_nodes_by_label(graph_ptr,
-                                          config->fnames,
+                                          filenames,
                                           foreground_labels,
                                           background_labels,
                                           diff_config, num_threads,
@@ -219,7 +220,7 @@ int assemble(Config *config) {
 
     logger->trace("Graph loaded in {} sec", timer.elapsed());
 
-    if (config->infbase_annotators.size()) {
+    if (config->infbase_annotators.size() || files.size() > 1) {
         logger->trace("Generating masked graphs...");
         std::mutex write_mutex;
         size_t num_threads = std::max(1u, get_num_threads());
@@ -250,13 +251,13 @@ int assemble(Config *config) {
             }
         };
 
-        if (files.size() > 1) {
+        if (files.size() > 1 or config->infbase_annotators.size() > 1) { // Checks the infbase_annotators in the case that annotation columns are provided in the commandline using '-a' or '--annotator'
             config->fnames.erase(config->fnames.begin(), config->fnames.begin() + 1);
-            call_masked_graphs(graph, config, graph_callback);
+            call_masked_graphs(graph, config, graph_callback); // Does NOT support kmer-counts assembly?
         } else {
             config->infbase = files.at(0);
             auto anno_graph = initialize_annotated_dbg(graph, *config);
-            call_masked_graphs(*anno_graph, config, graph_callback);
+            call_masked_graphs(*anno_graph, config, graph_callback); // Does support kmer-counts assembly?
         }
         return 0;
     }
diff --git a/metagraph/src/cli/config/config.cpp b/metagraph/src/cli/config/config.cpp
index 88f70ae322..91d58a2a1d 100644
--- a/metagraph/src/cli/config/config.cpp
+++ b/metagraph/src/cli/config/config.cpp
@@ -598,7 +598,7 @@ Config::Config(int argc, char *argv[]) {
             || identity == CLEAN
             || identity == ASSEMBLE
             || identity == RELAX_BRWT)
-                    && fnames.size() != 1) {
+                    && (fnames.size() != 1 && assembly_config_file.empty())) {
         std::cerr << "Error: exactly one graph must be provided for this mode" << std::endl;
         print_usage_and_exit = true;
     }
diff --git a/metagraph/src/common/vectors/vector_algorithm.hpp b/metagraph/src/common/vectors/vector_algorithm.hpp
index 7329805ba7..526b32c453 100644
--- a/metagraph/src/common/vectors/vector_algorithm.hpp
+++ b/metagraph/src/common/vectors/vector_algorithm.hpp
@@ -4,16 +4,14 @@
 #include <functional>
 #include <cassert>
 #include <cstdlib>
+
 #include <sdsl/int_vector.hpp>
 #include <sdsl/select_support_scan.hpp>
 
-#include "common/vectors/bitmap.hpp"
-
 class ThreadPool;
 class bit_vector;
 class bit_vector_adaptive;
 
-typedef std::function<void(uint64_t, uint64_t)> callback_template;
 
 sdsl::bit_vector to_sdsl(const std::vector<bool> &vector);
 sdsl::bit_vector to_sdsl(const std::vector<uint8_t> &vector);
@@ -165,26 +163,15 @@ sdsl::bit_vector generate_subindex(const bit_vector &column,
 sdsl::bit_vector autocorrelate(const sdsl::bit_vector &vector, uint8_t offset);
 
 
-
-
-void call_nonzeros(const sdsl::int_vector<> &vector,
-                   uint64_t begin, uint64_t end,
-                   callback_template callback);
-
-void call_nonzeros(const sdsl::bit_vector &vector,
-                   uint64_t begin, uint64_t end,
-                   callback_template callback);
-
-void call_nonzeros(const bitmap &vector,
-                   uint64_t begin, uint64_t end,
-                   callback_template callback);
+// Call (uint64_t index, uint64_t value) for each non-zero value in [begin, end)
+template <class Callback>
+inline void call_nonzeros(const sdsl::int_vector<> &vector,
+                          uint64_t begin, uint64_t end,
+                          Callback callback);
 
 // Call (uint64_t index, uint64_t value) for each non-zero value in |vector|.
-template <class vector_type>
-void call_nonzeros(const vector_type &vector, callback_template callback);
-
-template <class vector_type>
-void call_nonzeros(const vector_type &vector, callback_template callback) {
+template <class Callback>
+void call_nonzeros(const sdsl::int_vector<> &vector, Callback callback) {
     return call_nonzeros(vector, 0, vector.size(), callback);
 }
 
@@ -246,10 +233,10 @@ class select_support_scan_offset : public select_support_scan<t_b, t_pat_len> {
     using typename select_support_scan<t_b, t_pat_len>::size_type;
 
     explicit select_support_scan_offset(const bit_vector *v = nullptr)
-          : select_support_scan<t_b, t_pat_len>::select_support_scan(v) {}
+        : select_support_scan<t_b, t_pat_len>::select_support_scan(v) {}
 
     select_support_scan_offset(const select_support_scan<t_b,t_pat_len> &ss)
-          : select_support_scan<t_b, t_pat_len>::select_support_scan(ss.m_v) {}
+        : select_support_scan<t_b, t_pat_len>::select_support_scan(ss.m_v) {}
 
     inline size_type select_offset(size_type i, size_type offset = 0) const {
         using trait = select_support_trait<t_b, t_pat_len>;
@@ -260,7 +247,7 @@ class select_support_scan_offset : public select_support_scan<t_b, t_pat_len> {
         size_type args = trait::args_in_the_first_word(*data, word_off, carry);
         if (args >= i) {
             return (word_pos << 6)
-                + trait::ith_arg_pos_in_the_first_word(*data, i, word_off, carry);
+                    + trait::ith_arg_pos_in_the_first_word(*data, i, word_off, carry);
         }
         word_pos++;
         size_type sum_args = args;
@@ -275,7 +262,7 @@ class select_support_scan_offset : public select_support_scan<t_b, t_pat_len> {
             word_pos++;
         }
         return (word_pos << 6)
-            + trait::ith_arg_pos_in_the_word(*data, i - sum_args, old_carry);
+                + trait::ith_arg_pos_in_the_word(*data, i - sum_args, old_carry);
     }
 };
 
@@ -320,8 +307,8 @@ inline bool fetch_and_unset_bit(uint64_t *v, uint64_t i, bool atomic, int mo) {
 
 inline bool fetch_bit(const uint64_t *v, uint64_t i, bool atomic, int mo) {
     return atomic
-        ? ((__atomic_load_n(&v[i >> 6], mo) >> (i & 0x3F)) & 1)
-        : ((v[i >> 6] >> (i & 0x3F)) & 1);
+            ? ((__atomic_load_n(&v[i >> 6], mo) >> (i & 0x3F)) & 1)
+            : ((v[i >> 6] >> (i & 0x3F)) & 1);
 }
 
 inline void set_bit(uint64_t *v, uint64_t i, bool atomic, int mo) {
@@ -677,9 +664,10 @@ void call_zeros(const sdsl::bit_vector &vector,
     }
 }
 
-void call_nonzeros(const sdsl::int_vector<> & vector,
+template <class Callback>
+void call_nonzeros(const sdsl::int_vector<> &vector,
                    uint64_t begin, uint64_t end,
-                   callback_template callback) {
+                   Callback callback) {
     if (begin >= end)
         return;
 
@@ -688,25 +676,25 @@ void call_nonzeros(const sdsl::int_vector<> & vector,
         case 64:
             std::for_each(reinterpret_cast<const uint64_t*>(vector.data()) + begin,
                           reinterpret_cast<const uint64_t*>(vector.data()) + end,
-                          [&](uint64_t value) { if (value) callback(i, value); ++i; });
+                          [&](auto value) { if (value) callback(i, value); ++i; });
             break;
 
         case 32:
             std::for_each(reinterpret_cast<const uint32_t*>(vector.data()) + begin,
                           reinterpret_cast<const uint32_t*>(vector.data()) + end,
-                          [&](uint64_t value) { if (value) callback(i, value); ++i; });
+                          [&](auto value) { if (value) callback(i, value); ++i; });
             break;
 
         case 16:
             std::for_each(reinterpret_cast<const uint16_t*>(vector.data()) + begin,
                           reinterpret_cast<const uint16_t*>(vector.data()) + end,
-                          [&](uint64_t value) { if (value) callback(i, value); ++i; });
+                          [&](auto value) { if (value) callback(i, value); ++i; });
             break;
 
         case 8:
             std::for_each(reinterpret_cast<const uint8_t*>(vector.data()) + begin,
                           reinterpret_cast<const uint8_t*>(vector.data()) + end,
-                          [&](uint64_t value) { if (value) callback(i, value); ++i; });
+                          [&](auto value) { if (value) callback(i, value); ++i; });
             break;
 
         default:
@@ -723,7 +711,7 @@ void call_nonzeros(const sdsl::int_vector<> & vector,
                 it = std::max(it, w * 64 / vector.width());
 
                 auto it_end = std::min(end,
-                    ((w + 1) * 64 + vector.width() - 1) / vector.width());
+                                       ((w + 1) * 64 + vector.width() - 1) / vector.width());
 
                 for (; it < it_end; ++it) {
                     if (vector[it])
@@ -733,18 +721,6 @@ void call_nonzeros(const sdsl::int_vector<> & vector,
     }
 }
 
-void call_nonzeros(const sdsl::bit_vector &vector,
-                   uint64_t begin, uint64_t end,
-                   callback_template callback){
-    call_ones(vector, begin, end, [&](uint64_t i){callback(i, 1);});     // creates a lambda function that write a custom argument for the callback
-}
-
-void call_nonzeros(const bitmap &vector,
-                   uint64_t begin, uint64_t end,
-                   callback_template callback){
-    vector.call_ones_in_range(begin, end, [&](uint64_t i){callback(i, 1);});     // alternatively, use .call_ones() without begin or end.
-}
-
 template <typename BitVector>
 inline uint64_t next1(const BitVector &v, uint64_t pos, size_t num_steps) {
     assert(pos < v.size());
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index cc0c2b1e59..99f2859619 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1,5 +1,6 @@
 #include "annotated_graph_algorithm.hpp"
 
+#include "common/utils/string_utils.hpp"
 #include "common/logger.hpp"
 #include "common/vectors/vector_algorithm.hpp"
 #include "common/vectors/bitmap.hpp"
@@ -43,10 +44,26 @@ using ValueGenerator = std::function<void(const ValueCallback&)>;
 using ColumnCallback = std::function<void(uint64_t /* offset */, const Label& /* annotation column */, const ValueGenerator&)>;
 using ColumnGenerator = std::function<void(const ColumnCallback&)>;
 
+template <typename T>
+std::string outstring(std::vector<T> out_array);
+template <typename T>
+std::string outstring(std::vector<T> out_array){
+    std::string out = "";
+    for (auto &item: out_array) { std::string x = std::to_string(item); out = out + x + ", "; }
+    return out;
+}
+
+void kmer_distribution_table(const ColumnGenerator &generate_columns,
+                             const tsl::hopscotch_set<Label> &labels_in,
+                             const tsl::hopscotch_set<Label> &labels_out,
+                             size_t max_index);  // For kmer distribution.
+
 std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>>
 construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
+                                  const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
+                                  const DifferentialAssemblyConfig &config,
                                   size_t max_index,
                                   size_t num_labels,
                                   size_t num_threads,
@@ -89,7 +106,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const DifferentialAssemblyConfig &config,
                     size_t num_threads);
 
-std::shared_ptr<MaskedDeBruijnGraph>
+std::shared_ptr<MaskedDeBruijnGraph> // This version of mask_nodes_by_label is for when the columns are already loaded.
 mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     const tsl::hopscotch_set<Label> &labels_in,
                     const tsl::hopscotch_set<Label> &labels_out,
@@ -99,8 +116,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     size_t num_threads,
                     size_t num_parallel_files) {
     num_parallel_files = std::min(num_threads, num_parallel_files);
-//    size_t num_in_labels = labels_in.size() + labels_in_round2.size();
-//    size_t num_out_labels = labels_out.size() + labels_out_round2.size();
+
     auto graph_ptr = std::static_pointer_cast<const DeBruijnGraph>(
         anno_graph.get_graph_ptr()
     );
@@ -142,7 +158,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                 }
             };
         },
-        labels_in, labels_out, graph_ptr->max_index(), // Myrthe: error: ‘labels_in’ is not a type
+        { }, labels_in, labels_out, config, graph_ptr->max_index(),
         std::max(labels_in.size(), labels_out.size()), num_parallel_files,
         add_complement
     );
@@ -159,7 +175,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
 }
 
 std::shared_ptr<MaskedDeBruijnGraph>
-mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this version i don't need at first
+mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this version i don't need to change at first
                     const std::vector<std::string> &files,
                     const tsl::hopscotch_set<Label> &labels_in,
                     const tsl::hopscotch_set<Label> &labels_out,
@@ -177,17 +193,32 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
 
     logger->trace("Generating initial mask");
 
-    size_t sum_widths_in; size_t sum_widths_out; // calculate the sum of column widths to derive an upper bound for the required counts vector width.
+    if (config.family_wise_error_rate==0){
+        kmer_distribution_table(
+                [&](const ColumnCallback &column_callback) {
+                        annot::ColumnCompressed<>::load_columns_and_values(files,
+                           [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
+                               column_callback(offset, label, [&](const ValueCallback &value_callback) {
+                                   call_ones(*column, [&](uint64_t i) {
+                                       value_callback(i, column_values[column->rank1(i)-1]);
+                                   });
+                               });
+                           }
+                        );
+                },
+                labels_in, labels_out, graph_ptr->max_index()
+        );
+    std::exit(EXIT_SUCCESS);
+    }
+
     auto count_vector = construct_diff_label_count_vector(
             [&](const ColumnCallback &column_callback) {
             if (config.count_kmers) {
                 annot::ColumnCompressed<>::load_columns_and_values(files,
                     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
                         column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                           if (labels_in.count(label)) sum_widths_in += std::pow(2, column_values.width());
-                           if (labels_out.count(label)) sum_widths_out += std::pow(2, column_values.width());
                             call_ones(*column, [&](uint64_t i) {
-                                value_callback(i, column_values[column->rank1(i)]);
+                                value_callback(i, column_values[column->rank1(i)-1]);
                             });
                         });
                     }
@@ -204,7 +235,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                 );
             }
         },
-        labels_in, labels_out, graph_ptr->max_index(),
+        files, labels_in, labels_out, config, graph_ptr->max_index(),
         std::max(labels_in.size(), labels_out.size()), num_parallel_files,
         add_complement
     );
@@ -337,7 +368,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     // Filter unitigs from masked graph based on filtration criteria
     logger->trace("Filtering out background");
-    if (config.count_kmers){
+    if (config.count_kmers){ // Statistical testing part when k-mer counts are included.
         const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
         sdsl::bit_vector mask = in_mask;
         auto &[in_total_kmers, out_total_kmers] = total_kmers;
@@ -508,21 +539,39 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
 std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>>
 construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
+                                  const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
+                                  const DifferentialAssemblyConfig &config,
                                   size_t max_index,
                                   size_t num_labels,
                                   size_t num_threads,
                                   bool add_out_labels_to_mask) {
     bool parallel = (num_threads > 1);
     logger->trace("Allocating mask vector");
-    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask); // TODO Myrthe: this must be increased when including annotation counts. Think about how to. If we have disk buffering then 32 bit would work.
-    //Myrthe: otherwise we might estimate a good width based on the distributions / coverage. Maximum sum of in or outlabels < max(|inlabels|,|outlabels|) * max_coverage. An option could be to equalize the coverage.
-    //Myrthe: for the statistical tests we need for each k-mer the sum of its occurance over resp the in labels and out labels. Furthermore we need the sum of all kmers in inlabels resp outlabels. Extract the coverage.
 
-//    const auto &values_fname = remove_suffix(files[i], ColumnCompressed<>::kExtension) + ColumnCompressed<>::kCountExtension;
-//    then open an ifstream to that file and read the info using int_vector<>::read_header
-//
+    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
+
+    if (config.count_kmers){ // calculate the sum of column widths to derive an upper bound for the required counts vector width.
+        size_t sum_widths_in = 0; size_t sum_widths_out = 0;
+        for (size_t i = 0; i < files.size(); ++i) {
+            const auto &values_fname = utils::remove_suffix(files[i],
+                 annot::ColumnCompressed<>::kExtension) + annot::ColumnCompressed<>::kCountExtension;
+            sdsl::int_vector_size_type size; uint8_t col_width;
+            std::ifstream in_stream;
+            sdsl::int_vector<>::read_header(size, col_width, in_stream);
+            auto label_encoder = annot::ColumnCompressed<>::read_label_encoder(files[i]);
+            for (size_t c = 0; c < label_encoder.size(); ++c) {
+                auto label = label_encoder.decode(c);
+                if (labels_in.count(label)) sum_widths_in += std::pow(2, col_width); // QUESTION; Will arithmetics be ok despite type conversions? Seems correct
+                if (labels_out.count(label)) sum_widths_out += std::pow(2, col_width);
+            }
+        }
+        width = std::min((size_t) 32, (size_t) std::log(std::max(sum_widths_in, sum_widths_out)));
+    } else{
+        width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
+    }
+
 
     sdsl::bit_vector indicator(max_index + 1, false);
 
@@ -539,7 +588,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
             // Myrthe later: For the buffered_int_vector we would have to use operators [ ] [ ], but it might be problematic if it runs out of memory and push_back would need to be used instead.
             // Myrthe later: The second problem with buffered_int_vector might be that it is expensive, because we do random access on it. Instead a solution might be to use a piority queue of L columns, storing the smallest element as a triple (column , index, value), such that always k-mers in row x are processed before row x + 1.
 
-    size_t in_total_kmers; size_t out_total_kmers;
+    size_t in_total_kmers = 0; size_t out_total_kmers = 0;
     generate_columns([&](uint64_t offset, const Label &label, const ValueGenerator &value_generator) {
         uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
         if (labels_out.count(label))
@@ -590,6 +639,62 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                            std::make_tuple(std::move(in_total_kmers), std::move(out_total_kmers)));
 }
 
+
+
+void kmer_distribution_table(const ColumnGenerator &generate_columns,
+                                  const tsl::hopscotch_set<Label> &labels_in,
+                                  const tsl::hopscotch_set<Label> &labels_out,
+                                  size_t max_index) {
+
+    logger->trace("Allocating count vector");
+    std::vector<std::vector<int>> count_matrix(max_index*2 + 2); // outer array 'max_index + 1'*2, with arrays of initially zero lenght or of max size in labels/out_labels.
+
+    logger->trace("Populating count matrix");
+    //    out_stream << "offset_sum" + std::to_string(x) << std::endl; uint64_t x = 0;
+    generate_columns([&](uint64_t offset, const Label &label, const ValueGenerator &value_generator) {
+        std::ignore = offset;// to overcome the error that 'offset' is set but not used.
+        uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
+        if (labels_out.count(label))
+            col_indicator |= 2;
+
+        ValueCallback add_in = [&](Column i, uint64_t value) {
+            count_matrix[i * 2].push_back(value);
+        };
+
+        ValueCallback add_out = [&](Column i, uint64_t value) {
+            count_matrix[i * 2 +1 ].push_back(value);
+        };
+
+        ValueCallback add_both = [&](Column i, uint64_t value) { // theoretically a label should not be contained in both the in- and out-labels, but this is just in case.
+            count_matrix[i * 2].push_back(value);
+            count_matrix[i * 2 +1 ].push_back(value);
+
+        };
+
+        switch (col_indicator) {
+            case 1: { value_generator(add_in); } break;
+            case 2: { value_generator(add_out); } break;
+            case 3: { value_generator(add_both); } break;
+            default: {}
+        }
+    });
+
+    logger->trace("done");
+
+    std::ofstream out_stream("kmer_dist_table.py");
+    out_stream << "count_matrix_in = [" ;
+    for (size_t i = 0; i < count_matrix.size(); i+=2){
+        out_stream << "[" + outstring(count_matrix[i]) << "]" <<std::endl;
+    }
+    out_stream << "]" << std::endl;
+    out_stream << "count_matrix_out = [" ;
+    for (size_t i = 1; i < count_matrix.size(); i+=2){
+        out_stream << "[" + outstring(count_matrix[i]) << "]" <<std::endl;
+    }
+    out_stream << "]"<< std::endl;
+    out_stream.close();
+}
+
 void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
                                    const GetKeptIntervals &get_kept_intervals,
                                    size_t num_threads) {
diff --git a/metagraph/src/graph/differential_tests.cpp b/metagraph/src/graph/differential_tests.cpp
index 4110f25f27..c089e87691 100644
--- a/metagraph/src/graph/differential_tests.cpp
+++ b/metagraph/src/graph/differential_tests.cpp
@@ -1,9 +1,12 @@
-#include <cassert>
 #include <math.h>
+
 #include "differential_tests.hpp"
+#include "lookup_table_chisqrd_cdf.cpp"
+
 
 // https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/log_factorial_table.hpp
 // cpp https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/log_factorial_table.cpp
+namespace mtg{
 LogFactorialTable::LogFactorialTable(size_t size)
         : m_size(size)
 {
@@ -34,7 +37,7 @@ DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_h
 {}
 
 
-//double DifferentialTest::chi_sqrd_1_cdf(double x)// DOF = 1
+//double DifferentialTest::chi_sqrd_1_cdf(double x)// DOF = 1 // https://www.codeproject.com/Articles/432194/How-to-Calculate-the-Chi-Squared-P-Value
 //{
 //    assert(x > 0); // assert that it is positive.
 //    double igf = lower_incomplete_gamma_function(x/0.5);
@@ -88,6 +91,7 @@ std::tuple<double, bool> DifferentialTest::likelihood_ratio_test(double in_sum,
     double likelihood_ratio = alt_hypothesis - null_hypothesis;
 
     if (likelihood_ratio < 0) likelihood_ratio = 0;
+    Chi2PLookup Chi2PLookupTable; // generated with https://github.com/MoseleyBioinformaticsLab/chi2plookup
     double p_value = Chi2PLookupTable.getPValue(2 * likelihood_ratio, 1);
 
     auto out_sum_normalized = out_sum * in_total_kmers / out_total_kmers;
@@ -98,3 +102,4 @@ std::tuple<double, bool> DifferentialTest::likelihood_ratio_test(double in_sum,
     return std::make_tuple(p_value, sign);
 }
 
+} // namespace mtg
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index e958430e0a..ed495f6291 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -3,50 +3,50 @@
 #include <vector>
 #include <tuple>
 
-#include "differential_tests_notes.hpp" // TODO rename file when running cmake
-
-class LogFactorialTable
-{
-public:
+namespace mtg {
+class LogFactorialTable {
+  public:
     LogFactorialTable(size_t size);
 
-    double operator[](size_t i)
-    {
-        if (i < m_size) return m_table[i];
+    double operator[](size_t i) {
+        if (i < m_size)
+            return m_table[i];
         return log_factorial(i);
     }
 
-private:
+  private:
     double log_factorial(size_t k);
 
-private:
+  private:
     std::vector<double> m_table;
     size_t m_size;
 };
 
-class DifferentialTest
-{
-private:
+class DifferentialTest {
+  private:
     size_t preload_table_size;
-    Chi2PLookup Chi2PLookupTable; // generated with https://github.com/MoseleyBioinformaticsLab/chi2plookup
-    LogFactorialTable lf_table{preload_table_size};
+    LogFactorialTable lf_table { preload_table_size };
     double family_wise_error_rate;
     size_t total_hypotheses;
     size_t out_total_kmers;
     size_t in_total_kmers;
-    double gamma = std::sqrt(/* pi */ std::atan(1)*4);
-
-public:
-    DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t preload_table_size,
-                     size_t in_total_kmers, size_t out_total_kmers);
+    double gamma = std::sqrt(/* pi */ std::atan(1) * 4);
 
-    double chi_sqrd_1_cdf(double x);
+  public:
+    DifferentialTest(double family_wise_error_rate,
+                     size_t total_hypotheses,
+                     size_t preload_table_size,
+                     size_t in_total_kmers,
+                     size_t out_total_kmers);
 
-    static double lower_incomplete_gamma_function(double x);
+    //    double chi_sqrd_1_cdf(double x);
+    //
+    //    static double lower_incomplete_gamma_function(double x);
 
     double poisson_prob(int k, double lambda);
 
-    bool bonferroni_correction(double& pvalue);
+    bool bonferroni_correction(double &pvalue);
 
     std::tuple<double, bool> likelihood_ratio_test(double in_sum, double out_sum);
 };
+} // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/lookup_table_chisqrd_cdf.cpp b/metagraph/src/graph/lookup_table_chisqrd_cdf.cpp
new file mode 100644
index 0000000000..9b06e21934
--- /dev/null
+++ b/metagraph/src/graph/lookup_table_chisqrd_cdf.cpp
@@ -0,0 +1,9 @@
+#include "lookup_table_chisqrd_cdf.hpp"
+namespace mtg{
+static double pValues_1[] = {1.0, 0.9747728793699604, 0.9643294082703201, 0.9563199039155036, 0.9495709711511051, 0.9436280222029834, 0.9382578759648362, 0.9333219882913968, 0.9287300745648026, 0.9244194121866707, 0.920344325445942, 0.9164703270093635, 0.9127706190965621, 0.9092238872176422, 0.9058128467277439, 0.9025232501776794, 0.8993431885613663, 0.8962625866214807, 0.8932728301422118, 0.8903664853411608, 0.887537083981715, 0.884778956317105, 0.8820870994622765, 0.8794570724210615, 0.8768849114533788, 0.8743670611628918, 0.8719003178766185, 0.8694817827381617, 0.8671088225520472, 0.8647790368687397, 0.8624902301359216, 0.8602403879942262, 0.8580276569875211, 0.8558503271050759, 0.8537068166869359, 0.8515956593128358, 0.8495154923650348, 0.847465047011011, 0.8454431393963031, 0.8434486628734481, 0.841480581121794, 0.839537922036432, 0.8376197722836831, 0.83572527243636, 0.833853612615066, 0.8320040285726364, 0.8301757981678541, 0.8283682381821483, 0.826580701439357, 0.824812574194006, 0.8230632737581214, 0.8213322463404729, 0.819618965075448, 0.8179229282216066, 0.8162436575123876, 0.8145806966435467, 0.8129336098837189, 0.8113019807960693, 0.8096854110603592, 0.8080835193859561, 0.8064959405073401, 0.8049223242545844, 0.8033623346920775, 0.8018156493194588, 0.8002819583293617, 0.7987609639171032, 0.7972523796379415, 0.7957559298079551, 0.7942713489449782, 0.7927983812463655, 0.7913367801006603, 0.7898863076305189, 0.788446734264471, 0.7870178383353312, 0.7855994057032555, 0.7841912294016191, 0.7827931093040512, 0.7814048518110996, 0.7800262695551269, 0.778657181122161, 0.7772974107895214, 0.775946788278143, 0.7746051485185987, 0.7732723314299123, 0.7719481817103101, 0.7706325486391387, 0.769325285889223, 0.7680262513490048, 0.766735306953841, 0.7654523185258961, 0.7641771556220947, 0.7629096913896449, 0.7616498024286804, 0.7603973686615905, 0.7591522732086494, 0.7579144022695748, 0.7566836450106742, 0.7554598934572603, 0.7542430423910372, 0.7530329892521808, 0.7518296340458492, 0.7506328792528887, 0.7494426297444964, 0.7482587927006376, 0.747081277532011, 0.7459099958053774, 0.7447448611720762, 0.7435857892995648, 0.7424326978058251, 0.7412855061964934, 0.7401441358045748, 0.7390085097326141, 0.7378785527972025, 0.7367541914757052, 0.7356353538551013, 0.734521969582836, 0.7334139698195887, 0.7323112871938647, 0.731213855758329, 0.7301216109477959, 0.7290344895388039, 0.7279524296106983, 0.7268753705081562, 0.7258032528050896, 0.7247360182698619, 0.7236736098317629, 0.7226159715486866, 0.721563048575957, 0.7205147871362552, 0.7194711344906006, 0.7184320389103389, 0.7173974496500957, 0.7163673169216576, 0.7153415918687374, 0.7143202265425928, 0.7133031738784577, 0.7122903876727578, 0.7112818225610769, 0.7102774339968441, 0.7092771782307125, 0.7082810122906027, 0.7072888939623876, 0.7063007817711877, 0.70531663496326, 0.7043364134884518, 0.7033600779832025, 0.7023875897540707, 0.7014189107617657, 0.700454003605667, 0.6994928315088128, 0.6985353583033387, 0.6975815484163532, 0.696631366856231, 0.6956847791993109, 0.6947417515769843, 0.6938022506631576, 0.6928662436620792, 0.6919336982965139, 0.6910045827962572, 0.6900788658869739, 0.6891565167793516, 0.6882375051585587, 0.6873218011739946, 0.6864093754293241, 0.6855001989727865, 0.6845942432877679, 0.6836914802836315, 0.6827918822867933, 0.6818954220320397, 0.6810020726540733, 0.680111807679288, 0.6792246010177547, 0.6783404269554227, 0.6774592601465197, 0.6765810756061525, 0.6757058487030952, 0.6748335551527644, 0.6739641710103708, 0.6730976726642468, 0.6722340368293405, 0.6713732405408727, 0.6705152611481515, 0.6696600763085419, 0.668807663981581, 0.6679580024232398, 0.6671110701803225, 0.6662668460850041, 0.6654253092494974, 0.6645864390608476, 0.6637502151758536, 0.6629166175161068, 0.6620856262631476, 0.6612572218537375, 0.6604313849752381, 0.6596080965611015, 0.6587873377864597, 0.6579690900638204, 0.6571533350388584, 0.6563400545863027, 0.6555292308059188, 0.6547208460185768, 0.653914882762415, 0.6531113237890807, 0.65231015206006, 0.6515113507430879, 0.6507149032086343, 0.6499207930264701, 0.6491290039623059, 0.6483395199745046, 0.6475523252108636, 0.6467674040054683, 0.6459847408756088, 0.6452043205187664, 0.6444261278096608, 0.6436501477973608, 0.642876365702454, 0.6421047669142776, 0.6413353369882051, 0.6405680616429894, 0.63980292675816, 0.639039918371475, 0.6382790226764223, 0.6375202260197754, 0.6367635148991933, 0.6360088759608732, 0.6352562959972483, 0.634505761944731, 0.6337572608815012, 0.6330107800253391, 0.6322663067314984, 0.6315238284906244, 0.6307833329267092, 0.6300448077950888, 0.6293082409804776, 0.6285736204950417, 0.6278409344765085, 0.6271101711863123, 0.6263813190077749, 0.6256543664443213, 0.624929302117728, 0.624206114766406, 0.623484793243712, 0.6227653265162957, 0.6220477036624747, 0.6213319138706397, 0.6206179464376897, 0.6199057907674952, 0.6191954363693901, 0.6184868728566901, 0.6177800899452376, 0.6170750774519739, 0.6163718252935367, 0.6156703234848826, 0.6149705621379322, 0.6142725314602424, 0.6135762217536993, 0.6128816234132366, 0.6121887269255734, 0.6114975228679764, 0.610808001907044, 0.6101201547975077, 0.6094339723810585, 0.6087494455851904, 0.608066565422066, 0.6073853229874002, 0.6067057094593613, 0.6060277160974936, 0.6053513342416564, 0.604676555310981, 0.6040033708028456, 0.6033317722918664, 0.6026617514289063, 0.6019932999400995, 0.6013264096258926, 0.6006610723600998, 0.5999972800889765, 0.5993350248303055, 0.598674298672498, 0.5980150937737119, 0.59735740236098, 0.5967012167293563, 0.5960465292410733, 0.5953933323247131, 0.5947416184743941, 0.5940913802489658, 0.5934426102712209, 0.5927953012271178, 0.5921494458650145, 0.5915050369949164, 0.5908620674877341, 0.5902205302745531, 0.5895804183459157, 0.5889417247511126, 0.5883044425974859, 0.5876685650497439, 0.5870340853292839, 0.5864009967135273, 0.5857692925352638, 0.5851389661820058, 0.5845100110953532, 0.583882420770365, 0.5832561887549448, 0.5826313086492311, 0.582007774104998, 0.5813855788250666, 0.5807647165627225, 0.5801451811211438, 0.5795269663528356, 0.5789100661590744, 0.5782944744893597, 0.5776801853408733, 0.5770671927579482, 0.5764554908315416, 0.5758450736987198, 0.575235935542147, 0.574628070589583, 0.5740214731133877, 0.5734161374300326, 0.5728120578996192, 0.5722092289254042, 0.5716076449533314, 0.57100730047157, 0.5704081900100593, 0.5698103081400598, 0.5692136494737109, 0.5686182086635944, 0.568023980402303, 0.5674309594220168, 0.5668391404940838, 0.5662485184286077, 0.5656590880740384, 0.5650708443167728, 0.5644837820807553, 0.5638978963270893, 0.5633131820536491, 0.5627296342947006, 0.5621472481205246, 0.5615660186370466, 0.5609859409854712, 0.5604070103419199, 0.5598292219170768, 0.559252570955836, 0.5586770527369542, 0.5581026625727092, 0.5575293958085623, 0.5569572478228233, 0.5563862140263232, 0.5558162898620871, 0.5552474708050159, 0.5546797523615676, 0.5541131300694455, 0.5535475994972903, 0.5529831562443737, 0.5524197959402998, 0.5518575142447062, 0.5512963068469718, 0.5507361694659273, 0.5501770978495684, 0.5496190877747748, 0.54906213504703, 0.5485062355001471, 0.5479513849959966, 0.5473975794242372, 0.5468448147020512, 0.5462930867738827, 0.5457423916111782, 0.5451927252121317, 0.5446440836014315, 0.5440964628300113, 0.5435498589748047, 0.5430042681384988, 0.542459686449298, 0.5419161100606831, 0.541373535151178, 0.540831957924118, 0.5402913746074198, 0.5397517814533561, 0.5392131747383311, 0.5386755507626598, 0.5381389058503502, 0.537603236348886, 0.5370685386290148, 0.5365348090845363, 0.5360020441320945, 0.5354702402109708, 0.5349393937828826, 0.5344095013317793, 0.5338805593636464, 0.5333525644063062, 0.5328255130092256, 0.532299401743323, 0.5317742272007789, 0.5312499859948483, 0.5307266747596739, 0.5302042901501056, 0.5296828288415154, 0.5291622875296206, 0.5286426629303063, 0.5281239517794494, 0.5276061508327456, 0.5270892568655381, 0.5265732666726477, 0.526058177068206, 0.5255439848854889, 0.5250306869767538, 0.5245182802130763, 0.5240067614841899, 0.5234961276983292, 0.5229863757820714, 0.5224775026801816, 0.5219695053554594, 0.5214623807885884, 0.5209561259779845, 0.5204507379396484, 0.5199462137070181, 0.5194425503308249, 0.5189397448789479, 0.5184377944362738, 0.517936696104555, 0.5174364470022705, 0.5169370442644899, 0.5164384850427345, 0.515940766504845, 0.515443885834847, 0.5149478402328195, 0.5144526269147638, 0.5139582431124757, 0.5134646860734162, 0.5129719530605862, 0.5124800413524004, 0.5119889482425648, 0.5114986710399525, 0.5110092070684835, 0.5105205536670057, 0.5100327081891727, 0.5095456680033307, 0.5090594304923981, 0.5085739930537536, 0.5080893530991198, 0.5076055080544517, 0.507122455359825, 0.5066401924693249, 0.5061587168509367, 0.5056780259864386, 0.5051981173712928, 0.5047189885145416, 0.5042406369386991, 0.5037630601796503, 0.5032862557865465, 0.502810221321704, 0.502334954360502, 0.501860452491284, 0.5013867133152582, 0.5009137344463999, 0.5004415135113534, 0.499970048149337, 0.4994993360120481, 0.499029374763568, 0.4985601620802692, 0.4980916956507231, 0.49762397317560747, 0.4971569923676181, 0.4966907509513767, 0.49622524666334356, 0.49576047725172934, 0.49529644047640653, 0.49483313410882623, 0.4943705559319288, 0.49390870374006235, 0.49344757533889727, 0.4929871685453435, 0.4925274811874687, 0.4920685111044161, 0.49161025614632403, 0.49115271417424644, 0.4906958830600725, 0.4902397606864498, 0.489784344946705, 0.48932963374476834, 0.4888756249950953, 0.4884223166225936, 0.4879697065625459, 0.4875177927605382, 0.48706657317238333, 0.4866160457640505, 0.4861662085115922, 0.485717059401073, 0.4852685964284983, 0.4848208175997448, 0.4843737209304906, 0.48392730444614596, 0.4834815661817856, 0.48303650418208144, 0.4825921165012337, 0.4821484012029069, 0.48170535636016176, 0.48126298005539236, 0.4808212703802591, 0.4803802254356262, 0.4799398433314973, 0.47950012218695337, 0.47906106013008976, 0.47862265529795445, 0.47818490583648665, 0.47774780990045673, 0.47731136565340504, 0.47687557126758306, 0.47644042492389405, 0.4760059248118339, 0.4755720691294345, 0.47513885608320383, 0.47470628388807123, 0.4742743507673296, 0.4738430549525787, 0.473412394683671, 0.47298236820865647, 0.47255297378372574, 0.4721242096731584, 0.47169607414926906, 0.4712685654923524, 0.4708416819906315, 0.4704154219402058, 0.46998978364499866, 0.46956476541670644, 0.46914036557474637, 0.46871658244620695, 0.4682934143657984, 0.467870859675801, 0.46744891672601696, 0.46702758387372245, 0.4666068594836176, 0.4661867419277784, 0.4657672295856101, 0.4653483208438004, 0.46493001409626933, 0.46451230774412733, 0.46409520019562545, 0.46367868986611194, 0.46326277517798564, 0.4628474545606517, 0.4624327264504763, 0.46201858929074324, 0.46160504153160964, 0.46119208163006264, 0.460779708049876, 0.4603679192615673, 0.459956713742356, 0.459546089976121, 0.4591360464533588, 0.45872658167114233, 0.45831769413307955, 0.45790938234927314, 0.45750164483627975, 0.4570944801170703, 0.4566878867209898, 0.4562818631837181, 0.4558764080472305, 0.4554715198597594, 0.45506719717575617, 0.45466343855585156, 0.45426024256681874, 0.45385760778153605, 0.45345553277894846, 0.45305401614403285, 0.4526530564677582, 0.4522526523470516, 0.45185280238476144, 0.45145350518962113, 0.4510547593762142, 0.45065656356493844, 0.4502589163819718, 0.4498618164592356, 0.4494652624343629, 0.44906925295066213, 0.4486737866570849, 0.4482788622081898, 0.44788447826411193, 0.4474906334905274, 0.44709732655862244, 0.44670455614505866, 0.4463123209319426, 0.4459206196067923, 0.4455294508625064, 0.4451388133973311, 0.4447487059148302, 0.44435912712385295, 0.44397007573850433, 0.44358155047811265, 0.44319355006719985, 0.44280607323545274, 0.4424191187176897, 0.4420326852538343, 0.44164677158888443, 0.44126137647288155, 0.44087649866088363, 0.4404921369129361, 0.4401082899940416, 0.43972495667413236, 0.43934213572804304, 0.4389598259354811, 0.4385780260809997, 0.43819673495397016, 0.437815951348554, 0.43743567406367634, 0.43705590190299826, 0.43667663367489096, 0.4362978681924081, 0.43591960427325926, 0.4355418407397855, 0.4351645764189308, 0.43478781014221823, 0.4344115407457234, 0.4340357670700489, 0.4336604879602991, 0.43328570226605523, 0.43291140884135126, 0.43253760654464657, 0.4321642942388051, 0.4317914707910663, 0.43141913507302665, 0.43104728596061004, 0.43067592233404806, 0.43030504307785333, 0.42993464708079854, 0.4295647332358904, 0.42919530044034926, 0.4288263475955838, 0.4284578736071697, 0.42808987738482585, 0.4277223578423922, 0.4273553138978079, 0.4269887444730879, 0.42662264849430265, 0.4262570248915539, 0.4258918725989552, 0.4255271905546091, 0.42516297770058475, 0.42479923298289923, 0.4244359553514947, 0.4240731437602162, 0.4237107971667936, 0.4233489145328181, 0.4229874948237248, 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5.941923985064435e-07, 5.938842717601034e-07, 5.935763051079235e-07, 5.93268498660926e-07, 5.929608523080887e-07, 5.926533658273669e-07, 5.923460392187607e-07, 5.920388723712477e-07, 5.91731865284828e-07, 5.914250177374569e-07, 5.911183298401568e-07, 5.908118012598607e-07, 5.905054322186132e-07, 5.901992222723251e-07, 5.898931716430411e-07, 5.895872801087165e-07, 5.892815475583291e-07, 5.88975974102901e-07, 5.886705594093655e-07, 5.883653034777225e-07, 5.880602063079721e-07, 5.877552676780695e-07, 5.874504876990372e-07, 5.871458661488305e-07, 5.868414029164271e-07, 5.86537098001827e-07, 5.862329512940079e-07, 5.859289626819475e-07, 5.856251321656458e-07, 5.853214596340806e-07, 5.850179449762294e-07, 5.847145879700477e-07, 5.844113888375801e-07, 5.841083472457598e-07, 5.838054633056089e-07, 5.835027367950829e-07, 5.832001676031595e-07, 5.828977557298387e-07, 5.825955010640982e-07, 5.82293403605938e-07, 5.819914632443357e-07, 5.816896797572468e-07, 5.813880531446713e-07, 5.810865834066092e-07, 5.807852704320382e-07, 5.804841139989136e-07, 5.801831142182579e-07, 5.798822708680262e-07, 5.795815839482188e-07, 5.792810533478132e-07, 5.789806790668095e-07, 5.78680460883163e-07, 5.783803986858516e-07, 5.780804925858973e-07, 5.777807423612558e-07, 5.774811480119268e-07, 5.77181709315866e-07, 5.768824263840955e-07, 5.765832989945707e-07, 5.762843271472917e-07, 5.759855107312362e-07, 5.756868496353817e-07, 5.753883438597285e-07, 5.750899931822318e-07, 5.747917977139139e-07, 5.744937572327302e-07, 5.741958716276585e-07, 5.738981408986987e-07, 5.736005649348286e-07, 5.733031437360481e-07};
+
+const double * Chi2PLookup::pValues[] = {pValues_1};
+const int Chi2PLookup::divisor = 1000;
+const int Chi2PLookup::cutoff[] = {25};
+
+} // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/lookup_table_chisqrd_cdf.hpp b/metagraph/src/graph/lookup_table_chisqrd_cdf.hpp
new file mode 100644
index 0000000000..eb5fb3e532
--- /dev/null
+++ b/metagraph/src/graph/lookup_table_chisqrd_cdf.hpp
@@ -0,0 +1,18 @@
+#ifndef CHI2PLOOKUP_H
+#define CHI2PLOOKUP_H
+
+namespace mtg{
+struct Chi2PLookup
+{
+    static const double * pValues[];
+    static const int cutoff[];
+    static const int divisor;
+
+    inline double getPValue(double statistic, int df) {
+        return((statistic >= cutoff[df-1]) ? 0.0 : pValues[df-1][int(divisor * statistic)]);
+    }
+
+};
+} // namespace mtg
+#endif // CHI2PLOOKUP_H
+

From e42a16de41bc9e6ecb85ea1bff7a6fd0c13528d0 Mon Sep 17 00:00:00 2001
From: MyrtheW <willemsenmyrthe@gmail.com>
Date: Mon, 13 Mar 2023 15:46:09 +0100
Subject: [PATCH 035/410] k-mer distributions --> k=15, count_matrix is empty

---
 metagraph/experiments/kmer_distributions.py   | 63 ++++++++++++++++
 .../annotate_column_compressed.cpp            |  3 +
 .../src/graph/annotated_graph_algorithm.cpp   | 74 ++++++++++++++++---
 metagraph/src/graph/differential_tests.cpp    | 20 ++++-
 4 files changed, 147 insertions(+), 13 deletions(-)
 create mode 100644 metagraph/experiments/kmer_distributions.py

diff --git a/metagraph/experiments/kmer_distributions.py b/metagraph/experiments/kmer_distributions.py
new file mode 100644
index 0000000000..dad47927d2
--- /dev/null
+++ b/metagraph/experiments/kmer_distributions.py
@@ -0,0 +1,63 @@
+import matplotlib.pyplot as plt
+import importlib
+import scipy
+# plot histograms
+
+# statistical tests whether they are poisson?
+# import the python file from build/
+def evaluate_cpp_probability_distribution(dir):
+    i = importlib.import_module(dir + "_distribution")
+    i.results_exhaustive
+
+def statistical_test(kmer_counts_row):
+    result = scipy.stats.kstest(kmer_counts_row, "poisson")
+    return result.pvalue, result.sign
+
+def plot_distribution(kmer_counts_row, title):
+    n, bins, patches = plt.hist(x=kmer_counts_row, bins='auto', color='#0504aa',
+                                alpha=0.7, rwidth=0.85)
+    plt.grid(axis='y', alpha=0.75)
+    plt.xlabel("K-mer's occurance")
+    plt.ylabel('Frequency')
+    plt.title(title)
+    pvalue, sign = statistical_test(kmer_counts_row)
+    plt.suptitle("Kolmogorov-Smirnov: p-value "+ pvalue + ", sign: " + sign)
+    plt.savefig(dir.replace(".", "\\") + "_dist.png")
+    plt.show()
+
+print("start")
+folder = "C:/Users/myrth/Desktop/coding/metagraph/metagraph/build/"
+
+i = importlib.import_module(folder + "kmer_dist_table.py")
+counts_matrix = i.counts_matrix_in
+counts_matrix = i.counts_matrix_out
+
+random_rows = [2, 18, 20, 300, 240]# only sample even numbers
+for i in random_rows:
+    plot_distribution(counts_matrix_in[i], "in labels") # for in_labels
+    plot_distribution(counts_matrix_out[i], "out labels") # for in_labels
+    plot_distribution(counts_matrix_in[i] + counts_matrix_out[i], "union of in and out labels") # for in_labels
+
+
+
+# read in the .fai file, and select the hepatitis.  'C:/Users/myrth/Desktop/coding/diff_assembly_counts/split/virus.shuf1.100.fa.fai'
+#     f = open("C:/Users/myrth/Desktop/coding/metagraph/metagraph/build/virus_data/virus.shuf1.100.fa.fai","r")
+#     lines = f.readlines()
+#     labels_in = []
+#     labels_out = []
+#
+#     for line in lines:
+#         line = line.replace(",", "")
+#         line_list = line.split("|")
+#         #line_list = line_list.split("\t")
+#         filename = line_list[3]
+#         tag = line_list[4]
+#         if tag[:8] == "Hepatitis"[:8]:
+#             labels_in.append(filename + ".fa")
+#         else:
+#             labels_out.append(filename + ".fa")
+#     print("\", \"".join(labels_in))
+#     print("\", \"".join(labels_out))
+#
+
+# Once you have established this, the next question is whether the distribution is homogeneous or not. This means whether all parts of the data are handled by the same poisson distribution or is there a variation in this based on some aspect like time or space. Once you have convinced of these aspects, try the following three tests
\ No newline at end of file
diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
index 873bcca1cb..efd6695bd4 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
@@ -811,6 +811,9 @@ ::load_columns_and_values(const std::vector<std::string> &filenames,
                 if (column_values.size() != column->num_set_bits())
                     throw std::ifstream::failure("inconsistent size of the value vector");
 
+//                 for (auto &item: column_values) { std::string out_string = std::to_string(item); out = out + x + ", "; }
+//                    std::cout << out_string << ' ' << std::endl; }// Myrthe: temporary for debugging
+
                 callback(offsets[i] + c,
                          label_encoder_load.decode(c),
                          std::move(column), std::move(column_values));
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 99f2859619..2dd5214ab2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -374,7 +374,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         auto &[in_total_kmers, out_total_kmers] = total_kmers;
         auto total_hypotheses = counts.size()/2; // the total number of hypotheses tested.
         auto test_model = DifferentialTest(config.family_wise_error_rate, total_hypotheses,
-                                           counts.width(), in_total_kmers, out_total_kmers); // similar to the width of the counts vector, the size size of the log-factorial table should be the maximum joint coverage over the in_labels resp. out_labels
+                                           std::pow(2, counts.width()), in_total_kmers, out_total_kmers); // similar to the width of the counts vector, the size of the log-factorial table should be the maximum joint coverage over the in_labels resp. out_labels. Convert the width from bits to the number of stored values.
         size_t kept_nodes = 0;
         call_ones(in_mask, [&](node_index node) {
             uint64_t in_sum = counts[node * 2];
@@ -387,9 +387,16 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         });
 
+        masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+
+        size_t total_nodes = masked_graph->num_nodes();
+        logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes); // TODO this tracer is never printed and the breakpoints are never hit.
+
+        return masked_graph;
     }
 
 
+
     size_t min_label_in_count = std::ceil(config.label_mask_in_kmer_fraction
                                     * num_in_labels);
     size_t max_label_out_count = std::floor(config.label_mask_out_kmer_fraction
@@ -557,9 +564,11 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
         for (size_t i = 0; i < files.size(); ++i) {
             const auto &values_fname = utils::remove_suffix(files[i],
                  annot::ColumnCompressed<>::kExtension) + annot::ColumnCompressed<>::kCountExtension;
-            sdsl::int_vector_size_type size; uint8_t col_width;
+            sdsl::int_vector_size_type col_size; uint8_t col_width;
             std::ifstream in_stream;
-            sdsl::int_vector<>::read_header(size, col_width, in_stream);
+            sdsl::int_vector<>::read_header(col_size, col_width, in_stream);
+            logger->trace("colwidth");
+            std::cout << std::to_string(col_width) << std::endl;
             auto label_encoder = annot::ColumnCompressed<>::read_label_encoder(files[i]);
             for (size_t c = 0; c < label_encoder.size(); ++c) {
                 auto label = label_encoder.decode(c);
@@ -567,7 +576,9 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                 if (labels_out.count(label)) sum_widths_out += std::pow(2, col_width);
             }
         }
-        width = std::min((size_t) 32, (size_t) std::log(std::max(sum_widths_in, sum_widths_out)));
+        std::cout << std::to_string(sum_widths_in) << std::endl;
+        std::cout << std::to_string(sum_widths_out) << std::endl;
+        width = std::min((size_t) 32, (size_t) std::ceil(std::log(std::max(sum_widths_in, sum_widths_out)))); // TODO, check if this is really correct. this should be 8.5 -> 9 rather then 7.
     } else{
         width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
     }
@@ -662,12 +673,12 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
         };
 
         ValueCallback add_out = [&](Column i, uint64_t value) {
-            count_matrix[i * 2 +1 ].push_back(value);
+            count_matrix[i * 2 + 1].push_back(value);
         };
 
         ValueCallback add_both = [&](Column i, uint64_t value) { // theoretically a label should not be contained in both the in- and out-labels, but this is just in case.
             count_matrix[i * 2].push_back(value);
-            count_matrix[i * 2 +1 ].push_back(value);
+            count_matrix[i * 2 + 1].push_back(value);
 
         };
 
@@ -679,17 +690,33 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
         }
     });
 
-    logger->trace("done");
+    int matrix_sum = 0;
+    for (size_t i = 0; i < count_matrix.size(); i+=2){
+        matrix_sum += std::accumulate(count_matrix[i].begin(), count_matrix[i].end(), 0);
+    }
+    std::cout << "matrix sum ";
+    std::cout << matrix_sum;
+    logger->trace("Writing k-mer count matrix to table");
 
     std::ofstream out_stream("kmer_dist_table.py");
     out_stream << "count_matrix_in = [" ;
-    for (size_t i = 0; i < count_matrix.size(); i+=2){
-        out_stream << "[" + outstring(count_matrix[i]) << "]" <<std::endl;
+    for (size_t i = 0; i < std::min(count_matrix.size(), (size_t) 50000000); i+=2){
+        if (count_matrix[i+1].size() > 3 or count_matrix[i].size() > 3){ // labels_out.size()/3
+                std::vector<int> zeros_vector(labels_in.size()-count_matrix[i].size(), 0);
+                count_matrix[i].insert(count_matrix[i].end(), zeros_vector.begin(), zeros_vector.end());
+                out_stream << "[" + outstring(count_matrix[i]) << "]," <<std::endl;
+            }
     }
     out_stream << "]" << std::endl;
     out_stream << "count_matrix_out = [" ;
-    for (size_t i = 1; i < count_matrix.size(); i+=2){
-        out_stream << "[" + outstring(count_matrix[i]) << "]" <<std::endl;
+    for (size_t i = 1; i < std::min(count_matrix.size(), (size_t) 50000000); i+=2){
+        if (count_matrix[i].size() > 3 or count_matrix[i-1].size() > 3){ // labels_out.size()/3
+            std::cout << std::to_string(count_matrix[i].size()) <<std::endl;
+            std::cout << std::to_string(labels_out.size()) <<std::endl;
+            std::cout << "[" + outstring(count_matrix[i]) << "]" <<std::endl;
+            std::vector<int> zeros_vector(labels_out.size()-count_matrix[i].size(), 0);
+            count_matrix[i].insert(count_matrix[i].end(), zeros_vector.begin(), zeros_vector.end());
+            out_stream << "[" + outstring(count_matrix[i]) << "]," <<std::endl;        }
     }
     out_stream << "]"<< std::endl;
     out_stream.close();
@@ -697,7 +724,7 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
 
 void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
                                    const GetKeptIntervals &get_kept_intervals,
-                                   size_t num_threads) {
+                                   size_t num_threads) { // traverses unitigs and takes a callback that does the filtering.
     std::atomic<uint64_t> kept_unitigs(0);
     std::atomic<uint64_t> total_unitigs(0);
     std::atomic<uint64_t> num_kept_nodes(0);
@@ -738,6 +765,29 @@ void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
 }
 
 
+// TODO Myrthe:  filtering by unitig Myrthe todo
+//update_masked_graph_by_unitig(*masked_graph,
+//        [&](const std::string &, const std::vector<node_index> &path) -> Intervals { // Takes a path. Count vector per path.
+//            // return a set of intervals to keep in the graph
+//            size_t in_kmer_count = 0;
+//
+//            size_t begin = path.size();
+//            size_t end = 0;
+//            for (size_t i = 0; i < path.size(); ++i) { // first strategy is summing counts per unitig. QUESTION: where is the count vector created, such that iunitig k-mers can be accessed sequentially?
+//                in_kmer_count_unitig += counts[path[i] * 2];  // QUESTION Do more efficient std::accumulate for sdsl?
+//                out_kmer_count_unitig += counts[path[i] * 2 + 1];
+//            }
+//
+//            if model.likelihood_ratio_test(in_kmer_count_unitig, out_kmer_count_unitig) // todo can the model be accessed from the outside? Initiate total_hypotheses as number of unitigs.
+//                return { std::make_pair(begin, end) };
+//            else
+//                return {};
+//        },
+//        num_threads
+//);
+//
 
 } // namespace graph
 } // namespace mtg
+
+
diff --git a/metagraph/src/graph/differential_tests.cpp b/metagraph/src/graph/differential_tests.cpp
index c089e87691..2f30707f3c 100644
--- a/metagraph/src/graph/differential_tests.cpp
+++ b/metagraph/src/graph/differential_tests.cpp
@@ -70,9 +70,27 @@ double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipe
 // family-wise error rate (FWER)
 bool DifferentialTest::bonferroni_correction(double& pvalue) //  &family_wise_error_rate, std::size_t & total_hypotheses, --> can also be class parameters.
 {
-    return pvalue < (family_wise_error_rate / total_hypotheses);
+    return pvalue < (family_wise_error_rate / total_hypotheses); // TODO store "family_wise_error_rate / total_hypotheses" as a class variable, corrected_pvalue_threshold (2.4e-8)
 }
 
+
+//size_t lrt_threshold(double corrected_pvalue){
+//    auto & array = ;
+//    int low = 0;
+//    int high = array.size()-1;
+//    while (low <= high) {
+//        int mid = (low + high) >> 1;
+//        if (std::get<0>(array[mid]) < kmer_count)
+//        {low = mid + 1;}
+//        else if (std::get<0>(array[mid]) > kmer_count)
+//        {high = mid - 1;}
+//        else if (std::get<0>(array[mid]) == kmer_count)
+//        {return std::get<1>(array[mid]);} // exact kmer_count found
+//    }
+//    return std::get<1>(array[low]);
+//}
+
+
 // adapted code from kmdiff.
 // previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
 std::tuple<double, bool> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)

From cbe72a732599929259770c852241f2fc052f5040 Mon Sep 17 00:00:00 2001
From: MyrtheW <willemsenmyrthe@gmail.com>
Date: Mon, 13 Mar 2023 16:51:18 +0100
Subject: [PATCH 036/410] k-mer distributions --> k=15, count_matrix and count
 vector empty

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 2dd5214ab2..3572d2057c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -217,6 +217,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                 annot::ColumnCompressed<>::load_columns_and_values(files,
                     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
                         column_callback(offset, label, [&](const ValueCallback &value_callback) {
+                            assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0);
                             call_ones(*column, [&](uint64_t i) {
                                 value_callback(i, column_values[column->rank1(i)-1]);
                             });
@@ -374,7 +375,10 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         auto &[in_total_kmers, out_total_kmers] = total_kmers;
         auto total_hypotheses = counts.size()/2; // the total number of hypotheses tested.
         auto test_model = DifferentialTest(config.family_wise_error_rate, total_hypotheses,
-                                           std::pow(2, counts.width()), in_total_kmers, out_total_kmers); // similar to the width of the counts vector, the size of the log-factorial table should be the maximum joint coverage over the in_labels resp. out_labels. Convert the width from bits to the number of stored values.
+                                           std::min((int) std::distance(counts.begin(),
+                                                                       std::max_element(counts.begin(), counts.end())), (int) 1000), in_total_kmers, out_total_kmers); // similar to the width of the counts vector, the size of the log-factorial table should be the maximum joint coverage over the in_labels resp. out_labels. Convert the width from bits to the number of stored values.
+        // std::min(std::pow(2, counts.width()), 10000) TODO Myrthe: limit the power of the width by some reasonable number, that is lower than 4,294,967,296, the current maximum, i.e. (2^32) or calculate it by the maximum of the counts vector.
+
         size_t kept_nodes = 0;
         call_ones(in_mask, [&](node_index node) {
             uint64_t in_sum = counts[node * 2];
@@ -560,6 +564,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
     size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
 
     if (config.count_kmers){ // calculate the sum of column widths to derive an upper bound for the required counts vector width.
+        assert(labels_in.size() > 0);
         size_t sum_widths_in = 0; size_t sum_widths_out = 0;
         for (size_t i = 0; i < files.size(); ++i) {
             const auto &values_fname = utils::remove_suffix(files[i],
@@ -643,6 +648,8 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
         }
     });
 
+    assert( std::accumulate(counts.begin(), counts.end(), 0) > 0); // assert that the sum of the count vector is greater than 0
+
     std::atomic_thread_fence(std::memory_order_acquire);
     logger->trace("done");
 
@@ -694,6 +701,8 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
     for (size_t i = 0; i < count_matrix.size(); i+=2){
         matrix_sum += std::accumulate(count_matrix[i].begin(), count_matrix[i].end(), 0);
     }
+    assert(matrix_sum > 0);
+
     std::cout << "matrix sum ";
     std::cout << matrix_sum;
     logger->trace("Writing k-mer count matrix to table");

From b282d7b3c14a3c82254f575c00a39e510e41391f Mon Sep 17 00:00:00 2001
From: MyrtheW <willemsenmyrthe@gmail.com>
Date: Tue, 14 Mar 2023 09:35:27 +0100
Subject: [PATCH 037/410] k-mer distributions --> k=15, count_matrix and count
 vector empty assertion statements.

---
 .../src/graph/annotated_graph_algorithm.cpp   | 167 ++++++++++--------
 1 file changed, 92 insertions(+), 75 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 3572d2057c..51f00b4f66 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -217,8 +217,12 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                 annot::ColumnCompressed<>::load_columns_and_values(files,
                     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
                         column_callback(offset, label, [&](const ValueCallback &value_callback) {
+                            std::cout << "test column values ";
                             assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0);
+                            assert(column->num_set_bits() > 0);
+                            assert(column->num_set_bits() == column_values.size());
                             call_ones(*column, [&](uint64_t i) {
+                                std::cout << "add to count vector ";
                                 value_callback(i, column_values[column->rank1(i)-1]);
                             });
                         });
@@ -228,7 +232,10 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                 annot::ColumnCompressed<>::merge_load(files,
                     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column) {
                         column_callback(offset, label, [&](const ValueCallback &value_callback) {
+                            assert(column->num_set_bits() > 0);
+                            std::cout << "test columns ";
                             call_ones(*column, [&](uint64_t i) {
+                                std::cout << "add to count vector ";
                                 value_callback(i, 1);
                             });
                         });
@@ -307,13 +314,12 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     size_t num_out_labels = labels_out.size() + labels_out_round2.size();
 
     bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
-        && (config.add_complement || unitig_mode);
+            && (config.add_complement || unitig_mode);
 
     auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
                                                   add_complement, num_threads);
 
-    auto mask_or = [&](sdsl::bit_vector &a,
-                       const sdsl::bit_vector &b,
+    auto mask_or = [&](sdsl::bit_vector &a, const sdsl::bit_vector &b,
                        const std::vector<node_index> &id_map) {
         call_ones(b, [&](size_t i) {
             if (id_map[i])
@@ -324,43 +330,46 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     // check all other labels and round 2 labels
     if (anno_graph && (check_other || labels_in_round2.size() || labels_out_round2.size())) {
         sdsl::bit_vector union_mask
-            = static_cast<const bitmap_vector&>(masked_graph->get_mask()).data();
+                = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
         std::mutex vector_backup_mutex;
         std::atomic_thread_fence(std::memory_order_release);
 
-        auto count_merge = [&](sdsl::bit_vector &a,  // Myrthe ANSWER: what does count_merge do? Is a hack, I do not have to touch this now. Count_add would be better name. If you have a dense column, and include it in foreground or background. Wate time, Instead first create mask.  You only access dense columns, for instance a reference.
-                               const sdsl::bit_vector &b,
-                               const std::vector<node_index> &id_map,
-                               size_t offset = 0) {
-            call_ones(b, [&](size_t i) {
-                if (id_map[i]) {
-                    set_bit(a.data(), id_map[i], parallel, MO_RELAXED);
-                    atomic_fetch_and_add(counts, id_map[i] * 2 + offset, 1,
-                                         vector_backup_mutex, MO_RELAXED);
-                }
-            });
-        };
+        auto count_merge
+                = [&](sdsl::bit_vector
+                              &a, // Myrthe ANSWER: what does count_merge do? Is a hack, I do not have to touch this now. Count_add would be better name. If you have a dense column, and include it in foreground or background. Wate time, Instead first create mask.  You only access dense columns, for instance a reference.
+                      const sdsl::bit_vector &b, const std::vector<node_index> &id_map,
+                      size_t offset = 0) {
+                      call_ones(b, [&](size_t i) {
+                          if (id_map[i]) {
+                              set_bit(a.data(), id_map[i], parallel, MO_RELAXED);
+                              atomic_fetch_and_add(counts, id_map[i] * 2 + offset, 1,
+                                                   vector_backup_mutex, MO_RELAXED);
+                          }
+                      });
+                  };
 
         logger->trace("Checking shared and other labels");
-        masked_graph->call_sequences([&](const std::string &contig,
-                                         const std::vector<node_index> &path) {
-            for (const auto &[label, sig] : anno_graph->get_top_label_signatures(contig, num_labels)) {
-                bool found_in = labels_in.count(label);
-                bool found_out = labels_out.count(label);
-                bool found_in_round2 = labels_in_round2.count(label);
-                bool found_out_round2 = labels_out_round2.count(label);
-                if (!found_in && !found_out
-                        && !found_in_round2 && !found_out_round2 && check_other) {
-                    mask_or(other_mask, sig, path);
-                }
+        masked_graph->call_sequences(
+                [&](const std::string &contig, const std::vector<node_index> &path) {
+                    for (const auto &[label, sig] :
+                         anno_graph->get_top_label_signatures(contig, num_labels)) {
+                        bool found_in = labels_in.count(label);
+                        bool found_out = labels_out.count(label);
+                        bool found_in_round2 = labels_in_round2.count(label);
+                        bool found_out_round2 = labels_out_round2.count(label);
+                        if (!found_in && !found_out && !found_in_round2
+                            && !found_out_round2 && check_other) {
+                            mask_or(other_mask, sig, path);
+                        }
 
-                if (found_in_round2)
-                    count_merge(union_mask, sig, path);
+                        if (found_in_round2)
+                            count_merge(union_mask, sig, path);
 
-                if (found_out_round2)
-                    count_merge(union_mask, sig, path, 1);
-            }
-        }, num_threads);
+                        if (found_out_round2)
+                            count_merge(union_mask, sig, path, 1);
+                    }
+                },
+                num_threads);
 
         std::atomic_thread_fence(std::memory_order_acquire);
 
@@ -369,36 +378,64 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     // Filter unitigs from masked graph based on filtration criteria
     logger->trace("Filtering out background");
-    if (config.count_kmers){ // Statistical testing part when k-mer counts are included.
-        const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
-        sdsl::bit_vector mask = in_mask;
+    if (config.count_kmers) {
         auto &[in_total_kmers, out_total_kmers] = total_kmers;
         auto total_hypotheses = counts.size()/2; // the total number of hypotheses tested.
-        auto test_model = DifferentialTest(config.family_wise_error_rate, total_hypotheses,
+        auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_hypotheses,
                                            std::min((int) std::distance(counts.begin(),
                                                                        std::max_element(counts.begin(), counts.end())), (int) 1000), in_total_kmers, out_total_kmers); // similar to the width of the counts vector, the size of the log-factorial table should be the maximum joint coverage over the in_labels resp. out_labels. Convert the width from bits to the number of stored values.
         // std::min(std::pow(2, counts.width()), 10000) TODO Myrthe: limit the power of the width by some reasonable number, that is lower than 4,294,967,296, the current maximum, i.e. (2^32) or calculate it by the maximum of the counts vector.
 
-        size_t kept_nodes = 0;
-        call_ones(in_mask, [&](node_index node) {
-            uint64_t in_sum = counts[node * 2];
-            uint64_t out_sum = counts[node * 2 + 1];
-            auto [pvalue, sign] = test_model.likelihood_ratio_test(in_sum, out_sum);
-            if (test_model.bonferroni_correction(pvalue) and sign) {
-                ++kept_nodes;
-            } else {
-                mask[node] = false;
-            }
-        });
+        if (unitig_mode == false) { // Statistical testing part when k-mer counts are included.
+            const auto &in_mask
+                    = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
+            sdsl::bit_vector mask = in_mask;
+
+
+            size_t kept_nodes = 0;
+            call_ones(in_mask, [&](node_index node) {
+                uint64_t in_sum = counts[node * 2];
+                uint64_t out_sum = counts[node * 2 + 1];
+                auto [pvalue, sign] = statistical_model.likelihood_ratio_test(in_sum, out_sum);
+                if (statistical_model.bonferroni_correction(pvalue) and sign) {
+                    ++kept_nodes;
+                } else {
+                    mask[node] = false;
+                }
+            });
 
-        masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+            masked_graph->set_mask(new bitmap_vector(std::move(mask)));
 
-        size_t total_nodes = masked_graph->num_nodes();
-        logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes); // TODO this tracer is never printed and the breakpoints are never hit.
+            size_t total_nodes = masked_graph->num_nodes();
+            logger->trace("Kept {} out of {} nodes", kept_nodes,
+                          total_nodes); // TODO this tracer is never printed and the breakpoints are never hit.
 
-        return masked_graph;
-    }
+            return masked_graph;
+        } else if (config.count_kmers and unitig_mode == true) {
+        // TODO Myrthe:  filtering by unitig Myrthe todo
 
+        update_masked_graph_by_unitig(*masked_graph, // intitial mask
+                [&](const std::string &, const std::vector<node_index> &path)
+                        -> Intervals {
+                    // return a set of intervals to keep in the graph
+
+                    size_t begin = path.size();
+                    size_t end = 0;
+                    int in_kmer_count_unitig = 0;
+                    int out_kmer_count_unitig = 0;
+                    for (size_t i = 0; i < path.size(); ++i) { // first strategy is summing counts per unitig. // The update_mask.. function takes a path. Count vector per path. QUESTION: where is the count vector created, such that iunitig k-mers can be accessed sequentially?
+                        in_kmer_count_unitig += counts[path[i] * 2]; // QUESTION Do more efficient std::accumulate for sdsl?
+                        out_kmer_count_unitig += counts[path[i] * 2 + 1];
+                    }
+                    auto [pvalue, sign] = statistical_model.likelihood_ratio_test(in_kmer_count_unitig, out_kmer_count_unitig);
+                    if (statistical_model.bonferroni_correction(pvalue) and sign)
+                                return { std::make_pair(begin, end) };
+                    else
+                        return {};
+                },
+                num_threads);
+        }
+    }
 
 
     size_t min_label_in_count = std::ceil(config.label_mask_in_kmer_fraction
@@ -617,7 +654,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
             i = AnnotatedDBG::anno_to_graph_index(i); // indices in the annotation matrix have an offset of 1 compared to those in the graph. Since the mask should be compatible with the graph, we have to convert the index.
             assert(i != DeBruijnGraph::npos);
             set_bit(indicator.data(), i, parallel, MO_RELAXED); // set the corresponding bit to true in the mask, with the 'indicator' representing the mask.
-            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED);
+            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED); // TODO Myrthe later: make sure that values do not overflow in neighbouring cells.
             in_total_kmers += value;
         };
 
@@ -774,27 +811,7 @@ void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
 }
 
 
-// TODO Myrthe:  filtering by unitig Myrthe todo
-//update_masked_graph_by_unitig(*masked_graph,
-//        [&](const std::string &, const std::vector<node_index> &path) -> Intervals { // Takes a path. Count vector per path.
-//            // return a set of intervals to keep in the graph
-//            size_t in_kmer_count = 0;
-//
-//            size_t begin = path.size();
-//            size_t end = 0;
-//            for (size_t i = 0; i < path.size(); ++i) { // first strategy is summing counts per unitig. QUESTION: where is the count vector created, such that iunitig k-mers can be accessed sequentially?
-//                in_kmer_count_unitig += counts[path[i] * 2];  // QUESTION Do more efficient std::accumulate for sdsl?
-//                out_kmer_count_unitig += counts[path[i] * 2 + 1];
-//            }
-//
-//            if model.likelihood_ratio_test(in_kmer_count_unitig, out_kmer_count_unitig) // todo can the model be accessed from the outside? Initiate total_hypotheses as number of unitigs.
-//                return { std::make_pair(begin, end) };
-//            else
-//                return {};
-//        },
-//        num_threads
-//);
-//
+
 
 } // namespace graph
 } // namespace mtg

From 483d12e000615a4c417e222706029f80f62561fe Mon Sep 17 00:00:00 2001
From: MyrtheW <willemsenmyrthe@gmail.com>
Date: Thu, 16 Mar 2023 11:56:49 +0100
Subject: [PATCH 038/410] unitig mode

---
 metagraph/src/cli/assemble.cpp                |  11 +-
 .../src/common/vectors/vector_algorithm.hpp   |  53 +++--
 .../src/graph/annotated_graph_algorithm.cpp   | 185 +++++++++++-------
 .../src/graph/annotated_graph_algorithm.hpp   |   1 +
 metagraph/src/graph/differential_tests.cpp    |  63 ++++--
 metagraph/src/graph/differential_tests.hpp    |  14 +-
 .../representation/base/sequence_graph.hpp    |   1 +
 7 files changed, 210 insertions(+), 118 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index d2150564da..5ba4176332 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -52,6 +52,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.add_complement = experiment.get("forward_and_reverse", false).asBool();
     diff_config.count_kmers = experiment.get("count_kmers", false).asBool();
     diff_config.family_wise_error_rate = experiment.get("family_wise_error_rate", 0.05).asDouble();
+    diff_config.test_by_unitig = experiment.get("test_by_unitig", false).asBool();
 
     logger->trace("Per-kmer mask in fraction:\t\t{}", diff_config.label_mask_in_kmer_fraction);
     logger->trace("Per-unitig mask in fraction:\t\t{}", diff_config.label_mask_in_unitig_fraction);
@@ -59,9 +60,15 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     logger->trace("Per-unitig mask out fraction:\t\t{}", diff_config.label_mask_out_unitig_fraction);
     logger->trace("Per-unitig other label fraction:\t{}", diff_config.label_mask_other_unitig_fraction);
     logger->trace("Include reverse complements:\t\t{}", diff_config.add_complement);
-    logger->trace("Include k-mer counts if present in the graph:\t\t{}", diff_config.count_kmers); // if node weights (k-mer count) are present in the graph and should be included in the differential assembly.
+    logger->trace("Include k-mer counts if preseadd_complementnt in the graph:\t\t{}", diff_config.count_kmers); // if node weights (k-mer count) are present in the graph and should be included in the differential assembly.
     logger->trace("Family wise error rate for the Bonferroni test:\t{}", diff_config.family_wise_error_rate);
-    // TODO get the folder with the annotation columns from the .json or command, because it is redundant to have to give all of them as an input. How to? For all the in_labels and out_labels, collect the folder/<filename>.column.annodbg file and push them in the list of config->infbase_annotators.
+
+
+    // TODO get the folder with the annotation columns from the .json or command, because it is redundant to have to give all of them as an input.
+            //  How to? For all the in_labels and out_labels, collect the folder/<filename>.column.annodbg file and push them in the list of config->infbase_annotators.
+//    config->enumerate_out_sequences;
+//    config->infbase_annotators = ;
+    // but you would still need to match them?
     return diff_config;
 }
 
diff --git a/metagraph/src/common/vectors/vector_algorithm.hpp b/metagraph/src/common/vectors/vector_algorithm.hpp
index 526b32c453..96711cddbe 100644
--- a/metagraph/src/common/vectors/vector_algorithm.hpp
+++ b/metagraph/src/common/vectors/vector_algorithm.hpp
@@ -349,18 +349,24 @@ inline uint64_t atomic_fetch(const sdsl::int_vector<> &vector,
         return (__atomic_load_n(word, mo) >> (bit_pos & 0x7F)) & mask;
 #endif
     } else {
-        const uint8_t shift = bit_pos & 0x7;
-        const uint8_t *word = &reinterpret_cast<const uint8_t*>(vector.data())[bit_pos >> 3];
+        uint8_t shift = bit_pos & 0x7;
         if (shift + width <= 8) {
+            const uint8_t *word = &reinterpret_cast<const uint8_t*>(vector.data())[bit_pos >> 3];
             // read from a byte
             return (__atomic_load_n(word, mo) >> shift) & mask;
         } else if (shift + width <= 16) {
+            const uint16_t *word = &reinterpret_cast<const uint16_t*>(vector.data())[bit_pos >> 4];
+            shift = bit_pos & 0xF;
             // unaligned read from two bytes
             return (__atomic_load_n((const uint16_t*)word, mo) >> shift) & mask;
         } else if (shift + width <= 32) {
+            const uint32_t *word = &reinterpret_cast<const uint32_t*>(vector.data())[bit_pos >> 5];
+            shift = bit_pos & 0x1F;
             // unaligned read from four bytes
             return (__atomic_load_n((const uint32_t*)word, mo) >> shift) & mask;
         } else if (shift + width <= 64) {
+            const uint64_t *word = &vector.data()[bit_pos >> 6];
+            shift = bit_pos & 0x3F;
             // unaligned read from eight bytes
             return (__atomic_load_n((const uint64_t*)word, mo) >> shift) & mask;
         } else {
@@ -399,20 +405,26 @@ inline uint64_t atomic_fetch_and_add(sdsl::int_vector<> &vector,
         return (__sync_fetch_and_add(word, __uint128_t(val) << shift) >> shift) & mask;
 #endif
     } else {
-        const uint8_t shift = bit_pos & 0x7;
-        uint8_t *word = &reinterpret_cast<uint8_t*>(vector.data())[bit_pos >> 3];
+        uint8_t shift = bit_pos & 0x7;
         if (shift + width <= 8) {
             // read from a byte
+            uint8_t *word = &reinterpret_cast<uint8_t*>(vector.data())[bit_pos >> 3];
             return (__atomic_fetch_add(word, val << shift, mo) >> shift) & mask;
         } else if (shift + width <= 16) {
             // unaligned read from two bytes
-            return (__atomic_fetch_add((uint16_t*)word, val << shift, mo) >> shift) & mask;
+            uint16_t *word = &reinterpret_cast<uint16_t*>(vector.data())[bit_pos >> 4];
+            shift = bit_pos & 0xF;
+            return (__atomic_fetch_add(word, val << shift, mo) >> shift) & mask;
         } else if (shift + width <= 32) {
             // unaligned read from four bytes
-            return (__atomic_fetch_add((uint32_t*)word, val << shift, mo) >> shift) & mask;
+            uint32_t *word = &reinterpret_cast<uint32_t*>(vector.data())[bit_pos >> 5];
+            shift = bit_pos & 0x1F;
+            return (__atomic_fetch_add(word, val << shift, mo) >> shift) & mask;
         } else if (shift + width <= 64) {
             // unaligned read from eight bytes
-            return (__atomic_fetch_add((uint64_t*)word, val << shift, mo) >> shift) & mask;
+            uint64_t *word = &vector.data()[bit_pos >> 6];
+            shift = bit_pos & 0x3F;
+            return (__atomic_fetch_add(word, val << shift, mo) >> shift) & mask;
         } else {
             assert(false);
         }
@@ -464,9 +476,9 @@ inline uint64_t atomic_exchange(sdsl::int_vector<> &vector,
         return (exp >> shift) & mask;
 #endif
     } else {
-        const uint8_t shift = bit_pos & 0x7;
-        uint8_t *word = &reinterpret_cast<uint8_t*>(vector.data())[bit_pos >> 3];
+        uint8_t shift = bit_pos & 0x7;
         if (shift + width <= 8) {
+            uint8_t *word = &reinterpret_cast<uint8_t*>(vector.data())[bit_pos >> 3];
             // read from a byte
             uint8_t desired;
             uint8_t exp = *word;
@@ -477,37 +489,40 @@ inline uint64_t atomic_exchange(sdsl::int_vector<> &vector,
             } while (!__atomic_compare_exchange(word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
             return (exp >> shift) & mask;
         } else if (shift + width <= 16) {
+            uint16_t *word = &reinterpret_cast<uint16_t*>(vector.data())[bit_pos >> 4];
+            shift = bit_pos & 0xF;
             // unaligned read from two bytes
-            uint16_t *this_word = (uint16_t*)word;
             uint16_t desired;
-            uint16_t exp = *this_word;
+            uint16_t exp = *word;
             const uint16_t inv_mask = ~(mask << shift);
             val <<= shift;
             do {
                 desired = val | (inv_mask & exp);
-            } while (!__atomic_compare_exchange(this_word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
+            } while (!__atomic_compare_exchange(word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
             return (exp >> shift) & mask;
         } else if (shift + width <= 32) {
             // unaligned read from four bytes
-            uint32_t *this_word = (uint32_t*)word;
+            uint32_t *word = &reinterpret_cast<uint32_t*>(vector.data())[bit_pos >> 5];
+            shift = bit_pos & 0x1F;
             uint32_t desired;
-            uint32_t exp = *this_word;
+            uint32_t exp = *word;
             const uint32_t inv_mask = ~(mask << shift);
             val <<= shift;
             do {
                 desired = val | (inv_mask & exp);
-            } while (!__atomic_compare_exchange(this_word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
+            } while (!__atomic_compare_exchange(word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
             return (exp >> shift) & mask;
         } else if (shift + width <= 64) {
             // unaligned read from eight bytes
-            uint64_t *this_word = (uint64_t*)word;
+            uint64_t *word = &vector.data()[bit_pos >> 6];
+            shift = bit_pos & 0x3F;
             uint64_t desired;
-            uint64_t exp = *this_word;
+            uint64_t exp = *word;
             const uint64_t inv_mask = ~(mask << shift);
             val <<= shift;
             do {
                 desired = val | (inv_mask & exp);
-            } while (!__atomic_compare_exchange(this_word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
+            } while (!__atomic_compare_exchange(word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
             return (exp >> shift) & mask;
         } else {
             assert(false);
@@ -857,4 +872,4 @@ aligned_int_vector(size_t size, uint64_t val, uint8_t width, size_t alignment) {
     return v;
 }
 
-#endif // __VECTOR_ALGORITHM_HPP__
+#endif // __VECTOR_ALGORITHM_HPP__
\ No newline at end of file
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 51f00b4f66..6a197302ef 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -56,7 +56,10 @@ std::string outstring(std::vector<T> out_array){
 void kmer_distribution_table(const ColumnGenerator &generate_columns,
                              const tsl::hopscotch_set<Label> &labels_in,
                              const tsl::hopscotch_set<Label> &labels_out,
-                             size_t max_index);  // For kmer distribution.
+                             size_t max_index,
+                             MaskedDeBruijnGraph &masked_graph, // std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                             const DifferentialAssemblyConfig &config,
+                             size_t num_threads);  // For kmer distribution.
 
 std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>>
 construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
@@ -193,36 +196,20 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
 
     logger->trace("Generating initial mask");
 
-    if (config.family_wise_error_rate==0){
-        kmer_distribution_table(
-                [&](const ColumnCallback &column_callback) {
-                        annot::ColumnCompressed<>::load_columns_and_values(files,
-                           [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
-                               column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                                   call_ones(*column, [&](uint64_t i) {
-                                       value_callback(i, column_values[column->rank1(i)-1]);
-                                   });
-                               });
-                           }
-                        );
-                },
-                labels_in, labels_out, graph_ptr->max_index()
-        );
-    std::exit(EXIT_SUCCESS);
-    }
+
 
     auto count_vector = construct_diff_label_count_vector(
             [&](const ColumnCallback &column_callback) {
             if (config.count_kmers) {
+                assert(files.size() > 0);
                 annot::ColumnCompressed<>::load_columns_and_values(files,
                     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
+                        // TODO Myrthe: equalize coverage; std::ceil(column_values / (column_values sum
                         column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                            std::cout << "test column values ";
                             assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0);
                             assert(column->num_set_bits() > 0);
                             assert(column->num_set_bits() == column_values.size());
                             call_ones(*column, [&](uint64_t i) {
-                                std::cout << "add to count vector ";
                                 value_callback(i, column_values[column->rank1(i)-1]);
                             });
                         });
@@ -233,9 +220,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column) {
                         column_callback(offset, label, [&](const ValueCallback &value_callback) {
                             assert(column->num_set_bits() > 0);
-                            std::cout << "test columns ";
                             call_ones(*column, [&](uint64_t i) {
-                                std::cout << "add to count vector ";
                                 value_callback(i, 1);
                             });
                         });
@@ -277,6 +262,38 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         std::atomic_thread_fence(std::memory_order_acquire);
     }
 
+    if (config.family_wise_error_rate == 0){  // make k-mer distribution
+        logger->trace("K-mer distribution");
+
+        auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
+                                                      add_complement, num_threads);
+        kmer_distribution_table(
+                [&](const ColumnCallback &column_callback) {
+                    annot::ColumnCompressed<>::load_columns_and_values(files,
+                       [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
+                           column_callback(offset, label, [&](const ValueCallback &value_callback) {
+
+                               if (config.test_by_unitig == false) {
+                                   call_ones(*column, [&](uint64_t i) {
+                                       value_callback(i, column_values[column->rank1(i)- 1]);
+                                   });
+                               }
+                               else {
+                                   for(size_t i = 0; i < column->size(); i++){  // Myrthe actually here, we should for unitigs, call all values.
+                                       double value = (*column)[i];
+                                       if (value) value = column_values[column->rank1(i)- 1];
+                                       value_callback(i, value);
+                                   }
+                               }
+                           });
+                       }
+                    );
+                },
+                labels_in, labels_out, graph_ptr->max_index(), *masked_graph, config, num_threads
+        );
+        std::exit(EXIT_SUCCESS);
+    }
+
     return mask_nodes_by_label(graph_ptr, nullptr,
                                std::move(counts), std::move(init_mask),
                                std::move(other_mask),
@@ -380,61 +397,58 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     logger->trace("Filtering out background");
     if (config.count_kmers) {
         auto &[in_total_kmers, out_total_kmers] = total_kmers;
-        auto total_hypotheses = counts.size()/2; // the total number of hypotheses tested.
-        auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_hypotheses,
-                                           std::min((int) std::distance(counts.begin(),
-                                                                       std::max_element(counts.begin(), counts.end())), (int) 1000), in_total_kmers, out_total_kmers); // similar to the width of the counts vector, the size of the log-factorial table should be the maximum joint coverage over the in_labels resp. out_labels. Convert the width from bits to the number of stored values.
-        // std::min(std::pow(2, counts.width()), 10000) TODO Myrthe: limit the power of the width by some reasonable number, that is lower than 4,294,967,296, the current maximum, i.e. (2^32) or calculate it by the maximum of the counts vector.
-
-        if (unitig_mode == false) { // Statistical testing part when k-mer counts are included.
-            const auto &in_mask
-                    = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
-            sdsl::bit_vector mask = in_mask;
-
 
+        if (config.test_by_unitig == false) { // Statistical testing part when k-mer counts are included. // TODO change to test_by_unitig
+            auto total_hypotheses = counts.size()/2; // the total number of hypotheses tested.
+            auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_hypotheses,
+                                                      std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
+                                                      in_total_kmers, out_total_kmers); // similar to the width of the counts vector, the size of the log-factorial table should be the maximum joint coverage over the in_labels resp. out_labels. Convert the width from bits to the number of stored values.
+            // std::min(std::pow(2, counts.width()), 10000) TODO Myrthe: limit the power of the width by some reasonable number, that is lower than 4,294,967,296, the current maximum, i.e. (2^32) or calculate it by the maximum of the counts vector.
+            const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
+            sdsl::bit_vector mask = in_mask;
+            size_t total_nodes = masked_graph->num_nodes();
             size_t kept_nodes = 0;
             call_ones(in_mask, [&](node_index node) {
                 uint64_t in_sum = counts[node * 2];
                 uint64_t out_sum = counts[node * 2 + 1];
-                auto [pvalue, sign] = statistical_model.likelihood_ratio_test(in_sum, out_sum);
-                if (statistical_model.bonferroni_correction(pvalue) and sign) {
+                if (statistical_model.likelihood_ratio_test(in_sum, out_sum))
                     ++kept_nodes;
-                } else {
+                else
                     mask[node] = false;
-                }
             });
 
             masked_graph->set_mask(new bitmap_vector(std::move(mask)));
 
-            size_t total_nodes = masked_graph->num_nodes();
-            logger->trace("Kept {} out of {} nodes", kept_nodes,
-                          total_nodes); // TODO this tracer is never printed and the breakpoints are never hit.
-
-            return masked_graph;
-        } else if (config.count_kmers and unitig_mode == true) {
-        // TODO Myrthe:  filtering by unitig Myrthe todo
-
-        update_masked_graph_by_unitig(*masked_graph, // intitial mask
-                [&](const std::string &, const std::vector<node_index> &path)
-                        -> Intervals {
-                    // return a set of intervals to keep in the graph
+            logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
 
+        } else { // Myrthe: maybe it is more efficient to do this at an earlier stage, while calculating the counts vector.
+            std::atomic<uint64_t> total_unitigs(0);
+            masked_graph->call_unitigs([&](const std::string &, const std::vector<node_index> &) {
+                total_unitigs.fetch_add(1, MO_RELAXED);
+            });
+            auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, // total number of hypotheses equals the number of unitigs
+                                               std::min((int) std::distance(counts.begin(),
+                                               std::max_element(counts.begin(), counts.end())), (int) 100000), // Myrthe: the factorial should increase for the unitig mode.
+                                               in_total_kmers, out_total_kmers);
+            update_masked_graph_by_unitig(*masked_graph, // intitial mask
+                [&](const std::string &, const std::vector<node_index> &path) -> Intervals { // return a set of intervals to keep in the graph
+                //TODO Myrthe: set the number of test hypotheses to the number of unitigs
                     size_t begin = path.size();
                     size_t end = 0;
                     int in_kmer_count_unitig = 0;
                     int out_kmer_count_unitig = 0;
-                    for (size_t i = 0; i < path.size(); ++i) { // first strategy is summing counts per unitig. // The update_mask.. function takes a path. Count vector per path. QUESTION: where is the count vector created, such that iunitig k-mers can be accessed sequentially?
-                        in_kmer_count_unitig += counts[path[i] * 2]; // QUESTION Do more efficient std::accumulate for sdsl?
+                    for (size_t i = 0; i < path.size(); ++i) {
+                        in_kmer_count_unitig += counts[path[i] * 2];
                         out_kmer_count_unitig += counts[path[i] * 2 + 1];
                     }
-                    auto [pvalue, sign] = statistical_model.likelihood_ratio_test(in_kmer_count_unitig, out_kmer_count_unitig);
-                    if (statistical_model.bonferroni_correction(pvalue) and sign)
+                    if (statistical_model.likelihood_ratio_test(in_kmer_count_unitig, out_kmer_count_unitig))
                                 return { std::make_pair(begin, end) };
                     else
                         return {};
                 },
                 num_threads);
-        }
+            }
+            return masked_graph;
     }
 
 
@@ -598,7 +612,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
     bool parallel = (num_threads > 1);
     logger->trace("Allocating mask vector");
 
-    size_t width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
+    size_t width;
 
     if (config.count_kmers){ // calculate the sum of column widths to derive an upper bound for the required counts vector width.
         assert(labels_in.size() > 0);
@@ -607,10 +621,8 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
             const auto &values_fname = utils::remove_suffix(files[i],
                  annot::ColumnCompressed<>::kExtension) + annot::ColumnCompressed<>::kCountExtension;
             sdsl::int_vector_size_type col_size; uint8_t col_width;
-            std::ifstream in_stream;
+            std::ifstream in_stream(values_fname);
             sdsl::int_vector<>::read_header(col_size, col_width, in_stream);
-            logger->trace("colwidth");
-            std::cout << std::to_string(col_width) << std::endl;
             auto label_encoder = annot::ColumnCompressed<>::read_label_encoder(files[i]);
             for (size_t c = 0; c < label_encoder.size(); ++c) {
                 auto label = label_encoder.decode(c);
@@ -618,13 +630,11 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                 if (labels_out.count(label)) sum_widths_out += std::pow(2, col_width);
             }
         }
-        std::cout << std::to_string(sum_widths_in) << std::endl;
-        std::cout << std::to_string(sum_widths_out) << std::endl;
         width = std::min((size_t) 32, (size_t) std::ceil(std::log(std::max(sum_widths_in, sum_widths_out)))); // TODO, check if this is really correct. this should be 8.5 -> 9 rather then 7.
     } else{
         width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
     }
-
+    assert(width);
 
     sdsl::bit_vector indicator(max_index + 1, false);
 
@@ -642,6 +652,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
             // Myrthe later: The second problem with buffered_int_vector might be that it is expensive, because we do random access on it. Instead a solution might be to use a piority queue of L columns, storing the smallest element as a triple (column , index, value), such that always k-mers in row x are processed before row x + 1.
 
     size_t in_total_kmers = 0; size_t out_total_kmers = 0;
+
     generate_columns([&](uint64_t offset, const Label &label, const ValueGenerator &value_generator) {
         uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
         if (labels_out.count(label))
@@ -654,7 +665,8 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
             i = AnnotatedDBG::anno_to_graph_index(i); // indices in the annotation matrix have an offset of 1 compared to those in the graph. Since the mask should be compatible with the graph, we have to convert the index.
             assert(i != DeBruijnGraph::npos);
             set_bit(indicator.data(), i, parallel, MO_RELAXED); // set the corresponding bit to true in the mask, with the 'indicator' representing the mask.
-            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED); // TODO Myrthe later: make sure that values do not overflow in neighbouring cells.
+            //atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED); // TODO Myrthe later: make sure that values do not overflow in neighbouring cells.
+            counts[i*2] += value;
             in_total_kmers += value;
         };
 
@@ -663,7 +675,8 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
             assert(i != DeBruijnGraph::npos);
             if (add_out_labels_to_mask)
                 set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
+            //atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
+            counts[i*2 + 1] += value;
             out_total_kmers += value;
         };
 
@@ -685,10 +698,9 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
         }
     });
 
-    assert( std::accumulate(counts.begin(), counts.end(), 0) > 0); // assert that the sum of the count vector is greater than 0
-
     std::atomic_thread_fence(std::memory_order_acquire);
     logger->trace("done");
+    assert( std::accumulate(counts.begin(), counts.end(), 0) > 0); // assert that the sum of the count vector is greater than 0
 
     return std::make_tuple(std::move(counts), std::move(indicator), std::move(other_labels),
                            std::make_tuple(std::move(in_total_kmers), std::move(out_total_kmers)));
@@ -699,13 +711,15 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
 void kmer_distribution_table(const ColumnGenerator &generate_columns,
                                   const tsl::hopscotch_set<Label> &labels_in,
                                   const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index) {
+                                  size_t max_index,
+                                  MaskedDeBruijnGraph &masked_graph, // std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                             const DifferentialAssemblyConfig &config,
+                             size_t num_threads) {
 
     logger->trace("Allocating count vector");
     std::vector<std::vector<int>> count_matrix(max_index*2 + 2); // outer array 'max_index + 1'*2, with arrays of initially zero lenght or of max size in labels/out_labels.
 
     logger->trace("Populating count matrix");
-    //    out_stream << "offset_sum" + std::to_string(x) << std::endl; uint64_t x = 0;
     generate_columns([&](uint64_t offset, const Label &label, const ValueGenerator &value_generator) {
         std::ignore = offset;// to overcome the error that 'offset' is set but not used.
         uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
@@ -734,14 +748,44 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
         }
     });
 
+    if (config.test_by_unitig){         // unitig mode.
+        std::vector<std::vector<int>> unitig_count_matrix;
+        update_masked_graph_by_unitig(masked_graph, // intitial mask
+            [&](const std::string &, const std::vector<node_index> &path)
+                    -> Intervals {
+                // return a set of intervals to keep in the graph
+                std::vector<int> in_kmer_unitig(labels_in.size(), 0);
+                std::vector<int> out_kmer_unitig(labels_out.size(), 0);
+                for (size_t i = 0; i < path.size(); ++i) { // first strategy is summing counts per unitig. // The update_mask.. function takes a path. Count vector per path. QUESTION: where is the count vector created, such that unitig k-mers can be accessed sequentially?
+                    assert(count_matrix.size() >= path[i] * 2);
+                    if (count_matrix[path[i] * 2].size() != in_kmer_unitig.size() or count_matrix[path[i] * 2 + 1].size() != out_kmer_unitig.size()){
+                        std::cout << "sizes" << std::endl;
+                        std::cout << std::to_string(count_matrix[path[i] * 2].size()) << std::endl << std::flush;
+                        std::cout << std::to_string(in_kmer_unitig.size()) << std::endl << std::flush;
+                        std::cout << std::to_string(count_matrix[path[i] * 2 + 1].size()) << std::endl << std::flush;
+                        std::cout << std::to_string(out_kmer_unitig.size()) << std::endl << std::flush;
+                        std::cout << std::to_string(path[i] * 2) << std::endl << std::flush; // TODO: do I have to convert the index? It was not done in the original code
+                    }else{
+                        assert(count_matrix[path[i] * 2].size() == in_kmer_unitig.size());
+                        assert(count_matrix[path[i] * 2 + 1].size() == out_kmer_unitig.size());
+                        std::transform(in_kmer_unitig.begin(), in_kmer_unitig.end(), count_matrix[path[i] * 2].begin(), in_kmer_unitig.begin(), std::plus<int>()); // sum the values of count_matrix[x] to in_kmer_count.
+                        std::transform(out_kmer_unitig.begin(), out_kmer_unitig.end(), count_matrix[path[i] * 2 + 1].begin(), out_kmer_unitig.begin(), std::plus<int>());
+                    }
+                }
+                unitig_count_matrix.push_back(in_kmer_unitig);
+                unitig_count_matrix.push_back(out_kmer_unitig); // add to matrix
+                return { };
+                },
+            num_threads);
+        count_matrix = unitig_count_matrix;
+    }
+
     int matrix_sum = 0;
     for (size_t i = 0; i < count_matrix.size(); i+=2){
         matrix_sum += std::accumulate(count_matrix[i].begin(), count_matrix[i].end(), 0);
     }
     assert(matrix_sum > 0);
 
-    std::cout << "matrix sum ";
-    std::cout << matrix_sum;
     logger->trace("Writing k-mer count matrix to table");
 
     std::ofstream out_stream("kmer_dist_table.py");
@@ -757,9 +801,6 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
     out_stream << "count_matrix_out = [" ;
     for (size_t i = 1; i < std::min(count_matrix.size(), (size_t) 50000000); i+=2){
         if (count_matrix[i].size() > 3 or count_matrix[i-1].size() > 3){ // labels_out.size()/3
-            std::cout << std::to_string(count_matrix[i].size()) <<std::endl;
-            std::cout << std::to_string(labels_out.size()) <<std::endl;
-            std::cout << "[" + outstring(count_matrix[i]) << "]" <<std::endl;
             std::vector<int> zeros_vector(labels_out.size()-count_matrix[i].size(), 0);
             count_matrix[i].insert(count_matrix[i].end(), zeros_vector.begin(), zeros_vector.end());
             out_stream << "[" + outstring(count_matrix[i]) << "]," <<std::endl;        }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 3001681ed2..9f29b949c6 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -35,6 +35,7 @@ struct DifferentialAssemblyConfig {
     bool add_complement = false;
     bool count_kmers = false; // Myrthe
     double family_wise_error_rate = 0.05; //Myrthe: differential assembly statistical tests, or the family wise rate for bonferonni or a p-value
+    bool test_by_unitig = false; // Myrthe
 };
 
 /**
diff --git a/metagraph/src/graph/differential_tests.cpp b/metagraph/src/graph/differential_tests.cpp
index 2f30707f3c..f45b5d2195 100644
--- a/metagraph/src/graph/differential_tests.cpp
+++ b/metagraph/src/graph/differential_tests.cpp
@@ -1,4 +1,7 @@
 #include <math.h>
+#include <string>
+#include <numbers>
+#include <iostream>
 
 #include "differential_tests.hpp"
 #include "lookup_table_chisqrd_cdf.cpp"
@@ -15,7 +18,7 @@ LogFactorialTable::LogFactorialTable(size_t size)
         m_table.push_back(log_factorial(i));
 }
 
-double LogFactorialTable::log_factorial(size_t k)
+double LogFactorialTable::log_factorial(size_t k) // TODO this can be calculated more efficiently.
 {
     double res = 0;
     while (k > 1)
@@ -26,6 +29,9 @@ double LogFactorialTable::log_factorial(size_t k)
     return res;
 }
 
+double LogFactorialTable::approximate_log_factorial(size_t k) {
+    return k * log(k) - k * log2e_v;
+}
 
 DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t preload_table_size,
                                    size_t in_total_kmers, size_t out_total_kmers) :
@@ -74,26 +80,27 @@ bool DifferentialTest::bonferroni_correction(double& pvalue) //  &family_wise_er
 }
 
 
-//size_t lrt_threshold(double corrected_pvalue){
-//    auto & array = ;
-//    int low = 0;
-//    int high = array.size()-1;
-//    while (low <= high) {
-//        int mid = (low + high) >> 1;
-//        if (std::get<0>(array[mid]) < kmer_count)
-//        {low = mid + 1;}
-//        else if (std::get<0>(array[mid]) > kmer_count)
-//        {high = mid - 1;}
-//        else if (std::get<0>(array[mid]) == kmer_count)
-//        {return std::get<1>(array[mid]);} // exact kmer_count found
-//    }
-//    return std::get<1>(array[low]);
-//}
+size_t DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test
+    double corrected_pvalue = family_wise_error_rate/total_hypotheses; // Take into account the Bonferonni multiple testing correction
+    Chi2PLookup Chi2PLookupTable;
+    int low = 0;
+    int high = sizeof(pValues_1); //int high = Chi2PLookupTable.divisor*Chi2PLookupTable.cutoff - 1; //.size()-1;
+    while (low <= high) {
+        int mid = (low + high) >> 1;
+        if (Chi2PLookupTable.getPValue(mid, 1) < corrected_pvalue)
+        {low = mid + 1;}
+        else if (Chi2PLookupTable.getPValue(mid, 1) >= corrected_pvalue)
+        {high = mid - 1;}
+//        else if (Chi2PLookupTable.getPValue(mid, 1) == corrected_pvalue)
+//        {return mid/2;}
+    }
+    return low / 2; // Note that 2*LRT is Chi-squared distributed.
+}
 
 
 // adapted code from kmdiff.
 // previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
-std::tuple<double, bool> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
+bool DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
 {
     double mean = (out_sum + in_sum) / static_cast<double>(out_total_kmers + in_total_kmers);
 
@@ -108,16 +115,32 @@ std::tuple<double, bool> DifferentialTest::likelihood_ratio_test(double in_sum,
 
     double likelihood_ratio = alt_hypothesis - null_hypothesis;
 
+    // if (likelihood_ratio > likelihood_ratio_threshold) return true;  // TODO test this instead of the code below.
+
+
     if (likelihood_ratio < 0) likelihood_ratio = 0;
     Chi2PLookup Chi2PLookupTable; // generated with https://github.com/MoseleyBioinformaticsLab/chi2plookup
-    double p_value = Chi2PLookupTable.getPValue(2 * likelihood_ratio, 1);
+    double pvalue = Chi2PLookupTable.getPValue(2 * likelihood_ratio, 1);
+
 
     auto out_sum_normalized = out_sum * in_total_kmers / out_total_kmers;
 
     bool sign = false;
-    if (out_sum_normalized < in_sum) sign = true;
+    if (out_sum_normalized < in_sum) sign = true; // TODO shouldn't this always be the case if the p value is significant?
+
+    bool efficient_test = (likelihood_ratio > likelihood_ratio_threshold);
+    bool normal_test = (sign and bonferroni_correction(pvalue));
+    std::ignore = efficient_test;
+    std::ignore = normal_test;
+
+    //std::cout  << "efficient test: " + std::to_string(likelihood_ratio > likelihood_ratio_threshold) << std::endl;  // TODO test this instead of the code below.
+    //std::cout << "normal test: " + std::to_string(sign and bonferroni_correction(pvalue)) << std::endl;
+
+    if (sign)
+        if (bonferroni_correction(pvalue))
+            return true;
 
-    return std::make_tuple(p_value, sign);
+    return false;
 }
 
 } // namespace mtg
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index ed495f6291..a0e3ca92f7 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -8,14 +8,15 @@ class LogFactorialTable {
   public:
     LogFactorialTable(size_t size);
 
-    double operator[](size_t i) {
-        if (i < m_size)
-            return m_table[i];
-        return log_factorial(i);
+    double operator[](size_t k) {
+        if (k < m_size) return m_table[k];
+        return approximate_log_factorial(k);
     }
 
   private:
+    double log2e_v = std::log(std::exp(1.0));
     double log_factorial(size_t k);
+    double approximate_log_factorial(size_t k);
 
   private:
     std::vector<double> m_table;
@@ -31,6 +32,7 @@ class DifferentialTest {
     size_t out_total_kmers;
     size_t in_total_kmers;
     double gamma = std::sqrt(/* pi */ std::atan(1) * 4);
+    size_t likelihood_ratio_threshold = DifferentialTest::lrt_threshold();
 
   public:
     DifferentialTest(double family_wise_error_rate,
@@ -47,6 +49,8 @@ class DifferentialTest {
 
     bool bonferroni_correction(double &pvalue);
 
-    std::tuple<double, bool> likelihood_ratio_test(double in_sum, double out_sum);
+    size_t lrt_threshold();
+
+    bool likelihood_ratio_test(double in_sum, double out_sum);
 };
 } // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/representation/base/sequence_graph.hpp b/metagraph/src/graph/representation/base/sequence_graph.hpp
index 6de3b50f2a..0f54888a0d 100644
--- a/metagraph/src/graph/representation/base/sequence_graph.hpp
+++ b/metagraph/src/graph/representation/base/sequence_graph.hpp
@@ -160,6 +160,7 @@ class DeBruijnGraph : public SequenceGraph {
     // Traverse the incoming edge
     virtual node_index traverse_back(node_index node, char prev_char) const = 0;
 
+
     // Traverse graph mapping sequence to the graph nodes
     // and run callback for each node until the termination condition is satisfied.
     // Guarantees that nodes are called in the same order as the input sequence.

From be632a8e936bea01159925e80ed59d7783996954 Mon Sep 17 00:00:00 2001
From: MyrtheW <willemsenmyrthe@gmail.com>
Date: Fri, 17 Mar 2023 12:27:58 +0100
Subject: [PATCH 039/410] unitig mode and distribution

---
 metagraph/src/graph/annotated_graph_algorithm.cpp        | 2 +-
 metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 delete mode 100644 metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6a197302ef..c5650727bf 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -497,7 +497,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             size_t begin = path.size();
             size_t end = 0;
             for (size_t i = 0; i < path.size(); ++i) {
-                if (counts[path[i] * 2] >= min_label_in_count) {
+                if (counts[path[i] * 2] >= min_label_in_count) {f
                     if (begin == path.size())
                         begin = i;
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp b/metagraph/src/graph/annotated_graph_algorithm_myrthe.cpp
deleted file mode 100644
index e69de29bb2..0000000000

From 129274de2dbdef3dc09a410a9be321b88c950ba8 Mon Sep 17 00:00:00 2001
From: MyrtheW <willemsenmyrthe@gmail.com>
Date: Thu, 23 Mar 2023 21:42:41 +0100
Subject: [PATCH 040/410] unitig mode and distribution2

---
 metagraph/src/cli/assemble.cpp                |  2 +
 .../src/graph/annotated_graph_algorithm.cpp   | 54 ++++++++++++++++---
 .../src/graph/annotated_graph_algorithm.hpp   |  1 +
 3 files changed, 49 insertions(+), 8 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 5ba4176332..ce0fda1368 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -53,6 +53,8 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.count_kmers = experiment.get("count_kmers", false).asBool();
     diff_config.family_wise_error_rate = experiment.get("family_wise_error_rate", 0.05).asDouble();
     diff_config.test_by_unitig = experiment.get("test_by_unitig", false).asBool();
+    diff_config.evaluate_assembly = experiment.get("evaluate_assembly", false).asBool();
+
 
     logger->trace("Per-kmer mask in fraction:\t\t{}", diff_config.label_mask_in_kmer_fraction);
     logger->trace("Per-unitig mask in fraction:\t\t{}", diff_config.label_mask_in_unitig_fraction);
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c5650727bf..4dc9061a1e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -177,6 +177,8 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                                config, num_threads);
 }
 
+
+
 std::shared_ptr<MaskedDeBruijnGraph>
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this version i don't need to change at first
                     const std::vector<std::string> &files,
@@ -204,13 +206,19 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                 assert(files.size() > 0);
                 annot::ColumnCompressed<>::load_columns_and_values(files,
                     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
-                        // TODO Myrthe: equalize coverage; std::ceil(column_values / (column_values sum
                         column_callback(offset, label, [&](const ValueCallback &value_callback) {
                             assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0);
                             assert(column->num_set_bits() > 0);
                             assert(column->num_set_bits() == column_values.size());
+                            //Normalization. normalize_coverage(column); This constant should be the same for every column. Calculate it from  the max width column. with the max value over all columns being the width of that column devidied by its column_values_sum
+                            int normalization_constant = 1e9;   //(int) 2**16 / width * column_values_sum;
+                            auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0);
+                            auto normalization_factor = normalization_constant / total_kmers;
+                            int min_col_width = std::pow(2, 16); //column_values = std::min((int) std::ceil((*column_values)*normalization_factor), min_column_width); // limit to a certain number of bits.
                             call_ones(*column, [&](uint64_t i) {
-                                value_callback(i, column_values[column->rank1(i)-1]);
+                                auto column_value = column_values[column->rank1(i)-1];
+                                column_value = std::min((int) std::ceil(column_value*normalization_factor), min_col_width); // normalize value
+                                value_callback(i, column_value);
                             });
                         });
                     }
@@ -235,6 +243,37 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
 
 
     auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
+
+    // Myrthe: temporary
+    if (config.evaluate_assembly){     // Myrthe temporary: evaluate what k-mers overlap
+        logger->trace("Evaluate confusion table");
+        assert(counts.size() > 1);
+        assert( std::accumulate(counts.begin(), counts.end(), 0) > 0);
+        int true_positive = 0; int true_negative = 0; int false_positive = 0; int false_negative =0;
+        for (size_t i = 0; i < counts.size() ; i+=2){
+            auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
+            uint64_t true_count = counts[i];
+            uint64_t found_count = counts[i + 1];
+            if (true_count == found_count){
+                true_positive += true_count; // if 0 nothing will be added.
+            }else if (true_count > found_count){
+                false_negative += (true_count - found_count);
+                true_positive += found_count; // add to true_positive
+            }else{false_positive += (found_count - true_count);
+                true_positive += true_count;} // TODO :Why are there no false positives?
+        };
+        // true_negatives: total number of possible k-mers? Total number of k-mers in the original foreground samples from which the result had been calculated ?
+        auto precision = true_positive / ( true_positive - true_negative); // accuracy/precision = Ratio of true positives to total predicted positives.
+        auto recall = true_positive / (true_positive + false_negative);  //recall / sensitivity:  ratio of true positives to total (actual) positives in the data.
+        //auto specificity = true_negative / (true_negative + false_positive); // Specificity is the Ratio of true negatives to total negatives in the data.
+        std::cout<< "precision" << precision << std::endl << std::flush;
+        std::cout<< "recall" << recall << std::endl << std::flush;
+        std::cout << std::accumulate(counts.begin(), counts.end(), 0) << std::flush;
+        assert(true_positive + false_negative + false_positive == std::accumulate(counts.begin(), counts.end(), 0));
+        //std::cout<< "specificity" << specificity << std::endl << std::flush;
+    } // in mask probably only has sequences
+
+
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     if (check_other && sdsl::util::cnt_one_bits(other_labels)) {
         bool parallel = num_parallel_files > 1;
@@ -262,6 +301,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         std::atomic_thread_fence(std::memory_order_acquire);
     }
 
+
     if (config.family_wise_error_rate == 0){  // make k-mer distribution
         logger->trace("K-mer distribution");
 
@@ -393,6 +433,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         masked_graph->set_mask(new bitmap_vector(std::move(union_mask)));
     }
 
+
     // Filter unitigs from masked graph based on filtration criteria
     logger->trace("Filtering out background");
     if (config.count_kmers) {
@@ -432,9 +473,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                                in_total_kmers, out_total_kmers);
             update_masked_graph_by_unitig(*masked_graph, // intitial mask
                 [&](const std::string &, const std::vector<node_index> &path) -> Intervals { // return a set of intervals to keep in the graph
-                //TODO Myrthe: set the number of test hypotheses to the number of unitigs
-                    size_t begin = path.size();
-                    size_t end = 0;
                     int in_kmer_count_unitig = 0;
                     int out_kmer_count_unitig = 0;
                     for (size_t i = 0; i < path.size(); ++i) {
@@ -442,7 +480,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         out_kmer_count_unitig += counts[path[i] * 2 + 1];
                     }
                     if (statistical_model.likelihood_ratio_test(in_kmer_count_unitig, out_kmer_count_unitig))
-                                return { std::make_pair(begin, end) };
+                        return { std::make_pair(0, path.size()) };
                     else
                         return {};
                 },
@@ -497,7 +535,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             size_t begin = path.size();
             size_t end = 0;
             for (size_t i = 0; i < path.size(); ++i) {
-                if (counts[path[i] * 2] >= min_label_in_count) {f
+                if (counts[path[i] * 2] >= min_label_in_count) {
                     if (begin == path.size())
                         begin = i;
 
@@ -831,7 +869,7 @@ void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
             kept_unitigs.fetch_add(1, MO_RELAXED);
             num_kept_nodes.fetch_add(end - begin, MO_RELAXED);
             for ( ; last < begin; ++last) {
-                unset_bit(mask.data(), path[last], parallel, MO_RELAXED);
+                unset_bit(mask.data(), path[last], parallel, MO_RELAXED); // QUESTION Myrthe, why does it unset bits, rather than set bit?
             }
             last = end;
         }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 9f29b949c6..5e95fae8bd 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -36,6 +36,7 @@ struct DifferentialAssemblyConfig {
     bool count_kmers = false; // Myrthe
     double family_wise_error_rate = 0.05; //Myrthe: differential assembly statistical tests, or the family wise rate for bonferonni or a p-value
     bool test_by_unitig = false; // Myrthe
+    bool evaluate_assembly = false; // Myrthe temporary : evaluate an assembly
 };
 
 /**

From 70217809a8a929340a3a8bac9b6efcebf1f5b5d3 Mon Sep 17 00:00:00 2001
From: MyrtheW <willemsenmyrthe@gmail.com>
Date: Tue, 11 Apr 2023 16:38:28 +0200
Subject: [PATCH 041/410] cleaned

---
 metagraph/src/cli/assemble.cpp                |   6 +-
 .../src/graph/annotated_graph_algorithm.cpp   |  45 +++--
 metagraph/src/graph/differential_tests.cpp    | 176 ++++++++++--------
 metagraph/src/graph/differential_tests.hpp    |  50 +++--
 .../representation/base/sequence_graph.cpp    |  71 +++++--
 .../representation/base/sequence_graph.hpp    |  13 +-
 .../src/graph/representation/masked_graph.cpp |  28 +--
 .../src/graph/representation/masked_graph.hpp |   2 +
 8 files changed, 238 insertions(+), 153 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index ce0fda1368..a17620756a 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -252,6 +252,7 @@ int assemble(Config *config) {
                                    num_threads, config->min_tip_size,
                                    config->kmers_in_single_form);
             } else {
+                //assert(std::accumulate(graph.lrs.begin(), graph.lrs.end(), 0) > 0);
                 graph.call_sequences([&](const std::string &seq, auto&&) {
                                          std::lock_guard<std::mutex> lock(write_mutex);
                                          writer.write(seq);
@@ -262,12 +263,13 @@ int assemble(Config *config) {
 
         if (files.size() > 1 or config->infbase_annotators.size() > 1) { // Checks the infbase_annotators in the case that annotation columns are provided in the commandline using '-a' or '--annotator'
             config->fnames.erase(config->fnames.begin(), config->fnames.begin() + 1);
-            call_masked_graphs(graph, config, graph_callback); // Does NOT support kmer-counts assembly?
+            call_masked_graphs(graph, config, graph_callback); // Does NOT support kmer-counts assembly
         } else {
             config->infbase = files.at(0);
             auto anno_graph = initialize_annotated_dbg(graph, *config);
-            call_masked_graphs(*anno_graph, config, graph_callback); // Does support kmer-counts assembly?
+            call_masked_graphs(*anno_graph, config, graph_callback); // Does support kmer-counts assembly
         }
+
         return 0;
     }
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 4dc9061a1e..2318a7bd55 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -244,8 +244,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
 
     auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
 
-    // Myrthe: temporary
     if (config.evaluate_assembly){     // Myrthe temporary: evaluate what k-mers overlap
+
         logger->trace("Evaluate confusion table");
         assert(counts.size() > 1);
         assert( std::accumulate(counts.begin(), counts.end(), 0) > 0);
@@ -270,7 +270,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         std::cout<< "recall" << recall << std::endl << std::flush;
         std::cout << std::accumulate(counts.begin(), counts.end(), 0) << std::flush;
         assert(true_positive + false_negative + false_positive == std::accumulate(counts.begin(), counts.end(), 0));
-        //std::cout<< "specificity" << specificity << std::endl << std::flush;
     } // in mask probably only has sequences
 
 
@@ -302,7 +301,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
     }
 
 
-    if (config.family_wise_error_rate == 0){  // make k-mer distribution
+    if (config.family_wise_error_rate == 0){  // Temporary code to make k-mer distribution
         logger->trace("K-mer distribution");
 
         auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
@@ -319,7 +318,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                    });
                                }
                                else {
-                                   for(size_t i = 0; i < column->size(); i++){  // Myrthe actually here, we should for unitigs, call all values.
+                                   for(size_t i = 0; i < column->size(); i++){
                                        double value = (*column)[i];
                                        if (value) value = column_values[column->rank1(i)- 1];
                                        value_callback(i, value);
@@ -452,7 +451,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             call_ones(in_mask, [&](node_index node) {
                 uint64_t in_sum = counts[node * 2];
                 uint64_t out_sum = counts[node * 2 + 1];
-                if (statistical_model.likelihood_ratio_test(in_sum, out_sum))
+                if (std::get<0>(statistical_model.likelihood_ratio_test(in_sum, out_sum)))
                     ++kept_nodes;
                 else
                     mask[node] = false;
@@ -462,7 +461,9 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
 
-        } else { // Myrthe: maybe it is more efficient to do this at an earlier stage, while calculating the counts vector.
+        } else {
+            masked_graph->likelihood_ratios.resize(counts.size()/2+1);
+            std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0);
             std::atomic<uint64_t> total_unitigs(0);
             masked_graph->call_unitigs([&](const std::string &, const std::vector<node_index> &) {
                 total_unitigs.fetch_add(1, MO_RELAXED);
@@ -479,13 +480,20 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         in_kmer_count_unitig += counts[path[i] * 2];
                         out_kmer_count_unitig += counts[path[i] * 2 + 1];
                     }
-                    if (statistical_model.likelihood_ratio_test(in_kmer_count_unitig, out_kmer_count_unitig))
+                    auto [significance, likelihood_ratio] = statistical_model.likelihood_ratio_test(in_kmer_count_unitig, out_kmer_count_unitig);
+
+                    if (significance){
+                        for (size_t i = 0; i < path.size(); ++i) { // if significant add the likelihood ratio or the effect (in_kmer_count_unitig - out_kmer_count_unitig) to the likelihood_ratios vector. This is a temporary solution
+                            masked_graph->likelihood_ratios[path[i]] = in_kmer_count_unitig - out_kmer_count_unitig; //likelihood_ratio;
+                        }
                         return { std::make_pair(0, path.size()) };
+                    }
                     else
                         return {};
                 },
                 num_threads);
             }
+            //std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0.01); // Myrthe: For testing. temporary
             return masked_graph;
     }
 
@@ -664,7 +672,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
             auto label_encoder = annot::ColumnCompressed<>::read_label_encoder(files[i]);
             for (size_t c = 0; c < label_encoder.size(); ++c) {
                 auto label = label_encoder.decode(c);
-                if (labels_in.count(label)) sum_widths_in += std::pow(2, col_width); // QUESTION; Will arithmetics be ok despite type conversions? Seems correct
+                if (labels_in.count(label)) sum_widths_in += std::pow(2, col_width);
                 if (labels_out.count(label)) sum_widths_out += std::pow(2, col_width);
             }
         }
@@ -678,7 +686,8 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
 
     logger->trace("Allocating count vector");
     // the in and out counts are stored interleaved
-    sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16); //Myrthe later: int_vector_buffer. In that case the function also to be a template too, or a template based argument, if vector_type== buffered_int_vector, then it should be a buffered (// if constexpr (std::is_same_v<vector_type, Bitmap>) { })
+    sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16); //Myrthe later: int_vector_buffer. In that case the function also to be a template too, or a template based argument, if vector_type== buffered_int_vector, then it should be a buffered (// if constexpr (std::is_same_v<vector_type, Bitmap>) { } //sdsl::int_vector_buffer<> counts = aligned_int_vector(
+
     logger->trace("done");
 
     sdsl::bit_vector other_labels(num_labels, false);
@@ -703,8 +712,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
             i = AnnotatedDBG::anno_to_graph_index(i); // indices in the annotation matrix have an offset of 1 compared to those in the graph. Since the mask should be compatible with the graph, we have to convert the index.
             assert(i != DeBruijnGraph::npos);
             set_bit(indicator.data(), i, parallel, MO_RELAXED); // set the corresponding bit to true in the mask, with the 'indicator' representing the mask.
-            //atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED); // TODO Myrthe later: make sure that values do not overflow in neighbouring cells.
-            counts[i*2] += value;
+            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED); // TODO Myrthe later: make sure that values do not overflow in neighbouring cells.
             in_total_kmers += value;
         };
 
@@ -713,8 +721,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
             assert(i != DeBruijnGraph::npos);
             if (add_out_labels_to_mask)
                 set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            //atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
-            counts[i*2 + 1] += value;
+            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
             out_total_kmers += value;
         };
 
@@ -784,6 +791,8 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
             case 3: { value_generator(add_both); } break;
             default: {}
         }
+
+        // Calculate the product of the poisson probabilities and compare with negative binom
     });
 
     if (config.test_by_unitig){         // unitig mode.
@@ -869,7 +878,7 @@ void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
             kept_unitigs.fetch_add(1, MO_RELAXED);
             num_kept_nodes.fetch_add(end - begin, MO_RELAXED);
             for ( ; last < begin; ++last) {
-                unset_bit(mask.data(), path[last], parallel, MO_RELAXED); // QUESTION Myrthe, why does it unset bits, rather than set bit?
+                unset_bit(mask.data(), path[last], parallel, MO_RELAXED);
             }
             last = end;
         }
@@ -883,13 +892,15 @@ void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
 
     masked_graph.set_mask(new bitmap_vector(std::move(mask)));
 
-    logger->trace("Kept {} out of {} unitigs with average length {}",
+    logger->trace("Kept {} out of {} unitigs with average length {} and a total of {} nodes",
                   kept_unitigs, total_unitigs,
                   static_cast<double>(num_kept_nodes + kept_unitigs * (masked_graph.get_k() - 1))
-                      / kept_unitigs);
+                      / kept_unitigs,
+                  num_kept_nodes);
 }
 
-
+//%TODO A post-filtering step is done to filter out short contigs, percentage/cutoff , that is set to by default.  Short unitigs are removed afterwards
+//        % TODO For each contig the confidence score is output
 
 
 } // namespace graph
diff --git a/metagraph/src/graph/differential_tests.cpp b/metagraph/src/graph/differential_tests.cpp
index f45b5d2195..7d3b7d368e 100644
--- a/metagraph/src/graph/differential_tests.cpp
+++ b/metagraph/src/graph/differential_tests.cpp
@@ -2,6 +2,7 @@
 #include <string>
 #include <numbers>
 #include <iostream>
+#include <cassert>
 
 #include "differential_tests.hpp"
 #include "lookup_table_chisqrd_cdf.cpp"
@@ -10,37 +11,28 @@
 // https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/log_factorial_table.hpp
 // cpp https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/log_factorial_table.cpp
 namespace mtg{
-LogFactorialTable::LogFactorialTable(size_t size)
-        : m_size(size)
-{
-    m_table.reserve(m_size);
-    for (size_t i=0; i<size; i++)
-        m_table.push_back(log_factorial(i));
-}
-
-double LogFactorialTable::log_factorial(size_t k) // TODO this can be calculated more efficiently.
-{
-    double res = 0;
-    while (k > 1)
-    {
-        res += log(k);
-        k--;
-    }
-    return res;
-}
-
-double LogFactorialTable::approximate_log_factorial(size_t k) {
-    return k * log(k) - k * log2e_v;
-}
-
-DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t preload_table_size,
-                                   size_t in_total_kmers, size_t out_total_kmers) :
-        preload_table_size(preload_table_size), // similar to the width of the counts vector, this should be the maximum joint coverage over the in_labels resp. out_labels
-        family_wise_error_rate(family_wise_error_rate),
-        total_hypotheses(total_hypotheses),
-        out_total_kmers(out_total_kmers),
-        in_total_kmers(in_total_kmers)
-{}
+//LogFactorialTable::LogFactorialTable(size_t size)
+//        : m_size(size)
+//{
+//    m_table.reserve(m_size);
+//    for (size_t i=0; i<size; i++)
+//        m_table.push_back(log_factorial(i));
+//}
+//
+//double LogFactorialTable::log_factorial(size_t k) // TODO this can be calculated more efficiently.
+//{
+//    double res = 0;
+//    while (k > 1)
+//    {
+//        res += log(k);
+//        k--;
+//    }
+//    return res;
+//}
+//
+//double LogFactorialTable::approximate_log_factorial(size_t k) {
+//    return k * log(k) - k * log2e_v;
+//}
 
 
 //double DifferentialTest::chi_sqrd_1_cdf(double x)// DOF = 1 // https://www.codeproject.com/Articles/432194/How-to-Calculate-the-Chi-Squared-P-Value
@@ -65,82 +57,112 @@ DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_h
 //    return sum * Sc;
 //}
 
-double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution
+//double DifferentialTest::poisson_pmf(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
+//{
+//    if (lambda <= 0) return 0;
+//    if (k < 0) k = 0;
+//    return (-lambda + (k * std::log(lambda)) - lf_table[k]); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
+//}
+//
+//double DifferentialTest::binomial_pmf(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
+//{
+//    if (lambda <= 0) return 0;
+//    if (k < 0) k = 0;
+//    return (-lambda + (k * std::log(lambda)) - lf_table[k]); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
+//}
+//
+
+
+DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t ,
+                                   size_t in_total_kmers, size_t out_total_kmers) : //preload_table_size(preload_table_size), // similar to the width of the counts vector, this should be the maximum joint coverage over the in_labels resp. out_labels
+      family_wise_error_rate(family_wise_error_rate),
+      total_hypotheses(total_hypotheses),
+      out_total_kmers(out_total_kmers),
+      in_total_kmers(in_total_kmers)
+{}
+
+double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
 {
     if (lambda <= 0) return 0;
     if (k < 0) k = 0;
-    return (-lambda + (k * std::log(lambda) - lf_table[k]));
+    return (-lambda + (k * std::log(lambda))); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
 }
 
-//m be the total number of null hypotheses
-// family-wise error rate (FWER)
-bool DifferentialTest::bonferroni_correction(double& pvalue) //  &family_wise_error_rate, std::size_t & total_hypotheses, --> can also be class parameters.
+
+
+bool DifferentialTest::bonferroni_correction(double& pvalue)
 {
-    return pvalue < (family_wise_error_rate / total_hypotheses); // TODO store "family_wise_error_rate / total_hypotheses" as a class variable, corrected_pvalue_threshold (2.4e-8)
+    return pvalue < (family_wise_error_rate / total_hypotheses); //  store "family_wise_error_rate / total_hypotheses" as a class variable, corrected_pvalue_threshold (2.4e-8)
 }
 
 
-size_t DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test
+double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test
     double corrected_pvalue = family_wise_error_rate/total_hypotheses; // Take into account the Bonferonni multiple testing correction
-    Chi2PLookup Chi2PLookupTable;
+    if (family_wise_error_rate ==1)// Myrthe: ugly temporair method to put multiple testing off, and simply use a pvalue of 1 instead.
+        return (double) 3840 / 1000 / 2;//corrected_pvalue = 0.05;
+    Chi2PLookup lookup;
     int low = 0;
-    int high = sizeof(pValues_1); //int high = Chi2PLookupTable.divisor*Chi2PLookupTable.cutoff - 1; //.size()-1;
+    int high = lookup.divisor * lookup.cutoff[0] - 1;
     while (low <= high) {
-        int mid = (low + high) >> 1;
-        if (Chi2PLookupTable.getPValue(mid, 1) < corrected_pvalue)
+        int mid = (low + high) >> 1; // the mean of low and high
+        double current_pvalue = lookup.getPValue(mid / lookup.divisor, 1);
+        if (current_pvalue > corrected_pvalue)
         {low = mid + 1;}
-        else if (Chi2PLookupTable.getPValue(mid, 1) >= corrected_pvalue)
+        else
         {high = mid - 1;}
-//        else if (Chi2PLookupTable.getPValue(mid, 1) == corrected_pvalue)
-//        {return mid/2;}
-    }
-    return low / 2; // Note that 2*LRT is Chi-squared distributed.
+    } // this binary search is not perfect yet: for 0.05 mid = 4000, wheras it should be more like 3840
+    return (double)  low / lookup.divisor / 2.0; // Note that 2*LRT is Chi-squared distributed.
 }
 
 
 // adapted code from kmdiff.
 // previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
-bool DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
+std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
 {
     double mean = (out_sum + in_sum) / static_cast<double>(out_total_kmers + in_total_kmers);
 
-    double null_hypothesis = 0;
-    double alt_hypothesis = 0;
-
-    alt_hypothesis += poisson_prob(out_sum, out_sum);
-    alt_hypothesis += poisson_prob(in_sum, in_sum);
+    double alt_hypothesis = poisson_prob(out_sum, out_sum) +
+            poisson_prob(in_sum, in_sum); // K2 equals N2Theta2 because Theta2 = K2/N2 (?). Shouldn't Theta2 be = K2/|background labels| ?
 
-    null_hypothesis += poisson_prob(out_sum, mean * out_total_kmers);
-    null_hypothesis += poisson_prob(in_sum, mean * in_total_kmers);
+    double null_hypothesis = poisson_prob(out_sum, mean * out_total_kmers) +
+            poisson_prob(in_sum, mean * in_total_kmers);
 
     double likelihood_ratio = alt_hypothesis - null_hypothesis;
 
-    // if (likelihood_ratio > likelihood_ratio_threshold) return true;  // TODO test this instead of the code below.
-
-
-    if (likelihood_ratio < 0) likelihood_ratio = 0;
-    Chi2PLookup Chi2PLookupTable; // generated with https://github.com/MoseleyBioinformaticsLab/chi2plookup
-    double pvalue = Chi2PLookupTable.getPValue(2 * likelihood_ratio, 1);
-
-
-    auto out_sum_normalized = out_sum * in_total_kmers / out_total_kmers;
-
+    double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
     bool sign = false;
-    if (out_sum_normalized < in_sum) sign = true; // TODO shouldn't this always be the case if the p value is significant?
+    if (out_sum_normalized < in_sum) sign = true;
+    if (out_sum_normalized >= in_sum // This checks if the rate for the foreground group is higher compared to the background.
+        and likelihood_ratio > likelihood_ratio_threshold)
+        std::cout << sign;
+    if (out_sum_normalized < in_sum // This checks if the rate for the foreground group is higher compared to the background.
+        and likelihood_ratio > likelihood_ratio_threshold)
+        return std::make_tuple(true, likelihood_ratio);
 
-    bool efficient_test = (likelihood_ratio > likelihood_ratio_threshold);
-    bool normal_test = (sign and bonferroni_correction(pvalue));
-    std::ignore = efficient_test;
-    std::ignore = normal_test;
-
-    //std::cout  << "efficient test: " + std::to_string(likelihood_ratio > likelihood_ratio_threshold) << std::endl;  // TODO test this instead of the code below.
-    //std::cout << "normal test: " + std::to_string(sign and bonferroni_correction(pvalue)) << std::endl;
+//
+//
+//    if (likelihood_ratio < 0) likelihood_ratio = 0;
+//    Chi2PLookup Chi2PLookupTable; // generated with https://github.com/MoseleyBioinformaticsLab/chi2plookup
+//    double pvalue = Chi2PLookupTable.getPValue(2 * likelihood_ratio, 1);
+//
+//
 
-    if (sign)
-        if (bonferroni_correction(pvalue))
-            return true;
+//    bool efficient_test = (likelihood_ratio > likelihood_ratio_threshold);
+//    bool normal_test = (sign and bonferroni_correction(pvalue));
+//    std::ignore = efficient_test;
+//    std::ignore = normal_test;
+//
+//    if ((likelihood_ratio > likelihood_ratio_threshold) != (sign and bonferroni_correction(pvalue))){
+//        std::cout  << "efficient test: " + std::to_string(likelihood_ratio > likelihood_ratio_threshold) << std::endl;  // TODO test this instead of the code below.
+//        std::cout << "normal test: " + std::to_string(sign and bonferroni_correction(pvalue)) << std::endl; // The efficient test always returns false it seems.
+//    }
+//    assert((likelihood_ratio > likelihood_ratio_threshold) == (sign and bonferroni_correction(pvalue)));
+//
+//    if (sign)
+//        if (bonferroni_correction(pvalue))
+//            return std::make_tuple(true, likelihood_ratio);
 
-    return false;
+    return std::make_tuple(false, likelihood_ratio);
 }
 
 } // namespace mtg
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index a0e3ca92f7..23c26518bc 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -4,35 +4,35 @@
 #include <tuple>
 
 namespace mtg {
-class LogFactorialTable {
-  public:
-    LogFactorialTable(size_t size);
-
-    double operator[](size_t k) {
-        if (k < m_size) return m_table[k];
-        return approximate_log_factorial(k);
-    }
-
-  private:
-    double log2e_v = std::log(std::exp(1.0));
-    double log_factorial(size_t k);
-    double approximate_log_factorial(size_t k);
-
-  private:
-    std::vector<double> m_table;
-    size_t m_size;
-};
+//class LogFactorialTable {
+//  public:
+//    LogFactorialTable(size_t size);
+//
+//    double operator[](size_t k) {
+//        if (k < m_size) return m_table[k];
+//        return approximate_log_factorial(k);
+//    }
+//
+//  private:
+//    double log2e_v = std::log(std::exp(1.0));
+//    double log_factorial(size_t k);
+//    double approximate_log_factorial(size_t k);
+//
+//  private:
+//    std::vector<double> m_table;
+//    size_t m_size;
+//};
 
 class DifferentialTest {
   private:
-    size_t preload_table_size;
-    LogFactorialTable lf_table { preload_table_size };
+    // size_t preload_table_size;
+    //LogFactorialTable lf_table { preload_table_size };  // TODO : turn of, this is temporary to do distribution tests.
     double family_wise_error_rate;
     size_t total_hypotheses;
     size_t out_total_kmers;
     size_t in_total_kmers;
     double gamma = std::sqrt(/* pi */ std::atan(1) * 4);
-    size_t likelihood_ratio_threshold = DifferentialTest::lrt_threshold();
+    double likelihood_ratio_threshold = DifferentialTest::lrt_threshold();
 
   public:
     DifferentialTest(double family_wise_error_rate,
@@ -41,16 +41,12 @@ class DifferentialTest {
                      size_t in_total_kmers,
                      size_t out_total_kmers);
 
-    //    double chi_sqrd_1_cdf(double x);
-    //
-    //    static double lower_incomplete_gamma_function(double x);
-
     double poisson_prob(int k, double lambda);
 
     bool bonferroni_correction(double &pvalue);
 
-    size_t lrt_threshold();
+    double lrt_threshold();
 
-    bool likelihood_ratio_test(double in_sum, double out_sum);
+    std::tuple<bool, double> likelihood_ratio_test(double in_sum, double out_sum);
 };
 } // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/representation/base/sequence_graph.cpp b/metagraph/src/graph/representation/base/sequence_graph.cpp
index bc19dede84..5d2e1610b8 100644
--- a/metagraph/src/graph/representation/base/sequence_graph.cpp
+++ b/metagraph/src/graph/representation/base/sequence_graph.cpp
@@ -9,6 +9,7 @@
 #include "common/threads/threading.hpp"
 #include "common/vectors/vector_algorithm.hpp"
 #include "graph/representation/canonical_dbg.hpp"
+#include "graph/representation/masked_graph.hpp"
 
 
 namespace mtg {
@@ -114,7 +115,7 @@ void call_sequences_from(const DeBruijnGraph &graph,
                          bool call_unitigs,
                          uint64_t min_tip_size,
                          bool kmers_in_single_form) {
-    assert(start >= 1 && start <= graph.max_index());
+    assert(start >= 1 && start <= graph.max_index()); // TODO Myrthe: pick as starting node one with the highest signficance or effect .
     assert((min_tip_size <= 1 || call_unitigs)
                 && "tip pruning works only for unitig extraction");
     assert(visited);
@@ -128,6 +129,8 @@ void call_sequences_from(const DeBruijnGraph &graph,
     std::string sequence;
 
     std::vector<std::pair<node_index, char>> targets;
+    const mtg::graph::MaskedDeBruijnGraph *downcasted_graph = // Create a downcasted graph object, such that the likelihood ratio vector can be accessed.
+            dynamic_cast<const mtg::graph::MaskedDeBruijnGraph*>(&graph);
 
     // keep traversing until we have worked off all branches from the queue
     while (queue.size()) {
@@ -153,7 +156,7 @@ void call_sequences_from(const DeBruijnGraph &graph,
             ++progress_bar;
 
             targets.clear();
-            graph.call_outgoing_kmers(node,
+            graph.call_outgoing_kmers(node, // finds the subsequent nodes and adds them to the target vector
                 [&](node_index next, char c) { targets.emplace_back(next, c); }
             );
 
@@ -182,25 +185,48 @@ void call_sequences_from(const DeBruijnGraph &graph,
             }
 
             node_index next_node = DeBruijnGraph::npos;
+            char next_c;
             //  _____.___
             //      \.___
-            for (const auto &[next, c] : targets) {
-                if (next_node == DeBruijnGraph::npos
-                        && !call_unitigs
-                        && !(*visited)[next]) {
-                    (*discovered)[next] = true;
-                    next_node = next;
-                    sequence.push_back(c);
-                    path.push_back(next);
-                } else if (!(*discovered)[next]) {
-                    (*discovered)[next] = true;
-                    queue.push_back(next);
+
+
+                        // Original way
+                        node_index next_node_original = next_node;
+                        for (const auto &[next, c] : targets) {
+                            if (next_node_original == DeBruijnGraph::npos
+                                && !call_unitigs // Myrthe: if not in unitig mode, (which just returns the unitigs)
+                                && !(*visited)[next]) { // Myrthe: return call-back here.  input a callback as input to call_sequences_from , with default return false. also adapt in sequence_graph.hpp
+                                (*discovered)[next] = true; // Myrthe: if the node was not visited yet.
+                                next_node_original = next; // then  `next_node == DeBruijnGraph::npos` becomes false.
+                            }
+                        }
+
+            double next_likelihood = 0; // start with a likelihood of 0, s.t. only unitigs are added with a better Likelihood Ratio.
+            for (const auto &[next, c] : targets) { // If there are multiple descendents.
+                if (!(*visited)[next]){ // the node was not visited yet.
+                    if (!(*discovered)[next]) { // The node was not encountered before.
+                        (*discovered)[next] = true;
+                        queue.push_back(next); // The queue grows a bit faster compared to the original case.
+                        // TODO: make sure that the selected node is not added to the queue --> problems if run on  multiple threads.
+                    }
+                    if (!call_unitigs // In the unitig mode (i.e. if call_unitigs is true), simply the unitigs must be returned, rather than longer contigs
+                        && downcasted_graph // TODO feature is temporary
+                        && downcasted_graph->likelihood_ratios[next] > next_likelihood){ // If the likelihood of this unitig is better than the current likelihood
+                        next_likelihood = downcasted_graph->likelihood_ratios[next];
+                        next_node = next;
+                        next_c = c;
+                    }
                 }
             }
 
+            if (next_node != next_node_original)
+                std::cout << "different node selected compared to original method " <<std::flush;
             if (next_node == DeBruijnGraph::npos)
                 break;
-
+            else {
+                sequence.push_back(next_c);
+                path.push_back(next_node);
+            }
             node = next_node;
         }
 
@@ -321,6 +347,10 @@ void call_sequences(const DeBruijnGraph &graph,
                              "Traverse graph",
                              std::cerr, !common::get_verbose());
 
+//    // Myrthe: TODO define callback
+//    const std::function<bool(node_index, node_index)> &pick_edge
+//            = [](node_index, node_index){ return false; };
+
     auto call_paths_from = [&](node_index node) {
         call_sequences_from(graph,
                             node,
@@ -330,7 +360,9 @@ void call_sequences(const DeBruijnGraph &graph,
                             progress_bar,
                             call_unitigs,
                             min_tip_size,
-                            kmers_in_single_form);
+                            kmers_in_single_form
+                            //pick_edge
+                            );
     };
 
     if (call_unitigs) {
@@ -471,11 +503,20 @@ ::call_source_nodes(const std::function<void(node_index)> &callback) const {
 }
 
 
+void DeBruijnGraph::lrt_resize(size_t size){
+    lrs.reserve(size);
+}; // Myrthe
+void DeBruijnGraph::lrt_set_value(double value, size_t index){
+    lrs[index] = value;
+};
+
 std::ostream& operator<<(std::ostream &out, const DeBruijnGraph &graph) {
     graph.print(out);
     return out;
 }
 
+//DeBruijnGraph::DeBruijnGraph(const std::vector<double> &lrs) : lrs(lrs) {} //Myrthe
+
 // returns the edge rank, starting from zero
 size_t incoming_edge_rank(const SequenceGraph &graph,
                           SequenceGraph::node_index source,
diff --git a/metagraph/src/graph/representation/base/sequence_graph.hpp b/metagraph/src/graph/representation/base/sequence_graph.hpp
index 0f54888a0d..f935e92b33 100644
--- a/metagraph/src/graph/representation/base/sequence_graph.hpp
+++ b/metagraph/src/graph/representation/base/sequence_graph.hpp
@@ -146,6 +146,9 @@ class SequenceGraph {
 
 
 class DeBruijnGraph : public SequenceGraph {
+  private:
+    std::vector<double> lrs1{}; // likelihood ratios
+
   public:
     enum Mode { BASIC = 0, CANONICAL, PRIMARY };
 
@@ -190,7 +193,10 @@ class DeBruijnGraph : public SequenceGraph {
      */
     virtual void call_sequences(const CallPath &callback,
                                 size_t num_threads = 1,
-                                bool kmers_in_single_form = false) const;
+                                bool kmers_in_single_form = false
+//                              ,  const std::function<bool(node_index, node_index)> &pick_edge
+//                                = [](node_index, node_index){ return false; }
+            ) const; //Myrthe
     /**
      * Call all unitigs except short tips, where tips are
      * the unitigs with InDegree(first) + OutDegree(last) < 2.
@@ -239,6 +245,11 @@ class DeBruijnGraph : public SequenceGraph {
 
     // Call all nodes that have no incoming edges
     virtual void call_source_nodes(const std::function<void(node_index)> &callback) const;
+
+    void lrt_resize(size_t size); // Myrthe
+    void lrt_set_value(double value, size_t index);
+    mutable std::vector<double> lrs{}; // likelihood ratios
+
 };
 
 
diff --git a/metagraph/src/graph/representation/masked_graph.cpp b/metagraph/src/graph/representation/masked_graph.cpp
index fd8a109ba9..72dab44601 100644
--- a/metagraph/src/graph/representation/masked_graph.cpp
+++ b/metagraph/src/graph/representation/masked_graph.cpp
@@ -107,21 +107,21 @@ bit_vector_stat get_boss_mask(const DBGSuccinct &dbg_succ,
 void MaskedDeBruijnGraph::call_sequences(const CallPath &callback,
                                          size_t num_threads,
                                          bool kmers_in_single_form) const {
-    if (auto *dbg_succ = dynamic_cast<const DBGSuccinct*>(graph_.get())) {
-        bit_vector_stat mask = get_boss_mask(*dbg_succ, *kmers_in_graph_,
-                                             only_valid_nodes_in_mask_);
-
-        dbg_succ->get_boss().call_sequences([&](std::string&& sequence, auto&& path) {
-            for (auto &node : path) {
-                node = dbg_succ->boss_to_kmer_index(node);
-            }
-            callback(sequence, path);
-
-        }, num_threads, kmers_in_single_form, &mask);
-
-    } else {
+//    if (auto *dbg_succ = dynamic_cast<const DBGSuccinct*>(graph_.get())) {
+//            bit_vector_stat mask = get_boss_mask(*dbg_succ, *kmers_in_graph_,
+//                                             only_valid_nodes_in_mask_);
+//
+//        dbg_succ->get_boss().call_sequences([&](std::string&& sequence, auto&& path) {
+//            for (auto &node : path) {
+//                node = dbg_succ->boss_to_kmer_index(node);
+//            }
+//            callback(sequence, path);
+//
+//        }, num_threads, kmers_in_single_form, &mask);
+//
+//    } else {
         DeBruijnGraph::call_sequences(callback, num_threads, kmers_in_single_form);
-    }
+    //} Myrthe temporaryly mute
 }
 
 void MaskedDeBruijnGraph::call_unitigs(const CallPath &callback,
diff --git a/metagraph/src/graph/representation/masked_graph.hpp b/metagraph/src/graph/representation/masked_graph.hpp
index 20b0bacbee..d5b3bd19d5 100644
--- a/metagraph/src/graph/representation/masked_graph.hpp
+++ b/metagraph/src/graph/representation/masked_graph.hpp
@@ -129,6 +129,8 @@ class MaskedDeBruijnGraph : public DBGWrapper<DeBruijnGraph> {
         });
     }
 
+    std::vector<double> likelihood_ratios{}; // likelihood ratios
+
   private:
     std::unique_ptr<bitmap> kmers_in_graph_;
     bool only_valid_nodes_in_mask_;

From 59d643f0dbf7addf797ab306e15785ad681d04f4 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 18 Sep 2023 10:26:18 +0300
Subject: [PATCH 042/410] fixes

---
 metagraph/src/graph/annotated_graph_algorithm.cpp          | 2 +-
 metagraph/src/graph/representation/base/sequence_graph.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 948847e5da..1409a96caa 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -16,7 +16,7 @@ using mtg::common::logger;
 typedef AnnotatedDBG::node_index node_index;
 typedef AnnotatedDBG::Annotator Annotator;
 typedef AnnotatedDBG::Annotator::Label Label;
-using Column = annot::binmat::BinaryMatrix::Column;
+using Column = annot::matrix::BinaryMatrix::Column;
 
 typedef std::function<size_t()> LabelCountCallback;
 
diff --git a/metagraph/src/graph/representation/base/sequence_graph.cpp b/metagraph/src/graph/representation/base/sequence_graph.cpp
index 5d2e1610b8..f6823db55d 100644
--- a/metagraph/src/graph/representation/base/sequence_graph.cpp
+++ b/metagraph/src/graph/representation/base/sequence_graph.cpp
@@ -185,7 +185,7 @@ void call_sequences_from(const DeBruijnGraph &graph,
             }
 
             node_index next_node = DeBruijnGraph::npos;
-            char next_c;
+            char next_c = '\0';
             //  _____.___
             //      \.___
 

From a60f079978b7f4f7eea31e620f6f4b86b65f3351 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 4 Jan 2024 18:25:23 +0100
Subject: [PATCH 043/410] gitignore updated

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 9e165295f4..4ffe4a85f8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -29,3 +29,4 @@ metagraph/**/build*
 tags
 **/cmake-build-debug
 .idea
+.vscode

From c1f9e0a68527f280e8901b318fc8bbb8b4812004 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 4 Jan 2024 18:33:42 +0100
Subject: [PATCH 044/410] working myrthes differential tests

---
 .../src/graph/annotated_graph_algorithm.cpp   |  8 +-
 metagraph/src/graph/differential_tests.cpp    |  7 +-
 .../src/graph/differential_tests_uemmi.cpp    | 92 +++++++++++++++++++
 .../src/graph/differential_tests_uemmi.hpp    | 35 +++++++
 4 files changed, 138 insertions(+), 4 deletions(-)
 create mode 100644 metagraph/src/graph/differential_tests_uemmi.cpp
 create mode 100644 metagraph/src/graph/differential_tests_uemmi.hpp

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 1409a96caa..f423124c76 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -430,6 +430,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     // check all other labels and round 2 labels
     if (anno_graph && (check_other || labels_in_round2.size() || labels_out_round2.size())) {
+        logger->trace("1");
+
         sdsl::bit_vector union_mask
                 = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
         std::mutex vector_backup_mutex;
@@ -480,8 +482,10 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     // Filter unitigs from masked graph based on filtration criteria
     logger->trace("Filtering out background");
+    logger->trace("hello");
     if (config.count_kmers) {
         auto &[in_total_kmers, out_total_kmers] = total_kmers;
+        logger->trace("I'm here");
 
         if (config.test_by_unitig == false) { // Statistical testing part when k-mer counts are included. // TODO change to test_by_unitig
             auto total_hypotheses = counts.size()/2; // the total number of hypotheses tested.
@@ -490,23 +494,25 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                                       in_total_kmers, out_total_kmers); // similar to the width of the counts vector, the size of the log-factorial table should be the maximum joint coverage over the in_labels resp. out_labels. Convert the width from bits to the number of stored values.
             // std::min(std::pow(2, counts.width()), 10000) TODO Myrthe: limit the power of the width by some reasonable number, that is lower than 4,294,967,296, the current maximum, i.e. (2^32) or calculate it by the maximum of the counts vector.
             const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
+            logger->trace("AAAAAAAAAAAAA1");
             sdsl::bit_vector mask = in_mask;
             size_t total_nodes = masked_graph->num_nodes();
             size_t kept_nodes = 0;
             call_ones(in_mask, [&](node_index node) {
                 uint64_t in_sum = counts[node * 2];
                 uint64_t out_sum = counts[node * 2 + 1];
+                // logger->trace("in_sum {} out_sum {} ", in_sum, out_sum);
                 if (std::get<0>(statistical_model.likelihood_ratio_test(in_sum, out_sum)))
                     ++kept_nodes;
                 else
                     mask[node] = false;
             });
-
             masked_graph->set_mask(new bitmap_vector(std::move(mask)));
 
             logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
 
         } else {
+            logger->trace("AAAAAAAAAAAAA2");
             masked_graph->likelihood_ratios.resize(counts.size()/2+1);
             std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0);
             std::atomic<uint64_t> total_unitigs(0);
diff --git a/metagraph/src/graph/differential_tests.cpp b/metagraph/src/graph/differential_tests.cpp
index 7d3b7d368e..702abe0752 100644
--- a/metagraph/src/graph/differential_tests.cpp
+++ b/metagraph/src/graph/differential_tests.cpp
@@ -7,7 +7,6 @@
 #include "differential_tests.hpp"
 #include "lookup_table_chisqrd_cdf.cpp"
 
-
 // https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/log_factorial_table.hpp
 // cpp https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/log_factorial_table.cpp
 namespace mtg{
@@ -118,7 +117,8 @@ double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold
 // adapted code from kmdiff.
 // previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
 std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
-{
+{   
+    std::cout << "TEST";
     double mean = (out_sum + in_sum) / static_cast<double>(out_total_kmers + in_total_kmers);
 
     double alt_hypothesis = poisson_prob(out_sum, out_sum) +
@@ -128,13 +128,14 @@ std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum,
             poisson_prob(in_sum, mean * in_total_kmers);
 
     double likelihood_ratio = alt_hypothesis - null_hypothesis;
+    std::cout << "threshold " << likelihood_ratio << "ratio " << likelihood_ratio << "\n";
 
     double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
     bool sign = false;
     if (out_sum_normalized < in_sum) sign = true;
     if (out_sum_normalized >= in_sum // This checks if the rate for the foreground group is higher compared to the background.
         and likelihood_ratio > likelihood_ratio_threshold)
-        std::cout << sign;
+        // std::cout << sign;
     if (out_sum_normalized < in_sum // This checks if the rate for the foreground group is higher compared to the background.
         and likelihood_ratio > likelihood_ratio_threshold)
         return std::make_tuple(true, likelihood_ratio);
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
new file mode 100644
index 0000000000..5a1516219c
--- /dev/null
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -0,0 +1,92 @@
+#include <math.h>
+#include <string>
+#include <numbers>
+#include <iostream>
+#include <cassert>
+
+#include "differential_tests.hpp"
+#include "lookup_table_chisqrd_cdf.cpp"
+
+
+// https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/log_factorial_table.hpp
+// cpp https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/log_factorial_table.cpp
+namespace mtg{
+
+DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t ,
+                                   size_t in_total_kmers, size_t out_total_kmers) : 
+      family_wise_error_rate(family_wise_error_rate),
+      total_hypotheses(total_hypotheses),
+      out_total_kmers(out_total_kmers),
+      in_total_kmers(in_total_kmers)
+{}
+
+// loglikelihood of poisson dist.
+double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
+{
+    if (lambda <= 0) return 0;
+    if (k < 0) k = 0;
+    return (-lambda + (k * std::log(lambda))); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
+}
+
+// TODO: implement negative binomial
+
+
+// not in use at the moment
+bool DifferentialTest::bonferroni_correction(double& pvalue)
+{
+    return pvalue < (family_wise_error_rate / total_hypotheses); 
+}
+
+// using chi squared cdf??
+double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test 
+    double corrected_pvalue = family_wise_error_rate/total_hypotheses; // Take into account the Bonferonni multiple testing correction
+    if (family_wise_error_rate ==1)// Myrthe: ugly temporair method to put multiple testing off, and simply use a pvalue of 1 instead.
+        return (double) 3840 / 1000 / 2;//corrected_pvalue = 0.05;
+    Chi2PLookup lookup;
+    int low = 0;
+    int high = lookup.divisor * lookup.cutoff[0] - 1;
+    while (low <= high) {
+        int mid = (low + high) >> 1; // the mean of low and high
+        double current_pvalue = lookup.getPValue(mid / lookup.divisor, 1);
+        if (current_pvalue > corrected_pvalue)
+        {low = mid + 1;}
+        else
+        {high = mid - 1;}
+    } // this binary search is not perfect yet: for 0.05 mid = 4000, wheras it should be more like 3840
+    return (double)  low / lookup.divisor / 2.0; // Note that 2*LRT is Chi-squared distributed.
+}
+
+
+// adapted code from kmdiff.
+// previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
+std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
+{
+    double mean = (out_sum + in_sum) / static_cast<double>(out_total_kmers + in_total_kmers);
+    // std::cout << "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA" << mean << std::endl;
+
+    double alt_hypothesis = poisson_prob(out_sum, out_sum) +
+            poisson_prob(in_sum, in_sum); // K2 equals N2Theta2 because Theta2 = K2/N2 (?). Shouldn't Theta2 be = K2/|background labels| ?
+
+    double null_hypothesis = poisson_prob(out_sum, mean * out_total_kmers) +
+            poisson_prob(in_sum, mean * in_total_kmers);
+
+    double likelihood_ratio = alt_hypothesis - null_hypothesis;
+    // std::cout << "threshold " << likelihood_ratio_threshold << "ratio " << likelihood_ratio << "\n"; 
+    double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
+    //std::cout << "out_sum_normalizes " << out_sum_normalized << "in_sum " << in_sum << "\n";
+    bool sign = false;
+    if (out_sum_normalized < in_sum) sign = true;
+    if ((out_sum_normalized >= in_sum) // This checks if the rate for the foreground group is higher compared to the background.
+        && (likelihood_ratio > likelihood_ratio_threshold)){
+        int lol = 1;
+        lol = 2;}
+    std::cout << (out_sum_normalized < in_sum) << "\n";
+    std::cout << (likelihood_ratio > likelihood_ratio_threshold) << "\n";    
+    if ((out_sum_normalized < in_sum) && (likelihood_ratio > likelihood_ratio_threshold)){
+        std::cout<<"TRUE"<<"\n";
+        return std::make_tuple(true, likelihood_ratio);
+    }
+    return std::make_tuple(false, likelihood_ratio);
+}
+
+} // namespace mtg
diff --git a/metagraph/src/graph/differential_tests_uemmi.hpp b/metagraph/src/graph/differential_tests_uemmi.hpp
new file mode 100644
index 0000000000..839f10e415
--- /dev/null
+++ b/metagraph/src/graph/differential_tests_uemmi.hpp
@@ -0,0 +1,35 @@
+#include <cmath>
+#include <cstdint>
+#include <vector>
+#include <tuple>
+
+namespace mtg {
+
+class DifferentialTest {
+  private:
+    double family_wise_error_rate;
+    size_t total_hypotheses;
+    size_t out_total_kmers;
+    size_t in_total_kmers;
+    const double pi = std::atan(1) * 4;
+    double gamma = std::sqrt(pi);
+    double likelihood_ratio_threshold = DifferentialTest::lrt_threshold();
+
+  public:
+    DifferentialTest(double family_wise_error_rate,
+                     size_t total_hypotheses,
+                     size_t preload_table_size,
+                     size_t in_total_kmers,
+                     size_t out_total_kmers);
+
+    double poisson_prob(int k, double lambda);
+
+    double negative_binomial_prob(int k, double lambda);
+
+    bool bonferroni_correction(double &pvalue);
+
+    double lrt_threshold();
+
+    std::tuple<bool, double> likelihood_ratio_test(double in_sum, double out_sum);
+};
+} // namespace mtg
\ No newline at end of file

From c33ef870984bd3b9052d41d658f8c934d7acb5c0 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 25 Jan 2024 09:34:27 +0100
Subject: [PATCH 045/410] debugging

---
 .../src/graph/annotated_graph_algorithm.cpp   |  38 ++--
 metagraph/src/graph/differential_tests.cpp    | 169 ------------------
 .../src/graph/differential_tests_myrthe.cpp   | 169 ++++++++++++++++++
 .../src/graph/differential_tests_uemmi.cpp    |  25 +--
 4 files changed, 207 insertions(+), 194 deletions(-)
 delete mode 100644 metagraph/src/graph/differential_tests.cpp
 create mode 100644 metagraph/src/graph/differential_tests_myrthe.cpp

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index f423124c76..3787782560 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -245,24 +245,30 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
 
 
 
-    auto count_vector = construct_diff_label_count_vector(
+    auto count_vector = construct_diff_label_count_vector( // called for each group (in and out)
             [&](const ColumnCallback &column_callback) {
             if (config.count_kmers) {
                 assert(files.size() > 0);
                 annot::ColumnCompressed<>::load_columns_and_values(files,
-                    [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
+                    [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { // goes through all files in a group
+                        auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0);
+                        int normalization_constant = 1e9;   //(int) 2**16 / width * column_values_sum
+                        logger->trace("OOOOOOOOOO total_kmers {}", total_kmers);
+                        auto normalization_factor = normalization_constant / total_kmers;
+                        int min_col_width = std::pow(2, 16); //column_values = std::min((int) std::ceil((*column_values)*normalization_factor), min_column_width); // limit to a certain number of bits.
                         column_callback(offset, label, [&](const ValueCallback &value_callback) {
                             assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0);
+                            logger->trace("OOOOOOOOOOO inside count_kmers column_values.size {}", column_values.size());
+                            logger->trace("OOOOOOOOOO column size {}", column->size());
+                            logger->trace("OOOOOOOOOO 0s {}", column->size()-column->num_set_bits());
                             assert(column->num_set_bits() > 0);
                             assert(column->num_set_bits() == column_values.size());
+                            logger->trace("OOOOOOOOOOO inside count_kmers column->num_set_bits() {}", column->num_set_bits());
                             //Normalization. normalize_coverage(column); This constant should be the same for every column. Calculate it from  the max width column. with the max value over all columns being the width of that column devidied by its column_values_sum
-                            int normalization_constant = 1e9;   //(int) 2**16 / width * column_values_sum;
-                            auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0);
-                            auto normalization_factor = normalization_constant / total_kmers;
-                            int min_col_width = std::pow(2, 16); //column_values = std::min((int) std::ceil((*column_values)*normalization_factor), min_column_width); // limit to a certain number of bits.
                             call_ones(*column, [&](uint64_t i) {
                                 auto column_value = column_values[column->rank1(i)-1];
                                 column_value = std::min((int) std::ceil(column_value*normalization_factor), min_col_width); // normalize value
+                                // logger->trace("OOOOOOOOOO column_value {}", column_value);
                                 value_callback(i, column_value);
                             });
                         });
@@ -286,9 +292,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         add_complement
     );
 
-
     auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
-
+/*
     if (config.evaluate_assembly){     // Myrthe temporary: evaluate what k-mers overlap
 
         logger->trace("Evaluate confusion table");
@@ -316,10 +321,11 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         std::cout << std::accumulate(counts.begin(), counts.end(), 0) << std::flush;
         assert(true_positive + false_negative + false_positive == std::accumulate(counts.begin(), counts.end(), 0));
     } // in mask probably only has sequences
-
+*/
 
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     if (check_other && sdsl::util::cnt_one_bits(other_labels)) {
+        logger->trace("OOOOOOOOOOO Checking other labels");
         bool parallel = num_parallel_files > 1;
         size_t j = 0;
         std::atomic_thread_fence(std::memory_order_release);
@@ -345,7 +351,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         std::atomic_thread_fence(std::memory_order_acquire);
     }
 
-
+/*
     if (config.family_wise_error_rate == 0){  // Temporary code to make k-mer distribution
         logger->trace("K-mer distribution");
 
@@ -377,7 +383,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         );
         std::exit(EXIT_SUCCESS);
     }
-
+*/
     return mask_nodes_by_label(graph_ptr, nullptr,
                                std::move(counts), std::move(init_mask),
                                std::move(other_mask),
@@ -430,7 +436,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     // check all other labels and round 2 labels
     if (anno_graph && (check_other || labels_in_round2.size() || labels_out_round2.size())) {
-        logger->trace("1");
+        logger->trace("========= 1");
 
         sdsl::bit_vector union_mask
                 = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
@@ -482,7 +488,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     // Filter unitigs from masked graph based on filtration criteria
     logger->trace("Filtering out background");
-    logger->trace("hello");
     if (config.count_kmers) {
         auto &[in_total_kmers, out_total_kmers] = total_kmers;
         logger->trace("I'm here");
@@ -494,7 +499,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                                       in_total_kmers, out_total_kmers); // similar to the width of the counts vector, the size of the log-factorial table should be the maximum joint coverage over the in_labels resp. out_labels. Convert the width from bits to the number of stored values.
             // std::min(std::pow(2, counts.width()), 10000) TODO Myrthe: limit the power of the width by some reasonable number, that is lower than 4,294,967,296, the current maximum, i.e. (2^32) or calculate it by the maximum of the counts vector.
             const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
-            logger->trace("AAAAAAAAAAAAA1");
+            logger->trace("test_by_unitig == false");
             sdsl::bit_vector mask = in_mask;
             size_t total_nodes = masked_graph->num_nodes();
             size_t kept_nodes = 0;
@@ -502,6 +507,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 uint64_t in_sum = counts[node * 2];
                 uint64_t out_sum = counts[node * 2 + 1];
                 // logger->trace("in_sum {} out_sum {} ", in_sum, out_sum);
+                // logger->trace(std::get<0>(statistical_model.likelihood_ratio_test(in_sum, out_sum)));
                 if (std::get<0>(statistical_model.likelihood_ratio_test(in_sum, out_sum)))
                     ++kept_nodes;
                 else
@@ -817,12 +823,16 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
 
     logger->trace("Populating count matrix");
     generate_columns([&](uint64_t offset, const Label &label, const ValueGenerator &value_generator) {
+        logger->trace("OOOOOOOOOOOOOOOOOOO inside generate_columns");
         std::ignore = offset;// to overcome the error that 'offset' is set but not used.
         uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
+        std::cout << "col_indicator: " << std::to_string(col_indicator) << std::endl;
         if (labels_out.count(label))
             col_indicator |= 2;
 
         ValueCallback add_in = [&](Column i, uint64_t value) {
+            std::cout << "i: " << std::to_string(i) << std::endl;
+            std::cout << "value: " << std::to_string(value) << std::endl;
             count_matrix[i * 2].push_back(value);
         };
 
diff --git a/metagraph/src/graph/differential_tests.cpp b/metagraph/src/graph/differential_tests.cpp
deleted file mode 100644
index 702abe0752..0000000000
--- a/metagraph/src/graph/differential_tests.cpp
+++ /dev/null
@@ -1,169 +0,0 @@
-#include <math.h>
-#include <string>
-#include <numbers>
-#include <iostream>
-#include <cassert>
-
-#include "differential_tests.hpp"
-#include "lookup_table_chisqrd_cdf.cpp"
-
-// https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/log_factorial_table.hpp
-// cpp https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/log_factorial_table.cpp
-namespace mtg{
-//LogFactorialTable::LogFactorialTable(size_t size)
-//        : m_size(size)
-//{
-//    m_table.reserve(m_size);
-//    for (size_t i=0; i<size; i++)
-//        m_table.push_back(log_factorial(i));
-//}
-//
-//double LogFactorialTable::log_factorial(size_t k) // TODO this can be calculated more efficiently.
-//{
-//    double res = 0;
-//    while (k > 1)
-//    {
-//        res += log(k);
-//        k--;
-//    }
-//    return res;
-//}
-//
-//double LogFactorialTable::approximate_log_factorial(size_t k) {
-//    return k * log(k) - k * log2e_v;
-//}
-
-
-//double DifferentialTest::chi_sqrd_1_cdf(double x)// DOF = 1 // https://www.codeproject.com/Articles/432194/How-to-Calculate-the-Chi-Squared-P-Value
-//{
-//    assert(x > 0); // assert that it is positive.
-//    double igf = lower_incomplete_gamma_function(x/0.5);
-//    if(std::isnan(igf) or std::isinf(igf) or igf <= 1e-8) return 1e-14;
-//    return 1.0 - igf / gamma;
-//}
-//
-//double DifferentialTest::lower_incomplete_gamma_function(double x) // lower incomplete_gamma_function lower incomplete. with s = 1/2
-//{ // https://en.wikipedia.org/wiki/Incomplete_gamma_function#Lower_incomplete_gamma_function
-//    double Sc = 2 * std::sqrt(x) * exp(-x);
-//    double s = 0.5; double sum = 1.0; double nominator = 1.0; double denominator = 1.0;
-//    for(int I = 0; I < 200; I++)
-//    {
-//        s++;
-//        nominator *= x;
-//        denominator *= s;
-//        sum += (nominator / denominator);
-//    }
-//    return sum * Sc;
-//}
-
-//double DifferentialTest::poisson_pmf(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
-//{
-//    if (lambda <= 0) return 0;
-//    if (k < 0) k = 0;
-//    return (-lambda + (k * std::log(lambda)) - lf_table[k]); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
-//}
-//
-//double DifferentialTest::binomial_pmf(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
-//{
-//    if (lambda <= 0) return 0;
-//    if (k < 0) k = 0;
-//    return (-lambda + (k * std::log(lambda)) - lf_table[k]); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
-//}
-//
-
-
-DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t ,
-                                   size_t in_total_kmers, size_t out_total_kmers) : //preload_table_size(preload_table_size), // similar to the width of the counts vector, this should be the maximum joint coverage over the in_labels resp. out_labels
-      family_wise_error_rate(family_wise_error_rate),
-      total_hypotheses(total_hypotheses),
-      out_total_kmers(out_total_kmers),
-      in_total_kmers(in_total_kmers)
-{}
-
-double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
-{
-    if (lambda <= 0) return 0;
-    if (k < 0) k = 0;
-    return (-lambda + (k * std::log(lambda))); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
-}
-
-
-
-bool DifferentialTest::bonferroni_correction(double& pvalue)
-{
-    return pvalue < (family_wise_error_rate / total_hypotheses); //  store "family_wise_error_rate / total_hypotheses" as a class variable, corrected_pvalue_threshold (2.4e-8)
-}
-
-
-double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test
-    double corrected_pvalue = family_wise_error_rate/total_hypotheses; // Take into account the Bonferonni multiple testing correction
-    if (family_wise_error_rate ==1)// Myrthe: ugly temporair method to put multiple testing off, and simply use a pvalue of 1 instead.
-        return (double) 3840 / 1000 / 2;//corrected_pvalue = 0.05;
-    Chi2PLookup lookup;
-    int low = 0;
-    int high = lookup.divisor * lookup.cutoff[0] - 1;
-    while (low <= high) {
-        int mid = (low + high) >> 1; // the mean of low and high
-        double current_pvalue = lookup.getPValue(mid / lookup.divisor, 1);
-        if (current_pvalue > corrected_pvalue)
-        {low = mid + 1;}
-        else
-        {high = mid - 1;}
-    } // this binary search is not perfect yet: for 0.05 mid = 4000, wheras it should be more like 3840
-    return (double)  low / lookup.divisor / 2.0; // Note that 2*LRT is Chi-squared distributed.
-}
-
-
-// adapted code from kmdiff.
-// previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
-std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
-{   
-    std::cout << "TEST";
-    double mean = (out_sum + in_sum) / static_cast<double>(out_total_kmers + in_total_kmers);
-
-    double alt_hypothesis = poisson_prob(out_sum, out_sum) +
-            poisson_prob(in_sum, in_sum); // K2 equals N2Theta2 because Theta2 = K2/N2 (?). Shouldn't Theta2 be = K2/|background labels| ?
-
-    double null_hypothesis = poisson_prob(out_sum, mean * out_total_kmers) +
-            poisson_prob(in_sum, mean * in_total_kmers);
-
-    double likelihood_ratio = alt_hypothesis - null_hypothesis;
-    std::cout << "threshold " << likelihood_ratio << "ratio " << likelihood_ratio << "\n";
-
-    double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
-    bool sign = false;
-    if (out_sum_normalized < in_sum) sign = true;
-    if (out_sum_normalized >= in_sum // This checks if the rate for the foreground group is higher compared to the background.
-        and likelihood_ratio > likelihood_ratio_threshold)
-        // std::cout << sign;
-    if (out_sum_normalized < in_sum // This checks if the rate for the foreground group is higher compared to the background.
-        and likelihood_ratio > likelihood_ratio_threshold)
-        return std::make_tuple(true, likelihood_ratio);
-
-//
-//
-//    if (likelihood_ratio < 0) likelihood_ratio = 0;
-//    Chi2PLookup Chi2PLookupTable; // generated with https://github.com/MoseleyBioinformaticsLab/chi2plookup
-//    double pvalue = Chi2PLookupTable.getPValue(2 * likelihood_ratio, 1);
-//
-//
-
-//    bool efficient_test = (likelihood_ratio > likelihood_ratio_threshold);
-//    bool normal_test = (sign and bonferroni_correction(pvalue));
-//    std::ignore = efficient_test;
-//    std::ignore = normal_test;
-//
-//    if ((likelihood_ratio > likelihood_ratio_threshold) != (sign and bonferroni_correction(pvalue))){
-//        std::cout  << "efficient test: " + std::to_string(likelihood_ratio > likelihood_ratio_threshold) << std::endl;  // TODO test this instead of the code below.
-//        std::cout << "normal test: " + std::to_string(sign and bonferroni_correction(pvalue)) << std::endl; // The efficient test always returns false it seems.
-//    }
-//    assert((likelihood_ratio > likelihood_ratio_threshold) == (sign and bonferroni_correction(pvalue)));
-//
-//    if (sign)
-//        if (bonferroni_correction(pvalue))
-//            return std::make_tuple(true, likelihood_ratio);
-
-    return std::make_tuple(false, likelihood_ratio);
-}
-
-} // namespace mtg
diff --git a/metagraph/src/graph/differential_tests_myrthe.cpp b/metagraph/src/graph/differential_tests_myrthe.cpp
new file mode 100644
index 0000000000..e207aa70ac
--- /dev/null
+++ b/metagraph/src/graph/differential_tests_myrthe.cpp
@@ -0,0 +1,169 @@
+// #include <math.h>
+// #include <string>
+// #include <numbers>
+// #include <iostream>
+// #include <cassert>
+
+// #include "differential_tests.hpp"
+// #include "lookup_table_chisqrd_cdf.cpp"
+
+// // https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/log_factorial_table.hpp
+// // cpp https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/log_factorial_table.cpp
+// namespace mtg{
+// //LogFactorialTable::LogFactorialTable(size_t size)
+// //        : m_size(size)
+// //{
+// //    m_table.reserve(m_size);
+// //    for (size_t i=0; i<size; i++)
+// //        m_table.push_back(log_factorial(i));
+// //}
+// //
+// //double LogFactorialTable::log_factorial(size_t k) // TODO this can be calculated more efficiently.
+// //{
+// //    double res = 0;
+// //    while (k > 1)
+// //    {
+// //        res += log(k);
+// //        k--;
+// //    }
+// //    return res;
+// //}
+// //
+// //double LogFactorialTable::approximate_log_factorial(size_t k) {
+// //    return k * log(k) - k * log2e_v;
+// //}
+
+
+// //double DifferentialTest::chi_sqrd_1_cdf(double x)// DOF = 1 // https://www.codeproject.com/Articles/432194/How-to-Calculate-the-Chi-Squared-P-Value
+// //{
+// //    assert(x > 0); // assert that it is positive.
+// //    double igf = lower_incomplete_gamma_function(x/0.5);
+// //    if(std::isnan(igf) or std::isinf(igf) or igf <= 1e-8) return 1e-14;
+// //    return 1.0 - igf / gamma;
+// //}
+// //
+// //double DifferentialTest::lower_incomplete_gamma_function(double x) // lower incomplete_gamma_function lower incomplete. with s = 1/2
+// //{ // https://en.wikipedia.org/wiki/Incomplete_gamma_function#Lower_incomplete_gamma_function
+// //    double Sc = 2 * std::sqrt(x) * exp(-x);
+// //    double s = 0.5; double sum = 1.0; double nominator = 1.0; double denominator = 1.0;
+// //    for(int I = 0; I < 200; I++)
+// //    {
+// //        s++;
+// //        nominator *= x;
+// //        denominator *= s;
+// //        sum += (nominator / denominator);
+// //    }
+// //    return sum * Sc;
+// //}
+
+// //double DifferentialTest::poisson_pmf(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
+// //{
+// //    if (lambda <= 0) return 0;
+// //    if (k < 0) k = 0;
+// //    return (-lambda + (k * std::log(lambda)) - lf_table[k]); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
+// //}
+// //
+// //double DifferentialTest::binomial_pmf(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
+// //{
+// //    if (lambda <= 0) return 0;
+// //    if (k < 0) k = 0;
+// //    return (-lambda + (k * std::log(lambda)) - lf_table[k]); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
+// //}
+// //
+
+
+// DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t ,
+//                                    size_t in_total_kmers, size_t out_total_kmers) : //preload_table_size(preload_table_size), // similar to the width of the counts vector, this should be the maximum joint coverage over the in_labels resp. out_labels
+//       family_wise_error_rate(family_wise_error_rate),
+//       total_hypotheses(total_hypotheses),
+//       out_total_kmers(out_total_kmers),
+//       in_total_kmers(in_total_kmers)
+// {}
+
+// double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
+// {
+//     if (lambda <= 0) return 0;
+//     if (k < 0) k = 0;
+//     return (-lambda + (k * std::log(lambda))); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
+// }
+
+
+
+// bool DifferentialTest::bonferroni_correction(double& pvalue)
+// {
+//     return pvalue < (family_wise_error_rate / total_hypotheses); //  store "family_wise_error_rate / total_hypotheses" as a class variable, corrected_pvalue_threshold (2.4e-8)
+// }
+
+
+// double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test
+//     double corrected_pvalue = family_wise_error_rate/total_hypotheses; // Take into account the Bonferonni multiple testing correction
+//     if (family_wise_error_rate ==1)// Myrthe: ugly temporair method to put multiple testing off, and simply use a pvalue of 1 instead.
+//         return (double) 3840 / 1000 / 2;//corrected_pvalue = 0.05;
+//     Chi2PLookup lookup;
+//     int low = 0;
+//     int high = lookup.divisor * lookup.cutoff[0] - 1;
+//     while (low <= high) {
+//         int mid = (low + high) >> 1; // the mean of low and high
+//         double current_pvalue = lookup.getPValue(mid / lookup.divisor, 1);
+//         if (current_pvalue > corrected_pvalue)
+//         {low = mid + 1;}
+//         else
+//         {high = mid - 1;}
+//     } // this binary search is not perfect yet: for 0.05 mid = 4000, wheras it should be more like 3840
+//     return (double)  low / lookup.divisor / 2.0; // Note that 2*LRT is Chi-squared distributed.
+// }
+
+
+// // adapted code from kmdiff.
+// // previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
+// std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
+// {   
+//     std::cout << "TEST";
+//     double mean = (out_sum + in_sum) / static_cast<double>(out_total_kmers + in_total_kmers);
+
+//     double alt_hypothesis = poisson_prob(out_sum, out_sum) +
+//             poisson_prob(in_sum, in_sum); // K2 equals N2Theta2 because Theta2 = K2/N2 (?). Shouldn't Theta2 be = K2/|background labels| ?
+
+//     double null_hypothesis = poisson_prob(out_sum, mean * out_total_kmers) +
+//             poisson_prob(in_sum, mean * in_total_kmers);
+
+//     double likelihood_ratio = alt_hypothesis - null_hypothesis;
+//     std::cout << "threshold " << likelihood_ratio << "ratio " << likelihood_ratio << "\n";
+
+//     double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
+//     bool sign = false;
+//     if (out_sum_normalized < in_sum) sign = true;
+//     if (out_sum_normalized >= in_sum // This checks if the rate for the foreground group is higher compared to the background.
+//         and likelihood_ratio > likelihood_ratio_threshold)
+//         // std::cout << sign;
+//     if (out_sum_normalized < in_sum // This checks if the rate for the foreground group is higher compared to the background.
+//         and likelihood_ratio > likelihood_ratio_threshold)
+//         return std::make_tuple(true, likelihood_ratio);
+
+// //
+// //
+// //    if (likelihood_ratio < 0) likelihood_ratio = 0;
+// //    Chi2PLookup Chi2PLookupTable; // generated with https://github.com/MoseleyBioinformaticsLab/chi2plookup
+// //    double pvalue = Chi2PLookupTable.getPValue(2 * likelihood_ratio, 1);
+// //
+// //
+
+// //    bool efficient_test = (likelihood_ratio > likelihood_ratio_threshold);
+// //    bool normal_test = (sign and bonferroni_correction(pvalue));
+// //    std::ignore = efficient_test;
+// //    std::ignore = normal_test;
+// //
+// //    if ((likelihood_ratio > likelihood_ratio_threshold) != (sign and bonferroni_correction(pvalue))){
+// //        std::cout  << "efficient test: " + std::to_string(likelihood_ratio > likelihood_ratio_threshold) << std::endl;  // TODO test this instead of the code below.
+// //        std::cout << "normal test: " + std::to_string(sign and bonferroni_correction(pvalue)) << std::endl; // The efficient test always returns false it seems.
+// //    }
+// //    assert((likelihood_ratio > likelihood_ratio_threshold) == (sign and bonferroni_correction(pvalue)));
+// //
+// //    if (sign)
+// //        if (bonferroni_correction(pvalue))
+// //            return std::make_tuple(true, likelihood_ratio);
+
+//     return std::make_tuple(false, likelihood_ratio);
+// }
+
+// } // namespace mtg
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 5a1516219c..2c4dd2ea3b 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -40,6 +40,7 @@ bool DifferentialTest::bonferroni_correction(double& pvalue)
 // using chi squared cdf??
 double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test 
     double corrected_pvalue = family_wise_error_rate/total_hypotheses; // Take into account the Bonferonni multiple testing correction
+    std::cout << "total_hypotheses " << total_hypotheses << "\n";
     if (family_wise_error_rate ==1)// Myrthe: ugly temporair method to put multiple testing off, and simply use a pvalue of 1 instead.
         return (double) 3840 / 1000 / 2;//corrected_pvalue = 0.05;
     Chi2PLookup lookup;
@@ -62,8 +63,11 @@ double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold
 std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
 {
     double mean = (out_sum + in_sum) / static_cast<double>(out_total_kmers + in_total_kmers);
-    // std::cout << "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA" << mean << std::endl;
-
+    std::cout << "out sum " << out_sum << "\n";
+    std::cout << "in sum " << in_sum << "\n";
+    std::cout << "mean " << mean << "\n";
+    std::cout << "out_total_kmers " << out_total_kmers << "\n";
+    std::cout << "in_total_kmers " << in_total_kmers << "\n";
     double alt_hypothesis = poisson_prob(out_sum, out_sum) +
             poisson_prob(in_sum, in_sum); // K2 equals N2Theta2 because Theta2 = K2/N2 (?). Shouldn't Theta2 be = K2/|background labels| ?
 
@@ -74,16 +78,15 @@ std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum,
     // std::cout << "threshold " << likelihood_ratio_threshold << "ratio " << likelihood_ratio << "\n"; 
     double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
     //std::cout << "out_sum_normalizes " << out_sum_normalized << "in_sum " << in_sum << "\n";
-    bool sign = false;
-    if (out_sum_normalized < in_sum) sign = true;
-    if ((out_sum_normalized >= in_sum) // This checks if the rate for the foreground group is higher compared to the background.
-        && (likelihood_ratio > likelihood_ratio_threshold)){
-        int lol = 1;
-        lol = 2;}
-    std::cout << (out_sum_normalized < in_sum) << "\n";
-    std::cout << (likelihood_ratio > likelihood_ratio_threshold) << "\n";    
+    // bool sign = false;
+    // if (out_sum_normalized < in_sum) sign = true;
+    // if ((out_sum_normalized >= in_sum) // This checks if the rate for the foreground group is higher compared to the background.
+    //     && (likelihood_ratio > likelihood_ratio_threshold)){
+    //     int lol = 1;
+    //     lol = 2;}
+    // // std::cout << (out_sum_normalized < in_sum) << "\n";
+    // // std::cout << (likelihood_ratio > likelihood_ratio_threshold) << "\n";    
     if ((out_sum_normalized < in_sum) && (likelihood_ratio > likelihood_ratio_threshold)){
-        std::cout<<"TRUE"<<"\n";
         return std::make_tuple(true, likelihood_ratio);
     }
     return std::make_tuple(false, likelihood_ratio);

From ab86e6da3aaeac119213c8968d7fb768a101a62f Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 1 Feb 2024 11:50:04 +0100
Subject: [PATCH 046/410] add normalisation, dividing by mean and adjust number
 of tested hypotheses

---
 .../src/graph/annotated_graph_algorithm.cpp   | 118 +++++++++++-------
 metagraph/src/graph/differential_tests.hpp    |  33 ++---
 .../src/graph/differential_tests_uemmi.cpp    |  52 ++++----
 .../src/graph/differential_tests_uemmi.hpp    |  35 ------
 .../src/graph/lookup_table_chisqrd_cdf.hpp    |   2 +
 5 files changed, 113 insertions(+), 127 deletions(-)
 delete mode 100644 metagraph/src/graph/differential_tests_uemmi.hpp

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 3787782560..bb629a77cd 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -106,7 +106,7 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
                              const DifferentialAssemblyConfig &config,
                              size_t num_threads);  // For kmer distribution.
 
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>, size_t, size_t, size_t>
 construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
@@ -115,7 +115,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   size_t max_index,
                                   size_t num_labels,
                                   size_t num_threads,
-                                  bool add_out_labels_to_mask);
+                                  bool add_out_labels_to_mask);                              
 
 // Regions of a graph mask which should be kept (i.e., masked in)
 typedef std::vector<std::pair<size_t, size_t>> Intervals;
@@ -152,7 +152,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
                     std::tuple<size_t, size_t> total_kmers,
                     const DifferentialAssemblyConfig &config,
-                    size_t num_threads);
+                    size_t num_threads, size_t num_tested_kmers, size_t in_unique_kmers, size_t out_unique_kmers);
 
 std::shared_ptr<MaskedDeBruijnGraph> // This version of mask_nodes_by_label is for when the columns are already loaded.
 mask_nodes_by_label(const AnnotatedDBG &anno_graph,
@@ -211,7 +211,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
         add_complement
     );
 
-    auto &[counts, init_mask, other_labels, total_kmers] = count_vector; //ERROR  only 3 names provided for structured binding, std::vector<long unsigned int, std::allocator<long unsigned int> > >’ decomposes into 4 elements
+    auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers, in_unique_kmers, out_unique_kmers] = count_vector; //ERROR  only 3 names provided for structured binding, std::vector<long unsigned int, std::allocator<long unsigned int> > >’ decomposes into 4 elements
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     return mask_nodes_by_label(graph_ptr, &anno_graph,
                                std::move(counts), std::move(init_mask),
@@ -219,7 +219,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                                anno_graph.get_annotator().num_labels(),
                                labels_in, labels_out,
                                labels_in_round2, labels_out_round2, total_kmers,
-                               config, num_threads);
+                               config, num_threads, num_tested_kmers, in_unique_kmers, out_unique_kmers);
 }
 
 
@@ -243,32 +243,49 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
 
     logger->trace("Generating initial mask");
 
-
-
     auto count_vector = construct_diff_label_count_vector( // called for each group (in and out)
             [&](const ColumnCallback &column_callback) {
             if (config.count_kmers) {
                 assert(files.size() > 0);
+                // generate a vector of the total number of kmers per sample for each group (in and out)
+                std::vector<int> all_kmers_per_sample_in;
+                std::vector<int> all_kmers_per_sample_out;
+                annot::ColumnCompressed<>::load_columns_and_values(files,
+                    [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { 
+                        auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0) + column_values.size();
+                        if (labels_in.find(label) != labels_in.end())
+                        all_kmers_per_sample_in.push_back(total_kmers);
+                        else if (labels_out.find(label) != labels_out.end())
+                        all_kmers_per_sample_out.push_back(total_kmers);
+                        else throw std::runtime_error("Label not found in labels_in or labels_out");
+                    }
+                );
+                int min_kmer_count_in = *min_element(all_kmers_per_sample_in.begin(), all_kmers_per_sample_in.end());
+                int min_kmer_count_out = *min_element(all_kmers_per_sample_out.begin(), all_kmers_per_sample_out.end());
+                bool subsample = true; // subsample or normalize
                 annot::ColumnCompressed<>::load_columns_and_values(files,
                     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { // goes through all files in a group
-                        auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0);
-                        int normalization_constant = 1e9;   //(int) 2**16 / width * column_values_sum
-                        logger->trace("OOOOOOOOOO total_kmers {}", total_kmers);
+                        auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0) + column_values.size(); 
+                        int normalization_constant = 1e9;   // const to scale normalized values to save as int
                         auto normalization_factor = normalization_constant / total_kmers;
-                        int min_col_width = std::pow(2, 16); //column_values = std::min((int) std::ceil((*column_values)*normalization_factor), min_column_width); // limit to a certain number of bits.
+                        int max_col_width = std::pow(2, 16); // max value for int16_t
+                        int min_kmer_counts;
+                        if (labels_in.find(label) != labels_in.end())
+                            min_kmer_counts = min_kmer_count_in;
+                        else if (labels_out.find(label) != labels_out.end())
+                            min_kmer_counts = min_kmer_count_out;
+                        else throw std::runtime_error("Label not found in labels_in or labels_out");
+                        double scaling = (double) total_kmers / min_kmer_counts; 
                         column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                            assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0);
-                            logger->trace("OOOOOOOOOOO inside count_kmers column_values.size {}", column_values.size());
-                            logger->trace("OOOOOOOOOO column size {}", column->size());
-                            logger->trace("OOOOOOOOOO 0s {}", column->size()-column->num_set_bits());
-                            assert(column->num_set_bits() > 0);
-                            assert(column->num_set_bits() == column_values.size());
-                            logger->trace("OOOOOOOOOOO inside count_kmers column->num_set_bits() {}", column->num_set_bits());
-                            //Normalization. normalize_coverage(column); This constant should be the same for every column. Calculate it from  the max width column. with the max value over all columns being the width of that column devidied by its column_values_sum
+                            assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0); // at least one kmer in sample
+                            assert(column->num_set_bits() > 0); // at least one kmer present in sample
+                            assert(column->num_set_bits() == column_values.size()); // same number of kmers in column as in column_values
                             call_ones(*column, [&](uint64_t i) {
                                 auto column_value = column_values[column->rank1(i)-1];
-                                column_value = std::min((int) std::ceil(column_value*normalization_factor), min_col_width); // normalize value
-                                // logger->trace("OOOOOOOOOO column_value {}", column_value);
+                                if (subsample == true)
+                                    column_value = std::min((int) std::floor(column_value/scaling), max_col_width); // normalize value
+                                else
+                                    column_value = std::min((int) std::ceil(column_value*normalization_factor), max_col_width); // normalize value
                                 value_callback(i, column_value);
                             });
                         });
@@ -292,7 +309,14 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         add_complement
     );
 
-    auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
+    auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers, in_unique_kmers, out_unique_kmers] = count_vector; 
+    logger->trace("OOOOOOOOOOO num_tested_kmers {}", num_tested_kmers);
+    // total_kmers: tuple of in_total_kmers and out_total_kmers (number of kmers in foreground and background)
+    // init_mask: bit_vector of length graph_ptr->max_index() indicating which nodes are in the foreground (size = number of unique kmers total)
+    // counts: int_vector of length graph_ptr->max_index() * 2 indicating the number of in and out labels for each node (size = number of unique kmers total * 2)
+    // other_labels: bit_vector of length num_labels indicating which labels are not in or out labels 
+    logger->trace("OOOOOOOOOOO counts {}", counts.size());
+    logger->trace("OOOOOOOOOOO other_labels {}", other_labels);
 /*
     if (config.evaluate_assembly){     // Myrthe temporary: evaluate what k-mers overlap
 
@@ -324,7 +348,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
 */
 
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
-    if (check_other && sdsl::util::cnt_one_bits(other_labels)) {
+    if (check_other && sdsl::util::cnt_one_bits(other_labels)) { // currently not used
         logger->trace("OOOOOOOOOOO Checking other labels");
         bool parallel = num_parallel_files > 1;
         size_t j = 0;
@@ -334,7 +358,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
             if (count_ones(other_labels, j, j + num_labels_per_file)) {
                 annot::ColumnCompressed<>::merge_load({ files[i] },
                     [&](size_t offset, const auto &, auto&& column) {
-                        auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
+                        auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers, in_unique_kmers, out_unique_kmers] = count_vector;
                         if (other_labels[j + offset]) {
                             call_ones(init_mask, [&](size_t i) { // check how many set bits are also in other labels.
                                 if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
@@ -390,7 +414,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                files.size(),
                                labels_in, labels_out, {}, {},
                                total_kmers,
-                               config, num_threads);
+                               config, num_threads, num_tested_kmers, in_unique_kmers, out_unique_kmers);
 }
 
 std::shared_ptr<MaskedDeBruijnGraph>
@@ -406,8 +430,9 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
                     std::tuple<size_t, size_t> total_kmers,
                     const DifferentialAssemblyConfig &config,
-                    size_t num_threads) {
+                    size_t num_threads, size_t num_tested_kmers, size_t in_unique_kmers, size_t out_unique_kmers) {
     // in and out counts are stored interleaved in the counts vector
+    logger->trace("aaaaaaaaaaaaaaaaaaaaaA counts {}", counts.size());
     assert(counts.size() == init_mask.size() * 2);
 
     bool parallel = num_threads > 1;
@@ -435,8 +460,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     };
 
     // check all other labels and round 2 labels
-    if (anno_graph && (check_other || labels_in_round2.size() || labels_out_round2.size())) {
-        logger->trace("========= 1");
+    if (anno_graph && (check_other || labels_in_round2.size() || labels_out_round2.size())) { // currently not used? only in binary?
 
         sdsl::bit_vector union_mask
                 = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
@@ -456,7 +480,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                           }
                       });
                   };
-
         logger->trace("Checking shared and other labels");
         masked_graph->call_sequences(
                 [&](const std::string &contig, const std::vector<node_index> &path) {
@@ -491,23 +514,21 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     if (config.count_kmers) {
         auto &[in_total_kmers, out_total_kmers] = total_kmers;
         logger->trace("I'm here");
-
-        if (config.test_by_unitig == false) { // Statistical testing part when k-mer counts are included. // TODO change to test_by_unitig
-            auto total_hypotheses = counts.size()/2; // the total number of hypotheses tested.
+        logger->trace("AAAAAAAAAAaa in_unique_kmers {}", in_unique_kmers);
+        logger->trace("AAAAAAAAAAAAAAAA out_unique_kmers {}", out_unique_kmers);
+        if (config.test_by_unitig == false) { // Statistical testing part when k-mer counts are included.
+            auto total_hypotheses = num_tested_kmers; // the total number of hypotheses tested: kmers that are present more in the foreground than in the background.
             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_hypotheses,
                                                       std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
-                                                      in_total_kmers, out_total_kmers); // similar to the width of the counts vector, the size of the log-factorial table should be the maximum joint coverage over the in_labels resp. out_labels. Convert the width from bits to the number of stored values.
-            // std::min(std::pow(2, counts.width()), 10000) TODO Myrthe: limit the power of the width by some reasonable number, that is lower than 4,294,967,296, the current maximum, i.e. (2^32) or calculate it by the maximum of the counts vector.
+                                                      in_total_kmers, out_total_kmers, in_unique_kmers, out_unique_kmers); 
             const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
             logger->trace("test_by_unitig == false");
             sdsl::bit_vector mask = in_mask;
             size_t total_nodes = masked_graph->num_nodes();
             size_t kept_nodes = 0;
-            call_ones(in_mask, [&](node_index node) {
+            call_ones(in_mask, [&](node_index node) { // only run if counts_case > counts_control: tested inside likelihood_ratio_test 
                 uint64_t in_sum = counts[node * 2];
                 uint64_t out_sum = counts[node * 2 + 1];
-                // logger->trace("in_sum {} out_sum {} ", in_sum, out_sum);
-                // logger->trace(std::get<0>(statistical_model.likelihood_ratio_test(in_sum, out_sum)));
                 if (std::get<0>(statistical_model.likelihood_ratio_test(in_sum, out_sum)))
                     ++kept_nodes;
                 else
@@ -517,8 +538,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
 
-        } else {
-            logger->trace("AAAAAAAAAAAAA2");
+        } else { // binary approach
             masked_graph->likelihood_ratios.resize(counts.size()/2+1);
             std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0);
             std::atomic<uint64_t> total_unitigs(0);
@@ -528,7 +548,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, // total number of hypotheses equals the number of unitigs
                                                std::min((int) std::distance(counts.begin(),
                                                std::max_element(counts.begin(), counts.end())), (int) 100000), // Myrthe: the factorial should increase for the unitig mode.
-                                               in_total_kmers, out_total_kmers);
+                                               in_total_kmers, out_total_kmers, in_unique_kmers, out_unique_kmers); // Myrthe: I don't know what to do with the last two arguments.
             update_masked_graph_by_unitig(*masked_graph, // intitial mask
                 [&](const std::string &, const std::vector<node_index> &path) -> Intervals { // return a set of intervals to keep in the graph
                     int in_kmer_count_unitig = 0;
@@ -702,7 +722,7 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 }
 
 
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>, size_t, size_t, size_t>
 construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
@@ -720,14 +740,14 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
     if (config.count_kmers){ // calculate the sum of column widths to derive an upper bound for the required counts vector width.
         assert(labels_in.size() > 0);
         size_t sum_widths_in = 0; size_t sum_widths_out = 0;
-        for (size_t i = 0; i < files.size(); ++i) {
+        for (size_t i = 0; i < files.size(); ++i) { // loopes over files
             const auto &values_fname = utils::remove_suffix(files[i],
                  annot::ColumnCompressed<>::kExtension) + annot::ColumnCompressed<>::kCountExtension;
             sdsl::int_vector_size_type col_size; uint8_t col_width;
             std::ifstream in_stream(values_fname);
             sdsl::int_vector<>::read_header(col_size, col_width, in_stream);
             auto label_encoder = annot::ColumnCompressed<>::read_label_encoder(files[i]);
-            for (size_t c = 0; c < label_encoder.size(); ++c) {
+            for (size_t c = 0; c < label_encoder.size(); ++c) { // loopes over kmers
                 auto label = label_encoder.decode(c);
                 if (labels_in.count(label)) sum_widths_in += std::pow(2, col_width);
                 if (labels_out.count(label)) sum_widths_out += std::pow(2, col_width);
@@ -803,9 +823,19 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
     std::atomic_thread_fence(std::memory_order_acquire);
     logger->trace("done");
     assert( std::accumulate(counts.begin(), counts.end(), 0) > 0); // assert that the sum of the count vector is greater than 0
+    size_t num_tested_kmers = 0;
+    size_t in_unique_kmers = 0; 
+    size_t out_unique_kmers = 0;
+    for(size_t i = 0; i < counts.size(); i+=2){
+        if(counts[i]>0) in_unique_kmers += 1;
+        if(counts[i+1]>0) out_unique_kmers += 1;
+        if (counts[i] > counts[i+1])
+        num_tested_kmers += 1;
+    };
 
     return std::make_tuple(std::move(counts), std::move(indicator), std::move(other_labels),
-                           std::make_tuple(std::move(in_total_kmers), std::move(out_total_kmers)));
+                           std::make_tuple(std::move(in_total_kmers), std::move(out_total_kmers)), 
+                           std::move(num_tested_kmers), std::move(in_unique_kmers), std::move(out_unique_kmers));
 }
 
 
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index 23c26518bc..7de1ae0a59 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -4,34 +4,17 @@
 #include <tuple>
 
 namespace mtg {
-//class LogFactorialTable {
-//  public:
-//    LogFactorialTable(size_t size);
-//
-//    double operator[](size_t k) {
-//        if (k < m_size) return m_table[k];
-//        return approximate_log_factorial(k);
-//    }
-//
-//  private:
-//    double log2e_v = std::log(std::exp(1.0));
-//    double log_factorial(size_t k);
-//    double approximate_log_factorial(size_t k);
-//
-//  private:
-//    std::vector<double> m_table;
-//    size_t m_size;
-//};
 
 class DifferentialTest {
   private:
-    // size_t preload_table_size;
-    //LogFactorialTable lf_table { preload_table_size };  // TODO : turn of, this is temporary to do distribution tests.
     double family_wise_error_rate;
     size_t total_hypotheses;
-    size_t out_total_kmers;
     size_t in_total_kmers;
-    double gamma = std::sqrt(/* pi */ std::atan(1) * 4);
+    size_t out_total_kmers;
+    size_t in_unique_kmers;
+    size_t out_unique_kmers;
+    const double pi = std::atan(1) * 4;
+    double gamma = std::sqrt(pi);
     double likelihood_ratio_threshold = DifferentialTest::lrt_threshold();
 
   public:
@@ -39,10 +22,14 @@ class DifferentialTest {
                      size_t total_hypotheses,
                      size_t preload_table_size,
                      size_t in_total_kmers,
-                     size_t out_total_kmers);
+                     size_t out_total_kmers, 
+                     size_t in_unique_kmers, 
+                     size_t out_unique_kmers);
 
     double poisson_prob(int k, double lambda);
 
+    double negative_binomial_prob(int k, double lambda);
+
     bool bonferroni_correction(double &pvalue);
 
     double lrt_threshold();
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 2c4dd2ea3b..cf53fb2284 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -13,12 +13,14 @@
 namespace mtg{
 
 DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t ,
-                                   size_t in_total_kmers, size_t out_total_kmers) : 
-      family_wise_error_rate(family_wise_error_rate),
-      total_hypotheses(total_hypotheses),
-      out_total_kmers(out_total_kmers),
-      in_total_kmers(in_total_kmers)
-{}
+                                   size_t in_total_kmers, size_t out_total_kmers, size_t in_unique_kmers, size_t out_unique_kmers): 
+    family_wise_error_rate(family_wise_error_rate),
+    total_hypotheses(total_hypotheses),
+    in_total_kmers(in_total_kmers),
+    out_total_kmers(out_total_kmers), 
+    in_unique_kmers(in_unique_kmers),
+    out_unique_kmers(out_unique_kmers)
+    {}
 
 // loglikelihood of poisson dist.
 double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
@@ -28,7 +30,12 @@ double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipe
     return (-lambda + (k * std::log(lambda))); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
 }
 
-// TODO: implement negative binomial
+// TODO: implement negative binomial 
+// loglikelihood of negative binomial dist.
+// p = succes probability = avg occurance of kmers in sampels (either in and out or both together), 
+// r = number of failures = number of kmers in out group, 
+// k = x = number of successes = number kmers in in group
+// needs counts for all samples not just sum or avg
 
 
 // not in use at the moment
@@ -37,23 +44,23 @@ bool DifferentialTest::bonferroni_correction(double& pvalue)
     return pvalue < (family_wise_error_rate / total_hypotheses); 
 }
 
-// using chi squared cdf??
+// return likelihood ratio thereshold corresponding to corrected alpha value
 double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test 
     double corrected_pvalue = family_wise_error_rate/total_hypotheses; // Take into account the Bonferonni multiple testing correction
     std::cout << "total_hypotheses " << total_hypotheses << "\n";
-    if (family_wise_error_rate ==1)// Myrthe: ugly temporair method to put multiple testing off, and simply use a pvalue of 1 instead.
-        return (double) 3840 / 1000 / 2;//corrected_pvalue = 0.05;
+    if (family_wise_error_rate ==1) // TODO: change how to handle turning off mutliple testing correction. currtenly used like a flag, has no statistical meaning
+        return (double) 3840 / 1000 / 2; //corrected_pvalue = 0.05;
     Chi2PLookup lookup;
     int low = 0;
-    int high = lookup.divisor * lookup.cutoff[0] - 1;
-    while (low <= high) {
+    int high = lookup.divisor * lookup.cutoff[0] - 1; 
+    while (low <= high) { 
         int mid = (low + high) >> 1; // the mean of low and high
         double current_pvalue = lookup.getPValue(mid / lookup.divisor, 1);
         if (current_pvalue > corrected_pvalue)
         {low = mid + 1;}
         else
         {high = mid - 1;}
-    } // this binary search is not perfect yet: for 0.05 mid = 4000, wheras it should be more like 3840
+    } // get index where pvalue is closest to corrected pvalue, from that index get the corresponding likelihood ratio threshold
     return (double)  low / lookup.divisor / 2.0; // Note that 2*LRT is Chi-squared distributed.
 }
 
@@ -62,7 +69,12 @@ double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold
 // previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
 std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
 {
-    double mean = (out_sum + in_sum) / static_cast<double>(out_total_kmers + in_total_kmers);
+    // add pseudocounts
+    in_sum += 1;
+    out_sum += 1;
+    double mean = (out_sum + in_sum)*(out_total_kmers+in_total_kmers)/ static_cast<double>(out_unique_kmers + in_unique_kmers);
+    // mean = (out_sum + in_sum)/ static_cast<double>(out_total_kmers + in_total_kmers);
+    // double lambda = (in_sum/in_total_kmers + out_sum/out_total_kmers)/2 * ((out_total_kmers+in_total_kmers)/2);
     std::cout << "out sum " << out_sum << "\n";
     std::cout << "in sum " << in_sum << "\n";
     std::cout << "mean " << mean << "\n";
@@ -75,17 +87,7 @@ std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum,
             poisson_prob(in_sum, mean * in_total_kmers);
 
     double likelihood_ratio = alt_hypothesis - null_hypothesis;
-    // std::cout << "threshold " << likelihood_ratio_threshold << "ratio " << likelihood_ratio << "\n"; 
-    double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
-    //std::cout << "out_sum_normalizes " << out_sum_normalized << "in_sum " << in_sum << "\n";
-    // bool sign = false;
-    // if (out_sum_normalized < in_sum) sign = true;
-    // if ((out_sum_normalized >= in_sum) // This checks if the rate for the foreground group is higher compared to the background.
-    //     && (likelihood_ratio > likelihood_ratio_threshold)){
-    //     int lol = 1;
-    //     lol = 2;}
-    // // std::cout << (out_sum_normalized < in_sum) << "\n";
-    // // std::cout << (likelihood_ratio > likelihood_ratio_threshold) << "\n";    
+    double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;  
     if ((out_sum_normalized < in_sum) && (likelihood_ratio > likelihood_ratio_threshold)){
         return std::make_tuple(true, likelihood_ratio);
     }
diff --git a/metagraph/src/graph/differential_tests_uemmi.hpp b/metagraph/src/graph/differential_tests_uemmi.hpp
deleted file mode 100644
index 839f10e415..0000000000
--- a/metagraph/src/graph/differential_tests_uemmi.hpp
+++ /dev/null
@@ -1,35 +0,0 @@
-#include <cmath>
-#include <cstdint>
-#include <vector>
-#include <tuple>
-
-namespace mtg {
-
-class DifferentialTest {
-  private:
-    double family_wise_error_rate;
-    size_t total_hypotheses;
-    size_t out_total_kmers;
-    size_t in_total_kmers;
-    const double pi = std::atan(1) * 4;
-    double gamma = std::sqrt(pi);
-    double likelihood_ratio_threshold = DifferentialTest::lrt_threshold();
-
-  public:
-    DifferentialTest(double family_wise_error_rate,
-                     size_t total_hypotheses,
-                     size_t preload_table_size,
-                     size_t in_total_kmers,
-                     size_t out_total_kmers);
-
-    double poisson_prob(int k, double lambda);
-
-    double negative_binomial_prob(int k, double lambda);
-
-    bool bonferroni_correction(double &pvalue);
-
-    double lrt_threshold();
-
-    std::tuple<bool, double> likelihood_ratio_test(double in_sum, double out_sum);
-};
-} // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/lookup_table_chisqrd_cdf.hpp b/metagraph/src/graph/lookup_table_chisqrd_cdf.hpp
index eb5fb3e532..94295694da 100644
--- a/metagraph/src/graph/lookup_table_chisqrd_cdf.hpp
+++ b/metagraph/src/graph/lookup_table_chisqrd_cdf.hpp
@@ -2,6 +2,8 @@
 #define CHI2PLOOKUP_H
 
 namespace mtg{
+// https://www.codeproject.com/Articles/432194/How-to-Calculate-the-Chi-Squared-P-Value
+// table containing pvalues for chi squared distribution with degrees of freedom 1
 struct Chi2PLookup
 {
     static const double * pValues[];

From bbd2adf1db0b3807910d224a7063472cba9368bd Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 6 Feb 2024 14:33:49 +0100
Subject: [PATCH 047/410] remove unique kmer calculations

---
 .../src/graph/annotated_graph_algorithm.cpp   | 39 ++++++++-----------
 metagraph/src/graph/differential_tests.hpp    |  4 +-
 .../src/graph/differential_tests_uemmi.cpp    |  9 ++---
 metagraph/src/graph/timer.hpp                 | 23 +++++++++++
 4 files changed, 43 insertions(+), 32 deletions(-)
 create mode 100644 metagraph/src/graph/timer.hpp

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index bb629a77cd..c4813c200f 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -7,6 +7,7 @@
 #include "graph/representation/masked_graph.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 #include "differential_tests.hpp"
+#include "timer.hpp"
 
 namespace mtg {
 namespace graph {
@@ -106,7 +107,7 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
                              const DifferentialAssemblyConfig &config,
                              size_t num_threads);  // For kmer distribution.
 
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>, size_t, size_t, size_t>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>, size_t>
 construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
@@ -152,7 +153,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
                     std::tuple<size_t, size_t> total_kmers,
                     const DifferentialAssemblyConfig &config,
-                    size_t num_threads, size_t num_tested_kmers, size_t in_unique_kmers, size_t out_unique_kmers);
+                    size_t num_threads, size_t num_tested_kmers);
 
 std::shared_ptr<MaskedDeBruijnGraph> // This version of mask_nodes_by_label is for when the columns are already loaded.
 mask_nodes_by_label(const AnnotatedDBG &anno_graph,
@@ -211,7 +212,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
         add_complement
     );
 
-    auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers, in_unique_kmers, out_unique_kmers] = count_vector; //ERROR  only 3 names provided for structured binding, std::vector<long unsigned int, std::allocator<long unsigned int> > >’ decomposes into 4 elements
+    auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers] = count_vector; //ERROR  only 3 names provided for structured binding, std::vector<long unsigned int, std::allocator<long unsigned int> > >’ decomposes into 4 elements
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     return mask_nodes_by_label(graph_ptr, &anno_graph,
                                std::move(counts), std::move(init_mask),
@@ -219,7 +220,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                                anno_graph.get_annotator().num_labels(),
                                labels_in, labels_out,
                                labels_in_round2, labels_out_round2, total_kmers,
-                               config, num_threads, num_tested_kmers, in_unique_kmers, out_unique_kmers);
+                               config, num_threads, num_tested_kmers);
 }
 
 
@@ -248,6 +249,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
             if (config.count_kmers) {
                 assert(files.size() > 0);
                 // generate a vector of the total number of kmers per sample for each group (in and out)
+                Timer t;
                 std::vector<int> all_kmers_per_sample_in;
                 std::vector<int> all_kmers_per_sample_out;
                 annot::ColumnCompressed<>::load_columns_and_values(files,
@@ -262,6 +264,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                 );
                 int min_kmer_count_in = *min_element(all_kmers_per_sample_in.begin(), all_kmers_per_sample_in.end());
                 int min_kmer_count_out = *min_element(all_kmers_per_sample_out.begin(), all_kmers_per_sample_out.end());
+                logger->trace("OOOOOOOOOOO time to generate counts vector {}",  t.elapsed());
                 bool subsample = true; // subsample or normalize
                 annot::ColumnCompressed<>::load_columns_and_values(files,
                     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { // goes through all files in a group
@@ -289,6 +292,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                 value_callback(i, column_value);
                             });
                         });
+                        logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());    
                     }
                 );
             } else {
@@ -309,14 +313,11 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         add_complement
     );
 
-    auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers, in_unique_kmers, out_unique_kmers] = count_vector; 
-    logger->trace("OOOOOOOOOOO num_tested_kmers {}", num_tested_kmers);
+    auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers] = count_vector; 
     // total_kmers: tuple of in_total_kmers and out_total_kmers (number of kmers in foreground and background)
     // init_mask: bit_vector of length graph_ptr->max_index() indicating which nodes are in the foreground (size = number of unique kmers total)
     // counts: int_vector of length graph_ptr->max_index() * 2 indicating the number of in and out labels for each node (size = number of unique kmers total * 2)
     // other_labels: bit_vector of length num_labels indicating which labels are not in or out labels 
-    logger->trace("OOOOOOOOOOO counts {}", counts.size());
-    logger->trace("OOOOOOOOOOO other_labels {}", other_labels);
 /*
     if (config.evaluate_assembly){     // Myrthe temporary: evaluate what k-mers overlap
 
@@ -349,7 +350,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
 
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     if (check_other && sdsl::util::cnt_one_bits(other_labels)) { // currently not used
-        logger->trace("OOOOOOOOOOO Checking other labels");
         bool parallel = num_parallel_files > 1;
         size_t j = 0;
         std::atomic_thread_fence(std::memory_order_release);
@@ -358,7 +358,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
             if (count_ones(other_labels, j, j + num_labels_per_file)) {
                 annot::ColumnCompressed<>::merge_load({ files[i] },
                     [&](size_t offset, const auto &, auto&& column) {
-                        auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers, in_unique_kmers, out_unique_kmers] = count_vector;
+                        auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers] = count_vector;
                         if (other_labels[j + offset]) {
                             call_ones(init_mask, [&](size_t i) { // check how many set bits are also in other labels.
                                 if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
@@ -414,7 +414,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                files.size(),
                                labels_in, labels_out, {}, {},
                                total_kmers,
-                               config, num_threads, num_tested_kmers, in_unique_kmers, out_unique_kmers);
+                               config, num_threads, num_tested_kmers);
 }
 
 std::shared_ptr<MaskedDeBruijnGraph>
@@ -430,7 +430,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
                     std::tuple<size_t, size_t> total_kmers,
                     const DifferentialAssemblyConfig &config,
-                    size_t num_threads, size_t num_tested_kmers, size_t in_unique_kmers, size_t out_unique_kmers) {
+                    size_t num_threads, size_t num_tested_kmers) {
     // in and out counts are stored interleaved in the counts vector
     logger->trace("aaaaaaaaaaaaaaaaaaaaaA counts {}", counts.size());
     assert(counts.size() == init_mask.size() * 2);
@@ -514,13 +514,11 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     if (config.count_kmers) {
         auto &[in_total_kmers, out_total_kmers] = total_kmers;
         logger->trace("I'm here");
-        logger->trace("AAAAAAAAAAaa in_unique_kmers {}", in_unique_kmers);
-        logger->trace("AAAAAAAAAAAAAAAA out_unique_kmers {}", out_unique_kmers);
         if (config.test_by_unitig == false) { // Statistical testing part when k-mer counts are included.
             auto total_hypotheses = num_tested_kmers; // the total number of hypotheses tested: kmers that are present more in the foreground than in the background.
             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_hypotheses,
                                                       std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
-                                                      in_total_kmers, out_total_kmers, in_unique_kmers, out_unique_kmers); 
+                                                      in_total_kmers, out_total_kmers); 
             const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
             logger->trace("test_by_unitig == false");
             sdsl::bit_vector mask = in_mask;
@@ -548,7 +546,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, // total number of hypotheses equals the number of unitigs
                                                std::min((int) std::distance(counts.begin(),
                                                std::max_element(counts.begin(), counts.end())), (int) 100000), // Myrthe: the factorial should increase for the unitig mode.
-                                               in_total_kmers, out_total_kmers, in_unique_kmers, out_unique_kmers); // Myrthe: I don't know what to do with the last two arguments.
+                                               in_total_kmers, out_total_kmers); // Myrthe: I don't know what to do with the last two arguments.
             update_masked_graph_by_unitig(*masked_graph, // intitial mask
                 [&](const std::string &, const std::vector<node_index> &path) -> Intervals { // return a set of intervals to keep in the graph
                     int in_kmer_count_unitig = 0;
@@ -722,7 +720,7 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 }
 
 
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>, size_t, size_t, size_t>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>, size_t>
 construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
@@ -824,18 +822,14 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
     logger->trace("done");
     assert( std::accumulate(counts.begin(), counts.end(), 0) > 0); // assert that the sum of the count vector is greater than 0
     size_t num_tested_kmers = 0;
-    size_t in_unique_kmers = 0; 
-    size_t out_unique_kmers = 0;
     for(size_t i = 0; i < counts.size(); i+=2){
-        if(counts[i]>0) in_unique_kmers += 1;
-        if(counts[i+1]>0) out_unique_kmers += 1;
         if (counts[i] > counts[i+1])
         num_tested_kmers += 1;
     };
 
     return std::make_tuple(std::move(counts), std::move(indicator), std::move(other_labels),
                            std::make_tuple(std::move(in_total_kmers), std::move(out_total_kmers)), 
-                           std::move(num_tested_kmers), std::move(in_unique_kmers), std::move(out_unique_kmers));
+                           std::move(num_tested_kmers));
 }
 
 
@@ -853,7 +847,6 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
 
     logger->trace("Populating count matrix");
     generate_columns([&](uint64_t offset, const Label &label, const ValueGenerator &value_generator) {
-        logger->trace("OOOOOOOOOOOOOOOOOOO inside generate_columns");
         std::ignore = offset;// to overcome the error that 'offset' is set but not used.
         uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
         std::cout << "col_indicator: " << std::to_string(col_indicator) << std::endl;
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index 7de1ae0a59..65d20ab0d7 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -22,9 +22,7 @@ class DifferentialTest {
                      size_t total_hypotheses,
                      size_t preload_table_size,
                      size_t in_total_kmers,
-                     size_t out_total_kmers, 
-                     size_t in_unique_kmers, 
-                     size_t out_unique_kmers);
+                     size_t out_total_kmers);
 
     double poisson_prob(int k, double lambda);
 
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index cf53fb2284..ac1de1c435 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -13,13 +13,11 @@
 namespace mtg{
 
 DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t ,
-                                   size_t in_total_kmers, size_t out_total_kmers, size_t in_unique_kmers, size_t out_unique_kmers): 
+                                   size_t in_total_kmers, size_t out_total_kmers): 
     family_wise_error_rate(family_wise_error_rate),
     total_hypotheses(total_hypotheses),
     in_total_kmers(in_total_kmers),
-    out_total_kmers(out_total_kmers), 
-    in_unique_kmers(in_unique_kmers),
-    out_unique_kmers(out_unique_kmers)
+    out_total_kmers(out_total_kmers)
     {}
 
 // loglikelihood of poisson dist.
@@ -72,8 +70,7 @@ std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum,
     // add pseudocounts
     in_sum += 1;
     out_sum += 1;
-    double mean = (out_sum + in_sum)*(out_total_kmers+in_total_kmers)/ static_cast<double>(out_unique_kmers + in_unique_kmers);
-    // mean = (out_sum + in_sum)/ static_cast<double>(out_total_kmers + in_total_kmers);
+    double mean = (out_sum + in_sum)/ static_cast<double>(out_total_kmers + in_total_kmers);
     // double lambda = (in_sum/in_total_kmers + out_sum/out_total_kmers)/2 * ((out_total_kmers+in_total_kmers)/2);
     std::cout << "out sum " << out_sum << "\n";
     std::cout << "in sum " << in_sum << "\n";
diff --git a/metagraph/src/graph/timer.hpp b/metagraph/src/graph/timer.hpp
new file mode 100644
index 0000000000..733cba6052
--- /dev/null
+++ b/metagraph/src/graph/timer.hpp
@@ -0,0 +1,23 @@
+// timer 
+#include <chrono> // for std::chrono functions
+
+class Timer
+{
+private:
+	// Type aliases to make accessing nested type easier
+	using Clock = std::chrono::steady_clock;
+	using Second = std::chrono::duration<double, std::ratio<1> >;
+
+	std::chrono::time_point<Clock> m_beg { Clock::now() };
+
+public:
+	void reset()
+	{
+		m_beg = Clock::now();
+	}
+
+	double elapsed() const
+	{
+		return std::chrono::duration_cast<Second>(Clock::now() - m_beg).count();
+	}
+};
\ No newline at end of file

From 1881acf321d5890052f3f2a0ddbe3be669a20cb2 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 8 Feb 2024 11:35:02 +0100
Subject: [PATCH 048/410] ordered statistics vector added for
 benjamini-yekutieli process

---
 .../src/graph/annotated_graph_algorithm.cpp   | 26 ++++++++++++++++---
 metagraph/src/graph/differential_tests.hpp    |  2 +-
 .../src/graph/differential_tests_uemmi.cpp    | 10 +++++--
 3 files changed, 31 insertions(+), 7 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c4813c200f..ebaabd8f61 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -124,6 +124,11 @@ typedef std::vector<std::pair<size_t, size_t>> Intervals;
 // Returns a vector of kept regions given a unitig and its corresponding path
 typedef std::function<Intervals(const std::string&, const std::vector<node_index>&)> GetKeptIntervals;
 
+// Compare the first element of two pairs (used for sorting)
+bool sortByPValue(const std::pair<int, double>& a, const std::pair<int, double>& b) {
+    return a.first < b.first; // Sort in ascending order of p-values
+}
+
 // Assemble unitigs from the masked graph, then use get_kept_intervals to generate
 // regions which should be masked in. Update the graph mask accordingly.
 void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
@@ -524,14 +529,27 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             sdsl::bit_vector mask = in_mask;
             size_t total_nodes = masked_graph->num_nodes();
             size_t kept_nodes = 0;
-            call_ones(in_mask, [&](node_index node) { // only run if counts_case > counts_control: tested inside likelihood_ratio_test 
+            //std::vector<int> nodes;
+            std::vector<std::pair<double,int>> likelihood_ratios_nodes;
+            call_ones(in_mask, [&](node_index node) { // only run if counts_case > counts_control 
                 uint64_t in_sum = counts[node * 2];
                 uint64_t out_sum = counts[node * 2 + 1];
-                if (std::get<0>(statistical_model.likelihood_ratio_test(in_sum, out_sum)))
-                    ++kept_nodes;
+                double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
+                if (in_sum > out_sum_normalized) {
+                    auto [keep, likelihood_ratio] = statistical_model.likelihood_ratio_test(in_sum, out_sum);
+                    likelihood_ratios_nodes.push_back(std::make_pair(likelihood_ratio, node));
+                    if (keep)
+                        ++kept_nodes;
+                    else
+                        mask[node] = false;
+                }
                 else
-                    mask[node] = false;
+                    mask[node] = false;        
             });
+            std::sort(likelihood_ratios_nodes.begin(), likelihood_ratios_nodes.end(), sortByPValue);
+            logger->trace("likelihood_ratios__nodes_elem {}, {}, {}", likelihood_ratios_nodes[0].first, likelihood_ratios_nodes[1].first, likelihood_ratios_nodes[2].first);
+            logger->trace("likelihood_ratios_nodes_size {}", likelihood_ratios_nodes.size());
+            logger->trace("num_tests {}", total_hypotheses);
             masked_graph->set_mask(new bitmap_vector(std::move(mask)));
 
             logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index 65d20ab0d7..05e6ef8216 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -26,7 +26,7 @@ class DifferentialTest {
 
     double poisson_prob(int k, double lambda);
 
-    double negative_binomial_prob(int k, double lambda);
+    std::tuple<std::vector<int>, std::vector<double>> sorted_pvalues();
 
     bool bonferroni_correction(double &pvalue);
 
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index ac1de1c435..886b09b681 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -35,6 +35,12 @@ double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipe
 // k = x = number of successes = number kmers in in group
 // needs counts for all samples not just sum or avg
 
+std::tuple<std::vector<int>, std::vector<double>> sorted_pvalues(){
+    std::vector<int> nodes_sorted;
+    std::vector<double> pvalues_sorted;
+
+    return std::make_tuple(nodes_sorted, pvalues_sorted);
+}
 
 // not in use at the moment
 bool DifferentialTest::bonferroni_correction(double& pvalue)
@@ -84,8 +90,8 @@ std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum,
             poisson_prob(in_sum, mean * in_total_kmers);
 
     double likelihood_ratio = alt_hypothesis - null_hypothesis;
-    double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;  
-    if ((out_sum_normalized < in_sum) && (likelihood_ratio > likelihood_ratio_threshold)){
+    // double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;  
+    if (likelihood_ratio > likelihood_ratio_threshold){
         return std::make_tuple(true, likelihood_ratio);
     }
     return std::make_tuple(false, likelihood_ratio);

From e00addae8bf7c8c57c12bed19c2e99a85249db10 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 8 Feb 2024 14:31:29 +0100
Subject: [PATCH 049/410] benjamini yekutieli process added

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 15 +++++++++++----
 metagraph/src/graph/differential_tests.hpp        |  2 +-
 metagraph/src/graph/differential_tests_uemmi.cpp  | 15 +++++++++++++++
 3 files changed, 27 insertions(+), 5 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ebaabd8f61..6e2c3c4e0e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -538,15 +538,22 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (in_sum > out_sum_normalized) {
                     auto [keep, likelihood_ratio] = statistical_model.likelihood_ratio_test(in_sum, out_sum);
                     likelihood_ratios_nodes.push_back(std::make_pair(likelihood_ratio, node));
-                    if (keep)
-                        ++kept_nodes;
-                    else
-                        mask[node] = false;
+                    // if (keep)
+                    //     ++kept_nodes;
+                    // else
+                    //     mask[node] = false;
                 }
                 else
                     mask[node] = false;        
             });
+            // get index for benjamini-yekutieli correction
             std::sort(likelihood_ratios_nodes.begin(), likelihood_ratios_nodes.end(), sortByPValue);
+            int max_i = statistical_model.benjamini_yekutieli(likelihood_ratios_nodes, statistical_model.lrt_threshold());
+            // reject all hypotheses with index <= max_i
+            for (int i = 0; i <= max_i; i++){
+                mask[likelihood_ratios_nodes[i].second] = false;
+            }
+            logger->trace("max_i {}", max_i);
             logger->trace("likelihood_ratios__nodes_elem {}, {}, {}", likelihood_ratios_nodes[0].first, likelihood_ratios_nodes[1].first, likelihood_ratios_nodes[2].first);
             logger->trace("likelihood_ratios_nodes_size {}", likelihood_ratios_nodes.size());
             logger->trace("num_tests {}", total_hypotheses);
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index 05e6ef8216..217e0e4586 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -26,7 +26,7 @@ class DifferentialTest {
 
     double poisson_prob(int k, double lambda);
 
-    std::tuple<std::vector<int>, std::vector<double>> sorted_pvalues();
+    int benjamini_yekutieli(std::vector<std::pair<double, int>> likelihood_ratios, double lrt_threshold);
 
     bool bonferroni_correction(double &pvalue);
 
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 886b09b681..1deb2b90eb 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -68,6 +68,21 @@ double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold
     return (double)  low / lookup.divisor / 2.0; // Note that 2*LRT is Chi-squared distributed.
 }
 
+int DifferentialTest::benjamini_yekutieli(std::vector<std::pair<double, int>> likelihood_ratios, double lrt_threshold){
+    int m = likelihood_ratios.size();
+    double gamma = 0.5772156649; // euler-mascheroni constant
+    double c = std::log(m)  + gamma + 1/(2*m); // taylor expansion for sum(1/i) from 1 to m
+    int max_i = 0;
+    for (int i = 0; i <= m-1; i++){
+        double pvalue = likelihood_ratios[i].first;
+        double qvalue = lrt_threshold * (i+1) / (m * c);
+        if (pvalue <= qvalue){
+            max_i = i;
+        } 
+    }
+    return max_i;
+}
+
 
 // adapted code from kmdiff.
 // previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases

From 40531f0d81b9288d65ed42b783aa672de0ed52cc Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Wed, 14 Feb 2024 15:16:37 +0100
Subject: [PATCH 050/410] brunner munzel test added

---
 .../src/graph/annotated_graph_algorithm.cpp   |  38 ++--
 metagraph/src/graph/differential_tests.hpp    |   8 +
 .../src/graph/differential_tests_uemmi.cpp    |  71 ++++++-
 metagraph/src/graph/look_up_table_t_dist.cpp  | 177 ++++++++++++++++++
 metagraph/src/graph/look_up_table_t_dist.hpp  |  16 ++
 5 files changed, 293 insertions(+), 17 deletions(-)
 create mode 100644 metagraph/src/graph/look_up_table_t_dist.cpp
 create mode 100644 metagraph/src/graph/look_up_table_t_dist.hpp

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6e2c3c4e0e..a120e4a2b2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -8,6 +8,7 @@
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 #include "differential_tests.hpp"
 #include "timer.hpp"
+#include <sdust.h>
 
 namespace mtg {
 namespace graph {
@@ -129,6 +130,15 @@ bool sortByPValue(const std::pair<int, double>& a, const std::pair<int, double>&
     return a.first < b.first; // Sort in ascending order of p-values
 }
 
+inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
+    int n;
+    std::unique_ptr<uint64_t, decltype(std::free)*> r {
+        sdust(0, (const uint8_t*)s.data(), s.size(), T, W, &n),
+        std::free
+    };
+    return n > 0;
+}
+
 // Assemble unitigs from the masked graph, then use get_kept_intervals to generate
 // regions which should be masked in. Update the graph mask accordingly.
 void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
@@ -534,17 +544,23 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             call_ones(in_mask, [&](node_index node) { // only run if counts_case > counts_control 
                 uint64_t in_sum = counts[node * 2];
                 uint64_t out_sum = counts[node * 2 + 1];
-                double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
-                if (in_sum > out_sum_normalized) {
-                    auto [keep, likelihood_ratio] = statistical_model.likelihood_ratio_test(in_sum, out_sum);
-                    likelihood_ratios_nodes.push_back(std::make_pair(likelihood_ratio, node));
-                    // if (keep)
-                    //     ++kept_nodes;
-                    // else
-                    //     mask[node] = false;
-                }
-                else
-                    mask[node] = false;        
+                // filter kmers in samples less noise
+                // if (in_sum + out_sum >= int(total_nodes/20)) { // check for minimum occurance of k-mer
+                    double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
+                    if (in_sum > out_sum_normalized) { // check that kmer occurs more in foreground than in background
+                        // check for high complexity
+                        auto kmer_string = graph_ptr->get_node_sequence(node);
+                        auto complexity_low = is_low_complexity(kmer_string);
+                        logger->trace("kmer_string: {}", kmer_string);
+                        logger->trace("complexity_low: {}", complexity_low);
+                        auto [keep, likelihood_ratio] = statistical_model.likelihood_ratio_test(in_sum, out_sum);
+                        likelihood_ratios_nodes.push_back(std::make_pair(likelihood_ratio, node));
+                    }
+                    else
+                        mask[node] = false; 
+                // }
+                // else
+                //     mask[node] = false;               
             });
             // get index for benjamini-yekutieli correction
             std::sort(likelihood_ratios_nodes.begin(), likelihood_ratios_nodes.end(), sortByPValue);
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index 217e0e4586..585746a511 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -2,6 +2,8 @@
 #include <cstdint>
 #include <vector>
 #include <tuple>
+#include <numeric>
+#include "look_up_table_t_dist.hpp"
 
 namespace mtg {
 
@@ -13,6 +15,7 @@ class DifferentialTest {
     size_t out_total_kmers;
     size_t in_unique_kmers;
     size_t out_unique_kmers;
+    TDistributionTable t_table;
     const double pi = std::atan(1) * 4;
     double gamma = std::sqrt(pi);
     double likelihood_ratio_threshold = DifferentialTest::lrt_threshold();
@@ -25,6 +28,7 @@ class DifferentialTest {
                      size_t out_total_kmers);
 
     double poisson_prob(int k, double lambda);
+    std::tuple<bool, double> brunner_munzel_test(std::vector<int> in_counts, std::vector<int> out_counts);
 
     int benjamini_yekutieli(std::vector<std::pair<double, int>> likelihood_ratios, double lrt_threshold);
 
@@ -32,6 +36,10 @@ class DifferentialTest {
 
     double lrt_threshold();
 
+    double get_t_test_alpha(int df, double alpha);
+
+    std::vector<double> get_midranks(std::vector<int> in_counts, int size_in_counts);
+
     std::tuple<bool, double> likelihood_ratio_test(double in_sum, double out_sum);
 };
 } // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 1deb2b90eb..0f49e31196 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -3,9 +3,11 @@
 #include <numbers>
 #include <iostream>
 #include <cassert>
+#include<algorithm>
 
 #include "differential_tests.hpp"
 #include "lookup_table_chisqrd_cdf.cpp"
+#include "look_up_table_t_dist.cpp"
 
 
 // https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/log_factorial_table.hpp
@@ -28,12 +30,69 @@ double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipe
     return (-lambda + (k * std::log(lambda))); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
 }
 
-// TODO: implement negative binomial 
-// loglikelihood of negative binomial dist.
-// p = succes probability = avg occurance of kmers in sampels (either in and out or both together), 
-// r = number of failures = number of kmers in out group, 
-// k = x = number of successes = number kmers in in group
-// needs counts for all samples not just sum or avg
+double DifferentialTest::get_t_test_alpha(int df, double alpha=0.05){
+    return t_table.getCriticalValue(alpha, df);
+}
+
+std::vector<double> DifferentialTest::get_midranks(std::vector<int> in_counts, int size_in_counts){
+    std::vector<double> mid_ranks;
+    std::sort(in_counts.begin(), in_counts.end());
+    double curr_value = in_counts[0];
+    int tied = 1;
+    for (int i = 1; i<size_in_counts; i++){
+        if (in_counts[i] == curr_value){
+            tied++;
+        }
+        else{
+            for (int j = i-tied; j<i; j++){
+                mid_ranks.push_back((i-tied) + (tied+1)/2.0);
+            }
+            curr_value = in_counts[i];
+            tied = 1;
+        }
+    }
+    return mid_ranks;
+}
+
+std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<int> in_counts, std::vector<int> out_counts){
+    std::vector<double> mid_ranks_in;
+    std::vector<double> mid_ranks_out;
+    int m = in_counts.size();
+    int n = out_counts.size();
+    int N = m + n;
+    // populate mid_ranks_in and mid_ranks_out
+    mid_ranks_in = get_midranks(in_counts, m);
+    mid_ranks_out = get_midranks(out_counts, n);
+    // get means of midranks
+    double mean_mid_rank_in = std::accumulate(mid_ranks_in.begin(), mid_ranks_out.end(), 0.0) / mid_ranks_in.size();
+    double mean_mid_rank_out = std::accumulate(mid_ranks_out.begin(), mid_ranks_out.end(), 0.0) / mid_ranks_out.size();
+    // get means
+    double mean_in = std::accumulate(in_counts.begin(), in_counts.end(), 0.0) / m;
+    double mean_out = std::accumulate(out_counts.begin(), out_counts.end(), 0.0) / n;
+    // get variances
+    double var_in = 0;
+    for (int i = 0; i < m; i++) 
+        var_in += (in_counts[i] - mean_in) * (in_counts[i] - mean_in);
+    var_in /= m;
+    double var_out = 0;
+    for (int i = 0; i < n; i++) 
+        var_out += (out_counts[i] - mean_out) * (out_counts[i] - mean_out);
+    var_out /= n;               
+    // get test statistic
+    double b = (mean_mid_rank_out - mean_mid_rank_in) / (N * std::sqrt(var_in/(m*n*n) + var_out/(m*m*n)));
+    // get degrees of freedom with welch-satterthwaite equation
+    double df_approx = (var_in*var_in/m + var_out*var_out/n)*(var_in*var_in/m + var_out*var_out/n) / (((var_in*var_in/m)*(var_in*var_in/m)/(m - 1)) + ((var_out*var_out/n)*(var_out*var_out/n)/(n - 1)));
+    int df = std::floor(df_approx);
+    // get t statistic alpha value
+    double alpha = get_t_test_alpha(df, 0.05);
+    if( std::abs(b) > alpha){
+        return std::tuple(true, b);
+    }
+    else{
+        return std::tuple(false, b);
+    }
+}
+
 
 std::tuple<std::vector<int>, std::vector<double>> sorted_pvalues(){
     std::vector<int> nodes_sorted;
diff --git a/metagraph/src/graph/look_up_table_t_dist.cpp b/metagraph/src/graph/look_up_table_t_dist.cpp
new file mode 100644
index 0000000000..d286cf1dc5
--- /dev/null
+++ b/metagraph/src/graph/look_up_table_t_dist.cpp
@@ -0,0 +1,177 @@
+#include "look_up_table_t_dist.hpp"
+
+namespace mtg{
+
+TDistributionTable::TDistributionTable(){
+    // populate t table
+    ttable = {
+{63.65674116287404, 31.820515953757607, 21.20494878954778, 15.894544844102773, 12.706204736432102, 10.578894993648895, 9.057886686453278, 7.915815088487886, 7.026366229193259, 6.313751514800938, 5.729741646828793, 5.2421835812004245, 4.828817352266332, 4.473742829274344, 4.165299770144734, 3.8947428549775216, 3.6553843546946934, 3.4420225767075423, 3.2505508013349287, 3.077683537207806, 2.920760989329377, 2.7776068539439795, 2.6464232103144254, 2.5257116894741616, 2.414213562399234, 2.310863653908002, 2.2147544978385376, 2.1251081731820607, 2.0412539671463144, 1.9626105055295602, 1.888671341655297, 1.8189932473051216, 1.7531866324961058, 1.6909076558086666, 1.6318516871521922, 1.5757478599917516, 1.5223545069188738, 1.471455315842291, 1.422856077453704, 1.3763819204924506, 1.3318749515232495, 1.289192231805027, 1.248204036372255, 1.2087923504280027, 1.1708495661300697, 1.134277349272028, 1.0989856505519826, 1.0648918403395045, 1.031919949294224, 1.0000000000133888, 0.9690674172073598, 0.9390625058311636, 0.909929988191437, 0.8816185923768406, 0.8540806854769929, 0.8272719459858017, 0.8011510705718047, 0.7756795110623245, 0.7508212380510706, 0.7265425280172028, 0.7028117712516831, 0.679599298235292, 0.6568772224114475, 0.6346192975536905, 0.6128007881488275, 0.591398351407708, 0.570389929680869, 0.5497546521996847, 0.5294727451882802, 0.509525449500063, 0.48989494502750325, 0.47056428121669824, 0.4515173130908831, 0.43273864225081515, 0.41421356237601226, 0.3959280088002068, 0.3778685117779163, 0.3600221530975029, 0.34237652573012073, 0.32491969623407446, 0.3076401696608342, 0.29052685673265466, 0.27356904308281, 0.2567563603681629, 0.2400787590804417, 0.2235264828973866, 0.2070900444281076, 0.19076020221868345, 0.17452793889444299, 0.15838444032458612, 0.14232107570297353, 0.12632937844612596, 0.11040102781582847, 0.09452783117928666, 0.07870170682462048, 0.06291466725365039, 0.04715880287748072, 0.031426266043351345, 0.01570925532366499},
+{9.924843200918074, 6.9645567187558575, 5.642778356253855, 4.848732214442601, 4.3026527299112765, 3.8964253598141156, 3.5782466377934465, 3.3197640478484347, 3.10397669459001, 2.9199855803555175, 2.7604364908497363, 2.620161870372344, 2.4954091950215305, 2.3833781805370715, 2.281930587727806, 2.1894013475993677, 2.1044716744609335, 2.026081465540385, 1.9533673701027838, 1.88561808316415, 1.822241471315457, 1.7627399973517321, 1.7066920748538643, 1.6537377315791386, 1.603567451474554, 1.5559133940653724, 1.5105424144275605, 1.4672504626906038, 1.425858051738111, 1.386206560167348, 1.3481551943097179, 1.311578474677784, 1.2763641429907915, 1.2424114089714633, 1.2096294735180138, 1.177936278131698, 1.1472574406897713, 1.1175253455674767, 1.0886783622929808, 1.060660171779822, 1.0334191830297939, 1.0069080262652816, 0.9810831109075696, 0.9559042387984871, 0.9313342646680642, 0.9073387971584328, 0.8838859347845265, 0.8609460320922787, 0.838491492002007, 0.8164965809277266, 0.7949372637653906, 0.7737910562629935, 0.7530368926378882, 0.7326550066034949, 0.7126268242184065, 0.6929348671835833, 0.6735626653942307, 0.6544946777072819, 0.6357162200174828, 0.6172133998483679, 0.5989730567619356, 0.5809827079749208, 0.5632304986423031, 0.5457051563317493, 0.5283959492674782, 0.511292647969764, 0.4943854899579438, 0.47766514722122577, 0.4611226961935346, 0.44474958999666064, 0.428537632740655, 0.41247895569215276, 0.3965659951405059, 0.3807914718085806, 0.3651483716701108, 0.34962992804883897, 0.33422960488652037, 0.31894108107740377, 0.3037582357761879, 0.2886751346880066, 0.273686016791521, 0.2587852823734314, 0.24396748136845428, 0.22922730239640948, 0.21455956236869947, 0.19995919660748343, 0.1854212494311571, 0.17094086430843036, 0.15651327874141735, 0.14213381065899058, 0.12779785410201047, 0.1135008706242704, 0.09923838186828568, 0.08500596235965967, 0.07079923249231641, 0.05661385167813546, 0.042445511635597274, 0.02828992979293925, 0.014142842782062582},
+{5.840909299756432, 4.540702858698421, 3.896045934310326, 3.4819087603236154, 3.1824463052842638, 2.9505104700830853, 2.7625989631475267, 2.605426823077235, 2.470806798928964, 2.3533634348018277, 2.249391982623321, 2.1562391883828997, 2.071944671616096, 1.9950220773915692, 1.9243196567275627, 1.858928191025433, 1.7981182936180387, 1.7412965707442682, 1.687974263598821, 1.6377443572159065, 1.5902645687655401, 1.5452444955452138, 1.502435808210813, 1.4616245706263693, 1.4226252791180718, 1.3852760997961082, 1.3494350587418316, 1.3149769617707259, 1.2817908833385918, 1.2497781036789275, 1.2188504020687776, 1.1889286353689654, 1.1599415468283474, 1.131824762075477, 1.1045199382951365, 1.0779740395480302, 1.0521387165760707, 1.0269697736327967, 1.0024267081742795, 0.9784723118282994, 0.9550723232754882, 0.9321951249820855, 0.9098114775292693, 0.887894286029608, 0.8664183941244566, 0.8453604017661692, 0.8246985035799587, 0.8044123450904226, 0.7844828945044411, 0.764892328080891, 0.7456239274008808, 0.7266619870900545, 0.7079917317448512, 0.6895992409840244, 0.6714713816904055, 0.6535957466302154, 0.6359605987416174, 0.6185548204735917, 0.6013678676329675, 0.5843897272635452, 0.5676108791382846, 0.5510222604949113, 0.5346152336881659, 0.5183815564691496, 0.5023133546347311, 0.4864030968183422, 0.4706435712183413, 0.45502786408191614, 0.43954933978165817, 0.4242016223388162, 0.4089785782621171, 0.39387430058420564, 0.37888309398939823, 0.36399946093678015, 0.3492180886918387, 0.3345338371879919, 0.3199417276466278, 0.30543693189075377, 0.29101476229312667, 0.27667066230490933, 0.2624001975155142, 0.24819904719843036, 0.2340629963015331, 0.21998792784369317, 0.20596981568247813, 0.19200471762039606, 0.17808876881952823, 0.16421817549652357, 0.15038920887184487, 0.13659819934886067, 0.12284153089989633, 0.1091156356377043, 0.09541698855201315, 0.0817421023918594, 0.0680875226753272, 0.05444982280911219, 0.04082559930101041, 0.027211467048993205, 0.013604054691007653},
+{4.604094871415898, 3.7469473879811375, 3.29762972789088, 2.998527873206613, 2.7764451051977996, 2.6007619950497767, 2.4558919925977225, 2.332872560450991, 2.226099560783447, 2.1318467813362907, 2.0475172974516958, 1.9712324623164712, 1.9015915193277146, 1.8375235136750763, 1.7781921634033344, 1.7229325474018173, 1.6712076831926614, 1.6225779651662389, 1.5766791642054057, 1.5332062737131438, 1.4919014385929652, 1.4525447929995876, 1.4149474075811443, 1.3789457912641057, 1.3443975553416376, 1.311177958114628, 1.2791771247016042, 1.248297790275083, 1.2184534532210476, 1.1895668523501666, 1.1615687025080579, 1.1343966378985335, 1.107994323632405, 1.0823107044779783, 1.0572993662457801, 1.0329179902085548, 1.0091278848124179, 0.9858935819477416, 0.9631824874287078, 0.9409645771975298, 0.9192121322863114, 0.8978995067705849, 0.877002923918743, 0.8565002965357816, 0.8363710681442994, 0.8165960721750959, 0.7971574067759656, 0.7780383232084794, 0.7592231261027962, 0.7406970840912249, 0.7224463495513369, 0.7044578863661645, 0.6867194047581913, 0.6692193023802822, 0.6519466109541042, 0.6348909478382115, 0.6180424719863147, 0.6013918438234638, 0.5849301886256959, 0.5686490630385523, 0.5525404244130159, 0.5365966026747971, 0.5208102744754064, 0.5051744394017492, 0.4896823980457171, 0.47432773175685833, 0.45910428392019254, 0.44400614261789606, 0.4290276245482453, 0.4141632600881611, 0.3994077793971171, 0.3847560994702999, 0.3702033120578769, 0.3557446723752007, 0.3413755885358488, 0.3270916116457206, 0.3128884265020265, 0.2987618428460289, 0.2847077871228848, 0.2707222947059493, 0.25680150254649736, 0.24294164221304113, 0.2291390332873065, 0.2153900770865252, 0.20169125068401902, 0.1880391012021394, 0.17443024035349228, 0.16086133920805445, 0.1473291231652828, 0.13383036711166135, 0.12036189074532097, 0.10692055405043008, 0.09350325290499119, 0.08010691480650665, 0.06672849470070222, 0.05336497089911494, 0.040013341071897586, 0.026670618302631767, 0.013333827192318938},
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+{2.609002565753651, 2.3514645817344597, 2.1909313101671186, 2.0717618680097427, 1.9759053308869141, 1.8951237260856615, 1.8249370363331496, 1.762627336958542, 1.706416604289163, 1.655075500184607, 1.607717203995744, 1.5636801076633802, 1.5224569552519183, 1.4836499276202169, 1.4469410225656096, 1.4120718626905229, 1.3788295361595975, 1.3470364234978434, 1.316542731904429, 1.2872209136140762, 1.2589614234972892, 1.2316694468014109, 1.2052623416403443, 1.179667616143465, 1.1548213110900893, 1.130666693934008, 1.107153194704016, 1.0842355317613954, 1.0618729880249187, 1.0400288075109878, 1.018669688874223, 0.9977653577520378, 0.9772882035877614, 0.9572129695628966, 0.937516486546779, 0.918177443741689, 0.8991761900874957, 0.8804945615834412, 0.8621157305534525, 0.8440240735761362, 0.8262050553596757, 0.8086451262943476, 0.7913316317836735, 0.7742527317565583, 0.7573973290105693, 0.740755005241221, 0.7243159637820782, 0.7080709782221176, 0.6920113461853469, 0.6761288476573099, 0.6604157073271394, 0.6448645604849474, 0.6294684220747873, 0.6142206585549079, 0.5991149622610685, 0.584145328006435, 0.5693060316840769, 0.554591610666087, 0.5399968458175902, 0.525516744964918, 0.5111465276754984, 0.4968816112229256, 0.48271759762458655, 0.46865026165139695, 0.45467553971987623, 0.44078951958618734, 0.4269884307700366, 0.4132686356436407, 0.3996266211274115, 0.38605899093974316, 0.37256245835335694, 0.35913383941517113, 0.34577004659068356, 0.33246808279743206, 0.31922503579529876, 0.3060380729042809, 0.2929044360229078, 0.27982143692277994, 0.2667864527967573, 0.253796922040176, 0.24085034024612378, 0.22794425639730356, 0.21507626923834852, 0.20224402381366433, 0.18944520815695765, 0.17667755011958336, 0.16393881432573004, 0.1512267992432418, 0.13853933435959015, 0.12587427745313953, 0.1132295119504193, 0.10060294436061769, 0.08799250177896151, 0.07539612945104039, 0.06281178839048045, 0.05023745304266885, 0.03767110898749433, 0.02511075067427517, 0.012554379182232546}
+};
+};
+
+double TDistributionTable::getCriticalValue(double alpha, int df){
+    if (df > 150) {
+        throw std::invalid_argument("Degrees of freedom must be less than 150");
+        return 1;
+    }
+    if (alpha*100 != std::round(alpha*100)) {
+        throw std::invalid_argument("Alpha must be a multiple of 0.01");
+        return 1;
+    }
+    // get critical value from t table
+    df += 1;
+    std::vector<double> df_row = ttable[df];
+    int alpha_index = (int)(alpha * 100) - 1;
+    double t_value = df_row[alpha_index];
+    return t_value;
+}    
+}; // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/look_up_table_t_dist.hpp b/metagraph/src/graph/look_up_table_t_dist.hpp
new file mode 100644
index 0000000000..cef1b04253
--- /dev/null
+++ b/metagraph/src/graph/look_up_table_t_dist.hpp
@@ -0,0 +1,16 @@
+#include <iostream>
+#include <cmath>
+#include <vector>
+
+namespace mtg{
+class TDistributionTable {
+private:
+    std::vector<std::vector<double>> ttable;
+
+public:
+    TDistributionTable();
+
+    double getCriticalValue(double alpha, int df);
+
+};
+}
\ No newline at end of file

From 78fcd13e2e1a9f216fd072a5a1f514b12e925830 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 15 Feb 2024 22:19:02 +0100
Subject: [PATCH 051/410] added function for streaming count columns

---
 metagraph/src/common/vectors/transpose.hpp | 48 ++++++++++++++++++++++
 1 file changed, 48 insertions(+)

diff --git a/metagraph/src/common/vectors/transpose.hpp b/metagraph/src/common/vectors/transpose.hpp
index 6117dc30c2..2128a8e1e8 100644
--- a/metagraph/src/common/vectors/transpose.hpp
+++ b/metagraph/src/common/vectors/transpose.hpp
@@ -58,6 +58,54 @@ void call_rows(const std::vector<BitmapPtr> &columns,
     }
 }
 
+// Transpose all columns and call all rows.
+// If there are no columns, call `num_rows` empty rows.
+template <class BitmapPtr, class ValuesPtr, typename PairVector = std::vector<std::pair<uint64_t, uint8_t>>>
+void call_rows(const std::vector<BitmapPtr> &columns,
+               const std::vector<ValuesPtr> &column_values,
+               const std::function<void(const PairVector &)> &callback,
+               uint64_t num_rows = 0,
+               bool show_progress = mtg::common::get_verbose()) {
+    if (!columns.size()) {
+        ProgressBar progress_bar(num_rows, "Constructed rows", std::cerr, !show_progress);
+        PairVector empty;
+        while (num_rows--) {
+            callback(empty);
+        }
+        return;
+    }
+
+    num_rows = columns[0]->size();
+    std::vector<PairVector> rows;
+
+    ProgressBar progress_bar(num_rows, "Constructed rows", std::cerr, !show_progress);
+
+    #pragma omp parallel for ordered num_threads(get_num_threads()) schedule(dynamic) private(rows)
+    for (uint64_t i = 0; i < num_rows; i += kNumRowsInBlock) {
+        uint64_t begin = i;
+        uint64_t end = std::min(i + kNumRowsInBlock, num_rows);
+        assert(begin <= end);
+
+        rows.resize(end - begin);
+
+        for (size_t j = 0; j < columns.size(); ++j) {
+            columns[j]->call_ones_in_range(begin, end,
+                [&](uint64_t i) {
+                    rows[i - begin].emplace_back(j, (*column_values[j])[columns[j]->rank1(i) - 1]);
+                }
+            );
+        }
+        #pragma omp ordered
+        {
+            for (auto &row : rows) {
+                callback(row);
+                ++progress_bar;
+                row.resize(0);
+            }
+        }
+    }
+}
+
 } // namespace utils
 
 #endif // __BIT_VECTOR_UTILS_HPP__

From 462775c5a46dcba30657ad80aa0c33404c8fa5c3 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Mon, 19 Feb 2024 13:43:17 +0100
Subject: [PATCH 052/410] add median normalisation and tidy up brunner munzel
 function

---
 .../src/graph/annotated_graph_algorithm.cpp   | 65 ++++++++++++++++---
 metagraph/src/graph/differential_tests.hpp    |  4 ++
 .../src/graph/differential_tests_uemmi.cpp    | 36 ++++++----
 3 files changed, 83 insertions(+), 22 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a120e4a2b2..b88ffb6ec0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -9,6 +9,7 @@
 #include "differential_tests.hpp"
 #include "timer.hpp"
 #include <sdust.h>
+#include "common/vectors/bit_vector_sdsl.hpp"
 
 namespace mtg {
 namespace graph {
@@ -130,6 +131,48 @@ bool sortByPValue(const std::pair<int, double>& a, const std::pair<int, double>&
     return a.first < b.first; // Sort in ascending order of p-values
 }
 
+// Function for calculating 
+// the median 
+double findMedian(sdsl::int_vector<0> a, 
+                  int n) 
+{ 
+    // If size of the arr[] is even 
+    if (n % 2 == 0) { 
+  
+        // Applying nth_element 
+        // on n/2th index 
+        std::nth_element(a.begin(), 
+                    a.begin() + n / 2, 
+                    a.end()); 
+  
+        // Applying nth_element 
+        // on (n-1)/2 th index 
+        std::nth_element(a.begin(), 
+                    a.begin() + (n - 1) / 2, 
+                    a.end()); 
+  
+        // Find the average of value at 
+        // index N/2 and (N-1)/2 
+        return (double)(a[(n - 1) / 2] 
+                        + a[n / 2]) 
+               / 2.0; 
+    } 
+  
+    // If size of the arr[] is odd 
+    else { 
+  
+        // Applying nth_element 
+        // on n/2 
+        std::nth_element(a.begin(), 
+                    a.begin() + n / 2, 
+                    a.end()); 
+  
+        // Value at index (N/2)th 
+        // is the median 
+        return (double)a[n / 2]; 
+    } 
+} 
+
 inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
     int n;
     std::unique_ptr<uint64_t, decltype(std::free)*> r {
@@ -269,7 +312,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                 std::vector<int> all_kmers_per_sample_out;
                 annot::ColumnCompressed<>::load_columns_and_values(files,
                     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { 
-                        auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0) + column_values.size();
+                        auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0) + column_values.size(); // adding pseudocounts
                         if (labels_in.find(label) != labels_in.end())
                         all_kmers_per_sample_in.push_back(total_kmers);
                         else if (labels_out.find(label) != labels_out.end())
@@ -293,15 +336,17 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                         else if (labels_out.find(label) != labels_out.end())
                             min_kmer_counts = min_kmer_count_out;
                         else throw std::runtime_error("Label not found in labels_in or labels_out");
-                        double scaling = (double) total_kmers / min_kmer_counts; 
+                        // double scaling = (double) total_kmers / min_kmer_counts; 
                         column_callback(offset, label, [&](const ValueCallback &value_callback) {
                             assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0); // at least one kmer in sample
                             assert(column->num_set_bits() > 0); // at least one kmer present in sample
                             assert(column->num_set_bits() == column_values.size()); // same number of kmers in column as in column_values
+                            double median = findMedian(column_values, column_values.size());
                             call_ones(*column, [&](uint64_t i) {
                                 auto column_value = column_values[column->rank1(i)-1];
                                 if (subsample == true)
-                                    column_value = std::min((int) std::floor(column_value/scaling), max_col_width); // normalize value
+                                    // column_value = std::min((int) std::floor(column_value/scaling), max_col_width); // normalize value
+                                    column_value = std::min((int) std::ceil(column_value/median), max_col_width); // normalize value
                                 else
                                     column_value = std::min((int) std::ceil(column_value*normalization_factor), max_col_width); // normalize value
                                 value_callback(i, column_value);
@@ -457,8 +502,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             || config.label_mask_out_unitig_fraction != 1.0
             || config.label_mask_other_unitig_fraction != 1.0;
 
-    size_t num_in_labels = labels_in.size() + labels_in_round2.size();
-    size_t num_out_labels = labels_out.size() + labels_out_round2.size();
+    size_t num_in_labels = labels_in.size() + labels_in_round2.size(); // number of in labels before filtering
+    size_t num_out_labels = labels_out.size() + labels_out_round2.size(); // number of out labels before filtering
 
     bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
             && (config.add_complement || unitig_mode);
@@ -531,6 +576,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         logger->trace("I'm here");
         if (config.test_by_unitig == false) { // Statistical testing part when k-mer counts are included.
             auto total_hypotheses = num_tested_kmers; // the total number of hypotheses tested: kmers that are present more in the foreground than in the background.
+            logger->trace("total_hypotheses {}", total_hypotheses);
             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_hypotheses,
                                                       std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
                                                       in_total_kmers, out_total_kmers); 
@@ -549,10 +595,10 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
                     if (in_sum > out_sum_normalized) { // check that kmer occurs more in foreground than in background
                         // check for high complexity
-                        auto kmer_string = graph_ptr->get_node_sequence(node);
-                        auto complexity_low = is_low_complexity(kmer_string);
-                        logger->trace("kmer_string: {}", kmer_string);
-                        logger->trace("complexity_low: {}", complexity_low);
+                        // auto kmer_string = graph_ptr->get_node_sequence(node);
+                        // auto complexity_low = is_low_complexity(kmer_string);
+                        // logger->trace("kmer_string: {}", kmer_string);
+                        // logger->trace("complexity_low: {}", complexity_low);
                         auto [keep, likelihood_ratio] = statistical_model.likelihood_ratio_test(in_sum, out_sum);
                         likelihood_ratios_nodes.push_back(std::make_pair(likelihood_ratio, node));
                     }
@@ -564,6 +610,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             });
             // get index for benjamini-yekutieli correction
             std::sort(likelihood_ratios_nodes.begin(), likelihood_ratios_nodes.end(), sortByPValue);
+            // double alpha = statistical_model.get_t_test_alpha(statistical_model.get_df_approx(), 0.05) // 0.05 is the alpha value, TODO: add params.
             int max_i = statistical_model.benjamini_yekutieli(likelihood_ratios_nodes, statistical_model.lrt_threshold());
             // reject all hypotheses with index <= max_i
             for (int i = 0; i <= max_i; i++){
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index 585746a511..dd48ccf45d 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -41,5 +41,9 @@ class DifferentialTest {
     std::vector<double> get_midranks(std::vector<int> in_counts, int size_in_counts);
 
     std::tuple<bool, double> likelihood_ratio_test(double in_sum, double out_sum);
+
+    int get_df_approx(std::vector<int> in_counts, std::vector<int> out_counts);
+
+    double get_var(std::vector<int> counts, int n);
 };
 } // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 0f49e31196..691da48367 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -6,8 +6,7 @@
 #include<algorithm>
 
 #include "differential_tests.hpp"
-#include "lookup_table_chisqrd_cdf.cpp"
-#include "look_up_table_t_dist.cpp"
+#include "lookup_table_chisqrd_cdf.hpp"
 
 
 // https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/log_factorial_table.hpp
@@ -54,6 +53,24 @@ std::vector<double> DifferentialTest::get_midranks(std::vector<int> in_counts, i
     return mid_ranks;
 }
 
+int DifferentialTest::get_df_approx(std::vector<int> in_counts, std::vector<int> out_counts){
+    int m = in_counts.size();
+    int n = out_counts.size();
+    double var_in = get_var(in_counts, m);
+    double var_out = get_var(out_counts, n);
+    double df_approx = (var_in*var_in/m + var_out*var_out/n)*(var_in*var_in/m + var_out*var_out/n) / (((var_in*var_in/m)*(var_in*var_in/m)/(m - 1)) + ((var_out*var_out/n)*(var_out*var_out/n)/(n - 1)));
+    return int(std::floor(df_approx));
+}
+
+double DifferentialTest::get_var(std::vector<int> counts, int n){
+    double mean = std::accumulate(counts.begin(), counts.end(), 0.0) / n;
+    double var = 0;
+    for (int i = 0; i < n; i++) 
+        var += (counts[i] - mean) * (counts[i] - mean);
+    var /= n;
+    return var;
+}
+
 std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<int> in_counts, std::vector<int> out_counts){
     std::vector<double> mid_ranks_in;
     std::vector<double> mid_ranks_out;
@@ -70,22 +87,15 @@ std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<int>
     double mean_in = std::accumulate(in_counts.begin(), in_counts.end(), 0.0) / m;
     double mean_out = std::accumulate(out_counts.begin(), out_counts.end(), 0.0) / n;
     // get variances
-    double var_in = 0;
-    for (int i = 0; i < m; i++) 
-        var_in += (in_counts[i] - mean_in) * (in_counts[i] - mean_in);
-    var_in /= m;
-    double var_out = 0;
-    for (int i = 0; i < n; i++) 
-        var_out += (out_counts[i] - mean_out) * (out_counts[i] - mean_out);
-    var_out /= n;               
+    double var_in = get_var(in_counts, m);
+    double var_out = get_var(out_counts, n);              
     // get test statistic
     double b = (mean_mid_rank_out - mean_mid_rank_in) / (N * std::sqrt(var_in/(m*n*n) + var_out/(m*m*n)));
     // get degrees of freedom with welch-satterthwaite equation
-    double df_approx = (var_in*var_in/m + var_out*var_out/n)*(var_in*var_in/m + var_out*var_out/n) / (((var_in*var_in/m)*(var_in*var_in/m)/(m - 1)) + ((var_out*var_out/n)*(var_out*var_out/n)/(n - 1)));
-    int df = std::floor(df_approx);
+    double df = get_df_approx(in_counts, out_counts);
     // get t statistic alpha value
     double alpha = get_t_test_alpha(df, 0.05);
-    if( std::abs(b) > alpha){
+    if( std::abs(b) < alpha){
         return std::tuple(true, b);
     }
     else{

From c05c9741d4c2edeaa83da7939c7936308afdafae Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Mon, 19 Feb 2024 16:22:25 +0100
Subject: [PATCH 053/410] tested hypotheses count equals to number of unitigs

---
 .../src/graph/annotated_graph_algorithm.cpp   | 38 +++++++++----------
 1 file changed, 17 insertions(+), 21 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b88ffb6ec0..70895eff41 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -9,7 +9,7 @@
 #include "differential_tests.hpp"
 #include "timer.hpp"
 #include <sdust.h>
-#include "common/vectors/bit_vector_sdsl.hpp"
+#include "common/vectors/transpose.hpp"
 
 namespace mtg {
 namespace graph {
@@ -109,7 +109,7 @@ void kmer_distribution_table(const ColumnGenerator &generate_columns,
                              const DifferentialAssemblyConfig &config,
                              size_t num_threads);  // For kmer distribution.
 
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>, size_t>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>>
 construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
@@ -211,7 +211,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
                     std::tuple<size_t, size_t> total_kmers,
                     const DifferentialAssemblyConfig &config,
-                    size_t num_threads, size_t num_tested_kmers);
+                    size_t num_threads);
 
 std::shared_ptr<MaskedDeBruijnGraph> // This version of mask_nodes_by_label is for when the columns are already loaded.
 mask_nodes_by_label(const AnnotatedDBG &anno_graph,
@@ -270,7 +270,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
         add_complement
     );
 
-    auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers] = count_vector; //ERROR  only 3 names provided for structured binding, std::vector<long unsigned int, std::allocator<long unsigned int> > >’ decomposes into 4 elements
+    auto &[counts, init_mask, other_labels, total_kmers] = count_vector; //ERROR  only 3 names provided for structured binding, std::vector<long unsigned int, std::allocator<long unsigned int> > >’ decomposes into 4 elements
     sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
     return mask_nodes_by_label(graph_ptr, &anno_graph,
                                std::move(counts), std::move(init_mask),
@@ -278,7 +278,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                                anno_graph.get_annotator().num_labels(),
                                labels_in, labels_out,
                                labels_in_round2, labels_out_round2, total_kmers,
-                               config, num_threads, num_tested_kmers);
+                               config, num_threads);
 }
 
 
@@ -373,7 +373,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         add_complement
     );
 
-    auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers] = count_vector; 
+    auto &[counts, init_mask, other_labels, total_kmers] = count_vector; 
     // total_kmers: tuple of in_total_kmers and out_total_kmers (number of kmers in foreground and background)
     // init_mask: bit_vector of length graph_ptr->max_index() indicating which nodes are in the foreground (size = number of unique kmers total)
     // counts: int_vector of length graph_ptr->max_index() * 2 indicating the number of in and out labels for each node (size = number of unique kmers total * 2)
@@ -418,7 +418,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
             if (count_ones(other_labels, j, j + num_labels_per_file)) {
                 annot::ColumnCompressed<>::merge_load({ files[i] },
                     [&](size_t offset, const auto &, auto&& column) {
-                        auto &[counts, init_mask, other_labels, total_kmers, num_tested_kmers] = count_vector;
+                        auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
                         if (other_labels[j + offset]) {
                             call_ones(init_mask, [&](size_t i) { // check how many set bits are also in other labels.
                                 if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
@@ -474,7 +474,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                files.size(),
                                labels_in, labels_out, {}, {},
                                total_kmers,
-                               config, num_threads, num_tested_kmers);
+                               config, num_threads);
 }
 
 std::shared_ptr<MaskedDeBruijnGraph>
@@ -490,7 +490,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
                     std::tuple<size_t, size_t> total_kmers,
                     const DifferentialAssemblyConfig &config,
-                    size_t num_threads, size_t num_tested_kmers) {
+                    size_t num_threads) {
     // in and out counts are stored interleaved in the counts vector
     logger->trace("aaaaaaaaaaaaaaaaaaaaaA counts {}", counts.size());
     assert(counts.size() == init_mask.size() * 2);
@@ -575,9 +575,12 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         auto &[in_total_kmers, out_total_kmers] = total_kmers;
         logger->trace("I'm here");
         if (config.test_by_unitig == false) { // Statistical testing part when k-mer counts are included.
-            auto total_hypotheses = num_tested_kmers; // the total number of hypotheses tested: kmers that are present more in the foreground than in the background.
-            logger->trace("total_hypotheses {}", total_hypotheses);
-            auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_hypotheses,
+            std::atomic<uint64_t> total_unitigs(0);
+            masked_graph->call_unitigs([&](const std::string &, const std::vector<node_index> &) {
+                total_unitigs.fetch_add(1, MO_RELAXED);
+            });
+            logger->trace("ooooo unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
+            auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
                                                       std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
                                                       in_total_kmers, out_total_kmers); 
             const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
@@ -619,7 +622,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             logger->trace("max_i {}", max_i);
             logger->trace("likelihood_ratios__nodes_elem {}, {}, {}", likelihood_ratios_nodes[0].first, likelihood_ratios_nodes[1].first, likelihood_ratios_nodes[2].first);
             logger->trace("likelihood_ratios_nodes_size {}", likelihood_ratios_nodes.size());
-            logger->trace("num_tests {}", total_hypotheses);
             masked_graph->set_mask(new bitmap_vector(std::move(mask)));
 
             logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
@@ -808,7 +810,7 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 }
 
 
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>, size_t>
+std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>>
 construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   const std::vector<std::string> &files,
                                   const tsl::hopscotch_set<Label> &labels_in,
@@ -909,15 +911,9 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
     std::atomic_thread_fence(std::memory_order_acquire);
     logger->trace("done");
     assert( std::accumulate(counts.begin(), counts.end(), 0) > 0); // assert that the sum of the count vector is greater than 0
-    size_t num_tested_kmers = 0;
-    for(size_t i = 0; i < counts.size(); i+=2){
-        if (counts[i] > counts[i+1])
-        num_tested_kmers += 1;
-    };
 
     return std::make_tuple(std::move(counts), std::move(indicator), std::move(other_labels),
-                           std::make_tuple(std::move(in_total_kmers), std::move(out_total_kmers)), 
-                           std::move(num_tested_kmers));
+                           std::make_tuple(std::move(in_total_kmers), std::move(out_total_kmers)));
 }
 
 

From 6dec3ce38c20b8dc06ba46dce3636226a9a15e52 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Mon, 19 Feb 2024 16:23:09 +0100
Subject: [PATCH 054/410] remove unused var in brunner munzel test

---
 metagraph/src/graph/differential_tests_uemmi.cpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 691da48367..8548492fb3 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -83,9 +83,6 @@ std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<int>
     // get means of midranks
     double mean_mid_rank_in = std::accumulate(mid_ranks_in.begin(), mid_ranks_out.end(), 0.0) / mid_ranks_in.size();
     double mean_mid_rank_out = std::accumulate(mid_ranks_out.begin(), mid_ranks_out.end(), 0.0) / mid_ranks_out.size();
-    // get means
-    double mean_in = std::accumulate(in_counts.begin(), in_counts.end(), 0.0) / m;
-    double mean_out = std::accumulate(out_counts.begin(), out_counts.end(), 0.0) / n;
     // get variances
     double var_in = get_var(in_counts, m);
     double var_out = get_var(out_counts, n);              

From b8a923f36e10a33a7132ba9a3b069291b839fe41 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Mon, 19 Feb 2024 18:23:35 +0100
Subject: [PATCH 055/410] number of unitigs as number of hypotheses working,
 logging old hypotheses count for comaprison

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 70895eff41..1ee371ed3b 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -908,6 +908,13 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
         }
     });
 
+    size_t num_tested_kmers = 0;
+    for(size_t i = 0; i < counts.size(); i+=2){
+        if (counts[i] > counts[i+1])
+        num_tested_kmers += 1;
+    };
+    logger->trace("num_tested_kmers: {}", num_tested_kmers);
+
     std::atomic_thread_fence(std::memory_order_acquire);
     logger->trace("done");
     assert( std::accumulate(counts.begin(), counts.end(), 0) > 0); // assert that the sum of the count vector is greater than 0

From 0f61957e9f6c93216071ac01b17e79ff29ea7f08 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 20 Feb 2024 00:22:12 +0100
Subject: [PATCH 056/410] adding call to call_rows

---
 .../src/graph/annotated_graph_algorithm.cpp   | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 1ee371ed3b..fd130cddf2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -10,6 +10,7 @@
 #include "timer.hpp"
 #include <sdust.h>
 #include "common/vectors/transpose.hpp"
+#include <typeinfo>
 
 namespace mtg {
 namespace graph {
@@ -305,6 +306,24 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
     auto count_vector = construct_diff_label_count_vector( // called for each group (in and out)
             [&](const ColumnCallback &column_callback) {
             if (config.count_kmers) {
+                // brunner munzel test
+                // call kmer matrix per row
+                std::vector<std::unique_ptr<bit_vector>> columns_all;
+                std::vector<std::unique_ptr<sdsl::int_vector<>>> column_values_all;
+                annot::ColumnCompressed<>::load_columns_and_values(files,
+                    [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { 
+                        columns_all.push_back(std::move(column));
+                        column_values_all.push_back(std::make_unique<sdsl::int_vector<>>(std::move(column_values)));
+                    }
+                );
+                utils::call_rows<std::unique_ptr<bit_vector>,
+                                std::unique_ptr<sdsl::int_vector<>>,
+                                std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
+                                    //std::ignore = row;
+                                    //logger->trace("ooooooooooooooo Row1: {}, row2 {}", std::get<0>(row[0]), std::get<1>(row[0]));
+                                    logger->trace("ooooooooooooooo Row1: {}", typeid(std::get<0>(row[0])).name());
+                                    logger->trace("ooooooooooooooo Row2: {}", std::get<1>(row[0]));
+                });
                 assert(files.size() > 0);
                 // generate a vector of the total number of kmers per sample for each group (in and out)
                 Timer t;

From ebe1c65d8cb619eaa4552941dba139aea059764a Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 20 Feb 2024 14:27:53 +0100
Subject: [PATCH 057/410] set up call_rows to do filtering and testing

---
 .../src/graph/annotated_graph_algorithm.cpp   | 84 ++++++++++++++-----
 1 file changed, 64 insertions(+), 20 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fd130cddf2..de3b3af41a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -206,6 +206,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     sdsl::bit_vector&& mask,
                     sdsl::bit_vector&& other_mask,
                     size_t num_labels,
+                    const std::vector<std::string> &files,
                     const tsl::hopscotch_set<Label> &labels_in,
                     const tsl::hopscotch_set<Label> &labels_out,
                     const tsl::hopscotch_set<Label> &labels_in_round2,
@@ -277,13 +278,14 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                                std::move(counts), std::move(init_mask),
                                std::move(other_mask),
                                anno_graph.get_annotator().num_labels(),
+                               {},
                                labels_in, labels_out,
                                labels_in_round2, labels_out_round2, total_kmers,
                                config, num_threads);
 }
 
 
-
+// creates the necessary data structures for the differential assembly
 std::shared_ptr<MaskedDeBruijnGraph>
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this version i don't need to change at first
                     const std::vector<std::string> &files,
@@ -306,24 +308,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
     auto count_vector = construct_diff_label_count_vector( // called for each group (in and out)
             [&](const ColumnCallback &column_callback) {
             if (config.count_kmers) {
-                // brunner munzel test
-                // call kmer matrix per row
-                std::vector<std::unique_ptr<bit_vector>> columns_all;
-                std::vector<std::unique_ptr<sdsl::int_vector<>>> column_values_all;
-                annot::ColumnCompressed<>::load_columns_and_values(files,
-                    [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { 
-                        columns_all.push_back(std::move(column));
-                        column_values_all.push_back(std::make_unique<sdsl::int_vector<>>(std::move(column_values)));
-                    }
-                );
-                utils::call_rows<std::unique_ptr<bit_vector>,
-                                std::unique_ptr<sdsl::int_vector<>>,
-                                std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
-                                    //std::ignore = row;
-                                    //logger->trace("ooooooooooooooo Row1: {}, row2 {}", std::get<0>(row[0]), std::get<1>(row[0]));
-                                    logger->trace("ooooooooooooooo Row1: {}", typeid(std::get<0>(row[0])).name());
-                                    logger->trace("ooooooooooooooo Row2: {}", std::get<1>(row[0]));
-                });
                 assert(files.size() > 0);
                 // generate a vector of the total number of kmers per sample for each group (in and out)
                 Timer t;
@@ -491,18 +475,21 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                std::move(counts), std::move(init_mask),
                                std::move(other_mask),
                                files.size(),
+                               files,
                                labels_in, labels_out, {}, {},
                                total_kmers,
                                config, num_threads);
 }
 
-std::shared_ptr<MaskedDeBruijnGraph>
+// does differential testing
+std::shared_ptr<MaskedDeBruijnGraph> // TODO add all columns and columns label as param 
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const AnnotatedDBG *anno_graph,
                     sdsl::int_vector<>&& counts,
                     sdsl::bit_vector&& init_mask,
                     sdsl::bit_vector&& other_mask,
                     size_t num_labels,
+                    const std::vector<std::string> &files,
                     const tsl::hopscotch_set<Label> &labels_in,
                     const tsl::hopscotch_set<Label> &labels_out,
                     const tsl::hopscotch_set<Label> &labels_in_round2,
@@ -598,6 +585,62 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             masked_graph->call_unitigs([&](const std::string &, const std::vector<node_index> &) {
                 total_unitigs.fetch_add(1, MO_RELAXED);
             });
+            // brunner munzel test
+            // call kmer matrix per row
+            std::vector<std::unique_ptr<bit_vector>> columns_all;
+            std::vector<std::unique_ptr<sdsl::int_vector<>>> column_values_all;
+            std::vector<bool> column_label;
+            annot::ColumnCompressed<>::load_columns_and_values(files,
+                [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { 
+                    // TODO: add count filtering here as a function
+                    columns_all.push_back(std::move(column));
+                    column_values_all.push_back(std::make_unique<sdsl::int_vector<>>(std::move(column_values)));
+                    if (labels_in.find(label) != labels_in.end()) // if the label is in the in labels set column_label to 1, else 0
+                        column_label.push_back(1);
+                    else if (labels_out.find(label) != labels_out.end())
+                        column_label.push_back(0);
+                    else throw std::runtime_error("Label not found in labels_in or labels_out");
+                }
+            );
+            int row_id = 0;
+            utils::call_rows<std::unique_ptr<bit_vector>,
+                            std::unique_ptr<sdsl::int_vector<>>,
+                            std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
+                                // TODO filter for low complexity (make a function)
+                                // auto kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
+                                if (row.size() == 0) { // if the kmer is not present in any sample
+                                    row_id++; 
+                                    return;
+                                }
+                                else{
+                                    // populate the vector for the brunner munzel test with the kmer counts for each sample
+                                    std::vector<int> in_counts;
+                                    std::vector<int> out_counts;
+                                    for (size_t i = 0; i < row.size(); i++){
+                                        if (column_label[row[i].first] == 1)
+                                            in_counts.push_back(row[i].second);
+                                        else if (column_label[row[i].first] == 0)
+                                            out_counts.push_back(row[i].second);
+                                        else throw std::runtime_error("Label not found in labels_in or labels_out");
+                                    }
+                                    // add 0 to in_counts and out_counts if the kmer is not present in the sample
+                                    if (in_counts.size() < labels_in.size())
+                                        in_counts.resize(labels_in.size(), 0);
+                                    if (out_counts.size() < labels_out.size())
+                                        out_counts.resize(labels_out.size(), 0);
+                                    for (size_t i = 0; i < in_counts.size(); i++){
+                                        logger->trace("in_counts {}", in_counts[i]);
+                                    }
+                                    for (size_t i = 0; i < out_counts.size(); i++){
+                                        logger->trace("out_counts {}", out_counts[i]);
+                                    }        
+                                    // calculate the p-value for the brunner munzel test
+                                    auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
+                                                    std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
+                                                    0, 0);    
+                                    row_id++;
+                                }               
+            });
             logger->trace("ooooo unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
                                                       std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
@@ -646,6 +689,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
 
         } else { // binary approach
+            logger->trace("test_by_unitig == true");
             masked_graph->likelihood_ratios.resize(counts.size()/2+1);
             std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0);
             std::atomic<uint64_t> total_unitigs(0);

From ed56d424ddbafb046b4d713b6fa48d9b1e867735 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 20 Feb 2024 15:41:52 +0100
Subject: [PATCH 058/410] add function to run brunner munzel test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 127 ++++++++++--------
 1 file changed, 71 insertions(+), 56 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index de3b3af41a..e27abfe291 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -183,6 +183,76 @@ inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
     return n > 0;
 }
 
+std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<MaskedDeBruijnGraph> masked_graph, 
+                                                        const std::vector<std::string> &files, 
+                                                        const tsl::hopscotch_set<Label> &labels_in, 
+                                                        const tsl::hopscotch_set<Label> &labels_out, 
+                                                        const DifferentialAssemblyConfig &config,  
+                                                        size_t total_unitigs) {
+
+    const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
+    sdsl::bit_vector mask = in_mask;
+    // brunner munzel test
+    // call kmer matrix per row
+    std::vector<std::unique_ptr<bit_vector>> columns_all;
+    std::vector<std::unique_ptr<sdsl::int_vector<>>> column_values_all;
+    std::vector<bool> column_label;
+    annot::ColumnCompressed<>::load_columns_and_values(files,
+        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { 
+            // TODO: add count filtering here as a function
+            columns_all.push_back(std::move(column));
+            column_values_all.push_back(std::make_unique<sdsl::int_vector<>>(std::move(column_values)));
+            if (labels_in.find(label) != labels_in.end()) // if the label is in the in labels set column_label to 1, else 0
+                column_label.push_back(1);
+            else if (labels_out.find(label) != labels_out.end())
+                column_label.push_back(0);
+            else throw std::runtime_error("Label not found in labels_in or labels_out");
+        }
+    );
+    int row_id = 0;
+    utils::call_rows<std::unique_ptr<bit_vector>,
+                    std::unique_ptr<sdsl::int_vector<>>,
+                    std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
+                        // TODO filter for low complexity (make a function)
+                        // auto kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
+                        if (row.size() == 0) { // if the kmer is not present in any sample
+                            row_id++; 
+                            return;
+                        }
+                        else{
+                            // populate the vector for the brunner munzel test with the kmer counts for each sample
+                            std::vector<int> in_counts;
+                            std::vector<int> out_counts;
+                            for (size_t i = 0; i < row.size(); i++){
+                                if (column_label[row[i].first] == 1)
+                                    in_counts.push_back(row[i].second);
+                                else if (column_label[row[i].first] == 0)
+                                    out_counts.push_back(row[i].second);
+                                else throw std::runtime_error("Label not found in labels_in or labels_out");
+                            }
+                            // add 0 to in_counts and out_counts if the kmer is not present in the sample
+                            if (in_counts.size() < labels_in.size())
+                                in_counts.resize(labels_in.size(), 0);
+                            if (out_counts.size() < labels_out.size())
+                                out_counts.resize(labels_out.size(), 0);
+                            for (size_t i = 0; i < in_counts.size(); i++){
+                                logger->trace("in_counts {}", in_counts[i]);
+                            }
+                            for (size_t i = 0; i < out_counts.size(); i++){
+                                logger->trace("out_counts {}", out_counts[i]);
+                            }        
+                            // calculate the p-value for the brunner munzel test
+                            auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0);   
+                            // run test and adjust masked graph accordingly 
+                            auto [keep, pvalue] = statistical_model.brunner_munzel_test(in_counts, out_counts);
+                            if (keep == false)
+                                mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
+                            row_id++;
+                        }               
+    });
+    masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+    return masked_graph;
+}    
 // Assemble unitigs from the masked graph, then use get_kept_intervals to generate
 // regions which should be masked in. Update the graph mask accordingly.
 void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
@@ -585,62 +655,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             masked_graph->call_unitigs([&](const std::string &, const std::vector<node_index> &) {
                 total_unitigs.fetch_add(1, MO_RELAXED);
             });
-            // brunner munzel test
-            // call kmer matrix per row
-            std::vector<std::unique_ptr<bit_vector>> columns_all;
-            std::vector<std::unique_ptr<sdsl::int_vector<>>> column_values_all;
-            std::vector<bool> column_label;
-            annot::ColumnCompressed<>::load_columns_and_values(files,
-                [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { 
-                    // TODO: add count filtering here as a function
-                    columns_all.push_back(std::move(column));
-                    column_values_all.push_back(std::make_unique<sdsl::int_vector<>>(std::move(column_values)));
-                    if (labels_in.find(label) != labels_in.end()) // if the label is in the in labels set column_label to 1, else 0
-                        column_label.push_back(1);
-                    else if (labels_out.find(label) != labels_out.end())
-                        column_label.push_back(0);
-                    else throw std::runtime_error("Label not found in labels_in or labels_out");
-                }
-            );
-            int row_id = 0;
-            utils::call_rows<std::unique_ptr<bit_vector>,
-                            std::unique_ptr<sdsl::int_vector<>>,
-                            std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
-                                // TODO filter for low complexity (make a function)
-                                // auto kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
-                                if (row.size() == 0) { // if the kmer is not present in any sample
-                                    row_id++; 
-                                    return;
-                                }
-                                else{
-                                    // populate the vector for the brunner munzel test with the kmer counts for each sample
-                                    std::vector<int> in_counts;
-                                    std::vector<int> out_counts;
-                                    for (size_t i = 0; i < row.size(); i++){
-                                        if (column_label[row[i].first] == 1)
-                                            in_counts.push_back(row[i].second);
-                                        else if (column_label[row[i].first] == 0)
-                                            out_counts.push_back(row[i].second);
-                                        else throw std::runtime_error("Label not found in labels_in or labels_out");
-                                    }
-                                    // add 0 to in_counts and out_counts if the kmer is not present in the sample
-                                    if (in_counts.size() < labels_in.size())
-                                        in_counts.resize(labels_in.size(), 0);
-                                    if (out_counts.size() < labels_out.size())
-                                        out_counts.resize(labels_out.size(), 0);
-                                    for (size_t i = 0; i < in_counts.size(); i++){
-                                        logger->trace("in_counts {}", in_counts[i]);
-                                    }
-                                    for (size_t i = 0; i < out_counts.size(); i++){
-                                        logger->trace("out_counts {}", out_counts[i]);
-                                    }        
-                                    // calculate the p-value for the brunner munzel test
-                                    auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
-                                                    std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
-                                                    0, 0);    
-                                    row_id++;
-                                }               
-            });
+            masked_graph = brunner_munzel_test(masked_graph,files,labels_in,labels_out,config,total_unitigs);
             logger->trace("ooooo unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
                                                       std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),

From 460230e1707f5d2ac981244e54392a70bc966509 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 20 Feb 2024 23:48:11 +0100
Subject: [PATCH 059/410] add test type to config

---
 metagraph/src/cli/assemble.cpp                    | 2 ++
 metagraph/src/graph/annotated_graph_algorithm.hpp | 1 +
 2 files changed, 3 insertions(+)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index a17620756a..d2e7a2a350 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -54,6 +54,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.family_wise_error_rate = experiment.get("family_wise_error_rate", 0.05).asDouble();
     diff_config.test_by_unitig = experiment.get("test_by_unitig", false).asBool();
     diff_config.evaluate_assembly = experiment.get("evaluate_assembly", false).asBool();
+    diff_config.test_type = experiment.get("test_type", "brunner_munzel").asString();
 
 
     logger->trace("Per-kmer mask in fraction:\t\t{}", diff_config.label_mask_in_kmer_fraction);
@@ -64,6 +65,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     logger->trace("Include reverse complements:\t\t{}", diff_config.add_complement);
     logger->trace("Include k-mer counts if preseadd_complementnt in the graph:\t\t{}", diff_config.count_kmers); // if node weights (k-mer count) are present in the graph and should be included in the differential assembly.
     logger->trace("Family wise error rate for the Bonferroni test:\t{}", diff_config.family_wise_error_rate);
+    logger->trace("Test type:\t{}", diff_config.test_type);
 
 
     // TODO get the folder with the annotation columns from the .json or command, because it is redundant to have to give all of them as an input.
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 5e95fae8bd..913886b71d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -36,6 +36,7 @@ struct DifferentialAssemblyConfig {
     bool count_kmers = false; // Myrthe
     double family_wise_error_rate = 0.05; //Myrthe: differential assembly statistical tests, or the family wise rate for bonferonni or a p-value
     bool test_by_unitig = false; // Myrthe
+    std::string test_type = "brunner_munzel"; // default is "brunner_munzel", alternatives are: "likelihoodratio", "binary"
     bool evaluate_assembly = false; // Myrthe temporary : evaluate an assembly
 };
 

From 3862976ab3afd365b0c60a7238bdbaabf1b4725e Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 20 Feb 2024 23:48:56 +0100
Subject: [PATCH 060/410] add edge cases to degree of freedom and midranks
 calculations

---
 .../src/graph/annotated_graph_algorithm.cpp   | 10 +++---
 .../src/graph/differential_tests_uemmi.cpp    | 31 +++++++++++++++----
 2 files changed, 29 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index e27abfe291..fcfa1e5a24 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -216,6 +216,7 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<MaskedD
                         // TODO filter for low complexity (make a function)
                         // auto kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
                         if (row.size() == 0) { // if the kmer is not present in any sample
+                            mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
                             row_id++; 
                             return;
                         }
@@ -235,12 +236,6 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<MaskedD
                                 in_counts.resize(labels_in.size(), 0);
                             if (out_counts.size() < labels_out.size())
                                 out_counts.resize(labels_out.size(), 0);
-                            for (size_t i = 0; i < in_counts.size(); i++){
-                                logger->trace("in_counts {}", in_counts[i]);
-                            }
-                            for (size_t i = 0; i < out_counts.size(); i++){
-                                logger->trace("out_counts {}", out_counts[i]);
-                            }        
                             // calculate the p-value for the brunner munzel test
                             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0);   
                             // run test and adjust masked graph accordingly 
@@ -655,6 +650,9 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             masked_graph->call_unitigs([&](const std::string &, const std::vector<node_index> &) {
                 total_unitigs.fetch_add(1, MO_RELAXED);
             });
+            if (config.test_type == "brunner_munzel"){
+                logger->trace("Brunner Munzel test");
+            }
             masked_graph = brunner_munzel_test(masked_graph,files,labels_in,labels_out,config,total_unitigs);
             logger->trace("ooooo unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 8548492fb3..5071281e8a 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -4,6 +4,7 @@
 #include <iostream>
 #include <cassert>
 #include<algorithm>
+#include "common/logger.hpp"
 
 #include "differential_tests.hpp"
 #include "lookup_table_chisqrd_cdf.hpp"
@@ -41,13 +42,28 @@ std::vector<double> DifferentialTest::get_midranks(std::vector<int> in_counts, i
     for (int i = 1; i<size_in_counts; i++){
         if (in_counts[i] == curr_value){
             tied++;
+            if (i == size_in_counts-1){
+                i += 1;
+                for (int j = i-tied; j<i; j++){
+                    mid_ranks.push_back((i-tied) + (tied+1)/2.0);
+                }
+            }
         }
         else{
-            for (int j = i-tied; j<i; j++){
-                mid_ranks.push_back((i-tied) + (tied+1)/2.0);
+            if (i == size_in_counts-1){
+                for (int j = i-tied; j<i; j++){
+                    mid_ranks.push_back((i-tied) + (tied+1)/2.0);
+                }
+                mid_ranks.push_back(i+1);
+            }
+            else{
+                for (int j = i-tied; j<i; j++){
+                    mid_ranks.push_back((i-tied) + (tied+1)/2.0);
+                }
+                curr_value = in_counts[i];
+                tied = 1;
             }
-            curr_value = in_counts[i];
-            tied = 1;
+            
         }
     }
     return mid_ranks;
@@ -58,6 +74,9 @@ int DifferentialTest::get_df_approx(std::vector<int> in_counts, std::vector<int>
     int n = out_counts.size();
     double var_in = get_var(in_counts, m);
     double var_out = get_var(out_counts, n);
+    if (var_in == 0 && var_out == 0){
+        return std::min(m, n) - 1;
+    }
     double df_approx = (var_in*var_in/m + var_out*var_out/n)*(var_in*var_in/m + var_out*var_out/n) / (((var_in*var_in/m)*(var_in*var_in/m)/(m - 1)) + ((var_out*var_out/n)*(var_out*var_out/n)/(n - 1)));
     return int(std::floor(df_approx));
 }
@@ -81,11 +100,11 @@ std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<int>
     mid_ranks_in = get_midranks(in_counts, m);
     mid_ranks_out = get_midranks(out_counts, n);
     // get means of midranks
-    double mean_mid_rank_in = std::accumulate(mid_ranks_in.begin(), mid_ranks_out.end(), 0.0) / mid_ranks_in.size();
+    double mean_mid_rank_in = std::accumulate(mid_ranks_in.begin(), mid_ranks_in.end(), 0.0) / mid_ranks_in.size();
     double mean_mid_rank_out = std::accumulate(mid_ranks_out.begin(), mid_ranks_out.end(), 0.0) / mid_ranks_out.size();
     // get variances
     double var_in = get_var(in_counts, m);
-    double var_out = get_var(out_counts, n);              
+    double var_out = get_var(out_counts, n);         
     // get test statistic
     double b = (mean_mid_rank_out - mean_mid_rank_in) / (N * std::sqrt(var_in/(m*n*n) + var_out/(m*m*n)));
     // get degrees of freedom with welch-satterthwaite equation

From da121a6805c4acc053a75540c494e5c21fc1a301 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Wed, 21 Feb 2024 11:00:19 +0100
Subject: [PATCH 061/410] implement feedback, improve variance calc. and if
 statement for labels

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 5 ++---
 metagraph/src/graph/differential_tests_uemmi.cpp  | 6 ++----
 2 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fcfa1e5a24..1df470f9b9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -225,11 +225,10 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<MaskedD
                             std::vector<int> in_counts;
                             std::vector<int> out_counts;
                             for (size_t i = 0; i < row.size(); i++){
-                                if (column_label[row[i].first] == 1)
+                                if (column_label[row[i].first])
                                     in_counts.push_back(row[i].second);
-                                else if (column_label[row[i].first] == 0)
+                                else
                                     out_counts.push_back(row[i].second);
-                                else throw std::runtime_error("Label not found in labels_in or labels_out");
                             }
                             // add 0 to in_counts and out_counts if the kmer is not present in the sample
                             if (in_counts.size() < labels_in.size())
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 5071281e8a..bf80f1cad6 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -83,10 +83,8 @@ int DifferentialTest::get_df_approx(std::vector<int> in_counts, std::vector<int>
 
 double DifferentialTest::get_var(std::vector<int> counts, int n){
     double mean = std::accumulate(counts.begin(), counts.end(), 0.0) / n;
-    double var = 0;
-    for (int i = 0; i < n; i++) 
-        var += (counts[i] - mean) * (counts[i] - mean);
-    var /= n;
+    double mean_of_sqaures = std::inner_product(counts.begin(), counts.end(), counts.begin(), 0)/n;
+    double var = mean_of_sqaures - mean*mean;
     return var;
 }
 

From 0e6c36eba4b68593f9523c7cb94cd2a0dff3a119 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Wed, 21 Feb 2024 16:52:44 +0100
Subject: [PATCH 062/410] add median normalisation

---
 .../src/graph/annotated_graph_algorithm.cpp   | 204 +++++++++++-------
 metagraph/src/graph/differential_tests.hpp    |   8 +-
 .../src/graph/differential_tests_uemmi.cpp    |   8 +-
 3 files changed, 139 insertions(+), 81 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 1df470f9b9..33cff0c3c1 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -135,27 +135,33 @@ bool sortByPValue(const std::pair<int, double>& a, const std::pair<int, double>&
 // Function for calculating 
 // the median 
 double findMedian(sdsl::int_vector<0> a, 
-                  int n) 
-{ 
+                    int threshold) {   
+    logger->trace("non zero kmers: {}", a.size());
+    std::vector<double> a_filtered;
+    std::copy_if(a.begin(), a.end(), std::back_inserter(a_filtered), [&](int i) {
+        return (i > threshold);
+    });
+    logger->trace("kmers above threshold: {}", a_filtered.size());
+    int n = a_filtered.size();
     // If size of the arr[] is even 
     if (n % 2 == 0) { 
   
         // Applying nth_element 
         // on n/2th index 
-        std::nth_element(a.begin(), 
-                    a.begin() + n / 2, 
-                    a.end()); 
+        std::nth_element(a_filtered.begin(), 
+                    a_filtered.begin() + n / 2, 
+                    a_filtered.end()); 
   
         // Applying nth_element 
         // on (n-1)/2 th index 
-        std::nth_element(a.begin(), 
-                    a.begin() + (n - 1) / 2, 
-                    a.end()); 
+        std::nth_element(a_filtered.begin(), 
+                    a_filtered.begin() + (n - 1) / 2, 
+                    a_filtered.end()); 
   
         // Find the average of value at 
         // index N/2 and (N-1)/2 
-        return (double)(a[(n - 1) / 2] 
-                        + a[n / 2]) 
+        return (double)(a_filtered[(n - 1) / 2] 
+                        + a_filtered[n / 2]) 
                / 2.0; 
     } 
   
@@ -164,13 +170,13 @@ double findMedian(sdsl::int_vector<0> a,
   
         // Applying nth_element 
         // on n/2 
-        std::nth_element(a.begin(), 
-                    a.begin() + n / 2, 
-                    a.end()); 
+        std::nth_element(a_filtered.begin(), 
+                    a_filtered.begin() + n / 2, 
+                    a_filtered.end()); 
   
         // Value at index (N/2)th 
         // is the median 
-        return (double)a[n / 2]; 
+        return (double)a_filtered[n / 2]; 
     } 
 } 
 
@@ -188,7 +194,8 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<MaskedD
                                                         const tsl::hopscotch_set<Label> &labels_in, 
                                                         const tsl::hopscotch_set<Label> &labels_out, 
                                                         const DifferentialAssemblyConfig &config,  
-                                                        size_t total_unitigs) {
+                                                        size_t total_unitigs,
+                                                        std::vector<double> medians) {
 
     const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
     sdsl::bit_vector mask = in_mask;
@@ -222,19 +229,25 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<MaskedD
                         }
                         else{
                             // populate the vector for the brunner munzel test with the kmer counts for each sample
-                            std::vector<int> in_counts;
-                            std::vector<int> out_counts;
+                            std::vector<double> in_counts;
+                            std::vector<double> out_counts;
                             for (size_t i = 0; i < row.size(); i++){
+                                double median = medians[row[i].first];
+                                if (median == 0) median = 1; // if the median is 0, set it to 1 (to avoid division by 0)    
                                 if (column_label[row[i].first])
-                                    in_counts.push_back(row[i].second);
+                                    in_counts.push_back(row[i].second/median);
                                 else
-                                    out_counts.push_back(row[i].second);
+                                    out_counts.push_back(row[i].second/median);
                             }
                             // add 0 to in_counts and out_counts if the kmer is not present in the sample
                             if (in_counts.size() < labels_in.size())
                                 in_counts.resize(labels_in.size(), 0);
                             if (out_counts.size() < labels_out.size())
                                 out_counts.resize(labels_out.size(), 0);
+                            // for (size_t i = 0; i < in_counts.size(); i++){
+                            //     logger->trace("in_counts: {}", in_counts[i]);
+                            //     logger->trace("out_counts: {}", out_counts[i]);
+                            // }    
                             // calculate the p-value for the brunner munzel test
                             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0);   
                             // run test and adjust masked graph accordingly 
@@ -247,6 +260,49 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<MaskedD
     masked_graph->set_mask(new bitmap_vector(std::move(mask)));
     return masked_graph;
 }    
+void filtering_row(){
+    // minimum present in 20% of samples
+    // present more in in than out
+    // low complexity filter
+}
+std::vector<double> filtering_column(const std::vector<std::string> &files,
+               const tsl::hopscotch_set<Label> &labels_in,
+               const tsl::hopscotch_set<Label> &labels_out,
+               const DifferentialAssemblyConfig &config,
+               const ColumnCallback &column_callback
+               ) {
+    // median normalisation
+    // threshold of 2
+    double threshold = 2; // TODO get from config
+    assert(files.size() > 0);
+    // generate a vector of the total number of kmers per sample for each group (in and out)
+    Timer t;
+    logger->trace("OOOOOOOOOOO time to generate counts vector {}",  t.elapsed());
+    std::vector<double> medians;
+    annot::ColumnCompressed<>::load_columns_and_values(files,
+        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { // goes through all files in a group
+            double max_col_width = std::pow(2, 16); // max value for int16_t
+            column_callback(offset, label, [&](const ValueCallback &value_callback) {
+                assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0); // at least one kmer in sample
+                assert(column->num_set_bits() > 0); // at least one kmer present in sample
+                assert(column->num_set_bits() == column_values.size()); // same number of kmers in column as in column_values
+                double median = findMedian(column_values, threshold);
+                medians.push_back(median);
+                logger->trace("median: {}", median);
+                call_ones(*column, [&](uint64_t i) {
+                    double column_value = column_values[column->rank1(i)-1];
+                    if (column_value > threshold){
+                        column_value = std::min(column_value, max_col_width); // normalize value
+                        value_callback(i, column_value);
+                    }    
+                });
+            });
+            logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());    
+        }
+    );
+    return medians;
+}
+
 // Assemble unitigs from the masked graph, then use get_kept_intervals to generate
 // regions which should be masked in. Update the graph mask accordingly.
 void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
@@ -277,7 +333,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
                     std::tuple<size_t, size_t> total_kmers,
                     const DifferentialAssemblyConfig &config,
-                    size_t num_threads);
+                    size_t num_threads,
+                    std::vector<double> medians);
 
 std::shared_ptr<MaskedDeBruijnGraph> // This version of mask_nodes_by_label is for when the columns are already loaded.
 mask_nodes_by_label(const AnnotatedDBG &anno_graph,
@@ -345,7 +402,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                                {},
                                labels_in, labels_out,
                                labels_in_round2, labels_out_round2, total_kmers,
-                               config, num_threads);
+                               config, num_threads, {});
 }
 
 
@@ -368,60 +425,60 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         && (config.add_complement || unitig_mode);
 
     logger->trace("Generating initial mask");
-
+    bool filter = true;
+    std::vector<double> medians;
     auto count_vector = construct_diff_label_count_vector( // called for each group (in and out)
             [&](const ColumnCallback &column_callback) {
             if (config.count_kmers) {
-                assert(files.size() > 0);
-                // generate a vector of the total number of kmers per sample for each group (in and out)
-                Timer t;
-                std::vector<int> all_kmers_per_sample_in;
-                std::vector<int> all_kmers_per_sample_out;
-                annot::ColumnCompressed<>::load_columns_and_values(files,
-                    [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { 
-                        auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0) + column_values.size(); // adding pseudocounts
-                        if (labels_in.find(label) != labels_in.end())
-                        all_kmers_per_sample_in.push_back(total_kmers);
-                        else if (labels_out.find(label) != labels_out.end())
-                        all_kmers_per_sample_out.push_back(total_kmers);
-                        else throw std::runtime_error("Label not found in labels_in or labels_out");
-                    }
-                );
-                int min_kmer_count_in = *min_element(all_kmers_per_sample_in.begin(), all_kmers_per_sample_in.end());
-                int min_kmer_count_out = *min_element(all_kmers_per_sample_out.begin(), all_kmers_per_sample_out.end());
-                logger->trace("OOOOOOOOOOO time to generate counts vector {}",  t.elapsed());
-                bool subsample = true; // subsample or normalize
-                annot::ColumnCompressed<>::load_columns_and_values(files,
-                    [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { // goes through all files in a group
-                        auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0) + column_values.size(); 
-                        int normalization_constant = 1e9;   // const to scale normalized values to save as int
-                        auto normalization_factor = normalization_constant / total_kmers;
-                        int max_col_width = std::pow(2, 16); // max value for int16_t
-                        int min_kmer_counts;
-                        if (labels_in.find(label) != labels_in.end())
-                            min_kmer_counts = min_kmer_count_in;
-                        else if (labels_out.find(label) != labels_out.end())
-                            min_kmer_counts = min_kmer_count_out;
-                        else throw std::runtime_error("Label not found in labels_in or labels_out");
-                        // double scaling = (double) total_kmers / min_kmer_counts; 
-                        column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                            assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0); // at least one kmer in sample
-                            assert(column->num_set_bits() > 0); // at least one kmer present in sample
-                            assert(column->num_set_bits() == column_values.size()); // same number of kmers in column as in column_values
-                            double median = findMedian(column_values, column_values.size());
-                            call_ones(*column, [&](uint64_t i) {
-                                auto column_value = column_values[column->rank1(i)-1];
-                                if (subsample == true)
-                                    // column_value = std::min((int) std::floor(column_value/scaling), max_col_width); // normalize value
-                                    column_value = std::min((int) std::ceil(column_value/median), max_col_width); // normalize value
-                                else
+                if (filter){
+                    medians = filtering_column(files, labels_in, labels_out, config, column_callback);
+                }
+                else{
+                    assert(files.size() > 0);
+                    // generate a vector of the total number of kmers per sample for each group (in and out)
+                    Timer t;
+                    std::vector<int> all_kmers_per_sample_in;
+                    std::vector<int> all_kmers_per_sample_out;
+                    annot::ColumnCompressed<>::load_columns_and_values(files,
+                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { 
+                            auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0) + column_values.size(); // adding pseudocounts
+                            if (labels_in.find(label) != labels_in.end())
+                            all_kmers_per_sample_in.push_back(total_kmers);
+                            else if (labels_out.find(label) != labels_out.end())
+                            all_kmers_per_sample_out.push_back(total_kmers);
+                            else throw std::runtime_error("Label not found in labels_in or labels_out");
+                        }
+                    );
+                    int min_kmer_count_in = *min_element(all_kmers_per_sample_in.begin(), all_kmers_per_sample_in.end());
+                    int min_kmer_count_out = *min_element(all_kmers_per_sample_out.begin(), all_kmers_per_sample_out.end());
+                    logger->trace("OOOOOOOOOOO time to generate counts vector {}",  t.elapsed());
+                    annot::ColumnCompressed<>::load_columns_and_values(files,
+                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { // goes through all files in a group
+                            auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0) + column_values.size(); 
+                            int normalization_constant = 1e9;   // const to scale normalized values to save as int
+                            auto normalization_factor = normalization_constant / total_kmers;
+                            int max_col_width = std::pow(2, 16); // max value for int16_t
+                            int min_kmer_counts;
+                            if (labels_in.find(label) != labels_in.end())
+                                min_kmer_counts = min_kmer_count_in;
+                            else if (labels_out.find(label) != labels_out.end())
+                                min_kmer_counts = min_kmer_count_out;
+                            else throw std::runtime_error("Label not found in labels_in or labels_out");
+                            // double scaling = (double) total_kmers / min_kmer_counts; 
+                            column_callback(offset, label, [&](const ValueCallback &value_callback) {
+                                assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0); // at least one kmer in sample
+                                assert(column->num_set_bits() > 0); // at least one kmer present in sample
+                                assert(column->num_set_bits() == column_values.size()); // same number of kmers in column as in column_values
+                                call_ones(*column, [&](uint64_t i) {
+                                    auto column_value = column_values[column->rank1(i)-1];
                                     column_value = std::min((int) std::ceil(column_value*normalization_factor), max_col_width); // normalize value
-                                value_callback(i, column_value);
+                                    value_callback(i, column_value);
+                                });
                             });
-                        });
-                        logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());    
-                    }
-                );
+                            logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());    
+                        }
+                    );
+                }    
             } else {
                 annot::ColumnCompressed<>::merge_load(files,
                     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column) {
@@ -542,7 +599,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                files,
                                labels_in, labels_out, {}, {},
                                total_kmers,
-                               config, num_threads);
+                               config, num_threads, medians);
 }
 
 // does differential testing
@@ -560,7 +617,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const tsl::hopscotch_set<Label> &labels_out_round2,
                     std::tuple<size_t, size_t> total_kmers,
                     const DifferentialAssemblyConfig &config,
-                    size_t num_threads) {
+                    size_t num_threads,
+                    std::vector<double> medians) {
     // in and out counts are stored interleaved in the counts vector
     logger->trace("aaaaaaaaaaaaaaaaaaaaaA counts {}", counts.size());
     assert(counts.size() == init_mask.size() * 2);
@@ -652,7 +710,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (config.test_type == "brunner_munzel"){
                 logger->trace("Brunner Munzel test");
             }
-            masked_graph = brunner_munzel_test(masked_graph,files,labels_in,labels_out,config,total_unitigs);
+            masked_graph = brunner_munzel_test(masked_graph,files,labels_in,labels_out,config,total_unitigs, medians);
             logger->trace("ooooo unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
                                                       std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index dd48ccf45d..02ba094a51 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -28,7 +28,7 @@ class DifferentialTest {
                      size_t out_total_kmers);
 
     double poisson_prob(int k, double lambda);
-    std::tuple<bool, double> brunner_munzel_test(std::vector<int> in_counts, std::vector<int> out_counts);
+    std::tuple<bool, double> brunner_munzel_test(std::vector<double> in_counts, std::vector<double> out_counts);
 
     int benjamini_yekutieli(std::vector<std::pair<double, int>> likelihood_ratios, double lrt_threshold);
 
@@ -38,12 +38,12 @@ class DifferentialTest {
 
     double get_t_test_alpha(int df, double alpha);
 
-    std::vector<double> get_midranks(std::vector<int> in_counts, int size_in_counts);
+    std::vector<double> get_midranks(std::vector<double> in_counts, int size_in_counts);
 
     std::tuple<bool, double> likelihood_ratio_test(double in_sum, double out_sum);
 
-    int get_df_approx(std::vector<int> in_counts, std::vector<int> out_counts);
+    int get_df_approx(std::vector<double> in_counts, std::vector<double> out_counts);
 
-    double get_var(std::vector<int> counts, int n);
+    double get_var(std::vector<double> counts, int n);
 };
 } // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index bf80f1cad6..600388c08b 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -34,7 +34,7 @@ double DifferentialTest::get_t_test_alpha(int df, double alpha=0.05){
     return t_table.getCriticalValue(alpha, df);
 }
 
-std::vector<double> DifferentialTest::get_midranks(std::vector<int> in_counts, int size_in_counts){
+std::vector<double> DifferentialTest::get_midranks(std::vector<double> in_counts, int size_in_counts){
     std::vector<double> mid_ranks;
     std::sort(in_counts.begin(), in_counts.end());
     double curr_value = in_counts[0];
@@ -69,7 +69,7 @@ std::vector<double> DifferentialTest::get_midranks(std::vector<int> in_counts, i
     return mid_ranks;
 }
 
-int DifferentialTest::get_df_approx(std::vector<int> in_counts, std::vector<int> out_counts){
+int DifferentialTest::get_df_approx(std::vector<double> in_counts, std::vector<double> out_counts){
     int m = in_counts.size();
     int n = out_counts.size();
     double var_in = get_var(in_counts, m);
@@ -81,14 +81,14 @@ int DifferentialTest::get_df_approx(std::vector<int> in_counts, std::vector<int>
     return int(std::floor(df_approx));
 }
 
-double DifferentialTest::get_var(std::vector<int> counts, int n){
+double DifferentialTest::get_var(std::vector<double> counts, int n){
     double mean = std::accumulate(counts.begin(), counts.end(), 0.0) / n;
     double mean_of_sqaures = std::inner_product(counts.begin(), counts.end(), counts.begin(), 0)/n;
     double var = mean_of_sqaures - mean*mean;
     return var;
 }
 
-std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<int> in_counts, std::vector<int> out_counts){
+std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<double> in_counts, std::vector<double> out_counts){
     std::vector<double> mid_ranks_in;
     std::vector<double> mid_ranks_out;
     int m = in_counts.size();

From 0fc72916528a9146e90084c8b75c5c927518bb5e Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Wed, 21 Feb 2024 17:33:03 +0100
Subject: [PATCH 063/410] add row filtering

---
 .../src/graph/annotated_graph_algorithm.cpp   | 52 +++++++++++++------
 1 file changed, 36 insertions(+), 16 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 33cff0c3c1..0de0c973db 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -189,7 +189,13 @@ inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
     return n > 0;
 }
 
-std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<MaskedDeBruijnGraph> masked_graph, 
+bool filtering_row(std::vector<double> in_counts_non_zero,
+                    std::vector<double> out_counts_non_zero,
+                    int total_samples,
+                    std::string kmer_string);
+
+std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                                                        std::shared_ptr<MaskedDeBruijnGraph> masked_graph, 
                                                         const std::vector<std::string> &files, 
                                                         const tsl::hopscotch_set<Label> &labels_in, 
                                                         const tsl::hopscotch_set<Label> &labels_out, 
@@ -217,6 +223,7 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<MaskedD
         }
     );
     int row_id = 0;
+    int num_tests = 0;
     utils::call_rows<std::unique_ptr<bit_vector>,
                     std::unique_ptr<sdsl::int_vector<>>,
                     std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
@@ -240,30 +247,43 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<MaskedD
                                     out_counts.push_back(row[i].second/median);
                             }
                             // add 0 to in_counts and out_counts if the kmer is not present in the sample
-                            if (in_counts.size() < labels_in.size())
+                            std::string kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
+                            if (filtering_row(in_counts, out_counts, files.size(), kmer_string)) {
+                                num_tests++;
+                                if (in_counts.size() < labels_in.size())
                                 in_counts.resize(labels_in.size(), 0);
-                            if (out_counts.size() < labels_out.size())
-                                out_counts.resize(labels_out.size(), 0);
-                            // for (size_t i = 0; i < in_counts.size(); i++){
-                            //     logger->trace("in_counts: {}", in_counts[i]);
-                            //     logger->trace("out_counts: {}", out_counts[i]);
-                            // }    
-                            // calculate the p-value for the brunner munzel test
-                            auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0);   
-                            // run test and adjust masked graph accordingly 
-                            auto [keep, pvalue] = statistical_model.brunner_munzel_test(in_counts, out_counts);
-                            if (keep == false)
+                                if (out_counts.size() < labels_out.size())
+                                    out_counts.resize(labels_out.size(), 0);  
+                                // calculate the p-value for the brunner munzel test
+                                auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0);   
+                                // run test and adjust masked graph accordingly 
+                                auto [keep, pvalue] = statistical_model.brunner_munzel_test(in_counts, out_counts);
+                                if (keep == false)
+                                    mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
+                                row_id++;
+                            }
+                            else{
                                 mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
-                            row_id++;
+                                row_id++;
+                            }
+                            
                         }               
     });
+    logger->trace("number of tests: {}", num_tests);
     masked_graph->set_mask(new bitmap_vector(std::move(mask)));
     return masked_graph;
 }    
-void filtering_row(){
+bool filtering_row(std::vector<double> in_counts_non_zero,
+                    std::vector<double> out_counts_non_zero,
+                    int total_samples,
+                    std::string kmer_string){
     // minimum present in 20% of samples
+    bool is_present_in_20_percent = (in_counts_non_zero.size() + out_counts_non_zero.size()) > 0.2 * double(total_samples);
     // present more in in than out
+    bool is_present_more_in_foreground = in_counts_non_zero.size() > out_counts_non_zero.size();
     // low complexity filter
+    bool high_complexity =  !is_low_complexity(kmer_string);
+    return (is_present_in_20_percent && is_present_more_in_foreground && high_complexity);
 }
 std::vector<double> filtering_column(const std::vector<std::string> &files,
                const tsl::hopscotch_set<Label> &labels_in,
@@ -710,7 +730,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (config.test_type == "brunner_munzel"){
                 logger->trace("Brunner Munzel test");
             }
-            masked_graph = brunner_munzel_test(masked_graph,files,labels_in,labels_out,config,total_unitigs, medians);
+            masked_graph = brunner_munzel_test(graph_ptr, masked_graph,files,labels_in,labels_out,config,total_unitigs, medians);
             logger->trace("ooooo unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
             auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
                                                       std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),

From 00e4d32890aa042bcde90bb583e39989d14408e7 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Wed, 21 Feb 2024 17:50:49 +0100
Subject: [PATCH 064/410] make row filtering more efficient

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0de0c973db..8a29745b3a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -273,17 +273,22 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
     masked_graph->set_mask(new bitmap_vector(std::move(mask)));
     return masked_graph;
 }    
+// returns true if the kmer passes the filtering criteria
 bool filtering_row(std::vector<double> in_counts_non_zero,
                     std::vector<double> out_counts_non_zero,
                     int total_samples,
                     std::string kmer_string){
     // minimum present in 20% of samples
     bool is_present_in_20_percent = (in_counts_non_zero.size() + out_counts_non_zero.size()) > 0.2 * double(total_samples);
+    if (is_present_in_20_percent == false)
+        return false;
     // present more in in than out
     bool is_present_more_in_foreground = in_counts_non_zero.size() > out_counts_non_zero.size();
     // low complexity filter
+    if (is_present_more_in_foreground == false)
+        return false;
     bool high_complexity =  !is_low_complexity(kmer_string);
-    return (is_present_in_20_percent && is_present_more_in_foreground && high_complexity);
+    return (high_complexity);
 }
 std::vector<double> filtering_column(const std::vector<std::string> &files,
                const tsl::hopscotch_set<Label> &labels_in,

From 784900d89b4ee06f9feb087315edc32eaf13b62b Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 22 Feb 2024 00:25:09 +0100
Subject: [PATCH 065/410] update ttest lookup table

---
 .../src/graph/annotated_graph_algorithm.cpp   | 104 +++---
 metagraph/src/graph/differential_tests.hpp    |   1 +
 .../src/graph/differential_tests_uemmi.cpp    |  18 +-
 metagraph/src/graph/look_up_table_t_dist.cpp  | 348 ++++++++++--------
 metagraph/src/graph/look_up_table_t_dist.hpp  |   5 +-
 5 files changed, 264 insertions(+), 212 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 8a29745b3a..852c9000a0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -224,6 +224,9 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
     );
     int row_id = 0;
     int num_tests = 0;
+    int kept_nodes = 0;
+    // calculate the p-value for the brunner munzel test
+    auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0); 
     utils::call_rows<std::unique_ptr<bit_vector>,
                     std::unique_ptr<sdsl::int_vector<>>,
                     std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
@@ -253,13 +256,13 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
                                 if (in_counts.size() < labels_in.size())
                                 in_counts.resize(labels_in.size(), 0);
                                 if (out_counts.size() < labels_out.size())
-                                    out_counts.resize(labels_out.size(), 0);  
-                                // calculate the p-value for the brunner munzel test
-                                auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0);   
+                                    out_counts.resize(labels_out.size(), 0);    
                                 // run test and adjust masked graph accordingly 
                                 auto [keep, pvalue] = statistical_model.brunner_munzel_test(in_counts, out_counts);
                                 if (keep == false)
                                     mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
+                                else
+                                    kept_nodes++;  
                                 row_id++;
                             }
                             else{
@@ -271,6 +274,7 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
     });
     logger->trace("number of tests: {}", num_tests);
     masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+    logger->trace("number of kept nodes: {}", kept_nodes);
     return masked_graph;
 }    
 // returns true if the kmer passes the filtering criteria
@@ -734,54 +738,56 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             });
             if (config.test_type == "brunner_munzel"){
                 logger->trace("Brunner Munzel test");
+                masked_graph = brunner_munzel_test(graph_ptr, masked_graph,files,labels_in,labels_out,config,total_unitigs, medians);
             }
-            masked_graph = brunner_munzel_test(graph_ptr, masked_graph,files,labels_in,labels_out,config,total_unitigs, medians);
-            logger->trace("ooooo unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
-            auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
-                                                      std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
-                                                      in_total_kmers, out_total_kmers); 
-            const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
-            logger->trace("test_by_unitig == false");
-            sdsl::bit_vector mask = in_mask;
-            size_t total_nodes = masked_graph->num_nodes();
-            size_t kept_nodes = 0;
-            //std::vector<int> nodes;
-            std::vector<std::pair<double,int>> likelihood_ratios_nodes;
-            call_ones(in_mask, [&](node_index node) { // only run if counts_case > counts_control 
-                uint64_t in_sum = counts[node * 2];
-                uint64_t out_sum = counts[node * 2 + 1];
-                // filter kmers in samples less noise
-                // if (in_sum + out_sum >= int(total_nodes/20)) { // check for minimum occurance of k-mer
-                    double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
-                    if (in_sum > out_sum_normalized) { // check that kmer occurs more in foreground than in background
-                        // check for high complexity
-                        // auto kmer_string = graph_ptr->get_node_sequence(node);
-                        // auto complexity_low = is_low_complexity(kmer_string);
-                        // logger->trace("kmer_string: {}", kmer_string);
-                        // logger->trace("complexity_low: {}", complexity_low);
-                        auto [keep, likelihood_ratio] = statistical_model.likelihood_ratio_test(in_sum, out_sum);
-                        likelihood_ratios_nodes.push_back(std::make_pair(likelihood_ratio, node));
-                    }
-                    else
-                        mask[node] = false; 
-                // }
-                // else
-                //     mask[node] = false;               
-            });
-            // get index for benjamini-yekutieli correction
-            std::sort(likelihood_ratios_nodes.begin(), likelihood_ratios_nodes.end(), sortByPValue);
-            // double alpha = statistical_model.get_t_test_alpha(statistical_model.get_df_approx(), 0.05) // 0.05 is the alpha value, TODO: add params.
-            int max_i = statistical_model.benjamini_yekutieli(likelihood_ratios_nodes, statistical_model.lrt_threshold());
-            // reject all hypotheses with index <= max_i
-            for (int i = 0; i <= max_i; i++){
-                mask[likelihood_ratios_nodes[i].second] = false;
-            }
-            logger->trace("max_i {}", max_i);
-            logger->trace("likelihood_ratios__nodes_elem {}, {}, {}", likelihood_ratios_nodes[0].first, likelihood_ratios_nodes[1].first, likelihood_ratios_nodes[2].first);
-            logger->trace("likelihood_ratios_nodes_size {}", likelihood_ratios_nodes.size());
-            masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+            else{
+                logger->trace("ooooo unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
+                auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
+                                                        std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
+                                                        in_total_kmers, out_total_kmers); 
+                const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
+                logger->trace("test_by_unitig == false");
+                sdsl::bit_vector mask = in_mask;
+                size_t total_nodes = masked_graph->num_nodes();
+                size_t kept_nodes = 0;
+                //std::vector<int> nodes;
+                std::vector<std::pair<double,int>> likelihood_ratios_nodes;
+                call_ones(in_mask, [&](node_index node) { // only run if counts_case > counts_control 
+                    uint64_t in_sum = counts[node * 2];
+                    uint64_t out_sum = counts[node * 2 + 1];
+                    // filter kmers in samples less noise
+                    // if (in_sum + out_sum >= int(total_nodes/20)) { // check for minimum occurance of k-mer
+                        double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
+                        if (in_sum > out_sum_normalized) { // check that kmer occurs more in foreground than in background
+                            // check for high complexity
+                            // auto kmer_string = graph_ptr->get_node_sequence(node);
+                            // auto complexity_low = is_low_complexity(kmer_string);
+                            // logger->trace("kmer_string: {}", kmer_string);
+                            // logger->trace("complexity_low: {}", complexity_low);
+                            auto [keep, likelihood_ratio] = statistical_model.likelihood_ratio_test(in_sum, out_sum);
+                            likelihood_ratios_nodes.push_back(std::make_pair(likelihood_ratio, node));
+                        }
+                        else
+                            mask[node] = false; 
+                    // }
+                    // else
+                    //     mask[node] = false;               
+                });
+                // get index for benjamini-yekutieli correction
+                std::sort(likelihood_ratios_nodes.begin(), likelihood_ratios_nodes.end(), sortByPValue);
+                // double alpha = statistical_model.get_t_test_alpha(statistical_model.get_df_approx(), 0.05) // 0.05 is the alpha value, TODO: add params.
+                int max_i = statistical_model.benjamini_yekutieli(likelihood_ratios_nodes, statistical_model.lrt_threshold());
+                // reject all hypotheses with index <= max_i
+                for (int i = 0; i <= max_i; i++){
+                    mask[likelihood_ratios_nodes[i].second] = false;
+                }
+                logger->trace("max_i {}", max_i);
+                logger->trace("likelihood_ratios__nodes_elem {}, {}, {}", likelihood_ratios_nodes[0].first, likelihood_ratios_nodes[1].first, likelihood_ratios_nodes[2].first);
+                logger->trace("likelihood_ratios_nodes_size {}", likelihood_ratios_nodes.size());
+                masked_graph->set_mask(new bitmap_vector(std::move(mask)));
 
-            logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
+                logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
+            }
 
         } else { // binary approach
             logger->trace("test_by_unitig == true");
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index 02ba094a51..21d65f548e 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -16,6 +16,7 @@ class DifferentialTest {
     size_t in_unique_kmers;
     size_t out_unique_kmers;
     TDistributionTable t_table;
+    double alpha_precalc = -1;
     const double pi = std::atan(1) * 4;
     double gamma = std::sqrt(pi);
     double likelihood_ratio_threshold = DifferentialTest::lrt_threshold();
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 600388c08b..6189457021 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -31,7 +31,16 @@ double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipe
 }
 
 double DifferentialTest::get_t_test_alpha(int df, double alpha=0.05){
-    return t_table.getCriticalValue(alpha, df);
+    alpha = alpha/2; // convert to one sided test
+    alpha = alpha/total_hypotheses; // bonferroni correction
+    if (alpha > 0.01 || alpha < 0.0000000001){
+        common::logger->error("alpha value not supported");
+        return -1;
+    }
+    common::logger->trace("alpha: {}", alpha);
+    std::pair<double,double> out = t_table.getCriticalValue(alpha, df);
+    common::logger->trace("closest alpha: {}", out.second);
+    return out.first;
 }
 
 std::vector<double> DifferentialTest::get_midranks(std::vector<double> in_counts, int size_in_counts){
@@ -108,8 +117,11 @@ std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<doubl
     // get degrees of freedom with welch-satterthwaite equation
     double df = get_df_approx(in_counts, out_counts);
     // get t statistic alpha value
-    double alpha = get_t_test_alpha(df, 0.05);
-    if( std::abs(b) < alpha){
+    if (alpha_precalc == -1){
+        common::logger->trace("alpha_precalc not set, calculating alpha_precalc");
+        alpha_precalc = get_t_test_alpha(df, 0.05);
+    }
+    if( std::abs(b) < alpha_precalc){
         return std::tuple(true, b);
     }
     else{
diff --git a/metagraph/src/graph/look_up_table_t_dist.cpp b/metagraph/src/graph/look_up_table_t_dist.cpp
index d286cf1dc5..94e8093d11 100644
--- a/metagraph/src/graph/look_up_table_t_dist.cpp
+++ b/metagraph/src/graph/look_up_table_t_dist.cpp
@@ -4,174 +4,206 @@ namespace mtg{
 
 TDistributionTable::TDistributionTable(){
     // populate t table
+    alpha_values = {1e-10, 1.004508467431428e-10, 1.0090372611414361e-10, 1.0135864727703896e-10, 1.0181561943718107e-10, 1.0227465184142426e-10, 1.0273575377831195e-10, 1.0319893457826468e-10, 1.0366420361376885e-10, 1.0413157029956644e-10, 1.0460104409284548e-10, 1.0507263449343143e-10, 1.055463510439794e-10, 1.0602220333016724e-10, 1.0650020098088953e-10, 1.069803536684524e-10, 1.0746267110876926e-10, 1.079471630615574e-10, 1.0843383933053547e-10, 1.0892270976362188e-10, 1.0941378425313404e-10, 1.0990707273598859e-10, 1.1040258519390237e-10, 1.1090033165359453e-10, 1.1140032218698931e-10, 1.1190256691141994e-10, 1.1240707598983326e-10, 1.1291385963099547e-10, 1.1342292808969863e-10, 1.1393429166696823e-10, 1.1444796071027157e-10, 1.1496394561372718e-10, 1.1548225681831512e-10, 1.1600290481208828e-10, 1.1652590013038462e-10, 1.170512533560403e-10, 1.175789751196038e-10, 1.1810907609955122e-10, 1.186415670225021e-10, 1.1917645866343662e-10, 1.1971376184591365e-10, 1.2025348744228968e-10, 1.2079564637393885e-10, 1.2134024961147404e-10, 1.218873081749687e-10, 1.2243683313417999e-10, 1.229888356087726e-10, 1.23543326768544e-10, 1.2410031783365024e-10, 1.246598200748331e-10, 1.2522184481364816e-10, 1.2578640342269383e-10, 1.2635350732584148e-10, 1.2692316799846672e-10, 1.2749539696768148e-10, 1.2807020581256724e-10, 1.2864760616440948e-10, 1.292276097069329e-10, 1.2981022817653789e-10, 1.30395473362538e-10, 1.3098335710739866e-10, 1.3157389130697647e-10, 1.321670879107602e-10, 1.3276295892211254e-10, 1.3336151639851289e-10, 1.3396277245180142e-10, 1.3456673924842417e-10, 1.3517342900967914e-10, 1.3578285401196372e-10, 1.36395026587023e-10, 1.3700995912219932e-10, 1.3762766406068305e-10, 1.3824815390176413e-10, 1.3887144120108526e-10, 1.3949753857089583e-10, 1.4012645868030707e-10, 1.4075821425554856e-10, 1.4139281808022566e-10, 1.4203028299557817e-10, 1.4267062190074023e-10, 1.4331384775300128e-10, 1.4395997356806832e-10, 1.4460901242032917e-10, 1.452609774431172e-10, 1.4591588182897688e-10, 1.465737388299309e-10, 1.4723456175774827e-10, 1.4789836398421363e-10, 1.4856515894139794e-10, 1.4923496012193015e-10, 1.4990778107927033e-10, 1.5058363542798384e-10, 1.5126253684401694e-10, 1.5194449906497334e-10, 1.526295358903924e-10, 1.533176611820282e-10, 1.5400888886413007e-10, 1.5470323292372443e-10, 1.5540070741089763e-10, 1.5610132643908053e-10, 1.5680510418533384e-10, 1.5751205489063508e-10, 1.582221928601668e-10, 1.5893553246360596e-10, 1.596520881354148e-10, 1.6037187437513276e-10, 1.610949057476701e-10, 1.6182119688360243e-10, 1.6255076247946683e-10, 1.632836172980593e-10, 1.6401977616873333e-10, 1.6475925398770016e-10, 1.6550206571833007e-10, 1.662482263914552e-10, 1.6699775110567374e-10, 1.677506550276554e-10, 1.6850695339244828e-10, 1.6926666150378727e-10, 1.7002979473440363e-10, 1.7079636852633608e-10, 1.7156639839124323e-10, 1.7233989991071753e-10, 1.7311688873660054e-10, 1.7389738059129965e-10, 1.7468139126810616e-10, 1.7546893663151494e-10, 1.762600326175454e-10, 1.7705469523406404e-10, 1.7785294056110823e-10, 1.7865478475121167e-10, 1.7946024402973125e-10, 1.802693346951754e-10, 1.810820731195338e-10, 1.818984757486094e-10, 1.827185591023484e-10, 1.8354233977517877e-10, 1.8436983443634325e-10, 1.8520105983023727e-10, 1.8603603277674782e-10, 1.8687477017159386e-10, 1.8771728898666808e-10, 1.8856360627038042e-10, 1.8941373914800302e-10, 1.9026770482201678e-10, 1.9112552057245938e-10, 1.9198720375727503e-10, 1.9285277181266562e-10, 1.9372224225344363e-10, 1.9459563267338648e-10, 1.9547296074559256e-10, 1.9635424422283886e-10, 1.9723950093794015e-10, 1.9812874880410997e-10, 1.9902200581532286e-10, 1.9991929004667872e-10, 2.0082061965476835e-10, 2.0172601287804106e-10, 2.0263548803717351e-10, 2.0354906353544062e-10, 2.0446675785908781e-10, 2.0538858957770517e-10, 2.0631457734460317e-10, 2.0724473989719013e-10, 2.0817909605735137e-10, 2.0911766473183005e-10, 2.1006046491260976e-10, 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3.079265662734314, 3.077839544849525, 3.076412939893563, 3.074985847302966, 3.0735582665132504, 3.0721301969589074, 3.0707016380733956, 3.069272589289145, 3.06784305003755, 3.0664130197489685, 3.0649824978527183, 3.0635514837770765, 3.0621199769492735, 3.060687976795495, 3.059255482740873, 3.0578224942094896, 3.056389010624373, 3.05495503140749, 3.0535205559797474, 3.0520855837609915, 3.050650114169998, 3.0492141466244784, 3.04777768054107, 3.0463407153353375, 3.0449032504217675, 3.0434652852137667, 3.042026819123662, 3.040587851562692, 3.039148381941009, 3.037708409667675, 3.0362679341506578, 3.0348269547968307, 3.033385471011964, 3.03194348220073, 3.030500987766695, 3.0290579871123193, 3.0276144796389497, 3.0261704647468224, 3.024725941835056, 3.0232809103016525, 3.02183536954349, 3.020389318956323, 3.0189427579347785, 3.017495685872353, 3.0160481021614074, 3.0146000061931715, 3.0131513973577317, 3.011702275044033, 3.0102526386398756, 3.0088024875319137, 3.0073518211056474, 3.005900638745424, 3.004448939834435, 3.00299672375471, 3.0015439898871166, 3.0000907376113575, 2.9986369663059644, 2.9971826753482977, 2.9957278641145435, 2.994272531979709, 2.9928166783176207, 2.9913603025009188, 2.9899034039010597, 2.988445981888307, 2.9869880358317293, 2.985529565099203, 2.9840705690573994, 2.9826110470717913, 2.981150998506642, 2.979690422725007, 2.9782293190887303, 2.9767676869584387, 2.97530552569354, 2.973842834652222, 2.972379613191444, 2.97091586066694, 2.969451576433211, 2.967986759843523, 2.9665214102499022, 2.965055527003137, 2.9635891094527675, 2.9621221569470877, 2.96065466883314, 2.959186644456711, 2.9577180831623315, 2.956248984293267, 2.954779347191524, 2.953309171197835, 2.951838455651666, 2.950367199891205, 2.948895403253364, 2.947423065073771, 2.9459501846867715, 2.9444767614254217, 2.943002794621484, 2.9415282836054293, 2.940053227706426, 2.9385776262523424, 2.937101478569741, 2.9356247839838736, 2.934147541818681, 2.9326697513967863, 2.9311914120394933, 2.929712523066785, 2.928233083797312, 2.9267530935483994, 2.9252725516360365, 2.9237914573748736, 2.922309810078221, 2.9208276090580463, 2.919344853624965, 2.917861543088242, 2.916377676755786, 2.9148932539341468, 2.913408273928511, 2.9119227360426976, 2.9104366395791565, 2.908949983838962, 2.9074627681218113, 2.9059749917260174, 2.9044866539485126, 2.9029977540848346, 2.901508291429132, 2.900018265274155, 2.8985276749112523, 2.8970365196303707, 2.8955447987200453, 2.8940525114674016, 2.8925596571581482, 2.891066235076573, 2.889572244505541, 2.8880776847264893, 2.886582555019424, 2.885086854662914, 2.88359058293409, 2.882093739108639, 2.8805963224608013, 2.8790983322633634, 2.8775997677876575, 2.876100628303559, 2.8746009130794756, 2.873100621382349, 2.871599752477652, 2.870098305629377, 2.868596280100041, 2.8670936751506746, 2.86559049004082, 2.86408672402853, 2.8625823763703586, 2.8610774463213606, 2.859571933135086, 2.858065836063577, 2.856559154357359, 2.8550518872654465, 2.8535440340353277, 2.8520355939129662, 2.8505265661427974, 2.84901694996772, 2.847506744629096, 2.845995949366743, 2.844484563418934, 2.8429725860223884, 2.8414600164122703, 2.839946853822183, 2.838433097484167, 2.836918746628693, 2.8354038004846562, 2.833888258279376, 2.8323721192385922, 2.8308553825864506, 2.829338047545513, 2.827820113336742, 2.8263015791795003, 2.8247824442915457, 2.823262707889027, 2.821742369186479, 2.820221427396819, 2.8186998817313387, 2.8171777313997053, 2.815654975609952, 2.8141316135684735, 2.8126076444800265, 2.8110830675477185, 2.8095578819730065, 2.8080320869556936, 2.80650568169392, 2.804978665384162, 2.8034510372212256, 2.801922796398243, 2.800393942106666, 2.7988644735362604, 2.7973343898751057, 2.795803690309586, 2.7942723740243873, 2.7927404402024902, 2.7912078880251676, 2.7896747166719784, 2.788140925320763, 2.7866065131476394, 2.785071479326995, 2.7835358230314844, 2.7819995434320255, 2.78046263969779, 2.7789251109962025, 2.7773869564929323, 2.7758481753518924, 2.7743087667352295, 2.772768729803324, 2.771228063714778, 2.769686767626419, 2.768144840693286, 2.7666022820686305, 2.7650590909039083, 2.763515266348775, 2.7619708075510814, 2.7604257136568666, 2.7588799838103535, 2.757333617153945, 2.755786612828216, 2.7542389699719094, 2.7526906877219317, 2.751141765213346, 2.749592201579366, 2.748041995951353, 2.7464911474588085, 2.74493965522937, 2.7433875183888037, 2.741834736061002, 2.740281307367972, 2.7387272314298396, 2.737172507364834, 2.7356171342892885, 2.7340611113176307, 2.732504437562383, 2.7309471121341464, 2.7293891341416074, 2.7278305026915226, 2.726271216888719, 2.724711275836082, 2.723150678634557, 2.7215894243831373, 2.7200275121788646, 2.7184649411168142, 2.7169017102900987, 2.715337818789856, 2.7137732657052447, 2.712208050123441, 2.7106421711296274, 2.70907562780699, 2.7075084192367163, 2.705940544497981, 2.7043720026679448, 2.702802792821748, 2.701232914032503, 2.699662365371292, 2.6980911459071537, 2.6965192547070855, 2.69494669083603, 2.6933734533568745, 2.691799541330441, 2.6902249538154805, 2.688649689868669, 2.687073748544599, 2.6854971288957743, 2.6839198299726017, 2.6823418508233874, 2.6807631904943277, 2.6791838480295054, 2.6776038224708825, 2.6760231128582928, 2.6744417182294327, 2.6728596376198634, 2.6712768700629947, 2.669693414590083, 2.668109270230225, 2.6665244360103504, 2.664938910955214, 2.6633526940873913, 2.661765784427268, 2.660178180993039, 2.658589882800696, 2.657000888864025, 2.655411198194595, 2.6538208098017564, 2.65222972269263, 2.6506379358721013, 2.649045448342815, 2.647452259105166, 2.645858367157293, 2.644263771495073, 2.6426684711121124, 2.641072464999739, 2.6394757521469985, 2.637878331540645, 2.636280202165133, 2.6346813630026125, 2.6330818130329194, 2.6314815512335725, 2.6298805765797586, 2.6282788880443344, 2.6266764845978114, 2.625073365208354, 2.6234695288417678, 2.621864974461496, 2.6202597010286093, 2.6186537075017995, 2.617046992837373, 2.61543955598924, 2.6138313959089094, 2.612222511545483, 2.610612901845644, 2.609002565753651}
 };
 };
 
-double TDistributionTable::getCriticalValue(double alpha, int df){
+std::pair<double,double> TDistributionTable::getCriticalValue(double alpha, int df){
     if (df > 150) {
         throw std::invalid_argument("Degrees of freedom must be less than 150");
-        return 1;
+        return {};
     }
-    if (alpha*100 != std::round(alpha*100)) {
-        throw std::invalid_argument("Alpha must be a multiple of 0.01");
-        return 1;
+    int alpha_index = binary_search(alpha);
+    if (alpha_index == -1) {
+        throw std::invalid_argument("Alpha value out of bounds.");
+        return {};
     }
     // get critical value from t table
     df += 1;
     std::vector<double> df_row = ttable[df];
-    int alpha_index = (int)(alpha * 100) - 1;
     double t_value = df_row[alpha_index];
-    return t_value;
-}    
+    return std::make_pair(t_value, alpha_values[alpha_index]);
+}   
+int TDistributionTable::binary_search(double alpha){
+    int n = alpha_values.size();
+    int low = 0;
+    int high = n - 1;
+
+    while (low <= high) {
+        int mid = low + (high - low) / 2;
+
+        if (alpha_values[mid] == alpha) {
+            return mid; // Found exact match
+        } else if (alpha_values[mid] < alpha) {
+            low = mid + 1; // Target is in the right half
+        } else {
+            high = mid - 1; // Target is in the left half
+        }
+    }
+
+    // At this point, low and high indicate the range where the target would fit,
+    // so we compare the values at low and high to find the closest one.
+    if (high < 0) {
+        return low; // Target is smaller than all values
+    } else if (low >= n) {
+        return high; // Target is larger than all values
+    } else {
+        if (std::abs(alpha_values[low] - alpha) < std::abs(alpha_values[high] - alpha)) {
+            return low; // Value at low is closer to target
+        } else {
+            return high; // Value at high is closer to target
+        }
+    }
+}
 }; // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/look_up_table_t_dist.hpp b/metagraph/src/graph/look_up_table_t_dist.hpp
index cef1b04253..2c1a382a4f 100644
--- a/metagraph/src/graph/look_up_table_t_dist.hpp
+++ b/metagraph/src/graph/look_up_table_t_dist.hpp
@@ -6,11 +6,12 @@ namespace mtg{
 class TDistributionTable {
 private:
     std::vector<std::vector<double>> ttable;
+    std::vector<double> alpha_values;
 
 public:
     TDistributionTable();
 
-    double getCriticalValue(double alpha, int df);
-
+    std::pair<double,double> getCriticalValue(double alpha, int df);
+    int binary_search(double alpha);
 };
 }
\ No newline at end of file

From b1323a3d2d9b10241d3a421577427731f5dfd81b Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 22 Feb 2024 17:26:27 +0100
Subject: [PATCH 066/410] add num threads to unitig counting

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 852c9000a0..e2a786aaf1 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -735,7 +735,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             std::atomic<uint64_t> total_unitigs(0);
             masked_graph->call_unitigs([&](const std::string &, const std::vector<node_index> &) {
                 total_unitigs.fetch_add(1, MO_RELAXED);
-            });
+            }, get_num_threads());
             if (config.test_type == "brunner_munzel"){
                 logger->trace("Brunner Munzel test");
                 masked_graph = brunner_munzel_test(graph_ptr, masked_graph,files,labels_in,labels_out,config,total_unitigs, medians);

From b75c80ecb62656ea2b46719cf0501d68a06e5c16 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 22 Feb 2024 18:05:54 +0100
Subject: [PATCH 067/410] use boost to calculate t-value

---
 metagraph/src/graph/differential_tests.hpp    |   3 +
 .../src/graph/differential_tests_uemmi.cpp    |  25 ++-
 metagraph/src/graph/look_up_table_t_dist.cpp  | 208 +-----------------
 metagraph/src/graph/look_up_table_t_dist.hpp  |   6 +-
 4 files changed, 28 insertions(+), 214 deletions(-)

diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index 21d65f548e..ee56c5e3d5 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -17,6 +17,7 @@ class DifferentialTest {
     size_t out_unique_kmers;
     TDistributionTable t_table;
     double alpha_precalc = -1;
+    int df_precalc = -1;
     const double pi = std::atan(1) * 4;
     double gamma = std::sqrt(pi);
     double likelihood_ratio_threshold = DifferentialTest::lrt_threshold();
@@ -45,6 +46,8 @@ class DifferentialTest {
 
     int get_df_approx(std::vector<double> in_counts, std::vector<double> out_counts);
 
+    int get_df_conservative(std::vector<double> in_counts, std::vector<double> out_counts);
+
     double get_var(std::vector<double> counts, int n);
 };
 } // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 6189457021..b5ab6adaf0 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -33,14 +33,10 @@ double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipe
 double DifferentialTest::get_t_test_alpha(int df, double alpha=0.05){
     alpha = alpha/2; // convert to one sided test
     alpha = alpha/total_hypotheses; // bonferroni correction
-    if (alpha > 0.01 || alpha < 0.0000000001){
-        common::logger->error("alpha value not supported");
-        return -1;
-    }
     common::logger->trace("alpha: {}", alpha);
-    std::pair<double,double> out = t_table.getCriticalValue(alpha, df);
-    common::logger->trace("closest alpha: {}", out.second);
-    return out.first;
+    double critical_value = t_table.getCriticalValue(alpha, df);
+    common::logger->trace("Critical value: {}", critical_value);
+    return critical_value;
 }
 
 std::vector<double> DifferentialTest::get_midranks(std::vector<double> in_counts, int size_in_counts){
@@ -90,6 +86,12 @@ int DifferentialTest::get_df_approx(std::vector<double> in_counts, std::vector<d
     return int(std::floor(df_approx));
 }
 
+int DifferentialTest::get_df_conservative(std::vector<double> in_counts, std::vector<double> out_counts){
+    int m = in_counts.size();
+    int n = out_counts.size();
+    return std::min(m, n) - 1;
+}
+
 double DifferentialTest::get_var(std::vector<double> counts, int n){
     double mean = std::accumulate(counts.begin(), counts.end(), 0.0) / n;
     double mean_of_sqaures = std::inner_product(counts.begin(), counts.end(), counts.begin(), 0)/n;
@@ -114,12 +116,15 @@ std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<doubl
     double var_out = get_var(out_counts, n);         
     // get test statistic
     double b = (mean_mid_rank_out - mean_mid_rank_in) / (N * std::sqrt(var_in/(m*n*n) + var_out/(m*m*n)));
-    // get degrees of freedom with welch-satterthwaite equation
-    double df = get_df_approx(in_counts, out_counts);
+    // get degrees of freedom 
+    if (df_precalc == -1){
+        common::logger->trace("df_precalc not set, calculating df_precalc");
+        df_precalc = get_df_conservative(in_counts, out_counts);
+    }
     // get t statistic alpha value
     if (alpha_precalc == -1){
         common::logger->trace("alpha_precalc not set, calculating alpha_precalc");
-        alpha_precalc = get_t_test_alpha(df, 0.05);
+        alpha_precalc = get_t_test_alpha(df_precalc, 0.05);
     }
     if( std::abs(b) < alpha_precalc){
         return std::tuple(true, b);
diff --git a/metagraph/src/graph/look_up_table_t_dist.cpp b/metagraph/src/graph/look_up_table_t_dist.cpp
index 94e8093d11..825152cf0d 100644
--- a/metagraph/src/graph/look_up_table_t_dist.cpp
+++ b/metagraph/src/graph/look_up_table_t_dist.cpp
@@ -2,208 +2,16 @@
 
 namespace mtg{
 
-TDistributionTable::TDistributionTable(){
+TDistributionTable::TDistributionTable(){};
     // populate t table
-    alpha_values = {1e-10, 1.004508467431428e-10, 1.0090372611414361e-10, 1.0135864727703896e-10, 1.0181561943718107e-10, 1.0227465184142426e-10, 1.0273575377831195e-10, 1.0319893457826468e-10, 1.0366420361376885e-10, 1.0413157029956644e-10, 1.0460104409284548e-10, 1.0507263449343143e-10, 1.055463510439794e-10, 1.0602220333016724e-10, 1.0650020098088953e-10, 1.069803536684524e-10, 1.0746267110876926e-10, 1.079471630615574e-10, 1.0843383933053547e-10, 1.0892270976362188e-10, 1.0941378425313404e-10, 1.0990707273598859e-10, 1.1040258519390237e-10, 1.1090033165359453e-10, 1.1140032218698931e-10, 1.1190256691141994e-10, 1.1240707598983326e-10, 1.1291385963099547e-10, 1.1342292808969863e-10, 1.1393429166696823e-10, 1.1444796071027157e-10, 1.1496394561372718e-10, 1.1548225681831512e-10, 1.1600290481208828e-10, 1.1652590013038462e-10, 1.170512533560403e-10, 1.175789751196038e-10, 1.1810907609955122e-10, 1.186415670225021e-10, 1.1917645866343662e-10, 1.1971376184591365e-10, 1.2025348744228968e-10, 1.2079564637393885e-10, 1.2134024961147404e-10, 1.218873081749687e-10, 1.2243683313417999e-10, 1.229888356087726e-10, 1.23543326768544e-10, 1.2410031783365024e-10, 1.246598200748331e-10, 1.2522184481364816e-10, 1.2578640342269383e-10, 1.2635350732584148e-10, 1.2692316799846672e-10, 1.2749539696768148e-10, 1.2807020581256724e-10, 1.2864760616440948e-10, 1.292276097069329e-10, 1.2981022817653789e-10, 1.30395473362538e-10, 1.3098335710739866e-10, 1.3157389130697647e-10, 1.321670879107602e-10, 1.3276295892211254e-10, 1.3336151639851289e-10, 1.3396277245180142e-10, 1.3456673924842417e-10, 1.3517342900967914e-10, 1.3578285401196372e-10, 1.36395026587023e-10, 1.3700995912219932e-10, 1.3762766406068305e-10, 1.3824815390176413e-10, 1.3887144120108526e-10, 1.3949753857089583e-10, 1.4012645868030707e-10, 1.4075821425554856e-10, 1.4139281808022566e-10, 1.4203028299557817e-10, 1.4267062190074023e-10, 1.4331384775300128e-10, 1.4395997356806832e-10, 1.4460901242032917e-10, 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2.8550518872654465, 2.8535440340353277, 2.8520355939129662, 2.8505265661427974, 2.84901694996772, 2.847506744629096, 2.845995949366743, 2.844484563418934, 2.8429725860223884, 2.8414600164122703, 2.839946853822183, 2.838433097484167, 2.836918746628693, 2.8354038004846562, 2.833888258279376, 2.8323721192385922, 2.8308553825864506, 2.829338047545513, 2.827820113336742, 2.8263015791795003, 2.8247824442915457, 2.823262707889027, 2.821742369186479, 2.820221427396819, 2.8186998817313387, 2.8171777313997053, 2.815654975609952, 2.8141316135684735, 2.8126076444800265, 2.8110830675477185, 2.8095578819730065, 2.8080320869556936, 2.80650568169392, 2.804978665384162, 2.8034510372212256, 2.801922796398243, 2.800393942106666, 2.7988644735362604, 2.7973343898751057, 2.795803690309586, 2.7942723740243873, 2.7927404402024902, 2.7912078880251676, 2.7896747166719784, 2.788140925320763, 2.7866065131476394, 2.785071479326995, 2.7835358230314844, 2.7819995434320255, 2.78046263969779, 2.7789251109962025, 2.7773869564929323, 2.7758481753518924, 2.7743087667352295, 2.772768729803324, 2.771228063714778, 2.769686767626419, 2.768144840693286, 2.7666022820686305, 2.7650590909039083, 2.763515266348775, 2.7619708075510814, 2.7604257136568666, 2.7588799838103535, 2.757333617153945, 2.755786612828216, 2.7542389699719094, 2.7526906877219317, 2.751141765213346, 2.749592201579366, 2.748041995951353, 2.7464911474588085, 2.74493965522937, 2.7433875183888037, 2.741834736061002, 2.740281307367972, 2.7387272314298396, 2.737172507364834, 2.7356171342892885, 2.7340611113176307, 2.732504437562383, 2.7309471121341464, 2.7293891341416074, 2.7278305026915226, 2.726271216888719, 2.724711275836082, 2.723150678634557, 2.7215894243831373, 2.7200275121788646, 2.7184649411168142, 2.7169017102900987, 2.715337818789856, 2.7137732657052447, 2.712208050123441, 2.7106421711296274, 2.70907562780699, 2.7075084192367163, 2.705940544497981, 2.7043720026679448, 2.702802792821748, 2.701232914032503, 2.699662365371292, 2.6980911459071537, 2.6965192547070855, 2.69494669083603, 2.6933734533568745, 2.691799541330441, 2.6902249538154805, 2.688649689868669, 2.687073748544599, 2.6854971288957743, 2.6839198299726017, 2.6823418508233874, 2.6807631904943277, 2.6791838480295054, 2.6776038224708825, 2.6760231128582928, 2.6744417182294327, 2.6728596376198634, 2.6712768700629947, 2.669693414590083, 2.668109270230225, 2.6665244360103504, 2.664938910955214, 2.6633526940873913, 2.661765784427268, 2.660178180993039, 2.658589882800696, 2.657000888864025, 2.655411198194595, 2.6538208098017564, 2.65222972269263, 2.6506379358721013, 2.649045448342815, 2.647452259105166, 2.645858367157293, 2.644263771495073, 2.6426684711121124, 2.641072464999739, 2.6394757521469985, 2.637878331540645, 2.636280202165133, 2.6346813630026125, 2.6330818130329194, 2.6314815512335725, 2.6298805765797586, 2.6282788880443344, 2.6266764845978114, 2.625073365208354, 2.6234695288417678, 2.621864974461496, 2.6202597010286093, 2.6186537075017995, 2.617046992837373, 2.61543955598924, 2.6138313959089094, 2.612222511545483, 2.610612901845644, 2.609002565753651}
-};
-};
 
-std::pair<double,double> TDistributionTable::getCriticalValue(double alpha, int df){
-    if (df > 150) {
-        throw std::invalid_argument("Degrees of freedom must be less than 150");
-        return {};
-    }
-    int alpha_index = binary_search(alpha);
-    if (alpha_index == -1) {
-        throw std::invalid_argument("Alpha value out of bounds.");
-        return {};
-    }
-    // get critical value from t table
-    df += 1;
-    std::vector<double> df_row = ttable[df];
-    double t_value = df_row[alpha_index];
-    return std::make_pair(t_value, alpha_values[alpha_index]);
-}   
-int TDistributionTable::binary_search(double alpha){
-    int n = alpha_values.size();
-    int low = 0;
-    int high = n - 1;
-
-    while (low <= high) {
-        int mid = low + (high - low) / 2;
+double TDistributionTable::getCriticalValue(double alpha, int df){
+    // Create Student's t-distribution object
+    boost::math::students_t dist(df);
 
-        if (alpha_values[mid] == alpha) {
-            return mid; // Found exact match
-        } else if (alpha_values[mid] < alpha) {
-            low = mid + 1; // Target is in the right half
-        } else {
-            high = mid - 1; // Target is in the left half
-        }
-    }
+    // Get critical value for two-tailed test
+    double critical_value = boost::math::quantile(dist, 1 - alpha);
+    return critical_value;
+}   
 
-    // At this point, low and high indicate the range where the target would fit,
-    // so we compare the values at low and high to find the closest one.
-    if (high < 0) {
-        return low; // Target is smaller than all values
-    } else if (low >= n) {
-        return high; // Target is larger than all values
-    } else {
-        if (std::abs(alpha_values[low] - alpha) < std::abs(alpha_values[high] - alpha)) {
-            return low; // Value at low is closer to target
-        } else {
-            return high; // Value at high is closer to target
-        }
-    }
-}
 }; // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/look_up_table_t_dist.hpp b/metagraph/src/graph/look_up_table_t_dist.hpp
index 2c1a382a4f..e4e344e839 100644
--- a/metagraph/src/graph/look_up_table_t_dist.hpp
+++ b/metagraph/src/graph/look_up_table_t_dist.hpp
@@ -1,17 +1,15 @@
 #include <iostream>
 #include <cmath>
 #include <vector>
+#include <boost/math/distributions/students_t.hpp>
 
 namespace mtg{
 class TDistributionTable {
 private:
-    std::vector<std::vector<double>> ttable;
-    std::vector<double> alpha_values;
 
 public:
     TDistributionTable();
 
-    std::pair<double,double> getCriticalValue(double alpha, int df);
-    int binary_search(double alpha);
+    double getCriticalValue(double alpha, int df);
 };
 }
\ No newline at end of file

From 1f3a5adca8eadfb05e708efefa76d2ba25b54a7d Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 22 Feb 2024 18:29:19 +0100
Subject: [PATCH 068/410] remove include number line

---
 metagraph/src/graph/differential_tests_uemmi.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index b5ab6adaf0..57feefd4b2 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -1,9 +1,9 @@
 #include <math.h>
 #include <string>
-#include <numbers>
+// #include <numbers>
 #include <iostream>
 #include <cassert>
-#include<algorithm>
+#include <algorithm>
 #include "common/logger.hpp"
 
 #include "differential_tests.hpp"

From 309e006060d1e3e997d10f502d945d011ad8f311 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Sun, 25 Feb 2024 20:14:15 +0100
Subject: [PATCH 069/410] update likelihood ratio approach, add filter to
 config

---
 metagraph/src/cli/assemble.cpp                |  2 +
 .../src/graph/annotated_graph_algorithm.cpp   | 46 +++++++++----------
 .../src/graph/annotated_graph_algorithm.hpp   |  1 +
 3 files changed, 24 insertions(+), 25 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index d2e7a2a350..da4232c3fd 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -55,6 +55,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.test_by_unitig = experiment.get("test_by_unitig", false).asBool();
     diff_config.evaluate_assembly = experiment.get("evaluate_assembly", false).asBool();
     diff_config.test_type = experiment.get("test_type", "brunner_munzel").asString();
+    diff_config.filter = experiment.get("filter", true).asBool();
 
 
     logger->trace("Per-kmer mask in fraction:\t\t{}", diff_config.label_mask_in_kmer_fraction);
@@ -66,6 +67,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     logger->trace("Include k-mer counts if preseadd_complementnt in the graph:\t\t{}", diff_config.count_kmers); // if node weights (k-mer count) are present in the graph and should be included in the differential assembly.
     logger->trace("Family wise error rate for the Bonferroni test:\t{}", diff_config.family_wise_error_rate);
     logger->trace("Test type:\t{}", diff_config.test_type);
+    logger->trace("Filter:\t{}", diff_config.filter);
 
 
     // TODO get the folder with the annotation columns from the .json or command, because it is redundant to have to give all of them as an input.
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index e2a786aaf1..fabf199614 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -306,7 +306,6 @@ std::vector<double> filtering_column(const std::vector<std::string> &files,
     assert(files.size() > 0);
     // generate a vector of the total number of kmers per sample for each group (in and out)
     Timer t;
-    logger->trace("OOOOOOOOOOO time to generate counts vector {}",  t.elapsed());
     std::vector<double> medians;
     annot::ColumnCompressed<>::load_columns_and_values(files,
         [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { // goes through all files in a group
@@ -454,7 +453,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         && (config.add_complement || unitig_mode);
 
     logger->trace("Generating initial mask");
-    bool filter = true;
+    bool filter = config.filter;
     std::vector<double> medians;
     auto count_vector = construct_diff_label_count_vector( // called for each group (in and out)
             [&](const ColumnCallback &column_callback) {
@@ -478,8 +477,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                             else throw std::runtime_error("Label not found in labels_in or labels_out");
                         }
                     );
-                    int min_kmer_count_in = *min_element(all_kmers_per_sample_in.begin(), all_kmers_per_sample_in.end());
-                    int min_kmer_count_out = *min_element(all_kmers_per_sample_out.begin(), all_kmers_per_sample_out.end());
                     logger->trace("OOOOOOOOOOO time to generate counts vector {}",  t.elapsed());
                     annot::ColumnCompressed<>::load_columns_and_values(files,
                         [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { // goes through all files in a group
@@ -487,13 +484,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                             int normalization_constant = 1e9;   // const to scale normalized values to save as int
                             auto normalization_factor = normalization_constant / total_kmers;
                             int max_col_width = std::pow(2, 16); // max value for int16_t
-                            int min_kmer_counts;
-                            if (labels_in.find(label) != labels_in.end())
-                                min_kmer_counts = min_kmer_count_in;
-                            else if (labels_out.find(label) != labels_out.end())
-                                min_kmer_counts = min_kmer_count_out;
-                            else throw std::runtime_error("Label not found in labels_in or labels_out");
-                            // double scaling = (double) total_kmers / min_kmer_counts; 
                             column_callback(offset, label, [&](const ValueCallback &value_callback) {
                                 assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0); // at least one kmer in sample
                                 assert(column->num_set_bits() > 0); // at least one kmer present in sample
@@ -740,7 +730,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 logger->trace("Brunner Munzel test");
                 masked_graph = brunner_munzel_test(graph_ptr, masked_graph,files,labels_in,labels_out,config,total_unitigs, medians);
             }
-            else{
+            else if (config.test_type == "likelihoodratio"){
                 logger->trace("ooooo unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
                 auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
                                                         std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
@@ -765,7 +755,13 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             // logger->trace("kmer_string: {}", kmer_string);
                             // logger->trace("complexity_low: {}", complexity_low);
                             auto [keep, likelihood_ratio] = statistical_model.likelihood_ratio_test(in_sum, out_sum);
-                            likelihood_ratios_nodes.push_back(std::make_pair(likelihood_ratio, node));
+                            // likelihood_ratios_nodes.push_back(std::make_pair(likelihood_ratio, node));
+                            if (keep){
+                                mask[node] = true;
+                                kept_nodes++;
+                            }
+                            else
+                                mask[node] = false;
                         }
                         else
                             mask[node] = false; 
@@ -773,18 +769,18 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     // else
                     //     mask[node] = false;               
                 });
-                // get index for benjamini-yekutieli correction
-                std::sort(likelihood_ratios_nodes.begin(), likelihood_ratios_nodes.end(), sortByPValue);
-                // double alpha = statistical_model.get_t_test_alpha(statistical_model.get_df_approx(), 0.05) // 0.05 is the alpha value, TODO: add params.
-                int max_i = statistical_model.benjamini_yekutieli(likelihood_ratios_nodes, statistical_model.lrt_threshold());
-                // reject all hypotheses with index <= max_i
-                for (int i = 0; i <= max_i; i++){
-                    mask[likelihood_ratios_nodes[i].second] = false;
-                }
-                logger->trace("max_i {}", max_i);
-                logger->trace("likelihood_ratios__nodes_elem {}, {}, {}", likelihood_ratios_nodes[0].first, likelihood_ratios_nodes[1].first, likelihood_ratios_nodes[2].first);
-                logger->trace("likelihood_ratios_nodes_size {}", likelihood_ratios_nodes.size());
-                masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+                // // get index for benjamini-yekutieli correction
+                // std::sort(likelihood_ratios_nodes.begin(), likelihood_ratios_nodes.end(), sortByPValue);
+                // // double alpha = statistical_model.get_t_test_alpha(statistical_model.get_df_approx(), 0.05) // 0.05 is the alpha value, TODO: add params.
+                // int max_i = statistical_model.benjamini_yekutieli(likelihood_ratios_nodes, statistical_model.lrt_threshold());
+                // // reject all hypotheses with index <= max_i
+                // for (int i = 0; i <= max_i; i++){
+                //     mask[likelihood_ratios_nodes[i].second] = false;
+                // }
+                // logger->trace("max_i {}", max_i);
+                // logger->trace("likelihood_ratios__nodes_elem {}, {}, {}", likelihood_ratios_nodes[0].first, likelihood_ratios_nodes[1].first, likelihood_ratios_nodes[2].first);
+                // logger->trace("likelihood_ratios_nodes_size {}", likelihood_ratios_nodes.size());
+                // masked_graph->set_mask(new bitmap_vector(std::move(mask)));
 
                 logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
             }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 913886b71d..e0f7a1a5ca 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -38,6 +38,7 @@ struct DifferentialAssemblyConfig {
     bool test_by_unitig = false; // Myrthe
     std::string test_type = "brunner_munzel"; // default is "brunner_munzel", alternatives are: "likelihoodratio", "binary"
     bool evaluate_assembly = false; // Myrthe temporary : evaluate an assembly
+    bool filter = true;
 };
 
 /**

From daf9ddce3b7385b96692b5db9449eee97c59c3b2 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 27 Feb 2024 11:09:15 +0100
Subject: [PATCH 070/410] add log for reverse compliment

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fabf199614..9b63d58db5 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -387,6 +387,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
 
     bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
         && (config.add_complement || unitig_mode);
+    logger->trace("add_complement: {}", add_complement);    
 
     logger->trace("Generating initial mask");
 
@@ -451,7 +452,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
 
     bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
         && (config.add_complement || unitig_mode);
-
+    logger->trace("add_complement: {}", add_complement);
     logger->trace("Generating initial mask");
     bool filter = config.filter;
     std::vector<double> medians;
@@ -654,7 +655,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
             && (config.add_complement || unitig_mode);
-
+    logger->trace("add_complement: {}", add_complement);
     auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
                                                   add_complement, num_threads);
 

From 79353b916a6e9f5d961f65f04e23d51706d1a9f9 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 27 Feb 2024 12:06:29 +0100
Subject: [PATCH 071/410] change if statement for canonical

---
 metagraph/src/cli/load/load_annotated_graph.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/cli/load/load_annotated_graph.cpp b/metagraph/src/cli/load/load_annotated_graph.cpp
index fdf0fbc7ec..cf468e84c6 100644
--- a/metagraph/src/cli/load/load_annotated_graph.cpp
+++ b/metagraph/src/cli/load/load_annotated_graph.cpp
@@ -26,7 +26,7 @@ std::unique_ptr<AnnotatedDBG> initialize_annotated_dbg(std::shared_ptr<DeBruijnG
     uint64_t max_index = graph->max_index();
     const auto *dbg_graph = dynamic_cast<const DBGSuccinct*>(graph.get());
 
-    if (graph->get_mode() == DeBruijnGraph::PRIMARY) {
+    if (graph->get_mode() == DeBruijnGraph::PRIMARY && config.identity != Config::ASSEMBLE) {
         graph = std::make_shared<CanonicalDBG>(graph);
         logger->trace("Primary graph wrapped into canonical");
     }

From b32cb3f5bf8183799b1b36cdba7cc94c2fb5384c Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 27 Feb 2024 14:35:07 +0100
Subject: [PATCH 072/410] change back if statement

---
 metagraph/src/cli/assemble.cpp                  | 1 +
 metagraph/src/cli/load/load_annotated_graph.cpp | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index da4232c3fd..1cef9a18cb 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -66,6 +66,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     logger->trace("Include reverse complements:\t\t{}", diff_config.add_complement);
     logger->trace("Include k-mer counts if preseadd_complementnt in the graph:\t\t{}", diff_config.count_kmers); // if node weights (k-mer count) are present in the graph and should be included in the differential assembly.
     logger->trace("Family wise error rate for the Bonferroni test:\t{}", diff_config.family_wise_error_rate);
+    logger->trace("count_kmers:\t{}", diff_config.count_kmers);
     logger->trace("Test type:\t{}", diff_config.test_type);
     logger->trace("Filter:\t{}", diff_config.filter);
 
diff --git a/metagraph/src/cli/load/load_annotated_graph.cpp b/metagraph/src/cli/load/load_annotated_graph.cpp
index cf468e84c6..fdf0fbc7ec 100644
--- a/metagraph/src/cli/load/load_annotated_graph.cpp
+++ b/metagraph/src/cli/load/load_annotated_graph.cpp
@@ -26,7 +26,7 @@ std::unique_ptr<AnnotatedDBG> initialize_annotated_dbg(std::shared_ptr<DeBruijnG
     uint64_t max_index = graph->max_index();
     const auto *dbg_graph = dynamic_cast<const DBGSuccinct*>(graph.get());
 
-    if (graph->get_mode() == DeBruijnGraph::PRIMARY && config.identity != Config::ASSEMBLE) {
+    if (graph->get_mode() == DeBruijnGraph::PRIMARY) {
         graph = std::make_shared<CanonicalDBG>(graph);
         logger->trace("Primary graph wrapped into canonical");
     }

From 41773f76753b4c3d5ea003e2d628f4b4bd5ffe3f Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 27 Feb 2024 14:55:05 +0100
Subject: [PATCH 073/410] add line to make_shared

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9b63d58db5..54b6095c90 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -933,7 +933,8 @@ make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         add_complement
             ? std::make_unique<bitmap_vector>(mask)
             : std::make_unique<bitmap_vector>(std::move(mask)),
-        true
+            true,
+            graph_ptr->get_mode() == DeBruijnGraph::PRIMARY ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
     );
 
     logger->trace("Constructed masked graph with {} nodes", masked_graph->num_nodes());

From 9715f80254375c178991b75e76724b6e3d1c3cc4 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 27 Feb 2024 15:08:01 +0100
Subject: [PATCH 074/410] change load_annotated_graph

---
 metagraph/src/cli/load/load_annotated_graph.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/cli/load/load_annotated_graph.cpp b/metagraph/src/cli/load/load_annotated_graph.cpp
index fdf0fbc7ec..cf468e84c6 100644
--- a/metagraph/src/cli/load/load_annotated_graph.cpp
+++ b/metagraph/src/cli/load/load_annotated_graph.cpp
@@ -26,7 +26,7 @@ std::unique_ptr<AnnotatedDBG> initialize_annotated_dbg(std::shared_ptr<DeBruijnG
     uint64_t max_index = graph->max_index();
     const auto *dbg_graph = dynamic_cast<const DBGSuccinct*>(graph.get());
 
-    if (graph->get_mode() == DeBruijnGraph::PRIMARY) {
+    if (graph->get_mode() == DeBruijnGraph::PRIMARY && config.identity != Config::ASSEMBLE) {
         graph = std::make_shared<CanonicalDBG>(graph);
         logger->trace("Primary graph wrapped into canonical");
     }

From 41c8ed237f5ff810bc0e57511aed737053e23d40 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Wed, 28 Feb 2024 12:23:18 +0100
Subject: [PATCH 075/410] avoid seg. error

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 10 ++++++----
 metagraph/src/graph/differential_tests.hpp        |  1 +
 metagraph/src/graph/differential_tests_uemmi.cpp  |  6 +++++-
 3 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 54b6095c90..2fecf188c4 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -222,9 +222,9 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
             else throw std::runtime_error("Label not found in labels_in or labels_out");
         }
     );
-    int row_id = 0;
-    int num_tests = 0;
-    int kept_nodes = 0;
+    int64_t row_id = 0;
+    int64_t num_tests = 0;
+    int64_t kept_nodes = 0;
     // calculate the p-value for the brunner munzel test
     auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0); 
     utils::call_rows<std::unique_ptr<bit_vector>,
@@ -254,7 +254,7 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
                             if (filtering_row(in_counts, out_counts, files.size(), kmer_string)) {
                                 num_tests++;
                                 if (in_counts.size() < labels_in.size())
-                                in_counts.resize(labels_in.size(), 0);
+                                    in_counts.resize(labels_in.size(), 0);
                                 if (out_counts.size() < labels_out.size())
                                     out_counts.resize(labels_out.size(), 0);    
                                 // run test and adjust masked graph accordingly 
@@ -272,6 +272,7 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
                             
                         }               
     });
+    logger->trace("number of tests with 0 var: {}", statistical_model.var_0);
     logger->trace("number of tests: {}", num_tests);
     masked_graph->set_mask(new bitmap_vector(std::move(mask)));
     logger->trace("number of kept nodes: {}", kept_nodes);
@@ -750,6 +751,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     // if (in_sum + out_sum >= int(total_nodes/20)) { // check for minimum occurance of k-mer
                         double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
                         if (in_sum > out_sum_normalized) { // check that kmer occurs more in foreground than in background
+                            logger->trace("testing");
                             // check for high complexity
                             // auto kmer_string = graph_ptr->get_node_sequence(node);
                             // auto complexity_low = is_low_complexity(kmer_string);
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index ee56c5e3d5..096b81f7c5 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -23,6 +23,7 @@ class DifferentialTest {
     double likelihood_ratio_threshold = DifferentialTest::lrt_threshold();
 
   public:
+    int64_t var_0 = 0;
     DifferentialTest(double family_wise_error_rate,
                      size_t total_hypotheses,
                      size_t preload_table_size,
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 57feefd4b2..aa7d11dc12 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -113,7 +113,11 @@ std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<doubl
     double mean_mid_rank_out = std::accumulate(mid_ranks_out.begin(), mid_ranks_out.end(), 0.0) / mid_ranks_out.size();
     // get variances
     double var_in = get_var(in_counts, m);
-    double var_out = get_var(out_counts, n);         
+    double var_out = get_var(out_counts, n);
+    if (var_in == 0 && var_out == 0){
+        var_0++;
+        return std::tuple(false, 0);
+    }         
     // get test statistic
     double b = (mean_mid_rank_out - mean_mid_rank_in) / (N * std::sqrt(var_in/(m*n*n) + var_out/(m*m*n)));
     // get degrees of freedom 

From 051188f3d3c0126a030488d87bb99572ff02a8f9 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 29 Feb 2024 09:25:52 +0100
Subject: [PATCH 076/410] debug likelihood

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 2fecf188c4..92de61e428 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1074,6 +1074,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
 
     size_t num_tested_kmers = 0;
     for(size_t i = 0; i < counts.size(); i+=2){
+        logger->trace("counts: {}, {}", counts[i], counts[i+1]);
         if (counts[i] > counts[i+1])
         num_tested_kmers += 1;
     };

From 50e2c254f05100c59a02f7191ce56752972fe238 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Thu, 29 Feb 2024 21:29:38 +0100
Subject: [PATCH 077/410] buffered columns

---
 .../annotate_column_compressed.cpp            | 84 +++++++++++++++++++
 .../annotate_column_compressed.hpp            |  8 ++
 metagraph/src/common/vectors/transpose.hpp    |  2 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 66 ++++++---------
 .../src/graph/differential_tests_uemmi.cpp    |  9 +-
 5 files changed, 119 insertions(+), 50 deletions(-)

diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
index f85db12f63..9ccfe137ba 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
@@ -796,6 +796,90 @@ ::load_columns_and_values(const std::vector<std::string> &filenames,
         exit(1);
 }
 
+template <typename Label>
+void ColumnCompressed<Label>
+::load_columns_and_values(const std::vector<std::string> &filenames,
+                          const ColumnsValuesStreamCallback &callback,
+                          size_t num_threads) {
+    std::atomic<bool> error_occurred = false;
+    std::vector<uint64_t> offsets(filenames.size(), 0);
+
+    // load labels
+    #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+    for (size_t i = 1; i < filenames.size(); ++i) {
+        auto fname = make_suffix(filenames[i - 1], kExtension);
+        try {
+            offsets[i] = read_num_labels(fname);
+        } catch (...) {
+            logger->error("Can't load label encoder from {}", fname);
+            error_occurred = true;
+        }
+    }
+
+    if (error_occurred)
+        exit(1);
+
+    // compute global offsets (partial sums)
+    std::partial_sum(offsets.begin(), offsets.end(), offsets.begin());
+
+    // load annotations
+    #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+    for (size_t i = 0; i < filenames.size(); ++i) {
+        const auto &filename = make_suffix(filenames[i], kExtension);
+        logger->trace("Loading columns from {}", filename);
+        try {
+            std::unique_ptr<std::ifstream> in_ptr = utils::open_ifstream(filename);
+            auto &in = *in_ptr;
+            if (!in)
+                throw std::ifstream::failure("can't open file");
+
+            std::ignore = load_number(in);
+
+            LabelEncoder<Label> label_encoder_load;
+            if (!label_encoder_load.load(in))
+                throw std::ifstream::failure("can't load label encoder");
+
+            if (!label_encoder_load.size()) {
+                logger->warn("No columns in {}", filename);
+                continue;
+            }
+
+            const auto &values_fname
+                = utils::remove_suffix(filename, ColumnCompressed<>::kExtension)
+                                                + ColumnCompressed<>::kCountExtension;
+
+            std::unique_ptr<std::ifstream> values_in_ptr = utils::open_ifstream(values_fname);
+            auto &values_in = *values_in_ptr;
+            if (!values_in)
+                throw std::ifstream::failure("can't open file " + values_fname);
+
+            for (size_t c = 0; c < label_encoder_load.size(); ++c) {
+                auto column = std::make_unique<bit_vector_smart>();
+
+                if (!column->load(in))
+                    throw std::ifstream::failure("can't load next column");
+
+                sdsl::int_vector_buffer<> column_values(values_fname);
+                std::cout << "column.size () = " << column->num_set_bits() << std::endl;
+                std::cout << "column_values.size () = " << column_values.size() << std::endl;
+                callback(offsets[i] + c,
+                         label_encoder_load.decode(c),
+                         std::move(column), std::move(column_values));
+            }
+
+        } catch (const std::exception &e) {
+            logger->error("Caught exception when loading values for {}: {}", filename, e.what());
+            error_occurred = true;
+        } catch (...) {
+            logger->error("Unknown exception when loading values for {}", filename);
+            error_occurred = true;
+        }
+    }
+
+    if (error_occurred)
+        exit(1);
+}
+
 template <typename Label>
 void ColumnCompressed<Label>::load_columns_delims_and_values(
         const std::vector<std::string> &filenames,
diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
index 8a469e3677..5a25522025 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
@@ -100,6 +100,14 @@ class ColumnCompressed : public MultiLabelAnnotation<Label> {
                                         const ColumnsValuesCallback &callback,
                                         size_t num_threads = 1);
 
+    using ColumnsValuesStreamCallback = std::function<void(uint64_t offset,
+                                                     const Label &,
+                                                     std::unique_ptr<bit_vector>&&,
+                                                     sdsl::int_vector_buffer<>&&)>;
+    static void load_columns_and_values(const std::vector<std::string> &filenames,
+                                        const ColumnsValuesStreamCallback &callback,
+                                        size_t num_threads = 1);                                    
+
     using ColumnsDelimsValuesCallback = std::function<void(uint64_t offset,
                                                       const Label &,
                                                       std::unique_ptr<bit_vector>&&,
diff --git a/metagraph/src/common/vectors/transpose.hpp b/metagraph/src/common/vectors/transpose.hpp
index 2128a8e1e8..1c9c44401b 100644
--- a/metagraph/src/common/vectors/transpose.hpp
+++ b/metagraph/src/common/vectors/transpose.hpp
@@ -77,7 +77,7 @@ void call_rows(const std::vector<BitmapPtr> &columns,
 
     num_rows = columns[0]->size();
     std::vector<PairVector> rows;
-
+        
     ProgressBar progress_bar(num_rows, "Constructed rows", std::cerr, !show_progress);
 
     #pragma omp parallel for ordered num_threads(get_num_threads()) schedule(dynamic) private(rows)
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 92de61e428..9a34854587 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -134,7 +134,7 @@ bool sortByPValue(const std::pair<int, double>& a, const std::pair<int, double>&
 
 // Function for calculating 
 // the median 
-double findMedian(sdsl::int_vector<0> a, 
+double findMedian(sdsl::int_vector_buffer<0> a, 
                     int threshold) {   
     logger->trace("non zero kmers: {}", a.size());
     std::vector<double> a_filtered;
@@ -208,13 +208,12 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
     // brunner munzel test
     // call kmer matrix per row
     std::vector<std::unique_ptr<bit_vector>> columns_all;
-    std::vector<std::unique_ptr<sdsl::int_vector<>>> column_values_all;
+    std::vector<std::unique_ptr<sdsl::int_vector_buffer<>>> column_values_all;
     std::vector<bool> column_label;
     annot::ColumnCompressed<>::load_columns_and_values(files,
-        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { 
-            // TODO: add count filtering here as a function
+        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector_buffer<> && column_values) { 
             columns_all.push_back(std::move(column));
-            column_values_all.push_back(std::make_unique<sdsl::int_vector<>>(std::move(column_values)));
+            column_values_all.push_back(std::make_unique<sdsl::int_vector_buffer<>>(std::move(column_values)));
             if (labels_in.find(label) != labels_in.end()) // if the label is in the in labels set column_label to 1, else 0
                 column_label.push_back(1);
             else if (labels_out.find(label) != labels_out.end())
@@ -228,9 +227,8 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
     // calculate the p-value for the brunner munzel test
     auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0); 
     utils::call_rows<std::unique_ptr<bit_vector>,
-                    std::unique_ptr<sdsl::int_vector<>>,
+                    std::unique_ptr<sdsl::int_vector_buffer<>>,
                     std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
-                        // TODO filter for low complexity (make a function)
                         // auto kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
                         if (row.size() == 0) { // if the kmer is not present in any sample
                             mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
@@ -256,13 +254,14 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
                                 if (in_counts.size() < labels_in.size())
                                     in_counts.resize(labels_in.size(), 0);
                                 if (out_counts.size() < labels_out.size())
-                                    out_counts.resize(labels_out.size(), 0);    
+                                    out_counts.resize(labels_out.size(), 0);       
                                 // run test and adjust masked graph accordingly 
                                 auto [keep, pvalue] = statistical_model.brunner_munzel_test(in_counts, out_counts);
                                 if (keep == false)
                                     mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
-                                else
-                                    kept_nodes++;  
+                                else{
+                                    kept_nodes++; 
+                                }     
                                 row_id++;
                             }
                             else{
@@ -309,17 +308,17 @@ std::vector<double> filtering_column(const std::vector<std::string> &files,
     Timer t;
     std::vector<double> medians;
     annot::ColumnCompressed<>::load_columns_and_values(files,
-        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { // goes through all files in a group
-            double max_col_width = std::pow(2, 16); // max value for int16_t
+        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector_buffer<> && column_values) { // goes through all files in a group
+            uint64_t max_col_width = std::pow(2, 16); // max value for int16_t
             column_callback(offset, label, [&](const ValueCallback &value_callback) {
                 assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0); // at least one kmer in sample
                 assert(column->num_set_bits() > 0); // at least one kmer present in sample
                 assert(column->num_set_bits() == column_values.size()); // same number of kmers in column as in column_values
-                double median = findMedian(column_values, threshold);
+                double median = findMedian(std::move(column_values), threshold);
                 medians.push_back(median);
                 logger->trace("median: {}", median);
                 call_ones(*column, [&](uint64_t i) {
-                    double column_value = column_values[column->rank1(i)-1];
+                    uint64_t column_value = column_values[column->rank1(i)-1];
                     if (column_value > threshold){
                         column_value = std::min(column_value, max_col_width); // normalize value
                         value_callback(i, column_value);
@@ -467,32 +466,17 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                     assert(files.size() > 0);
                     // generate a vector of the total number of kmers per sample for each group (in and out)
                     Timer t;
-                    std::vector<int> all_kmers_per_sample_in;
-                    std::vector<int> all_kmers_per_sample_out;
-                    annot::ColumnCompressed<>::load_columns_and_values(files,
-                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { 
-                            auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0) + column_values.size(); // adding pseudocounts
-                            if (labels_in.find(label) != labels_in.end())
-                            all_kmers_per_sample_in.push_back(total_kmers);
-                            else if (labels_out.find(label) != labels_out.end())
-                            all_kmers_per_sample_out.push_back(total_kmers);
-                            else throw std::runtime_error("Label not found in labels_in or labels_out");
-                        }
-                    );
                     logger->trace("OOOOOOOOOOO time to generate counts vector {}",  t.elapsed());
                     annot::ColumnCompressed<>::load_columns_and_values(files,
-                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) { // goes through all files in a group
-                            auto total_kmers = std::accumulate(column_values.begin(), column_values.end(), 0) + column_values.size(); 
-                            int normalization_constant = 1e9;   // const to scale normalized values to save as int
-                            auto normalization_factor = normalization_constant / total_kmers;
+                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector_buffer<> && column_values) { // goes through all files in a group
                             int max_col_width = std::pow(2, 16); // max value for int16_t
                             column_callback(offset, label, [&](const ValueCallback &value_callback) {
                                 assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0); // at least one kmer in sample
                                 assert(column->num_set_bits() > 0); // at least one kmer present in sample
                                 assert(column->num_set_bits() == column_values.size()); // same number of kmers in column as in column_values
                                 call_ones(*column, [&](uint64_t i) {
-                                    auto column_value = column_values[column->rank1(i)-1];
-                                    column_value = std::min((int) std::ceil(column_value*normalization_factor), max_col_width); // normalize value
+                                    uint64_t column_value = column_values[column->rank1(i)-1];
+                                    column_value = std::min((int) column_value, max_col_width); // normalize value
                                     value_callback(i, column_value);
                                 });
                             });
@@ -733,7 +717,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 masked_graph = brunner_munzel_test(graph_ptr, masked_graph,files,labels_in,labels_out,config,total_unitigs, medians);
             }
             else if (config.test_type == "likelihoodratio"){
-                logger->trace("ooooo unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
+                logger->trace("Likelihood ratio test");
+                logger->trace("ooooo total unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
                 auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
                                                         std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
                                                         in_total_kmers, out_total_kmers); 
@@ -751,7 +736,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     // if (in_sum + out_sum >= int(total_nodes/20)) { // check for minimum occurance of k-mer
                         double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
                         if (in_sum > out_sum_normalized) { // check that kmer occurs more in foreground than in background
-                            logger->trace("testing");
                             // check for high complexity
                             // auto kmer_string = graph_ptr->get_node_sequence(node);
                             // auto complexity_low = is_low_complexity(kmer_string);
@@ -783,12 +767,11 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 // logger->trace("max_i {}", max_i);
                 // logger->trace("likelihood_ratios__nodes_elem {}, {}, {}", likelihood_ratios_nodes[0].first, likelihood_ratios_nodes[1].first, likelihood_ratios_nodes[2].first);
                 // logger->trace("likelihood_ratios_nodes_size {}", likelihood_ratios_nodes.size());
-                // masked_graph->set_mask(new bitmap_vector(std::move(mask)));
-
+                masked_graph->set_mask(new bitmap_vector(std::move(mask)));
                 logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
             }
 
-        } else { // binary approach
+        } else { 
             logger->trace("test_by_unitig == true");
             masked_graph->likelihood_ratios.resize(counts.size()/2+1);
             std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0);
@@ -819,10 +802,10 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     else
                         return {};
                 },
-                num_threads);
-            }
-            //std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0.01); // Myrthe: For testing. temporary
-            return masked_graph;
+            num_threads);
+        }
+            //std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0.01); // Myrthe: For testing. temporary    
+        return masked_graph;
     }
 
 
@@ -1074,7 +1057,6 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
 
     size_t num_tested_kmers = 0;
     for(size_t i = 0; i < counts.size(); i+=2){
-        logger->trace("counts: {}, {}", counts[i], counts[i+1]);
         if (counts[i] > counts[i+1])
         num_tested_kmers += 1;
     };
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index aa7d11dc12..9243d4a4a4 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -116,7 +116,8 @@ std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<doubl
     double var_out = get_var(out_counts, n);
     if (var_in == 0 && var_out == 0){
         var_0++;
-        return std::tuple(false, 0);
+        var_in = 1;
+        var_out = 1;
     }         
     // get test statistic
     double b = (mean_mid_rank_out - mean_mid_rank_in) / (N * std::sqrt(var_in/(m*n*n) + var_out/(m*m*n)));
@@ -196,12 +197,6 @@ std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum,
     in_sum += 1;
     out_sum += 1;
     double mean = (out_sum + in_sum)/ static_cast<double>(out_total_kmers + in_total_kmers);
-    // double lambda = (in_sum/in_total_kmers + out_sum/out_total_kmers)/2 * ((out_total_kmers+in_total_kmers)/2);
-    std::cout << "out sum " << out_sum << "\n";
-    std::cout << "in sum " << in_sum << "\n";
-    std::cout << "mean " << mean << "\n";
-    std::cout << "out_total_kmers " << out_total_kmers << "\n";
-    std::cout << "in_total_kmers " << in_total_kmers << "\n";
     double alt_hypothesis = poisson_prob(out_sum, out_sum) +
             poisson_prob(in_sum, in_sum); // K2 equals N2Theta2 because Theta2 = K2/N2 (?). Shouldn't Theta2 be = K2/|background labels| ?
 

From b9891f78fa7d7d41996e86b4d61b9967c94a18a7 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Sun, 3 Mar 2024 15:43:48 +0100
Subject: [PATCH 078/410] move median calculations

---
 .../src/graph/annotated_graph_algorithm.cpp   | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9a34854587..a880bf0a4b 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -138,9 +138,15 @@ double findMedian(sdsl::int_vector_buffer<0> a,
                     int threshold) {   
     logger->trace("non zero kmers: {}", a.size());
     std::vector<double> a_filtered;
-    std::copy_if(a.begin(), a.end(), std::back_inserter(a_filtered), [&](int i) {
-        return (i > threshold);
-    });
+    for (size_t i = 0; i < a.size(); i++){
+        int value = a[i];
+        if (value > threshold){
+            a_filtered.push_back(value);
+        }
+    }
+    // auto it = std::copy_if(a.begin(), a.end(), std::back_inserter(a_filtered), [&](int i) {
+    //     return (i > threshold);
+    // });
     logger->trace("kmers above threshold: {}", a_filtered.size());
     int n = a_filtered.size();
     // If size of the arr[] is even 
@@ -307,6 +313,7 @@ std::vector<double> filtering_column(const std::vector<std::string> &files,
     // generate a vector of the total number of kmers per sample for each group (in and out)
     Timer t;
     std::vector<double> medians;
+    logger->trace("filtering_column");
     annot::ColumnCompressed<>::load_columns_and_values(files,
         [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector_buffer<> && column_values) { // goes through all files in a group
             uint64_t max_col_width = std::pow(2, 16); // max value for int16_t
@@ -314,9 +321,6 @@ std::vector<double> filtering_column(const std::vector<std::string> &files,
                 assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0); // at least one kmer in sample
                 assert(column->num_set_bits() > 0); // at least one kmer present in sample
                 assert(column->num_set_bits() == column_values.size()); // same number of kmers in column as in column_values
-                double median = findMedian(std::move(column_values), threshold);
-                medians.push_back(median);
-                logger->trace("median: {}", median);
                 call_ones(*column, [&](uint64_t i) {
                     uint64_t column_value = column_values[column->rank1(i)-1];
                     if (column_value > threshold){
@@ -324,6 +328,9 @@ std::vector<double> filtering_column(const std::vector<std::string> &files,
                         value_callback(i, column_value);
                     }    
                 });
+                double median = findMedian(std::move(column_values), threshold);
+                medians.push_back(median);
+                logger->trace("median: {}", median);
             });
             logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());    
         }

From cd6abdb0ff05abafe2ba332adee8a013cc205dc8 Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Mon, 4 Mar 2024 17:15:34 +0100
Subject: [PATCH 079/410] update brunner munzel test

---
 metagraph/src/graph/differential_tests.hpp    |  4 +
 .../src/graph/differential_tests_uemmi.cpp    | 77 +++++++++++++++++--
 2 files changed, 73 insertions(+), 8 deletions(-)

diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
index 096b81f7c5..69160a656f 100644
--- a/metagraph/src/graph/differential_tests.hpp
+++ b/metagraph/src/graph/differential_tests.hpp
@@ -41,6 +41,10 @@ class DifferentialTest {
 
     double get_t_test_alpha(int df, double alpha);
 
+    std::tuple<std::vector<double>, std::vector<double>> get_overall_midrank(std::vector<double> in_counts, std::vector<double> out_counts, int N);
+
+    double get_var_midranks(std::vector<double> mid_ranks_within, std::vector<double> mid_ranks_overall, int m);
+
     std::vector<double> get_midranks(std::vector<double> in_counts, int size_in_counts);
 
     std::tuple<bool, double> likelihood_ratio_test(double in_sum, double out_sum);
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 9243d4a4a4..961d0eb36c 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -74,6 +74,57 @@ std::vector<double> DifferentialTest::get_midranks(std::vector<double> in_counts
     return mid_ranks;
 }
 
+std::tuple<std::vector<double>, std::vector<double>> DifferentialTest::get_overall_midrank(std::vector<double> in_counts, std::vector<double> out_counts, int N){
+    std::vector<std::tuple<double, int>> all_counts;
+    for (int i = 0; i < (int) in_counts.size(); i++){
+        all_counts.push_back(std::make_tuple(in_counts[i], 1));
+    }
+    for (int i = 0; i < (int) out_counts.size(); i++){
+        all_counts.push_back(std::make_tuple(out_counts[i], 0));
+    }
+    std::vector<std::tuple<double, int>> midranks_all;
+    double curr_value = std::get<0>(all_counts[0]);
+    int tied = 1;
+    for (int i = 1; i<N; i++){
+        if (std::get<0>(all_counts[i]) == curr_value){
+            tied++;
+            if (i == N-1){
+                i += 1;
+                for (int j = i-tied; j<i; j++){
+                    midranks_all.push_back(std::make_tuple((i-tied) + (tied+1)/2.0, std::get<1>(all_counts[midranks_all.size()])));
+                }
+            }
+        }
+        else{
+            if (i == N-1){
+                for (int j = i-tied; j<i; j++){
+                    midranks_all.push_back(std::make_tuple((i-tied) + (tied+1)/2.0, std::get<1>(all_counts[midranks_all.size()])));
+                }
+                midranks_all.push_back(std::make_tuple(i+1, std::get<1>(all_counts[midranks_all.size()])));
+            }
+            else{
+                for (int j = i-tied; j<i; j++){
+                    midranks_all.push_back(std::make_tuple((i-tied) + (tied+1)/2.0, std::get<1>(all_counts[midranks_all.size()])));
+                }
+                curr_value = std::get<0>(all_counts[i]);
+                tied = 1;
+            }
+            
+        }
+    }
+    std::vector<double> in_ranks;
+    std::vector<double> out_ranks;
+    for (int i = 0; i<N; i++){
+        if (std::get<1>(midranks_all[i]) == 1){
+            in_ranks.push_back(std::get<0>(midranks_all[i]));
+        }
+        else{
+            out_ranks.push_back(std::get<0>(midranks_all[i]));
+        }
+    }
+    return std::make_tuple(in_ranks, out_ranks);
+}
+
 int DifferentialTest::get_df_approx(std::vector<double> in_counts, std::vector<double> out_counts){
     int m = in_counts.size();
     int n = out_counts.size();
@@ -99,21 +150,31 @@ double DifferentialTest::get_var(std::vector<double> counts, int n){
     return var;
 }
 
+double DifferentialTest::get_var_midranks(std::vector<double> mid_ranks_within, std::vector<double> mid_ranks_overall, int m){
+    double mean_mid_rank = std::accumulate(mid_ranks_overall.begin(), mid_ranks_overall.end(), 0.0) / mid_ranks_overall.size();
+    double var = 0;
+    for (int i = 0; i < m; i++){
+        var = var + (mid_ranks_overall[i] - mid_ranks_within[i] - mean_mid_rank + (m+1)/2) * (mid_ranks_overall[i] - mid_ranks_within[i] - mean_mid_rank + (m+1)/2);
+    }
+    return var/(m-1);
+}
+
+
 std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<double> in_counts, std::vector<double> out_counts){
-    std::vector<double> mid_ranks_in;
-    std::vector<double> mid_ranks_out;
     int m = in_counts.size();
     int n = out_counts.size();
     int N = m + n;
-    // populate mid_ranks_in and mid_ranks_out
-    mid_ranks_in = get_midranks(in_counts, m);
-    mid_ranks_out = get_midranks(out_counts, n);
+    // get overall midranks
+    auto [mid_ranks_in, mid_ranks_out] = get_overall_midrank(in_counts, out_counts, N);
     // get means of midranks
     double mean_mid_rank_in = std::accumulate(mid_ranks_in.begin(), mid_ranks_in.end(), 0.0) / mid_ranks_in.size();
     double mean_mid_rank_out = std::accumulate(mid_ranks_out.begin(), mid_ranks_out.end(), 0.0) / mid_ranks_out.size();
+    // get midranks within groups
+    std::vector<double> in_ranks = get_midranks(in_counts, m);
+    std::vector<double> out_ranks = get_midranks(out_counts, n);
     // get variances
-    double var_in = get_var(in_counts, m);
-    double var_out = get_var(out_counts, n);
+    double var_in = get_var_midranks(in_ranks, mid_ranks_in, m);
+    double var_out = get_var_midranks(out_ranks, mid_ranks_out, n);
     if (var_in == 0 && var_out == 0){
         var_0++;
         var_in = 1;
@@ -131,7 +192,7 @@ std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<doubl
         common::logger->trace("alpha_precalc not set, calculating alpha_precalc");
         alpha_precalc = get_t_test_alpha(df_precalc, 0.05);
     }
-    if( std::abs(b) < alpha_precalc){
+    if( std::abs(b) > alpha_precalc){
         return std::tuple(true, b);
     }
     else{

From ba906902e6af46c724f69bd0ab6997a9f11b9e8c Mon Sep 17 00:00:00 2001
From: Uemmi <armaganummi@gmail.com>
Date: Tue, 5 Mar 2024 17:18:58 +0100
Subject: [PATCH 080/410] move get_node_sequence into filter_row function

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a880bf0a4b..599571c408 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -198,7 +198,8 @@ inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
 bool filtering_row(std::vector<double> in_counts_non_zero,
                     std::vector<double> out_counts_non_zero,
                     int total_samples,
-                    std::string kmer_string);
+                    std::shared_ptr<const DeBruijnGraph> graph_ptr, 
+                    int64_t row_id);
 
 std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                                         std::shared_ptr<MaskedDeBruijnGraph> masked_graph, 
@@ -254,8 +255,7 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
                                     out_counts.push_back(row[i].second/median);
                             }
                             // add 0 to in_counts and out_counts if the kmer is not present in the sample
-                            std::string kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
-                            if (filtering_row(in_counts, out_counts, files.size(), kmer_string)) {
+                            if (filtering_row(in_counts, out_counts, files.size(), graph_ptr, row_id)) {
                                 num_tests++;
                                 if (in_counts.size() < labels_in.size())
                                     in_counts.resize(labels_in.size(), 0);
@@ -287,7 +287,8 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
 bool filtering_row(std::vector<double> in_counts_non_zero,
                     std::vector<double> out_counts_non_zero,
                     int total_samples,
-                    std::string kmer_string){
+                    std::shared_ptr<const DeBruijnGraph> graph_ptr, 
+                    int64_t row_id){
     // minimum present in 20% of samples
     bool is_present_in_20_percent = (in_counts_non_zero.size() + out_counts_non_zero.size()) > 0.2 * double(total_samples);
     if (is_present_in_20_percent == false)
@@ -297,6 +298,7 @@ bool filtering_row(std::vector<double> in_counts_non_zero,
     // low complexity filter
     if (is_present_more_in_foreground == false)
         return false;
+    std::string kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
     bool high_complexity =  !is_low_complexity(kmer_string);
     return (high_complexity);
 }

From 17322341cbfdf63e4689457bd5235c9424fa360b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 29 Feb 2024 17:33:08 +0100
Subject: [PATCH 081/410] median coverage

---
 metagraph/src/cli/assemble.cpp                |   6 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 263 ++++++++++++++++--
 .../src/graph/annotated_graph_algorithm.hpp   |   4 +-
 metagraph/src/graph/graph_cleaning.cpp        |  32 +++
 metagraph/src/graph/graph_cleaning.hpp        |   6 +
 metagraph/src/graph/look_up_table_t_dist.hpp  |   7 +-
 6 files changed, 295 insertions(+), 23 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 1cef9a18cb..cea1c65815 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -56,6 +56,8 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.evaluate_assembly = experiment.get("evaluate_assembly", false).asBool();
     diff_config.test_type = experiment.get("test_type", "brunner_munzel").asString();
     diff_config.filter = experiment.get("filter", true).asBool();
+    diff_config.clean = experiment.get("clean", false).asBool();
+    diff_config.min_count = experiment.get("min_count", 1).asInt();
 
 
     logger->trace("Per-kmer mask in fraction:\t\t{}", diff_config.label_mask_in_kmer_fraction);
@@ -79,7 +81,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     return diff_config;
 }
 
-typedef std::function<void(const graph::MaskedDeBruijnGraph&,
+typedef std::function<void(const graph::DeBruijnGraph&,
                            const std::string& /* header */)> CallMaskedGraphHeader;
 
 void call_masked_graphs(const AnnotatedDBG &anno_graph,
@@ -243,7 +245,7 @@ int assemble(Config *config) {
             utils::remove_suffix(config->outfbase, ".gz", ".fasta") + ".fasta.gz"
         );
 
-        auto graph_callback = [&](const graph::MaskedDeBruijnGraph &graph, const std::string &header) {
+        auto graph_callback = [&](const graph::DeBruijnGraph &graph, const std::string &header) {
             seq_io::FastaWriter writer(config->outfbase, header,
                                        config->enumerate_out_sequences,
                                        num_threads > 1, /* async write */
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 599571c408..109f23c25f 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -4,7 +4,10 @@
 #include "common/logger.hpp"
 #include "common/vectors/vector_algorithm.hpp"
 #include "common/vectors/bitmap.hpp"
+#include "common/vector_map.hpp"
+#include "common/vectors/transpose.hpp"
 #include "graph/representation/masked_graph.hpp"
+#include "graph/representation/canonical_dbg.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 #include "differential_tests.hpp"
 #include "timer.hpp"
@@ -444,8 +447,8 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
 }
 
 
-// creates the necessary data structures for the differential assembly
-std::shared_ptr<MaskedDeBruijnGraph>
+
+std::shared_ptr<DeBruijnGraph>
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this version i don't need to change at first
                     const std::vector<std::string> &files,
                     const tsl::hopscotch_set<Label> &labels_in,
@@ -489,33 +492,153 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                     value_callback(i, column_value);
                                 });
                             });
-                            logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());    
+                            logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());
                         }
                     );
-                }    
+                }
             } else {
-                annot::ColumnCompressed<>::merge_load(files,
-                    [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column) {
-                        column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                            assert(column->num_set_bits() > 0);
-                            call_ones(*column, [&](uint64_t i) {
-                                value_callback(i, 1);
+                if (!config.clean) {
+                    annot::ColumnCompressed<>::merge_load(files,
+                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column) {
+                            column_callback(offset, label, [&](const ValueCallback &value_callback) {
+                                call_ones(*column, [&](uint64_t i) {
+                                    value_callback(i, 1);
+                                });
                             });
-                        });
-                    }
-                );
+                        }
+                    );
+                } else {
+                    annot::ColumnCompressed<>::load_columns_and_values(files,
+                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
+                            VectorMap<uint64_t,uint64_t> hist_map;
+                            for (auto c : column_values) {
+                                ++hist_map[c];
+                            }
+                            std::vector<std::pair<uint64_t,uint64_t>> hist(
+                                const_cast<std::vector<std::pair<uint64_t,uint64_t>>&&>(hist_map.values_container())
+                            );
+                            std::sort(hist.begin(), hist.end(), utils::LessFirst());
+                            // common::logger->trace("Min: {}, Max: {}", hist[0].first, hist.back().first);
+
+                            uint64_t max_count = (1llu << column_values.width()) - 1;
+                            if (hist.back().first == max_count)
+                                hist.pop_back();
+
+                            // estimate a zero-truncated poisson from the first touse counts
+                            // (from https://doi.org/10.1016/S0167-9473(99)00111-5)
+                            // set a cutoff that covers 95% of this distribution
+                            uint64_t touse = 3;
+                            size_t max_iter = 1000;
+                            double cdf_cutoff = 0.95;
+
+                            size_t nupto_c = 0;
+                            uint64_t sumupto = 0;
+                            for (const auto &[count,nkmers] : hist) {
+                                if (count > touse)
+                                    break;
+
+                                // common::logger->trace("c: {}; n: {}", count, nkmers);
+                                nupto_c += nkmers;
+                                sumupto += count * nkmers;
+                            }
+
+                            double sum = sumupto;
+                            double nupto = nupto_c;
+                            double mean_est = sum / nupto;
+
+                            for (size_t i = 0; i < max_iter; ++i) {
+                                double last_mean_est = mean_est;
+                                double total_est = nupto/(1.0-exp(-mean_est));
+                                mean_est = sum / total_est;
+                                if (abs(mean_est - last_mean_est)/mean_est < 1e-3) {
+                                    common::logger->trace("Fitted mean {} after {} iterations", mean_est, i + 1);
+                                    break;
+                                }
+                            }
+
+                            double p_num = mean_est * exp(-mean_est);
+                            double p_denom = 1;
+                            double cdf = p_num;
+                            double denom = 1.0 - exp(-mean_est);
+
+                            size_t min_count = 1;
+                            for (size_t i = 0; i < max_iter; ++i) {
+                                if (cdf / denom >= cdf_cutoff)
+                                    break;
+
+                                ++min_count;
+                                p_num *= mean_est;
+                                p_denom *= min_count;
+                                cdf += p_num / p_denom;
+                            }
+                            ++min_count;
+
+                            common::logger->trace("Cleaning threshold: {}; noise CDF: {}", min_count, cdf / denom);
+
+                            auto mask = std::make_unique<bitmap_lazy>([&](node_index node) {
+                                if (!node)
+                                    return false;
+
+                                return (*column)[AnnotatedDBG::graph_to_anno_index(node)];
+                            }, column->size() + 1);
+                            bool is_primary = (graph_ptr->get_mode() == DeBruijnGraph::PRIMARY);
+                            std::shared_ptr<DeBruijnGraph> masked
+                                = std::make_shared<MaskedDeBruijnGraph>(graph_ptr, std::move(mask), true,
+                                                       is_primary
+                                                        ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+
+                            std::shared_ptr<CanonicalDBG> wrapped;
+                            if (is_primary) {
+                                wrapped = std::make_shared<CanonicalDBG>(masked);
+                                masked = wrapped;
+                            }
+                            column_callback(offset, label, [&](const ValueCallback &value_callback) {
+                                assert(column->num_set_bits() == column_values.size());
+                                // call_ones(*column, [&](uint64_t i) {
+                                //     auto val = column_values[column->rank1(i) - 1];
+                                //     if (val >= min_count && val < max_count)
+                                //         value_callback(i, 1);
+                                // });
+                                masked->call_unitigs([&](const std::string&, const auto &path) {
+                                    std::vector<uint64_t> counts;
+                                    std::vector<uint64_t> rows;
+                                    counts.reserve(path.size());
+                                    rows.reserve(path.size());
+                                    for (node_index node : path) {
+                                        auto row = AnnotatedDBG::graph_to_anno_index(
+                                            is_primary ? wrapped->get_base_node(node) : node
+                                        );
+                                        rows.emplace_back(row);
+                                        counts.emplace_back(column_values[column->rank1(row) - 1]);
+                                    }
+                                    std::sort(counts.begin(), counts.end());
+                                    double median = counts[counts.size() / 2];
+
+                                    if (counts.size() % 2 == 1)
+                                        median = (median + counts[counts.size() / 2 - 1])/2;
+
+                                    if (median >= min_count && median < max_count) {
+                                        for (auto row : rows) {
+                                            value_callback(row, 1);
+                                        }
+                                    }
+                                });
+                            });
+                        }
+                    );
+                }
             }
         },
         files, labels_in, labels_out, config, graph_ptr->max_index(),
         std::max(labels_in.size(), labels_out.size()), num_parallel_files,
-        add_complement
+        config.test_by_unitig
     );
 
-    auto &[counts, init_mask, other_labels, total_kmers] = count_vector; 
+    auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
     // total_kmers: tuple of in_total_kmers and out_total_kmers (number of kmers in foreground and background)
     // init_mask: bit_vector of length graph_ptr->max_index() indicating which nodes are in the foreground (size = number of unique kmers total)
     // counts: int_vector of length graph_ptr->max_index() * 2 indicating the number of in and out labels for each node (size = number of unique kmers total * 2)
-    // other_labels: bit_vector of length num_labels indicating which labels are not in or out labels 
+    // other_labels: bit_vector of length num_labels indicating which labels are not in or out labels
 /*
     if (config.evaluate_assembly){     // Myrthe temporary: evaluate what k-mers overlap
 
@@ -606,7 +729,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
         std::exit(EXIT_SUCCESS);
     }
 */
-    return mask_nodes_by_label(graph_ptr, nullptr,
+    auto masked_graph = mask_nodes_by_label(graph_ptr, nullptr,
                                std::move(counts), std::move(init_mask),
                                std::move(other_mask),
                                files.size(),
@@ -823,6 +946,108 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     size_t max_label_out_count = std::floor(config.label_mask_out_kmer_fraction
                                     * num_out_labels);
 
+    if (config.test_by_unitig) {
+        sdsl::bit_vector mask(masked_graph->get_mask().size(), false);
+
+        std::shared_ptr<DeBruijnGraph> test_graph = masked_graph;
+        std::shared_ptr<CanonicalDBG> wrapped;
+        if (masked_graph->get_mode() == DeBruijnGraph::PRIMARY) {
+            wrapped = std::make_shared<CanonicalDBG>(masked_graph);
+            test_graph = wrapped;
+        }
+
+        test_graph->call_unitigs([&](const std::string &, const auto &path) {
+            uint64_t total_in_count = 0;
+            uint64_t total_out_count = 0;
+            auto begin = path.end();
+            auto end = path.begin();
+            for (auto it = path.begin(); it != path.end(); ++it) {
+                node_index node = wrapped ? wrapped->get_base_node(*it) : *it;
+                auto in_count = counts[node * 2];
+                total_in_count += in_count;
+                total_out_count += counts[node * 2 + 1];
+                if (in_count) {
+                    begin = std::min(it, begin);
+                    end = std::max(it, end);
+                }
+            }
+
+            size_t path_size = end - begin;
+            if (total_in_count >= min_label_in_count*path_size && total_out_count <= max_label_out_count*path_size) {
+                std::for_each(begin, end, [&](node_index node) {
+                    mask[wrapped ? wrapped->get_base_node(node) : node] = true;
+                });
+            }
+        });
+
+        /*
+        size_t kept_nodes = 0;
+
+        call_ones(masked_graph->get_mask(), [&](node_index node) {
+            uint64_t in_count = counts[node * 2];
+            uint64_t out_count = counts[node * 2 + 1];
+
+            if (in_count >= min_label_in_count && out_count <= max_label_out_count) {
+                ++kept_nodes;
+            } else {
+                mask[node] = false;
+            }
+        });
+
+        size_t num_filled = 0;
+        masked_graph->call_unitigs([&](const std::string &, const auto &path) {
+            auto in_mask = [&](node_index node) { return mask[node]; };
+            auto begin = std::find_if(path.begin(), path.end(), in_mask);
+            if (begin == path.end())
+                return;
+
+            auto end = std::find_if(path.rbegin(), path.rend(), in_mask).base();
+
+            size_t num_in_mask = std::count_if(begin, end, in_mask);
+
+            if (static_cast<double>(num_in_mask) / path.size() >= 0.8) {
+                std::for_each(path.begin(), path.end(), [&](node_index node) {
+                    if (!mask[node]) {
+                        mask[node] = true;
+                        ++kept_nodes;
+                        ++num_filled;
+                    }
+                });
+                return;
+            }
+
+            if (static_cast<double>(num_in_mask) / (end - begin) >= 0.8) {
+                std::for_each(begin, end, [&](node_index node) {
+                    if (!mask[node]) {
+                        mask[node] = true;
+                        ++kept_nodes;
+                        ++num_filled;
+                    }
+                });
+            }
+
+            for (size_t i = 1; i + 1 < path.size(); ++i) {
+                node_index node = path[i];
+                if (!mask[node]) {
+                    if (mask[path[i - 1]] && mask[path[i + 1]]) {
+                        mask[node] = true;
+                        ++kept_nodes;
+                        ++num_filled;
+                    }
+                }
+            }
+        });
+        */
+
+        masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+
+        // logger->trace("Kept {} out of {} nodes, {} filled", kept_nodes, masked_graph->get_graph().num_nodes(), num_filled);
+        masked_graph->likelihood_ratios.resize(counts.size()/2+1);
+        std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0);
+
+        return masked_graph;
+    }
+
     if (config.label_mask_in_unitig_fraction == 0.0
             && config.label_mask_out_unitig_fraction == 1.0
             && config.label_mask_other_unitig_fraction == 1.0) {
@@ -844,11 +1069,11 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         });
 
-
-
         masked_graph->set_mask(new bitmap_vector(std::move(mask)));
 
         logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
+        masked_graph->likelihood_ratios.resize(counts.size()/2+1);
+        std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0);
 
         return masked_graph;
     }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index e0f7a1a5ca..e9d5b35831 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -34,11 +34,13 @@ struct DifferentialAssemblyConfig {
     double label_mask_other_unitig_fraction = 1.0;
     bool add_complement = false;
     bool count_kmers = false; // Myrthe
+    bool clean = false;
     double family_wise_error_rate = 0.05; //Myrthe: differential assembly statistical tests, or the family wise rate for bonferonni or a p-value
     bool test_by_unitig = false; // Myrthe
     std::string test_type = "brunner_munzel"; // default is "brunner_munzel", alternatives are: "likelihoodratio", "binary"
     bool evaluate_assembly = false; // Myrthe temporary : evaluate an assembly
     bool filter = true;
+    uint64_t min_count = 1;
 };
 
 /**
@@ -65,7 +67,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                     size_t num_threads = 1,
                     size_t num_parallel_files = std::numeric_limits<size_t>::max());
 
-std::shared_ptr<MaskedDeBruijnGraph>
+std::shared_ptr<DeBruijnGraph>
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const std::vector<std::string> &files,
                     const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
diff --git a/metagraph/src/graph/graph_cleaning.cpp b/metagraph/src/graph/graph_cleaning.cpp
index 249a80c49d..0544e9ff44 100644
--- a/metagraph/src/graph/graph_cleaning.cpp
+++ b/metagraph/src/graph/graph_cleaning.cpp
@@ -64,6 +64,38 @@ uint64_t estimate_min_kmer_abundance(const DeBruijnGraph &graph,
                                         &false_pos_ptr, &false_neg_ptr);
 }
 
+std::tuple<int64_t,double,double> estimate_min_kmer_abundance(const bit_vector &idx,
+                                     const sdsl::int_vector<> &values,
+                                     uint64_t num_singleton_kmers,
+                                     bool discard_last_count) {
+    std::vector<uint64_t> hist;
+    idx.call_ones([&](auto i) {
+        uint64_t kmer_count = values[idx.rank1(i) - 1];
+        assert(kmer_count && "All k-mers in graph must have non-zero counts");
+        while (kmer_count >= hist.size()) {
+            hist.push_back(0);
+        }
+        hist[kmer_count]++;
+    });
+
+    if (discard_last_count && hist.size() >= 10 && hist.back() > hist[hist.size() - 2])
+        hist.pop_back();
+
+    hist.resize(std::max((uint64_t)hist.size(), (uint64_t)10), 0);
+
+    if (num_singleton_kmers) {
+        logger->info("The count for singleton k-mers in histogram is reset from {} to {}",
+                     hist[1], num_singleton_kmers);
+        hist[1] = num_singleton_kmers;
+    }
+
+    double alpha_est_ptr, beta_est_ptr, false_pos_ptr, false_neg_ptr;
+    int64_t threshold = cleaning_pick_kmer_threshold(hist.data(), hist.size(),
+                                        &alpha_est_ptr, &beta_est_ptr,
+                                        &false_pos_ptr, &false_neg_ptr);
+    return std::make_tuple(threshold,alpha_est_ptr,beta_est_ptr);
+}
+
 
 /** The next routines were copied from:
  * https://github.com/mcveanlab/mccortex/blob/97aba198d632ee98ac1aa496db33d1a7a8cb7e51/src/tools/clean_graph.c
diff --git a/metagraph/src/graph/graph_cleaning.hpp b/metagraph/src/graph/graph_cleaning.hpp
index 1647a63a8e..4d9b0bb52d 100644
--- a/metagraph/src/graph/graph_cleaning.hpp
+++ b/metagraph/src/graph/graph_cleaning.hpp
@@ -2,6 +2,7 @@
 #define __GRAPH_CLEANING_HPP__
 
 #include "representation/base/sequence_graph.hpp"
+#include "common/vectors/bit_vector.hpp"
 #include "graph_extensions/node_weights.hpp"
 
 
@@ -16,6 +17,11 @@ uint64_t estimate_min_kmer_abundance(const DeBruijnGraph &graph,
                                      const NodeWeights &node_weights,
                                      uint64_t num_singleton_kmers = 0);
 
+std::tuple<int64_t,double,double> estimate_min_kmer_abundance(const bit_vector &idx,
+                                     const sdsl::int_vector<> &values,
+                                     uint64_t num_singleton_kmers = 0,
+                                     bool discard_last_count = false);
+
 } // namespace graph
 } // namespace mtg
 
diff --git a/metagraph/src/graph/look_up_table_t_dist.hpp b/metagraph/src/graph/look_up_table_t_dist.hpp
index e4e344e839..4ed3edf424 100644
--- a/metagraph/src/graph/look_up_table_t_dist.hpp
+++ b/metagraph/src/graph/look_up_table_t_dist.hpp
@@ -1,3 +1,6 @@
+#ifndef __TLOOKUP_HPP__
+#define __TLOOKUP_HPP__
+
 #include <iostream>
 #include <cmath>
 #include <vector>
@@ -12,4 +15,6 @@ class TDistributionTable {
 
     double getCriticalValue(double alpha, int df);
 };
-}
\ No newline at end of file
+}
+
+#endif // __TLOOKUP_HPP__
\ No newline at end of file

From e0464e3cece06687f1d47930cfb1ae8b9694339e Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 1 Mar 2024 14:11:07 +0100
Subject: [PATCH 082/410] ztp based cleaning

---
 metagraph/src/cli/clean.cpp                   |  6 +-
 metagraph/src/cli/config/config.cpp           |  2 +
 metagraph/src/cli/config/config.hpp           |  1 +
 metagraph/src/graph/graph_cleaning.cpp        | 71 +++++++++++++++++++
 metagraph/src/graph/graph_cleaning.hpp        |  4 ++
 .../graph/graph_extensions/node_weights.hpp   |  1 +
 6 files changed, 83 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/cli/clean.cpp b/metagraph/src/cli/clean.cpp
index 945bb03510..26d877c6aa 100644
--- a/metagraph/src/cli/clean.cpp
+++ b/metagraph/src/cli/clean.cpp
@@ -80,8 +80,10 @@ int clean_graph(Config *config) {
                         [&](auto i) { return (*node_weights)[i] > 0; }, true)
                     : graph;
 
-            uint64_t cutoff
-                = estimate_min_kmer_abundance(*_graph, *node_weights,
+            uint64_t cutoff = config->clean_ztp
+                ? estimate_min_kmer_abundance_ztp(*_graph, *node_weights,
+                                                  config->num_singleton_kmers)
+                : estimate_min_kmer_abundance(*_graph, *node_weights,
                                               config->num_singleton_kmers);
 
             if (cutoff != static_cast<uint64_t>(-1)) {
diff --git a/metagraph/src/cli/config/config.cpp b/metagraph/src/cli/config/config.cpp
index e292423255..40f1e629ce 100644
--- a/metagraph/src/cli/config/config.cpp
+++ b/metagraph/src/cli/config/config.cpp
@@ -339,6 +339,8 @@ Config::Config(int argc, char *argv[]) {
             adjrc = true;
         } else if (!strcmp(argv[i], "--compacted")) {
             output_compacted = true;
+        } else if (!strcmp(argv[i], "--clean-ztp")) {
+            clean_ztp = true;
         } else if (!strcmp(argv[i], "--json")) {
             output_json = true;
         } else if (!strcmp(argv[i], "--unitigs")) {
diff --git a/metagraph/src/cli/config/config.hpp b/metagraph/src/cli/config/config.hpp
index a4cb1f790f..d834a14597 100644
--- a/metagraph/src/cli/config/config.hpp
+++ b/metagraph/src/cli/config/config.hpp
@@ -62,6 +62,7 @@ class Config {
     bool aggregate_columns = false;
     bool coordinates = false;
     bool advanced = false;
+    bool clean_ztp = false;
 
     unsigned int k = 3;
 
diff --git a/metagraph/src/graph/graph_cleaning.cpp b/metagraph/src/graph/graph_cleaning.cpp
index 0544e9ff44..863b59cc51 100644
--- a/metagraph/src/graph/graph_cleaning.cpp
+++ b/metagraph/src/graph/graph_cleaning.cpp
@@ -64,6 +64,77 @@ uint64_t estimate_min_kmer_abundance(const DeBruijnGraph &graph,
                                         &false_pos_ptr, &false_neg_ptr);
 }
 
+// returns -1 if the automatic estimation fails
+uint64_t estimate_min_kmer_abundance_ztp(const DeBruijnGraph &graph,
+                                         const NodeWeights &node_weights,
+                                         uint64_t num_singleton_kmers) {
+    std::vector<uint64_t> hist;
+    graph.call_nodes([&](auto i) {
+        uint64_t kmer_count = node_weights[i];
+        assert(kmer_count && "All k-mers in graph must have non-zero counts");
+        while (kmer_count >= hist.size()) {
+            hist.push_back(0);
+        }
+        hist[kmer_count]++;
+    });
+
+    if (num_singleton_kmers) {
+        logger->info("The count for singleton k-mers in histogram is reset from {} to {}",
+                     hist[1], num_singleton_kmers);
+        hist[1] = num_singleton_kmers;
+    }
+
+    uint64_t max_size = 1llu << node_weights.get_data().width();
+    if (hist.size() == max_size)
+        hist.pop_back();
+
+    // estimate a zero-truncated poisson from the first touse counts
+    // (from https://doi.org/10.1016/S0167-9473(99)00111-5)
+    // set a cutoff that covers 95% of this distribution
+    uint64_t touse = 3;
+    size_t max_iter = 1000;
+    double cdf_cutoff = 0.95;
+
+    size_t nupto_c = 0;
+    uint64_t sumupto = 0;
+    for (size_t count = 1; count <= touse; ++count) {
+        uint64_t nkmers = hist[count];
+        nupto_c += nkmers;
+        sumupto += count * nkmers;
+    }
+
+    double sum = sumupto;
+    double nupto = nupto_c;
+    double mean_est = sum / nupto;
+
+    for (size_t i = 0; i < max_iter; ++i) {
+        double last_mean_est = mean_est;
+        double total_est = nupto/(1.0-exp(-mean_est));
+        mean_est = sum / total_est;
+        if (abs(mean_est - last_mean_est)/mean_est < 1e-3) {
+            common::logger->trace("Fitted mean {} after {} iterations", mean_est, i + 1);
+            break;
+        }
+    }
+
+    double p_num = mean_est * exp(-mean_est);
+    double p_denom = 1;
+    double cdf = p_num;
+    double denom = 1.0 - exp(-mean_est);
+
+    uint64_t min_count = 1;
+    for (size_t i = 0; i < max_iter; ++i) {
+        if (cdf / denom >= cdf_cutoff)
+            break;
+
+        ++min_count;
+        p_num *= mean_est;
+        p_denom *= min_count;
+        cdf += p_num / p_denom;
+    }
+    return min_count + 1;
+}
+
 std::tuple<int64_t,double,double> estimate_min_kmer_abundance(const bit_vector &idx,
                                      const sdsl::int_vector<> &values,
                                      uint64_t num_singleton_kmers,
diff --git a/metagraph/src/graph/graph_cleaning.hpp b/metagraph/src/graph/graph_cleaning.hpp
index 4d9b0bb52d..58c0cd2a99 100644
--- a/metagraph/src/graph/graph_cleaning.hpp
+++ b/metagraph/src/graph/graph_cleaning.hpp
@@ -17,6 +17,10 @@ uint64_t estimate_min_kmer_abundance(const DeBruijnGraph &graph,
                                      const NodeWeights &node_weights,
                                      uint64_t num_singleton_kmers = 0);
 
+uint64_t estimate_min_kmer_abundance_ztp(const DeBruijnGraph &graph,
+                                         const NodeWeights &node_weights,
+                                         uint64_t num_singleton_kmers = 0);
+
 std::tuple<int64_t,double,double> estimate_min_kmer_abundance(const bit_vector &idx,
                                      const sdsl::int_vector<> &values,
                                      uint64_t num_singleton_kmers = 0,
diff --git a/metagraph/src/graph/graph_extensions/node_weights.hpp b/metagraph/src/graph/graph_extensions/node_weights.hpp
index 57dbf750ea..a2c6adcd53 100644
--- a/metagraph/src/graph/graph_extensions/node_weights.hpp
+++ b/metagraph/src/graph/graph_extensions/node_weights.hpp
@@ -44,6 +44,7 @@ class NodeWeights : public SequenceGraph::GraphExtension {
     bool is_compatible(const SequenceGraph &graph, bool verbose = true) const;
 
     sdsl::int_vector<>& get_data() { return weights_; }
+    const sdsl::int_vector<>& get_data() const { return weights_; }
 
   private:
     sdsl::int_vector<> weights_;

From 12ee22ed90912f4e88e8009402ea58c2e9352f5d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 1 Mar 2024 14:16:25 +0100
Subject: [PATCH 083/410] fix

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 109f23c25f..9971a8ad6d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -737,6 +737,11 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                labels_in, labels_out, {}, {},
                                total_kmers,
                                config, num_threads, medians);
+
+    if (masked_graph->get_mode() != DeBruijnGraph::PRIMARY)
+        return masked_graph;
+
+    return std::make_shared<CanonicalDBG>(masked_graph);
 }
 
 // does differential testing

From f5a216099fa9d318784b0ff034b090ca46772548 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 1 Mar 2024 14:17:54 +0100
Subject: [PATCH 084/410] fix df

---
 metagraph/src/graph/differential_tests_uemmi.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 961d0eb36c..3e602d82d5 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -33,7 +33,7 @@ double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipe
 double DifferentialTest::get_t_test_alpha(int df, double alpha=0.05){
     alpha = alpha/2; // convert to one sided test
     alpha = alpha/total_hypotheses; // bonferroni correction
-    common::logger->trace("alpha: {}", alpha);
+    common::logger->trace("alpha: {}\tdf:{}", alpha, df);
     double critical_value = t_table.getCriticalValue(alpha, df);
     common::logger->trace("Critical value: {}", critical_value);
     return critical_value;
@@ -140,7 +140,7 @@ int DifferentialTest::get_df_approx(std::vector<double> in_counts, std::vector<d
 int DifferentialTest::get_df_conservative(std::vector<double> in_counts, std::vector<double> out_counts){
     int m = in_counts.size();
     int n = out_counts.size();
-    return std::min(m, n) - 1;
+    return std::max(1,std::min(m, n) - 1);
 }
 
 double DifferentialTest::get_var(std::vector<double> counts, int n){

From 5c329cd96d3693c23f2aa1705cf5175f90167351 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 5 Mar 2024 16:15:41 +0100
Subject: [PATCH 085/410] test

---
 metagraph/src/cli/assemble.cpp                |   2 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 322 ++++++++----------
 .../representation/base/sequence_graph.cpp    |   3 +-
 3 files changed, 139 insertions(+), 188 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index cea1c65815..82f7203c75 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -57,7 +57,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.test_type = experiment.get("test_type", "brunner_munzel").asString();
     diff_config.filter = experiment.get("filter", true).asBool();
     diff_config.clean = experiment.get("clean", false).asBool();
-    diff_config.min_count = experiment.get("min_count", 1).asInt();
+    diff_config.min_count = experiment.get("min_count", 0).asInt();
 
 
     logger->trace("Per-kmer mask in fraction:\t\t{}", diff_config.label_mask_in_kmer_fraction);
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9971a8ad6d..0d2a707c81 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -122,7 +122,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
                                   size_t max_index,
                                   size_t num_labels,
                                   size_t num_threads,
-                                  bool add_out_labels_to_mask);                              
+                                  bool add_out_labels_to_mask);
 
 // Regions of a graph mask which should be kept (i.e., masked in)
 typedef std::vector<std::pair<size_t, size_t>> Intervals;
@@ -135,10 +135,10 @@ bool sortByPValue(const std::pair<int, double>& a, const std::pair<int, double>&
     return a.first < b.first; // Sort in ascending order of p-values
 }
 
-// Function for calculating 
-// the median 
-double findMedian(sdsl::int_vector_buffer<0> a, 
-                    int threshold) {   
+// Function for calculating
+// the median
+double findMedian(sdsl::int_vector_buffer<0> a,
+                    int threshold) {
     logger->trace("non zero kmers: {}", a.size());
     std::vector<double> a_filtered;
     for (size_t i = 0; i < a.size(); i++){
@@ -152,42 +152,42 @@ double findMedian(sdsl::int_vector_buffer<0> a,
     // });
     logger->trace("kmers above threshold: {}", a_filtered.size());
     int n = a_filtered.size();
-    // If size of the arr[] is even 
-    if (n % 2 == 0) { 
-  
-        // Applying nth_element 
-        // on n/2th index 
-        std::nth_element(a_filtered.begin(), 
-                    a_filtered.begin() + n / 2, 
-                    a_filtered.end()); 
-  
-        // Applying nth_element 
-        // on (n-1)/2 th index 
-        std::nth_element(a_filtered.begin(), 
-                    a_filtered.begin() + (n - 1) / 2, 
-                    a_filtered.end()); 
-  
-        // Find the average of value at 
-        // index N/2 and (N-1)/2 
-        return (double)(a_filtered[(n - 1) / 2] 
-                        + a_filtered[n / 2]) 
-               / 2.0; 
-    } 
-  
-    // If size of the arr[] is odd 
-    else { 
-  
-        // Applying nth_element 
-        // on n/2 
-        std::nth_element(a_filtered.begin(), 
-                    a_filtered.begin() + n / 2, 
-                    a_filtered.end()); 
-  
-        // Value at index (N/2)th 
-        // is the median 
-        return (double)a_filtered[n / 2]; 
-    } 
-} 
+    // If size of the arr[] is even
+    if (n % 2 == 0) {
+
+        // Applying nth_element
+        // on n/2th index
+        std::nth_element(a_filtered.begin(),
+                    a_filtered.begin() + n / 2,
+                    a_filtered.end());
+
+        // Applying nth_element
+        // on (n-1)/2 th index
+        std::nth_element(a_filtered.begin(),
+                    a_filtered.begin() + (n - 1) / 2,
+                    a_filtered.end());
+
+        // Find the average of value at
+        // index N/2 and (N-1)/2
+        return (double)(a_filtered[(n - 1) / 2]
+                        + a_filtered[n / 2])
+               / 2.0;
+    }
+
+    // If size of the arr[] is odd
+    else {
+
+        // Applying nth_element
+        // on n/2
+        std::nth_element(a_filtered.begin(),
+                    a_filtered.begin() + n / 2,
+                    a_filtered.end());
+
+        // Value at index (N/2)th
+        // is the median
+        return (double)a_filtered[n / 2];
+    }
+}
 
 inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
     int n;
@@ -205,11 +205,11 @@ bool filtering_row(std::vector<double> in_counts_non_zero,
                     int64_t row_id);
 
 std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                                                        std::shared_ptr<MaskedDeBruijnGraph> masked_graph, 
-                                                        const std::vector<std::string> &files, 
-                                                        const tsl::hopscotch_set<Label> &labels_in, 
-                                                        const tsl::hopscotch_set<Label> &labels_out, 
-                                                        const DifferentialAssemblyConfig &config,  
+                                                        std::shared_ptr<MaskedDeBruijnGraph> masked_graph,
+                                                        const std::vector<std::string> &files,
+                                                        const tsl::hopscotch_set<Label> &labels_in,
+                                                        const tsl::hopscotch_set<Label> &labels_out,
+                                                        const DifferentialAssemblyConfig &config,
                                                         size_t total_unitigs,
                                                         std::vector<double> medians) {
 
@@ -221,7 +221,7 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
     std::vector<std::unique_ptr<sdsl::int_vector_buffer<>>> column_values_all;
     std::vector<bool> column_label;
     annot::ColumnCompressed<>::load_columns_and_values(files,
-        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector_buffer<> && column_values) { 
+        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector_buffer<> && column_values) {
             columns_all.push_back(std::move(column));
             column_values_all.push_back(std::make_unique<sdsl::int_vector_buffer<>>(std::move(column_values)));
             if (labels_in.find(label) != labels_in.end()) // if the label is in the in labels set column_label to 1, else 0
@@ -235,14 +235,14 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
     int64_t num_tests = 0;
     int64_t kept_nodes = 0;
     // calculate the p-value for the brunner munzel test
-    auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0); 
+    auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0);
     utils::call_rows<std::unique_ptr<bit_vector>,
                     std::unique_ptr<sdsl::int_vector_buffer<>>,
                     std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
                         // auto kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
                         if (row.size() == 0) { // if the kmer is not present in any sample
                             mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
-                            row_id++; 
+                            row_id++;
                             return;
                         }
                         else{
@@ -251,7 +251,7 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
                             std::vector<double> out_counts;
                             for (size_t i = 0; i < row.size(); i++){
                                 double median = medians[row[i].first];
-                                if (median == 0) median = 1; // if the median is 0, set it to 1 (to avoid division by 0)    
+                                if (median == 0) median = 1; // if the median is 0, set it to 1 (to avoid division by 0)
                                 if (column_label[row[i].first])
                                     in_counts.push_back(row[i].second/median);
                                 else
@@ -263,29 +263,29 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
                                 if (in_counts.size() < labels_in.size())
                                     in_counts.resize(labels_in.size(), 0);
                                 if (out_counts.size() < labels_out.size())
-                                    out_counts.resize(labels_out.size(), 0);       
-                                // run test and adjust masked graph accordingly 
+                                    out_counts.resize(labels_out.size(), 0);
+                                // run test and adjust masked graph accordingly
                                 auto [keep, pvalue] = statistical_model.brunner_munzel_test(in_counts, out_counts);
                                 if (keep == false)
                                     mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
                                 else{
-                                    kept_nodes++; 
-                                }     
+                                    kept_nodes++;
+                                }
                                 row_id++;
                             }
                             else{
                                 mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
                                 row_id++;
                             }
-                            
-                        }               
+
+                        }
     });
     logger->trace("number of tests with 0 var: {}", statistical_model.var_0);
     logger->trace("number of tests: {}", num_tests);
     masked_graph->set_mask(new bitmap_vector(std::move(mask)));
     logger->trace("number of kept nodes: {}", kept_nodes);
     return masked_graph;
-}    
+}
 // returns true if the kmer passes the filtering criteria
 bool filtering_row(std::vector<double> in_counts_non_zero,
                     std::vector<double> out_counts_non_zero,
@@ -313,7 +313,7 @@ std::vector<double> filtering_column(const std::vector<std::string> &files,
                ) {
     // median normalisation
     // threshold of 2
-    double threshold = 2; // TODO get from config
+    double threshold = config.min_count; // TODO get from config
     assert(files.size() > 0);
     // generate a vector of the total number of kmers per sample for each group (in and out)
     Timer t;
@@ -331,13 +331,13 @@ std::vector<double> filtering_column(const std::vector<std::string> &files,
                     if (column_value > threshold){
                         column_value = std::min(column_value, max_col_width); // normalize value
                         value_callback(i, column_value);
-                    }    
+                    }
                 });
                 double median = findMedian(std::move(column_values), threshold);
                 medians.push_back(median);
                 logger->trace("median: {}", median);
             });
-            logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());    
+            logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());
         }
     );
     return medians;
@@ -399,7 +399,7 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
 
     bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
         && (config.add_complement || unitig_mode);
-    logger->trace("add_complement: {}", add_complement);    
+    logger->trace("add_complement: {}", add_complement);
 
     logger->trace("Generating initial mask");
 
@@ -489,7 +489,8 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                 call_ones(*column, [&](uint64_t i) {
                                     uint64_t column_value = column_values[column->rank1(i)-1];
                                     column_value = std::min((int) column_value, max_col_width); // normalize value
-                                    value_callback(i, column_value);
+                                    if (column_value > config.min_count)
+                                        value_callback(i, column_value);
                                 });
                             });
                             logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());
@@ -509,121 +510,65 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                     );
                 } else {
                     annot::ColumnCompressed<>::load_columns_and_values(files,
-                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
-                            VectorMap<uint64_t,uint64_t> hist_map;
-                            for (auto c : column_values) {
-                                ++hist_map[c];
-                            }
-                            std::vector<std::pair<uint64_t,uint64_t>> hist(
-                                const_cast<std::vector<std::pair<uint64_t,uint64_t>>&&>(hist_map.values_container())
-                            );
-                            std::sort(hist.begin(), hist.end(), utils::LessFirst());
-                            // common::logger->trace("Min: {}, Max: {}", hist[0].first, hist.back().first);
-
-                            uint64_t max_count = (1llu << column_values.width()) - 1;
-                            if (hist.back().first == max_count)
-                                hist.pop_back();
-
-                            // estimate a zero-truncated poisson from the first touse counts
-                            // (from https://doi.org/10.1016/S0167-9473(99)00111-5)
-                            // set a cutoff that covers 95% of this distribution
-                            uint64_t touse = 3;
-                            size_t max_iter = 1000;
-                            double cdf_cutoff = 0.95;
-
-                            size_t nupto_c = 0;
-                            uint64_t sumupto = 0;
-                            for (const auto &[count,nkmers] : hist) {
-                                if (count > touse)
-                                    break;
-
-                                // common::logger->trace("c: {}; n: {}", count, nkmers);
-                                nupto_c += nkmers;
-                                sumupto += count * nkmers;
-                            }
-
-                            double sum = sumupto;
-                            double nupto = nupto_c;
-                            double mean_est = sum / nupto;
-
-                            for (size_t i = 0; i < max_iter; ++i) {
-                                double last_mean_est = mean_est;
-                                double total_est = nupto/(1.0-exp(-mean_est));
-                                mean_est = sum / total_est;
-                                if (abs(mean_est - last_mean_est)/mean_est < 1e-3) {
-                                    common::logger->trace("Fitted mean {} after {} iterations", mean_est, i + 1);
-                                    break;
-                                }
-                            }
-
-                            double p_num = mean_est * exp(-mean_est);
-                            double p_denom = 1;
-                            double cdf = p_num;
-                            double denom = 1.0 - exp(-mean_est);
-
-                            size_t min_count = 1;
-                            for (size_t i = 0; i < max_iter; ++i) {
-                                if (cdf / denom >= cdf_cutoff)
-                                    break;
-
-                                ++min_count;
-                                p_num *= mean_est;
-                                p_denom *= min_count;
-                                cdf += p_num / p_denom;
-                            }
-                            ++min_count;
-
-                            common::logger->trace("Cleaning threshold: {}; noise CDF: {}", min_count, cdf / denom);
-
-                            auto mask = std::make_unique<bitmap_lazy>([&](node_index node) {
-                                if (!node)
-                                    return false;
-
-                                return (*column)[AnnotatedDBG::graph_to_anno_index(node)];
-                            }, column->size() + 1);
-                            bool is_primary = (graph_ptr->get_mode() == DeBruijnGraph::PRIMARY);
-                            std::shared_ptr<DeBruijnGraph> masked
-                                = std::make_shared<MaskedDeBruijnGraph>(graph_ptr, std::move(mask), true,
-                                                       is_primary
-                                                        ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
-
-                            std::shared_ptr<CanonicalDBG> wrapped;
-                            if (is_primary) {
-                                wrapped = std::make_shared<CanonicalDBG>(masked);
-                                masked = wrapped;
-                            }
+                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector<>&& column_values) {
                             column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                                assert(column->num_set_bits() == column_values.size());
-                                // call_ones(*column, [&](uint64_t i) {
-                                //     auto val = column_values[column->rank1(i) - 1];
-                                //     if (val >= min_count && val < max_count)
-                                //         value_callback(i, 1);
-                                // });
-                                masked->call_unitigs([&](const std::string&, const auto &path) {
-                                    std::vector<uint64_t> counts;
-                                    std::vector<uint64_t> rows;
-                                    counts.reserve(path.size());
-                                    rows.reserve(path.size());
-                                    for (node_index node : path) {
-                                        auto row = AnnotatedDBG::graph_to_anno_index(
-                                            is_primary ? wrapped->get_base_node(node) : node
-                                        );
-                                        rows.emplace_back(row);
-                                        counts.emplace_back(column_values[column->rank1(row) - 1]);
-                                    }
-                                    std::sort(counts.begin(), counts.end());
-                                    double median = counts[counts.size() / 2];
-
-                                    if (counts.size() % 2 == 1)
-                                        median = (median + counts[counts.size() / 2 - 1])/2;
-
-                                    if (median >= min_count && median < max_count) {
-                                        for (auto row : rows) {
-                                            value_callback(row, 1);
-                                        }
-                                    }
+                                call_ones(*column, [&](uint64_t i) {
+                                    auto val = column_values[column->rank1(i) - 1];
+                                    if (val > config.min_count)
+                                        value_callback(i, 1);
                                 });
                             });
+                            // size_t min_count = config.min_count;
+
+                            // auto mask = std::make_unique<bitmap_lazy>([&](node_index node) {
+                            //     if (!node)
+                            //         return false;
+
+                            //     return (*column)[AnnotatedDBG::graph_to_anno_index(node)];
+                            // }, column->size() + 1);
+                            // bool is_primary = (graph_ptr->get_mode() == DeBruijnGraph::PRIMARY);
+                            // std::shared_ptr<DeBruijnGraph> masked
+                            //     = std::make_shared<MaskedDeBruijnGraph>(graph_ptr, std::move(mask), true,
+                            //                            is_primary
+                            //                             ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+
+                            // std::shared_ptr<CanonicalDBG> wrapped;
+                            // if (is_primary) {
+                            //     wrapped = std::make_shared<CanonicalDBG>(masked);
+                            //     masked = wrapped;
+                            // }
+                            // column_callback(offset, label, [&](const ValueCallback &value_callback) {
+                            //     assert(column->num_set_bits() == column_values.size());
+                            //     // call_ones(*column, [&](uint64_t i) {
+                            //     //     auto val = column_values[column->rank1(i) - 1];
+                            //     //     if (val >= min_count && val < max_count)
+                            //     //         value_callback(i, 1);
+                            //     // });
+                            //     masked->call_unitigs([&](const std::string&, const auto &path) {
+                            //         std::vector<uint64_t> counts;
+                            //         std::vector<uint64_t> rows;
+                            //         counts.reserve(path.size());
+                            //         rows.reserve(path.size());
+                            //         for (node_index node : path) {
+                            //             auto row = AnnotatedDBG::graph_to_anno_index(
+                            //                 is_primary ? wrapped->get_base_node(node) : node
+                            //             );
+                            //             rows.emplace_back(row);
+                            //             counts.emplace_back(column_values[column->rank1(row) - 1]);
+                            //         }
+                            //         std::sort(counts.begin(), counts.end());
+                            //         double median = counts[counts.size() / 2];
+
+                            //         if (counts.size() % 2 == 1)
+                            //             median = (median + counts[counts.size() / 2 - 1])/2;
+
+                            //         if (median >= min_count && median < max_count) {
+                            //             for (auto row : rows) {
+                            //                 value_callback(row, 1);
+                            //             }
+                            //         }
+                            //     });
+                            // });
                         }
                     );
                 }
@@ -745,7 +690,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
 }
 
 // does differential testing
-std::shared_ptr<MaskedDeBruijnGraph> // TODO add all columns and columns label as param 
+std::shared_ptr<MaskedDeBruijnGraph> // TODO add all columns and columns label as param
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const AnnotatedDBG *anno_graph,
                     sdsl::int_vector<>&& counts,
@@ -846,27 +791,31 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         logger->trace("I'm here");
         if (config.test_by_unitig == false) { // Statistical testing part when k-mer counts are included.
             std::atomic<uint64_t> total_unitigs(0);
-            masked_graph->call_unitigs([&](const std::string &, const std::vector<node_index> &) {
-                total_unitigs.fetch_add(1, MO_RELAXED);
-            }, get_num_threads());
             if (config.test_type == "brunner_munzel"){
                 logger->trace("Brunner Munzel test");
+                masked_graph->likelihood_ratios.resize(counts.size()/2+1);
+                masked_graph->call_unitigs([&](const std::string &, const std::vector<node_index> &) {
+                    ++total_unitigs;
+                    // total_unitigs.fetch_add(1, MO_RELAXED);
+                }, get_num_threads());
                 masked_graph = brunner_munzel_test(graph_ptr, masked_graph,files,labels_in,labels_out,config,total_unitigs, medians);
+                masked_graph->likelihood_ratios.resize(counts.size()/2+1);
             }
             else if (config.test_type == "likelihoodratio"){
+                const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
+                total_unitigs = static_cast<const bitmap_vector &>(masked_graph->get_mask()).num_set_bits();
                 logger->trace("Likelihood ratio test");
                 logger->trace("ooooo total unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
                 auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
                                                         std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
-                                                        in_total_kmers, out_total_kmers); 
-                const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
+                                                        in_total_kmers, out_total_kmers);
                 logger->trace("test_by_unitig == false");
                 sdsl::bit_vector mask = in_mask;
                 size_t total_nodes = masked_graph->num_nodes();
                 size_t kept_nodes = 0;
                 //std::vector<int> nodes;
                 std::vector<std::pair<double,int>> likelihood_ratios_nodes;
-                call_ones(in_mask, [&](node_index node) { // only run if counts_case > counts_control 
+                call_ones(in_mask, [&](node_index node) { // only run if counts_case > counts_control
                     uint64_t in_sum = counts[node * 2];
                     uint64_t out_sum = counts[node * 2 + 1];
                     // filter kmers in samples less noise
@@ -888,10 +837,10 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                 mask[node] = false;
                         }
                         else
-                            mask[node] = false; 
+                            mask[node] = false;
                     // }
                     // else
-                    //     mask[node] = false;               
+                    //     mask[node] = false;
                 });
                 // // get index for benjamini-yekutieli correction
                 // std::sort(likelihood_ratios_nodes.begin(), likelihood_ratios_nodes.end(), sortByPValue);
@@ -905,10 +854,11 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 // logger->trace("likelihood_ratios__nodes_elem {}, {}, {}", likelihood_ratios_nodes[0].first, likelihood_ratios_nodes[1].first, likelihood_ratios_nodes[2].first);
                 // logger->trace("likelihood_ratios_nodes_size {}", likelihood_ratios_nodes.size());
                 masked_graph->set_mask(new bitmap_vector(std::move(mask)));
+                masked_graph->likelihood_ratios.resize(counts.size()/2+1);
                 logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
             }
 
-        } else { 
+        } else {
             logger->trace("test_by_unitig == true");
             masked_graph->likelihood_ratios.resize(counts.size()/2+1);
             std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0);
@@ -941,7 +891,7 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 },
             num_threads);
         }
-            //std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0.01); // Myrthe: For testing. temporary    
+            //std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0.01); // Myrthe: For testing. temporary
         return masked_graph;
     }
 
@@ -1270,7 +1220,7 @@ construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
         ValueCallback add_out = [&](Column i, uint64_t value) {
             i = AnnotatedDBG::anno_to_graph_index(i);
             assert(i != DeBruijnGraph::npos);
-            if (add_out_labels_to_mask)
+            if (add_out_labels_to_mask || config.test_type == "likelihoodratio")
                 set_bit(indicator.data(), i, parallel, MO_RELAXED);
             atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
             out_total_kmers += value;
diff --git a/metagraph/src/graph/representation/base/sequence_graph.cpp b/metagraph/src/graph/representation/base/sequence_graph.cpp
index f6823db55d..b91e7431b2 100644
--- a/metagraph/src/graph/representation/base/sequence_graph.cpp
+++ b/metagraph/src/graph/representation/base/sequence_graph.cpp
@@ -130,7 +130,8 @@ void call_sequences_from(const DeBruijnGraph &graph,
 
     std::vector<std::pair<node_index, char>> targets;
     const mtg::graph::MaskedDeBruijnGraph *downcasted_graph = // Create a downcasted graph object, such that the likelihood ratio vector can be accessed.
-            dynamic_cast<const mtg::graph::MaskedDeBruijnGraph*>(&graph);
+            // dynamic_cast<const mtg::graph::MaskedDeBruijnGraph*>(&graph);
+            nullptr;
 
     // keep traversing until we have worked off all branches from the queue
     while (queue.size()) {

From 35f6047fe321afd27f635d93523f467759ca7d17 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 7 Mar 2024 17:05:52 +0100
Subject: [PATCH 086/410] restructure

---
 metagraph/src/cli/assemble.cpp                |  14 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 605 +++++++++++++++++-
 .../src/graph/annotated_graph_algorithm.hpp   |   9 +
 .../src/graph/differential_tests_uemmi.cpp    |  24 +-
 metagraph/src/graph/graph_cleaning.cpp        |  68 +-
 metagraph/src/graph/graph_cleaning.hpp        |   5 +
 .../src/graph/representation/masked_graph.hpp |   1 +
 7 files changed, 681 insertions(+), 45 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 82f7203c75..fb9deaffee 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -203,12 +203,14 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             auto filenames = (config->infbase_annotators.size() > 1) ? config->infbase_annotators : config->fnames;
 
-            callback(*mask_nodes_by_label(graph_ptr,
-                                          filenames,
-                                          foreground_labels,
-                                          background_labels,
-                                          diff_config, num_threads,
-                                          config->parallel_nodes), exp_name);
+            auto [ingraph, outgraph] = mask_nodes_by_label_dual(graph_ptr,
+                                        filenames,
+                                        foreground_labels,
+                                        background_labels,
+                                        diff_config, num_threads,
+                                        config->parallel_nodes);
+            callback(*ingraph, exp_name + "in.");
+            callback(*outgraph, exp_name + "out.");
         }
     }
 }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0d2a707c81..8949f51ccd 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1,5 +1,9 @@
 #include "annotated_graph_algorithm.hpp"
 
+#include <boost/math/distributions/chi_squared.hpp>
+#include <boost/math/statistics/t_test.hpp>
+#include <boost/math/statistics/univariate_statistics.hpp>
+
 #include "common/utils/string_utils.hpp"
 #include "common/logger.hpp"
 #include "common/vectors/vector_algorithm.hpp"
@@ -8,6 +12,7 @@
 #include "common/vectors/transpose.hpp"
 #include "graph/representation/masked_graph.hpp"
 #include "graph/representation/canonical_dbg.hpp"
+#include "graph/graph_cleaning.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 #include "differential_tests.hpp"
 #include "timer.hpp"
@@ -137,7 +142,8 @@ bool sortByPValue(const std::pair<int, double>& a, const std::pair<int, double>&
 
 // Function for calculating
 // the median
-double findMedian(sdsl::int_vector_buffer<0> a,
+template <class Container>
+double findMedian(const Container &a,
                     int threshold) {
     logger->trace("non zero kmers: {}", a.size());
     std::vector<double> a_filtered;
@@ -201,7 +207,7 @@ inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
 bool filtering_row(std::vector<double> in_counts_non_zero,
                     std::vector<double> out_counts_non_zero,
                     int total_samples,
-                    std::shared_ptr<const DeBruijnGraph> graph_ptr, 
+                    std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     int64_t row_id);
 
 std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const DeBruijnGraph> graph_ptr,
@@ -290,7 +296,7 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
 bool filtering_row(std::vector<double> in_counts_non_zero,
                     std::vector<double> out_counts_non_zero,
                     int total_samples,
-                    std::shared_ptr<const DeBruijnGraph> graph_ptr, 
+                    std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     int64_t row_id){
     // minimum present in 20% of samples
     bool is_present_in_20_percent = (in_counts_non_zero.size() + out_counts_non_zero.size()) > 0.2 * double(total_samples);
@@ -446,7 +452,600 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                                config, num_threads, {});
 }
 
+std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
+mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                         const std::vector<std::string> &files,
+                         const tsl::hopscotch_set<Label> &labels_in,
+                         const tsl::hopscotch_set<Label> &labels_out,
+                         const DifferentialAssemblyConfig &config,
+                         size_t num_threads,
+                         size_t num_parallel_files) {
+    bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
+    common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
+
+    size_t max_index = graph_ptr->max_index();
+    std::vector<double> medians;
+    std::vector<size_t> n_kmers;
+    std::vector<uint64_t> sums;
+    std::vector<std::unique_ptr<bit_vector>> columns_all;
+
+    using ValuesContainer = sdsl::int_vector<>;
+    std::vector<std::unique_ptr<ValuesContainer>> column_values_all;
+    std::vector<bool> groups;
+    uint8_t max_width = 0;
+    uint64_t max_val = 0;
+
+    annot::ColumnCompressed<>::load_columns_and_values(files,
+        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
+            groups.push_back(labels_out.count(label));
+            columns_all.emplace_back(std::move(column));
+
+            max_width = std::max(max_width, column_values.width());
+            max_val = (1llu << max_width) - 1;
+
+            medians.push_back(findMedian(column_values, 0));
+            if (medians.back() == max_val)
+                common::logger->warn("Column {}: Median == {}", label, max_val);
+
+            uint64_t sum = 0;
+            size_t n = 0;
+            for (uint64_t val : column_values) {
+                if (val < max_val) {
+                    sum += val;
+                    ++n;
+                }
+            }
+            sums.push_back(sum);
+            n_kmers.push_back(n);
+
+            column_values_all.push_back(std::make_unique<ValuesContainer>(std::move(column_values)));
+        }
+    );
+
+    double in_kmers = 0;
+    double out_kmers = 0;
+    for (size_t j = 0; j < sums.size(); ++j) {
+        if (groups[j]) {
+            out_kmers += sums[j];
+        } else {
+            in_kmers += sums[j];
+        }
+    }
+    double total_kmers = in_kmers + out_kmers;
+
+    uint64_t num_unitigs = 0;
+    if (config.test_type != "likelihoodratio") {
+        graph_ptr->call_unitigs([&](const auto &, const auto &path) {
+            if (path.size() > 1)
+                ++num_unitigs;
+        });
+    } else {
+        num_unitigs = total_kmers;
+    }
+
+    sdsl::bit_vector indicator_in(max_index + 1, false);
+    sdsl::bit_vector indicator_out(max_index + 1, false);
+    common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_unitigs);
+
+    if (config.test_type == "likelihoodratio") {
+        sdsl::int_vector<> counts(2 * (AnnotatedDBG::graph_to_anno_index(max_index) + 1), 0, 32);
+        for (size_t j = 0; j < groups.size(); ++j) {
+            size_t offset = groups[j];
+            const auto &col = *columns_all[j]
+            const auto &col_vals = *column_values_all[j];
+            uint64_t rank = 0;
+            col.call_ones([&](uint64_t row_i) {
+                counts[row_i * 2 + offset] += col_vals[rank++];
+            });
+        }
+
+        for (size_t i = 0; i < counts.size(); i += 2) {
+            uint64_t in_sum = counts[i];
+            uint64_t out_sum = counts[i + 1];
+            double log_likelihood_num_in = (log(static_cast<double>(in_sum)) - 1) * in_sum;
+            double log_likelihood_num_out = (log(static_cast<double>(out_sum)) - 1) * out_sum;
+            double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
+
+            double mean_denom_in = theta * in_kmers;
+            double mean_denom_out = theta * out_kmers;
+            log_likelihood_denoms.emplace_back(log(mean_denom_in) * in_sum - mean_denom_in + log(mean_denom_out) * out_sum - mean_denom_out);
+        }
+    } else if (config.test_type == "nonparametric") {
+        uint64_t row_i = 0;
+        auto statistical_model = DifferentialTest(config.family_wise_error_rate, num_unitigs, 0, 0, 0);
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                         std::unique_ptr<sdsl::int_vector_buffer<>>,
+                         std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
+                                                                    [&](const auto &row) {
+            if (row.empty()) {
+                ++row_i;
+                return;
+            }
+
+            std::vector<double> in_counts;
+            std::vector<double> out_counts;
+            for (size_t i = 0; i < row.size(); i++){
+                double median = medians[row[i].first];
+                if (median == 0) median = 1; // if the median is 0, set it to 1 (to avoid division by 0)
+                if (!groups[row[i].first])
+                    in_counts.push_back(row[i].second/median);
+                else
+                    out_counts.push_back(row[i].second/median);
+            }
+
+            auto [keep, pvalue] = statistical_model.brunner_munzel_test(in_counts, out_counts);
+            if (keep)
+
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+
+            ++row_i;
+        });
+    } else {
+        graph_ptr->call_unitigs([&](const auto &, const auto &path) {
+            if (path.size() == 1)
+                return;
+
+            if (config.test_type == "zmp") {
+                std::vector<double> in_counts;
+                std::vector<double> out_counts;
+                for (node_index node : path) {
+                    uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                    for (size_t j = 0; j < columns_all.size(); ++j) {
+                        auto &bucket = groups[j] ? out_counts : in_counts;
+                        const auto &col = *columns_all[j];
+                        if (uint64_t rk = col.conditional_rank1(row_i)) {
+                            uint64_t val = (*column_values_all[j])[rk - 1];
+                            if (val < max_val) {
+                                bucket.push_back(static_cast<double>(val) / medians[j]);
+                            } else {
+                                bucket.push_back(0.0);
+                            }
+                        } else {
+                            bucket.push_back(0.0);
+                        }
+                    }
+                }
+
+                if (std::equal(in_counts.begin() + 1, in_counts.end(), in_counts.begin())
+                        && std::equal(out_counts.begin() + 1, out_counts.end(), out_counts.begin())
+                        && in_counts[0] == out_counts[0])
+                    return;
+
+                try {
+                    auto [t, p] = boost::math::statistics::two_sample_t_test(in_counts, out_counts);
+                    if (p * num_unitigs >= 0.05)
+                        return;
+
+                    auto &indicator = t > 0 ? indicator_in : indicator_out;
+                    for (node_index node : path) {
+                        indicator[node] = true;
+                    }
+                } catch (...) {
+                    common::logger->trace("f\t{}\t{}",fmt::join(in_counts, ","),fmt::join(out_counts,","));
+                    throw std::runtime_error("f");
+                }
+            } else if (config.test_type == "likelihoodratio_unitig") {
+                std::vector<double> log_likelihood_num_ins;
+                std::vector<double> log_likelihood_num_outs;
+                std::vector<double> log_likelihood_denoms;
+                log_likelihood_num_ins.reserve(path.size());
+                log_likelihood_num_outs.reserve(path.size());
+                log_likelihood_denoms.reserve(path.size());
+                for (node_index node : path) {
+                    uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                    double in_sum = 0;
+                    double out_sum = 0;
+                    for (size_t j = 0; j < columns_all.size(); ++j) {
+                        const auto &col = *columns_all[j];
+                        const auto &col_values = *column_values_all[j];
+                        uint64_t val = 0;
+                        if (uint64_t rk = col.conditional_rank1(row_i)) {
+                            val = col_values[rk - 1];
+                            if (val >= max_val)
+                                val = 0;
+                        }
+
+                        if (groups[j]) {
+                            out_sum += val;
+                        } else {
+                            in_sum += val;
+                        }
+                    }
 
+                    log_likelihood_num_ins.emplace_back((log(static_cast<double>(in_sum)) - 1) * in_sum);
+                    log_likelihood_num_outs.emplace_back((log(static_cast<double>(out_sum)) - 1) * out_sum);
+                    double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
+
+                    double mean_denom_in = theta * in_kmers;
+                    double mean_denom_out = theta * out_kmers;
+                    log_likelihood_denoms.emplace_back(log(mean_denom_in) * in_sum - mean_denom_in + log(mean_denom_out) * out_sum - mean_denom_out);
+                }
+
+                std::vector<double> lstats;
+                lstats.reserve(log_likelihood_num_ins.size());
+                for (size_t i = 0; i < log_likelihood_num_ins.size(); ++i) {
+                    lstats.emplace_back(2 * (log_likelihood_num_ins[i] + log_likelihood_num_outs[i] - log_likelihood_denoms[i]));
+                }
+
+                std::vector<double> lstats_sums;
+                std::vector<double> num_diffs;
+                std::vector<double> log_likelihood_num_out_sums;
+                lstats_sums.reserve(lstats.size() + 1);
+                num_diffs.reserve(lstats.size() + 1);
+                log_likelihood_num_out_sums.reserve(lstats.size() + 1);
+                lstats_sums.push_back(0);
+                num_diffs.push_back(0);
+                log_likelihood_num_out_sums.push_back(0);
+                std::partial_sum(lstats.begin(), lstats.end(), std::back_inserter(lstats_sums));
+                std::partial_sum(log_likelihood_num_ins.begin(), log_likelihood_num_ins.end(), std::back_inserter(num_diffs));
+                std::partial_sum(log_likelihood_num_outs.begin(), log_likelihood_num_outs.end(), std::back_inserter(log_likelihood_num_out_sums));
+                for (size_t i = 0; i < log_likelihood_num_outs.size(); ++i) {
+                    num_diffs[i] -= log_likelihood_num_outs[i];
+                }
+
+                double lstat_best = lstats_sums.back();
+                auto begin_best = lstats_sums.begin();
+                auto end_best = lstats_sums.end() - 1;
+                // for (auto begin = lstats_sums.begin(); begin < lstats_sums.end(); ++begin) {
+                //     for (auto end = begin + 1; end < lstats_sums.end(); ++end) {
+                //         if (*end - *begin > lstat_best) {
+                //             lstat_best = *end - *begin;
+                //             begin_best = begin;
+                //             end_best = end;
+                //         }
+                //     }
+                // }
+
+                boost::math::chi_squared dist(2*(end_best - begin_best) - 1);
+                if (lstat_best > 0 && boost::math::cdf(boost::math::complement(dist, lstat_best)) * num_unitigs < 0.05) {
+                    size_t i_begin = begin_best - lstats_sums.begin();
+                    size_t i_end = end_best - lstats_sums.begin();
+                    double num_diff = num_diffs[i_end] - num_diffs[i_begin];
+                    auto &indicator = num_diff > 0 ? indicator_in : indicator_out;
+                    for (size_t i = i_begin; i < i_end; ++i) {
+                        indicator[path[i]] = true;
+                    }
+                }
+            }
+        });
+    }
+
+    auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
+        graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
+        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+    );
+
+    auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
+        graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_out)), true,
+        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+    );
+
+    if (!is_primary)
+        return std::make_pair(masked_graph_in, masked_graph_out);
+
+    return std::make_pair(std::make_shared<CanonicalDBG>(masked_graph_in),
+                          std::make_shared<CanonicalDBG>(masked_graph_out));
+
+    // sdsl::bit_vector indicator(max_index + 1, false);
+    // std::vector<bool> groups;
+    // std::vector<uint64_t> sums;
+
+    // uint8_t max_width = 0;
+
+    // annot::ColumnCompressed<>::load_columns_and_values(files,
+    //     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, sdsl::int_vector_buffer<> && column_values) {
+    //         groups.push_back(labels_out.count(label));
+    //         sums.push_back(std::accumulate(column_values.begin(),
+    //                                        column_values.end(),
+    //                                        0llu));
+    //         column->call_ones([&](uint64_t row_i) {
+    //             node_index i = AnnotatedDBG::anno_to_graph_index(row_i);
+    //             indicator[i] = true;
+    //         });
+    //         max_width = std::max(max_width, column_values.width());
+    //         columns_all.emplace_back(std::move(column));
+    //         column_values_all.push_back(std::make_unique<sdsl::int_vector_buffer<>>(std::move(column_values)));
+    //     }
+    // );
+
+    // // size_t total_unique_kmers = sdsl::util::cnt_one_bits(indicator);
+    // size_t num_tests = 1;
+    // boost::math::chi_squared dist(groups.size() - 1);
+
+    // uint64_t row_i = 0;
+    // utils::call_rows<std::unique_ptr<bit_vector>,
+    //                  std::unique_ptr<sdsl::int_vector_buffer<>>,
+    //                  std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
+    //     if (row.empty()) {
+    //         ++row_i;
+    //         return;
+    //     }
+
+    //     std::vector<uint64_t> all_counts(groups.size());
+    //     std::vector<uint64_t> counts[2];
+    //     uint64_t sums[2] = { 0, 0 };
+    //     for (const auto &[j, c] : row) {
+    //         if (c) {
+    //             sums[groups[j]] += c;
+    //             counts[groups[j]].emplace_back(c);
+    //             all_counts[j] = c;
+    //         }
+    //     }
+
+    //     double in_mean = 0;
+    //     double in_nzeros = 0;
+    //     double out_mean = 0;
+    //     double out_nzeros = 0;
+    //     double null_mean = 0;
+    //     double null_nzeros = 0;
+
+    //     if (counts[0].size()) {
+    //         std::tie(in_mean, in_nzeros) = estimate_ztp_mean(
+    //             [&](const auto &callback) { std::for_each(counts[0].begin(), counts[0].end(), callback); },
+    //             max_width
+    //         );
+    //     }
+
+    //     if (counts[1].size()) {
+    //         std::tie(out_mean, out_nzeros) = estimate_ztp_mean(
+    //             [&](const auto &callback) { std::for_each(counts[1].begin(), counts[1].end(), callback); },
+    //             max_width
+    //         );
+    //     }
+
+    //     std::tie(null_mean, null_nzeros) = estimate_ztp_mean(
+    //         [&](const auto &callback) {
+    //             std::for_each(counts[0].begin(), counts[0].end(), callback);
+    //             std::for_each(counts[1].begin(), counts[1].end(), callback);
+    //         },
+    //         max_width
+    //     );
+
+    //     // double log_likelihood_alt = 0;
+    //     // if (in_mean > 0)
+    //     //     log_likelihood_alt += log(in_mean) * sums[0] - (in_mean + log1p(-exp(-in_mean))) * counts[0].size();
+
+    //     // if (out_mean > 0)
+    //     //     log_likelihood_alt += log(out_mean) * sums[1] - (out_mean + log1p(-exp(-out_mean))) * counts[1].size();
+
+    //     // double log_likelihood_null = log(null_mean) * (sums[0] + sums[1]) - (null_mean + log1p(-exp(-null_mean))) * (counts[0].size() + counts[1].size());
+
+    //     double log_likelihood_alt = 0;
+    //     double log_likelihood_null = 0;
+    //     for (size_t j = 0; j < all_counts.size(); ++j) {
+    //         if (all_counts[j]) {
+    //             double cur_mean = static_cast<double>(sums[j]) / (groups[j] ? out_mean : in_mean);
+    //             double cur_null_mean = static_cast<double>(sums[j]) / null_mean;
+    //             log_likelihood_alt += log(cur_mean) * all_counts[j] - cur_mean - log1p(-exp(-cur_mean));
+    //             log_likelihood_null += log(cur_null_mean) * all_counts[j] - cur_null_mean - log1p(-exp(-cur_null_mean));
+    //         }
+    //     }
+
+    //     double lstat = 2 * (log_likelihood_alt - log_likelihood_null);
+
+    //     double pval = lstat > 0
+    //         ? (1.0 - boost::math::cdf(dist, lstat)) * num_tests
+    //         : 1.0;
+
+    //     common::logger->trace("{}\t{}\t{} {}",
+    //         std::min(pval, double(1.0)),
+    //         fmt::join(all_counts, ","),
+    //         log_likelihood_alt,
+    //         log_likelihood_null
+    //     );
+
+    //     node_index i = AnnotatedDBG::anno_to_graph_index(row_i);
+    //     if (pval >= 0.05)
+    //         indicator[i] = false;
+
+    //     ++row_i;
+    // });
+
+
+    // sdsl::bit_vector in_indicator(max_index + 1, false);
+    // sdsl::bit_vector out_indicator(max_index + 1, false);
+    // sdsl::bit_vector indicator(max_index + 1, false);
+    // std::vector<std::unique_ptr<bit_vector>> columns_all;
+    // std::vector<bool> groups;
+    // size_t num_set_bits = 0;
+
+    // std::vector<size_t> n_kmers;
+    // std::vector<size_t> count_sums;
+    // // std::vector<std::pair<double, double>> ztp_params;
+    // // double in_rate = 0;
+    // // double out_rate = 0;
+    // uint8_t max_width = 8;
+    // uint64_t in_sum = 0;
+    // uint64_t out_sum = 0;
+    // double ztp_out_mean = 0;
+    // double ztp_out_nzeros = 0;
+    // double ztp_in_mean = 0;
+    // double ztp_in_nzeros = 0;
+
+    // auto overall_iterator = [&](const auto &global_callback) {
+    //     auto in_iterator = [&](const auto &in_callback) {
+    //         auto out_iterator = [&](const auto &out_callback) {
+    //             annot::ColumnCompressed<>::load_columns_and_values(files,
+    //                 [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector<> && column_values) {
+    //                     num_set_bits += column_values.size();
+    //                     groups.push_back(labels_out.count(label));
+    //                     uint64_t sum = 0;
+    //                     size_t rank = 0;
+    //                     column->call_ones([&](uint64_t row_i) {
+    //                         node_index i = AnnotatedDBG::anno_to_graph_index(row_i);
+    //                         indicator[i] = true;
+    //                         uint64_t val = column_values[rank++];
+    //                         global_callback(val);
+
+    //                         if (groups.back()) {
+    //                             out_callback(val);
+    //                             out_indicator[i] = true;
+    //                             out_sum += val;
+    //                         } else {
+    //                             in_callback(val);
+    //                             in_indicator[i] = true;
+    //                             in_sum += val;
+    //                         }
+    //                         sum += val;
+    //                     });
+    //                     count_sums.push_back(sum);
+    //                     n_kmers.push_back(column_values.size());
+    //                     columns_all.emplace_back(std::move(column));
+    //                 }
+    //             );
+    //         };
+    //         std::tie(ztp_out_mean, ztp_out_nzeros) = estimate_ztp_mean(out_iterator, max_width);
+    //         // out_rate = ztp_out_mean / out_sum;
+    //     };
+
+    //     std::tie(ztp_in_mean, ztp_in_nzeros) = estimate_ztp_mean(in_iterator, max_width);
+    //     // in_rate = ztp_in_mean / in_sum;
+    // };
+
+    // auto [overall_ztp_mean, overall_ztp_nzeros] = estimate_ztp_mean(overall_iterator, max_width);
+    // // double null_rate = overall_ztp_mean / (in_sum + out_sum);
+
+    // // common::logger->trace("Rates: in: {}\tout: {}\tnull: {}",in_rate, out_rate, null_rate);
+
+    // assert(!indicator[DeBruijnGraph::npos]);
+    // auto ind_p = std::make_unique<bitmap_vector>(std::move(indicator));
+    // double overall_prob = static_cast<double>(num_set_bits) / (ind_p->num_set_bits() * groups.size());
+    // double log_overall_denom = log1p(-exp(-overall_ztp_mean));
+
+    // boost::math::chi_squared dist(groups.size() - 1);
+
+    // std::vector<std::unique_ptr<sdsl::int_vector_buffer<>>> column_values_all;
+    // annot::ColumnCompressed<>::load_columns_and_values(files,
+    //     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&, sdsl::int_vector_buffer<> && column_values) {
+    //         column_values_all.push_back(std::make_unique<sdsl::int_vector_buffer<>>(std::move(column_values)));
+    //     }
+    // );
+
+    // bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
+
+    // // auto initial_masked_graph = std::make_shared<MaskedDeBruijnGraph>(
+    // //     graph_ptr, std::move(ind_p), true,
+    // //     is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+    // // );
+    // // std::shared_ptr<DeBruijnGraph> test_graph = initial_masked_graph;
+    // // // if (is_primary)
+    // // //     test_graph = std::make_shared<CanonicalDBG>(initial_masked_graph);
+
+    // // size_t num_tests = 0;
+    // // test_graph->call_unitigs([&](const auto&, const auto&) {
+    // //     ++num_tests;
+    // // });
+    // // // if (is_primary)
+    // // //     num_tests /= 2;
+
+    // // ind_p = std::unique_ptr<bitmap_vector>(static_cast<bitmap_vector*>(initial_masked_graph->release_mask()));
+    // size_t num_tests = ind_p->num_set_bits();
+
+    // // num_tests = 1;
+    // common::logger->trace("Num tests: {}", num_tests);
+
+    // for (size_t i = 0; i < groups.size(); ++i) {
+    //     common::logger->trace("{}: zero prob: {}", i, 1.0 - static_cast<double>(n_kmers[i])/ind_p->num_set_bits());
+    // }
+    // common::logger->trace("null: zero prob: {}", 1.0 - overall_prob);
+
+    // uint64_t row_i = 0;
+    // // ProgressBar progress(indicator.size() - 1,
+    // //                      "Testing k-mers zmp",
+    // //                      std::cerr, !common::get_verbose());
+    // utils::call_rows<std::unique_ptr<bit_vector>,
+    //                 std::unique_ptr<sdsl::int_vector_buffer<>>,
+    //                 std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
+    //     [&](const auto &row) {
+    //         // ++progress;
+    //         if (row.empty()) {
+    //             ++row_i;
+    //             return;
+    //         }
+
+    //         std::vector<uint64_t> counts(groups.size());
+    //         for (const auto &[j, c] : row) {
+    //             counts[j] = c;
+    //         }
+
+    //         // double likelihood_num = 1;
+    //         // double likelihood_denom = 1;
+    //         double log_likelihood_num = 0;
+    //         double log_likelihood_denom = 0;
+    //         for (size_t j = 0; j < counts.size(); ++j) {
+    //             uint64_t c = counts[j];
+    //             double log_p_denom = 0;
+    //             for (uint64_t cc = 2; cc <= c; ++cc) {
+    //                 log_p_denom += log(cc);
+    //             }
+
+    //             // the distribution is a zero-inflated, zero-truncated poisson
+    //             {
+    //                 // double mean_est = ztp_params[j].first;
+    //                 // double mean_est = (groups[j] ? out_rate : in_rate) * count_sums[j];
+    //                 double mean_est = groups[j] ? ztp_out_mean : ztp_in_mean;
+    //                 double log_prob = log(static_cast<double>(n_kmers[j])) - log(static_cast<double>(ind_p->num_set_bits()));
+    //                 if (!c) {
+    //                     // we are in the zero-count zone
+    //                     log_prob = log(static_cast<double>(ind_p->num_set_bits() - n_kmers[j])) - log(static_cast<double>(ind_p->num_set_bits()));
+    //                 } else {
+    //                     // we are now in the ztp zone
+    //                     double log_denom = log1p(-exp(-mean_est));
+    //                     double log_p_num = c * log(mean_est) - mean_est;
+    //                     double log_ztp_prob = log_p_num - log_p_denom - log_denom;
+    //                     log_prob += log_ztp_prob;
+    //                 }
+
+    //                 log_likelihood_num += log_prob;
+    //             }
+
+    //             {
+    //                 double mean_est = overall_ztp_mean;
+    //                 // double mean_est = null_rate * count_sums[j];
+    //                 double log_prob = log(overall_prob);
+    //                 if (!c) {
+    //                     // we are in the zero-count zone
+    //                     log_prob = log1p(-overall_prob);
+    //                 } else {
+    //                     // we are now in the ztp zone
+    //                     double log_p_num = c * log(mean_est) - mean_est;
+    //                     double log_ztp_prob = log_p_num - log_p_denom - log_overall_denom;
+    //                     log_prob += log_ztp_prob;
+    //                 }
+
+    //                 log_likelihood_denom += log_prob;
+    //             }
+    //         }
+
+    //         // common::logger->trace("{} {}", likelihood_num, likelihood_denom);
+    //         double lstat = 2 * (log_likelihood_num - log_likelihood_denom);
+    //         double pval = lstat > 0
+    //             ? (1.0 - boost::math::cdf(dist, lstat)) * num_tests
+    //             : 1.0;
+
+    //         // if (counts[0] + counts[2] > counts[1] && counts[0] && counts[2]) {
+    //         //     common::logger->trace("{}\t{} {}\t{}", pval, log_likelihood_num, log_likelihood_denom, lstat);
+    //         //     common::logger->trace("{}", fmt::join(groups, "\t"));
+    //         //     common::logger->trace("{}", fmt::join(counts, "\t"));
+    //         // }
+
+    //         node_index i = AnnotatedDBG::anno_to_graph_index(row_i);
+    //         if (pval >= 0.05)
+    //             ind_p->set(i, false);
+
+    //         ++row_i;
+    //     }
+    // );
+
+    // auto masked_graph = std::make_shared<MaskedDeBruijnGraph>(
+    //     graph_ptr, std::move(ind_p), true,
+    //     is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+    // );
+    // if (!is_primary)
+    //     return masked_graph;
+
+    // return std::make_shared<CanonicalDBG>(masked_graph);
+}
 
 std::shared_ptr<DeBruijnGraph>
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this version i don't need to change at first
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index e9d5b35831..ed75f83bb2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -76,6 +76,15 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     size_t num_threads = 1,
                     size_t num_parallel_files = std::numeric_limits<size_t>::max());
 
+std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
+mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                         const std::vector<std::string> &files,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
+                         const DifferentialAssemblyConfig &config,
+                         size_t num_threads = 1,
+                         size_t num_parallel_files = std::numeric_limits<size_t>::max());
+
 } // namespace graph
 } // namespace mtg
 
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
index 3e602d82d5..12b15a093d 100644
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ b/metagraph/src/graph/differential_tests_uemmi.cpp
@@ -15,7 +15,7 @@
 namespace mtg{
 
 DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t ,
-                                   size_t in_total_kmers, size_t out_total_kmers): 
+                                   size_t in_total_kmers, size_t out_total_kmers):
     family_wise_error_rate(family_wise_error_rate),
     total_hypotheses(total_hypotheses),
     in_total_kmers(in_total_kmers),
@@ -31,7 +31,7 @@ double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipe
 }
 
 double DifferentialTest::get_t_test_alpha(int df, double alpha=0.05){
-    alpha = alpha/2; // convert to one sided test
+    // alpha = alpha/2; // convert to one sided test
     alpha = alpha/total_hypotheses; // bonferroni correction
     common::logger->trace("alpha: {}\tdf:{}", alpha, df);
     double critical_value = t_table.getCriticalValue(alpha, df);
@@ -68,7 +68,7 @@ std::vector<double> DifferentialTest::get_midranks(std::vector<double> in_counts
                 curr_value = in_counts[i];
                 tied = 1;
             }
-            
+
         }
     }
     return mid_ranks;
@@ -109,7 +109,7 @@ std::tuple<std::vector<double>, std::vector<double>> DifferentialTest::get_overa
                 curr_value = std::get<0>(all_counts[i]);
                 tied = 1;
             }
-            
+
         }
     }
     std::vector<double> in_ranks;
@@ -179,10 +179,10 @@ std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<doubl
         var_0++;
         var_in = 1;
         var_out = 1;
-    }         
+    }
     // get test statistic
     double b = (mean_mid_rank_out - mean_mid_rank_in) / (N * std::sqrt(var_in/(m*n*n) + var_out/(m*m*n)));
-    // get degrees of freedom 
+    // get degrees of freedom
     if (df_precalc == -1){
         common::logger->trace("df_precalc not set, calculating df_precalc");
         df_precalc = get_df_conservative(in_counts, out_counts);
@@ -211,19 +211,19 @@ std::tuple<std::vector<int>, std::vector<double>> sorted_pvalues(){
 // not in use at the moment
 bool DifferentialTest::bonferroni_correction(double& pvalue)
 {
-    return pvalue < (family_wise_error_rate / total_hypotheses); 
+    return pvalue < (family_wise_error_rate / total_hypotheses);
 }
 
 // return likelihood ratio thereshold corresponding to corrected alpha value
-double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test 
+double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test
     double corrected_pvalue = family_wise_error_rate/total_hypotheses; // Take into account the Bonferonni multiple testing correction
     std::cout << "total_hypotheses " << total_hypotheses << "\n";
     if (family_wise_error_rate ==1) // TODO: change how to handle turning off mutliple testing correction. currtenly used like a flag, has no statistical meaning
         return (double) 3840 / 1000 / 2; //corrected_pvalue = 0.05;
     Chi2PLookup lookup;
     int low = 0;
-    int high = lookup.divisor * lookup.cutoff[0] - 1; 
-    while (low <= high) { 
+    int high = lookup.divisor * lookup.cutoff[0] - 1;
+    while (low <= high) {
         int mid = (low + high) >> 1; // the mean of low and high
         double current_pvalue = lookup.getPValue(mid / lookup.divisor, 1);
         if (current_pvalue > corrected_pvalue)
@@ -244,7 +244,7 @@ int DifferentialTest::benjamini_yekutieli(std::vector<std::pair<double, int>> li
         double qvalue = lrt_threshold * (i+1) / (m * c);
         if (pvalue <= qvalue){
             max_i = i;
-        } 
+        }
     }
     return max_i;
 }
@@ -265,7 +265,7 @@ std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum,
             poisson_prob(in_sum, mean * in_total_kmers);
 
     double likelihood_ratio = alt_hypothesis - null_hypothesis;
-    // double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;  
+    // double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
     if (likelihood_ratio > likelihood_ratio_threshold){
         return std::make_tuple(true, likelihood_ratio);
     }
diff --git a/metagraph/src/graph/graph_cleaning.cpp b/metagraph/src/graph/graph_cleaning.cpp
index 863b59cc51..357e5e2f6a 100644
--- a/metagraph/src/graph/graph_cleaning.cpp
+++ b/metagraph/src/graph/graph_cleaning.cpp
@@ -68,9 +68,44 @@ uint64_t estimate_min_kmer_abundance(const DeBruijnGraph &graph,
 uint64_t estimate_min_kmer_abundance_ztp(const DeBruijnGraph &graph,
                                          const NodeWeights &node_weights,
                                          uint64_t num_singleton_kmers) {
+    // set a cutoff that covers 95% of this distribution
+    double cdf_cutoff = 0.95;
+    size_t max_iter = 1000;
+
+    auto [mean_est, nzeros_est] = estimate_ztp_mean(
+        [&](const auto &callback) {
+            graph.call_nodes([&](DeBruijnGraph::node_index i) {
+                callback(node_weights[i]);
+            });
+        },
+        node_weights.get_data().width(),
+        num_singleton_kmers,
+        3 // estimate from the first 3 counts
+    );
+    double p_num = mean_est * exp(-mean_est);
+    double p_denom = 1;
+    double cdf = p_num;
+    double denom = 1.0 - exp(-mean_est);
+
+    uint64_t min_count = 1;
+    for (size_t i = 0; i < max_iter; ++i) {
+        if (cdf / denom >= cdf_cutoff)
+            break;
+
+        ++min_count;
+        p_num *= mean_est;
+        p_denom *= min_count;
+        cdf += p_num / p_denom;
+    }
+    return min_count + 1;
+}
+
+std::pair<double, double> estimate_ztp_mean(const std::function<void(const std::function<void(uint64_t)>&)> &generator,
+                                            uint8_t width,
+                                            uint64_t num_singleton_kmers,
+                                            size_t touse) {
     std::vector<uint64_t> hist;
-    graph.call_nodes([&](auto i) {
-        uint64_t kmer_count = node_weights[i];
+    generator([&](uint64_t kmer_count) {
         assert(kmer_count && "All k-mers in graph must have non-zero counts");
         while (kmer_count >= hist.size()) {
             hist.push_back(0);
@@ -84,19 +119,18 @@ uint64_t estimate_min_kmer_abundance_ztp(const DeBruijnGraph &graph,
         hist[1] = num_singleton_kmers;
     }
 
-    uint64_t max_size = 1llu << node_weights.get_data().width();
+    uint64_t max_size = 1llu << width;
     if (hist.size() == max_size)
         hist.pop_back();
 
     // estimate a zero-truncated poisson from the first touse counts
     // (from https://doi.org/10.1016/S0167-9473(99)00111-5)
-    // set a cutoff that covers 95% of this distribution
-    uint64_t touse = 3;
+
     size_t max_iter = 1000;
-    double cdf_cutoff = 0.95;
 
     size_t nupto_c = 0;
     uint64_t sumupto = 0;
+    touse = std::min(touse, hist.size() - 1);
     for (size_t count = 1; count <= touse; ++count) {
         uint64_t nkmers = hist[count];
         nupto_c += nkmers;
@@ -106,33 +140,19 @@ uint64_t estimate_min_kmer_abundance_ztp(const DeBruijnGraph &graph,
     double sum = sumupto;
     double nupto = nupto_c;
     double mean_est = sum / nupto;
+    double total_est = nupto;
 
     for (size_t i = 0; i < max_iter; ++i) {
         double last_mean_est = mean_est;
-        double total_est = nupto/(1.0-exp(-mean_est));
+        total_est = nupto/(1.0-exp(-mean_est));
         mean_est = sum / total_est;
-        if (abs(mean_est - last_mean_est)/mean_est < 1e-3) {
+        if (abs(mean_est - last_mean_est)/mean_est < 1e-5) {
             common::logger->trace("Fitted mean {} after {} iterations", mean_est, i + 1);
             break;
         }
     }
 
-    double p_num = mean_est * exp(-mean_est);
-    double p_denom = 1;
-    double cdf = p_num;
-    double denom = 1.0 - exp(-mean_est);
-
-    uint64_t min_count = 1;
-    for (size_t i = 0; i < max_iter; ++i) {
-        if (cdf / denom >= cdf_cutoff)
-            break;
-
-        ++min_count;
-        p_num *= mean_est;
-        p_denom *= min_count;
-        cdf += p_num / p_denom;
-    }
-    return min_count + 1;
+    return std::make_pair(mean_est, total_est - nupto);
 }
 
 std::tuple<int64_t,double,double> estimate_min_kmer_abundance(const bit_vector &idx,
diff --git a/metagraph/src/graph/graph_cleaning.hpp b/metagraph/src/graph/graph_cleaning.hpp
index 58c0cd2a99..6d5771fefb 100644
--- a/metagraph/src/graph/graph_cleaning.hpp
+++ b/metagraph/src/graph/graph_cleaning.hpp
@@ -17,6 +17,11 @@ uint64_t estimate_min_kmer_abundance(const DeBruijnGraph &graph,
                                      const NodeWeights &node_weights,
                                      uint64_t num_singleton_kmers = 0);
 
+std::pair<double, double> estimate_ztp_mean(const std::function<void(const std::function<void(uint64_t)>&)> &generator,
+                                            uint8_t width = 64,
+                                            uint64_t num_singleton_kmers = 0,
+                                            size_t touse = std::numeric_limits<size_t>::max());
+
 uint64_t estimate_min_kmer_abundance_ztp(const DeBruijnGraph &graph,
                                          const NodeWeights &node_weights,
                                          uint64_t num_singleton_kmers = 0);
diff --git a/metagraph/src/graph/representation/masked_graph.hpp b/metagraph/src/graph/representation/masked_graph.hpp
index d5b3bd19d5..0918731a92 100644
--- a/metagraph/src/graph/representation/masked_graph.hpp
+++ b/metagraph/src/graph/representation/masked_graph.hpp
@@ -114,6 +114,7 @@ class MaskedDeBruijnGraph : public DBGWrapper<DeBruijnGraph> {
     virtual void set_mask(bitmap *mask) { kmers_in_graph_.reset(mask); }
 
     virtual const bitmap& get_mask() const { return *kmers_in_graph_; }
+    bitmap* release_mask() { return kmers_in_graph_.release(); }
 
     virtual void call_kmers(const std::function<void(node_index, const std::string&)> &callback) const override;
 

From 39b8116f3980eb3e8f125ca33fd79533894fdf09 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 11 Mar 2024 09:12:11 +0100
Subject: [PATCH 087/410] revised tests

---
 .../src/graph/annotated_graph_algorithm.cpp   | 603 +++++++++++++-----
 .../representation/base/sequence_graph.cpp    |  21 +-
 .../src/graph/representation/masked_graph.cpp |  28 +-
 3 files changed, 485 insertions(+), 167 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 8949f51ccd..9d2377155c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -462,6 +462,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          size_t num_parallel_files) {
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
     common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
+    common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
+    common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
 
     size_t max_index = graph_ptr->max_index();
     std::vector<double> medians;
@@ -474,22 +476,29 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<bool> groups;
     uint8_t max_width = 0;
     uint64_t max_val = 0;
+    uint64_t max_observed_value = 0;
 
     annot::ColumnCompressed<>::load_columns_and_values(files,
         [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
             groups.push_back(labels_out.count(label));
+            common::logger->trace("{}: {}", label, groups.back() ? "out": "in");
             columns_all.emplace_back(std::move(column));
 
             max_width = std::max(max_width, column_values.width());
-            max_val = (1llu << max_width) - 1;
+            max_val = sdsl::bits::lo_set[max_width];
 
-            medians.push_back(findMedian(column_values, 0));
+            // median calculation modifies the underlying vector, so we have to make a copy
+            std::vector<uint8_t> vals;
+            vals.reserve(column_values.size());
+            std::copy(column_values.begin(), column_values.end(), std::back_inserter(vals));
+            medians.push_back(boost::math::statistics::median(vals));
             if (medians.back() == max_val)
                 common::logger->warn("Column {}: Median == {}", label, max_val);
 
             uint64_t sum = 0;
             size_t n = 0;
             for (uint64_t val : column_values) {
+                max_observed_value = std::max(max_observed_value, val);
                 if (val < max_val) {
                     sum += val;
                     ++n;
@@ -502,6 +511,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     );
 
+    common::logger->trace("Max width: {}\tMax val: {}\tMax obs. val: {}", max_width, max_val, max_observed_value);
+
     double in_kmers = 0;
     double out_kmers = 0;
     for (size_t j = 0; j < sums.size(); ++j) {
@@ -513,48 +524,65 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
     double total_kmers = in_kmers + out_kmers;
 
-    uint64_t num_unitigs = 0;
-    if (config.test_type != "likelihoodratio") {
-        graph_ptr->call_unitigs([&](const auto &, const auto &path) {
-            if (path.size() > 1)
-                ++num_unitigs;
-        });
-    } else {
-        num_unitigs = total_kmers;
-    }
-
     sdsl::bit_vector indicator_in(max_index + 1, false);
     sdsl::bit_vector indicator_out(max_index + 1, false);
-    common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_unitigs);
 
-    if (config.test_type == "likelihoodratio") {
-        sdsl::int_vector<> counts(2 * (AnnotatedDBG::graph_to_anno_index(max_index) + 1), 0, 32);
+    uint64_t num_unitigs = 0;
+    if (config.test_type != "likelihoodratio")
+        graph_ptr->call_unitigs([&](const auto &, const auto &) { ++num_unitigs; });
+
+    if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig") {
+        boost::math::chi_squared dist(1);
+        sdsl::int_vector<> counts(columns_all[0]->size() * 2, 0, 32);
+        size_t num_nonzeros = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
             size_t offset = groups[j];
-            const auto &col = *columns_all[j]
+            const auto &col = *columns_all[j];
             const auto &col_vals = *column_values_all[j];
             uint64_t rank = 0;
             col.call_ones([&](uint64_t row_i) {
-                counts[row_i * 2 + offset] += col_vals[rank++];
+                uint64_t val = col_vals[rank++];
+                if (val < max_val) {
+                    num_nonzeros += (counts[row_i * 2] == 0 && counts[row_i * 2 + 1] == 0);
+                    counts[row_i * 2 + offset] += val;
+                }
             });
         }
 
+        size_t num_tests = config.test_type == "likelihoodratio"
+            ? num_nonzeros : num_unitigs;
+
+        common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_tests);
+
         for (size_t i = 0; i < counts.size(); i += 2) {
-            uint64_t in_sum = counts[i];
-            uint64_t out_sum = counts[i + 1];
-            double log_likelihood_num_in = (log(static_cast<double>(in_sum)) - 1) * in_sum;
-            double log_likelihood_num_out = (log(static_cast<double>(out_sum)) - 1) * out_sum;
-            double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
+            double in_sum = static_cast<uint64_t>(counts[i]);
+            double out_sum = static_cast<uint64_t>(counts[i + 1]);
+
+            double log_likelihood_num_in = in_sum > 0 ? in_sum * log(in_sum) - in_sum : 0.0;
+            double log_likelihood_num_out = out_sum > 0 ? out_sum * log(out_sum) - out_sum : 0.0;
+            double theta = (in_sum + out_sum) / total_kmers;
 
             double mean_denom_in = theta * in_kmers;
             double mean_denom_out = theta * out_kmers;
-            log_likelihood_denoms.emplace_back(log(mean_denom_in) * in_sum - mean_denom_in + log(mean_denom_out) * out_sum - mean_denom_out);
+            double log_likelihood_denom = log(mean_denom_in) * in_sum - mean_denom_in + log(mean_denom_out) * out_sum - mean_denom_out;
+
+            double lstat = log_likelihood_num_in + log_likelihood_num_out - log_likelihood_denom;
+            if (lstat > 0 && boost::math::cdf(boost::math::complement(dist, lstat * 2.0)) * num_tests < 0.05) {
+                node_index node = AnnotatedDBG::anno_to_graph_index(i / 2);
+                double scaled_outsum = out_sum / out_kmers * in_kmers;
+                if (scaled_outsum < in_sum) {
+                    indicator_in[node] = true;
+                } else if (scaled_outsum > in_sum) {
+                    indicator_out[node] = true;
+                }
+            }
         }
     } else if (config.test_type == "nonparametric") {
+        size_t num_tests = num_unitigs;
+        common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_tests);
         uint64_t row_i = 0;
-        auto statistical_model = DifferentialTest(config.family_wise_error_rate, num_unitigs, 0, 0, 0);
         utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<sdsl::int_vector_buffer<>>,
+                         std::unique_ptr<ValuesContainer>,
                          std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
                                                                     [&](const auto &row) {
             if (row.empty()) {
@@ -563,153 +591,442 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             std::vector<double> in_counts;
+            in_counts.reserve(labels_in.size());
             std::vector<double> out_counts;
-            for (size_t i = 0; i < row.size(); i++){
-                double median = medians[row[i].first];
-                if (median == 0) median = 1; // if the median is 0, set it to 1 (to avoid division by 0)
-                if (!groups[row[i].first])
-                    in_counts.push_back(row[i].second/median);
-                else
-                    out_counts.push_back(row[i].second/median);
+            out_counts.reserve(labels_out.size());
+            for (const auto &[j, c] : row) {
+                if (c < max_val) {
+                    auto &bucket = groups[j] ? out_counts : in_counts;
+                    bucket.emplace_back(static_cast<double>(c) / medians[j]);
+                }
             }
+            in_counts.resize(labels_in.size());
+            out_counts.resize(labels_out.size());
+
+            auto rankdata = [&](const std::vector<double> &c) {
+                std::vector<std::pair<double, double>> c_p;
+                c_p.reserve(c.size());
+                for (size_t i = 0; i < c.size(); ++i) {
+                    c_p.emplace_back(c[i], i + 1);
+                }
+                std::sort(c_p.begin(), c_p.end());
+                tsl::hopscotch_map<double, std::pair<size_t, size_t>> counts;
+                for (const auto &[v, r] : c_p) {
+                    counts[v].first += r;
+                    ++counts[v].second;
+                }
 
-            auto [keep, pvalue] = statistical_model.brunner_munzel_test(in_counts, out_counts);
-            if (keep)
+                std::vector<double> ranks;
+                ranks.reserve(c.size());
+                for (double v : c) {
+                    ranks.emplace_back(static_cast<double>(counts[v].first)/counts[v].second);
+                }
 
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                return std::make_pair(std::move(ranks), std::move(counts));
+            };
+
+            size_t nx = in_counts.size();
+            size_t ny = out_counts.size();
+
+            std::vector<double> c;
+            c.reserve(in_counts.size() + out_counts.size());
+            std::copy(in_counts.begin(), in_counts.end(), std::back_inserter(c));
+            std::copy(out_counts.begin(), out_counts.end(), std::back_inserter(c));
+            auto [rankc, countsc] = rankdata(c);
+
+            if (ny == 1) {
+                if (std::all_of(in_counts.begin(), in_counts.end(), [&](double c) { return c > out_counts[0]; })) {
+                    indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
+                } else if (std::all_of(in_counts.begin(), in_counts.end(), [&](double c) { return c < out_counts[0]; })) {
+                    indicator_out[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
+                }
+            } else if (nx == 1) {
+                if (std::all_of(out_counts.begin(), out_counts.end(), [&](double c) { return c > in_counts[0]; })) {
+                    indicator_out[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
+                } else if (std::all_of(out_counts.begin(), out_counts.end(), [&](double c) { return c < in_counts[0]; })) {
+                    indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
+                }
+            } else {
+                double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
+                double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
+
+                auto [rankx, countsx] = rankdata(in_counts);
+                auto [ranky, countsy] = rankdata(out_counts);
+                double rankx_mean = boost::math::statistics::mean(rankx.begin(), rankx.end());
+                double ranky_mean = boost::math::statistics::mean(ranky.begin(), ranky.end());
+
+                double Sx = 0;
+                for (size_t i = 0; i < nx; ++i) {
+                    Sx += std::pow(rankc[i] - rankx[i] - rankcx_mean + rankx_mean, 2.0);
+                }
+                Sx /= nx - 1;
+                double Sy = 0;
+                for (size_t i = 0; i < ny; ++i) {
+                    Sy += std::pow(rankc[nx + i] - ranky[i] - rankcy_mean + ranky_mean, 2.0);
+                }
+                Sy /= ny - 1;
+
+                double wbfn = (rankcy_mean - rankcx_mean) * nx * ny;
+                wbfn /= std::sqrt(Sx * nx + Sy * ny) * (nx + ny);
+
+                double df_numer = std::pow(Sx * nx + Sy * ny, 2.0);
+                double df_denom = std::pow(Sx * nx, 2.0) / (nx - 1) + std::pow(Sy * ny, 2.0) / (ny - 1);
+                double df = df_numer / df_denom;
+
+                if (df_denom == 0) {
+                    if (*std::min_element(ranky.begin(), ranky.end()) > *std::max_element(rankx.begin(), rankx.end())) {
+                        indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
+                    } else if (*std::min_element(rankx.begin(), rankx.end()) > *std::max_element(ranky.begin(), ranky.end())) {
+                        indicator_out[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
+                    }
+                } else {
+                    boost::math::students_t dist(df);
+                    double p = boost::math::cdf(dist, wbfn);
+                    double p1 = boost::math::cdf(boost::math::complement(dist, wbfn));
+                    double p2 = 2 * std::min(p, p1);
+
+                    if (p2 * num_tests < 0.05) {
+                        auto &indicator = rankcy_mean > rankcx_mean ? indicator_in : indicator_out;
+                        indicator[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
+                    }
+                }
+            }
 
             ++row_i;
         });
-    } else {
+    } else if (config.test_type == "likelihoodratio_perunitig") {
         graph_ptr->call_unitigs([&](const auto &, const auto &path) {
-            if (path.size() == 1)
-                return;
+            std::vector<double> log_likelihood_num_ins;
+            std::vector<double> log_likelihood_num_outs;
+            std::vector<double> log_likelihood_denoms;
+            log_likelihood_num_ins.reserve(path.size());
+            log_likelihood_num_outs.reserve(path.size());
+            log_likelihood_denoms.reserve(path.size());
+            for (node_index node : path) {
+                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                double in_sum = 0;
+                double out_sum = 0;
+                for (size_t j = 0; j < columns_all.size(); ++j) {
+                    const auto &col = *columns_all[j];
+                    const auto &col_values = *column_values_all[j];
+                    uint64_t val = 0;
+                    if (uint64_t rk = col.conditional_rank1(row_i)) {
+                        val = col_values[rk - 1];
+                        if (val >= max_val)
+                            val = 0;
+                    }
 
-            if (config.test_type == "zmp") {
-                std::vector<double> in_counts;
-                std::vector<double> out_counts;
-                for (node_index node : path) {
-                    uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-                    for (size_t j = 0; j < columns_all.size(); ++j) {
-                        auto &bucket = groups[j] ? out_counts : in_counts;
-                        const auto &col = *columns_all[j];
-                        if (uint64_t rk = col.conditional_rank1(row_i)) {
-                            uint64_t val = (*column_values_all[j])[rk - 1];
-                            if (val < max_val) {
-                                bucket.push_back(static_cast<double>(val) / medians[j]);
-                            } else {
-                                bucket.push_back(0.0);
-                            }
-                        } else {
-                            bucket.push_back(0.0);
-                        }
+                    if (groups[j]) {
+                        out_sum += val;
+                    } else {
+                        in_sum += val;
                     }
                 }
 
-                if (std::equal(in_counts.begin() + 1, in_counts.end(), in_counts.begin())
-                        && std::equal(out_counts.begin() + 1, out_counts.end(), out_counts.begin())
-                        && in_counts[0] == out_counts[0])
-                    return;
+                log_likelihood_num_ins.emplace_back(in_sum > 0 ? in_sum * log(in_sum) - in_sum : 0.0);
+                log_likelihood_num_outs.emplace_back(out_sum > 0 ? out_sum * log(out_sum) - out_sum : 0.0);
+                double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
 
-                try {
-                    auto [t, p] = boost::math::statistics::two_sample_t_test(in_counts, out_counts);
-                    if (p * num_unitigs >= 0.05)
-                        return;
+                double mean_denom_in = theta * in_kmers;
+                double mean_denom_out = theta * out_kmers;
+                log_likelihood_denoms.emplace_back(log(mean_denom_in) * in_sum - mean_denom_in + log(mean_denom_out) * out_sum - mean_denom_out);
+            }
 
-                    auto &indicator = t > 0 ? indicator_in : indicator_out;
-                    for (node_index node : path) {
-                        indicator[node] = true;
-                    }
-                } catch (...) {
-                    common::logger->trace("f\t{}\t{}",fmt::join(in_counts, ","),fmt::join(out_counts,","));
-                    throw std::runtime_error("f");
+            std::vector<double> lstats;
+            lstats.reserve(log_likelihood_num_ins.size());
+            for (size_t i = 0; i < log_likelihood_num_ins.size(); ++i) {
+                lstats.emplace_back(2 * (log_likelihood_num_ins[i] + log_likelihood_num_outs[i] - log_likelihood_denoms[i]));
+            }
+
+            std::vector<double> lstats_sums;
+            std::vector<double> num_diffs;
+            std::vector<double> log_likelihood_num_out_sums;
+            lstats_sums.reserve(lstats.size() + 1);
+            num_diffs.reserve(lstats.size() + 1);
+            log_likelihood_num_out_sums.reserve(lstats.size() + 1);
+            lstats_sums.push_back(0);
+            num_diffs.push_back(0);
+            log_likelihood_num_out_sums.push_back(0);
+            std::partial_sum(lstats.begin(), lstats.end(), std::back_inserter(lstats_sums));
+            std::partial_sum(log_likelihood_num_ins.begin(), log_likelihood_num_ins.end(), std::back_inserter(num_diffs));
+            std::partial_sum(log_likelihood_num_outs.begin(), log_likelihood_num_outs.end(), std::back_inserter(log_likelihood_num_out_sums));
+            for (size_t i = 0; i < log_likelihood_num_outs.size(); ++i) {
+                num_diffs[i] -= log_likelihood_num_outs[i];
+            }
+
+            double lstat_best = lstats_sums.back();
+            auto begin_best = lstats_sums.begin();
+            auto end_best = lstats_sums.end() - 1;
+            // for (auto begin = lstats_sums.begin(); begin < lstats_sums.end(); ++begin) {
+            //     for (auto end = begin + 1; end < lstats_sums.end(); ++end) {
+            //         if (*end - *begin > lstat_best) {
+            //             lstat_best = *end - *begin;
+            //             begin_best = begin;
+            //             end_best = end;
+            //         }
+            //     }
+            // }
+
+            boost::math::chi_squared dist(2*(end_best - begin_best) - 1);
+            if (lstat_best > 0 && boost::math::cdf(boost::math::complement(dist, lstat_best)) * num_unitigs < 0.05) {
+                size_t i_begin = begin_best - lstats_sums.begin();
+                size_t i_end = end_best - lstats_sums.begin();
+                double num_diff = num_diffs[i_end] - num_diffs[i_begin];
+                auto &indicator = num_diff > 0 ? indicator_in : indicator_out;
+                for (size_t i = i_begin; i < i_end; ++i) {
+                    indicator[path[i]] = true;
                 }
-            } else if (config.test_type == "likelihoodratio_unitig") {
-                std::vector<double> log_likelihood_num_ins;
-                std::vector<double> log_likelihood_num_outs;
-                std::vector<double> log_likelihood_denoms;
-                log_likelihood_num_ins.reserve(path.size());
-                log_likelihood_num_outs.reserve(path.size());
-                log_likelihood_denoms.reserve(path.size());
-                for (node_index node : path) {
-                    uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-                    double in_sum = 0;
-                    double out_sum = 0;
-                    for (size_t j = 0; j < columns_all.size(); ++j) {
-                        const auto &col = *columns_all[j];
-                        const auto &col_values = *column_values_all[j];
-                        uint64_t val = 0;
-                        if (uint64_t rk = col.conditional_rank1(row_i)) {
-                            val = col_values[rk - 1];
-                            if (val >= max_val)
-                                val = 0;
-                        }
+            }
+        });
 
-                        if (groups[j]) {
-                            out_sum += val;
-                        } else {
-                            in_sum += val;
-                        }
+    } else if (config.test_type == "nonparametric_perunitig") {
+        size_t num_tests = num_unitigs;
+        graph_ptr->call_unitigs([&](const auto &, const auto &path) {
+            std::vector<double> in_counts;
+            std::vector<double> out_counts;
+            for (node_index node : path) {
+                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                for (size_t j = 0; j < columns_all.size(); ++j) {
+                    double scaled_val = 0.0;
+                    if (uint64_t rk = columns_all[j]->conditional_rank1(row_i)) {
+                        uint64_t val = (*column_values_all[j])[rk - 1];
+                        if (val < max_val)
+                            scaled_val = static_cast<double>(val) / medians[j];
                     }
+                    auto &bucket = groups[j] ? out_counts : in_counts;
+                    bucket.emplace_back(scaled_val);
+                }
+            }
 
-                    log_likelihood_num_ins.emplace_back((log(static_cast<double>(in_sum)) - 1) * in_sum);
-                    log_likelihood_num_outs.emplace_back((log(static_cast<double>(out_sum)) - 1) * out_sum);
-                    double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
+            auto rankdata = [&](const std::vector<double> &c) {
+                std::vector<std::pair<double, double>> c_p;
+                c_p.reserve(c.size());
+                for (size_t i = 0; i < c.size(); ++i) {
+                    c_p.emplace_back(c[i], i + 1);
+                }
+                std::sort(c_p.begin(), c_p.end());
+                tsl::hopscotch_map<double, std::pair<size_t, size_t>> counts;
+                for (const auto &[v, r] : c_p) {
+                    counts[v].first += r;
+                    ++counts[v].second;
+                }
 
-                    double mean_denom_in = theta * in_kmers;
-                    double mean_denom_out = theta * out_kmers;
-                    log_likelihood_denoms.emplace_back(log(mean_denom_in) * in_sum - mean_denom_in + log(mean_denom_out) * out_sum - mean_denom_out);
+                std::vector<double> ranks;
+                ranks.reserve(c.size());
+                for (double v : c) {
+                    ranks.emplace_back(static_cast<double>(counts[v].first)/counts[v].second);
                 }
 
-                std::vector<double> lstats;
-                lstats.reserve(log_likelihood_num_ins.size());
-                for (size_t i = 0; i < log_likelihood_num_ins.size(); ++i) {
-                    lstats.emplace_back(2 * (log_likelihood_num_ins[i] + log_likelihood_num_outs[i] - log_likelihood_denoms[i]));
+                return std::make_pair(std::move(ranks), std::move(counts));
+            };
+
+            size_t nx = in_counts.size();
+            size_t ny = out_counts.size();
+
+            auto add_to = [&](auto &indicator) {
+                for (node_index node : path) {
+                    indicator[node] = true;
                 }
+            };
 
-                std::vector<double> lstats_sums;
-                std::vector<double> num_diffs;
-                std::vector<double> log_likelihood_num_out_sums;
-                lstats_sums.reserve(lstats.size() + 1);
-                num_diffs.reserve(lstats.size() + 1);
-                log_likelihood_num_out_sums.reserve(lstats.size() + 1);
-                lstats_sums.push_back(0);
-                num_diffs.push_back(0);
-                log_likelihood_num_out_sums.push_back(0);
-                std::partial_sum(lstats.begin(), lstats.end(), std::back_inserter(lstats_sums));
-                std::partial_sum(log_likelihood_num_ins.begin(), log_likelihood_num_ins.end(), std::back_inserter(num_diffs));
-                std::partial_sum(log_likelihood_num_outs.begin(), log_likelihood_num_outs.end(), std::back_inserter(log_likelihood_num_out_sums));
-                for (size_t i = 0; i < log_likelihood_num_outs.size(); ++i) {
-                    num_diffs[i] -= log_likelihood_num_outs[i];
+            if (ny == 1) {
+                if (std::all_of(in_counts.begin(), in_counts.end(), [&](double c) { return c > out_counts[0]; })) {
+                    add_to(indicator_in);
+                } else if (std::all_of(in_counts.begin(), in_counts.end(), [&](double c) { return c < out_counts[0]; })) {
+                    add_to(indicator_out);
                 }
+            } else if (nx == 1) {
+                if (std::all_of(out_counts.begin(), out_counts.end(), [&](double c) { return c > in_counts[0]; })) {
+                    add_to(indicator_out);
+                } else if (std::all_of(out_counts.begin(), out_counts.end(), [&](double c) { return c < in_counts[0]; })) {
+                    add_to(indicator_in);
+                }
+            } else {
+                std::vector<double> c;
+                c.reserve(in_counts.size() + out_counts.size());
+                std::copy(in_counts.begin(), in_counts.end(), std::back_inserter(c));
+                std::copy(out_counts.begin(), out_counts.end(), std::back_inserter(c));
+                auto [rankc, countsc] = rankdata(c);
+
+                double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
+                double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
+
+                auto [rankx, countsx] = rankdata(in_counts);
+                auto [ranky, countsy] = rankdata(out_counts);
+                double rankx_mean = boost::math::statistics::mean(rankx.begin(), rankx.end());
+                double ranky_mean = boost::math::statistics::mean(ranky.begin(), ranky.end());
+
+                double Sx = 0;
+                for (size_t i = 0; i < nx; ++i) {
+                    Sx += std::pow(rankc[i] - rankx[i] - rankcx_mean + rankx_mean, 2.0);
+                }
+                Sx /= nx - 1;
+                double Sy = 0;
+                for (size_t i = 0; i < ny; ++i) {
+                    Sy += std::pow(rankc[nx + i] - ranky[i] - rankcy_mean + ranky_mean, 2.0);
+                }
+                Sy /= ny - 1;
 
-                double lstat_best = lstats_sums.back();
-                auto begin_best = lstats_sums.begin();
-                auto end_best = lstats_sums.end() - 1;
-                // for (auto begin = lstats_sums.begin(); begin < lstats_sums.end(); ++begin) {
-                //     for (auto end = begin + 1; end < lstats_sums.end(); ++end) {
-                //         if (*end - *begin > lstat_best) {
-                //             lstat_best = *end - *begin;
-                //             begin_best = begin;
-                //             end_best = end;
-                //         }
-                //     }
-                // }
+                double wbfn = (rankcy_mean - rankcx_mean) * nx * ny;
+                wbfn /= std::sqrt(Sx * nx + Sy * ny) * (nx + ny);
 
-                boost::math::chi_squared dist(2*(end_best - begin_best) - 1);
-                if (lstat_best > 0 && boost::math::cdf(boost::math::complement(dist, lstat_best)) * num_unitigs < 0.05) {
-                    size_t i_begin = begin_best - lstats_sums.begin();
-                    size_t i_end = end_best - lstats_sums.begin();
-                    double num_diff = num_diffs[i_end] - num_diffs[i_begin];
-                    auto &indicator = num_diff > 0 ? indicator_in : indicator_out;
-                    for (size_t i = i_begin; i < i_end; ++i) {
-                        indicator[path[i]] = true;
+                double df_numer = std::pow(Sx * nx + Sy * ny, 2.0);
+                double df_denom = std::pow(Sx * nx, 2.0) / (nx - 1) + std::pow(Sy * ny, 2.0) / (ny - 1);
+                double df = df_numer / df_denom;
+
+                if (df_denom == 0) {
+                    if (*std::min_element(ranky.begin(), ranky.end()) > *std::max_element(rankx.begin(), rankx.end())) {
+                        add_to(indicator_in);
+                    } else if (*std::min_element(rankx.begin(), rankx.end()) > *std::max_element(ranky.begin(), ranky.end())) {
+                        add_to(indicator_out);
                     }
+                } else {
+                    boost::math::students_t dist(df);
+                    double p = boost::math::cdf(dist, wbfn);
+                    double p1 = boost::math::cdf(boost::math::complement(dist, wbfn));
+                    double p2 = 2 * std::min(p, p1);
+
+                    if (p2 * num_tests < 0.05)
+                        add_to(rankcy_mean > rankcx_mean ? indicator_in : indicator_out);
                 }
             }
         });
     }
 
+    // common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_unitigs);
+
+    // if (config.test_type == "nonparametric") {
+
+    // } else {
+    //     graph_ptr->call_unitigs([&](const auto &, const auto &path) {
+    //         if (path.size() == 1)
+    //             return;
+
+    //         if (config.test_type == "zmp") {
+    //             std::vector<double> in_counts;
+    //             std::vector<double> out_counts;
+    //             for (node_index node : path) {
+    //                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+    //                 for (size_t j = 0; j < columns_all.size(); ++j) {
+    //                     auto &bucket = groups[j] ? out_counts : in_counts;
+    //                     const auto &col = *columns_all[j];
+    //                     if (uint64_t rk = col.conditional_rank1(row_i)) {
+    //                         uint64_t val = (*column_values_all[j])[rk - 1];
+    //                         if (val < max_val) {
+    //                             bucket.push_back(static_cast<double>(val) / medians[j]);
+    //                         } else {
+    //                             bucket.push_back(0.0);
+    //                         }
+    //                     } else {
+    //                         bucket.push_back(0.0);
+    //                     }
+    //                 }
+    //             }
+
+    //             if (std::equal(in_counts.begin() + 1, in_counts.end(), in_counts.begin())
+    //                     && std::equal(out_counts.begin() + 1, out_counts.end(), out_counts.begin())
+    //                     && in_counts[0] == out_counts[0])
+    //                 return;
+
+    //             try {
+    //                 auto [t, p] = boost::math::statistics::two_sample_t_test(in_counts, out_counts);
+    //                 if (p * num_unitigs >= 0.05)
+    //                     return;
+
+    //                 auto &indicator = t > 0 ? indicator_in : indicator_out;
+    //                 for (node_index node : path) {
+    //                     indicator[node] = true;
+    //                 }
+    //             } catch (...) {
+    //                 common::logger->trace("f\t{}\t{}",fmt::join(in_counts, ","),fmt::join(out_counts,","));
+    //                 throw std::runtime_error("f");
+    //             }
+    //         } else if (config.test_type == "likelihoodratio_unitig") {
+    //             std::vector<double> log_likelihood_num_ins;
+    //             std::vector<double> log_likelihood_num_outs;
+    //             std::vector<double> log_likelihood_denoms;
+    //             log_likelihood_num_ins.reserve(path.size());
+    //             log_likelihood_num_outs.reserve(path.size());
+    //             log_likelihood_denoms.reserve(path.size());
+    //             for (node_index node : path) {
+    //                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+    //                 double in_sum = 0;
+    //                 double out_sum = 0;
+    //                 for (size_t j = 0; j < columns_all.size(); ++j) {
+    //                     const auto &col = *columns_all[j];
+    //                     const auto &col_values = *column_values_all[j];
+    //                     uint64_t val = 0;
+    //                     if (uint64_t rk = col.conditional_rank1(row_i)) {
+    //                         val = col_values[rk - 1];
+    //                         if (val >= max_val)
+    //                             val = 0;
+    //                     }
+
+    //                     if (groups[j]) {
+    //                         out_sum += val;
+    //                     } else {
+    //                         in_sum += val;
+    //                     }
+    //                 }
+
+    //                 log_likelihood_num_ins.emplace_back((log(static_cast<double>(in_sum)) - 1) * in_sum);
+    //                 log_likelihood_num_outs.emplace_back((log(static_cast<double>(out_sum)) - 1) * out_sum);
+    //                 double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
+
+    //                 double mean_denom_in = theta * in_kmers;
+    //                 double mean_denom_out = theta * out_kmers;
+    //                 log_likelihood_denoms.emplace_back(log(mean_denom_in) * in_sum - mean_denom_in + log(mean_denom_out) * out_sum - mean_denom_out);
+    //             }
+
+    //             std::vector<double> lstats;
+    //             lstats.reserve(log_likelihood_num_ins.size());
+    //             for (size_t i = 0; i < log_likelihood_num_ins.size(); ++i) {
+    //                 lstats.emplace_back(2 * (log_likelihood_num_ins[i] + log_likelihood_num_outs[i] - log_likelihood_denoms[i]));
+    //             }
+
+    //             std::vector<double> lstats_sums;
+    //             std::vector<double> num_diffs;
+    //             std::vector<double> log_likelihood_num_out_sums;
+    //             lstats_sums.reserve(lstats.size() + 1);
+    //             num_diffs.reserve(lstats.size() + 1);
+    //             log_likelihood_num_out_sums.reserve(lstats.size() + 1);
+    //             lstats_sums.push_back(0);
+    //             num_diffs.push_back(0);
+    //             log_likelihood_num_out_sums.push_back(0);
+    //             std::partial_sum(lstats.begin(), lstats.end(), std::back_inserter(lstats_sums));
+    //             std::partial_sum(log_likelihood_num_ins.begin(), log_likelihood_num_ins.end(), std::back_inserter(num_diffs));
+    //             std::partial_sum(log_likelihood_num_outs.begin(), log_likelihood_num_outs.end(), std::back_inserter(log_likelihood_num_out_sums));
+    //             for (size_t i = 0; i < log_likelihood_num_outs.size(); ++i) {
+    //                 num_diffs[i] -= log_likelihood_num_outs[i];
+    //             }
+
+    //             double lstat_best = lstats_sums.back();
+    //             auto begin_best = lstats_sums.begin();
+    //             auto end_best = lstats_sums.end() - 1;
+    //             // for (auto begin = lstats_sums.begin(); begin < lstats_sums.end(); ++begin) {
+    //             //     for (auto end = begin + 1; end < lstats_sums.end(); ++end) {
+    //             //         if (*end - *begin > lstat_best) {
+    //             //             lstat_best = *end - *begin;
+    //             //             begin_best = begin;
+    //             //             end_best = end;
+    //             //         }
+    //             //     }
+    //             // }
+
+    //             boost::math::chi_squared dist(2*(end_best - begin_best) - 1);
+    //             if (lstat_best > 0 && boost::math::cdf(boost::math::complement(dist, lstat_best)) * num_unitigs < 0.05) {
+    //                 size_t i_begin = begin_best - lstats_sums.begin();
+    //                 size_t i_end = end_best - lstats_sums.begin();
+    //                 double num_diff = num_diffs[i_end] - num_diffs[i_begin];
+    //                 auto &indicator = num_diff > 0 ? indicator_in : indicator_out;
+    //                 for (size_t i = i_begin; i < i_end; ++i) {
+    //                     indicator[path[i]] = true;
+    //                 }
+    //             }
+    //         }
+    //     });
+    // }
+
     auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
         graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
@@ -720,11 +1037,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
     );
 
-    if (!is_primary)
+    // if (!is_primary)
         return std::make_pair(masked_graph_in, masked_graph_out);
 
-    return std::make_pair(std::make_shared<CanonicalDBG>(masked_graph_in),
-                          std::make_shared<CanonicalDBG>(masked_graph_out));
+    // return std::make_pair(std::make_shared<CanonicalDBG>(masked_graph_in),
+    //                       std::make_shared<CanonicalDBG>(masked_graph_out));
 
     // sdsl::bit_vector indicator(max_index + 1, false);
     // std::vector<bool> groups;
diff --git a/metagraph/src/graph/representation/base/sequence_graph.cpp b/metagraph/src/graph/representation/base/sequence_graph.cpp
index b91e7431b2..c366d742fe 100644
--- a/metagraph/src/graph/representation/base/sequence_graph.cpp
+++ b/metagraph/src/graph/representation/base/sequence_graph.cpp
@@ -202,7 +202,7 @@ void call_sequences_from(const DeBruijnGraph &graph,
                             }
                         }
 
-            double next_likelihood = 0; // start with a likelihood of 0, s.t. only unitigs are added with a better Likelihood Ratio.
+            // double next_likelihood = 0; // start with a likelihood of 0, s.t. only unitigs are added with a better Likelihood Ratio.
             for (const auto &[next, c] : targets) { // If there are multiple descendents.
                 if (!(*visited)[next]){ // the node was not visited yet.
                     if (!(*discovered)[next]) { // The node was not encountered before.
@@ -210,18 +210,19 @@ void call_sequences_from(const DeBruijnGraph &graph,
                         queue.push_back(next); // The queue grows a bit faster compared to the original case.
                         // TODO: make sure that the selected node is not added to the queue --> problems if run on  multiple threads.
                     }
-                    if (!call_unitigs // In the unitig mode (i.e. if call_unitigs is true), simply the unitigs must be returned, rather than longer contigs
-                        && downcasted_graph // TODO feature is temporary
-                        && downcasted_graph->likelihood_ratios[next] > next_likelihood){ // If the likelihood of this unitig is better than the current likelihood
-                        next_likelihood = downcasted_graph->likelihood_ratios[next];
-                        next_node = next;
-                        next_c = c;
-                    }
+                    std::ignore = downcasted_graph;
+                    // if (!call_unitigs // In the unitig mode (i.e. if call_unitigs is true), simply the unitigs must be returned, rather than longer contigs
+                    //     && downcasted_graph // TODO feature is temporary
+                    //     && downcasted_graph->likelihood_ratios[next] > next_likelihood){ // If the likelihood of this unitig is better than the current likelihood
+                    //     next_likelihood = downcasted_graph->likelihood_ratios[next];
+                    //     next_node = next;
+                    //     next_c = c;
+                    // }
                 }
             }
 
-            if (next_node != next_node_original)
-                std::cout << "different node selected compared to original method " <<std::flush;
+            // if (next_node != next_node_original)
+            //     std::cout << "different node selected compared to original method " <<std::flush;
             if (next_node == DeBruijnGraph::npos)
                 break;
             else {
diff --git a/metagraph/src/graph/representation/masked_graph.cpp b/metagraph/src/graph/representation/masked_graph.cpp
index 72dab44601..3494247608 100644
--- a/metagraph/src/graph/representation/masked_graph.cpp
+++ b/metagraph/src/graph/representation/masked_graph.cpp
@@ -107,21 +107,21 @@ bit_vector_stat get_boss_mask(const DBGSuccinct &dbg_succ,
 void MaskedDeBruijnGraph::call_sequences(const CallPath &callback,
                                          size_t num_threads,
                                          bool kmers_in_single_form) const {
-//    if (auto *dbg_succ = dynamic_cast<const DBGSuccinct*>(graph_.get())) {
-//            bit_vector_stat mask = get_boss_mask(*dbg_succ, *kmers_in_graph_,
-//                                             only_valid_nodes_in_mask_);
-//
-//        dbg_succ->get_boss().call_sequences([&](std::string&& sequence, auto&& path) {
-//            for (auto &node : path) {
-//                node = dbg_succ->boss_to_kmer_index(node);
-//            }
-//            callback(sequence, path);
-//
-//        }, num_threads, kmers_in_single_form, &mask);
-//
-//    } else {
+   if (auto *dbg_succ = dynamic_cast<const DBGSuccinct*>(graph_.get())) {
+           bit_vector_stat mask = get_boss_mask(*dbg_succ, *kmers_in_graph_,
+                                            only_valid_nodes_in_mask_);
+
+       dbg_succ->get_boss().call_sequences([&](std::string&& sequence, auto&& path) {
+           for (auto &node : path) {
+               node = dbg_succ->boss_to_kmer_index(node);
+           }
+           callback(sequence, path);
+
+       }, num_threads, kmers_in_single_form, &mask);
+
+   } else {
         DeBruijnGraph::call_sequences(callback, num_threads, kmers_in_single_form);
-    //} Myrthe temporaryly mute
+    } //Myrthe temporaryly mute
 }
 
 void MaskedDeBruijnGraph::call_unitigs(const CallPath &callback,

From 9c85ca0585b61e737a9151bb24fa388e91011697 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 13 Mar 2024 14:19:38 +0100
Subject: [PATCH 088/410] test fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 103 +++++++-----------
 1 file changed, 38 insertions(+), 65 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9d2377155c..3a59668012 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -627,6 +627,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             size_t nx = in_counts.size();
             size_t ny = out_counts.size();
+            double p2 = 0.0;
 
             std::vector<double> c;
             c.reserve(in_counts.size() + out_counts.size());
@@ -634,22 +635,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             std::copy(out_counts.begin(), out_counts.end(), std::back_inserter(c));
             auto [rankc, countsc] = rankdata(c);
 
-            if (ny == 1) {
-                if (std::all_of(in_counts.begin(), in_counts.end(), [&](double c) { return c > out_counts[0]; })) {
-                    indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
-                } else if (std::all_of(in_counts.begin(), in_counts.end(), [&](double c) { return c < out_counts[0]; })) {
-                    indicator_out[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
-                }
-            } else if (nx == 1) {
-                if (std::all_of(out_counts.begin(), out_counts.end(), [&](double c) { return c > in_counts[0]; })) {
-                    indicator_out[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
-                } else if (std::all_of(out_counts.begin(), out_counts.end(), [&](double c) { return c < in_counts[0]; })) {
-                    indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
-                }
-            } else {
-                double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
-                double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
+            double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
+            double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
 
+            if (nx > 1 && ny > 1) {
                 auto [rankx, countsx] = rankdata(in_counts);
                 auto [ranky, countsy] = rankdata(out_counts);
                 double rankx_mean = boost::math::statistics::mean(rankx.begin(), rankx.end());
@@ -671,24 +660,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 double df_numer = std::pow(Sx * nx + Sy * ny, 2.0);
                 double df_denom = std::pow(Sx * nx, 2.0) / (nx - 1) + std::pow(Sy * ny, 2.0) / (ny - 1);
-                double df = df_numer / df_denom;
 
-                if (df_denom == 0) {
-                    if (*std::min_element(ranky.begin(), ranky.end()) > *std::max_element(rankx.begin(), rankx.end())) {
-                        indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
-                    } else if (*std::min_element(rankx.begin(), rankx.end()) > *std::max_element(ranky.begin(), ranky.end())) {
-                        indicator_out[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
-                    }
-                } else {
+                if (df_denom != 0) {
+                    double df = df_numer / df_denom;
                     boost::math::students_t dist(df);
                     double p = boost::math::cdf(dist, wbfn);
                     double p1 = boost::math::cdf(boost::math::complement(dist, wbfn));
-                    double p2 = 2 * std::min(p, p1);
+                    p2 = 2 * std::min(p, p1);
+                }
+            }
 
-                    if (p2 * num_tests < 0.05) {
-                        auto &indicator = rankcy_mean > rankcx_mean ? indicator_in : indicator_out;
-                        indicator[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
-                    }
+            if (p2 * num_tests < 0.05) {
+                if (rankcy_mean > rankcx_mean) {
+                    indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
+                } else if (rankcy_mean < rankcx_mean) {
+                    indicator_out[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
                 }
             }
 
@@ -822,35 +808,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             size_t nx = in_counts.size();
             size_t ny = out_counts.size();
+            double p2 = 0.0;
 
-            auto add_to = [&](auto &indicator) {
-                for (node_index node : path) {
-                    indicator[node] = true;
-                }
-            };
-
-            if (ny == 1) {
-                if (std::all_of(in_counts.begin(), in_counts.end(), [&](double c) { return c > out_counts[0]; })) {
-                    add_to(indicator_in);
-                } else if (std::all_of(in_counts.begin(), in_counts.end(), [&](double c) { return c < out_counts[0]; })) {
-                    add_to(indicator_out);
-                }
-            } else if (nx == 1) {
-                if (std::all_of(out_counts.begin(), out_counts.end(), [&](double c) { return c > in_counts[0]; })) {
-                    add_to(indicator_out);
-                } else if (std::all_of(out_counts.begin(), out_counts.end(), [&](double c) { return c < in_counts[0]; })) {
-                    add_to(indicator_in);
-                }
-            } else {
-                std::vector<double> c;
-                c.reserve(in_counts.size() + out_counts.size());
-                std::copy(in_counts.begin(), in_counts.end(), std::back_inserter(c));
-                std::copy(out_counts.begin(), out_counts.end(), std::back_inserter(c));
-                auto [rankc, countsc] = rankdata(c);
+            std::vector<double> c;
+            c.reserve(in_counts.size() + out_counts.size());
+            std::copy(in_counts.begin(), in_counts.end(), std::back_inserter(c));
+            std::copy(out_counts.begin(), out_counts.end(), std::back_inserter(c));
+            auto [rankc, countsc] = rankdata(c);
 
-                double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
-                double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
+            double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
+            double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
 
+            if (nx > 1 && ny > 1) {
                 auto [rankx, countsx] = rankdata(in_counts);
                 auto [ranky, countsy] = rankdata(out_counts);
                 double rankx_mean = boost::math::statistics::mean(rankx.begin(), rankx.end());
@@ -872,22 +841,26 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 double df_numer = std::pow(Sx * nx + Sy * ny, 2.0);
                 double df_denom = std::pow(Sx * nx, 2.0) / (nx - 1) + std::pow(Sy * ny, 2.0) / (ny - 1);
-                double df = df_numer / df_denom;
 
-                if (df_denom == 0) {
-                    if (*std::min_element(ranky.begin(), ranky.end()) > *std::max_element(rankx.begin(), rankx.end())) {
-                        add_to(indicator_in);
-                    } else if (*std::min_element(rankx.begin(), rankx.end()) > *std::max_element(ranky.begin(), ranky.end())) {
-                        add_to(indicator_out);
-                    }
-                } else {
+                if (df_denom != 0) {
+                    double df = df_numer / df_denom;
                     boost::math::students_t dist(df);
                     double p = boost::math::cdf(dist, wbfn);
                     double p1 = boost::math::cdf(boost::math::complement(dist, wbfn));
-                    double p2 = 2 * std::min(p, p1);
+                    p2 = 2 * std::min(p, p1);
+                }
+            }
 
-                    if (p2 * num_tests < 0.05)
-                        add_to(rankcy_mean > rankcx_mean ? indicator_in : indicator_out);
+            if (p2 * num_tests < 0.05) {
+                auto add_to = [&](auto &indicator) {
+                    for (node_index node : path) {
+                        indicator[node] = true;
+                    }
+                };
+                if (rankcy_mean > rankcx_mean) {
+                    add_to(indicator_in);
+                } else if (rankcy_mean < rankcx_mean) {
+                    add_to(indicator_out);
                 }
             }
         });

From 4a8e844da04808257b68c65bb9708a86ca64c104 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 5 Apr 2024 17:48:52 +0200
Subject: [PATCH 089/410] new tests

---
 metagraph/src/cli/assemble.cpp                |   50 +-
 metagraph/src/cli/clean.cpp                   |    3 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 1244 +++++------------
 metagraph/src/seq_io/sequence_io.cpp          |    3 +-
 4 files changed, 399 insertions(+), 901 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index fb9deaffee..1a16e581e6 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -247,26 +247,50 @@ int assemble(Config *config) {
             utils::remove_suffix(config->outfbase, ".gz", ".fasta") + ".fasta.gz"
         );
 
+        std::filesystem::remove(config->outfbase + ".loglikelihoodratio");
+
         auto graph_callback = [&](const graph::DeBruijnGraph &graph, const std::string &header) {
-            seq_io::FastaWriter writer(config->outfbase, header,
+            const auto *masked_graph = dynamic_cast<const graph::MaskedDeBruijnGraph*>(&graph);
+            seq_io::FastaWriter writer(config->outfbase,
+                                       header,
                                        config->enumerate_out_sequences,
                                        num_threads > 1, /* async write */
                                        "a" /* append mode */);
+            std::ofstream fout(config->outfbase + ".loglikelihoodratio", ios::out | ios::app);
 
             if (config->unitigs || config->min_tip_size > 1) {
-                graph.call_unitigs([&](const std::string &unitig, auto&&) {
-                                       std::lock_guard<std::mutex> lock(write_mutex);
-                                       writer.write(unitig);
-                                   },
-                                   num_threads, config->min_tip_size,
-                                   config->kmers_in_single_form);
+                graph.call_unitigs(
+                    [&](const std::string &unitig, auto&& path) {
+                        std::vector<double> lstats;
+                        lstats.reserve(path.size());
+                        for (auto node : path) {
+                            lstats.emplace_back(masked_graph->likelihood_ratios[node]);
+                        }
+                        std::lock_guard<std::mutex> lock(write_mutex);
+                        writer.write(unitig);
+                        for (double l : lstats) {
+                            fout << l << "\n";
+                        }
+                    },
+                    num_threads, config->min_tip_size,
+                    config->kmers_in_single_form
+                );
             } else {
-                //assert(std::accumulate(graph.lrs.begin(), graph.lrs.end(), 0) > 0);
-                graph.call_sequences([&](const std::string &seq, auto&&) {
-                                         std::lock_guard<std::mutex> lock(write_mutex);
-                                         writer.write(seq);
-                                     },
-                                     num_threads, config->kmers_in_single_form);
+                graph.call_sequences(
+                    [&](const std::string &seq, auto&& path) {
+                        std::vector<double> lstats;
+                        lstats.reserve(path.size());
+                        for (auto node : path) {
+                            lstats.emplace_back(masked_graph->likelihood_ratios[node]);
+                        }
+                        std::lock_guard<std::mutex> lock(write_mutex);
+                        writer.write(seq);
+                        for (double l : lstats) {
+                            fout << l << "\n";
+                        }
+                    },
+                    num_threads, config->kmers_in_single_form
+                );
             }
         };
 
diff --git a/metagraph/src/cli/clean.cpp b/metagraph/src/cli/clean.cpp
index 26d877c6aa..cf923abee9 100644
--- a/metagraph/src/cli/clean.cpp
+++ b/metagraph/src/cli/clean.cpp
@@ -148,7 +148,8 @@ int clean_graph(Config *config) {
             graph->call_unitigs(callback,
                                 num_threads,
                                 config->min_tip_size,
-                                graph->get_mode() == graph::DeBruijnGraph::CANONICAL);
+                                config->kmers_in_single_form);
+                                // graph->get_mode() == graph::DeBruijnGraph::CANONICAL);
 
         } else {
             graph->call_sequences(callback, num_threads,
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 3a59668012..6fca2b91dd 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1,7 +1,6 @@
 #include "annotated_graph_algorithm.hpp"
 
 #include <boost/math/distributions/chi_squared.hpp>
-#include <boost/math/statistics/t_test.hpp>
 #include <boost/math/statistics/univariate_statistics.hpp>
 
 #include "common/utils/string_utils.hpp"
@@ -30,6 +29,364 @@ typedef AnnotatedDBG::Annotator Annotator;
 typedef AnnotatedDBG::Annotator::Label Label;
 using Column = annot::matrix::BinaryMatrix::Column;
 
+std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
+mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                         const std::vector<std::string> &files,
+                         const tsl::hopscotch_set<Label> &labels_in,
+                         const tsl::hopscotch_set<Label> &labels_out,
+                         const DifferentialAssemblyConfig &config,
+                         size_t num_threads,
+                         size_t num_parallel_files) {
+    bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
+    common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
+    common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
+    common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
+
+    size_t max_index = graph_ptr->max_index();
+    std::vector<double> medians;
+    std::vector<uint64_t> max_vals;
+    std::vector<size_t> n_kmers;
+    std::vector<uint64_t> sums;
+    std::vector<uint64_t> sums_of_squares;
+    std::vector<std::unique_ptr<bit_vector>> columns_all;
+
+    using ValuesContainer = sdsl::int_vector<>;
+    std::vector<std::unique_ptr<ValuesContainer>> column_values_all;
+    std::vector<bool> groups;
+    uint8_t max_width = 0;
+    uint64_t max_val = 0;
+    uint64_t max_observed_value = 0;
+
+    annot::ColumnCompressed<>::load_columns_and_values(files,
+        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
+            groups.push_back(labels_out.count(label));
+
+            max_width = std::max(max_width, column_values.width());
+            max_val = sdsl::bits::lo_set[max_width];
+
+            // median calculation modifies the underlying vector, so we have to make a copy
+            std::vector<uint8_t> vals;
+            vals.reserve(column_values.size());
+            std::copy(column_values.begin(), column_values.end(), std::back_inserter(vals));
+            medians.push_back(boost::math::statistics::median(vals));
+            max_vals.push_back(max_val);
+
+            uint64_t sum = std::accumulate(column_values.begin(), column_values.end(),
+                                           uint64_t(0));
+            uint64_t sum_of_squares = std::accumulate(
+                column_values.begin(), column_values.end(), uint64_t(0),
+                [&](auto&& prev, uint64_t cur) { return std::move(prev) + cur * cur; }
+            );
+            size_t n = column_values.size();
+
+            sums.push_back(sum);
+            sums_of_squares.push_back(sum_of_squares);
+            n_kmers.push_back(n);
+
+            columns_all.emplace_back(std::move(column));
+            column_values_all.push_back(std::make_unique<ValuesContainer>(std::move(column_values)));
+
+            common::logger->trace("{}: {}\tMean: {}\tCutoff: {}",
+                                  label, groups.back() ? "out": "in",
+                                  static_cast<double>(sum) / n,
+                                  max_vals.back());
+        }
+    );
+
+    common::logger->trace("Max width: {}\tMax val: {}\tMax obs. val: {}", max_width, max_val, max_observed_value);
+
+    double in_kmers = 0;
+    double out_kmers = 0;
+    for (size_t j = 0; j < sums.size(); ++j) {
+        if (groups[j]) {
+            out_kmers += sums[j];
+        } else {
+            in_kmers += sums[j];
+        }
+    }
+    double total_kmers = in_kmers + out_kmers;
+
+    sdsl::bit_vector indicator_in(max_index + 1, false);
+    sdsl::bit_vector indicator_out(max_index + 1, false);
+
+    std::vector<double> likelihoods;
+    likelihoods.reserve(max_index + 1);
+    likelihoods.emplace_back(0);
+
+    if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig" || config.test_type == "nbinom" || config.test_type == "nbinom_unitig") {
+        boost::math::chi_squared dist(1);
+        sdsl::int_vector<> counts(columns_all[0]->size() * 2, 0, 32);
+        sdsl::int_vector<> nnzero(columns_all[0]->size() * 2, 0, 32);
+        sdsl::int_vector<> foundtotal(columns_all[0]->size() * 2, 0, 64);
+        std::vector<double> foundlogtotal(columns_all[0]->size() * 2);
+        std::vector<double> stuff(columns_all[0]->size() * 2);
+        for (size_t j = 0; j < groups.size(); ++j) {
+            size_t offset = groups[j];
+            const auto &col = *columns_all[j];
+            const auto &col_vals = *column_values_all[j];
+            uint64_t rank = 0;
+            col.call_ones([&](uint64_t row_i) {
+                uint64_t val = col_vals[rank++];
+                counts[row_i * 2 + offset] += val;
+                ++nnzero[row_i * 2 + offset];
+                foundtotal[row_i * 2 + offset] += sums[j];
+                foundlogtotal[row_i * 2 + offset] += log(static_cast<double>(sums[j]));
+                if (val > 1) {
+                    stuff[row_i * 2 + offset] += (val - 1) * (log(static_cast<double>(val - 1)) - 1)
+                                                - lgamma(static_cast<double>(val + 1));
+                }
+            });
+        }
+
+        size_t num_tests = 0;
+        ProgressBar progress(columns_all[0]->size(),
+                             "Testing k-mers",
+                             std::cerr, !common::get_verbose());
+        for (size_t i = 0; i < counts.size(); i += 2) {
+            ++progress;
+            double in_sum = static_cast<uint64_t>(counts[i]);
+            double out_sum = static_cast<uint64_t>(counts[i + 1]);
+
+            if (in_sum == 0 && out_sum == 0) {
+                likelihoods.emplace_back(0);
+                continue;
+            }
+
+            size_t in_nnzero = nnzero[i];
+            size_t out_nnzero = nnzero[i + 1];
+
+            if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig") {
+                double log_likelihood_in = in_sum > 0 ? -in_sum + in_sum * log(in_sum) : 0.0;
+                double log_likelihood_out = out_sum > 0 ? -out_sum + out_sum * log(out_sum) : 0.0;
+                double theta = (in_sum + out_sum) / total_kmers;
+
+                double mean_denom_in = theta * in_kmers;
+                double mean_denom_out = theta * out_kmers;
+                double log_likelihood_null = mean_denom_in > 0 && in_sum >= 0 ? -mean_denom_in + in_sum * log(mean_denom_in) : 0.0;
+                log_likelihood_null += mean_denom_out > 0 && out_sum >= 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
+
+                double lstat = log_likelihood_in + log_likelihood_out - log_likelihood_null;
+                num_tests += (lstat > 0);
+                likelihoods.emplace_back(lstat);
+            } else {
+                // generalized poisson
+                double log_likelihood_in = 0;
+                if (in_sum > 0) {
+                    size_t in_notfoundtotal = in_kmers - foundtotal[i];
+                    double rho_in = static_cast<double>(in_nnzero) / in_notfoundtotal;
+                    log_likelihood_in = rho_in * in_notfoundtotal + rho_in * in_nnzero + foundlogtotal[i] + stuff[i];
+                } else if (in_nnzero == labels_in.size()) {
+                    log_likelihood_in = stuff[i] + foundlogtotal[i];
+                }
+
+                double log_likelihood_out = 0;
+                if (out_sum > 0) {
+                    size_t out_notfoundtotal = out_kmers - foundtotal[i + 1];
+                    double rho_out = static_cast<double>(out_nnzero) / out_notfoundtotal;
+                    log_likelihood_out = rho_out * out_notfoundtotal + rho_out * out_nnzero + foundlogtotal[i + 1] + stuff[i + 1];
+                } else if (out_nnzero == labels_out.size()) {
+                    log_likelihood_out = stuff[i + 1] + foundlogtotal[i + 1];
+                }
+
+                double log_likelihood_null = 0;
+                if (in_sum > 0 && out_sum > 0) {
+                    log_likelihood_null = stuff[i] + stuff[i + 1] + foundlogtotal[i] + foundlogtotal[i + 1];
+                } else {
+                    size_t null_notfoundtotal = total_kmers - foundtotal[i] - foundtotal[i + 1];
+                    double rho_null = static_cast<double>(in_nnzero + out_nnzero) / null_notfoundtotal;
+                    log_likelihood_null = rho_null * null_notfoundtotal + rho_null * (in_nnzero + out_nnzero) + foundlogtotal[i] + foundlogtotal[i + 1] + stuff[i] + stuff[i + 1];
+                }
+
+                double lstat = log_likelihood_in + log_likelihood_out - log_likelihood_null;
+                num_tests += (lstat > 0);
+                likelihoods.emplace_back(lstat);
+            }
+        }
+
+        if (config.test_type == "likelihoodratio_unitig" || config.test_type == "nbinom_unitig") {
+            num_tests = 0;
+            graph_ptr->call_unitigs([&](const auto &, const auto &path) {
+                for (node_index node : path) {
+                    if (likelihoods[node] > 0) {
+                        ++num_tests;
+                        break;
+                    }
+                }
+            });
+        }
+
+        for (node_index node = 0; node < likelihoods.size(); ++node) {
+            double lstat = likelihoods[node];
+            if (lstat > 0) {
+                double pval = boost::math::cdf(boost::math::complement(dist, lstat * 2.0));
+                if (pval * num_tests < 0.05) {
+                    uint64_t i = AnnotatedDBG::graph_to_anno_index(node);
+                    double in_sum = static_cast<uint64_t>(counts[i * 2]);
+                    double out_sum = static_cast<uint64_t>(counts[i * 2 + 1]);
+                    double scaled_outsum = out_sum / out_kmers * in_kmers;
+                    if (scaled_outsum < in_sum) {
+                        indicator_in[node] = true;
+                    } else if (scaled_outsum > in_sum) {
+                        indicator_out[node] = true;
+                    }
+                }
+            }
+        }
+    } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u") {
+        size_t num_tests = 0;
+        graph_ptr->call_unitigs([&](const auto &, const auto &path) {
+            for (node_index node : path) {
+                if (likelihoods[node] > 0) {
+                    ++num_tests;
+                    break;
+                }
+            }
+        });
+        common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_tests);
+        uint64_t row_i = 0;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                         std::unique_ptr<ValuesContainer>,
+                         std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
+                                                                    [&](const auto &row) {
+            if (row.empty()) {
+                likelihoods.emplace_back(0);
+                ++row_i;
+                return;
+            }
+
+            std::vector<double> in_counts;
+            in_counts.reserve(labels_in.size());
+            std::vector<double> out_counts;
+            out_counts.reserve(labels_out.size());
+            for (const auto &[j, c] : row) {
+                auto &bucket = groups[j] ? out_counts : in_counts;
+                bucket.emplace_back(static_cast<double>(c) / medians[j]);
+            }
+            in_counts.resize(labels_in.size());
+            out_counts.resize(labels_out.size());
+
+            auto rankdata = [&](const std::vector<double> &c) {
+                auto c_p = c;
+                std::sort(c_p.begin(), c_p.end());
+                tsl::hopscotch_map<double, std::pair<size_t, size_t>> counts;
+                for (size_t i = 0; i < c_p.size(); ++i) {
+                    auto &[rank_sum, nranks] = counts[c_p[i]];
+                    rank_sum += i + 1;
+                    ++nranks;
+                }
+
+                std::vector<double> ranks;
+                ranks.reserve(c.size());
+                for (double v : c) {
+                    ranks.emplace_back(static_cast<double>(counts[v].first)/counts[v].second);
+                }
+
+                return std::make_pair(std::move(ranks), std::move(counts));
+            };
+
+            size_t nx = in_counts.size();
+            size_t ny = out_counts.size();
+            double p2 = 0.0;
+            double likelihood = std::numeric_limits<double>::max();
+
+            std::vector<double> c;
+            c.reserve(in_counts.size() + out_counts.size());
+            std::copy(in_counts.begin(), in_counts.end(), std::back_inserter(c));
+            std::copy(out_counts.begin(), out_counts.end(), std::back_inserter(c));
+            auto [rankc, countsc] = rankdata(c);
+            double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
+            double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
+
+            if (config.test_type == "nonparametric") {
+                if (nx > 1 && ny > 1) {
+                    auto [rankx, countsx] = rankdata(in_counts);
+                    auto [ranky, countsy] = rankdata(out_counts);
+                    double rankx_mean = boost::math::statistics::mean(rankx.begin(), rankx.end());
+                    double ranky_mean = boost::math::statistics::mean(ranky.begin(), ranky.end());
+
+                    double Sx = 0;
+                    for (size_t i = 0; i < nx; ++i) {
+                        Sx += std::pow(rankc[i] - rankx[i] - rankcx_mean + rankx_mean, 2.0);
+                    }
+                    Sx /= nx - 1;
+                    double Sy = 0;
+                    for (size_t i = 0; i < ny; ++i) {
+                        Sy += std::pow(rankc[nx + i] - ranky[i] - rankcy_mean + ranky_mean, 2.0);
+                    }
+                    Sy /= ny - 1;
+
+                    double wbfn = (rankcy_mean - rankcx_mean) * nx * ny;
+                    wbfn /= std::sqrt(Sx * nx + Sy * ny) * (nx + ny);
+
+                    double df_numer = std::pow(Sx * nx + Sy * ny, 2.0);
+                    double df_denom = std::pow(Sx * nx, 2.0) / (nx - 1) + std::pow(Sy * ny, 2.0) / (ny - 1);
+
+                    if (df_denom != 0) {
+                        double df = df_numer / df_denom;
+                        boost::math::students_t dist(df);
+                        double p = boost::math::cdf(dist, wbfn);
+                        double p1 = boost::math::cdf(boost::math::complement(dist, wbfn));
+                        p2 = 2 * std::min(p, p1);
+                        likelihood = abs(wbfn);
+                    }
+                }
+            } else {
+                // https://datatab.net/tutorial/mann-whitney-u-test
+                double corr = 0;
+                for (const auto &[val,rks] : countsc) {
+                    const auto &[rk, c] = rks;
+                    if (c > 0)
+                        corr += c * c * c - c;
+                }
+
+                double rankcx_sum = std::accumulate(rankc.begin(), rankc.begin() + nx, double(0.0));
+                double rankcy_sum = std::accumulate(rankc.begin() + nx, rankc.end(), double(0.0));
+                double u1 = nx * ny + static_cast<double>(nx * (nx + 1))/2 - rankcx_sum;
+                double u2 = nx * ny + static_cast<double>(ny * (ny + 1))/2 - rankcy_sum;
+                double u = std::min(u1, u2);
+                double mu = static_cast<double>(nx * nx) / 2;
+                double n = nx + ny;
+                double sd = sqrt(static_cast<double>(nx * ny) / n / (n - 1)) * sqrt((n * n * n - n - corr) / 12);
+
+                double z = abs((u - mu) / sd);
+                auto dist = boost::math::normal_distribution();
+                p2 = boost::math::cdf(boost::math::complement(dist, z)) * 2;
+                likelihood = z;
+            }
+
+            likelihoods.emplace_back(likelihood);
+
+            if (p2 * num_tests < 0.05) {
+                if (rankcx_mean > rankcy_mean) {
+                    indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
+                } else if (rankcx_mean < rankcy_mean) {
+                    indicator_out[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
+                }
+            }
+
+            ++row_i;
+        });
+    } else {
+        throw std::runtime_error("Test not implemented");
+    }
+
+    auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
+        graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
+        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+    );
+    masked_graph_in->likelihood_ratios = likelihoods;
+
+    auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
+        graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_out)), true,
+        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+    );
+    masked_graph_out->likelihood_ratios = likelihoods;
+
+    return std::make_pair(masked_graph_in, masked_graph_out);
+}
+
+
+
 typedef std::function<size_t()> LabelCountCallback;
 
 constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
@@ -452,891 +809,6 @@ mask_nodes_by_label(const AnnotatedDBG &anno_graph,
                                config, num_threads, {});
 }
 
-std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
-mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         const std::vector<std::string> &files,
-                         const tsl::hopscotch_set<Label> &labels_in,
-                         const tsl::hopscotch_set<Label> &labels_out,
-                         const DifferentialAssemblyConfig &config,
-                         size_t num_threads,
-                         size_t num_parallel_files) {
-    bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
-    common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
-    common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
-    common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
-
-    size_t max_index = graph_ptr->max_index();
-    std::vector<double> medians;
-    std::vector<size_t> n_kmers;
-    std::vector<uint64_t> sums;
-    std::vector<std::unique_ptr<bit_vector>> columns_all;
-
-    using ValuesContainer = sdsl::int_vector<>;
-    std::vector<std::unique_ptr<ValuesContainer>> column_values_all;
-    std::vector<bool> groups;
-    uint8_t max_width = 0;
-    uint64_t max_val = 0;
-    uint64_t max_observed_value = 0;
-
-    annot::ColumnCompressed<>::load_columns_and_values(files,
-        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
-            groups.push_back(labels_out.count(label));
-            common::logger->trace("{}: {}", label, groups.back() ? "out": "in");
-            columns_all.emplace_back(std::move(column));
-
-            max_width = std::max(max_width, column_values.width());
-            max_val = sdsl::bits::lo_set[max_width];
-
-            // median calculation modifies the underlying vector, so we have to make a copy
-            std::vector<uint8_t> vals;
-            vals.reserve(column_values.size());
-            std::copy(column_values.begin(), column_values.end(), std::back_inserter(vals));
-            medians.push_back(boost::math::statistics::median(vals));
-            if (medians.back() == max_val)
-                common::logger->warn("Column {}: Median == {}", label, max_val);
-
-            uint64_t sum = 0;
-            size_t n = 0;
-            for (uint64_t val : column_values) {
-                max_observed_value = std::max(max_observed_value, val);
-                if (val < max_val) {
-                    sum += val;
-                    ++n;
-                }
-            }
-            sums.push_back(sum);
-            n_kmers.push_back(n);
-
-            column_values_all.push_back(std::make_unique<ValuesContainer>(std::move(column_values)));
-        }
-    );
-
-    common::logger->trace("Max width: {}\tMax val: {}\tMax obs. val: {}", max_width, max_val, max_observed_value);
-
-    double in_kmers = 0;
-    double out_kmers = 0;
-    for (size_t j = 0; j < sums.size(); ++j) {
-        if (groups[j]) {
-            out_kmers += sums[j];
-        } else {
-            in_kmers += sums[j];
-        }
-    }
-    double total_kmers = in_kmers + out_kmers;
-
-    sdsl::bit_vector indicator_in(max_index + 1, false);
-    sdsl::bit_vector indicator_out(max_index + 1, false);
-
-    uint64_t num_unitigs = 0;
-    if (config.test_type != "likelihoodratio")
-        graph_ptr->call_unitigs([&](const auto &, const auto &) { ++num_unitigs; });
-
-    if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig") {
-        boost::math::chi_squared dist(1);
-        sdsl::int_vector<> counts(columns_all[0]->size() * 2, 0, 32);
-        size_t num_nonzeros = 0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            size_t offset = groups[j];
-            const auto &col = *columns_all[j];
-            const auto &col_vals = *column_values_all[j];
-            uint64_t rank = 0;
-            col.call_ones([&](uint64_t row_i) {
-                uint64_t val = col_vals[rank++];
-                if (val < max_val) {
-                    num_nonzeros += (counts[row_i * 2] == 0 && counts[row_i * 2 + 1] == 0);
-                    counts[row_i * 2 + offset] += val;
-                }
-            });
-        }
-
-        size_t num_tests = config.test_type == "likelihoodratio"
-            ? num_nonzeros : num_unitigs;
-
-        common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_tests);
-
-        for (size_t i = 0; i < counts.size(); i += 2) {
-            double in_sum = static_cast<uint64_t>(counts[i]);
-            double out_sum = static_cast<uint64_t>(counts[i + 1]);
-
-            double log_likelihood_num_in = in_sum > 0 ? in_sum * log(in_sum) - in_sum : 0.0;
-            double log_likelihood_num_out = out_sum > 0 ? out_sum * log(out_sum) - out_sum : 0.0;
-            double theta = (in_sum + out_sum) / total_kmers;
-
-            double mean_denom_in = theta * in_kmers;
-            double mean_denom_out = theta * out_kmers;
-            double log_likelihood_denom = log(mean_denom_in) * in_sum - mean_denom_in + log(mean_denom_out) * out_sum - mean_denom_out;
-
-            double lstat = log_likelihood_num_in + log_likelihood_num_out - log_likelihood_denom;
-            if (lstat > 0 && boost::math::cdf(boost::math::complement(dist, lstat * 2.0)) * num_tests < 0.05) {
-                node_index node = AnnotatedDBG::anno_to_graph_index(i / 2);
-                double scaled_outsum = out_sum / out_kmers * in_kmers;
-                if (scaled_outsum < in_sum) {
-                    indicator_in[node] = true;
-                } else if (scaled_outsum > in_sum) {
-                    indicator_out[node] = true;
-                }
-            }
-        }
-    } else if (config.test_type == "nonparametric") {
-        size_t num_tests = num_unitigs;
-        common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_tests);
-        uint64_t row_i = 0;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
-                                                                    [&](const auto &row) {
-            if (row.empty()) {
-                ++row_i;
-                return;
-            }
-
-            std::vector<double> in_counts;
-            in_counts.reserve(labels_in.size());
-            std::vector<double> out_counts;
-            out_counts.reserve(labels_out.size());
-            for (const auto &[j, c] : row) {
-                if (c < max_val) {
-                    auto &bucket = groups[j] ? out_counts : in_counts;
-                    bucket.emplace_back(static_cast<double>(c) / medians[j]);
-                }
-            }
-            in_counts.resize(labels_in.size());
-            out_counts.resize(labels_out.size());
-
-            auto rankdata = [&](const std::vector<double> &c) {
-                std::vector<std::pair<double, double>> c_p;
-                c_p.reserve(c.size());
-                for (size_t i = 0; i < c.size(); ++i) {
-                    c_p.emplace_back(c[i], i + 1);
-                }
-                std::sort(c_p.begin(), c_p.end());
-                tsl::hopscotch_map<double, std::pair<size_t, size_t>> counts;
-                for (const auto &[v, r] : c_p) {
-                    counts[v].first += r;
-                    ++counts[v].second;
-                }
-
-                std::vector<double> ranks;
-                ranks.reserve(c.size());
-                for (double v : c) {
-                    ranks.emplace_back(static_cast<double>(counts[v].first)/counts[v].second);
-                }
-
-                return std::make_pair(std::move(ranks), std::move(counts));
-            };
-
-            size_t nx = in_counts.size();
-            size_t ny = out_counts.size();
-            double p2 = 0.0;
-
-            std::vector<double> c;
-            c.reserve(in_counts.size() + out_counts.size());
-            std::copy(in_counts.begin(), in_counts.end(), std::back_inserter(c));
-            std::copy(out_counts.begin(), out_counts.end(), std::back_inserter(c));
-            auto [rankc, countsc] = rankdata(c);
-
-            double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
-            double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
-
-            if (nx > 1 && ny > 1) {
-                auto [rankx, countsx] = rankdata(in_counts);
-                auto [ranky, countsy] = rankdata(out_counts);
-                double rankx_mean = boost::math::statistics::mean(rankx.begin(), rankx.end());
-                double ranky_mean = boost::math::statistics::mean(ranky.begin(), ranky.end());
-
-                double Sx = 0;
-                for (size_t i = 0; i < nx; ++i) {
-                    Sx += std::pow(rankc[i] - rankx[i] - rankcx_mean + rankx_mean, 2.0);
-                }
-                Sx /= nx - 1;
-                double Sy = 0;
-                for (size_t i = 0; i < ny; ++i) {
-                    Sy += std::pow(rankc[nx + i] - ranky[i] - rankcy_mean + ranky_mean, 2.0);
-                }
-                Sy /= ny - 1;
-
-                double wbfn = (rankcy_mean - rankcx_mean) * nx * ny;
-                wbfn /= std::sqrt(Sx * nx + Sy * ny) * (nx + ny);
-
-                double df_numer = std::pow(Sx * nx + Sy * ny, 2.0);
-                double df_denom = std::pow(Sx * nx, 2.0) / (nx - 1) + std::pow(Sy * ny, 2.0) / (ny - 1);
-
-                if (df_denom != 0) {
-                    double df = df_numer / df_denom;
-                    boost::math::students_t dist(df);
-                    double p = boost::math::cdf(dist, wbfn);
-                    double p1 = boost::math::cdf(boost::math::complement(dist, wbfn));
-                    p2 = 2 * std::min(p, p1);
-                }
-            }
-
-            if (p2 * num_tests < 0.05) {
-                if (rankcy_mean > rankcx_mean) {
-                    indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
-                } else if (rankcy_mean < rankcx_mean) {
-                    indicator_out[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
-                }
-            }
-
-            ++row_i;
-        });
-    } else if (config.test_type == "likelihoodratio_perunitig") {
-        graph_ptr->call_unitigs([&](const auto &, const auto &path) {
-            std::vector<double> log_likelihood_num_ins;
-            std::vector<double> log_likelihood_num_outs;
-            std::vector<double> log_likelihood_denoms;
-            log_likelihood_num_ins.reserve(path.size());
-            log_likelihood_num_outs.reserve(path.size());
-            log_likelihood_denoms.reserve(path.size());
-            for (node_index node : path) {
-                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-                double in_sum = 0;
-                double out_sum = 0;
-                for (size_t j = 0; j < columns_all.size(); ++j) {
-                    const auto &col = *columns_all[j];
-                    const auto &col_values = *column_values_all[j];
-                    uint64_t val = 0;
-                    if (uint64_t rk = col.conditional_rank1(row_i)) {
-                        val = col_values[rk - 1];
-                        if (val >= max_val)
-                            val = 0;
-                    }
-
-                    if (groups[j]) {
-                        out_sum += val;
-                    } else {
-                        in_sum += val;
-                    }
-                }
-
-                log_likelihood_num_ins.emplace_back(in_sum > 0 ? in_sum * log(in_sum) - in_sum : 0.0);
-                log_likelihood_num_outs.emplace_back(out_sum > 0 ? out_sum * log(out_sum) - out_sum : 0.0);
-                double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
-
-                double mean_denom_in = theta * in_kmers;
-                double mean_denom_out = theta * out_kmers;
-                log_likelihood_denoms.emplace_back(log(mean_denom_in) * in_sum - mean_denom_in + log(mean_denom_out) * out_sum - mean_denom_out);
-            }
-
-            std::vector<double> lstats;
-            lstats.reserve(log_likelihood_num_ins.size());
-            for (size_t i = 0; i < log_likelihood_num_ins.size(); ++i) {
-                lstats.emplace_back(2 * (log_likelihood_num_ins[i] + log_likelihood_num_outs[i] - log_likelihood_denoms[i]));
-            }
-
-            std::vector<double> lstats_sums;
-            std::vector<double> num_diffs;
-            std::vector<double> log_likelihood_num_out_sums;
-            lstats_sums.reserve(lstats.size() + 1);
-            num_diffs.reserve(lstats.size() + 1);
-            log_likelihood_num_out_sums.reserve(lstats.size() + 1);
-            lstats_sums.push_back(0);
-            num_diffs.push_back(0);
-            log_likelihood_num_out_sums.push_back(0);
-            std::partial_sum(lstats.begin(), lstats.end(), std::back_inserter(lstats_sums));
-            std::partial_sum(log_likelihood_num_ins.begin(), log_likelihood_num_ins.end(), std::back_inserter(num_diffs));
-            std::partial_sum(log_likelihood_num_outs.begin(), log_likelihood_num_outs.end(), std::back_inserter(log_likelihood_num_out_sums));
-            for (size_t i = 0; i < log_likelihood_num_outs.size(); ++i) {
-                num_diffs[i] -= log_likelihood_num_outs[i];
-            }
-
-            double lstat_best = lstats_sums.back();
-            auto begin_best = lstats_sums.begin();
-            auto end_best = lstats_sums.end() - 1;
-            // for (auto begin = lstats_sums.begin(); begin < lstats_sums.end(); ++begin) {
-            //     for (auto end = begin + 1; end < lstats_sums.end(); ++end) {
-            //         if (*end - *begin > lstat_best) {
-            //             lstat_best = *end - *begin;
-            //             begin_best = begin;
-            //             end_best = end;
-            //         }
-            //     }
-            // }
-
-            boost::math::chi_squared dist(2*(end_best - begin_best) - 1);
-            if (lstat_best > 0 && boost::math::cdf(boost::math::complement(dist, lstat_best)) * num_unitigs < 0.05) {
-                size_t i_begin = begin_best - lstats_sums.begin();
-                size_t i_end = end_best - lstats_sums.begin();
-                double num_diff = num_diffs[i_end] - num_diffs[i_begin];
-                auto &indicator = num_diff > 0 ? indicator_in : indicator_out;
-                for (size_t i = i_begin; i < i_end; ++i) {
-                    indicator[path[i]] = true;
-                }
-            }
-        });
-
-    } else if (config.test_type == "nonparametric_perunitig") {
-        size_t num_tests = num_unitigs;
-        graph_ptr->call_unitigs([&](const auto &, const auto &path) {
-            std::vector<double> in_counts;
-            std::vector<double> out_counts;
-            for (node_index node : path) {
-                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-                for (size_t j = 0; j < columns_all.size(); ++j) {
-                    double scaled_val = 0.0;
-                    if (uint64_t rk = columns_all[j]->conditional_rank1(row_i)) {
-                        uint64_t val = (*column_values_all[j])[rk - 1];
-                        if (val < max_val)
-                            scaled_val = static_cast<double>(val) / medians[j];
-                    }
-                    auto &bucket = groups[j] ? out_counts : in_counts;
-                    bucket.emplace_back(scaled_val);
-                }
-            }
-
-            auto rankdata = [&](const std::vector<double> &c) {
-                std::vector<std::pair<double, double>> c_p;
-                c_p.reserve(c.size());
-                for (size_t i = 0; i < c.size(); ++i) {
-                    c_p.emplace_back(c[i], i + 1);
-                }
-                std::sort(c_p.begin(), c_p.end());
-                tsl::hopscotch_map<double, std::pair<size_t, size_t>> counts;
-                for (const auto &[v, r] : c_p) {
-                    counts[v].first += r;
-                    ++counts[v].second;
-                }
-
-                std::vector<double> ranks;
-                ranks.reserve(c.size());
-                for (double v : c) {
-                    ranks.emplace_back(static_cast<double>(counts[v].first)/counts[v].second);
-                }
-
-                return std::make_pair(std::move(ranks), std::move(counts));
-            };
-
-            size_t nx = in_counts.size();
-            size_t ny = out_counts.size();
-            double p2 = 0.0;
-
-            std::vector<double> c;
-            c.reserve(in_counts.size() + out_counts.size());
-            std::copy(in_counts.begin(), in_counts.end(), std::back_inserter(c));
-            std::copy(out_counts.begin(), out_counts.end(), std::back_inserter(c));
-            auto [rankc, countsc] = rankdata(c);
-
-            double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
-            double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
-
-            if (nx > 1 && ny > 1) {
-                auto [rankx, countsx] = rankdata(in_counts);
-                auto [ranky, countsy] = rankdata(out_counts);
-                double rankx_mean = boost::math::statistics::mean(rankx.begin(), rankx.end());
-                double ranky_mean = boost::math::statistics::mean(ranky.begin(), ranky.end());
-
-                double Sx = 0;
-                for (size_t i = 0; i < nx; ++i) {
-                    Sx += std::pow(rankc[i] - rankx[i] - rankcx_mean + rankx_mean, 2.0);
-                }
-                Sx /= nx - 1;
-                double Sy = 0;
-                for (size_t i = 0; i < ny; ++i) {
-                    Sy += std::pow(rankc[nx + i] - ranky[i] - rankcy_mean + ranky_mean, 2.0);
-                }
-                Sy /= ny - 1;
-
-                double wbfn = (rankcy_mean - rankcx_mean) * nx * ny;
-                wbfn /= std::sqrt(Sx * nx + Sy * ny) * (nx + ny);
-
-                double df_numer = std::pow(Sx * nx + Sy * ny, 2.0);
-                double df_denom = std::pow(Sx * nx, 2.0) / (nx - 1) + std::pow(Sy * ny, 2.0) / (ny - 1);
-
-                if (df_denom != 0) {
-                    double df = df_numer / df_denom;
-                    boost::math::students_t dist(df);
-                    double p = boost::math::cdf(dist, wbfn);
-                    double p1 = boost::math::cdf(boost::math::complement(dist, wbfn));
-                    p2 = 2 * std::min(p, p1);
-                }
-            }
-
-            if (p2 * num_tests < 0.05) {
-                auto add_to = [&](auto &indicator) {
-                    for (node_index node : path) {
-                        indicator[node] = true;
-                    }
-                };
-                if (rankcy_mean > rankcx_mean) {
-                    add_to(indicator_in);
-                } else if (rankcy_mean < rankcx_mean) {
-                    add_to(indicator_out);
-                }
-            }
-        });
-    }
-
-    // common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_unitigs);
-
-    // if (config.test_type == "nonparametric") {
-
-    // } else {
-    //     graph_ptr->call_unitigs([&](const auto &, const auto &path) {
-    //         if (path.size() == 1)
-    //             return;
-
-    //         if (config.test_type == "zmp") {
-    //             std::vector<double> in_counts;
-    //             std::vector<double> out_counts;
-    //             for (node_index node : path) {
-    //                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-    //                 for (size_t j = 0; j < columns_all.size(); ++j) {
-    //                     auto &bucket = groups[j] ? out_counts : in_counts;
-    //                     const auto &col = *columns_all[j];
-    //                     if (uint64_t rk = col.conditional_rank1(row_i)) {
-    //                         uint64_t val = (*column_values_all[j])[rk - 1];
-    //                         if (val < max_val) {
-    //                             bucket.push_back(static_cast<double>(val) / medians[j]);
-    //                         } else {
-    //                             bucket.push_back(0.0);
-    //                         }
-    //                     } else {
-    //                         bucket.push_back(0.0);
-    //                     }
-    //                 }
-    //             }
-
-    //             if (std::equal(in_counts.begin() + 1, in_counts.end(), in_counts.begin())
-    //                     && std::equal(out_counts.begin() + 1, out_counts.end(), out_counts.begin())
-    //                     && in_counts[0] == out_counts[0])
-    //                 return;
-
-    //             try {
-    //                 auto [t, p] = boost::math::statistics::two_sample_t_test(in_counts, out_counts);
-    //                 if (p * num_unitigs >= 0.05)
-    //                     return;
-
-    //                 auto &indicator = t > 0 ? indicator_in : indicator_out;
-    //                 for (node_index node : path) {
-    //                     indicator[node] = true;
-    //                 }
-    //             } catch (...) {
-    //                 common::logger->trace("f\t{}\t{}",fmt::join(in_counts, ","),fmt::join(out_counts,","));
-    //                 throw std::runtime_error("f");
-    //             }
-    //         } else if (config.test_type == "likelihoodratio_unitig") {
-    //             std::vector<double> log_likelihood_num_ins;
-    //             std::vector<double> log_likelihood_num_outs;
-    //             std::vector<double> log_likelihood_denoms;
-    //             log_likelihood_num_ins.reserve(path.size());
-    //             log_likelihood_num_outs.reserve(path.size());
-    //             log_likelihood_denoms.reserve(path.size());
-    //             for (node_index node : path) {
-    //                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-    //                 double in_sum = 0;
-    //                 double out_sum = 0;
-    //                 for (size_t j = 0; j < columns_all.size(); ++j) {
-    //                     const auto &col = *columns_all[j];
-    //                     const auto &col_values = *column_values_all[j];
-    //                     uint64_t val = 0;
-    //                     if (uint64_t rk = col.conditional_rank1(row_i)) {
-    //                         val = col_values[rk - 1];
-    //                         if (val >= max_val)
-    //                             val = 0;
-    //                     }
-
-    //                     if (groups[j]) {
-    //                         out_sum += val;
-    //                     } else {
-    //                         in_sum += val;
-    //                     }
-    //                 }
-
-    //                 log_likelihood_num_ins.emplace_back((log(static_cast<double>(in_sum)) - 1) * in_sum);
-    //                 log_likelihood_num_outs.emplace_back((log(static_cast<double>(out_sum)) - 1) * out_sum);
-    //                 double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
-
-    //                 double mean_denom_in = theta * in_kmers;
-    //                 double mean_denom_out = theta * out_kmers;
-    //                 log_likelihood_denoms.emplace_back(log(mean_denom_in) * in_sum - mean_denom_in + log(mean_denom_out) * out_sum - mean_denom_out);
-    //             }
-
-    //             std::vector<double> lstats;
-    //             lstats.reserve(log_likelihood_num_ins.size());
-    //             for (size_t i = 0; i < log_likelihood_num_ins.size(); ++i) {
-    //                 lstats.emplace_back(2 * (log_likelihood_num_ins[i] + log_likelihood_num_outs[i] - log_likelihood_denoms[i]));
-    //             }
-
-    //             std::vector<double> lstats_sums;
-    //             std::vector<double> num_diffs;
-    //             std::vector<double> log_likelihood_num_out_sums;
-    //             lstats_sums.reserve(lstats.size() + 1);
-    //             num_diffs.reserve(lstats.size() + 1);
-    //             log_likelihood_num_out_sums.reserve(lstats.size() + 1);
-    //             lstats_sums.push_back(0);
-    //             num_diffs.push_back(0);
-    //             log_likelihood_num_out_sums.push_back(0);
-    //             std::partial_sum(lstats.begin(), lstats.end(), std::back_inserter(lstats_sums));
-    //             std::partial_sum(log_likelihood_num_ins.begin(), log_likelihood_num_ins.end(), std::back_inserter(num_diffs));
-    //             std::partial_sum(log_likelihood_num_outs.begin(), log_likelihood_num_outs.end(), std::back_inserter(log_likelihood_num_out_sums));
-    //             for (size_t i = 0; i < log_likelihood_num_outs.size(); ++i) {
-    //                 num_diffs[i] -= log_likelihood_num_outs[i];
-    //             }
-
-    //             double lstat_best = lstats_sums.back();
-    //             auto begin_best = lstats_sums.begin();
-    //             auto end_best = lstats_sums.end() - 1;
-    //             // for (auto begin = lstats_sums.begin(); begin < lstats_sums.end(); ++begin) {
-    //             //     for (auto end = begin + 1; end < lstats_sums.end(); ++end) {
-    //             //         if (*end - *begin > lstat_best) {
-    //             //             lstat_best = *end - *begin;
-    //             //             begin_best = begin;
-    //             //             end_best = end;
-    //             //         }
-    //             //     }
-    //             // }
-
-    //             boost::math::chi_squared dist(2*(end_best - begin_best) - 1);
-    //             if (lstat_best > 0 && boost::math::cdf(boost::math::complement(dist, lstat_best)) * num_unitigs < 0.05) {
-    //                 size_t i_begin = begin_best - lstats_sums.begin();
-    //                 size_t i_end = end_best - lstats_sums.begin();
-    //                 double num_diff = num_diffs[i_end] - num_diffs[i_begin];
-    //                 auto &indicator = num_diff > 0 ? indicator_in : indicator_out;
-    //                 for (size_t i = i_begin; i < i_end; ++i) {
-    //                     indicator[path[i]] = true;
-    //                 }
-    //             }
-    //         }
-    //     });
-    // }
-
-    auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
-        graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
-        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    );
-
-    auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
-        graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_out)), true,
-        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    );
-
-    // if (!is_primary)
-        return std::make_pair(masked_graph_in, masked_graph_out);
-
-    // return std::make_pair(std::make_shared<CanonicalDBG>(masked_graph_in),
-    //                       std::make_shared<CanonicalDBG>(masked_graph_out));
-
-    // sdsl::bit_vector indicator(max_index + 1, false);
-    // std::vector<bool> groups;
-    // std::vector<uint64_t> sums;
-
-    // uint8_t max_width = 0;
-
-    // annot::ColumnCompressed<>::load_columns_and_values(files,
-    //     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, sdsl::int_vector_buffer<> && column_values) {
-    //         groups.push_back(labels_out.count(label));
-    //         sums.push_back(std::accumulate(column_values.begin(),
-    //                                        column_values.end(),
-    //                                        0llu));
-    //         column->call_ones([&](uint64_t row_i) {
-    //             node_index i = AnnotatedDBG::anno_to_graph_index(row_i);
-    //             indicator[i] = true;
-    //         });
-    //         max_width = std::max(max_width, column_values.width());
-    //         columns_all.emplace_back(std::move(column));
-    //         column_values_all.push_back(std::make_unique<sdsl::int_vector_buffer<>>(std::move(column_values)));
-    //     }
-    // );
-
-    // // size_t total_unique_kmers = sdsl::util::cnt_one_bits(indicator);
-    // size_t num_tests = 1;
-    // boost::math::chi_squared dist(groups.size() - 1);
-
-    // uint64_t row_i = 0;
-    // utils::call_rows<std::unique_ptr<bit_vector>,
-    //                  std::unique_ptr<sdsl::int_vector_buffer<>>,
-    //                  std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
-    //     if (row.empty()) {
-    //         ++row_i;
-    //         return;
-    //     }
-
-    //     std::vector<uint64_t> all_counts(groups.size());
-    //     std::vector<uint64_t> counts[2];
-    //     uint64_t sums[2] = { 0, 0 };
-    //     for (const auto &[j, c] : row) {
-    //         if (c) {
-    //             sums[groups[j]] += c;
-    //             counts[groups[j]].emplace_back(c);
-    //             all_counts[j] = c;
-    //         }
-    //     }
-
-    //     double in_mean = 0;
-    //     double in_nzeros = 0;
-    //     double out_mean = 0;
-    //     double out_nzeros = 0;
-    //     double null_mean = 0;
-    //     double null_nzeros = 0;
-
-    //     if (counts[0].size()) {
-    //         std::tie(in_mean, in_nzeros) = estimate_ztp_mean(
-    //             [&](const auto &callback) { std::for_each(counts[0].begin(), counts[0].end(), callback); },
-    //             max_width
-    //         );
-    //     }
-
-    //     if (counts[1].size()) {
-    //         std::tie(out_mean, out_nzeros) = estimate_ztp_mean(
-    //             [&](const auto &callback) { std::for_each(counts[1].begin(), counts[1].end(), callback); },
-    //             max_width
-    //         );
-    //     }
-
-    //     std::tie(null_mean, null_nzeros) = estimate_ztp_mean(
-    //         [&](const auto &callback) {
-    //             std::for_each(counts[0].begin(), counts[0].end(), callback);
-    //             std::for_each(counts[1].begin(), counts[1].end(), callback);
-    //         },
-    //         max_width
-    //     );
-
-    //     // double log_likelihood_alt = 0;
-    //     // if (in_mean > 0)
-    //     //     log_likelihood_alt += log(in_mean) * sums[0] - (in_mean + log1p(-exp(-in_mean))) * counts[0].size();
-
-    //     // if (out_mean > 0)
-    //     //     log_likelihood_alt += log(out_mean) * sums[1] - (out_mean + log1p(-exp(-out_mean))) * counts[1].size();
-
-    //     // double log_likelihood_null = log(null_mean) * (sums[0] + sums[1]) - (null_mean + log1p(-exp(-null_mean))) * (counts[0].size() + counts[1].size());
-
-    //     double log_likelihood_alt = 0;
-    //     double log_likelihood_null = 0;
-    //     for (size_t j = 0; j < all_counts.size(); ++j) {
-    //         if (all_counts[j]) {
-    //             double cur_mean = static_cast<double>(sums[j]) / (groups[j] ? out_mean : in_mean);
-    //             double cur_null_mean = static_cast<double>(sums[j]) / null_mean;
-    //             log_likelihood_alt += log(cur_mean) * all_counts[j] - cur_mean - log1p(-exp(-cur_mean));
-    //             log_likelihood_null += log(cur_null_mean) * all_counts[j] - cur_null_mean - log1p(-exp(-cur_null_mean));
-    //         }
-    //     }
-
-    //     double lstat = 2 * (log_likelihood_alt - log_likelihood_null);
-
-    //     double pval = lstat > 0
-    //         ? (1.0 - boost::math::cdf(dist, lstat)) * num_tests
-    //         : 1.0;
-
-    //     common::logger->trace("{}\t{}\t{} {}",
-    //         std::min(pval, double(1.0)),
-    //         fmt::join(all_counts, ","),
-    //         log_likelihood_alt,
-    //         log_likelihood_null
-    //     );
-
-    //     node_index i = AnnotatedDBG::anno_to_graph_index(row_i);
-    //     if (pval >= 0.05)
-    //         indicator[i] = false;
-
-    //     ++row_i;
-    // });
-
-
-    // sdsl::bit_vector in_indicator(max_index + 1, false);
-    // sdsl::bit_vector out_indicator(max_index + 1, false);
-    // sdsl::bit_vector indicator(max_index + 1, false);
-    // std::vector<std::unique_ptr<bit_vector>> columns_all;
-    // std::vector<bool> groups;
-    // size_t num_set_bits = 0;
-
-    // std::vector<size_t> n_kmers;
-    // std::vector<size_t> count_sums;
-    // // std::vector<std::pair<double, double>> ztp_params;
-    // // double in_rate = 0;
-    // // double out_rate = 0;
-    // uint8_t max_width = 8;
-    // uint64_t in_sum = 0;
-    // uint64_t out_sum = 0;
-    // double ztp_out_mean = 0;
-    // double ztp_out_nzeros = 0;
-    // double ztp_in_mean = 0;
-    // double ztp_in_nzeros = 0;
-
-    // auto overall_iterator = [&](const auto &global_callback) {
-    //     auto in_iterator = [&](const auto &in_callback) {
-    //         auto out_iterator = [&](const auto &out_callback) {
-    //             annot::ColumnCompressed<>::load_columns_and_values(files,
-    //                 [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector<> && column_values) {
-    //                     num_set_bits += column_values.size();
-    //                     groups.push_back(labels_out.count(label));
-    //                     uint64_t sum = 0;
-    //                     size_t rank = 0;
-    //                     column->call_ones([&](uint64_t row_i) {
-    //                         node_index i = AnnotatedDBG::anno_to_graph_index(row_i);
-    //                         indicator[i] = true;
-    //                         uint64_t val = column_values[rank++];
-    //                         global_callback(val);
-
-    //                         if (groups.back()) {
-    //                             out_callback(val);
-    //                             out_indicator[i] = true;
-    //                             out_sum += val;
-    //                         } else {
-    //                             in_callback(val);
-    //                             in_indicator[i] = true;
-    //                             in_sum += val;
-    //                         }
-    //                         sum += val;
-    //                     });
-    //                     count_sums.push_back(sum);
-    //                     n_kmers.push_back(column_values.size());
-    //                     columns_all.emplace_back(std::move(column));
-    //                 }
-    //             );
-    //         };
-    //         std::tie(ztp_out_mean, ztp_out_nzeros) = estimate_ztp_mean(out_iterator, max_width);
-    //         // out_rate = ztp_out_mean / out_sum;
-    //     };
-
-    //     std::tie(ztp_in_mean, ztp_in_nzeros) = estimate_ztp_mean(in_iterator, max_width);
-    //     // in_rate = ztp_in_mean / in_sum;
-    // };
-
-    // auto [overall_ztp_mean, overall_ztp_nzeros] = estimate_ztp_mean(overall_iterator, max_width);
-    // // double null_rate = overall_ztp_mean / (in_sum + out_sum);
-
-    // // common::logger->trace("Rates: in: {}\tout: {}\tnull: {}",in_rate, out_rate, null_rate);
-
-    // assert(!indicator[DeBruijnGraph::npos]);
-    // auto ind_p = std::make_unique<bitmap_vector>(std::move(indicator));
-    // double overall_prob = static_cast<double>(num_set_bits) / (ind_p->num_set_bits() * groups.size());
-    // double log_overall_denom = log1p(-exp(-overall_ztp_mean));
-
-    // boost::math::chi_squared dist(groups.size() - 1);
-
-    // std::vector<std::unique_ptr<sdsl::int_vector_buffer<>>> column_values_all;
-    // annot::ColumnCompressed<>::load_columns_and_values(files,
-    //     [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&, sdsl::int_vector_buffer<> && column_values) {
-    //         column_values_all.push_back(std::make_unique<sdsl::int_vector_buffer<>>(std::move(column_values)));
-    //     }
-    // );
-
-    // bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
-
-    // // auto initial_masked_graph = std::make_shared<MaskedDeBruijnGraph>(
-    // //     graph_ptr, std::move(ind_p), true,
-    // //     is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    // // );
-    // // std::shared_ptr<DeBruijnGraph> test_graph = initial_masked_graph;
-    // // // if (is_primary)
-    // // //     test_graph = std::make_shared<CanonicalDBG>(initial_masked_graph);
-
-    // // size_t num_tests = 0;
-    // // test_graph->call_unitigs([&](const auto&, const auto&) {
-    // //     ++num_tests;
-    // // });
-    // // // if (is_primary)
-    // // //     num_tests /= 2;
-
-    // // ind_p = std::unique_ptr<bitmap_vector>(static_cast<bitmap_vector*>(initial_masked_graph->release_mask()));
-    // size_t num_tests = ind_p->num_set_bits();
-
-    // // num_tests = 1;
-    // common::logger->trace("Num tests: {}", num_tests);
-
-    // for (size_t i = 0; i < groups.size(); ++i) {
-    //     common::logger->trace("{}: zero prob: {}", i, 1.0 - static_cast<double>(n_kmers[i])/ind_p->num_set_bits());
-    // }
-    // common::logger->trace("null: zero prob: {}", 1.0 - overall_prob);
-
-    // uint64_t row_i = 0;
-    // // ProgressBar progress(indicator.size() - 1,
-    // //                      "Testing k-mers zmp",
-    // //                      std::cerr, !common::get_verbose());
-    // utils::call_rows<std::unique_ptr<bit_vector>,
-    //                 std::unique_ptr<sdsl::int_vector_buffer<>>,
-    //                 std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
-    //     [&](const auto &row) {
-    //         // ++progress;
-    //         if (row.empty()) {
-    //             ++row_i;
-    //             return;
-    //         }
-
-    //         std::vector<uint64_t> counts(groups.size());
-    //         for (const auto &[j, c] : row) {
-    //             counts[j] = c;
-    //         }
-
-    //         // double likelihood_num = 1;
-    //         // double likelihood_denom = 1;
-    //         double log_likelihood_num = 0;
-    //         double log_likelihood_denom = 0;
-    //         for (size_t j = 0; j < counts.size(); ++j) {
-    //             uint64_t c = counts[j];
-    //             double log_p_denom = 0;
-    //             for (uint64_t cc = 2; cc <= c; ++cc) {
-    //                 log_p_denom += log(cc);
-    //             }
-
-    //             // the distribution is a zero-inflated, zero-truncated poisson
-    //             {
-    //                 // double mean_est = ztp_params[j].first;
-    //                 // double mean_est = (groups[j] ? out_rate : in_rate) * count_sums[j];
-    //                 double mean_est = groups[j] ? ztp_out_mean : ztp_in_mean;
-    //                 double log_prob = log(static_cast<double>(n_kmers[j])) - log(static_cast<double>(ind_p->num_set_bits()));
-    //                 if (!c) {
-    //                     // we are in the zero-count zone
-    //                     log_prob = log(static_cast<double>(ind_p->num_set_bits() - n_kmers[j])) - log(static_cast<double>(ind_p->num_set_bits()));
-    //                 } else {
-    //                     // we are now in the ztp zone
-    //                     double log_denom = log1p(-exp(-mean_est));
-    //                     double log_p_num = c * log(mean_est) - mean_est;
-    //                     double log_ztp_prob = log_p_num - log_p_denom - log_denom;
-    //                     log_prob += log_ztp_prob;
-    //                 }
-
-    //                 log_likelihood_num += log_prob;
-    //             }
-
-    //             {
-    //                 double mean_est = overall_ztp_mean;
-    //                 // double mean_est = null_rate * count_sums[j];
-    //                 double log_prob = log(overall_prob);
-    //                 if (!c) {
-    //                     // we are in the zero-count zone
-    //                     log_prob = log1p(-overall_prob);
-    //                 } else {
-    //                     // we are now in the ztp zone
-    //                     double log_p_num = c * log(mean_est) - mean_est;
-    //                     double log_ztp_prob = log_p_num - log_p_denom - log_overall_denom;
-    //                     log_prob += log_ztp_prob;
-    //                 }
-
-    //                 log_likelihood_denom += log_prob;
-    //             }
-    //         }
-
-    //         // common::logger->trace("{} {}", likelihood_num, likelihood_denom);
-    //         double lstat = 2 * (log_likelihood_num - log_likelihood_denom);
-    //         double pval = lstat > 0
-    //             ? (1.0 - boost::math::cdf(dist, lstat)) * num_tests
-    //             : 1.0;
-
-    //         // if (counts[0] + counts[2] > counts[1] && counts[0] && counts[2]) {
-    //         //     common::logger->trace("{}\t{} {}\t{}", pval, log_likelihood_num, log_likelihood_denom, lstat);
-    //         //     common::logger->trace("{}", fmt::join(groups, "\t"));
-    //         //     common::logger->trace("{}", fmt::join(counts, "\t"));
-    //         // }
-
-    //         node_index i = AnnotatedDBG::anno_to_graph_index(row_i);
-    //         if (pval >= 0.05)
-    //             ind_p->set(i, false);
-
-    //         ++row_i;
-    //     }
-    // );
-
-    // auto masked_graph = std::make_shared<MaskedDeBruijnGraph>(
-    //     graph_ptr, std::move(ind_p), true,
-    //     is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    // );
-    // if (!is_primary)
-    //     return masked_graph;
-
-    // return std::make_shared<CanonicalDBG>(masked_graph);
-}
-
 std::shared_ptr<DeBruijnGraph>
 mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this version i don't need to change at first
                     const std::vector<std::string> &files,
diff --git a/metagraph/src/seq_io/sequence_io.cpp b/metagraph/src/seq_io/sequence_io.cpp
index 289dc3b658..803abdb439 100644
--- a/metagraph/src/seq_io/sequence_io.cpp
+++ b/metagraph/src/seq_io/sequence_io.cpp
@@ -111,7 +111,7 @@ ExtendedFastaWriter<T>::ExtendedFastaWriter(const std::string &filebase,
 
     auto filename = utils::remove_suffix(filebase, ".gz", ".fasta") + "." + feature_name + ".gz";
 
-    feature_gz_out_ = gzopen(filename.c_str(), "w");
+    feature_gz_out_ = gzopen(filename.c_str(), mode);
     if (feature_gz_out_ == Z_NULL
             || gzwrite(feature_gz_out_, &kmer_length_, 4) != sizeof(kmer_length_)) {
         std::cerr << "ERROR: Can't write to " << filename << std::endl;
@@ -186,6 +186,7 @@ template class ExtendedFastaWriter<uint8_t>;
 template class ExtendedFastaWriter<uint16_t>;
 template class ExtendedFastaWriter<uint32_t>;
 template class ExtendedFastaWriter<uint64_t>;
+template class ExtendedFastaWriter<double>;
 
 
 bool write_fasta(gzFile gz_out, const kseq_t &kseq) {

From 7c5d34c2111f2d36379bc5a2ac41b26118fe6d96 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 5 Apr 2024 17:53:37 +0200
Subject: [PATCH 090/410] fix

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6fca2b91dd..630ba3fea8 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -234,14 +234,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u") {
         size_t num_tests = 0;
-        graph_ptr->call_unitigs([&](const auto &, const auto &path) {
-            for (node_index node : path) {
-                if (likelihoods[node] > 0) {
-                    ++num_tests;
-                    break;
-                }
-            }
-        });
+        graph_ptr->call_unitigs([&](const auto &, const auto &) { ++num_tests; });
         common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_tests);
         uint64_t row_i = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,

From e8643f9d3c0c9c3f4321b2298788953be27d87b2 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 5 Apr 2024 18:22:24 +0200
Subject: [PATCH 091/410] correct effect size

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 630ba3fea8..6525be4933 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -50,7 +50,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<uint64_t> sums_of_squares;
     std::vector<std::unique_ptr<bit_vector>> columns_all;
 
-    using ValuesContainer = sdsl::int_vector<>;
+    using ValuesContainer = sdsl::int_vector_buffer<>;
     std::vector<std::unique_ptr<ValuesContainer>> column_values_all;
     std::vector<bool> groups;
     uint8_t max_width = 0;
@@ -68,17 +68,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             std::vector<uint8_t> vals;
             vals.reserve(column_values.size());
             std::copy(column_values.begin(), column_values.end(), std::back_inserter(vals));
-            medians.push_back(boost::math::statistics::median(vals));
-            max_vals.push_back(max_val);
-
-            uint64_t sum = std::accumulate(column_values.begin(), column_values.end(),
-                                           uint64_t(0));
+            uint64_t sum = std::accumulate(vals.begin(), vals.end(), uint64_t(0));
             uint64_t sum_of_squares = std::accumulate(
-                column_values.begin(), column_values.end(), uint64_t(0),
+                vals.begin(), vals.end(), uint64_t(0),
                 [&](auto&& prev, uint64_t cur) { return std::move(prev) + cur * cur; }
             );
             size_t n = column_values.size();
 
+            medians.push_back(boost::math::statistics::median(vals));
+            max_vals.push_back(max_val);
+
             sums.push_back(sum);
             sums_of_squares.push_back(sum_of_squares);
             n_kmers.push_back(n);
@@ -344,7 +343,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double z = abs((u - mu) / sd);
                 auto dist = boost::math::normal_distribution();
                 p2 = boost::math::cdf(boost::math::complement(dist, z)) * 2;
-                likelihood = z;
+                likelihood = z / sqrt(n);
             }
 
             likelihoods.emplace_back(likelihood);

From b499b538df9288c420d6620c4f99f18a409f7326 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 5 Apr 2024 18:31:25 +0200
Subject: [PATCH 092/410] cleanup

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6525be4933..fdf3ba06d0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -278,7 +278,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             size_t nx = in_counts.size();
             size_t ny = out_counts.size();
-            double p2 = 0.0;
+            double p = 0.0;
             double likelihood = std::numeric_limits<double>::max();
 
             std::vector<double> c;
@@ -314,11 +314,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     double df_denom = std::pow(Sx * nx, 2.0) / (nx - 1) + std::pow(Sy * ny, 2.0) / (ny - 1);
 
                     if (df_denom != 0) {
+                        wbfn = abs(wbfn);
                         double df = df_numer / df_denom;
                         boost::math::students_t dist(df);
-                        double p = boost::math::cdf(dist, wbfn);
-                        double p1 = boost::math::cdf(boost::math::complement(dist, wbfn));
-                        p2 = 2 * std::min(p, p1);
+                        p = 2 * boost::math::cdf(boost::math::complement(dist, wbfn));
                         likelihood = abs(wbfn);
                     }
                 }
@@ -342,13 +341,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 double z = abs((u - mu) / sd);
                 auto dist = boost::math::normal_distribution();
-                p2 = boost::math::cdf(boost::math::complement(dist, z)) * 2;
+                p = boost::math::cdf(boost::math::complement(dist, z)) * 2;
                 likelihood = z / sqrt(n);
             }
 
             likelihoods.emplace_back(likelihood);
 
-            if (p2 * num_tests < 0.05) {
+            if (p * num_tests < 0.05) {
                 if (rankcx_mean > rankcy_mean) {
                     indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
                 } else if (rankcx_mean < rankcy_mean) {

From 02eaef5cf22effb0c1f9e332f5adeb451e85db30 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 8 Apr 2024 02:07:12 +0200
Subject: [PATCH 093/410] dump count columns as tsv

---
 metagraph/src/cli/transform_annotation.cpp | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/metagraph/src/cli/transform_annotation.cpp b/metagraph/src/cli/transform_annotation.cpp
index 6b5ac12c18..d7549de9b3 100644
--- a/metagraph/src/cli/transform_annotation.cpp
+++ b/metagraph/src/cli/transform_annotation.cpp
@@ -4,6 +4,7 @@
 
 #include "common/logger.hpp"
 #include "common/unix_tools.hpp"
+#include "common/utils/string_utils.hpp"
 #include "common/threads/threading.hpp"
 #include "annotation/representation/row_compressed/annotate_row_compressed.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
@@ -324,7 +325,18 @@ int transform_annotation(Config *config) {
         logger->trace("Loading annotation...");
 
         if (input_anno_type == Config::ColumnCompressed) {
-            if (!dynamic_cast<ColumnCompressed<>&>(*annotation).merge_load(files)) {
+            if (config->count_kmers) {
+                ColumnCompressed<>::load_columns_and_values(files,
+                    [&](uint64_t offset, const auto &label, std::unique_ptr<bit_vector> &&column, sdsl::int_vector_buffer<>&& column_values) {
+                        std::ofstream outstream(utils::remove_suffix(config->outfbase, ColumnCompressed<>::kExtension)
+                                    + fmt::format(".{}.tsv", offset));
+                        uint64_t rk = 0;
+                        column->call_ones([&](uint64_t pos) {
+                            outstream << pos << "\t" << column_values[rk++] << "\n";
+                        });
+                    }
+                );
+            } else if (!dynamic_cast<ColumnCompressed<>&>(*annotation).merge_load(files)) {
                 logger->error("Cannot load annotations");
                 exit(1);
             }

From a2b6ff01f9ce72fa11930beed765924c3b1f6bac Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 8 Apr 2024 02:09:08 +0200
Subject: [PATCH 094/410] report the number of times traversal was started when
 assembling

---
 metagraph/src/graph/representation/succinct/boss.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/metagraph/src/graph/representation/succinct/boss.cpp b/metagraph/src/graph/representation/succinct/boss.cpp
index 5888399197..fb5d11c78f 100644
--- a/metagraph/src/graph/representation/succinct/boss.cpp
+++ b/metagraph/src/graph/representation/succinct/boss.cpp
@@ -2109,7 +2109,10 @@ void BOSS::call_paths(Call<std::vector<edge_index> &&, std::vector<TAlphabet> &&
     ThreadPool thread_pool(num_threads ? num_threads : 1, TASK_POOL_SIZE);
     bool async = true;
 
+    size_t num_starts = 0;
+
     auto enqueue_start = [&](ThreadPool &thread_pool, edge_index start) {
+        ++num_starts;
         thread_pool.enqueue([&,start]() {
             ::mtg::graph::boss::call_paths(
                     *this, EdgeQueue(start), callback,
@@ -2217,6 +2220,7 @@ void BOSS::call_paths(Call<std::vector<edge_index> &&, std::vector<TAlphabet> &&
         // Ensures that call_path is called only once for each cycle
         auto rep = std::min_element(path.begin(), path.end());
         if (!fetch_bit(visited.data(), *rep, async)) {
+            __atomic_add_fetch(&num_starts, 1, __ATOMIC_SEQ_CST);
             EdgeQueue queue;
             queue.emplace_back(*rep,
                                sequence.begin() + (rep - path.begin()),
@@ -2262,6 +2266,8 @@ void BOSS::call_paths(Call<std::vector<edge_index> &&, std::vector<TAlphabet> &&
 #ifndef NDEBUG
     assert_no_leftovers(*this, visited);
 #endif
+
+    common::logger->trace("Starts:\t{}", num_starts);
 }
 
 void call_paths(const BOSS &boss,

From 6149397554edf0660d70c098988d940f43a8a31f Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 8 Apr 2024 02:10:47 +0200
Subject: [PATCH 095/410] support entering the number of tests in the json

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 15 ++++++++++-----
 metagraph/src/graph/annotated_graph_algorithm.hpp |  1 +
 2 files changed, 11 insertions(+), 5 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fdf3ba06d0..fe348c1658 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -137,7 +137,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             });
         }
 
-        size_t num_tests = 0;
+        size_t num_tests = config.num_tests;
+        bool count_tests = !num_tests;
         ProgressBar progress(columns_all[0]->size(),
                              "Testing k-mers",
                              std::cerr, !common::get_verbose());
@@ -197,12 +198,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 double lstat = log_likelihood_in + log_likelihood_out - log_likelihood_null;
-                num_tests += (lstat > 0);
+                if (count_tests)
+                    num_tests += (lstat > 0);
+
                 likelihoods.emplace_back(lstat);
             }
         }
 
-        if (config.test_type == "likelihoodratio_unitig" || config.test_type == "nbinom_unitig") {
+        if (count_tests && (config.test_type == "likelihoodratio_unitig" || config.test_type == "nbinom_unitig")) {
             num_tests = 0;
             graph_ptr->call_unitigs([&](const auto &, const auto &path) {
                 for (node_index node : path) {
@@ -232,8 +235,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u") {
-        size_t num_tests = 0;
-        graph_ptr->call_unitigs([&](const auto &, const auto &) { ++num_tests; });
+        size_t num_tests = config.num_tests;
+        if (!num_tests)
+            graph_ptr->call_unitigs([&](const auto &, const auto &) { ++num_tests; });
+
         common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_tests);
         uint64_t row_i = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index ed75f83bb2..d0e7b9f2bd 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -41,6 +41,7 @@ struct DifferentialAssemblyConfig {
     bool evaluate_assembly = false; // Myrthe temporary : evaluate an assembly
     bool filter = true;
     uint64_t min_count = 1;
+    uint64_t num_tests = 0;
 };
 
 /**

From 25f64b82401fd7555213cac22cc8117cf10f137b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 11 Apr 2024 18:09:59 +0200
Subject: [PATCH 096/410] cleanup

---
 metagraph/src/cli/align.cpp                   | 43 +++++++++++++-
 metagraph/src/cli/assemble.cpp                |  1 +
 metagraph/src/cli/config/config.cpp           |  2 +
 metagraph/src/cli/config/config.hpp           |  1 +
 metagraph/src/cli/stats.cpp                   | 59 +++++++++++++++++++
 .../src/graph/annotated_graph_algorithm.cpp   | 54 +++++++++--------
 6 files changed, 133 insertions(+), 27 deletions(-)

diff --git a/metagraph/src/cli/align.cpp b/metagraph/src/cli/align.cpp
index a4d91a1bad..0237e7e803 100644
--- a/metagraph/src/cli/align.cpp
+++ b/metagraph/src/cli/align.cpp
@@ -15,6 +15,7 @@
 #include "config/config.hpp"
 #include "load/load_graph.hpp"
 #include "load/load_annotated_graph.hpp"
+#include "graph/representation/hash/dbg_hash_fast.hpp"
 
 namespace mtg {
 namespace cli {
@@ -85,7 +86,42 @@ void map_sequences_in_file(const std::string &file,
 
     Timer data_reading_timer;
 
+    sdsl::bit_vector visited;
+    size_t num_testable_kmers = graph.max_index();
+    const auto *graph_ptr = &graph;
+    const auto *canonical = dynamic_cast<const CanonicalDBG*>(&graph);
+
+    if (canonical)
+        graph_ptr = &canonical->get_graph();
+
+    if (config.count_testable_kmers) {
+        if (const auto *succ = dynamic_cast<const DBGSuccinct*>(graph_ptr)) {
+            visited = succ->get_boss().mark_all_dummy_edges(get_num_threads());
+            num_testable_kmers = visited.size() - sdsl::util::cnt_one_bits(visited);
+        } else {
+            visited = sdsl::bit_vector(graph_ptr->max_index(), false);
+            num_testable_kmers = visited.size();
+        }
+    }
+
+    size_t total_kmers = num_testable_kmers;
+
     seq_io::read_fasta_file_critical(file, [&](kseq_t *read_stream) {
+        if (config.count_testable_kmers) {
+            auto nodes = canonical
+                ? map_to_nodes(graph, read_stream->seq.s)
+                : map_to_nodes_sequentially(graph, read_stream->seq.s);
+
+            for (size_t i = 0; i + 1 < nodes.size(); ++i) {
+                if (!visited[nodes[i + 1]])
+                    --num_testable_kmers;
+
+                visited[nodes[i + 1]] = true;
+            }
+
+            return;
+        }
+
         logger->trace("Sequence: {}", read_stream->seq.s);
 
         if (config.query_presence
@@ -174,6 +210,11 @@ void map_sequences_in_file(const std::string &file,
 
     }, config.forward_and_reverse);
 
+    if (config.count_testable_kmers) {
+        logger->info("Num k-mers:\t{}\tNum testable k-mers:\t{}",
+                     total_kmers, num_testable_kmers);
+    }
+
     logger->trace("File {} processed in {} sec, current mem usage: {} MB, total time {} sec",
                   file, data_reading_timer.elapsed(), get_curr_RSS() / 1e6, timer.elapsed());
 }
@@ -314,7 +355,7 @@ int align_to_graph(Config *config) {
     ThreadPool thread_pool(get_num_threads());
     std::mutex print_mutex;
 
-    if (config->map_sequences) {
+    if (config->map_sequences || config->count_testable_kmers) {
         if (graph->get_mode() == DeBruijnGraph::PRIMARY) {
             graph = std::make_shared<CanonicalDBG>(graph);
             logger->trace("Primary graph wrapped into canonical");
diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 1a16e581e6..200fbe81e5 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -58,6 +58,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.filter = experiment.get("filter", true).asBool();
     diff_config.clean = experiment.get("clean", false).asBool();
     diff_config.min_count = experiment.get("min_count", 0).asInt();
+    diff_config.num_tests = experiment.get("num_tests", 0).asInt();
 
 
     logger->trace("Per-kmer mask in fraction:\t\t{}", diff_config.label_mask_in_kmer_fraction);
diff --git a/metagraph/src/cli/config/config.cpp b/metagraph/src/cli/config/config.cpp
index 40f1e629ce..c911421c19 100644
--- a/metagraph/src/cli/config/config.cpp
+++ b/metagraph/src/cli/config/config.cpp
@@ -341,6 +341,8 @@ Config::Config(int argc, char *argv[]) {
             output_compacted = true;
         } else if (!strcmp(argv[i], "--clean-ztp")) {
             clean_ztp = true;
+        } else if (!strcmp(argv[i], "--count-testable-kmers")) {
+            count_testable_kmers = true;
         } else if (!strcmp(argv[i], "--json")) {
             output_json = true;
         } else if (!strcmp(argv[i], "--unitigs")) {
diff --git a/metagraph/src/cli/config/config.hpp b/metagraph/src/cli/config/config.hpp
index d834a14597..57b329e87a 100644
--- a/metagraph/src/cli/config/config.hpp
+++ b/metagraph/src/cli/config/config.hpp
@@ -63,6 +63,7 @@ class Config {
     bool coordinates = false;
     bool advanced = false;
     bool clean_ztp = false;
+    bool count_testable_kmers = false;
 
     unsigned int k = 3;
 
diff --git a/metagraph/src/cli/stats.cpp b/metagraph/src/cli/stats.cpp
index f4a2cee9c6..e848dd716d 100644
--- a/metagraph/src/cli/stats.cpp
+++ b/metagraph/src/cli/stats.cpp
@@ -1,6 +1,7 @@
 #include "stats.hpp"
 
 #include <ips4o/ips4o.hpp>
+#include <progress_bar.hpp>
 
 #include "common/algorithms.hpp"
 #include "common/logger.hpp"
@@ -10,6 +11,8 @@
 #include "graph/representation/succinct/dbg_succinct.hpp"
 #include "graph/representation/succinct/boss.hpp"
 #include "graph/graph_extensions/node_weights.hpp"
+#include "graph/annotated_dbg.hpp"
+#include "graph/representation/canonical_dbg.hpp"
 #include "annotation/representation/row_compressed/annotate_row_compressed.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 #include "annotation/representation/annotation_matrix/static_annotators_def.hpp"
@@ -385,6 +388,62 @@ int print_stats(Config *config) {
 
     const auto &files = config->fnames;
 
+    if (config->count_testable_kmers) {
+        auto graph = load_critical_dbg(files[0]);
+
+        uint64_t num_testable_kmers = graph->max_index();
+        if (auto dbg_succ = dynamic_cast<graph::DBGSuccinct*>(graph.get())) {
+            const auto &boss_graph = dbg_succ->get_boss();
+            uint64_t num_source_dummy_edges
+                = boss_graph.mark_source_dummy_edges(NULL, get_num_threads());
+            uint64_t num_sink_dummy_edges = boss_graph.mark_sink_dummy_edges(NULL);
+            num_testable_kmers -= num_source_dummy_edges + num_sink_dummy_edges;
+        }
+
+        uint64_t total_kmers = num_testable_kmers;
+
+        std::shared_ptr<graph::CanonicalDBG> canonical;
+        if (graph->get_mode() == graph::DeBruijnGraph::PRIMARY) {
+            canonical = std::make_shared<graph::CanonicalDBG>(graph);
+            graph = canonical;
+            logger->trace("Primary graph wrapped into canonical");
+        }
+
+        using ValuesContainer = sdsl::int_vector<>;
+        sdsl::bit_vector visited(graph->max_index(), false);
+        annot::ColumnCompressed<>::load_columns_and_values(config->infbase_annotators,
+            [&](uint64_t, const auto&, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
+                uint64_t rk = 0;
+                ProgressBar progress(column_values.size(),
+                                    "Testing k-mers",
+                                    std::cerr, !common::get_verbose());
+                column->call_ones([&](uint64_t row_i) {
+                    ++progress;
+                    uint64_t cur_count = column_values[rk++];
+                    auto node = graph::AnnotatedDBG::anno_to_graph_index(row_i);
+                    graph->adjacent_outgoing_nodes(node, [&](auto next) {
+                        if (canonical)
+                            next = canonical->get_base_node(next);
+
+                        uint64_t row_j = graph::AnnotatedDBG::graph_to_anno_index(next);
+                        if (uint64_t rk_next = column->conditional_rank1(row_j)) {
+                            uint64_t next_count = column_values[rk_next];
+                            if (cur_count == next_count && !visited[next])
+                                --num_testable_kmers;
+
+                            visited[next] = true;
+                        }
+                    });
+                });
+            }
+        );
+
+        common::logger->info("Total k-mers:\t{}\tNum testable k-mers:\t{}",
+                             total_kmers, num_testable_kmers);
+
+        return 0;
+    }
+
     for (const auto &file : files) {
         if (utils::ends_with(file, ".annodbg")) {
             print_annotation_stats(file, *config);
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fe348c1658..9dca209882 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -152,24 +152,26 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 continue;
             }
 
-            size_t in_nnzero = nnzero[i];
-            size_t out_nnzero = nnzero[i + 1];
-
             if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig") {
                 double log_likelihood_in = in_sum > 0 ? -in_sum + in_sum * log(in_sum) : 0.0;
                 double log_likelihood_out = out_sum > 0 ? -out_sum + out_sum * log(out_sum) : 0.0;
-                double theta = (in_sum + out_sum) / total_kmers;
 
+                double theta = (in_sum + out_sum) / total_kmers;
                 double mean_denom_in = theta * in_kmers;
                 double mean_denom_out = theta * out_kmers;
                 double log_likelihood_null = mean_denom_in > 0 && in_sum >= 0 ? -mean_denom_in + in_sum * log(mean_denom_in) : 0.0;
                 log_likelihood_null += mean_denom_out > 0 && out_sum >= 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
 
                 double lstat = log_likelihood_in + log_likelihood_out - log_likelihood_null;
-                num_tests += (lstat > 0);
+                if (count_tests)
+                    num_tests += (lstat > 0);
+
                 likelihoods.emplace_back(lstat);
             } else {
                 // generalized poisson
+                size_t in_nnzero = nnzero[i];
+                size_t out_nnzero = nnzero[i + 1];
+
                 double log_likelihood_in = 0;
                 if (in_sum > 0) {
                     size_t in_notfoundtotal = in_kmers - foundtotal[i];
@@ -235,6 +237,25 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u") {
+        auto rankdata = [&](const std::vector<double> &c) {
+            auto c_p = c;
+            std::sort(c_p.begin(), c_p.end());
+            tsl::hopscotch_map<double, std::pair<size_t, size_t>> counts;
+            for (size_t i = 0; i < c_p.size(); ++i) {
+                auto &[rank_sum, nranks] = counts[c_p[i]];
+                rank_sum += i + 1;
+                ++nranks;
+            }
+
+            std::vector<double> ranks;
+            ranks.reserve(c.size());
+            for (double v : c) {
+                ranks.emplace_back(static_cast<double>(counts[v].first)/counts[v].second);
+            }
+
+            return std::make_pair(std::move(ranks), std::move(counts));
+        };
+
         size_t num_tests = config.num_tests;
         if (!num_tests)
             graph_ptr->call_unitigs([&](const auto &, const auto &) { ++num_tests; });
@@ -242,8 +263,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_tests);
         uint64_t row_i = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
+                        std::unique_ptr<ValuesContainer>,
+                        std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
                                                                     [&](const auto &row) {
             if (row.empty()) {
                 likelihoods.emplace_back(0);
@@ -262,25 +283,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             in_counts.resize(labels_in.size());
             out_counts.resize(labels_out.size());
 
-            auto rankdata = [&](const std::vector<double> &c) {
-                auto c_p = c;
-                std::sort(c_p.begin(), c_p.end());
-                tsl::hopscotch_map<double, std::pair<size_t, size_t>> counts;
-                for (size_t i = 0; i < c_p.size(); ++i) {
-                    auto &[rank_sum, nranks] = counts[c_p[i]];
-                    rank_sum += i + 1;
-                    ++nranks;
-                }
-
-                std::vector<double> ranks;
-                ranks.reserve(c.size());
-                for (double v : c) {
-                    ranks.emplace_back(static_cast<double>(counts[v].first)/counts[v].second);
-                }
-
-                return std::make_pair(std::move(ranks), std::move(counts));
-            };
-
             size_t nx = in_counts.size();
             size_t ny = out_counts.size();
             double p = 0.0;

From 4e940fdde3fee04908843df62968a2de5ae252bb Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 11 Apr 2024 18:12:17 +0200
Subject: [PATCH 097/410] remove nbinom tests

---
 .../src/graph/annotated_graph_algorithm.cpp   | 79 ++++---------------
 1 file changed, 14 insertions(+), 65 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9dca209882..9de16c5380 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -112,28 +112,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     likelihoods.reserve(max_index + 1);
     likelihoods.emplace_back(0);
 
-    if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig" || config.test_type == "nbinom" || config.test_type == "nbinom_unitig") {
+    if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig") {
         boost::math::chi_squared dist(1);
         sdsl::int_vector<> counts(columns_all[0]->size() * 2, 0, 32);
-        sdsl::int_vector<> nnzero(columns_all[0]->size() * 2, 0, 32);
-        sdsl::int_vector<> foundtotal(columns_all[0]->size() * 2, 0, 64);
-        std::vector<double> foundlogtotal(columns_all[0]->size() * 2);
-        std::vector<double> stuff(columns_all[0]->size() * 2);
         for (size_t j = 0; j < groups.size(); ++j) {
             size_t offset = groups[j];
             const auto &col = *columns_all[j];
             const auto &col_vals = *column_values_all[j];
             uint64_t rank = 0;
             col.call_ones([&](uint64_t row_i) {
-                uint64_t val = col_vals[rank++];
-                counts[row_i * 2 + offset] += val;
-                ++nnzero[row_i * 2 + offset];
-                foundtotal[row_i * 2 + offset] += sums[j];
-                foundlogtotal[row_i * 2 + offset] += log(static_cast<double>(sums[j]));
-                if (val > 1) {
-                    stuff[row_i * 2 + offset] += (val - 1) * (log(static_cast<double>(val - 1)) - 1)
-                                                - lgamma(static_cast<double>(val + 1));
-                }
+                counts[row_i * 2 + offset] += col_vals[rank++];
             });
         }
 
@@ -152,62 +140,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 continue;
             }
 
-            if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig") {
-                double log_likelihood_in = in_sum > 0 ? -in_sum + in_sum * log(in_sum) : 0.0;
-                double log_likelihood_out = out_sum > 0 ? -out_sum + out_sum * log(out_sum) : 0.0;
-
-                double theta = (in_sum + out_sum) / total_kmers;
-                double mean_denom_in = theta * in_kmers;
-                double mean_denom_out = theta * out_kmers;
-                double log_likelihood_null = mean_denom_in > 0 && in_sum >= 0 ? -mean_denom_in + in_sum * log(mean_denom_in) : 0.0;
-                log_likelihood_null += mean_denom_out > 0 && out_sum >= 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
-
-                double lstat = log_likelihood_in + log_likelihood_out - log_likelihood_null;
-                if (count_tests)
-                    num_tests += (lstat > 0);
+            double log_likelihood_in = in_sum > 0 ? -in_sum + in_sum * log(in_sum) : 0.0;
+            double log_likelihood_out = out_sum > 0 ? -out_sum + out_sum * log(out_sum) : 0.0;
 
-                likelihoods.emplace_back(lstat);
-            } else {
-                // generalized poisson
-                size_t in_nnzero = nnzero[i];
-                size_t out_nnzero = nnzero[i + 1];
-
-                double log_likelihood_in = 0;
-                if (in_sum > 0) {
-                    size_t in_notfoundtotal = in_kmers - foundtotal[i];
-                    double rho_in = static_cast<double>(in_nnzero) / in_notfoundtotal;
-                    log_likelihood_in = rho_in * in_notfoundtotal + rho_in * in_nnzero + foundlogtotal[i] + stuff[i];
-                } else if (in_nnzero == labels_in.size()) {
-                    log_likelihood_in = stuff[i] + foundlogtotal[i];
-                }
+            double theta = (in_sum + out_sum) / total_kmers;
+            double mean_denom_in = theta * in_kmers;
+            double mean_denom_out = theta * out_kmers;
+            double log_likelihood_null = mean_denom_in > 0 && in_sum >= 0 ? -mean_denom_in + in_sum * log(mean_denom_in) : 0.0;
+            log_likelihood_null += mean_denom_out > 0 && out_sum >= 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
 
-                double log_likelihood_out = 0;
-                if (out_sum > 0) {
-                    size_t out_notfoundtotal = out_kmers - foundtotal[i + 1];
-                    double rho_out = static_cast<double>(out_nnzero) / out_notfoundtotal;
-                    log_likelihood_out = rho_out * out_notfoundtotal + rho_out * out_nnzero + foundlogtotal[i + 1] + stuff[i + 1];
-                } else if (out_nnzero == labels_out.size()) {
-                    log_likelihood_out = stuff[i + 1] + foundlogtotal[i + 1];
-                }
+            double lstat = log_likelihood_in + log_likelihood_out - log_likelihood_null;
+            if (count_tests)
+                num_tests += (lstat > 0);
 
-                double log_likelihood_null = 0;
-                if (in_sum > 0 && out_sum > 0) {
-                    log_likelihood_null = stuff[i] + stuff[i + 1] + foundlogtotal[i] + foundlogtotal[i + 1];
-                } else {
-                    size_t null_notfoundtotal = total_kmers - foundtotal[i] - foundtotal[i + 1];
-                    double rho_null = static_cast<double>(in_nnzero + out_nnzero) / null_notfoundtotal;
-                    log_likelihood_null = rho_null * null_notfoundtotal + rho_null * (in_nnzero + out_nnzero) + foundlogtotal[i] + foundlogtotal[i + 1] + stuff[i] + stuff[i + 1];
-                }
-
-                double lstat = log_likelihood_in + log_likelihood_out - log_likelihood_null;
-                if (count_tests)
-                    num_tests += (lstat > 0);
-
-                likelihoods.emplace_back(lstat);
-            }
+            likelihoods.emplace_back(lstat);
         }
 
-        if (count_tests && (config.test_type == "likelihoodratio_unitig" || config.test_type == "nbinom_unitig")) {
+        if (count_tests && config.test_type == "likelihoodratio_unitig") {
             num_tests = 0;
             graph_ptr->call_unitigs([&](const auto &, const auto &path) {
                 for (node_index node : path) {

From 04bb14e8be51ab55d093c7897b0723370d6523c1 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 11 Apr 2024 18:38:48 +0200
Subject: [PATCH 098/410] store counts in tmp file

---
 metagraph/src/cli/assemble.cpp                |  2 +
 .../src/graph/annotated_graph_algorithm.cpp   | 44 +++++++++++++------
 .../src/graph/annotated_graph_algorithm.hpp   |  1 +
 .../test_annotated_graph_algorithm.cpp        | 12 +++--
 4 files changed, 42 insertions(+), 17 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 200fbe81e5..80553b2c61 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -127,6 +127,7 @@ void call_masked_graphs(const AnnotatedDBG &anno_graph,
             tsl::hopscotch_set<std::string> background_labels;
 
             DifferentialAssemblyConfig diff_config = diff_assembly_config(experiment);
+            diff_config.outfbase = config->outfbase;
 
             std::string exp_name = experiment["name"].asString()
                                     + (config->enumerate_out_sequences ? "." : "");
@@ -188,6 +189,7 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             tsl::hopscotch_set<std::string> background_labels;
 
             DifferentialAssemblyConfig diff_config = diff_assembly_config(experiment);
+            diff_config.outfbase = config->outfbase;
 
             std::string exp_name = experiment["name"].asString()
                                     + (config->enumerate_out_sequences ? "." : "");
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9de16c5380..cf9aff15d5 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -113,27 +113,43 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     likelihoods.emplace_back(0);
 
     if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig") {
-        boost::math::chi_squared dist(1);
-        sdsl::int_vector<> counts(columns_all[0]->size() * 2, 0, 32);
-        for (size_t j = 0; j < groups.size(); ++j) {
-            size_t offset = groups[j];
-            const auto &col = *columns_all[j];
-            const auto &col_vals = *column_values_all[j];
-            uint64_t rank = 0;
-            col.call_ones([&](uint64_t row_i) {
-                counts[row_i * 2 + offset] += col_vals[rank++];
-            });
+        {
+            sdsl::int_vector_buffer<> counts(config.outfbase + "counts.tmp",
+                                             std::ios::binary | std::ios::out,
+                                             1024*1024,
+                                             32);
+            for (size_t j = 0; j < groups.size(); ++j) {
+                size_t offset = groups[j];
+                const auto &col = *columns_all[j];
+                const auto &col_vals = *column_values_all[j];
+                uint64_t rank = 0;
+                col.call_ones([&](uint64_t row_i) {
+                    size_t idx = row_i * 2 + offset;
+                    while (counts.size() <= idx) {
+                        counts.push_back(0);
+                    }
+
+                    counts[idx] += col_vals[rank++];
+                });
+            }
         }
 
+        sdsl::int_vector_buffer<> counts(config.outfbase + "counts.tmp",
+                                         std::ios::binary | std::ios::in,
+                                         1024*1024,
+                                         32);
+
+        boost::math::chi_squared dist(1);
         size_t num_tests = config.num_tests;
         bool count_tests = !num_tests;
         ProgressBar progress(columns_all[0]->size(),
                              "Testing k-mers",
                              std::cerr, !common::get_verbose());
-        for (size_t i = 0; i < counts.size(); i += 2) {
+        for (size_t i = 0; i < columns_all[0]->size(); ++i) {
             ++progress;
-            double in_sum = static_cast<uint64_t>(counts[i]);
-            double out_sum = static_cast<uint64_t>(counts[i + 1]);
+            size_t j = i * 2;
+            double in_sum = j < counts.size() ? static_cast<uint64_t>(counts[j]) : 0;
+            double out_sum = j + 1 < counts.size() ? static_cast<uint64_t>(counts[j + 1]) : 0;
 
             if (in_sum == 0 && out_sum == 0) {
                 likelihoods.emplace_back(0);
@@ -156,6 +172,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             likelihoods.emplace_back(lstat);
         }
 
+        counts.close(true);
+
         if (count_tests && config.test_type == "likelihoodratio_unitig") {
             num_tests = 0;
             graph_ptr->call_unitigs([&](const auto &, const auto &path) {
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index d0e7b9f2bd..12fb8e066b 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -42,6 +42,7 @@ struct DifferentialAssemblyConfig {
     bool filter = true;
     uint64_t min_count = 1;
     uint64_t num_tests = 0;
+    std::string outfbase;
 };
 
 /**
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index 5030d0ef08..069db76059 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -62,7 +62,8 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
                 .label_mask_in_kmer_fraction = 1.0,
                 .label_mask_out_unitig_fraction = 1.0,
                 .label_mask_out_kmer_fraction = 0.0,
-                .label_mask_other_unitig_fraction = 1.0
+                .label_mask_other_unitig_fraction = 1.0,
+                .outfbase = "",
             };
 
             auto masked_dbg = mask_nodes_by_label(*anno_graph,
@@ -110,7 +111,8 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelOverlap) {
             .label_mask_in_kmer_fraction = 0.0,
             .label_mask_out_unitig_fraction = 1.0,
             .label_mask_out_kmer_fraction = 0.99,
-            .label_mask_other_unitig_fraction = 1.0
+            .label_mask_other_unitig_fraction = 1.0,
+            .outfbase = "",
         };
 
         auto masked_dbg = mask_nodes_by_label(*anno_graph,
@@ -161,7 +163,8 @@ void test_mask_unitigs(double inlabel_fraction,
                 .label_mask_in_kmer_fraction = 1.0,
                 .label_mask_out_unitig_fraction = outlabel_fraction,
                 .label_mask_out_kmer_fraction = 0.0,
-                .label_mask_other_unitig_fraction = other_label_fraction
+                .label_mask_other_unitig_fraction = other_label_fraction,
+                .outfbase = "",
             };
 
             auto masked_dbg = mask_nodes_by_label(*anno_graph,
@@ -258,7 +261,8 @@ test_mask_unitigs_canonical(double inlabel_fraction,
                 .label_mask_out_unitig_fraction = outlabel_fraction,
                 .label_mask_out_kmer_fraction = 0.0,
                 .label_mask_other_unitig_fraction = other_label_fraction,
-                .add_complement = add_complement
+                .add_complement = add_complement,
+                .outfbase = "",
             };
 
             auto masked_dbg = mask_nodes_by_label(*anno_graph,

From bb25c135f0041cfd049aa1dd786a899bd1fde535 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 12 Apr 2024 15:21:33 +0200
Subject: [PATCH 099/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 95 +++++++------------
 1 file changed, 36 insertions(+), 59 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index cf9aff15d5..c2c6da3ee2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -113,47 +113,44 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     likelihoods.emplace_back(0);
 
     if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig") {
-        {
-            sdsl::int_vector_buffer<> counts(config.outfbase + "counts.tmp",
-                                             std::ios::binary | std::ios::out,
-                                             1024*1024,
-                                             32);
-            for (size_t j = 0; j < groups.size(); ++j) {
-                size_t offset = groups[j];
-                const auto &col = *columns_all[j];
-                const auto &col_vals = *column_values_all[j];
-                uint64_t rank = 0;
-                col.call_ones([&](uint64_t row_i) {
-                    size_t idx = row_i * 2 + offset;
-                    while (counts.size() <= idx) {
-                        counts.push_back(0);
+        size_t num_tests = config.num_tests;
+        boost::math::chi_squared dist(1);
+        if (!num_tests) {
+            if (config.test_type == "likelihoodratio_unitig") {
+                num_tests = 0;
+                graph_ptr->call_unitigs([&](const auto &, const auto &path) {
+                    for (node_index node : path) {
+                        if (likelihoods[node] > 0) {
+                            ++num_tests;
+                            break;
+                        }
                     }
-
-                    counts[idx] += col_vals[rank++];
                 });
+            } else {
+                num_tests = max_index;
             }
         }
 
-        sdsl::int_vector_buffer<> counts(config.outfbase + "counts.tmp",
-                                         std::ios::binary | std::ios::in,
-                                         1024*1024,
-                                         32);
-
-        boost::math::chi_squared dist(1);
-        size_t num_tests = config.num_tests;
-        bool count_tests = !num_tests;
-        ProgressBar progress(columns_all[0]->size(),
-                             "Testing k-mers",
-                             std::cerr, !common::get_verbose());
-        for (size_t i = 0; i < columns_all[0]->size(); ++i) {
-            ++progress;
-            size_t j = i * 2;
-            double in_sum = j < counts.size() ? static_cast<uint64_t>(counts[j]) : 0;
-            double out_sum = j + 1 < counts.size() ? static_cast<uint64_t>(counts[j + 1]) : 0;
-
-            if (in_sum == 0 && out_sum == 0) {
+        uint64_t row_i = 0;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                        std::unique_ptr<ValuesContainer>,
+                        std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
+                                                                    [&](const auto &row) {
+            if (row.empty()) {
                 likelihoods.emplace_back(0);
-                continue;
+                ++row_i;
+                return;
+            }
+
+            double in_sum = 0;
+            double out_sum = 0;
+
+            for (const auto &[j, c] : row) {
+                if (groups[j]) {
+                    out_sum += c;
+                } else {
+                    in_sum += c;
+                }
             }
 
             double log_likelihood_in = in_sum > 0 ? -in_sum + in_sum * log(in_sum) : 0.0;
@@ -166,34 +163,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             log_likelihood_null += mean_denom_out > 0 && out_sum >= 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
 
             double lstat = log_likelihood_in + log_likelihood_out - log_likelihood_null;
-            if (count_tests)
-                num_tests += (lstat > 0);
-
             likelihoods.emplace_back(lstat);
-        }
 
-        counts.close(true);
-
-        if (count_tests && config.test_type == "likelihoodratio_unitig") {
-            num_tests = 0;
-            graph_ptr->call_unitigs([&](const auto &, const auto &path) {
-                for (node_index node : path) {
-                    if (likelihoods[node] > 0) {
-                        ++num_tests;
-                        break;
-                    }
-                }
-            });
-        }
-
-        for (node_index node = 0; node < likelihoods.size(); ++node) {
-            double lstat = likelihoods[node];
             if (lstat > 0) {
                 double pval = boost::math::cdf(boost::math::complement(dist, lstat * 2.0));
                 if (pval * num_tests < 0.05) {
-                    uint64_t i = AnnotatedDBG::graph_to_anno_index(node);
-                    double in_sum = static_cast<uint64_t>(counts[i * 2]);
-                    double out_sum = static_cast<uint64_t>(counts[i * 2 + 1]);
+                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
                     double scaled_outsum = out_sum / out_kmers * in_kmers;
                     if (scaled_outsum < in_sum) {
                         indicator_in[node] = true;
@@ -202,7 +177,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 }
             }
-        }
+
+            ++row_i;
+        });
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u") {
         auto rankdata = [&](const std::vector<double> &c) {
             auto c_p = c;

From 43113c58de4091a54928463ee96135b63c16209e Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 12 Apr 2024 15:29:28 +0200
Subject: [PATCH 100/410] fix

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c2c6da3ee2..121ce0a883 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -117,15 +117,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         boost::math::chi_squared dist(1);
         if (!num_tests) {
             if (config.test_type == "likelihoodratio_unitig") {
-                num_tests = 0;
-                graph_ptr->call_unitigs([&](const auto &, const auto &path) {
-                    for (node_index node : path) {
-                        if (likelihoods[node] > 0) {
-                            ++num_tests;
-                            break;
-                        }
-                    }
-                });
+                graph_ptr->call_unitigs([&](const auto &, const auto &) { ++num_tests; });
             } else {
                 num_tests = max_index;
             }

From 0c890fcb193a658c08a7f6d1035ed1649b537f61 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 18 Apr 2024 20:48:05 +0200
Subject: [PATCH 101/410] implemented negative binomial

---
 metagraph/src/cli/assemble.cpp                |  29 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 332 +++++++++++++++---
 2 files changed, 304 insertions(+), 57 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 80553b2c61..5111c927c4 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -251,6 +251,7 @@ int assemble(Config *config) {
         );
 
         std::filesystem::remove(config->outfbase + ".loglikelihoodratio");
+        std::filesystem::remove(config->outfbase + ".all.loglikelihoodratio");
 
         auto graph_callback = [&](const graph::DeBruijnGraph &graph, const std::string &header) {
             const auto *masked_graph = dynamic_cast<const graph::MaskedDeBruijnGraph*>(&graph);
@@ -259,20 +260,26 @@ int assemble(Config *config) {
                                        config->enumerate_out_sequences,
                                        num_threads > 1, /* async write */
                                        "a" /* append mode */);
-            std::ofstream fout(config->outfbase + ".loglikelihoodratio", ios::out | ios::app);
+            std::ofstream fout(config->outfbase + ".pvals", ios::out | ios::app);
+            {
+                std::ofstream fout_all(config->outfbase + ".all.pvals", ios::out | ios::app);
+                for (double pval : masked_graph->likelihood_ratios) {
+                    fout_all << pval << "\n";
+                }
+            }
 
             if (config->unitigs || config->min_tip_size > 1) {
                 graph.call_unitigs(
                     [&](const std::string &unitig, auto&& path) {
-                        std::vector<double> lstats;
-                        lstats.reserve(path.size());
+                        std::vector<double> pvals;
+                        pvals.reserve(path.size());
                         for (auto node : path) {
-                            lstats.emplace_back(masked_graph->likelihood_ratios[node]);
+                            pvals.emplace_back(masked_graph->likelihood_ratios[node]);
                         }
                         std::lock_guard<std::mutex> lock(write_mutex);
                         writer.write(unitig);
-                        for (double l : lstats) {
-                            fout << l << "\n";
+                        for (double p : pvals) {
+                            fout << p << "\n";
                         }
                     },
                     num_threads, config->min_tip_size,
@@ -281,15 +288,15 @@ int assemble(Config *config) {
             } else {
                 graph.call_sequences(
                     [&](const std::string &seq, auto&& path) {
-                        std::vector<double> lstats;
-                        lstats.reserve(path.size());
+                        std::vector<double> pvals;
+                        pvals.reserve(path.size());
                         for (auto node : path) {
-                            lstats.emplace_back(masked_graph->likelihood_ratios[node]);
+                            pvals.emplace_back(masked_graph->likelihood_ratios[node]);
                         }
                         std::lock_guard<std::mutex> lock(write_mutex);
                         writer.write(seq);
-                        for (double l : lstats) {
-                            fout << l << "\n";
+                        for (double p : pvals) {
+                            fout << p << "\n";
                         }
                     },
                     num_threads, config->kmers_in_single_form
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 121ce0a883..826310f88b 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -2,6 +2,10 @@
 
 #include <boost/math/distributions/chi_squared.hpp>
 #include <boost/math/statistics/univariate_statistics.hpp>
+#include <boost/math/distributions/negative_binomial.hpp>
+#include <boost/math/distributions/poisson.hpp>
+#include <boost/math/statistics/t_test.hpp>
+#include <boost/math/tools/roots.hpp>
 
 #include "common/utils/string_utils.hpp"
 #include "common/logger.hpp"
@@ -29,6 +33,23 @@ typedef AnnotatedDBG::Annotator Annotator;
 typedef AnnotatedDBG::Annotator::Label Label;
 using Column = annot::matrix::BinaryMatrix::Column;
 
+struct IDist {
+    virtual ~IDist() {}
+    virtual double cdf(double x) const = 0;
+};
+
+template <class T>
+struct Dist : public IDist {
+    template <typename... Args>
+    Dist(Args&&... args) : d(std::forward<Args>(args)...) {}
+
+    double cdf(double x) const override final { return boost::math::cdf(d, x); }
+    const T d;
+};
+
+template struct Dist<boost::math::negative_binomial>;
+template struct Dist<boost::math::poisson>;
+
 std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const std::vector<std::string> &files,
@@ -65,9 +86,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             max_val = sdsl::bits::lo_set[max_width];
 
             // median calculation modifies the underlying vector, so we have to make a copy
-            std::vector<uint8_t> vals;
+            std::vector<uint64_t> vals;
             vals.reserve(column_values.size());
             std::copy(column_values.begin(), column_values.end(), std::back_inserter(vals));
+            max_observed_value = std::max(max_observed_value,
+                                          *std::max_element(vals.begin(), vals.end()));
             uint64_t sum = std::accumulate(vals.begin(), vals.end(), uint64_t(0));
             uint64_t sum_of_squares = std::accumulate(
                 vals.begin(), vals.end(), uint64_t(0),
@@ -85,14 +108,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             columns_all.emplace_back(std::move(column));
             column_values_all.push_back(std::make_unique<ValuesContainer>(std::move(column_values)));
 
-            common::logger->trace("{}: {}\tMean: {}\tCutoff: {}",
-                                  label, groups.back() ? "out": "in",
+            common::logger->trace("{}: {}\tTotal: {}\tMean: {}\tCutoff: {}",
+                                  label, sum, groups.back() ? "out": "in",
                                   static_cast<double>(sum) / n,
                                   max_vals.back());
         }
     );
 
-    common::logger->trace("Max width: {}\tMax val: {}\tMax obs. val: {}", max_width, max_val, max_observed_value);
+    common::logger->trace("Max width: {}\tMax val: {}\tMax obs. val: {}",
+                          max_width, max_val, max_observed_value);
 
     double in_kmers = 0;
     double out_kmers = 0;
@@ -108,36 +132,123 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     sdsl::bit_vector indicator_in(max_index + 1, false);
     sdsl::bit_vector indicator_out(max_index + 1, false);
 
-    std::vector<double> likelihoods;
-    likelihoods.reserve(max_index + 1);
-    likelihoods.emplace_back(0);
+    std::vector<double> pvals;
+    pvals.reserve(max_index + 1);
+    pvals.emplace_back(1.0);
 
-    if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig") {
-        size_t num_tests = config.num_tests;
+    if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig"
+            || config.test_type == "cmh"
+            || config.test_type == "fisher") {
+        bool found_bad_chi2_stat = false;
         boost::math::chi_squared dist(1);
-        if (!num_tests) {
-            if (config.test_type == "likelihoodratio_unitig") {
-                graph_ptr->call_unitigs([&](const auto &, const auto &) { ++num_tests; });
+        uint64_t row_i = 0;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                         std::unique_ptr<ValuesContainer>,
+                         std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
+                                                                    [&](const auto &row) {
+            if (row.empty()) {
+                pvals.emplace_back(1);
+                ++row_i;
+                return;
+            }
+
+            double in_sum = 0;
+            double out_sum = 0;
+
+            for (const auto &[j, c] : row) {
+                if (groups[j]) {
+                    out_sum += c;
+                } else {
+                    in_sum += c;
+                }
+            }
+
+            double chi_stat = 0;
+            double pval = -1;
+            if (config.test_type == "cmh") {
+                double xi = in_sum * out_kmers - out_sum * in_kmers;
+                xi *= xi * (total_kmers - 1) / in_kmers / out_kmers / (in_sum + out_sum) / (total_kmers - in_sum - out_sum);
+                chi_stat = xi;
+            } else if (config.test_type == "fisher") {
+                double lp_base = lgamma(in_kmers + 1) - lgamma(in_kmers - in_sum + 1) - lgamma(in_sum + 1);
+                lp_base += lgamma(out_kmers + 1) - lgamma(out_kmers - out_sum + 1) - lgamma(out_sum + 1);
+
+                double denom_in = lgamma(total_kmers + 1) - lgamma(total_kmers - in_sum - out_sum + 1) - lgamma(in_sum + out_sum + 1);
+
+                pval = exp(lp_base - denom_in);
+                chi_stat = boost::math::quantile(boost::math::complement(dist, pval));
             } else {
-                num_tests = max_index;
+                double log_likelihood_in = in_sum > 0 ? -in_sum + in_sum * log(in_sum) : 0.0;
+                double log_likelihood_out = out_sum > 0 ? -out_sum + out_sum * log(out_sum) : 0.0;
+
+                double theta = (in_sum + out_sum) / total_kmers;
+                double mean_denom_in = theta * in_kmers;
+                double mean_denom_out = theta * out_kmers;
+                double log_likelihood_null = mean_denom_in > 0 && in_sum >= 0 ? -mean_denom_in + in_sum * log(mean_denom_in) : 0.0;
+                log_likelihood_null += mean_denom_out > 0 && out_sum >= 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
+
+                chi_stat = (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
             }
+
+            if (chi_stat > 0) {
+                if (pval == -1)
+                    pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
+
+                if (pval < 0.05) {
+                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                    double scaled_outsum = out_sum / out_kmers * in_kmers;
+                    if (scaled_outsum < in_sum) {
+                        indicator_in[node] = true;
+                    } else if (scaled_outsum > in_sum) {
+                        indicator_out[node] = true;
+                    }
+                }
+            } else {
+                found_bad_chi2_stat |= (chi_stat < 0);
+                pval = 1.0;
+            }
+
+            pvals.emplace_back(pval);
+
+            ++row_i;
+        });
+
+        if (found_bad_chi2_stat)
+            common::logger->warn("Detected likelihood ratio(s) < 0");
+
+    } else if (config.test_type == "nbinom") {
+        bool found_bad_chi2_stat = false;
+        double r_num = 0;
+        double r_denom = 0;
+        for (size_t i = 0; i < groups.size(); ++i) {
+            double mean = static_cast<double>(sums[i]) / n_kmers[i];
+            r_num += mean;
+
+            double var = static_cast<double>(sums_of_squares[i]) / n_kmers[i] - mean * mean;
+            r_denom += var / mean;
         }
 
+        r_denom -= groups.size();
+        double r = r_num / r_denom;
+        common::logger->trace("r est: {}", r);
+
+        boost::math::chi_squared dist(1);
         uint64_t row_i = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,
-                        std::unique_ptr<ValuesContainer>,
-                        std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
+                         std::unique_ptr<ValuesContainer>,
+                         std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
                                                                     [&](const auto &row) {
             if (row.empty()) {
-                likelihoods.emplace_back(0);
+                pvals.emplace_back(1.0);
                 ++row_i;
                 return;
             }
 
             double in_sum = 0;
             double out_sum = 0;
-
+            sdsl::bit_vector found(groups.size());
             for (const auto &[j, c] : row) {
+                found[j] = true;
                 if (groups[j]) {
                     out_sum += c;
                 } else {
@@ -145,21 +256,102 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            double log_likelihood_in = in_sum > 0 ? -in_sum + in_sum * log(in_sum) : 0.0;
-            double log_likelihood_out = out_sum > 0 ? -out_sum + out_sum * log(out_sum) : 0.0;
+            std::vector<size_t> unfound;
+            for (size_t i = 0; i < found.size(); ++i) {
+                if (!found[i])
+                    unfound.emplace_back(i);
+            }
 
-            double theta = (in_sum + out_sum) / total_kmers;
-            double mean_denom_in = theta * in_kmers;
-            double mean_denom_out = theta * out_kmers;
-            double log_likelihood_null = mean_denom_in > 0 && in_sum >= 0 ? -mean_denom_in + in_sum * log(mean_denom_in) : 0.0;
-            log_likelihood_null += mean_denom_out > 0 && out_sum >= 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
+            double total_sum = in_sum + out_sum;
+
+            double lambda_in = boost::math::tools::newton_raphson_iterate([&](double lambda) {
+                double val = 0;
+                double dval = 0;
+                for (const auto &[j, c] : row) {
+                    if (!groups[j]) {
+                        double prop = static_cast<double>(sums[j]) * lambda;
+                        double shift_prop = r + prop;
+                        val += (c - prop) / shift_prop;
+                        dval -= prop * (r + c) / shift_prop / shift_prop;
+                    }
+                }
 
-            double lstat = log_likelihood_in + log_likelihood_out - log_likelihood_null;
-            likelihoods.emplace_back(lstat);
+                for (size_t j : unfound) {
+                    uint64_t c = 0;
+                    if (!groups[j]) {
+                        double prop = static_cast<double>(sums[j]) * lambda;
+                        double shift_prop = r + prop;
+                        val += (c - prop) / shift_prop;
+                        dval -= prop * (r + c) / shift_prop / shift_prop;
+                    }
+                }
 
-            if (lstat > 0) {
-                double pval = boost::math::cdf(boost::math::complement(dist, lstat * 2.0));
-                if (pval * num_tests < 0.05) {
+                return std::make_pair(val, dval);
+            }, in_sum / in_kmers, 0.0, 1.0, 5);
+
+            double lambda_out = boost::math::tools::newton_raphson_iterate([&](double lambda) {
+                double val = 0;
+                double dval = 0;
+                for (const auto &[j, c] : row) {
+                    if (groups[j]) {
+                        double prop = static_cast<double>(sums[j]) * lambda;
+                        double shift_prop = r + prop;
+                        val += (c - prop) / shift_prop;
+                        dval -= prop * (r + c) / shift_prop / shift_prop;
+                    }
+                }
+
+                for (size_t j : unfound) {
+                    uint64_t c = 0;
+                    if (groups[j]) {
+                        double prop = static_cast<double>(sums[j]) * lambda;
+                        double shift_prop = r + prop;
+                        val += (c - prop) / shift_prop;
+                        dval -= prop * (r + c) / shift_prop / shift_prop;
+                    }
+                }
+
+                return std::make_pair(val, dval);
+            }, out_sum / out_kmers, 0.0, 1.0, 5);
+
+            double lambda_null = boost::math::tools::newton_raphson_iterate([&](double lambda) {
+                double val = 0;
+                double dval = 0;
+                for (const auto &[j, c] : row) {
+                    double prop = static_cast<double>(sums[j]) * lambda;
+                    double shift_prop = r + prop;
+                    val += (c - prop) / shift_prop;
+                    dval -= prop * (r + c) / shift_prop / shift_prop;
+                }
+
+                for (size_t j : unfound) {
+                    uint64_t c = 0;
+                    double prop = static_cast<double>(sums[j]) * lambda;
+                    double shift_prop = r + prop;
+                    val += (c - prop) / shift_prop;
+                    dval -= prop * (r + c) / shift_prop / shift_prop;
+                }
+
+                return std::make_pair(val, dval);
+            }, total_sum / total_kmers, 0.0, 1.0, 5);
+
+            double loglikelihood_alt = 0;
+            double loglikelihood_null = 0;
+
+            for (const auto &[j, c] : row) {
+                double lambda = groups[j] ? lambda_out : lambda_in;
+                double prop = lambda * sums[j];
+                loglikelihood_alt += log(prop) * c - log(r + prop) * (r + c);
+
+                double prop_null = lambda_null * sums[j];
+                loglikelihood_null += log(prop_null) * c - log(r + prop_null) * (r + c);
+            }
+
+            double chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
+
+            if (chi_stat > 0) {
+                double pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
+                if (pval < 0.05) {
                     node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
                     double scaled_outsum = out_sum / out_kmers * in_kmers;
                     if (scaled_outsum < in_sum) {
@@ -168,11 +360,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         indicator_out[node] = true;
                     }
                 }
+
+                pvals.emplace_back(pval);
+            } else {
+                found_bad_chi2_stat |= (chi_stat < 0);
+                pvals.emplace_back(1.0);
             }
 
             ++row_i;
         });
-    } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u") {
+
+        if (found_bad_chi2_stat)
+            common::logger->warn("Detected likelihood ratio(s) < 0");
+
+    } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u" || config.test_type == "quantile") {
+        auto norm = boost::math::normal_distribution();
         auto rankdata = [&](const std::vector<double> &c) {
             auto c_p = c;
             std::sort(c_p.begin(), c_p.end());
@@ -192,18 +394,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_pair(std::move(ranks), std::move(counts));
         };
 
-        size_t num_tests = config.num_tests;
-        if (!num_tests)
-            graph_ptr->call_unitigs([&](const auto &, const auto &) { ++num_tests; });
-
-        common::logger->trace("Test type: {}\tNum tests: {}", config.test_type, num_tests);
         uint64_t row_i = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,
                         std::unique_ptr<ValuesContainer>,
                         std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
                                                                     [&](const auto &row) {
             if (row.empty()) {
-                likelihoods.emplace_back(0);
+                pvals.emplace_back(1.0);
                 ++row_i;
                 return;
             }
@@ -212,17 +409,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             in_counts.reserve(labels_in.size());
             std::vector<double> out_counts;
             out_counts.reserve(labels_out.size());
+
             for (const auto &[j, c] : row) {
                 auto &bucket = groups[j] ? out_counts : in_counts;
                 bucket.emplace_back(static_cast<double>(c) / medians[j]);
             }
+
             in_counts.resize(labels_in.size());
             out_counts.resize(labels_out.size());
 
+            double p = 0.0;
             size_t nx = in_counts.size();
             size_t ny = out_counts.size();
-            double p = 0.0;
-            double likelihood = std::numeric_limits<double>::max();
 
             std::vector<double> c;
             c.reserve(in_counts.size() + out_counts.size());
@@ -261,7 +459,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         double df = df_numer / df_denom;
                         boost::math::students_t dist(df);
                         p = 2 * boost::math::cdf(boost::math::complement(dist, wbfn));
-                        likelihood = abs(wbfn);
                     }
                 }
             } else {
@@ -283,14 +480,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double sd = sqrt(static_cast<double>(nx * ny) / n / (n - 1)) * sqrt((n * n * n - n - corr) / 12);
 
                 double z = abs((u - mu) / sd);
-                auto dist = boost::math::normal_distribution();
-                p = boost::math::cdf(boost::math::complement(dist, z)) * 2;
-                likelihood = z / sqrt(n);
+                p = boost::math::cdf(boost::math::complement(norm, z)) * 2;
             }
 
-            likelihoods.emplace_back(likelihood);
+            pvals.emplace_back(p);
 
-            if (p * num_tests < 0.05) {
+            if (p < 0.05) {
                 if (rankcx_mean > rankcy_mean) {
                     indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
                 } else if (rankcx_mean < rankcy_mean) {
@@ -304,17 +499,62 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         throw std::runtime_error("Test not implemented");
     }
 
+    for (auto &col : columns_all) {
+        col.reset();
+    }
+
+    for (auto &col_vals : column_values_all) {
+        col_vals.reset();
+    }
+
+    {
+        VectorMap<double, std::vector<size_t>> stats_map;
+        call_ones(indicator_in, [&](size_t i) {
+            stats_map[pvals[i]].emplace_back(i);
+        });
+        call_ones(indicator_out, [&](size_t i) {
+            stats_map[pvals[i]].emplace_back(i);
+        });
+        auto stats = const_cast<std::vector<std::pair<double, std::vector<size_t>>>&&>(
+            stats_map.values_container()
+        );
+        std::sort(stats.begin(), stats.end(), utils::LessFirst());
+
+        size_t acc = 0;
+        auto it = stats.begin();
+        for ( ; it != stats.end(); ++it) {
+            const auto &[p, bucket] = *it;
+            double k = 0.05 / p;
+            if (acc + bucket.size() > k)
+                break;
+
+            acc += bucket.size();
+        }
+
+        common::logger->trace("Corr: {}\tp: {}", acc, 0.05 / acc);
+
+        for (auto jt = stats.begin(); jt != stats.end(); ++jt) {
+            const auto &[p, bucket] = *jt;
+            if (jt >= it || p * acc >= 0.05) {
+                for (size_t i : bucket) {
+                    indicator_in[i] = false;
+                    indicator_out[i] = false;
+                }
+            }
+        }
+    }
+
     auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
         graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
     );
-    masked_graph_in->likelihood_ratios = likelihoods;
+    masked_graph_in->likelihood_ratios = pvals;
 
     auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
         graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_out)), true,
         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
     );
-    masked_graph_out->likelihood_ratios = likelihoods;
+    masked_graph_out->likelihood_ratios = std::move(pvals);
 
     return std::make_pair(masked_graph_in, masked_graph_out);
 }

From 1ffd85aa183ec8c2d51d93aa569b3ac5ad4eeff1 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 19 Apr 2024 01:40:33 +0200
Subject: [PATCH 102/410] fixes

---
 metagraph/src/cli/assemble.cpp                |  4 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 92 +++++++++----------
 2 files changed, 43 insertions(+), 53 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 5111c927c4..4541de7f6d 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -250,8 +250,8 @@ int assemble(Config *config) {
             utils::remove_suffix(config->outfbase, ".gz", ".fasta") + ".fasta.gz"
         );
 
-        std::filesystem::remove(config->outfbase + ".loglikelihoodratio");
-        std::filesystem::remove(config->outfbase + ".all.loglikelihoodratio");
+        std::filesystem::remove(config->outfbase + ".pvals");
+        std::filesystem::remove(config->outfbase + ".all.pvals");
 
         auto graph_callback = [&](const graph::DeBruijnGraph &graph, const std::string &header) {
             const auto *masked_graph = dynamic_cast<const graph::MaskedDeBruijnGraph*>(&graph);
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 826310f88b..bc6aecb730 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -33,23 +33,6 @@ typedef AnnotatedDBG::Annotator Annotator;
 typedef AnnotatedDBG::Annotator::Label Label;
 using Column = annot::matrix::BinaryMatrix::Column;
 
-struct IDist {
-    virtual ~IDist() {}
-    virtual double cdf(double x) const = 0;
-};
-
-template <class T>
-struct Dist : public IDist {
-    template <typename... Args>
-    Dist(Args&&... args) : d(std::forward<Args>(args)...) {}
-
-    double cdf(double x) const override final { return boost::math::cdf(d, x); }
-    const T d;
-};
-
-template struct Dist<boost::math::negative_binomial>;
-template struct Dist<boost::math::poisson>;
-
 std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const std::vector<std::string> &files,
@@ -139,13 +122,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig"
             || config.test_type == "cmh"
             || config.test_type == "fisher") {
-        bool found_bad_chi2_stat = false;
         boost::math::chi_squared dist(1);
         uint64_t row_i = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,
                          std::unique_ptr<ValuesContainer>,
-                         std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
-                                                                    [&](const auto &row) {
+                         std::vector<std::pair<uint64_t, uint64_t>>>(columns_all, column_values_all,
+                                                                     [&](const auto &row) {
             if (row.empty()) {
                 pvals.emplace_back(1);
                 ++row_i;
@@ -203,21 +185,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         indicator_out[node] = true;
                     }
                 }
-            } else {
-                found_bad_chi2_stat |= (chi_stat < 0);
+            } else if (chi_stat == 0) {
                 pval = 1.0;
+            } else {
+                common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+                throw std::runtime_error("Test failed");
             }
 
             pvals.emplace_back(pval);
 
             ++row_i;
         });
-
-        if (found_bad_chi2_stat)
-            common::logger->warn("Detected likelihood ratio(s) < 0");
-
     } else if (config.test_type == "nbinom") {
-        bool found_bad_chi2_stat = false;
         double r_num = 0;
         double r_denom = 0;
         for (size_t i = 0; i < groups.size(); ++i) {
@@ -236,8 +215,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         uint64_t row_i = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,
                          std::unique_ptr<ValuesContainer>,
-                         std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
-                                                                    [&](const auto &row) {
+                         std::vector<std::pair<uint64_t, uint64_t>>>(columns_all, column_values_all,
+                                                                     [&](const auto &row) {
             if (row.empty()) {
                 pvals.emplace_back(1.0);
                 ++row_i;
@@ -263,6 +242,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             double total_sum = in_sum + out_sum;
+            size_t ndigits = 20;
 
             double lambda_in = boost::math::tools::newton_raphson_iterate([&](double lambda) {
                 double val = 0;
@@ -272,22 +252,22 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         double prop = static_cast<double>(sums[j]) * lambda;
                         double shift_prop = r + prop;
                         val += (c - prop) / shift_prop;
-                        dval -= prop * (r + c) / shift_prop / shift_prop;
+                        dval -= (r + c) / shift_prop / shift_prop * sums[j];
                     }
                 }
 
                 for (size_t j : unfound) {
-                    uint64_t c = 0;
                     if (!groups[j]) {
+                        uint64_t c = 0;
                         double prop = static_cast<double>(sums[j]) * lambda;
                         double shift_prop = r + prop;
                         val += (c - prop) / shift_prop;
-                        dval -= prop * (r + c) / shift_prop / shift_prop;
+                        dval -= (r + c) / shift_prop / shift_prop * sums[j];
                     }
                 }
 
                 return std::make_pair(val, dval);
-            }, in_sum / in_kmers, 0.0, 1.0, 5);
+            }, in_sum / in_kmers, 0.0, 1.0, ndigits);
 
             double lambda_out = boost::math::tools::newton_raphson_iterate([&](double lambda) {
                 double val = 0;
@@ -297,22 +277,22 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         double prop = static_cast<double>(sums[j]) * lambda;
                         double shift_prop = r + prop;
                         val += (c - prop) / shift_prop;
-                        dval -= prop * (r + c) / shift_prop / shift_prop;
+                        dval -= (r + c) / shift_prop / shift_prop * sums[j];
                     }
                 }
 
                 for (size_t j : unfound) {
-                    uint64_t c = 0;
                     if (groups[j]) {
+                        uint64_t c = 0;
                         double prop = static_cast<double>(sums[j]) * lambda;
                         double shift_prop = r + prop;
                         val += (c - prop) / shift_prop;
-                        dval -= prop * (r + c) / shift_prop / shift_prop;
+                        dval -= (r + c) / shift_prop / shift_prop * sums[j];
                     }
                 }
 
                 return std::make_pair(val, dval);
-            }, out_sum / out_kmers, 0.0, 1.0, 5);
+            }, out_sum / out_kmers, 0.0, 1.0, ndigits);
 
             double lambda_null = boost::math::tools::newton_raphson_iterate([&](double lambda) {
                 double val = 0;
@@ -321,7 +301,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     double prop = static_cast<double>(sums[j]) * lambda;
                     double shift_prop = r + prop;
                     val += (c - prop) / shift_prop;
-                    dval -= prop * (r + c) / shift_prop / shift_prop;
+                    dval -= (r + c) / shift_prop / shift_prop * sums[j];
                 }
 
                 for (size_t j : unfound) {
@@ -329,11 +309,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     double prop = static_cast<double>(sums[j]) * lambda;
                     double shift_prop = r + prop;
                     val += (c - prop) / shift_prop;
-                    dval -= prop * (r + c) / shift_prop / shift_prop;
+                    dval -= (r + c) / shift_prop / shift_prop * sums[j];
                 }
 
                 return std::make_pair(val, dval);
-            }, total_sum / total_kmers, 0.0, 1.0, 5);
+            }, total_sum / total_kmers, 0.0, 1.0, ndigits);
 
             double loglikelihood_alt = 0;
             double loglikelihood_null = 0;
@@ -341,10 +321,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             for (const auto &[j, c] : row) {
                 double lambda = groups[j] ? lambda_out : lambda_in;
                 double prop = lambda * sums[j];
-                loglikelihood_alt += log(prop) * c - log(r + prop) * (r + c);
+                loglikelihood_alt += log(lambda) * c - log(r + prop) * (r + c);
+
+                double prop_null = lambda_null * sums[j];
+                loglikelihood_null += log(lambda_null) * c - log(r + prop_null) * (r + c);
+            }
+
+            for (size_t j : unfound) {
+                double lambda = groups[j] ? lambda_out : lambda_in;
+                double prop = lambda * sums[j];
+                loglikelihood_alt -= log(r + prop) * r;
 
                 double prop_null = lambda_null * sums[j];
-                loglikelihood_null += log(prop_null) * c - log(r + prop_null) * (r + c);
+                loglikelihood_null -= log(r + prop_null) * r;
             }
 
             double chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
@@ -362,17 +351,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 pvals.emplace_back(pval);
-            } else {
-                found_bad_chi2_stat |= (chi_stat < 0);
+            } else if (chi_stat == 0) {
                 pvals.emplace_back(1.0);
+            } else {
+                common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+                throw std::runtime_error("Test failed");
             }
 
             ++row_i;
         });
-
-        if (found_bad_chi2_stat)
-            common::logger->warn("Detected likelihood ratio(s) < 0");
-
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u" || config.test_type == "quantile") {
         auto norm = boost::math::normal_distribution();
         auto rankdata = [&](const std::vector<double> &c) {
@@ -396,9 +383,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         uint64_t row_i = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,
-                        std::unique_ptr<ValuesContainer>,
-                        std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
-                                                                    [&](const auto &row) {
+                         std::unique_ptr<ValuesContainer>,
+                         std::vector<std::pair<uint64_t, uint64_t>>>(columns_all, column_values_all,
+                                                                     [&](const auto &row) {
             if (row.empty()) {
                 pvals.emplace_back(1.0);
                 ++row_i;
@@ -508,6 +495,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
 
     {
+        uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
+        common::logger->trace("Correcting {}/{} significant p-values", total_sig, max_index);
+
         VectorMap<double, std::vector<size_t>> stats_map;
         call_ones(indicator_in, [&](size_t i) {
             stats_map[pvals[i]].emplace_back(i);

From f0b0ccb6d18d6a5f48724b738ddcbbf08b95bd59 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 19 Apr 2024 05:11:15 +0200
Subject: [PATCH 103/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 26 ++++++-------------
 1 file changed, 8 insertions(+), 18 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index bc6aecb730..f1e2f898d7 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1,27 +1,22 @@
 #include "annotated_graph_algorithm.hpp"
 
+#include <typeinfo>
+
+#include <sdust.h>
+
 #include <boost/math/distributions/chi_squared.hpp>
 #include <boost/math/statistics/univariate_statistics.hpp>
-#include <boost/math/distributions/negative_binomial.hpp>
-#include <boost/math/distributions/poisson.hpp>
-#include <boost/math/statistics/t_test.hpp>
 #include <boost/math/tools/roots.hpp>
 
 #include "common/utils/string_utils.hpp"
 #include "common/logger.hpp"
-#include "common/vectors/vector_algorithm.hpp"
 #include "common/vectors/bitmap.hpp"
 #include "common/vector_map.hpp"
 #include "common/vectors/transpose.hpp"
 #include "graph/representation/masked_graph.hpp"
 #include "graph/representation/canonical_dbg.hpp"
-#include "graph/graph_cleaning.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 #include "differential_tests.hpp"
-#include "timer.hpp"
-#include <sdust.h>
-#include "common/vectors/transpose.hpp"
-#include <typeinfo>
 
 namespace mtg {
 namespace graph {
@@ -117,7 +112,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     std::vector<double> pvals;
     pvals.reserve(max_index + 1);
-    pvals.emplace_back(1.0);
+    pvals.emplace_back(std::numeric_limits<double>::max());
 
     if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig"
             || config.test_type == "cmh"
@@ -129,7 +124,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          std::vector<std::pair<uint64_t, uint64_t>>>(columns_all, column_values_all,
                                                                      [&](const auto &row) {
             if (row.empty()) {
-                pvals.emplace_back(1);
+                pvals.emplace_back(std::numeric_limits<double>::max());
                 ++row_i;
                 return;
             }
@@ -218,7 +213,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          std::vector<std::pair<uint64_t, uint64_t>>>(columns_all, column_values_all,
                                                                      [&](const auto &row) {
             if (row.empty()) {
-                pvals.emplace_back(1.0);
+                pvals.emplace_back(std::numeric_limits<double>::max());
                 ++row_i;
                 return;
             }
@@ -387,7 +382,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          std::vector<std::pair<uint64_t, uint64_t>>>(columns_all, column_values_all,
                                                                      [&](const auto &row) {
             if (row.empty()) {
-                pvals.emplace_back(1.0);
+                pvals.emplace_back(std::numeric_limits<double>::max());
                 ++row_i;
                 return;
             }
@@ -843,7 +838,6 @@ std::vector<double> filtering_column(const std::vector<std::string> &files,
     double threshold = config.min_count; // TODO get from config
     assert(files.size() > 0);
     // generate a vector of the total number of kmers per sample for each group (in and out)
-    Timer t;
     std::vector<double> medians;
     logger->trace("filtering_column");
     annot::ColumnCompressed<>::load_columns_and_values(files,
@@ -864,7 +858,6 @@ std::vector<double> filtering_column(const std::vector<std::string> &files,
                 medians.push_back(median);
                 logger->trace("median: {}", median);
             });
-            logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());
         }
     );
     return medians;
@@ -1002,8 +995,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                 else{
                     assert(files.size() > 0);
                     // generate a vector of the total number of kmers per sample for each group (in and out)
-                    Timer t;
-                    logger->trace("OOOOOOOOOOO time to generate counts vector {}",  t.elapsed());
                     annot::ColumnCompressed<>::load_columns_and_values(files,
                         [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector_buffer<> && column_values) { // goes through all files in a group
                             int max_col_width = std::pow(2, 16); // max value for int16_t
@@ -1018,7 +1009,6 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: t
                                         value_callback(i, column_value);
                                 });
                             });
-                            logger->trace("OOOOOOOOOOO time to load columns and values {}",  t.elapsed());
                         }
                     );
                 }

From f1c96926a4b406e3f52a481d0cd14138d51cd249 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 20 Apr 2024 19:58:09 +0200
Subject: [PATCH 104/410] better estimate of r

---
 .../src/graph/annotated_graph_algorithm.cpp   | 25 +++++++++++--------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index f1e2f898d7..32537cf29a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -192,18 +192,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             ++row_i;
         });
     } else if (config.test_type == "nbinom") {
-        double r_num = 0;
-        double r_denom = 0;
-        for (size_t i = 0; i < groups.size(); ++i) {
-            double mean = static_cast<double>(sums[i]) / n_kmers[i];
-            r_num += mean;
-
-            double var = static_cast<double>(sums_of_squares[i]) / n_kmers[i] - mean * mean;
-            r_denom += var / mean;
-        }
+        // use moments to fit the r parameter of a beta negative binomial
+        double total_size = max_index * groups.size();
+        double bnb_mean = static_cast<double>(total_kmers) / total_size;
+        double bnb_m2 = static_cast<double>(std::accumulate(sums_of_squares.begin(),
+                                                            sums_of_squares.end(),
+                                                            size_t(0))) / total_size - bnb_mean;
+        double a = bnb_mean * bnb_m2;
+        double b = bnb_m2 * (2 * bnb_mean * bnb_mean - bnb_m2 + bnb_mean);
+        double c = 2 * bnb_mean * bnb_mean * bnb_m2;
+
+        double r = (-b + sqrt(b * b - 4 * a * c)) / 2 / a;
+        double r_low = (-b - sqrt(b * b - 4 * a * c)) / 2 / a;
+        if (r_low > 0)
+            common::logger->warn("Two possible solutions for r: {} and {}", r_low, r);
 
-        r_denom -= groups.size();
-        double r = r_num / r_denom;
         common::logger->trace("r est: {}", r);
 
         boost::math::chi_squared dist(1);

From e111c890b49167dbc6bc0549a25f95c201dc4c86 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 26 Apr 2024 13:47:52 +0200
Subject: [PATCH 105/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 190 +++++++++++-------
 1 file changed, 121 insertions(+), 69 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 32537cf29a..218d32eb5c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -27,6 +27,7 @@ typedef AnnotatedDBG::node_index node_index;
 typedef AnnotatedDBG::Annotator Annotator;
 typedef AnnotatedDBG::Annotator::Label Label;
 using Column = annot::matrix::BinaryMatrix::Column;
+using PairContainer = std::vector<std::pair<uint64_t, uint64_t>>;
 
 std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
@@ -107,6 +108,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
     double total_kmers = in_kmers + out_kmers;
 
+    PairContainer in_best;
+    PairContainer out_best;
+
+    for (size_t i = 0; i < groups.size(); ++i) {
+        if (groups[i]) {
+            out_best.emplace_back(i, sums[i]);
+        } else {
+            in_best.emplace_back(i, sums[i]);
+        }
+    }
+
+    tsl::hopscotch_map<size_t, size_t> m;
+
     sdsl::bit_vector indicator_in(max_index + 1, false);
     sdsl::bit_vector indicator_out(max_index + 1, false);
 
@@ -193,33 +207,40 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         });
     } else if (config.test_type == "nbinom") {
         // use moments to fit the r parameter of a beta negative binomial
-        double total_size = max_index * groups.size();
-        double bnb_mean = static_cast<double>(total_kmers) / total_size;
-        double bnb_m2 = static_cast<double>(std::accumulate(sums_of_squares.begin(),
-                                                            sums_of_squares.end(),
-                                                            size_t(0))) / total_size - bnb_mean;
-        double a = bnb_mean * bnb_m2;
-        double b = bnb_m2 * (2 * bnb_mean * bnb_mean - bnb_m2 + bnb_mean);
-        double c = 2 * bnb_mean * bnb_mean * bnb_m2;
-
-        double r = (-b + sqrt(b * b - 4 * a * c)) / 2 / a;
-        double r_low = (-b - sqrt(b * b - 4 * a * c)) / 2 / a;
-        if (r_low > 0)
-            common::logger->warn("Two possible solutions for r: {} and {}", r_low, r);
+        double mu_sum = 0;
+        double sum_of_vars = 0;
+        double other_stat = 0;
+        for (size_t i = 0; i < groups.size(); ++i) {
+            mu_sum += sums[i];
 
-        common::logger->trace("r est: {}", r);
+            double mu = static_cast<double>(sums[i]) / max_index;
+            double var = static_cast<double>(sums_of_squares[i]) / max_index - mu * mu;
 
-        boost::math::chi_squared dist(1);
-        uint64_t row_i = 0;
+            sum_of_vars += var;
+            other_stat += (var - mu) / mu;
+        }
+        mu_sum /= max_index;
+
+        double var_sum = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,
                          std::unique_ptr<ValuesContainer>,
-                         std::vector<std::pair<uint64_t, uint64_t>>>(columns_all, column_values_all,
-                                                                     [&](const auto &row) {
-            if (row.empty()) {
-                pvals.emplace_back(std::numeric_limits<double>::max());
-                ++row_i;
-                return;
+                         PairContainer>(columns_all, column_values_all, [&](const auto &row) {
+            double sum = 0;
+            for (const auto &[j, c] : row) {
+                sum += c;
             }
+            var_sum += sum * sum;
+        });
+        var_sum = var_sum / max_index - mu_sum * mu_sum;
+
+        double r = mu_sum / other_stat * sqrt((sum_of_vars - mu_sum)/(var_sum - mu_sum));
+        common::logger->trace("r est: {}", r);
+
+        boost::math::chi_squared dist(1);
+
+        auto compute_pval = [&](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
             double in_sum = 0;
             double out_sum = 0;
@@ -242,7 +263,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double total_sum = in_sum + out_sum;
             size_t ndigits = 20;
 
-            double lambda_in = boost::math::tools::newton_raphson_iterate([&](double lambda) {
+            auto ll_dx_ddx_in = [&](double lambda) {
                 double val = 0;
                 double dval = 0;
                 for (const auto &[j, c] : row) {
@@ -265,9 +286,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 return std::make_pair(val, dval);
-            }, in_sum / in_kmers, 0.0, 1.0, ndigits);
+            };
 
-            double lambda_out = boost::math::tools::newton_raphson_iterate([&](double lambda) {
+            auto ll_dx_ddx_out = [&](double lambda) {
                 double val = 0;
                 double dval = 0;
                 for (const auto &[j, c] : row) {
@@ -290,9 +311,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 return std::make_pair(val, dval);
-            }, out_sum / out_kmers, 0.0, 1.0, ndigits);
+            };
 
-            double lambda_null = boost::math::tools::newton_raphson_iterate([&](double lambda) {
+            auto ll_dx_ddx_null = [&](double lambda) {
                 double val = 0;
                 double dval = 0;
                 for (const auto &[j, c] : row) {
@@ -311,51 +332,88 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 return std::make_pair(val, dval);
-            }, total_sum / total_kmers, 0.0, 1.0, ndigits);
+            };
 
-            double loglikelihood_alt = 0;
-            double loglikelihood_null = 0;
+            double lambda_null = boost::math::tools::newton_raphson_iterate(
+                ll_dx_ddx_null, total_sum / total_kmers, 0.0, 1.0, ndigits
+            );
 
-            for (const auto &[j, c] : row) {
-                double lambda = groups[j] ? lambda_out : lambda_in;
-                double prop = lambda * sums[j];
-                loglikelihood_alt += log(lambda) * c - log(r + prop) * (r + c);
+            double chi_stat = 0;
+            if (true) {
+                // score test
+                auto [val_in, dval_in] = ll_dx_ddx_in(lambda_null);
+                auto [val_out, dval_out] = ll_dx_ddx_out(lambda_null);
+                val_in *= r / lambda_null;
+                val_out *= r / lambda_null;
+
+                chi_stat = val_in * val_in / r / (r + lambda_null) * lambda_null / in_kmers
+                            + val_out * val_out / r / (r + lambda_null) * lambda_null / out_kmers;
+            } else {
+                // log likelihood test
+                double lambda_in = boost::math::tools::newton_raphson_iterate(
+                    ll_dx_ddx_in, in_sum / in_kmers, 0.0, 1.0, ndigits
+                );
 
-                double prop_null = lambda_null * sums[j];
-                loglikelihood_null += log(lambda_null) * c - log(r + prop_null) * (r + c);
-            }
+                double lambda_out = boost::math::tools::newton_raphson_iterate(
+                    ll_dx_ddx_out, out_sum / out_kmers, 0.0, 1.0, ndigits
+                );
 
-            for (size_t j : unfound) {
-                double lambda = groups[j] ? lambda_out : lambda_in;
-                double prop = lambda * sums[j];
-                loglikelihood_alt -= log(r + prop) * r;
+                double loglikelihood_alt = 0;
+                double loglikelihood_null = 0;
 
-                double prop_null = lambda_null * sums[j];
-                loglikelihood_null -= log(r + prop_null) * r;
-            }
+                for (const auto &[j, c] : row) {
+                    double lambda = groups[j] ? lambda_out : lambda_in;
+                    double prop = lambda * sums[j];
+                    loglikelihood_alt += log(lambda) * c - log(r + prop) * (r + c);
 
-            double chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
+                    double prop_null = lambda_null * sums[j];
+                    loglikelihood_null += log(lambda_null) * c - log(r + prop_null) * (r + c);
+                }
 
-            if (chi_stat > 0) {
-                double pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
-                if (pval < 0.05) {
-                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                    double scaled_outsum = out_sum / out_kmers * in_kmers;
-                    if (scaled_outsum < in_sum) {
-                        indicator_in[node] = true;
-                    } else if (scaled_outsum > in_sum) {
-                        indicator_out[node] = true;
-                    }
+                for (size_t j : unfound) {
+                    double lambda = groups[j] ? lambda_out : lambda_in;
+                    double prop = lambda * sums[j];
+                    loglikelihood_alt -= log(r + prop) * r;
+
+                    double prop_null = lambda_null * sums[j];
+                    loglikelihood_null -= log(r + prop_null) * r;
                 }
 
-                pvals.emplace_back(pval);
-            } else if (chi_stat == 0) {
-                pvals.emplace_back(1.0);
-            } else {
+                chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
+            }
+
+            if (chi_stat == 0)
+                return std::make_tuple(1.0, in_sum, out_sum);
+
+            if (chi_stat < 0) {
                 common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
                 throw std::runtime_error("Test failed");
             }
 
+            double pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
+            return std::make_tuple(
+                pval,
+                in_sum,
+                out_sum
+            );
+        };
+
+        uint64_t row_i = 0;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                         std::unique_ptr<ValuesContainer>,
+                         PairContainer>(columns_all, column_values_all, [&](const auto &row) {
+            // TODO: compute min p value based on lambda, keep track of m(k) in dictionary
+            auto [pval, in_sum, out_sum, pval_min] = compute_pval(row);
+            pvals.emplace_back(pval);
+            if (pval < 0.05) {
+                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                double scaled_outsum = out_sum / out_kmers * in_kmers;
+                if (scaled_outsum < in_sum) {
+                    indicator_in[node] = true;
+                } else if (scaled_outsum > in_sum) {
+                    indicator_out[node] = true;
+                }
+            }
             ++row_i;
         });
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u" || config.test_type == "quantile") {
@@ -508,18 +566,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         );
         std::sort(stats.begin(), stats.end(), utils::LessFirst());
 
-        size_t acc = 0;
-        auto it = stats.begin();
-        for ( ; it != stats.end(); ++it) {
-            const auto &[p, bucket] = *it;
-            double k = 0.05 / p;
-            if (acc + bucket.size() > k)
-                break;
+        // we need k * pval_min < 0.05 => k < 0.05 / pval_min
+        size_t k = 0.05 / pval_min;
 
-            acc += bucket.size();
-        }
+        // we need to decrease k until total_sig <= k
 
-        common::logger->trace("Corr: {}\tp: {}", acc, 0.05 / acc);
+        common::logger->trace("Corr: {}\tp: {}", k, 0.05 / k);
 
         for (auto jt = stats.begin(); jt != stats.end(); ++jt) {
             const auto &[p, bucket] = *jt;

From 5f021206cea3bb4a32675f702cc9e5ff04c46a4d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 26 Apr 2024 15:37:48 +0200
Subject: [PATCH 106/410] tt

---
 .../src/graph/annotated_graph_algorithm.cpp   | 66 ++++++++++---------
 1 file changed, 36 insertions(+), 30 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 218d32eb5c..36fd1fea42 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -119,7 +119,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
-    tsl::hopscotch_map<size_t, size_t> m;
+    double pval_min = 0;
 
     sdsl::bit_vector indicator_in(max_index + 1, false);
     sdsl::bit_vector indicator_out(max_index + 1, false);
@@ -207,10 +207,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         });
     } else if (config.test_type == "nbinom") {
         // use moments to fit the r parameter of a beta negative binomial
+        double in_sq_kmers = 0;
+        double out_sq_kmers = 0;
         double mu_sum = 0;
         double sum_of_vars = 0;
         double other_stat = 0;
         for (size_t i = 0; i < groups.size(); ++i) {
+            if (groups[i]) {
+                out_sq_kmers += sums[i] * sums[i];
+            } else {
+                in_sq_kmers += sums[i] * sums[i];
+            }
             mu_sum += sums[i];
 
             double mu = static_cast<double>(sums[i]) / max_index;
@@ -240,7 +247,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         auto compute_pval = [&](const auto &row) {
             if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+                return std::make_tuple(1.1, 0.0, 0.0);
 
             double in_sum = 0;
             double out_sum = 0;
@@ -339,15 +346,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             );
 
             double chi_stat = 0;
-            if (true) {
+            if (false) {
                 // score test
                 auto [val_in, dval_in] = ll_dx_ddx_in(lambda_null);
                 auto [val_out, dval_out] = ll_dx_ddx_out(lambda_null);
+
                 val_in *= r / lambda_null;
                 val_out *= r / lambda_null;
 
-                chi_stat = val_in * val_in / r / (r + lambda_null) * lambda_null / in_kmers
-                            + val_out * val_out / r / (r + lambda_null) * lambda_null / out_kmers;
+                dval_in = -(dval_in * r / lambda_null - val_in / lambda_null) / labels_in.size();
+                dval_out = -(dval_out * r / lambda_null - val_out / lambda_null) / labels_out.size();
+                chi_stat = val_in * val_in / dval_in + val_out * val_out / dval_out;
+                // chi_stat = val_in * val_in / (in_kmers + lambda_null / r * in_sq_kmers)
+                //             + val_out * val_out / (out_kmers + lambda_null / r * out_sq_kmers);
             } else {
                 // log likelihood test
                 double lambda_in = boost::math::tools::newton_raphson_iterate(
@@ -398,12 +409,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             );
         };
 
+        auto [pval_min_in, max_sum_in, min_sum_out] = compute_pval(in_best);
+        auto [pval_min_out, min_sum_in, max_sum_out] = compute_pval(out_best);
+        pval_min = std::min(pval_min_in, pval_min_out);
+
         uint64_t row_i = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,
                          std::unique_ptr<ValuesContainer>,
                          PairContainer>(columns_all, column_values_all, [&](const auto &row) {
-            // TODO: compute min p value based on lambda, keep track of m(k) in dictionary
-            auto [pval, in_sum, out_sum, pval_min] = compute_pval(row);
+            auto [pval, in_sum, out_sum] = compute_pval(row);
+            if (pval < pval_min) {
+                common::logger->error("Invalid pval_min: {} > {}", pval_min, pval);
+                throw std::runtime_error("Test failed");
+            }
             pvals.emplace_back(pval);
             if (pval < 0.05) {
                 node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
@@ -554,29 +572,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
         common::logger->trace("Correcting {}/{} significant p-values", total_sig, max_index);
 
-        VectorMap<double, std::vector<size_t>> stats_map;
-        call_ones(indicator_in, [&](size_t i) {
-            stats_map[pvals[i]].emplace_back(i);
-        });
-        call_ones(indicator_out, [&](size_t i) {
-            stats_map[pvals[i]].emplace_back(i);
-        });
-        auto stats = const_cast<std::vector<std::pair<double, std::vector<size_t>>>&&>(
-            stats_map.values_container()
-        );
-        std::sort(stats.begin(), stats.end(), utils::LessFirst());
-
-        // we need k * pval_min < 0.05 => k < 0.05 / pval_min
-        size_t k = 0.05 / pval_min;
-
-        // we need to decrease k until total_sig <= k
-
-        common::logger->trace("Corr: {}\tp: {}", k, 0.05 / k);
-
-        for (auto jt = stats.begin(); jt != stats.end(); ++jt) {
-            const auto &[p, bucket] = *jt;
-            if (jt >= it || p * acc >= 0.05) {
-                for (size_t i : bucket) {
+        // we need to find min k s.t.
+        // k * pval_min < 0.05 and total_sig <= k
+        // so
+        // total_sig <= k < 0.05 / pval_min
+        common::logger->trace("Min p-val: {}\talpha_corr: {}", pval_min, 0.05 / total_sig);
+        if (pval_min * total_sig >= 0.05) {
+            sdsl::util::set_to_value(indicator_in, false);
+            sdsl::util::set_to_value(indicator_out, false);
+        } else {
+            for (size_t i = 0; i < pvals.size(); ++i) {
+                if (pvals[i] < 0.05 && pvals[i] * total_sig >= 0.05) {
                     indicator_in[i] = false;
                     indicator_out[i] = false;
                 }

From a57c1c8afc4b1a18d2bc2b45fd2742ca45f62885 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 26 Apr 2024 23:23:03 +0200
Subject: [PATCH 107/410] try2

---
 .../src/graph/annotated_graph_algorithm.cpp   | 88 ++++++++++---------
 1 file changed, 47 insertions(+), 41 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 36fd1fea42..5884174d05 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -108,19 +108,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
     double total_kmers = in_kmers + out_kmers;
 
-    PairContainer in_best;
-    PairContainer out_best;
-
-    for (size_t i = 0; i < groups.size(); ++i) {
-        if (groups[i]) {
-            out_best.emplace_back(i, sums[i]);
-        } else {
-            in_best.emplace_back(i, sums[i]);
-        }
-    }
-
-    double pval_min = 0;
-
     sdsl::bit_vector indicator_in(max_index + 1, false);
     sdsl::bit_vector indicator_out(max_index + 1, false);
 
@@ -128,6 +115,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     pvals.reserve(max_index + 1);
     pvals.emplace_back(std::numeric_limits<double>::max());
 
+    VectorMap<size_t, size_t> m;
+
     if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig"
             || config.test_type == "cmh"
             || config.test_type == "fisher") {
@@ -247,7 +236,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         auto compute_pval = [&](const auto &row) {
             if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0);
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
             double in_sum = 0;
             double out_sum = 0;
@@ -346,6 +335,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             );
 
             double chi_stat = 0;
+            double pval_min = 0;
             if (false) {
                 // score test
                 auto [val_in, dval_in] = ll_dx_ddx_in(lambda_null);
@@ -354,11 +344,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 val_in *= r / lambda_null;
                 val_out *= r / lambda_null;
 
-                dval_in = -(dval_in * r / lambda_null - val_in / lambda_null) / labels_in.size();
-                dval_out = -(dval_out * r / lambda_null - val_out / lambda_null) / labels_out.size();
-                chi_stat = val_in * val_in / dval_in + val_out * val_out / dval_out;
-                // chi_stat = val_in * val_in / (in_kmers + lambda_null / r * in_sq_kmers)
-                //             + val_out * val_out / (out_kmers + lambda_null / r * out_sq_kmers);
+                dval_in = (val_in / lambda_null - dval_in * r / lambda_null) / labels_in.size();
+                dval_out = (val_out / lambda_null - dval_out * r / lambda_null) / labels_out.size();
+                chi_stat = std::max(val_in * val_in / dval_in + val_out * val_out / dval_out,
+                                    double(0.0));
             } else {
                 // log likelihood test
                 double lambda_in = boost::math::tools::newton_raphson_iterate(
@@ -372,10 +361,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double loglikelihood_alt = 0;
                 double loglikelihood_null = 0;
 
+                double loglikelihood_max = 0;
+
                 for (const auto &[j, c] : row) {
                     double lambda = groups[j] ? lambda_out : lambda_in;
                     double prop = lambda * sums[j];
                     loglikelihood_alt += log(lambda) * c - log(r + prop) * (r + c);
+                    loglikelihood_max -= log(r + prop) * r;
 
                     double prop_null = lambda_null * sums[j];
                     loglikelihood_null += log(lambda_null) * c - log(r + prop_null) * (r + c);
@@ -385,45 +377,49 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     double lambda = groups[j] ? lambda_out : lambda_in;
                     double prop = lambda * sums[j];
                     loglikelihood_alt -= log(r + prop) * r;
+                    loglikelihood_max -= log(r + prop) * r;
 
                     double prop_null = lambda_null * sums[j];
                     loglikelihood_null -= log(r + prop_null) * r;
                 }
 
                 chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
+
+                if (chi_stat < 0) {
+                    common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+                    throw std::runtime_error("Test failed");
+                }
+                pval_min = boost::math::cdf(boost::math::complement(dist, (loglikelihood_max - loglikelihood_null) * 2));
             }
 
-            if (chi_stat == 0)
-                return std::make_tuple(1.0, in_sum, out_sum);
+            double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
 
-            if (chi_stat < 0) {
-                common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+            if (pval_min > pval) {
+                common::logger->error("Failed to compute min pval: {} > {}", pval_min, pval);
                 throw std::runtime_error("Test failed");
             }
 
-            double pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
             return std::make_tuple(
                 pval,
                 in_sum,
-                out_sum
+                out_sum,
+                pval_min
             );
         };
 
-        auto [pval_min_in, max_sum_in, min_sum_out] = compute_pval(in_best);
-        auto [pval_min_out, min_sum_in, max_sum_out] = compute_pval(out_best);
-        pval_min = std::min(pval_min_in, pval_min_out);
-
         uint64_t row_i = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,
                          std::unique_ptr<ValuesContainer>,
                          PairContainer>(columns_all, column_values_all, [&](const auto &row) {
-            auto [pval, in_sum, out_sum] = compute_pval(row);
-            if (pval < pval_min) {
-                common::logger->error("Invalid pval_min: {} > {}", pval_min, pval);
-                throw std::runtime_error("Test failed");
-            }
+            auto [pval, in_sum, out_sum, pval_min] = compute_pval(row);
             pvals.emplace_back(pval);
             if (pval < 0.05) {
+                if (pval_min == 0) {
+                    ++m[std::numeric_limits<size_t>::max()];
+                } else {
+                    size_t k = 0.05 / pval_min;
+                    ++m[k];
+                }
                 node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
                 double scaled_outsum = out_sum / out_kmers * in_kmers;
                 if (scaled_outsum < in_sum) {
@@ -572,17 +568,27 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
         common::logger->trace("Correcting {}/{} significant p-values", total_sig, max_index);
 
-        // we need to find min k s.t.
-        // k * pval_min < 0.05 and total_sig <= k
-        // so
-        // total_sig <= k < 0.05 / pval_min
-        common::logger->trace("Min p-val: {}\talpha_corr: {}", pval_min, 0.05 / total_sig);
-        if (pval_min * total_sig >= 0.05) {
+        auto m_data = const_cast<std::vector<std::pair<size_t, size_t>>&&>(m.values_container());
+        std::sort(m_data.begin(), m_data.end(), utils::GreaterFirst());
+
+        size_t acc = 0;
+        size_t k = 0;
+        for (const auto &[cur_k, c] : m_data) {
+            acc += c;
+            common::logger->trace("Checking: m(k)={}\tk={}", acc, cur_k);
+            if (acc <= cur_k)
+                k = cur_k;
+        }
+
+        if (k == 0) {
+            common::logger->trace("No good k value found");
             sdsl::util::set_to_value(indicator_in, false);
             sdsl::util::set_to_value(indicator_out, false);
         } else {
+            common::logger->trace("k: {}\talpha_corr: {}", k, 0.05 / k);
+
             for (size_t i = 0; i < pvals.size(); ++i) {
-                if (pvals[i] < 0.05 && pvals[i] * total_sig >= 0.05) {
+                if (pvals[i] < 0.05 && pvals[i] * k >= 0.05) {
                     indicator_in[i] = false;
                     indicator_out[i] = false;
                 }

From 60212838aa40872f7c2edf2fd6c10bc142a6a1bd Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 29 Apr 2024 00:52:01 +0200
Subject: [PATCH 108/410] test3

---
 .../src/graph/annotated_graph_algorithm.cpp   | 226 +++++++++++++++---
 1 file changed, 194 insertions(+), 32 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5884174d05..d43e1af9ae 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -230,13 +230,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         var_sum = var_sum / max_index - mu_sum * mu_sum;
 
         double r = mu_sum / other_stat * sqrt((sum_of_vars - mu_sum)/(var_sum - mu_sum));
+        // double mu = static_cast<double>(total_kmers) / max_index / groups.size();
+        // double var = static_cast<double>(std::accumulate(sums_of_squares.begin(),
+        //                                                  sums_of_squares.end(),
+        //                                                  size_t(0))) / max_index / groups.size() - mu * mu;
+        // double r = mu * mu / (var - mu);
         common::logger->trace("r est: {}", r);
 
         boost::math::chi_squared dist(1);
 
         auto compute_pval = [&](const auto &row) {
             if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+                return std::make_tuple(1.1, 0.0, 0.0);
 
             double in_sum = 0;
             double out_sum = 0;
@@ -335,7 +340,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             );
 
             double chi_stat = 0;
-            double pval_min = 0;
             if (false) {
                 // score test
                 auto [val_in, dval_in] = ll_dx_ddx_in(lambda_null);
@@ -361,13 +365,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double loglikelihood_alt = 0;
                 double loglikelihood_null = 0;
 
-                double loglikelihood_max = 0;
-
                 for (const auto &[j, c] : row) {
                     double lambda = groups[j] ? lambda_out : lambda_in;
                     double prop = lambda * sums[j];
                     loglikelihood_alt += log(lambda) * c - log(r + prop) * (r + c);
-                    loglikelihood_max -= log(r + prop) * r;
 
                     double prop_null = lambda_null * sums[j];
                     loglikelihood_null += log(lambda_null) * c - log(r + prop_null) * (r + c);
@@ -377,10 +378,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     double lambda = groups[j] ? lambda_out : lambda_in;
                     double prop = lambda * sums[j];
                     loglikelihood_alt -= log(r + prop) * r;
-                    loglikelihood_max -= log(r + prop) * r;
 
                     double prop_null = lambda_null * sums[j];
-                    loglikelihood_null -= log(r + prop_null) * r;
+                    loglikelihood_null -= log(r + prop_null) * r;\
                 }
 
                 chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
@@ -389,42 +389,47 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
                     throw std::runtime_error("Test failed");
                 }
-                pval_min = boost::math::cdf(boost::math::complement(dist, (loglikelihood_max - loglikelihood_null) * 2));
             }
 
             double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
 
-            if (pval_min > pval) {
-                common::logger->error("Failed to compute min pval: {} > {}", pval_min, pval);
-                throw std::runtime_error("Test failed");
-            }
-
-            return std::make_tuple(
-                pval,
-                in_sum,
-                out_sum,
-                pval_min
-            );
+            return std::make_tuple(pval, in_sum, out_sum);
         };
 
         uint64_t row_i = 0;
         utils::call_rows<std::unique_ptr<bit_vector>,
                          std::unique_ptr<ValuesContainer>,
                          PairContainer>(columns_all, column_values_all, [&](const auto &row) {
-            auto [pval, in_sum, out_sum, pval_min] = compute_pval(row);
+            auto [pval, in_sum, out_sum] = compute_pval(row);
             pvals.emplace_back(pval);
             if (pval < 0.05) {
-                if (pval_min == 0) {
-                    ++m[std::numeric_limits<size_t>::max()];
-                } else {
-                    size_t k = 0.05 / pval_min;
-                    ++m[k];
+                double scaled_outsum = out_sum / out_kmers * in_kmers;
+                if (scaled_outsum == in_sum)
+                    return;
+
+                bool in_kmer = scaled_outsum < in_sum;
+                bool out_kmer = scaled_outsum > in_sum;
+
+                PairContainer best;
+                for (const auto &[j, c] : row) {
+                    if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
+                        best.emplace_back(j, sums[j]);
                 }
+
+                auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
+
+                if (pval_min > pval) {
+                    common::logger->trace("Min p-val estimate too high: {} > {}", pval_min, pval);
+                    throw std::runtime_error("Test failed");
+                }
+
+                size_t k = 0.05 / pval_min;
+                ++m[k];
+
                 node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                double scaled_outsum = out_sum / out_kmers * in_kmers;
-                if (scaled_outsum < in_sum) {
+                if (in_kmer) {
                     indicator_in[node] = true;
-                } else if (scaled_outsum > in_sum) {
+                } else if (out_kmer) {
                     indicator_out[node] = true;
                 }
             }
@@ -552,6 +557,161 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             ++row_i;
         });
+    } else if (config.test_type == "zinb") {
+        // size_t nelem_orig = std::accumulate(n_kmers.begin(), n_kmers.end(), size_t(0));
+        // size_t total_sq_kmers = std::accumulate(sums_of_squares.begin(),
+        //                                         sums_of_squares.end(),
+        //                                         size_t(0));
+        // size_t nelem = nelem_orig;
+        // double r = 0;
+        // for (size_t i = 0; i < 100; ++i) {
+        //     double mu = static_cast<double>(total_kmers) / nelem;
+        //     double var = static_cast<double>(total_sq_kmers) / nelem - mu * mu;
+        //     double old_r = r;
+        //     r = mu * mu / (var - mu);
+        //     double p = mu / var;
+        //     nelem = nelem_orig / (1 - pow(p, r));
+        //     if (abs(r - old_r) / r < 1e-5)
+        //         break;
+        // }
+
+        std::vector<double> pi;
+        // double r_prod = 1;
+        double r_sum = 0;
+        pi.reserve(n_kmers.size());
+        for (size_t j = 0; j < n_kmers.size(); ++j) {
+            size_t nelem_orig = n_kmers[j];
+            size_t nelem = nelem_orig;
+            double p = 0;
+            double r = 0;
+            for (size_t i = 0; i < 100; ++i) {
+                double mu = static_cast<double>(sums[j]) / nelem;
+                double var = static_cast<double>(sums_of_squares[j]) / nelem;
+                double p_old = p;
+                r = mu * mu / (var - mu);
+                p = mu / var;
+                nelem = nelem_orig / (1 - pow(p, r));
+                if (abs(p - p_old) / p < 1e-5)
+                    break;
+            }
+            double mu = static_cast<double>(sums[j]) / max_index;
+            pi.emplace_back(1.0 - mu / (r * (1 - p) / p));
+            common::logger->trace("Label: {}\tr: {}\tp: {}\tpi: {}", j, r, p, pi.back());
+            r_sum += r;
+        }
+        double r = r_sum / groups.size();
+        common::logger->trace("r est: {}", r);
+
+        boost::math::chi_squared dist(1);
+        auto compute_pval = [&](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0);
+
+            std::vector<uint64_t> row_c(groups.size());
+            double in_sum = 0;
+            double out_sum = 0;
+            for (const auto &[j, c] : row) {
+                if (groups[j]) {
+                    out_sum += c;
+                } else {
+                    in_sum += c;
+                }
+                row_c[j] = c;
+            }
+
+            auto ll_dx_ddx_pick = [&](const auto &picker) {
+                return [&](double lambda) {
+                    double val = 0;
+                    double dval = 0;
+                    for (size_t j = 0; j < row_c.size(); ++j) {
+                        if (picker(j)) {
+                            uint64_t c = row_c[j];
+                            double mu = static_cast<double>(sums[j]) * lambda;
+                            double rshift = r + mu;
+                            double p = r / rshift;
+                            if (c > 0) {
+                                double interm = p / lambda * (c - mu);
+                                val += interm;
+                                dval -= interm / lambda + p / lambda * sums[j] * (r + c) / rshift;
+                            } else {
+                                double p_p = pow(p, r);
+                                double denom = pi[j] + (1 - pi[j]) * p_p;
+                                val -= (1 - pi[j]) * sums[j] * p_p * p / (pi[j] + (1 - pi[j]) * p_p);
+                                dval -= r * sums[j] * sums[j] * (1 - pi[j]) / rshift * p_p * p / denom / denom * (denom + r * pi[j]);
+                            }
+                        }
+                    }
+
+                    return std::make_pair(val, dval);
+                };
+            };
+
+            size_t ndigits = 20;
+            double lambda_in = boost::math::tools::newton_raphson_iterate(
+                ll_dx_ddx_pick([&](size_t j) { return !groups[j]; }),
+                0.5, -0.01, 1.0, ndigits
+            );
+            double lambda_out = boost::math::tools::newton_raphson_iterate(
+                ll_dx_ddx_pick([&](size_t j) { return groups[j]; }),
+                0.5, -0.01, 1.0, ndigits
+            );
+            double lambda_null = boost::math::tools::newton_raphson_iterate(
+                ll_dx_ddx_pick([&](size_t) { return true; }),
+                0.5, 0.0, 1.0, ndigits
+            );
+
+            if (lambda_in < 0 || lambda_out < 0) {
+                common::logger->error("Fit out of bounds: in: {}\tout: {}", lambda_in, lambda_out);
+                throw std::runtime_error("Fit failed");
+            }
+
+            double loglikelihood_alt = 0;
+            double loglikelihood_null = 0;
+            for (size_t j = 0; j < row_c.size(); ++j) {
+                uint64_t c = row_c[j];
+                double lambda = groups[j] ? lambda_out : lambda_in;
+                double mu = static_cast<double>(sums[j]) / lambda;
+                double mu_null = static_cast<double>(sums[j]) / lambda_null;
+                double rshift = r + mu;
+                double rshift_null = r + mu_null;
+
+                if (c > 0) {
+                    loglikelihood_alt += c * log(mu) - c * log(rshift) - r * log(rshift);
+                    loglikelihood_null += c * log(mu_null) - c * log(rshift_null) - r * log(rshift_null);
+                } else {
+                    loglikelihood_alt += log(pi[j] + (1 - pi[j]) * pow(r / rshift, r));
+                    loglikelihood_null += log(pi[j] + (1 - pi[j]) * pow(r / rshift_null, r));
+                }
+            }
+
+            double chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
+            if (chi_stat < 0) {
+                common::logger->error("stat {} < 0", chi_stat);
+                throw std::runtime_error("Failed test");
+            }
+
+            double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
+            return std::make_tuple(pval, in_sum, out_sum);
+        };
+
+        uint64_t row_i = 0;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                         std::unique_ptr<ValuesContainer>,
+                         PairContainer>(columns_all, column_values_all, [&](const auto &row) {
+            auto [pval, in_sum, out_sum] = compute_pval(row);
+            pvals.emplace_back(pval);
+            if (pval < 0.05) {
+                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                double scaled_outsum = out_sum / out_kmers * in_kmers;
+                if (scaled_outsum < in_sum) {
+                    indicator_in[node] = true;
+                } else if (scaled_outsum > in_sum) {
+                    indicator_out[node] = true;
+                }
+            }
+            ++row_i;
+        });
+
     } else {
         throw std::runtime_error("Test not implemented");
     }
@@ -569,15 +729,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Correcting {}/{} significant p-values", total_sig, max_index);
 
         auto m_data = const_cast<std::vector<std::pair<size_t, size_t>>&&>(m.values_container());
-        std::sort(m_data.begin(), m_data.end(), utils::GreaterFirst());
+        std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
 
         size_t acc = 0;
         size_t k = 0;
         for (const auto &[cur_k, c] : m_data) {
             acc += c;
-            common::logger->trace("Checking: m(k)={}\tk={}", acc, cur_k);
-            if (acc <= cur_k)
-                k = cur_k;
+            common::logger->trace("Checking: m(k) = {}\tk <= {}", acc, cur_k);
+            if (acc <= cur_k) {
+                k = acc;
+                break;
+            }
         }
 
         if (k == 0) {

From df28fe775dc731c7dcac6b041a6561899067e89d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 29 Apr 2024 02:00:58 +0200
Subject: [PATCH 109/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 34 +++++++++++++------
 1 file changed, 23 insertions(+), 11 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index d43e1af9ae..ca90b14306 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -115,7 +115,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     pvals.reserve(max_index + 1);
     pvals.emplace_back(std::numeric_limits<double>::max());
 
-    VectorMap<size_t, size_t> m;
+    VectorMap<size_t, std::vector<uint64_t>> m;
 
     if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig"
             || config.test_type == "cmh"
@@ -424,9 +424,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 size_t k = 0.05 / pval_min;
-                ++m[k];
 
                 node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                m[k].emplace_back(node);
+
                 if (in_kmer) {
                     indicator_in[node] = true;
                 } else if (out_kmer) {
@@ -728,18 +729,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
         common::logger->trace("Correcting {}/{} significant p-values", total_sig, max_index);
 
-        auto m_data = const_cast<std::vector<std::pair<size_t, size_t>>&&>(m.values_container());
+        auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
         std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
 
-        size_t acc = 0;
+        size_t acc = total_sig;
         size_t k = 0;
-        for (const auto &[cur_k, c] : m_data) {
-            acc += c;
+        auto it = m_data.begin();
+        for (; it != m_data.end(); ++it) {
+            const auto &[cur_k, bucket] = *it;
             common::logger->trace("Checking: m(k) = {}\tk <= {}", acc, cur_k);
             if (acc <= cur_k) {
                 k = acc;
                 break;
             }
+            acc -= bucket.size();
         }
 
         if (k == 0) {
@@ -748,13 +751,22 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             sdsl::util::set_to_value(indicator_out, false);
         } else {
             common::logger->trace("k: {}\talpha_corr: {}", k, 0.05 / k);
-
-            for (size_t i = 0; i < pvals.size(); ++i) {
-                if (pvals[i] < 0.05 && pvals[i] * k >= 0.05) {
-                    indicator_in[i] = false;
-                    indicator_out[i] = false;
+            for (auto jt = m_data.begin(); jt != m_data.end(); ++jt) {
+                const auto &[cur_k, bucket] = *jt;
+                for (uint64_t i : bucket) {
+                    if (jt < it || pvals[i] * k >= 0.05) {
+                        indicator_in[i] = false;
+                        indicator_out[i] = false;
+                    }
                 }
             }
+
+            // for (size_t i = 0; i < pvals.size(); ++i) {
+            //     if (pvals[i] < 0.05 && pvals[i] * k >= 0.05) {
+            //         indicator_in[i] = false;
+            //         indicator_out[i] = false;
+            //     }
+            // }
         }
     }
 

From 70b404eaa34fde03846cfe020b9ea01d36d459bd Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 29 Apr 2024 18:57:39 +0200
Subject: [PATCH 110/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 157 +++++++++++-------
 1 file changed, 101 insertions(+), 56 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ca90b14306..7dfa9a269d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -196,46 +196,81 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         });
     } else if (config.test_type == "nbinom") {
         // use moments to fit the r parameter of a beta negative binomial
-        double in_sq_kmers = 0;
-        double out_sq_kmers = 0;
-        double mu_sum = 0;
-        double sum_of_vars = 0;
-        double other_stat = 0;
-        for (size_t i = 0; i < groups.size(); ++i) {
-            if (groups[i]) {
-                out_sq_kmers += sums[i] * sums[i];
-            } else {
-                in_sq_kmers += sums[i] * sums[i];
-            }
-            mu_sum += sums[i];
-
-            double mu = static_cast<double>(sums[i]) / max_index;
-            double var = static_cast<double>(sums_of_squares[i]) / max_index - mu * mu;
-
-            sum_of_vars += var;
-            other_stat += (var - mu) / mu;
-        }
-        mu_sum /= max_index;
-
-        double var_sum = 0;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         PairContainer>(columns_all, column_values_all, [&](const auto &row) {
-            double sum = 0;
-            for (const auto &[j, c] : row) {
-                sum += c;
-            }
-            var_sum += sum * sum;
-        });
-        var_sum = var_sum / max_index - mu_sum * mu_sum;
-
-        double r = mu_sum / other_stat * sqrt((sum_of_vars - mu_sum)/(var_sum - mu_sum));
+        // double in_sq_kmers = 0;
+        // double out_sq_kmers = 0;
+        // double mu_sum = 0;
+        // double sum_of_vars = 0;
+        // double other_stat = 0;
+        // for (size_t i = 0; i < groups.size(); ++i) {
+        //     if (groups[i]) {
+        //         out_sq_kmers += sums[i] * sums[i];
+        //     } else {
+        //         in_sq_kmers += sums[i] * sums[i];
+        //     }
+        //     mu_sum += sums[i];
+
+        //     double mu = static_cast<double>(sums[i]) / max_index;
+        //     double var = static_cast<double>(sums_of_squares[i]) / max_index - mu * mu;
+
+        //     sum_of_vars += var;
+        //     other_stat += (var - mu) / mu;
+        // }
+        // mu_sum /= max_index;
+
+        // double var_sum = 0;
+        // utils::call_rows<std::unique_ptr<bit_vector>,
+        //                  std::unique_ptr<ValuesContainer>,
+        //                  PairContainer>(columns_all, column_values_all, [&](const auto &row) {
+        //     double sum = 0;
+        //     for (const auto &[j, c] : row) {
+        //         sum += c;
+        //     }
+        //     var_sum += sum * sum;
+        // });
+        // var_sum = var_sum / max_index - mu_sum * mu_sum;
+
+        // double r = mu_sum / other_stat * sqrt((sum_of_vars - mu_sum)/(var_sum - mu_sum));
         // double mu = static_cast<double>(total_kmers) / max_index / groups.size();
         // double var = static_cast<double>(std::accumulate(sums_of_squares.begin(),
         //                                                  sums_of_squares.end(),
         //                                                  size_t(0))) / max_index / groups.size() - mu * mu;
         // double r = mu * mu / (var - mu);
-        common::logger->trace("r est: {}", r);
+        uint64_t sum_of_cubes = 0;
+        for (const auto &count_ptr : column_values_all) {
+            sum_of_cubes += std::accumulate(count_ptr->begin(), count_ptr->end(),
+                                            uint64_t(0),
+                                            [&](uint64_t sum, int64_t c) { return sum + c * (c - 1) * (c - 2); });
+        }
+        double mu = static_cast<double>(total_kmers) / groups.size() / max_index;
+        double mu2 = static_cast<double>(std::accumulate(sums_of_squares.begin(),
+                                                         sums_of_squares.end(), uint64_t(0))) / groups.size() / max_index - mu;
+        double mu3 = static_cast<double>(sum_of_cubes) / groups.size() / max_index;
+        double a = mu3 * mu + mu * mu * mu2 + 2 * mu2 * mu2;
+        double b = 2 * mu3 * mu * mu + mu3 * mu - mu2 * mu3 - mu * mu2 * mu2 + 3 * mu * mu + mu2 - 4 * mu2 * mu2;
+        double c = -2 * mu * (mu2 * mu2 - mu * mu2 - mu3 * mu);
+        double r_low = (-b - sqrt(b * b - 4 * a * c)) / 2 / a;
+        double r_high = (-b + sqrt(b * b - 4 * a * c)) / 2 / a;
+
+        double r_low_var_sqerr = 0;
+        double r_high_var_sqerr = 0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            double mu = static_cast<double>(sums[j]) / max_index;
+            double var = static_cast<double>(sums_of_squares[j]) / max_index - mu * mu;
+            double p_low = r_low / (r_low + mu);
+            double p_high = r_high / (r_high + mu);
+            double t_var_low = mu / p_low;
+            double t_var_high = mu / p_high;
+            r_low_var_sqerr += pow(t_var_low - var, 2.0);
+            r_high_var_sqerr += pow(t_var_high - var, 2.0);
+            common::logger->trace("label: {}\tvar: {}\tp_low: {}\tvar_low: {}\tp_high: {}\tvar_high: {}",
+                                  j, var, p_low, t_var_low, p_high, t_var_high);
+        }
+
+        common::logger->trace("r low: {} (sqerr: {})\t r high: {} (sqerr: {})",
+                              r_low, r_low_var_sqerr, r_high, r_high_var_sqerr);
+
+        double r = r_low_var_sqerr < r_high_var_sqerr ? r_low : r_high;
+        common::logger->trace("r: {}", r);
 
         boost::math::chi_squared dist(1);
 
@@ -340,6 +375,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             );
 
             double chi_stat = 0;
+            double loglikelihood_null = 0;
             if (false) {
                 // score test
                 auto [val_in, dval_in] = ll_dx_ddx_in(lambda_null);
@@ -363,7 +399,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 );
 
                 double loglikelihood_alt = 0;
-                double loglikelihood_null = 0;
 
                 for (const auto &[j, c] : row) {
                     double lambda = groups[j] ? lambda_out : lambda_in;
@@ -380,7 +415,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     loglikelihood_alt -= log(r + prop) * r;
 
                     double prop_null = lambda_null * sums[j];
-                    loglikelihood_null -= log(r + prop_null) * r;\
+                    loglikelihood_null -= log(r + prop_null) * r;
                 }
 
                 chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
@@ -419,11 +454,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
 
                 if (pval_min > pval) {
-                    common::logger->trace("Min p-val estimate too high: {} > {}", pval_min, pval);
+                    common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
                     throw std::runtime_error("Test failed");
                 }
 
-                size_t k = 0.05 / pval_min;
+                double lkd = log(0.05) - log(pval_min);
+                uint64_t k = lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max()))
+                    ? std::numeric_limits<uint64_t>::max()
+                    : exp(lkd);
+
+                if (k == 0) {
+                    common::logger->error("k: {}\tpval_min: {}", k, pval_min);
+                    throw std::runtime_error("Min failed");
+                }
 
                 node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
                 m[k].emplace_back(node);
@@ -730,19 +773,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Correcting {}/{} significant p-values", total_sig, max_index);
 
         auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
-        std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
 
-        size_t acc = total_sig;
-        size_t k = 0;
-        auto it = m_data.begin();
-        for (; it != m_data.end(); ++it) {
-            const auto &[cur_k, bucket] = *it;
-            common::logger->trace("Checking: m(k) = {}\tk <= {}", acc, cur_k);
-            if (acc <= cur_k) {
+        std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
+        size_t acc = 0;
+        size_t k = 1;
+        auto begin = m_data.begin();
+        auto it = begin;
+        while (it < m_data.end()) {
+            common::logger->trace("k: {}\tp: {}", k, 0.05 / k);
+            while (acc <= k && it < m_data.end()) {
+                acc += it->second.size();
+                ++it;
+            }
+            if (acc > k) {
+                size_t old_k = k;
                 k = acc;
+                while (begin < it && k >= begin->first && begin->first > old_k) {
+                    acc -= begin->second.size();
+                    ++begin;
+                }
+            } else {
                 break;
             }
-            acc -= bucket.size();
         }
 
         if (k == 0) {
@@ -754,19 +806,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             for (auto jt = m_data.begin(); jt != m_data.end(); ++jt) {
                 const auto &[cur_k, bucket] = *jt;
                 for (uint64_t i : bucket) {
-                    if (jt < it || pvals[i] * k >= 0.05) {
+                    if (jt < begin || pvals[i] * k >= 0.05) {
                         indicator_in[i] = false;
                         indicator_out[i] = false;
                     }
                 }
             }
-
-            // for (size_t i = 0; i < pvals.size(); ++i) {
-            //     if (pvals[i] < 0.05 && pvals[i] * k >= 0.05) {
-            //         indicator_in[i] = false;
-            //         indicator_out[i] = false;
-            //     }
-            // }
         }
     }
 

From fd03f659938d540353e3c715990c03185384b266 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 29 Apr 2024 19:29:55 +0200
Subject: [PATCH 111/410] fix

---
 .../src/graph/annotated_graph_algorithm.cpp   | 107 ++++++++++--------
 1 file changed, 61 insertions(+), 46 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 7dfa9a269d..fca198e6c6 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -437,40 +437,38 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          PairContainer>(columns_all, column_values_all, [&](const auto &row) {
             auto [pval, in_sum, out_sum] = compute_pval(row);
             pvals.emplace_back(pval);
-            if (pval < 0.05) {
-                double scaled_outsum = out_sum / out_kmers * in_kmers;
-                if (scaled_outsum == in_sum)
-                    return;
-
-                bool in_kmer = scaled_outsum < in_sum;
-                bool out_kmer = scaled_outsum > in_sum;
-
-                PairContainer best;
-                for (const auto &[j, c] : row) {
-                    if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
-                        best.emplace_back(j, sums[j]);
-                }
+            double scaled_outsum = out_sum / out_kmers * in_kmers;
+            if (scaled_outsum == in_sum)
+                return;
 
-                auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
+            bool in_kmer = scaled_outsum < in_sum;
+            bool out_kmer = scaled_outsum > in_sum;
 
-                if (pval_min > pval) {
-                    common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
-                    throw std::runtime_error("Test failed");
-                }
+            PairContainer best;
+            for (const auto &[j, c] : row) {
+                if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
+                    best.emplace_back(j, sums[j]);
+            }
 
-                double lkd = log(0.05) - log(pval_min);
-                uint64_t k = lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max()))
-                    ? std::numeric_limits<uint64_t>::max()
-                    : exp(lkd);
+            auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
 
-                if (k == 0) {
-                    common::logger->error("k: {}\tpval_min: {}", k, pval_min);
-                    throw std::runtime_error("Min failed");
-                }
+            if (pval_min > pval) {
+                common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
+                throw std::runtime_error("Test failed");
+            }
 
-                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                m[k].emplace_back(node);
+            double lkd = log(0.05) - log(pval_min);
+            uint64_t k = lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max()))
+                ? std::numeric_limits<uint64_t>::max()
+                : exp(lkd);
 
+            if (k == 0) {
+                common::logger->error("k: {}\tpval_min: {}", k, pval_min);
+                throw std::runtime_error("Min failed");
+            }
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+            m[k].emplace_back(node);
+            if (pval < 0.05) {
                 if (in_kmer) {
                     indicator_in[node] = true;
                 } else if (out_kmer) {
@@ -770,32 +768,49 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     {
         uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
-        common::logger->trace("Correcting {}/{} significant p-values", total_sig, max_index);
+
 
         auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
 
         std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
-        size_t acc = 0;
-        size_t k = 1;
+        size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
+                                     [](size_t sum, const auto &a) { return sum + a.second.size(); });
+        common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
+
         auto begin = m_data.begin();
-        auto it = begin;
-        while (it < m_data.end()) {
-            common::logger->trace("k: {}\tp: {}", k, 0.05 / k);
-            while (acc <= k && it < m_data.end()) {
-                acc += it->second.size();
-                ++it;
-            }
-            if (acc > k) {
-                size_t old_k = k;
-                k = acc;
-                while (begin < it && k >= begin->first && begin->first > old_k) {
-                    acc -= begin->second.size();
-                    ++begin;
-                }
-            } else {
+        size_t k = 0;
+        size_t last_k = 0;
+        for ( ; begin != m_data.end(); ++begin) {
+            const auto &[cur_k, bucket] = *begin;
+            if (acc <= cur_k) {
+                k = last_k + 1;
                 break;
             }
+
+            last_k = cur_k;
+            acc -= bucket.size();
         }
+        // size_t acc = 0;
+        // size_t k = 1;
+        // auto begin = m_data.begin();
+        // auto it = begin;
+        // while (it < m_data.end()) {
+        //     common::logger->trace("k: {}\tp: {}", k, 0.05 / k);
+        //     while (acc <= k && it < m_data.end()) {
+        //         acc += it->second.size();
+        //         ++it;
+        //     }
+        //     if (acc > k) {
+        //         size_t old_k = k;
+        //         k = acc;
+        //         while (begin < it && k >= begin->first && begin->first > old_k) {
+        //             acc -= begin->second.size();
+        //             ++begin;
+        //         }
+        //     } else {
+        //         break;
+        //     }
+        // }
 
         if (k == 0) {
             common::logger->trace("No good k value found");

From 9fc7bcbb4e5eca0cca90e2235aa822cdc138c352 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 29 Apr 2024 20:19:37 +0200
Subject: [PATCH 112/410] testable p-vals for poisson test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 311 ++++--------------
 1 file changed, 66 insertions(+), 245 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fca198e6c6..6418519624 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -45,6 +45,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     size_t max_index = graph_ptr->max_index();
     std::vector<double> medians;
     std::vector<uint64_t> max_vals;
+    std::vector<uint64_t> max_obs_vals;
     std::vector<size_t> n_kmers;
     std::vector<uint64_t> sums;
     std::vector<uint64_t> sums_of_squares;
@@ -68,8 +69,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             std::vector<uint64_t> vals;
             vals.reserve(column_values.size());
             std::copy(column_values.begin(), column_values.end(), std::back_inserter(vals));
-            max_observed_value = std::max(max_observed_value,
-                                          *std::max_element(vals.begin(), vals.end()));
+            max_obs_vals.emplace_back(*std::max_element(vals.begin(), vals.end()));
+            max_observed_value = std::max(max_observed_value, max_obs_vals.back());
             uint64_t sum = std::accumulate(vals.begin(), vals.end(), uint64_t(0));
             uint64_t sum_of_squares = std::accumulate(
                 vals.begin(), vals.end(), uint64_t(0),
@@ -121,16 +122,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             || config.test_type == "cmh"
             || config.test_type == "fisher") {
         boost::math::chi_squared dist(1);
-        uint64_t row_i = 0;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         std::vector<std::pair<uint64_t, uint64_t>>>(columns_all, column_values_all,
-                                                                     [&](const auto &row) {
-            if (row.empty()) {
-                pvals.emplace_back(std::numeric_limits<double>::max());
-                ++row_i;
-                return;
-            }
+        auto compute_pval = [&](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0);
 
             double in_sum = 0;
             double out_sum = 0;
@@ -170,71 +164,71 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 chi_stat = (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
             }
 
-            if (chi_stat > 0) {
-                if (pval == -1)
-                    pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
-
-                if (pval < 0.05) {
-                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                    double scaled_outsum = out_sum / out_kmers * in_kmers;
-                    if (scaled_outsum < in_sum) {
-                        indicator_in[node] = true;
-                    } else if (scaled_outsum > in_sum) {
-                        indicator_out[node] = true;
-                    }
-                }
-            } else if (chi_stat == 0) {
-                pval = 1.0;
-            } else {
+            if (chi_stat == 0)
+                return std::make_tuple(1.0, in_sum, out_sum);
+
+            if (chi_stat < 0) {
                 common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
                 throw std::runtime_error("Test failed");
             }
 
+            if (pval == -1)
+                pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
+
+            return std::make_tuple(pval, in_sum, out_sum);
+        };
+        uint64_t row_i = 0;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                         std::unique_ptr<ValuesContainer>,
+                         PairContainer>(columns_all, column_values_all,
+                                        [&](const auto &row) {
+            auto [pval, in_sum, out_sum] = compute_pval(row);
             pvals.emplace_back(pval);
+            double scaled_outsum = out_sum / out_kmers * in_kmers;
+
+            if (pval > 1.0 || scaled_outsum == in_sum) {
+                ++row_i;
+                return;
+            }
 
+            bool in_kmer = scaled_outsum < in_sum;
+            bool out_kmer = scaled_outsum > in_sum;
+
+            PairContainer best;
+            for (const auto &[j, c] : row) {
+                if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
+                    best.emplace_back(j, sums[j]);
+            }
+
+            auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
+
+            if (pval_min > pval) {
+                common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
+                throw std::runtime_error("Test failed");
+            }
+
+            double lkd = log(0.05) - log(pval_min);
+            uint64_t k = lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max()))
+                ? std::numeric_limits<uint64_t>::max()
+                : exp(lkd);
+
+            if (k == 0) {
+                common::logger->error("k: {}\tpval_min: {}", k, pval_min);
+                throw std::runtime_error("Min failed");
+            }
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+            m[k].emplace_back(node);
+            if (pval < 0.05) {
+                if (in_kmer) {
+                    indicator_in[node] = true;
+                } else if (out_kmer) {
+                    indicator_out[node] = true;
+                }
+            }
             ++row_i;
         });
     } else if (config.test_type == "nbinom") {
         // use moments to fit the r parameter of a beta negative binomial
-        // double in_sq_kmers = 0;
-        // double out_sq_kmers = 0;
-        // double mu_sum = 0;
-        // double sum_of_vars = 0;
-        // double other_stat = 0;
-        // for (size_t i = 0; i < groups.size(); ++i) {
-        //     if (groups[i]) {
-        //         out_sq_kmers += sums[i] * sums[i];
-        //     } else {
-        //         in_sq_kmers += sums[i] * sums[i];
-        //     }
-        //     mu_sum += sums[i];
-
-        //     double mu = static_cast<double>(sums[i]) / max_index;
-        //     double var = static_cast<double>(sums_of_squares[i]) / max_index - mu * mu;
-
-        //     sum_of_vars += var;
-        //     other_stat += (var - mu) / mu;
-        // }
-        // mu_sum /= max_index;
-
-        // double var_sum = 0;
-        // utils::call_rows<std::unique_ptr<bit_vector>,
-        //                  std::unique_ptr<ValuesContainer>,
-        //                  PairContainer>(columns_all, column_values_all, [&](const auto &row) {
-        //     double sum = 0;
-        //     for (const auto &[j, c] : row) {
-        //         sum += c;
-        //     }
-        //     var_sum += sum * sum;
-        // });
-        // var_sum = var_sum / max_index - mu_sum * mu_sum;
-
-        // double r = mu_sum / other_stat * sqrt((sum_of_vars - mu_sum)/(var_sum - mu_sum));
-        // double mu = static_cast<double>(total_kmers) / max_index / groups.size();
-        // double var = static_cast<double>(std::accumulate(sums_of_squares.begin(),
-        //                                                  sums_of_squares.end(),
-        //                                                  size_t(0))) / max_index / groups.size() - mu * mu;
-        // double r = mu * mu / (var - mu);
         uint64_t sum_of_cubes = 0;
         for (const auto &count_ptr : column_values_all) {
             sum_of_cubes += std::accumulate(count_ptr->begin(), count_ptr->end(),
@@ -438,8 +432,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto [pval, in_sum, out_sum] = compute_pval(row);
             pvals.emplace_back(pval);
             double scaled_outsum = out_sum / out_kmers * in_kmers;
-            if (scaled_outsum == in_sum)
+            if (pval > 1.0 || scaled_outsum == in_sum) {
+                ++row_i;
                 return;
+            }
 
             bool in_kmer = scaled_outsum < in_sum;
             bool out_kmer = scaled_outsum > in_sum;
@@ -599,161 +595,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             ++row_i;
         });
-    } else if (config.test_type == "zinb") {
-        // size_t nelem_orig = std::accumulate(n_kmers.begin(), n_kmers.end(), size_t(0));
-        // size_t total_sq_kmers = std::accumulate(sums_of_squares.begin(),
-        //                                         sums_of_squares.end(),
-        //                                         size_t(0));
-        // size_t nelem = nelem_orig;
-        // double r = 0;
-        // for (size_t i = 0; i < 100; ++i) {
-        //     double mu = static_cast<double>(total_kmers) / nelem;
-        //     double var = static_cast<double>(total_sq_kmers) / nelem - mu * mu;
-        //     double old_r = r;
-        //     r = mu * mu / (var - mu);
-        //     double p = mu / var;
-        //     nelem = nelem_orig / (1 - pow(p, r));
-        //     if (abs(r - old_r) / r < 1e-5)
-        //         break;
-        // }
-
-        std::vector<double> pi;
-        // double r_prod = 1;
-        double r_sum = 0;
-        pi.reserve(n_kmers.size());
-        for (size_t j = 0; j < n_kmers.size(); ++j) {
-            size_t nelem_orig = n_kmers[j];
-            size_t nelem = nelem_orig;
-            double p = 0;
-            double r = 0;
-            for (size_t i = 0; i < 100; ++i) {
-                double mu = static_cast<double>(sums[j]) / nelem;
-                double var = static_cast<double>(sums_of_squares[j]) / nelem;
-                double p_old = p;
-                r = mu * mu / (var - mu);
-                p = mu / var;
-                nelem = nelem_orig / (1 - pow(p, r));
-                if (abs(p - p_old) / p < 1e-5)
-                    break;
-            }
-            double mu = static_cast<double>(sums[j]) / max_index;
-            pi.emplace_back(1.0 - mu / (r * (1 - p) / p));
-            common::logger->trace("Label: {}\tr: {}\tp: {}\tpi: {}", j, r, p, pi.back());
-            r_sum += r;
-        }
-        double r = r_sum / groups.size();
-        common::logger->trace("r est: {}", r);
-
-        boost::math::chi_squared dist(1);
-        auto compute_pval = [&](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0);
-
-            std::vector<uint64_t> row_c(groups.size());
-            double in_sum = 0;
-            double out_sum = 0;
-            for (const auto &[j, c] : row) {
-                if (groups[j]) {
-                    out_sum += c;
-                } else {
-                    in_sum += c;
-                }
-                row_c[j] = c;
-            }
-
-            auto ll_dx_ddx_pick = [&](const auto &picker) {
-                return [&](double lambda) {
-                    double val = 0;
-                    double dval = 0;
-                    for (size_t j = 0; j < row_c.size(); ++j) {
-                        if (picker(j)) {
-                            uint64_t c = row_c[j];
-                            double mu = static_cast<double>(sums[j]) * lambda;
-                            double rshift = r + mu;
-                            double p = r / rshift;
-                            if (c > 0) {
-                                double interm = p / lambda * (c - mu);
-                                val += interm;
-                                dval -= interm / lambda + p / lambda * sums[j] * (r + c) / rshift;
-                            } else {
-                                double p_p = pow(p, r);
-                                double denom = pi[j] + (1 - pi[j]) * p_p;
-                                val -= (1 - pi[j]) * sums[j] * p_p * p / (pi[j] + (1 - pi[j]) * p_p);
-                                dval -= r * sums[j] * sums[j] * (1 - pi[j]) / rshift * p_p * p / denom / denom * (denom + r * pi[j]);
-                            }
-                        }
-                    }
-
-                    return std::make_pair(val, dval);
-                };
-            };
-
-            size_t ndigits = 20;
-            double lambda_in = boost::math::tools::newton_raphson_iterate(
-                ll_dx_ddx_pick([&](size_t j) { return !groups[j]; }),
-                0.5, -0.01, 1.0, ndigits
-            );
-            double lambda_out = boost::math::tools::newton_raphson_iterate(
-                ll_dx_ddx_pick([&](size_t j) { return groups[j]; }),
-                0.5, -0.01, 1.0, ndigits
-            );
-            double lambda_null = boost::math::tools::newton_raphson_iterate(
-                ll_dx_ddx_pick([&](size_t) { return true; }),
-                0.5, 0.0, 1.0, ndigits
-            );
-
-            if (lambda_in < 0 || lambda_out < 0) {
-                common::logger->error("Fit out of bounds: in: {}\tout: {}", lambda_in, lambda_out);
-                throw std::runtime_error("Fit failed");
-            }
-
-            double loglikelihood_alt = 0;
-            double loglikelihood_null = 0;
-            for (size_t j = 0; j < row_c.size(); ++j) {
-                uint64_t c = row_c[j];
-                double lambda = groups[j] ? lambda_out : lambda_in;
-                double mu = static_cast<double>(sums[j]) / lambda;
-                double mu_null = static_cast<double>(sums[j]) / lambda_null;
-                double rshift = r + mu;
-                double rshift_null = r + mu_null;
-
-                if (c > 0) {
-                    loglikelihood_alt += c * log(mu) - c * log(rshift) - r * log(rshift);
-                    loglikelihood_null += c * log(mu_null) - c * log(rshift_null) - r * log(rshift_null);
-                } else {
-                    loglikelihood_alt += log(pi[j] + (1 - pi[j]) * pow(r / rshift, r));
-                    loglikelihood_null += log(pi[j] + (1 - pi[j]) * pow(r / rshift_null, r));
-                }
-            }
-
-            double chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
-            if (chi_stat < 0) {
-                common::logger->error("stat {} < 0", chi_stat);
-                throw std::runtime_error("Failed test");
-            }
-
-            double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
-            return std::make_tuple(pval, in_sum, out_sum);
-        };
-
-        uint64_t row_i = 0;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         PairContainer>(columns_all, column_values_all, [&](const auto &row) {
-            auto [pval, in_sum, out_sum] = compute_pval(row);
-            pvals.emplace_back(pval);
-            if (pval < 0.05) {
-                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                double scaled_outsum = out_sum / out_kmers * in_kmers;
-                if (scaled_outsum < in_sum) {
-                    indicator_in[node] = true;
-                } else if (scaled_outsum > in_sum) {
-                    indicator_out[node] = true;
-                }
-            }
-            ++row_i;
-        });
-
     } else {
         throw std::runtime_error("Test not implemented");
     }
@@ -782,35 +623,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         size_t last_k = 0;
         for ( ; begin != m_data.end(); ++begin) {
             const auto &[cur_k, bucket] = *begin;
+            common::logger->trace("k: {}\tm(k): {}", cur_k, acc);
             if (acc <= cur_k) {
-                k = last_k + 1;
+                k = std::max(acc, last_k + 1);
                 break;
             }
 
             last_k = cur_k;
             acc -= bucket.size();
         }
-        // size_t acc = 0;
-        // size_t k = 1;
-        // auto begin = m_data.begin();
-        // auto it = begin;
-        // while (it < m_data.end()) {
-        //     common::logger->trace("k: {}\tp: {}", k, 0.05 / k);
-        //     while (acc <= k && it < m_data.end()) {
-        //         acc += it->second.size();
-        //         ++it;
-        //     }
-        //     if (acc > k) {
-        //         size_t old_k = k;
-        //         k = acc;
-        //         while (begin < it && k >= begin->first && begin->first > old_k) {
-        //             acc -= begin->second.size();
-        //             ++begin;
-        //         }
-        //     } else {
-        //         break;
-        //     }
-        // }
 
         if (k == 0) {
             common::logger->trace("No good k value found");

From bf65fe35b4527cf20fb742b9c832a43f8065828b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 30 Apr 2024 14:27:16 +0200
Subject: [PATCH 113/410] set boundaries

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6418519624..939d5eb20e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -197,7 +197,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             PairContainer best;
             for (const auto &[j, c] : row) {
                 if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
-                    best.emplace_back(j, sums[j]);
+                    best.emplace_back(j, max_obs_vals[j]);
             }
 
             auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
@@ -443,7 +443,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             PairContainer best;
             for (const auto &[j, c] : row) {
                 if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
-                    best.emplace_back(j, sums[j]);
+                    best.emplace_back(j, max_obs_vals[j]);
             }
 
             auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
@@ -459,8 +459,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 : exp(lkd);
 
             if (k == 0) {
-                common::logger->error("k: {}\tpval_min: {}", k, pval_min);
-                throw std::runtime_error("Min failed");
+                ++row_i;
+                return;
             }
             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
             m[k].emplace_back(node);

From 9af2291742a4e0fa1f87af3e3c54ab78015a7bcb Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 30 Apr 2024 14:44:29 +0200
Subject: [PATCH 114/410] update non-parametric

---
 .../src/graph/annotated_graph_algorithm.cpp   | 67 ++++++++++++++-----
 1 file changed, 50 insertions(+), 17 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 939d5eb20e..9aec1c11c7 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -494,16 +494,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_pair(std::move(ranks), std::move(counts));
         };
 
-        uint64_t row_i = 0;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         std::vector<std::pair<uint64_t, uint64_t>>>(columns_all, column_values_all,
-                                                                     [&](const auto &row) {
-            if (row.empty()) {
-                pvals.emplace_back(std::numeric_limits<double>::max());
-                ++row_i;
-                return;
-            }
+        auto compute_pval = [&](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0);
 
             std::vector<double> in_counts;
             in_counts.reserve(labels_in.size());
@@ -583,16 +576,56 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 p = boost::math::cdf(boost::math::complement(norm, z)) * 2;
             }
 
-            pvals.emplace_back(p);
+            return std::make_tuple(p, rankcx_mean, rankcy_mean);
+        };
 
-            if (p < 0.05) {
-                if (rankcx_mean > rankcy_mean) {
-                    indicator_in[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
-                } else if (rankcx_mean < rankcy_mean) {
-                    indicator_out[AnnotatedDBG::anno_to_graph_index(row_i)] = true;
-                }
+        uint64_t row_i = 0;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                         std::unique_ptr<ValuesContainer>,
+                         std::vector<std::pair<uint64_t, uint64_t>>>(columns_all, column_values_all,
+                                                                     [&](const auto &row) {
+            auto [pval, rankcx_mean, rankcy_mean] = compute_pval(row);
+            pvals.emplace_back(pval);
+            bool in_kmer = rankcx_mean > rankcy_mean;
+            bool out_kmer = rankcx_mean < rankcy_mean;
+
+            if (pval > 1.0 || (!in_kmer && !out_kmer)) {
+                ++row_i;
+                return;
+            }
+
+            PairContainer best;
+            for (const auto &[j, c] : row) {
+                if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
+                    best.emplace_back(j, max_obs_vals[j]);
+            }
+
+            auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
+
+            if (pval_min > pval) {
+                common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
+                throw std::runtime_error("Test failed");
             }
 
+            double lkd = log(0.05) - log(pval_min);
+            uint64_t k = lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max()))
+                ? std::numeric_limits<uint64_t>::max()
+                : exp(lkd);
+
+            if (k == 0) {
+                ++row_i;
+                return;
+            }
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+            m[k].emplace_back(node);
+
+            if (pval < 0.05) {
+                if (in_kmer) {
+                    indicator_in[node] = true;
+                } else if (out_kmer) {
+                    indicator_out[node] = true;
+                }
+            }
             ++row_i;
         });
     } else {

From 3633d69676641e9337580631f69e855730cd3009 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 30 Apr 2024 14:54:26 +0200
Subject: [PATCH 115/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 255 ++++++------------
 1 file changed, 78 insertions(+), 177 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9aec1c11c7..b2d631591c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -117,12 +117,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     pvals.emplace_back(std::numeric_limits<double>::max());
 
     VectorMap<size_t, std::vector<uint64_t>> m;
+    using RowStats = std::tuple<double, double, double>;
+    std::function<RowStats(const PairContainer&)> compute_pval;
 
     if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig"
             || config.test_type == "cmh"
             || config.test_type == "fisher") {
         boost::math::chi_squared dist(1);
-        auto compute_pval = [&](const auto &row) {
+        compute_pval = [&,dist](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0);
 
@@ -175,58 +177,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (pval == -1)
                 pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
 
-            return std::make_tuple(pval, in_sum, out_sum);
+            return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
-        uint64_t row_i = 0;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         PairContainer>(columns_all, column_values_all,
-                                        [&](const auto &row) {
-            auto [pval, in_sum, out_sum] = compute_pval(row);
-            pvals.emplace_back(pval);
-            double scaled_outsum = out_sum / out_kmers * in_kmers;
-
-            if (pval > 1.0 || scaled_outsum == in_sum) {
-                ++row_i;
-                return;
-            }
-
-            bool in_kmer = scaled_outsum < in_sum;
-            bool out_kmer = scaled_outsum > in_sum;
-
-            PairContainer best;
-            for (const auto &[j, c] : row) {
-                if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
-                    best.emplace_back(j, max_obs_vals[j]);
-            }
-
-            auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
-
-            if (pval_min > pval) {
-                common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
-                throw std::runtime_error("Test failed");
-            }
-
-            double lkd = log(0.05) - log(pval_min);
-            uint64_t k = lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max()))
-                ? std::numeric_limits<uint64_t>::max()
-                : exp(lkd);
-
-            if (k == 0) {
-                common::logger->error("k: {}\tpval_min: {}", k, pval_min);
-                throw std::runtime_error("Min failed");
-            }
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            m[k].emplace_back(node);
-            if (pval < 0.05) {
-                if (in_kmer) {
-                    indicator_in[node] = true;
-                } else if (out_kmer) {
-                    indicator_out[node] = true;
-                }
-            }
-            ++row_i;
-        });
     } else if (config.test_type == "nbinom") {
         // use moments to fit the r parameter of a beta negative binomial
         uint64_t sum_of_cubes = 0;
@@ -268,7 +220,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         boost::math::chi_squared dist(1);
 
-        auto compute_pval = [&](const auto &row) {
+        compute_pval = [&,r,dist](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0);
 
@@ -422,81 +374,32 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
 
-            return std::make_tuple(pval, in_sum, out_sum);
+            return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
-
-        uint64_t row_i = 0;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         PairContainer>(columns_all, column_values_all, [&](const auto &row) {
-            auto [pval, in_sum, out_sum] = compute_pval(row);
-            pvals.emplace_back(pval);
-            double scaled_outsum = out_sum / out_kmers * in_kmers;
-            if (pval > 1.0 || scaled_outsum == in_sum) {
-                ++row_i;
-                return;
-            }
-
-            bool in_kmer = scaled_outsum < in_sum;
-            bool out_kmer = scaled_outsum > in_sum;
-
-            PairContainer best;
-            for (const auto &[j, c] : row) {
-                if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
-                    best.emplace_back(j, max_obs_vals[j]);
-            }
-
-            auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
-
-            if (pval_min > pval) {
-                common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
-                throw std::runtime_error("Test failed");
-            }
-
-            double lkd = log(0.05) - log(pval_min);
-            uint64_t k = lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max()))
-                ? std::numeric_limits<uint64_t>::max()
-                : exp(lkd);
-
-            if (k == 0) {
-                ++row_i;
-                return;
-            }
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            m[k].emplace_back(node);
-            if (pval < 0.05) {
-                if (in_kmer) {
-                    indicator_in[node] = true;
-                } else if (out_kmer) {
-                    indicator_out[node] = true;
-                }
-            }
-            ++row_i;
-        });
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u" || config.test_type == "quantile") {
-        auto norm = boost::math::normal_distribution();
-        auto rankdata = [&](const std::vector<double> &c) {
-            auto c_p = c;
-            std::sort(c_p.begin(), c_p.end());
-            tsl::hopscotch_map<double, std::pair<size_t, size_t>> counts;
-            for (size_t i = 0; i < c_p.size(); ++i) {
-                auto &[rank_sum, nranks] = counts[c_p[i]];
-                rank_sum += i + 1;
-                ++nranks;
-            }
+        compute_pval = [&](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0);
 
-            std::vector<double> ranks;
-            ranks.reserve(c.size());
-            for (double v : c) {
-                ranks.emplace_back(static_cast<double>(counts[v].first)/counts[v].second);
-            }
+            auto norm = boost::math::normal_distribution();
+            auto rankdata = [&](const std::vector<double> &c) {
+                auto c_p = c;
+                std::sort(c_p.begin(), c_p.end());
+                tsl::hopscotch_map<double, std::pair<size_t, size_t>> counts;
+                for (size_t i = 0; i < c_p.size(); ++i) {
+                    auto &[rank_sum, nranks] = counts[c_p[i]];
+                    rank_sum += i + 1;
+                    ++nranks;
+                }
 
-            return std::make_pair(std::move(ranks), std::move(counts));
-        };
+                std::vector<double> ranks;
+                ranks.reserve(c.size());
+                for (double v : c) {
+                    ranks.emplace_back(static_cast<double>(counts[v].first)/counts[v].second);
+                }
 
-        auto compute_pval = [&](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0);
+                return std::make_pair(std::move(ranks), std::move(counts));
+            };
 
             std::vector<double> in_counts;
             in_counts.reserve(labels_in.size());
@@ -578,59 +481,58 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return std::make_tuple(p, rankcx_mean, rankcy_mean);
         };
+    } else {
+        throw std::runtime_error("Test not implemented");
+    }
 
-        uint64_t row_i = 0;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         std::vector<std::pair<uint64_t, uint64_t>>>(columns_all, column_values_all,
-                                                                     [&](const auto &row) {
-            auto [pval, rankcx_mean, rankcy_mean] = compute_pval(row);
-            pvals.emplace_back(pval);
-            bool in_kmer = rankcx_mean > rankcy_mean;
-            bool out_kmer = rankcx_mean < rankcy_mean;
-
-            if (pval > 1.0 || (!in_kmer && !out_kmer)) {
-                ++row_i;
-                return;
-            }
+    uint64_t row_i = 0;
+    utils::call_rows<std::unique_ptr<bit_vector>,
+                     std::unique_ptr<ValuesContainer>,
+                     PairContainer>(columns_all, column_values_all, [&](const auto &row) {
+        auto [pval, in_stat, out_stat] = compute_pval(row);
+        pvals.emplace_back(pval);
 
-            PairContainer best;
-            for (const auto &[j, c] : row) {
-                if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
-                    best.emplace_back(j, max_obs_vals[j]);
-            }
+        if (pval > 1.0 || in_stat == out_stat) {
+            ++row_i;
+            return;
+        }
 
-            auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
+        bool in_kmer = in_stat > out_stat;
+        bool out_kmer = in_stat < out_stat;
 
-            if (pval_min > pval) {
-                common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
-                throw std::runtime_error("Test failed");
-            }
+        PairContainer best;
+        for (const auto &[j, c] : row) {
+            if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
+                best.emplace_back(j, max_obs_vals[j]);
+        }
 
-            double lkd = log(0.05) - log(pval_min);
-            uint64_t k = lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max()))
-                ? std::numeric_limits<uint64_t>::max()
-                : exp(lkd);
+        auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
 
-            if (k == 0) {
-                ++row_i;
-                return;
-            }
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            m[k].emplace_back(node);
-
-            if (pval < 0.05) {
-                if (in_kmer) {
-                    indicator_in[node] = true;
-                } else if (out_kmer) {
-                    indicator_out[node] = true;
-                }
+        if (pval_min > pval) {
+            common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
+            throw std::runtime_error("Test failed");
+        }
+
+        double lkd = log(0.05) - log(pval_min);
+        uint64_t k = lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max()))
+            ? std::numeric_limits<uint64_t>::max()
+            : exp(lkd);
+
+        if (k == 0) {
+            common::logger->error("k: {}\tpval_min: {}", k, pval_min);
+            throw std::runtime_error("Min failed");
+        }
+        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+        m[k].emplace_back(node);
+        if (pval < 0.05) {
+            if (in_kmer) {
+                indicator_in[node] = true;
+            } else if (out_kmer) {
+                indicator_out[node] = true;
             }
-            ++row_i;
-        });
-    } else {
-        throw std::runtime_error("Test not implemented");
-    }
+        }
+        ++row_i;
+    });
 
     for (auto &col : columns_all) {
         col.reset();
@@ -640,17 +542,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         col_vals.reset();
     }
 
-    {
-        uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
-
+    uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
 
-        auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
+    auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
 
-        std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
-        size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
-                                     [](size_t sum, const auto &a) { return sum + a.second.size(); });
-        common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
+    std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
+    size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
+                                 [](size_t sum, const auto &a) { return sum + a.second.size(); });
+    common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
 
+    if (total_sig) {
         auto begin = m_data.begin();
         size_t k = 0;
         size_t last_k = 0;

From 43df0632cbca78da941c7d4f1e1e89218d89bbf7 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 1 May 2024 11:33:02 +0200
Subject: [PATCH 116/410] changes

---
 metagraph/src/common/vectors/transpose.hpp    |   2 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 253 ++++++++++++++----
 2 files changed, 198 insertions(+), 57 deletions(-)

diff --git a/metagraph/src/common/vectors/transpose.hpp b/metagraph/src/common/vectors/transpose.hpp
index 1c9c44401b..2128a8e1e8 100644
--- a/metagraph/src/common/vectors/transpose.hpp
+++ b/metagraph/src/common/vectors/transpose.hpp
@@ -77,7 +77,7 @@ void call_rows(const std::vector<BitmapPtr> &columns,
 
     num_rows = columns[0]->size();
     std::vector<PairVector> rows;
-        
+
     ProgressBar progress_bar(num_rows, "Constructed rows", std::cerr, !show_progress);
 
     #pragma omp parallel for ordered num_threads(get_num_threads()) schedule(dynamic) private(rows)
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b2d631591c..755d73c7ca 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -5,6 +5,7 @@
 #include <sdust.h>
 
 #include <boost/math/distributions/chi_squared.hpp>
+#include <boost/math/distributions/poisson.hpp>
 #include <boost/math/statistics/univariate_statistics.hpp>
 #include <boost/math/tools/roots.hpp>
 
@@ -29,6 +30,58 @@ typedef AnnotatedDBG::Annotator::Label Label;
 using Column = annot::matrix::BinaryMatrix::Column;
 using PairContainer = std::vector<std::pair<uint64_t, uint64_t>>;
 
+template <typename ForwardIterator>
+inline auto quartiles(ForwardIterator first, ForwardIterator last)
+{
+    auto m = std::distance(first,last);
+    BOOST_MATH_ASSERT_MSG(m >= 3, "At least 3 samples are required to compute the interquartile range.");
+    auto k = m/4;
+    auto j = m - (4*k);
+    // m = 4k+j.
+    // If j = 0 or j = 1, then there are an even number of samples below the median, and an even number above the median.
+    //    Then we must average adjacent elements to get the quartiles.
+    // If j = 2 or j = 3, there are an odd number of samples above and below the median, these elements may be directly extracted to get the quartiles.
+
+    double Q1;
+    double Q3;
+    if (j==2 || j==3)
+    {
+        auto q1 = first + k;
+        auto q3 = first + 3*k + j - 1;
+        std::nth_element(first, q1, last);
+        Q1 = *q1;
+        std::nth_element(q1, q3, last);
+        Q3 = *q3;
+    } else {
+        // j == 0 or j==1:
+        auto q1 = first + k - 1;
+        auto q3 = first + 3*k - 1 + j;
+        std::nth_element(first, q1, last);
+        double a = *q1;
+        std::nth_element(q1, q1 + 1, last);
+        double b = *(q1 + 1);
+        Q1 = (a+b)/2;
+        std::nth_element(q1, q3, last);
+        a = *q3;
+        std::nth_element(q3, q3 + 1, last);
+        b = *(q3 + 1);
+        Q3 = (a+b)/2;
+    }
+
+    // return std::make_tuple(Q1, boost::math::statistics::median(first, last), Q3);
+    return std::make_pair(Q1, Q3);
+}
+
+inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
+    int n;
+    std::unique_ptr<uint64_t, decltype(std::free)*> r {
+        sdust(0, (const uint8_t*)s.data(), s.size(), T, W, &n),
+        std::free
+    };
+    return n > 0;
+}
+
+
 std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const std::vector<std::string> &files,
@@ -45,6 +98,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     size_t max_index = graph_ptr->max_index();
     std::vector<double> medians;
     std::vector<uint64_t> max_vals;
+    std::vector<uint64_t> check_cutoff;
     std::vector<uint64_t> max_obs_vals;
     std::vector<size_t> n_kmers;
     std::vector<uint64_t> sums;
@@ -56,7 +110,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<bool> groups;
     uint8_t max_width = 0;
     uint64_t max_val = 0;
-    uint64_t max_observed_value = 0;
+
+    auto get_count = [&](uint64_t raw_count, uint64_t cutoff, uint64_t row_i) {
+        return raw_count <= cutoff ? raw_count : 0;
+        // if (raw_count <= cutoff)
+        //     return raw_count;
+
+        // node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+        // return is_low_complexity(graph_ptr->get_node_sequence(node)) ? 0 : raw_count;
+    };
 
     annot::ColumnCompressed<>::load_columns_and_values(files,
         [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
@@ -64,22 +126,62 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             max_width = std::max(max_width, column_values.width());
             max_val = sdsl::bits::lo_set[max_width];
+            max_vals.push_back(max_val);
 
             // median calculation modifies the underlying vector, so we have to make a copy
             std::vector<uint64_t> vals;
             vals.reserve(column_values.size());
             std::copy(column_values.begin(), column_values.end(), std::back_inserter(vals));
-            max_obs_vals.emplace_back(*std::max_element(vals.begin(), vals.end()));
-            max_observed_value = std::max(max_observed_value, max_obs_vals.back());
-            uint64_t sum = std::accumulate(vals.begin(), vals.end(), uint64_t(0));
-            uint64_t sum_of_squares = std::accumulate(
-                vals.begin(), vals.end(), uint64_t(0),
-                [&](auto&& prev, uint64_t cur) { return std::move(prev) + cur * cur; }
-            );
-            size_t n = column_values.size();
 
-            medians.push_back(boost::math::statistics::median(vals));
-            max_vals.push_back(max_val);
+            uint64_t in_max_obs_val = *std::max_element(vals.begin(), vals.end());
+
+            auto [q1, q3] = quartiles(vals.begin(), vals.end());
+            double q2 = boost::math::statistics::median(vals);
+            double outlier_cutoff = q3 + 1.5 * (q3 - q1);
+
+            double repeats_mu = 0;
+            double repeats_cutoff = 0;
+            size_t num_large = 0;
+            for (uint64_t c : vals) {
+                if (c >= outlier_cutoff) {
+                    repeats_mu += c;
+                    ++num_large;
+                }
+            }
+            if (num_large) {
+                repeats_mu /= num_large;
+
+                repeats_cutoff = boost::math::quantile(boost::math::complement(
+                    boost::math::poisson(repeats_mu), 0.95
+                ));
+
+                if (repeats_cutoff > outlier_cutoff) {
+                    check_cutoff.emplace_back(repeats_cutoff);
+                } else {
+                    check_cutoff.emplace_back(max_val);
+                }
+            } else {
+                check_cutoff.emplace_back(max_val);
+            }
+
+            uint64_t sum = 0;
+            uint64_t sum_of_squares = 0;
+            uint64_t max_obs_val = 0;
+            size_t i = 0;
+            size_t n = 0;
+            column->call_ones([&](uint64_t row_i) {
+                if (uint64_t c = get_count(column_values[i], check_cutoff.back(), row_i)) {
+                    sum += c;
+                    sum_of_squares += c * c;
+                    max_obs_val = std::max(max_obs_val, c);
+                    ++n;
+                }
+                ++i;
+            });
+
+            max_obs_vals.emplace_back(max_obs_val);
+
+            medians.push_back(q2);
 
             sums.push_back(sum);
             sums_of_squares.push_back(sum_of_squares);
@@ -88,13 +190,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             columns_all.emplace_back(std::move(column));
             column_values_all.push_back(std::make_unique<ValuesContainer>(std::move(column_values)));
 
-            common::logger->trace("{}: {}\tTotal: {}\tMean: {}\tCutoff: {}",
-                                  label, sum, groups.back() ? "out": "in",
-                                  static_cast<double>(sum) / n,
-                                  max_vals.back());
+            common::logger->trace("{}: {}\tGroup: {}\tTotal: {}\tQ1: {}\tQ2: {}\tQ3: {}\tMax val: {}\tOutlier cutoff: {}\tRepeat mu: {}\tRepeat lb: {}\tCheck cutoff: {}",
+                                  groups.size(), label, groups.back() ? "out": "in", sum,
+                                  q1, q2, q3,
+                                  in_max_obs_val,
+                                  outlier_cutoff,
+                                  repeats_mu, repeats_cutoff, check_cutoff.back());
         }
     );
 
+    uint64_t max_observed_value = *std::max_element(max_obs_vals.begin(), max_obs_vals.end());
+
     common::logger->trace("Max width: {}\tMax val: {}\tMax obs. val: {}",
                           max_width, max_val, max_observed_value);
 
@@ -142,6 +248,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double chi_stat = 0;
             double pval = -1;
             if (config.test_type == "cmh") {
+                if (in_sum + out_sum == total_kmers || total_kmers <= 1 || in_sum + out_sum == 0) {
+                    common::logger->error("Invalid counts: in_sum: {}\tout_sum: {}\ttotal_kmers: {}",
+                                          in_sum, out_sum, total_kmers);
+                }
+
                 double xi = in_sum * out_kmers - out_sum * in_kmers;
                 xi *= xi * (total_kmers - 1) / in_kmers / out_kmers / (in_sum + out_sum) / (total_kmers - in_sum - out_sum);
                 chi_stat = xi;
@@ -182,10 +293,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     } else if (config.test_type == "nbinom") {
         // use moments to fit the r parameter of a beta negative binomial
         uint64_t sum_of_cubes = 0;
-        for (const auto &count_ptr : column_values_all) {
-            sum_of_cubes += std::accumulate(count_ptr->begin(), count_ptr->end(),
-                                            uint64_t(0),
-                                            [&](uint64_t sum, int64_t c) { return sum + c * (c - 1) * (c - 2); });
+        for (size_t j = 0; j < groups.size(); ++j) {
+            const auto &column = columns_all[j];
+            const auto &count_ptr = column_values_all[j];
+            size_t i = 0;
+            column->call_ones([&](uint64_t row_i) {
+                int64_t c = get_count((*count_ptr)[i], check_cutoff[j], row_i);
+                if (c > 0)
+                    sum_of_cubes += c * (c - 1) * (c - 2);
+                ++i;
+            });
         }
         double mu = static_cast<double>(total_kmers) / groups.size() / max_index;
         double mu2 = static_cast<double>(std::accumulate(sums_of_squares.begin(),
@@ -209,7 +326,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             r_low_var_sqerr += pow(t_var_low - var, 2.0);
             r_high_var_sqerr += pow(t_var_high - var, 2.0);
             common::logger->trace("label: {}\tvar: {}\tp_low: {}\tvar_low: {}\tp_high: {}\tvar_high: {}",
-                                  j, var, p_low, t_var_low, p_high, t_var_high);
+                                  j + 1, var, p_low, t_var_low, p_high, t_var_high);
         }
 
         common::logger->trace("r low: {} (sqerr: {})\t r high: {} (sqerr: {})",
@@ -462,7 +579,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double corr = 0;
                 for (const auto &[val,rks] : countsc) {
                     const auto &[rk, c] = rks;
-                    if (c > 0)
+                    if (c > 1)
                         corr += c * c * c - c;
                 }
 
@@ -471,9 +588,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double u1 = nx * ny + static_cast<double>(nx * (nx + 1))/2 - rankcx_sum;
                 double u2 = nx * ny + static_cast<double>(ny * (ny + 1))/2 - rankcy_sum;
                 double u = std::min(u1, u2);
-                double mu = static_cast<double>(nx * nx) / 2;
+                double mu = static_cast<double>(nx * ny) / 2;
                 double n = nx + ny;
-                double sd = sqrt(static_cast<double>(nx * ny) / n / (n - 1)) * sqrt((n * n * n - n - corr) / 12);
+                double sd = sqrt(static_cast<double>(nx * ny) / n / (n - 1)) * sqrt((n * n * n - n)/12 - corr / 12);
 
                 double z = abs((u - mu) / sd);
                 p = boost::math::cdf(boost::math::complement(norm, z)) * 2;
@@ -488,7 +605,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     uint64_t row_i = 0;
     utils::call_rows<std::unique_ptr<bit_vector>,
                      std::unique_ptr<ValuesContainer>,
-                     PairContainer>(columns_all, column_values_all, [&](const auto &row) {
+                     PairContainer>(columns_all, column_values_all, [&](const auto &raw_row) {
+        PairContainer row;
+        size_t in_count = 0;
+        size_t out_count = 0;
+        for (const auto &[j, raw_c] : raw_row) {
+            if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i)) {
+                if (groups[j]) {
+                    ++out_count;
+                } else {
+                    ++in_count;
+                }
+                row.emplace_back(j, c);
+            }
+        }
+
         auto [pval, in_stat, out_stat] = compute_pval(row);
         pvals.emplace_back(pval);
 
@@ -499,20 +630,33 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         bool in_kmer = in_stat > out_stat;
         bool out_kmer = in_stat < out_stat;
+        double pval_min = 0;
+        if (config.test_type == "nonparametric_u") {
+            pval_min = exp(lgamma(in_count + 1) + lgamma(out_count + 1) - lgamma(in_count + out_count + 1));
+        } else {
+            PairContainer best;
+            for (const auto &[j, c] : row) {
+                if ((in_kmer && !groups[j]) || (out_kmer && groups[j])) {
+                    best.emplace_back(j, max_obs_vals[j]);
+                } else {
+                    best.emplace_back(j, 1);
+                }
+            }
 
-        PairContainer best;
-        for (const auto &[j, c] : row) {
-            if ((in_kmer && !groups[j]) || (out_kmer && groups[j]))
-                best.emplace_back(j, max_obs_vals[j]);
+            auto [pm, in_sum_m, out_sum_m] = compute_pval(best);
+            pval_min = pm;
         }
 
-        auto [pval_min, in_sum_m, out_sum_m] = compute_pval(best);
-
         if (pval_min > pval) {
             common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
             throw std::runtime_error("Test failed");
         }
 
+        if (pval_min >= 0.05) {
+            ++row_i;
+            return;
+        }
+
         double lkd = log(0.05) - log(pval_min);
         uint64_t k = lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max()))
             ? std::numeric_limits<uint64_t>::max()
@@ -524,13 +668,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
         node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
         m[k].emplace_back(node);
-        if (pval < 0.05) {
-            if (in_kmer) {
-                indicator_in[node] = true;
-            } else if (out_kmer) {
-                indicator_out[node] = true;
-            }
-        }
+        auto &indicator = in_kmer ? indicator_in : indicator_out;
+        if (pval < 0.05)
+            indicator[node] = true;
+
         ++row_i;
     });
 
@@ -544,14 +685,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
 
-    auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
-
-    std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
-    size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
-                                 [](size_t sum, const auto &a) { return sum + a.second.size(); });
-    common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
-
     if (total_sig) {
+        auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
+
+        std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
+        size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
+                                    [](size_t sum, const auto &a) { return sum + a.second.size(); });
+        size_t total_tests = acc;
+        common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
         auto begin = m_data.begin();
         size_t k = 0;
         size_t last_k = 0;
@@ -568,9 +709,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         if (k == 0) {
-            common::logger->trace("No good k value found");
-            sdsl::util::set_to_value(indicator_in, false);
-            sdsl::util::set_to_value(indicator_out, false);
+            common::logger->trace("No good k value found, defaulting to Bonferroni cutoff");
+            k = total_tests;
+            common::logger->trace("k: {}\talpha_corr: {}", k, 0.05 / k);
+            for (auto jt = m_data.begin(); jt != m_data.end(); ++jt) {
+                const auto &[cur_k, bucket] = *jt;
+                for (uint64_t i : bucket) {
+                    if (pvals[i] < 0.05 && pvals[i] * k >= 0.05) {
+                        indicator_in[i] = false;
+                        indicator_out[i] = false;
+                    }
+                }
+            }
         } else {
             common::logger->trace("k: {}\talpha_corr: {}", k, 0.05 / k);
             for (auto jt = m_data.begin(); jt != m_data.end(); ++jt) {
@@ -767,15 +917,6 @@ double findMedian(const Container &a,
     }
 }
 
-inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
-    int n;
-    std::unique_ptr<uint64_t, decltype(std::free)*> r {
-        sdust(0, (const uint8_t*)s.data(), s.size(), T, W, &n),
-        std::free
-    };
-    return n > 0;
-}
-
 bool filtering_row(std::vector<double> in_counts_non_zero,
                     std::vector<double> out_counts_non_zero,
                     int total_samples,

From bee9ac217dfad3d797bb85de778df3810bd293f8 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 1 May 2024 12:36:10 +0200
Subject: [PATCH 117/410] set min p-values

---
 .../src/graph/annotated_graph_algorithm.cpp   | 89 +++++++++----------
 1 file changed, 43 insertions(+), 46 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 755d73c7ca..b9426181ab 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -576,20 +576,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             } else {
                 // https://datatab.net/tutorial/mann-whitney-u-test
-                double corr = 0;
-                for (const auto &[val,rks] : countsc) {
-                    const auto &[rk, c] = rks;
-                    if (c > 1)
-                        corr += c * c * c - c;
-                }
-
                 double rankcx_sum = std::accumulate(rankc.begin(), rankc.begin() + nx, double(0.0));
                 double rankcy_sum = std::accumulate(rankc.begin() + nx, rankc.end(), double(0.0));
                 double u1 = nx * ny + static_cast<double>(nx * (nx + 1))/2 - rankcx_sum;
                 double u2 = nx * ny + static_cast<double>(ny * (ny + 1))/2 - rankcy_sum;
                 double u = std::min(u1, u2);
-                double mu = static_cast<double>(nx * ny) / 2;
+
                 double n = nx + ny;
+                    // normal approximation
+                double mu = static_cast<double>(nx * ny) / 2;
+                double corr = 0;
+                for (const auto &[val,rks] : countsc) {
+                    const auto &[rk, c] = rks;
+                    if (c > 1)
+                        corr += c * c * c - c;
+                }
                 double sd = sqrt(static_cast<double>(nx * ny) / n / (n - 1)) * sqrt((n * n * n - n)/12 - corr / 12);
 
                 double z = abs((u - mu) / sd);
@@ -607,17 +608,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                      std::unique_ptr<ValuesContainer>,
                      PairContainer>(columns_all, column_values_all, [&](const auto &raw_row) {
         PairContainer row;
-        size_t in_count = 0;
-        size_t out_count = 0;
         for (const auto &[j, raw_c] : raw_row) {
-            if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i)) {
-                if (groups[j]) {
-                    ++out_count;
-                } else {
-                    ++in_count;
-                }
+            if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i))
                 row.emplace_back(j, c);
-            }
         }
 
         auto [pval, in_stat, out_stat] = compute_pval(row);
@@ -630,42 +623,46 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         bool in_kmer = in_stat > out_stat;
         bool out_kmer = in_stat < out_stat;
-        double pval_min = 0;
-        if (config.test_type == "nonparametric_u") {
-            pval_min = exp(lgamma(in_count + 1) + lgamma(out_count + 1) - lgamma(in_count + out_count + 1));
-        } else {
-            PairContainer best;
-            for (const auto &[j, c] : row) {
-                if ((in_kmer && !groups[j]) || (out_kmer && groups[j])) {
-                    best.emplace_back(j, max_obs_vals[j]);
-                } else {
-                    best.emplace_back(j, 1);
+        uint64_t k = std::numeric_limits<uint64_t>::max();
+
+        if (config.test_type != "nonparametric") {
+            double pval_min = 0;
+            if (config.test_type == "nonparametric_u") {
+                pval_min = 2.0 / exp(lgamma(groups.size() + 1) - lgamma(labels_in.size() + 1) - lgamma(labels_out.size() + 1));
+            } else {
+                PairContainer best;
+                for (const auto &[j, c] : row) {
+                    if ((in_kmer && !groups[j]) || (out_kmer && groups[j])) {
+                        best.emplace_back(j, max_obs_vals[j]);
+                    } else {
+                        best.emplace_back(j, 1);
+                    }
                 }
-            }
 
-            auto [pm, in_sum_m, out_sum_m] = compute_pval(best);
-            pval_min = pm;
-        }
+                auto [pm, in_sum_m, out_sum_m] = compute_pval(best);
+                pval_min = pm;
+            }
 
-        if (pval_min > pval) {
-            common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
-            throw std::runtime_error("Test failed");
-        }
+            if (pval_min > pval) {
+                common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
+                throw std::runtime_error("Test failed");
+            }
 
-        if (pval_min >= 0.05) {
-            ++row_i;
-            return;
-        }
+            if (pval_min >= 0.05) {
+                ++row_i;
+                return;
+            }
 
-        double lkd = log(0.05) - log(pval_min);
-        uint64_t k = lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max()))
-            ? std::numeric_limits<uint64_t>::max()
-            : exp(lkd);
+            double lkd = log(0.05) - log(pval_min);
+            if (lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max())))
+                k = exp(lkd);
 
-        if (k == 0) {
-            common::logger->error("k: {}\tpval_min: {}", k, pval_min);
-            throw std::runtime_error("Min failed");
+            if (k == 0) {
+                common::logger->error("k: {}\tpval_min: {}", k, pval_min);
+                throw std::runtime_error("Min failed");
+            }
         }
+
         node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
         m[k].emplace_back(node);
         auto &indicator = in_kmer ? indicator_in : indicator_out;

From cb300fc9f7e31201d41597f452eda07c2d6a209a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 2 May 2024 12:04:41 +0200
Subject: [PATCH 118/410] fix

---
 .../src/graph/annotated_graph_algorithm.cpp   | 258 +++++++++++-------
 1 file changed, 161 insertions(+), 97 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b9426181ab..612c592dcf 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -360,105 +360,62 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             double total_sum = in_sum + out_sum;
-            size_t ndigits = 20;
-
-            auto ll_dx_ddx_in = [&](double lambda) {
-                double val = 0;
-                double dval = 0;
-                for (const auto &[j, c] : row) {
-                    if (!groups[j]) {
-                        double prop = static_cast<double>(sums[j]) * lambda;
-                        double shift_prop = r + prop;
-                        val += (c - prop) / shift_prop;
-                        dval -= (r + c) / shift_prop / shift_prop * sums[j];
-                    }
-                }
-
-                for (size_t j : unfound) {
-                    if (!groups[j]) {
-                        uint64_t c = 0;
-                        double prop = static_cast<double>(sums[j]) * lambda;
-                        double shift_prop = r + prop;
-                        val += (c - prop) / shift_prop;
-                        dval -= (r + c) / shift_prop / shift_prop * sums[j];
-                    }
-                }
-
-                return std::make_pair(val, dval);
-            };
-
-            auto ll_dx_ddx_out = [&](double lambda) {
-                double val = 0;
-                double dval = 0;
-                for (const auto &[j, c] : row) {
-                    if (groups[j]) {
-                        double prop = static_cast<double>(sums[j]) * lambda;
-                        double shift_prop = r + prop;
-                        val += (c - prop) / shift_prop;
-                        dval -= (r + c) / shift_prop / shift_prop * sums[j];
+            size_t ndigits = 30;
+
+            auto ll_dx_ddx_pick = [&](const auto &picker) {
+                return [&](double lambda) {
+                    double val = 0;
+                    double dval = 0;
+                    for (const auto &[j, c] : row) {
+                        if (picker(j)) {
+                            double prop = static_cast<double>(sums[j]) * lambda;
+                            double shift_prop = r + prop;
+                            val += (c - prop) / shift_prop;
+                            dval -= (r + c) / shift_prop / shift_prop * sums[j];
+                        }
                     }
-                }
 
-                for (size_t j : unfound) {
-                    if (groups[j]) {
-                        uint64_t c = 0;
-                        double prop = static_cast<double>(sums[j]) * lambda;
-                        double shift_prop = r + prop;
-                        val += (c - prop) / shift_prop;
-                        dval -= (r + c) / shift_prop / shift_prop * sums[j];
+                    for (size_t j : unfound) {
+                        if (picker(j)) {
+                            double prop = static_cast<double>(sums[j]) * lambda;
+                            double shift_prop = r + prop;
+                            val -= prop / shift_prop;
+                            dval -= r / shift_prop / shift_prop * sums[j];
+                        }
                     }
-                }
 
-                return std::make_pair(val, dval);
-            };
-
-            auto ll_dx_ddx_null = [&](double lambda) {
-                double val = 0;
-                double dval = 0;
-                for (const auto &[j, c] : row) {
-                    double prop = static_cast<double>(sums[j]) * lambda;
-                    double shift_prop = r + prop;
-                    val += (c - prop) / shift_prop;
-                    dval -= (r + c) / shift_prop / shift_prop * sums[j];
-                }
-
-                for (size_t j : unfound) {
-                    uint64_t c = 0;
-                    double prop = static_cast<double>(sums[j]) * lambda;
-                    double shift_prop = r + prop;
-                    val += (c - prop) / shift_prop;
-                    dval -= (r + c) / shift_prop / shift_prop * sums[j];
-                }
-
-                return std::make_pair(val, dval);
+                    return std::make_pair(val, dval);
+                };
             };
 
             double lambda_null = boost::math::tools::newton_raphson_iterate(
-                ll_dx_ddx_null, total_sum / total_kmers, 0.0, 1.0, ndigits
+                ll_dx_ddx_pick([&](size_t) { return true; }),
+                total_sum / total_kmers, -0.01, 1.01, ndigits
             );
 
             double chi_stat = 0;
             double loglikelihood_null = 0;
-            if (false) {
-                // score test
-                auto [val_in, dval_in] = ll_dx_ddx_in(lambda_null);
-                auto [val_out, dval_out] = ll_dx_ddx_out(lambda_null);
-
-                val_in *= r / lambda_null;
-                val_out *= r / lambda_null;
-
-                dval_in = (val_in / lambda_null - dval_in * r / lambda_null) / labels_in.size();
-                dval_out = (val_out / lambda_null - dval_out * r / lambda_null) / labels_out.size();
-                chi_stat = std::max(val_in * val_in / dval_in + val_out * val_out / dval_out,
-                                    double(0.0));
-            } else {
+            // if (false) {
+            //     // score test
+            //     auto [val_in, dval_in] = ll_dx_ddx_in(lambda_null);
+            //     auto [val_out, dval_out] = ll_dx_ddx_out(lambda_null);
+
+            //     val_in *= r / lambda_null;
+            //     val_out *= r / lambda_null;
+
+            //     dval_in = (val_in / lambda_null - dval_in * r / lambda_null) / labels_in.size();
+            //     dval_out = (val_out / lambda_null - dval_out * r / lambda_null) / labels_out.size();
+            //     chi_stat = val_in * val_in / dval_in + val_out * val_out / dval_out;
+            // } else {
                 // log likelihood test
                 double lambda_in = boost::math::tools::newton_raphson_iterate(
-                    ll_dx_ddx_in, in_sum / in_kmers, 0.0, 1.0, ndigits
+                    ll_dx_ddx_pick([&](size_t j) { return !groups[j]; }),
+                    in_sum / in_kmers, -0.01, 1.01, ndigits
                 );
 
                 double lambda_out = boost::math::tools::newton_raphson_iterate(
-                    ll_dx_ddx_out, out_sum / out_kmers, 0.0, 1.0, ndigits
+                    ll_dx_ddx_pick([&](size_t j) { return groups[j]; }),
+                    out_sum / out_kmers, -0.01, 1.01, ndigits
                 );
 
                 double loglikelihood_alt = 0;
@@ -485,8 +442,113 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 if (chi_stat < 0) {
                     common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+                    common::logger->error("Theta null: {}\ttheta in: {}\ttheta out: {}",
+                                          lambda_null, lambda_in, lambda_out);
                     throw std::runtime_error("Test failed");
                 }
+            // }
+
+            double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
+
+            return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
+        };
+    } else if (config.test_type == "bp") {
+        boost::math::chi_squared dist(1);
+
+        double a_sum_in = 0;
+        double a_sum_out = 0;
+        double b_sum_in = 0;
+        double b_sum_out = 0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            double n = static_cast<double>(sums[j]);
+            double mu = n / max_index;
+            double var = static_cast<double>(sums_of_squares[j]) / max_index - mu * mu;
+            double overdisp = var / mu;
+            double common = (overdisp - sums[j] + mu * n - 1) / n / (1 - overdisp * n);
+
+            double a = mu * common;
+            double b = (n - mu) * common;
+            common::logger->trace("Label: {}\ta: {}\tb: {}", j, a, b);
+
+            if (a <= 0 || b <= 0)
+                throw std::runtime_error("Fail");
+
+            if (groups[j]) {
+                a_sum_out += a;
+                b_sum_out += b;
+            } else {
+                a_sum_in += a;
+                b_sum_in += b;
+            }
+        }
+
+        double a_sum_null = a_sum_in + a_sum_out;
+        double b_sum_null = b_sum_in + b_sum_out;
+
+        auto get_theta = [&](double total, double k_sum, double a_sum, double b_sum) {
+            double b = 2 - k_sum - a_sum - b_sum - total;
+            double c = k_sum + a_sum - 1;
+            double common = sqrt(b * b - 4 * total * c);
+            double theta_low = (-b - common) / 2.0 / total;
+            double theta_high = (-b + common) / 2.0 / total;
+
+            bool low_valid = theta_low >= 0 && theta_low <= 1.0;
+            bool high_valid = theta_high >= 0 && theta_high <= 1.0;
+
+            if (low_valid && high_valid) {
+                common::logger->error("Two valid thetas: {}\t{}", theta_low, theta_high);
+                throw std::runtime_error("Fail");
+            }
+
+            if (!low_valid && !high_valid) {
+                common::logger->error("No valid thetas: {}\t{}", theta_low, theta_high);
+                throw std::runtime_error("Fail");
+            }
+
+            return low_valid ? theta_low : theta_high;
+        };
+
+        compute_pval = [&,dist](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0);
+
+            sdsl::bit_vector unfound(groups.size(), true);
+            double in_sum = 0;
+            double out_sum = 0;
+            for (const auto &[j, c] : row) {
+                unfound[j] = false;
+                if (groups[j]) {
+                    out_sum += c;
+                } else {
+                    in_sum += c;
+                }
+            }
+            double null_sum = in_sum + out_sum;
+
+            double theta_in = get_theta(in_kmers, in_sum, a_sum_in, b_sum_in);
+            double theta_out = get_theta(out_kmers, out_sum, a_sum_out, b_sum_out);
+            double theta_null = get_theta(total_kmers, null_sum, a_sum_null, b_sum_null);
+
+            double loglikelihood_alt = 0;
+            double loglikelihood_null = -theta_null * total_kmers;
+
+            for (size_t j = 0; j < groups.size(); ++j) {
+                double theta = groups[j] ? theta_out : theta_in;
+                loglikelihood_alt -= theta * sums[j];
+            }
+
+            for (const auto &[j, c] : row) {
+                double theta = groups[j] ? theta_out : theta_in;
+                loglikelihood_alt += log(theta) * c;
+                loglikelihood_null += log(theta_null) * c;
+            }
+
+            double chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
+            if (chi_stat < 0) {
+                common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+                common::logger->error("theta in: {}\ttheta out: {}\ttheta null: {}",
+                                      theta_in, theta_out, theta_null);
+                throw std::runtime_error("Test failed");
             }
 
             double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
@@ -583,7 +645,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double u = std::min(u1, u2);
 
                 double n = nx + ny;
-                    // normal approximation
+                // normal approximation
                 double mu = static_cast<double>(nx * ny) / 2;
                 double corr = 0;
                 for (const auto &[val,rks] : countsc) {
@@ -653,13 +715,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return;
             }
 
-            double lkd = log(0.05) - log(pval_min);
-            if (lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max())))
-                k = exp(lkd);
+            if (pval_min > 0) {
+                double lkd = log(0.05) - log(pval_min);
+                if (lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max())))
+                    k = exp(lkd);
 
-            if (k == 0) {
-                common::logger->error("k: {}\tpval_min: {}", k, pval_min);
-                throw std::runtime_error("Min failed");
+                if (k == 0) {
+                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
+                    throw std::runtime_error("Min failed");
+                }
             }
         }
 
@@ -681,15 +745,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
 
     uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
+    auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
 
-    if (total_sig) {
-        auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
+    std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
+    size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
+                                 [](size_t sum, const auto &a) { return sum + a.second.size(); });
+    size_t total_tests = acc;
+    common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
 
-        std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
-        size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
-                                    [](size_t sum, const auto &a) { return sum + a.second.size(); });
-        size_t total_tests = acc;
-        common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
+    if (total_sig) {
         auto begin = m_data.begin();
         size_t k = 0;
         size_t last_k = 0;

From c01a2e9b9fa1b64c164a8e659d086e46b9212ccf Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 3 May 2024 02:25:47 +0200
Subject: [PATCH 119/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 200 +++++++++++++-----
 1 file changed, 144 insertions(+), 56 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 612c592dcf..691dd43235 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -455,53 +455,58 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     } else if (config.test_type == "bp") {
         boost::math::chi_squared dist(1);
 
-        double a_sum_in = 0;
-        double a_sum_out = 0;
-        double b_sum_in = 0;
-        double b_sum_out = 0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            double n = static_cast<double>(sums[j]);
-            double mu = n / max_index;
-            double var = static_cast<double>(sums_of_squares[j]) / max_index - mu * mu;
-            double overdisp = var / mu;
-            double common = (overdisp - sums[j] + mu * n - 1) / n / (1 - overdisp * n);
+        uint64_t row_i = 0;
+        uint64_t sum_of_squares = 0;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                        std::unique_ptr<ValuesContainer>,
+                        PairContainer>(columns_all, column_values_all, [&](const auto &raw_row) {
+            uint64_t sum = 0;
+            for (const auto &[j, raw_c] : raw_row) {
+                if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i))
+                    sum += c;
+            }
 
-            double a = mu * common;
-            double b = (n - mu) * common;
-            common::logger->trace("Label: {}\ta: {}\tb: {}", j, a, b);
+            sum_of_squares += sum * sum;
+            ++row_i;
+        });
 
-            if (a <= 0 || b <= 0)
-                throw std::runtime_error("Fail");
+        double n = static_cast<double>(total_kmers);
+        double mu = n / max_index;
+        double var = static_cast<double>(sum_of_squares) / max_index;
 
-            if (groups[j]) {
-                a_sum_out += a;
-                b_sum_out += b;
-            } else {
-                a_sum_in += a;
-                b_sum_in += b;
-            }
-        }
+        double overdisp = var / mu;
+        double common = (overdisp - n + mu * n - 1) / n / (1 - overdisp * n);
 
-        double a_sum_null = a_sum_in + a_sum_out;
-        double b_sum_null = b_sum_in + b_sum_out;
+        double a = mu * common;
+        double b = (n - mu) * common;
+        common::logger->trace("mu: {}\tvar: {}\ta: {}\tb: {}", mu, var, a, b);
 
-        auto get_theta = [&](double total, double k_sum, double a_sum, double b_sum) {
-            double b = 2 - k_sum - a_sum - b_sum - total;
-            double c = k_sum + a_sum - 1;
-            double common = sqrt(b * b - 4 * total * c);
-            double theta_low = (-b - common) / 2.0 / total;
-            double theta_high = (-b + common) / 2.0 / total;
+        if (a <= 0 || b <= 0)
+            throw std::runtime_error("Fail");
+
+        auto get_theta = [&](double total, double k_sum) {
+            double qb = 2 - k_sum - a - b - total;
+            double qc = k_sum + a - 1;
+            double common = sqrt(qb * qb - 4 * total * qc);
+            double theta_low = (-qb - common) / 2.0 / total;
+            double theta_high = (-qb + common) / 2.0 / total;
 
             bool low_valid = theta_low >= 0 && theta_low <= 1.0;
             bool high_valid = theta_high >= 0 && theta_high <= 1.0;
 
             if (low_valid && high_valid) {
-                common::logger->error("Two valid thetas: {}\t{}", theta_low, theta_high);
-                throw std::runtime_error("Fail");
+                // pick the one that's closest to the measured count
+                if (abs(total * theta_low - k_sum) < abs(total * theta_high - k_sum)) {
+                    high_valid = false;
+                } else {
+                    low_valid = false;
+                }
             }
 
             if (!low_valid && !high_valid) {
-                common::logger->error("No valid thetas: {}\t{}", theta_low, theta_high);
+                common::logger->error("No valid thetas: {}\t{}\t{},{}",
+                                      theta_low, theta_high,
+                                      total, k_sum);
                 throw std::runtime_error("Fail");
             }
 
@@ -512,11 +517,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0);
 
-            sdsl::bit_vector unfound(groups.size(), true);
             double in_sum = 0;
             double out_sum = 0;
             for (const auto &[j, c] : row) {
-                unfound[j] = false;
                 if (groups[j]) {
                     out_sum += c;
                 } else {
@@ -525,23 +528,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
             double null_sum = in_sum + out_sum;
 
-            double theta_in = get_theta(in_kmers, in_sum, a_sum_in, b_sum_in);
-            double theta_out = get_theta(out_kmers, out_sum, a_sum_out, b_sum_out);
-            double theta_null = get_theta(total_kmers, null_sum, a_sum_null, b_sum_null);
-
-            double loglikelihood_alt = 0;
-            double loglikelihood_null = -theta_null * total_kmers;
+            double theta_in = get_theta(in_kmers, in_sum);
+            double theta_out = get_theta(out_kmers, out_sum);
+            double theta_null = get_theta(total_kmers, null_sum);
 
-            for (size_t j = 0; j < groups.size(); ++j) {
-                double theta = groups[j] ? theta_out : theta_in;
-                loglikelihood_alt -= theta * sums[j];
-            }
-
-            for (const auto &[j, c] : row) {
-                double theta = groups[j] ? theta_out : theta_in;
-                loglikelihood_alt += log(theta) * c;
-                loglikelihood_null += log(theta_null) * c;
-            }
+            double loglikelihood_alt = in_sum > 0 ? in_sum * log(theta_in) - theta_in * in_kmers : 0;
+            loglikelihood_alt += out_sum > 0 ? out_sum * log(theta_out) - theta_out * out_kmers : 0;
+            double loglikelihood_null = null_sum * log(theta_null) - theta_null * total_kmers;
 
             double chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
             if (chi_stat < 0) {
@@ -555,6 +548,101 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
+
+        // double a_sum_in = 0;
+        // double a_sum_out = 0;
+        // double b_sum_in = 0;
+        // double b_sum_out = 0;
+        // for (size_t j = 0; j < groups.size(); ++j) {
+        //     double n = static_cast<double>(sums[j]);
+        //     double mu = n / max_index;
+        //     double var = static_cast<double>(sums_of_squares[j]) / max_index - mu * mu;
+        //     double overdisp = var / mu;
+        //     double common = (overdisp - sums[j] + mu * n - 1) / n / (1 - overdisp * n);
+
+        //     double a = mu * common;
+        //     double b = (n - mu) * common;
+        //     common::logger->trace("Label: {}\ta: {}\tb: {}", j, a, b);
+
+        //     if (a <= 0 || b <= 0)
+        //         throw std::runtime_error("Fail");
+
+        //     if (groups[j]) {
+        //         a_sum_out += a;
+        //         b_sum_out += b;
+        //     } else {
+        //         a_sum_in += a;
+        //         b_sum_in += b;
+        //     }
+        // }
+
+        // double a_sum_null = a_sum_in + a_sum_out;
+        // double b_sum_null = b_sum_in + b_sum_out;
+
+        // auto get_theta = [&](double total, double k_sum, double a_sum, double b_sum, size_t nelem) {
+        //     double b = 2 * nelem - k_sum - a_sum - b_sum - total;
+        //     double c = k_sum + a_sum - nelem;
+        //     double common = sqrt(b * b - 4 * total * c);
+        //     double theta_low = (-b - common) / 2.0 / total;
+        //     double theta_high = (-b + common) / 2.0 / total;
+
+        //     bool low_valid = theta_low >= 0 && theta_low <= 1.0;
+        //     bool high_valid = theta_high >= 0 && theta_high <= 1.0;
+
+        //     if (low_valid && high_valid) {
+        //         // pick the one that's closest to the measured count
+        //         if (abs(total * theta_low - k_sum) < abs(total * theta_high - k_sum)) {
+        //             high_valid = false;
+        //         } else {
+        //             low_valid = false;
+        //         }
+        //     }
+
+        //     if (!low_valid && !high_valid) {
+        //         common::logger->error("No valid thetas: {}\t{}\t{},{},{},{}",
+        //                               theta_low, theta_high,
+        //                               total, k_sum, a_sum, b_sum);
+        //         throw std::runtime_error("Fail");
+        //     }
+
+        //     return low_valid ? theta_low : theta_high;
+        // };
+
+        // compute_pval = [&,dist](const auto &row) {
+        //     if (row.empty())
+        //         return std::make_tuple(1.1, 0.0, 0.0);
+
+        //     double in_sum = 0;
+        //     double out_sum = 0;
+        //     for (const auto &[j, c] : row) {
+        //         if (groups[j]) {
+        //             out_sum += c;
+        //         } else {
+        //             in_sum += c;
+        //         }
+        //     }
+        //     double null_sum = in_sum + out_sum;
+
+        //     double theta_in = get_theta(in_kmers, in_sum, a_sum_in, b_sum_in, labels_in.size());
+        //     double theta_out = get_theta(out_kmers, out_sum, a_sum_out, b_sum_out, labels_out.size());
+        //     double theta_null = get_theta(total_kmers, null_sum, a_sum_null, b_sum_null, groups.size());
+
+        //     double loglikelihood_alt = in_sum > 0 ? in_sum * log(theta_in) - theta_in * in_kmers : 0;
+        //     loglikelihood_alt += out_sum > 0 ? out_sum * log(theta_out) - theta_out * out_kmers : 0;
+        //     double loglikelihood_null = null_sum * log(theta_null) - theta_null * total_kmers;
+
+        //     double chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
+        //     if (chi_stat < 0) {
+        //         common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+        //         common::logger->error("theta in: {}\ttheta out: {}\ttheta null: {}",
+        //                               theta_in, theta_out, theta_null);
+        //         throw std::runtime_error("Test failed");
+        //     }
+
+        //     double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
+
+        //     return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
+        // };
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u" || config.test_type == "quantile") {
         compute_pval = [&](const auto &row) {
             if (row.empty())
@@ -706,7 +794,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             if (pval_min > pval) {
-                common::logger->error("Min p-val estimate too high: {} > {}", pval_min, pval);
+                common::logger->error("Min p-val estimate too high: min {} > cur {}", pval_min, pval);
                 throw std::runtime_error("Test failed");
             }
 
@@ -716,9 +804,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             if (pval_min > 0) {
-                double lkd = log(0.05) - log(pval_min);
-                if (lkd > log(static_cast<double>(std::numeric_limits<uint64_t>::max())))
-                    k = exp(lkd);
+                double lkd = log2(0.05) - log2(pval_min);
+                if (lkd <= 64)
+                    k = pow(2.0, lkd);
 
                 if (k == 0) {
                     common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);

From b42533942df8fb08165dac54ca69ddcaf7bf7029 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 3 May 2024 03:18:24 +0200
Subject: [PATCH 120/410] tt

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 691dd43235..ecb2e34208 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -474,17 +474,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         double mu = n / max_index;
         double var = static_cast<double>(sum_of_squares) / max_index;
 
-        double overdisp = var / mu;
-        double common = (overdisp - n + mu * n - 1) / n / (1 - overdisp * n);
+        double common = (mu * (n - mu) - var + mu) / n / (var - mu);
 
         double a = mu * common;
         double b = (n - mu) * common;
-        common::logger->trace("mu: {}\tvar: {}\ta: {}\tb: {}", mu, var, a, b);
+        common::logger->trace("mu: {}\tvar: {}\ta: {}\tb: {}\ttheory mean: {}\ttheory var: {}",
+                              mu, var, a, b, n * a / (a + b),
+                              mu * (1 + n * b / (a + b) / (a + b + 1)));
 
         if (a <= 0 || b <= 0)
             throw std::runtime_error("Fail");
 
         auto get_theta = [&](double total, double k_sum) {
+            if (k_sum == 0)
+                return 0.0;
+
             double qb = 2 - k_sum - a - b - total;
             double qc = k_sum + a - 1;
             double common = sqrt(qb * qb - 4 * total * qc);

From 720b6250b7602488cd4d8b1303b5ec68331c18e6 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 4 May 2024 00:30:42 +0200
Subject: [PATCH 121/410] t2

---
 .../src/graph/annotated_graph_algorithm.cpp   | 146 ++++--------------
 1 file changed, 26 insertions(+), 120 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ecb2e34208..88b7ee9e06 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -486,28 +486,34 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             throw std::runtime_error("Fail");
 
         auto get_theta = [&](double total, double k_sum) {
-            if (k_sum == 0)
-                return 0.0;
-
-            double qb = 2 - k_sum - a - b - total;
+            double qb = b - a - k_sum - total;
             double qc = k_sum + a - 1;
             double common = sqrt(qb * qb - 4 * total * qc);
             double theta_low = (-qb - common) / 2.0 / total;
             double theta_high = (-qb + common) / 2.0 / total;
 
-            bool low_valid = theta_low >= 0 && theta_low <= 1.0;
-            bool high_valid = theta_high >= 0 && theta_high <= 1.0;
+            bool low_valid = theta_low > 0 && theta_low < 1.0;
+            bool high_valid = theta_high > 0 && theta_high < 1.0;
 
             if (low_valid && high_valid) {
-                // pick the one that's closest to the measured count
-                if (abs(total * theta_low - k_sum) < abs(total * theta_high - k_sum)) {
+                double stat_low = 0;
+                double stat_high = 0;
+                if (theta_low != 0 && theta_high != 0) {
+                    stat_low = (k_sum + a - 1) * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low;
+                    stat_high = (k_sum + a - 1) * log(theta_high) + (b - 1) * log1p(-theta_high) - total * theta_high;
+                }
+
+                if (stat_low == stat_high) {
+                    common::logger->error("Two valid thetas: {}\t{}\tTotal: {}\tK: {}",
+                                          theta_low, theta_high,
+                                          total, k_sum);
+                    throw std::runtime_error("Fail");
+                } else if (stat_low > stat_high) {
                     high_valid = false;
                 } else {
                     low_valid = false;
                 }
-            }
-
-            if (!low_valid && !high_valid) {
+            } else if (!low_valid && !high_valid) {
                 common::logger->error("No valid thetas: {}\t{}\t{},{}",
                                       theta_low, theta_high,
                                       total, k_sum);
@@ -532,121 +538,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
             double null_sum = in_sum + out_sum;
 
+            double theta_null = get_theta(total_kmers, null_sum);
             double theta_in = get_theta(in_kmers, in_sum);
             double theta_out = get_theta(out_kmers, out_sum);
-            double theta_null = get_theta(total_kmers, null_sum);
-
-            double loglikelihood_alt = in_sum > 0 ? in_sum * log(theta_in) - theta_in * in_kmers : 0;
-            loglikelihood_alt += out_sum > 0 ? out_sum * log(theta_out) - theta_out * out_kmers : 0;
-            double loglikelihood_null = null_sum * log(theta_null) - theta_null * total_kmers;
 
-            double chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
-            if (chi_stat < 0) {
-                common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
-                common::logger->error("theta in: {}\ttheta out: {}\ttheta null: {}",
-                                      theta_in, theta_out, theta_null);
-                throw std::runtime_error("Test failed");
-            }
+            auto get_stat = [&](double theta, double k, double total) -> double {
+                return (k + a - 1) * log(theta) + (b - 1) * log1p(-theta) - total * theta;
+            };
+            double log_in_stat = get_stat(theta_in, in_sum, in_kmers);
+            double log_out_stat = get_stat(theta_out, out_sum, out_kmers);
 
-            double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
+            double map_pval_in = exp(get_stat(theta_null, in_sum, in_kmers) - log_in_stat);
+            double map_pval_out = exp(get_stat(theta_null, out_sum, out_kmers) - log_out_stat);
 
-            return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
+            return std::make_tuple(std::min(map_pval_in, map_pval_out), in_sum, out_sum / out_kmers * in_kmers);
         };
-
-        // double a_sum_in = 0;
-        // double a_sum_out = 0;
-        // double b_sum_in = 0;
-        // double b_sum_out = 0;
-        // for (size_t j = 0; j < groups.size(); ++j) {
-        //     double n = static_cast<double>(sums[j]);
-        //     double mu = n / max_index;
-        //     double var = static_cast<double>(sums_of_squares[j]) / max_index - mu * mu;
-        //     double overdisp = var / mu;
-        //     double common = (overdisp - sums[j] + mu * n - 1) / n / (1 - overdisp * n);
-
-        //     double a = mu * common;
-        //     double b = (n - mu) * common;
-        //     common::logger->trace("Label: {}\ta: {}\tb: {}", j, a, b);
-
-        //     if (a <= 0 || b <= 0)
-        //         throw std::runtime_error("Fail");
-
-        //     if (groups[j]) {
-        //         a_sum_out += a;
-        //         b_sum_out += b;
-        //     } else {
-        //         a_sum_in += a;
-        //         b_sum_in += b;
-        //     }
-        // }
-
-        // double a_sum_null = a_sum_in + a_sum_out;
-        // double b_sum_null = b_sum_in + b_sum_out;
-
-        // auto get_theta = [&](double total, double k_sum, double a_sum, double b_sum, size_t nelem) {
-        //     double b = 2 * nelem - k_sum - a_sum - b_sum - total;
-        //     double c = k_sum + a_sum - nelem;
-        //     double common = sqrt(b * b - 4 * total * c);
-        //     double theta_low = (-b - common) / 2.0 / total;
-        //     double theta_high = (-b + common) / 2.0 / total;
-
-        //     bool low_valid = theta_low >= 0 && theta_low <= 1.0;
-        //     bool high_valid = theta_high >= 0 && theta_high <= 1.0;
-
-        //     if (low_valid && high_valid) {
-        //         // pick the one that's closest to the measured count
-        //         if (abs(total * theta_low - k_sum) < abs(total * theta_high - k_sum)) {
-        //             high_valid = false;
-        //         } else {
-        //             low_valid = false;
-        //         }
-        //     }
-
-        //     if (!low_valid && !high_valid) {
-        //         common::logger->error("No valid thetas: {}\t{}\t{},{},{},{}",
-        //                               theta_low, theta_high,
-        //                               total, k_sum, a_sum, b_sum);
-        //         throw std::runtime_error("Fail");
-        //     }
-
-        //     return low_valid ? theta_low : theta_high;
-        // };
-
-        // compute_pval = [&,dist](const auto &row) {
-        //     if (row.empty())
-        //         return std::make_tuple(1.1, 0.0, 0.0);
-
-        //     double in_sum = 0;
-        //     double out_sum = 0;
-        //     for (const auto &[j, c] : row) {
-        //         if (groups[j]) {
-        //             out_sum += c;
-        //         } else {
-        //             in_sum += c;
-        //         }
-        //     }
-        //     double null_sum = in_sum + out_sum;
-
-        //     double theta_in = get_theta(in_kmers, in_sum, a_sum_in, b_sum_in, labels_in.size());
-        //     double theta_out = get_theta(out_kmers, out_sum, a_sum_out, b_sum_out, labels_out.size());
-        //     double theta_null = get_theta(total_kmers, null_sum, a_sum_null, b_sum_null, groups.size());
-
-        //     double loglikelihood_alt = in_sum > 0 ? in_sum * log(theta_in) - theta_in * in_kmers : 0;
-        //     loglikelihood_alt += out_sum > 0 ? out_sum * log(theta_out) - theta_out * out_kmers : 0;
-        //     double loglikelihood_null = null_sum * log(theta_null) - theta_null * total_kmers;
-
-        //     double chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
-        //     if (chi_stat < 0) {
-        //         common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
-        //         common::logger->error("theta in: {}\ttheta out: {}\ttheta null: {}",
-        //                               theta_in, theta_out, theta_null);
-        //         throw std::runtime_error("Test failed");
-        //     }
-
-        //     double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
-
-        //     return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
-        // };
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u" || config.test_type == "quantile") {
         compute_pval = [&](const auto &row) {
             if (row.empty())

From 4e63d3642fe1ca58d2fb41f7d87f052e5e698dc3 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 7 May 2024 14:54:11 +0200
Subject: [PATCH 122/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   |  216 ++-
 projects/BIGSI/scalability.ipynb              | 1374 ++++++++---------
 2 files changed, 834 insertions(+), 756 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 88b7ee9e06..30205661b4 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -8,6 +8,8 @@
 #include <boost/math/distributions/poisson.hpp>
 #include <boost/math/statistics/univariate_statistics.hpp>
 #include <boost/math/tools/roots.hpp>
+#include <boost/math/special_functions/digamma.hpp>
+#include <boost/math/tools/minima.hpp>
 
 #include "common/utils/string_utils.hpp"
 #include "common/logger.hpp"
@@ -220,7 +222,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     std::vector<double> pvals;
     pvals.reserve(max_index + 1);
-    pvals.emplace_back(std::numeric_limits<double>::max());
+    pvals.emplace_back(1.1);
 
     VectorMap<size_t, std::vector<uint64_t>> m;
     using RowStats = std::tuple<double, double, double>;
@@ -453,82 +455,148 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "bp") {
-        boost::math::chi_squared dist(1);
-
+        double pseudocount = 0;
+        in_kmers += pseudocount * max_index;
+        out_kmers += pseudocount * max_index;
+        total_kmers += pseudocount * 2.0 * max_index;
         uint64_t row_i = 0;
-        uint64_t sum_of_squares = 0;
+        double sum_of_squares_in = 0;
+        double sum_of_squares_out = 0;
+        double sum_of_squares_null = 0;
+        std::vector<std::pair<double, double>> counts;
         utils::call_rows<std::unique_ptr<bit_vector>,
                         std::unique_ptr<ValuesContainer>,
                         PairContainer>(columns_all, column_values_all, [&](const auto &raw_row) {
-            uint64_t sum = 0;
+            double sum_in = pseudocount;
+            double sum_out = pseudocount;
             for (const auto &[j, raw_c] : raw_row) {
-                if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i))
-                    sum += c;
+                if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i)) {
+                    if (groups[j]) {
+                        sum_in += c;
+                    } else {
+                        sum_out += c;
+                    }
+                }
             }
 
-            sum_of_squares += sum * sum;
+            if (sum_in + sum_out > 0) {
+                counts.emplace_back(sum_in, sum_out);
+                sum_of_squares_in += sum_in * sum_in;
+                sum_of_squares_out += sum_out * sum_out;
+                sum_of_squares_null += pow(sum_in + sum_out, 2.0);
+            }
             ++row_i;
         });
 
-        double n = static_cast<double>(total_kmers);
-        double mu = n / max_index;
-        double var = static_cast<double>(sum_of_squares) / max_index;
+        double nelem = counts.size();
+
+        // double n = total_kmers;
+
+        auto get_a = [&](double k, double k2, uint8_t pick) {
+            double mu = k / nelem;
+            auto get_ll = [&](double r) {
+                double p = r / (r + mu);
+                double ll = nelem * r * log(p) - nelem * lgamma(r);
+                for (const auto &[si, so] : counts) {
+                    double s = 0;
+                    if (pick == 0) {
+                        s = si;
+                    } else if (pick == 1) {
+                        s = so;
+                    } else {
+                        s = si + so;
+                    }
+                    ll += lgamma(r + s);
+                }
+
+                return -ll;
+            };
 
-        double common = (mu * (n - mu) - var + mu) / n / (var - mu);
+            return boost::math::tools::brent_find_minima(
+                get_ll, 0.0, nelem, 64
+            ).first * nelem / (nelem - 1);
+        };
 
-        double a = mu * common;
-        double b = (n - mu) * common;
-        common::logger->trace("mu: {}\tvar: {}\ta: {}\tb: {}\ttheory mean: {}\ttheory var: {}",
-                              mu, var, a, b, n * a / (a + b),
-                              mu * (1 + n * b / (a + b) / (a + b + 1)));
+        double a_in = get_a(in_kmers, sum_of_squares_in, 0);
+        double a_out = get_a(out_kmers, sum_of_squares_out, 1);
+        double a_null = get_a(total_kmers, sum_of_squares_null, 2);
 
-        if (a <= 0 || b <= 0)
-            throw std::runtime_error("Fail");
+        double b_in = (nelem - 1) * a_in;
+        double b_out = (nelem - 1) * a_out;
+        double b_null = (nelem - 1) * a_null;
 
-        auto get_theta = [&](double total, double k_sum) {
-            double qb = b - a - k_sum - total;
+        common::logger->trace("In: {},{}\tOut: {},{}\tNull: {},{}",
+                              a_in,b_in,a_out,b_out,a_null,b_null);
+
+        auto get_theta = [&](double a, double b, double total, double k_sum) {
+            double qb = 2.0 - total - k_sum - a - b;
             double qc = k_sum + a - 1;
             double common = sqrt(qb * qb - 4 * total * qc);
             double theta_low = (-qb - common) / 2.0 / total;
             double theta_high = (-qb + common) / 2.0 / total;
 
             bool low_valid = theta_low > 0 && theta_low < 1.0;
-            bool high_valid = theta_high > 0 && theta_high < 1.0;
-
-            if (low_valid && high_valid) {
-                double stat_low = 0;
-                double stat_high = 0;
-                if (theta_low != 0 && theta_high != 0) {
-                    stat_low = (k_sum + a - 1) * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low;
-                    stat_high = (k_sum + a - 1) * log(theta_high) + (b - 1) * log1p(-theta_high) - total * theta_high;
-                }
-
-                if (stat_low == stat_high) {
-                    common::logger->error("Two valid thetas: {}\t{}\tTotal: {}\tK: {}",
-                                          theta_low, theta_high,
-                                          total, k_sum);
-                    throw std::runtime_error("Fail");
-                } else if (stat_low > stat_high) {
-                    high_valid = false;
-                } else {
-                    low_valid = false;
-                }
-            } else if (!low_valid && !high_valid) {
-                common::logger->error("No valid thetas: {}\t{}\t{},{}",
-                                      theta_low, theta_high,
-                                      total, k_sum);
+            bool high_valid = common != 0 && theta_high > 0 && theta_high < 1.0;
+
+            // double stat_zero = k_sum + a - 1 < 1
+            //     ? std::numeric_limits<double>::infinity()
+            //     : -std::numeric_limits<double>::infinity();
+            // double stat_one = -total;
+            double stat_zero = -std::numeric_limits<double>::infinity();
+            double stat_one = -std::numeric_limits<double>::infinity();
+            double stat_low = low_valid
+                ? (k_sum + a - 1) * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low
+                : -std::numeric_limits<double>::infinity();
+            double stat_high = high_valid
+                ? (k_sum + a - 1) * log(theta_high) + (b - 1) * log1p(-theta_high) - total * theta_high
+                : -std::numeric_limits<double>::infinity();
+
+            double stat_max = std::max({ stat_zero, stat_one, stat_low, stat_high });
+
+            bool stat_is_zero = stat_max == stat_zero;
+            bool stat_is_one = stat_max == stat_one;
+            bool stat_is_low = low_valid && stat_max == stat_low;
+            bool stat_is_high = high_valid && stat_max == stat_high;
+
+            if ((stat_is_zero && stat_is_one)
+                    || (stat_is_zero && stat_is_low)
+                    || (stat_is_zero && stat_is_high)
+                    || (stat_is_one && stat_is_low)
+                    || (stat_is_one && stat_is_high)
+                    || (stat_is_low && stat_is_high)) {
+                common::logger->error("Multiple valid thetas\t{}", stat_max);
+                common::logger->error("{}\t{}\t{}\t{}", 0.0, 1.0, theta_low, theta_high);
+                common::logger->error("{}\t{}\t{}\t{}", stat_zero, stat_one, stat_low, stat_high);
+                common::logger->error("{}\t{}\t{}\t{}", stat_is_zero, stat_is_one, stat_is_low, stat_is_high);
+                common::logger->error("Ratio: {} / {}", k_sum, total);
                 throw std::runtime_error("Fail");
             }
 
-            return low_valid ? theta_low : theta_high;
+            if (stat_is_zero)
+                return 0.0;
+
+            if (stat_is_one)
+                return 1.0;
+
+            if (stat_is_low)
+                return theta_low;
+
+            if (stat_is_high)
+                return theta_high;
+
+            common::logger->error("No valid thetas\t{}", stat_max);
+            common::logger->error("{}\t{}\t{}\t{}", 0.0, 1.0, theta_low, theta_high);
+            common::logger->error("{}\t{}\t{}\t{}", stat_zero, stat_one, stat_low, stat_high);
+            common::logger->error("{}\t{}\t{}\t{}", stat_is_zero, stat_is_one, stat_is_low, stat_is_high);
+            throw std::runtime_error("Fail");
         };
 
-        compute_pval = [&,dist](const auto &row) {
+        compute_pval = [&](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0);
 
-            double in_sum = 0;
-            double out_sum = 0;
+            double in_sum = pseudocount;
+            double out_sum = pseudocount;
             for (const auto &[j, c] : row) {
                 if (groups[j]) {
                     out_sum += c;
@@ -536,22 +604,54 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     in_sum += c;
                 }
             }
+
             double null_sum = in_sum + out_sum;
 
-            double theta_null = get_theta(total_kmers, null_sum);
-            double theta_in = get_theta(in_kmers, in_sum);
-            double theta_out = get_theta(out_kmers, out_sum);
+            double theta_null = get_theta(a_null, b_null, total_kmers, null_sum);
+            double theta_in = get_theta(a_in, b_in, in_kmers, in_sum);
+            double theta_out = get_theta(a_out, b_out, out_kmers, out_sum);
 
-            auto get_stat = [&](double theta, double k, double total) -> double {
+            auto get_stat_log = [&](double a, double b, double theta, double k, double total) -> double {
                 return (k + a - 1) * log(theta) + (b - 1) * log1p(-theta) - total * theta;
             };
-            double log_in_stat = get_stat(theta_in, in_sum, in_kmers);
-            double log_out_stat = get_stat(theta_out, out_sum, out_kmers);
 
-            double map_pval_in = exp(get_stat(theta_null, in_sum, in_kmers) - log_in_stat);
-            double map_pval_out = exp(get_stat(theta_null, out_sum, out_kmers) - log_out_stat);
+            auto get_stat = [&](double a, double b, double theta, double k, double total) -> double {
+                return pow(theta, k + a - 1) * pow(1.0 - theta, b - 1) * exp(-total * theta);
+            };
+
+            double map_pval_in = theta_in > 0
+                ? exp(get_stat_log(a_in, b_in, theta_null, in_sum, in_kmers)
+                        - get_stat_log(a_in, b_in, theta_in, in_sum, in_kmers))
+                : get_stat(a_in, b_in, theta_null, in_sum, in_kmers);
+
+            double map_pval_out = theta_out > 0
+                ? exp(get_stat_log(a_out, b_out, theta_null, out_sum, out_kmers)
+                        - get_stat_log(a_out, b_out, theta_out, out_sum, out_kmers))
+                : get_stat(a_out, b_out, theta_null, out_sum, out_kmers);
+
+            // double map_pval_in = theta_in > 0
+            //     ? exp(get_stat_log(theta_in, null_sum, total_kmers)
+            //             - get_stat_log(theta_null, null_sum, total_kmers))
+            //     : exp(-get_stat_log(theta_null, null_sum, total_kmers));
+
+            // double map_pval_out = theta_out > 0
+            //     ? exp(get_stat_log(theta_out, null_sum, total_kmers)
+            //             - get_stat_log(theta_null, null_sum, total_kmers))
+            //     : exp(-get_stat_log(theta_null, null_sum, total_kmers));
+
+            if (map_pval_in > 1.0 || map_pval_in < 0.0
+                    || map_pval_out > 1.0 || map_pval_out < 0.0) {
+                common::logger->error("Failed to compute MAP of thetas");
+                common::logger->error("Theta in: {}\tTheta out: {}\tTheta null: {}",
+                                      theta_in, theta_out, theta_null);
+                common::logger->error("In sum: {}\tOut sum: {}", in_sum, out_sum);
+                common::logger->error("In p-val: {}\tOut p-val: {}", map_pval_in, map_pval_out);
+                throw std::runtime_error("Fail");
+            }
 
-            return std::make_tuple(std::min(map_pval_in, map_pval_out), in_sum, out_sum / out_kmers * in_kmers);
+            return std::make_tuple(std::min(std::min(map_pval_in, map_pval_out) * 2.0, 1.0),
+                                   in_sum,
+                                   out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u" || config.test_type == "quantile") {
         compute_pval = [&](const auto &row) {
diff --git a/projects/BIGSI/scalability.ipynb b/projects/BIGSI/scalability.ipynb
index 700774f97b..4c356947be 100644
--- a/projects/BIGSI/scalability.ipynb
+++ b/projects/BIGSI/scalability.ipynb
@@ -2,23 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-13T07:53:35.346932Z",
      "start_time": "2020-08-13T07:53:35.304112Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import glob\n",
     "import os\n",
@@ -53,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-13T07:53:52.184306Z",
@@ -62,18 +53,20 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "33"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '/cluster/work/grlab/projects/metagenome/data/BIGSI/subsets/graph_subset_750.dbg'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 16\u001b[0m\n\u001b[1;32m     13\u001b[0m annotation_size_rb_brwt_primary \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m750\u001b[39m, \u001b[38;5;241m24751\u001b[39m, \u001b[38;5;241m750\u001b[39m):\n\u001b[0;32m---> 16\u001b[0m     graph_size\u001b[38;5;241m.\u001b[39mappend(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mgetsize(DIR \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgraph_subset_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.dbg\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(i)))\n\u001b[1;32m     17\u001b[0m     graph_primary_size\u001b[38;5;241m.\u001b[39mappend(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mgetsize(DIR \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgraph_subset_\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m_primary.dbg\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(i)))\n\u001b[1;32m     18\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "File \u001b[0;32m<frozen genericpath>:50\u001b[0m, in \u001b[0;36mgetsize\u001b[0;34m(filename)\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/cluster/work/grlab/projects/metagenome/data/BIGSI/subsets/graph_subset_750.dbg'"
+     ]
     }
    ],
    "source": [
-    "DIR = '/cluster/home/mikhaika/metagenome/data/BIGSI/subsets/'\n",
+    "DIR = '/cluster/work/grlab/projects/metagenome/data/BIGSI/subsets/'\n",
     "\n",
     "graph_size = []\n",
     "graph_primary_size = []\n",
@@ -151,660 +144,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-13T07:53:52.226101Z",
      "start_time": "2020-08-13T07:53:52.188486Z"
-    },
-    "scrolled": false
+    }
    },
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>n_samples</th>\n",
-       "      <th>graph</th>\n",
-       "      <th>graph_primary</th>\n",
-       "      <th>anno_column</th>\n",
-       "      <th>anno_flat</th>\n",
-       "      <th>anno_brwt</th>\n",
-       "      <th>anno_brwt_relaxed</th>\n",
-       "      <th>anno_rbfish</th>\n",
-       "      <th>anno_rb_brwt</th>\n",
-       "      <th>anno_brwt_primary</th>\n",
-       "      <th>anno_brwt_primary_relaxed</th>\n",
-       "      <th>anno_rb_brwt_primary</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>750</td>\n",
-       "      <td>680840187</td>\n",
-       "      <td>365273397</td>\n",
-       "      <td>3467282309</td>\n",
-       "      <td>3403187568</td>\n",
-       "      <td>1452939063</td>\n",
-       "      <td>1258618962</td>\n",
-       "      <td>1069386178</td>\n",
-       "      <td>981916827</td>\n",
-       "      <td>1368942507</td>\n",
-       "      <td>1198597461</td>\n",
-       "      <td>811159748</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1500</td>\n",
-       "      <td>1097441441</td>\n",
-       "      <td>584008411</td>\n",
-       "      <td>6944099422</td>\n",
-       "      <td>7392805676</td>\n",
-       "      <td>2669114564</td>\n",
-       "      <td>2339253260</td>\n",
-       "      <td>2060912922</td>\n",
-       "      <td>1718434604</td>\n",
-       "      <td>2527734236</td>\n",
-       "      <td>2176665666</td>\n",
-       "      <td>1431942712</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2250</td>\n",
-       "      <td>1458808225</td>\n",
-       "      <td>775600027</td>\n",
-       "      <td>11395383787</td>\n",
-       "      <td>11421318886</td>\n",
-       "      <td>3803000524</td>\n",
-       "      <td>3331726412</td>\n",
-       "      <td>3163502112</td>\n",
-       "      <td>2406339596</td>\n",
-       "      <td>3634402819</td>\n",
-       "      <td>3164242999</td>\n",
-       "      <td>2020974859</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3000</td>\n",
-       "      <td>1791219361</td>\n",
-       "      <td>951690661</td>\n",
-       "      <td>15114539807</td>\n",
-       "      <td>16032242791</td>\n",
-       "      <td>4866896980</td>\n",
-       "      <td>4204070206</td>\n",
-       "      <td>4352412225</td>\n",
-       "      <td>3056285478</td>\n",
-       "      <td>4655137134</td>\n",
-       "      <td>4141684554</td>\n",
-       "      <td>2577599110</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3750</td>\n",
-       "      <td>2050869703</td>\n",
-       "      <td>1088471572</td>\n",
-       "      <td>18950759854</td>\n",
-       "      <td>19438476437</td>\n",
-       "      <td>5756163635</td>\n",
-       "      <td>5095047195</td>\n",
-       "      <td>5720092738</td>\n",
-       "      <td>3604309006</td>\n",
-       "      <td>5579940590</td>\n",
-       "      <td>4879372106</td>\n",
-       "      <td>3052699352</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>4500</td>\n",
-       "      <td>2417864162</td>\n",
-       "      <td>1278806702</td>\n",
-       "      <td>22712604705</td>\n",
-       "      <td>24544295866</td>\n",
-       "      <td>6995844891</td>\n",
-       "      <td>6164045164</td>\n",
-       "      <td>7327513539</td>\n",
-       "      <td>4324783609</td>\n",
-       "      <td>6634230155</td>\n",
-       "      <td>5867330389</td>\n",
-       "      <td>3664249366</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>5250</td>\n",
-       "      <td>2770969378</td>\n",
-       "      <td>1462866336</td>\n",
-       "      <td>28639265474</td>\n",
-       "      <td>28284468848</td>\n",
-       "      <td>8081394693</td>\n",
-       "      <td>7058643047</td>\n",
-       "      <td>8902006918</td>\n",
-       "      <td>5032477995</td>\n",
-       "      <td>7786719492</td>\n",
-       "      <td>6841964833</td>\n",
-       "      <td>4275191738</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>6000</td>\n",
-       "      <td>3029609610</td>\n",
-       "      <td>1597784679</td>\n",
-       "      <td>32715054437</td>\n",
-       "      <td>32192999047</td>\n",
-       "      <td>9062934266</td>\n",
-       "      <td>8028452704</td>\n",
-       "      <td>10067634234</td>\n",
-       "      <td>5570061727</td>\n",
-       "      <td>8590052865</td>\n",
-       "      <td>7606608586</td>\n",
-       "      <td>4734489812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>6750</td>\n",
-       "      <td>3279011641</td>\n",
-       "      <td>1728942675</td>\n",
-       "      <td>36646285747</td>\n",
-       "      <td>38067083121</td>\n",
-       "      <td>9942224825</td>\n",
-       "      <td>8786859184</td>\n",
-       "      <td>12106433275</td>\n",
-       "      <td>6107492792</td>\n",
-       "      <td>9430694882</td>\n",
-       "      <td>8336879752</td>\n",
-       "      <td>5206338505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>7500</td>\n",
-       "      <td>3629828589</td>\n",
-       "      <td>1911388019</td>\n",
-       "      <td>40645802986</td>\n",
-       "      <td>41650273004</td>\n",
-       "      <td>11017517019</td>\n",
-       "      <td>9729211358</td>\n",
-       "      <td>13651203469</td>\n",
-       "      <td>6822510201</td>\n",
-       "      <td>10494946828</td>\n",
-       "      <td>9269793179</td>\n",
-       "      <td>5818759893</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>8250</td>\n",
-       "      <td>3833921284</td>\n",
-       "      <td>2018310958</td>\n",
-       "      <td>44683465745</td>\n",
-       "      <td>45270345736</td>\n",
-       "      <td>11784526032</td>\n",
-       "      <td>10443111911</td>\n",
-       "      <td>15106282398</td>\n",
-       "      <td>7290361853</td>\n",
-       "      <td>11375755476</td>\n",
-       "      <td>10029880371</td>\n",
-       "      <td>6232202959</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>9000</td>\n",
-       "      <td>4070050111</td>\n",
-       "      <td>2143003364</td>\n",
-       "      <td>48691597755</td>\n",
-       "      <td>48863689264</td>\n",
-       "      <td>12693407257</td>\n",
-       "      <td>11169272073</td>\n",
-       "      <td>17598356597</td>\n",
-       "      <td>7825914638</td>\n",
-       "      <td>12247517332</td>\n",
-       "      <td>10762079611</td>\n",
-       "      <td>6692243494</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>9750</td>\n",
-       "      <td>4344680891</td>\n",
-       "      <td>2286330740</td>\n",
-       "      <td>52879033484</td>\n",
-       "      <td>56445289456</td>\n",
-       "      <td>13695358937</td>\n",
-       "      <td>12097010492</td>\n",
-       "      <td>19151641321</td>\n",
-       "      <td>8420316987</td>\n",
-       "      <td>13092518865</td>\n",
-       "      <td>11548076633</td>\n",
-       "      <td>7203503404</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>10500</td>\n",
-       "      <td>4618961427</td>\n",
-       "      <td>2431135124</td>\n",
-       "      <td>56944088553</td>\n",
-       "      <td>60381580178</td>\n",
-       "      <td>14522140383</td>\n",
-       "      <td>12749433269</td>\n",
-       "      <td>20563114468</td>\n",
-       "      <td>8995298384</td>\n",
-       "      <td>14037971419</td>\n",
-       "      <td>12483899930</td>\n",
-       "      <td>7720973386</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>11250</td>\n",
-       "      <td>4942492973</td>\n",
-       "      <td>2599904384</td>\n",
-       "      <td>61150229936</td>\n",
-       "      <td>65267054472</td>\n",
-       "      <td>15610919952</td>\n",
-       "      <td>13751136393</td>\n",
-       "      <td>22314329077</td>\n",
-       "      <td>9712647672</td>\n",
-       "      <td>15056770098</td>\n",
-       "      <td>13298903040</td>\n",
-       "      <td>8333667443</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>12000</td>\n",
-       "      <td>5080413111</td>\n",
-       "      <td>2674181295</td>\n",
-       "      <td>65173115329</td>\n",
-       "      <td>68869815771</td>\n",
-       "      <td>16284256030</td>\n",
-       "      <td>14577247405</td>\n",
-       "      <td>23641228138</td>\n",
-       "      <td>10087812789</td>\n",
-       "      <td>15777709350</td>\n",
-       "      <td>13809234634</td>\n",
-       "      <td>8689068889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>12750</td>\n",
-       "      <td>5337333315</td>\n",
-       "      <td>2807096637</td>\n",
-       "      <td>74385241935</td>\n",
-       "      <td>72663587659</td>\n",
-       "      <td>17219344648</td>\n",
-       "      <td>15192629084</td>\n",
-       "      <td>25229718280</td>\n",
-       "      <td>10642711700</td>\n",
-       "      <td>16583599085</td>\n",
-       "      <td>14666962616</td>\n",
-       "      <td>9142506260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>13500</td>\n",
-       "      <td>5576095447</td>\n",
-       "      <td>2933456531</td>\n",
-       "      <td>78692066219</td>\n",
-       "      <td>76268082147</td>\n",
-       "      <td>18082369780</td>\n",
-       "      <td>16040587337</td>\n",
-       "      <td>29302771591</td>\n",
-       "      <td>11182331415</td>\n",
-       "      <td>17298557849</td>\n",
-       "      <td>15220445610</td>\n",
-       "      <td>9622788429</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>14250</td>\n",
-       "      <td>5846359637</td>\n",
-       "      <td>3074565247</td>\n",
-       "      <td>83122549046</td>\n",
-       "      <td>85569012741</td>\n",
-       "      <td>18928843383</td>\n",
-       "      <td>16700405969</td>\n",
-       "      <td>30973064076</td>\n",
-       "      <td>11764253798</td>\n",
-       "      <td>18179825623</td>\n",
-       "      <td>16022851696</td>\n",
-       "      <td>10119447307</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>15000</td>\n",
-       "      <td>6060915067</td>\n",
-       "      <td>3186311980</td>\n",
-       "      <td>87369172897</td>\n",
-       "      <td>89402550879</td>\n",
-       "      <td>19867580691</td>\n",
-       "      <td>17533688391</td>\n",
-       "      <td>32461544225</td>\n",
-       "      <td>12276024499</td>\n",
-       "      <td>19126372967</td>\n",
-       "      <td>16830472236</td>\n",
-       "      <td>10588442172</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>15750</td>\n",
-       "      <td>6299858471</td>\n",
-       "      <td>3313640540</td>\n",
-       "      <td>91755864568</td>\n",
-       "      <td>93368431400</td>\n",
-       "      <td>20721155524</td>\n",
-       "      <td>18609455464</td>\n",
-       "      <td>34005811102</td>\n",
-       "      <td>12855713839</td>\n",
-       "      <td>19942605327</td>\n",
-       "      <td>17541676624</td>\n",
-       "      <td>11081353503</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>16500</td>\n",
-       "      <td>6509998249</td>\n",
-       "      <td>3424202471</td>\n",
-       "      <td>96228599285</td>\n",
-       "      <td>97414065316</td>\n",
-       "      <td>21536360234</td>\n",
-       "      <td>19169242064</td>\n",
-       "      <td>35621328409</td>\n",
-       "      <td>13367252849</td>\n",
-       "      <td>20880280270</td>\n",
-       "      <td>18193671376</td>\n",
-       "      <td>11543485705</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>17250</td>\n",
-       "      <td>6713586133</td>\n",
-       "      <td>3530941389</td>\n",
-       "      <td>100584690303</td>\n",
-       "      <td>101349631754</td>\n",
-       "      <td>22415587331</td>\n",
-       "      <td>19780201589</td>\n",
-       "      <td>37137763582</td>\n",
-       "      <td>13859514186</td>\n",
-       "      <td>21571886542</td>\n",
-       "      <td>19014999225</td>\n",
-       "      <td>11984085789</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>18000</td>\n",
-       "      <td>6966770152</td>\n",
-       "      <td>3660004669</td>\n",
-       "      <td>104856124724</td>\n",
-       "      <td>105199548967</td>\n",
-       "      <td>23262022502</td>\n",
-       "      <td>20610052847</td>\n",
-       "      <td>38744619636</td>\n",
-       "      <td>14441643040</td>\n",
-       "      <td>22417863049</td>\n",
-       "      <td>19881351255</td>\n",
-       "      <td>12462011528</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>18750</td>\n",
-       "      <td>7170097818</td>\n",
-       "      <td>3767303220</td>\n",
-       "      <td>109013492169</td>\n",
-       "      <td>108950988422</td>\n",
-       "      <td>24137621426</td>\n",
-       "      <td>21306961371</td>\n",
-       "      <td>44035130014</td>\n",
-       "      <td>14945275059</td>\n",
-       "      <td>23250382467</td>\n",
-       "      <td>20460930556</td>\n",
-       "      <td>12927410755</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>19500</td>\n",
-       "      <td>7327146337</td>\n",
-       "      <td>3852100787</td>\n",
-       "      <td>113442185938</td>\n",
-       "      <td>112952667488</td>\n",
-       "      <td>24900248625</td>\n",
-       "      <td>21936882323</td>\n",
-       "      <td>45764169308</td>\n",
-       "      <td>15386067828</td>\n",
-       "      <td>23820423528</td>\n",
-       "      <td>21146381309</td>\n",
-       "      <td>13315684087</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>20250</td>\n",
-       "      <td>7515544854</td>\n",
-       "      <td>3951571275</td>\n",
-       "      <td>117838710888</td>\n",
-       "      <td>116927321214</td>\n",
-       "      <td>25594970535</td>\n",
-       "      <td>22962159673</td>\n",
-       "      <td>47585696473</td>\n",
-       "      <td>15875376291</td>\n",
-       "      <td>24634861102</td>\n",
-       "      <td>21864282298</td>\n",
-       "      <td>13740171758</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>21000</td>\n",
-       "      <td>7736880330</td>\n",
-       "      <td>4066889007</td>\n",
-       "      <td>122167280580</td>\n",
-       "      <td>120837098278</td>\n",
-       "      <td>26408528700</td>\n",
-       "      <td>23615705241</td>\n",
-       "      <td>49295986308</td>\n",
-       "      <td>16404603055</td>\n",
-       "      <td>25506135797</td>\n",
-       "      <td>22560293509</td>\n",
-       "      <td>14204493149</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>21750</td>\n",
-       "      <td>7938542197</td>\n",
-       "      <td>4171438261</td>\n",
-       "      <td>126517258272</td>\n",
-       "      <td>135204438642</td>\n",
-       "      <td>27165219647</td>\n",
-       "      <td>24103456251</td>\n",
-       "      <td>51040900568</td>\n",
-       "      <td>16857619187</td>\n",
-       "      <td>26326626164</td>\n",
-       "      <td>23241111073</td>\n",
-       "      <td>14612896625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>22500</td>\n",
-       "      <td>8077990788</td>\n",
-       "      <td>4246616760</td>\n",
-       "      <td>130810276693</td>\n",
-       "      <td>139077834813</td>\n",
-       "      <td>27685076997</td>\n",
-       "      <td>24532269013</td>\n",
-       "      <td>53543169482</td>\n",
-       "      <td>17252285386</td>\n",
-       "      <td>26792621685</td>\n",
-       "      <td>23839527632</td>\n",
-       "      <td>14978142643</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>23250</td>\n",
-       "      <td>8285521781</td>\n",
-       "      <td>4353182638</td>\n",
-       "      <td>135105490424</td>\n",
-       "      <td>142950262939</td>\n",
-       "      <td>28571468192</td>\n",
-       "      <td>25425481107</td>\n",
-       "      <td>55210298868</td>\n",
-       "      <td>17765891998</td>\n",
-       "      <td>27558104309</td>\n",
-       "      <td>24609005610</td>\n",
-       "      <td>15427223528</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>24000</td>\n",
-       "      <td>8454873634</td>\n",
-       "      <td>4442659665</td>\n",
-       "      <td>139433968003</td>\n",
-       "      <td>146861017464</td>\n",
-       "      <td>29302629086</td>\n",
-       "      <td>25862996482</td>\n",
-       "      <td>56718031383</td>\n",
-       "      <td>18201526764</td>\n",
-       "      <td>28305803512</td>\n",
-       "      <td>24495427504</td>\n",
-       "      <td>15795240703</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>24750</td>\n",
-       "      <td>8590558374</td>\n",
-       "      <td>4513368082</td>\n",
-       "      <td>143674082582</td>\n",
-       "      <td>150679889791</td>\n",
-       "      <td>29873379300</td>\n",
-       "      <td>26473427889</td>\n",
-       "      <td>58233452088</td>\n",
-       "      <td>18580300648</td>\n",
-       "      <td>28949591765</td>\n",
-       "      <td>25728392632</td>\n",
-       "      <td>16127357752</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    n_samples       graph  graph_primary   anno_column     anno_flat  \\\n",
-       "0         750   680840187      365273397    3467282309    3403187568   \n",
-       "1        1500  1097441441      584008411    6944099422    7392805676   \n",
-       "2        2250  1458808225      775600027   11395383787   11421318886   \n",
-       "3        3000  1791219361      951690661   15114539807   16032242791   \n",
-       "4        3750  2050869703     1088471572   18950759854   19438476437   \n",
-       "5        4500  2417864162     1278806702   22712604705   24544295866   \n",
-       "6        5250  2770969378     1462866336   28639265474   28284468848   \n",
-       "7        6000  3029609610     1597784679   32715054437   32192999047   \n",
-       "8        6750  3279011641     1728942675   36646285747   38067083121   \n",
-       "9        7500  3629828589     1911388019   40645802986   41650273004   \n",
-       "10       8250  3833921284     2018310958   44683465745   45270345736   \n",
-       "11       9000  4070050111     2143003364   48691597755   48863689264   \n",
-       "12       9750  4344680891     2286330740   52879033484   56445289456   \n",
-       "13      10500  4618961427     2431135124   56944088553   60381580178   \n",
-       "14      11250  4942492973     2599904384   61150229936   65267054472   \n",
-       "15      12000  5080413111     2674181295   65173115329   68869815771   \n",
-       "16      12750  5337333315     2807096637   74385241935   72663587659   \n",
-       "17      13500  5576095447     2933456531   78692066219   76268082147   \n",
-       "18      14250  5846359637     3074565247   83122549046   85569012741   \n",
-       "19      15000  6060915067     3186311980   87369172897   89402550879   \n",
-       "20      15750  6299858471     3313640540   91755864568   93368431400   \n",
-       "21      16500  6509998249     3424202471   96228599285   97414065316   \n",
-       "22      17250  6713586133     3530941389  100584690303  101349631754   \n",
-       "23      18000  6966770152     3660004669  104856124724  105199548967   \n",
-       "24      18750  7170097818     3767303220  109013492169  108950988422   \n",
-       "25      19500  7327146337     3852100787  113442185938  112952667488   \n",
-       "26      20250  7515544854     3951571275  117838710888  116927321214   \n",
-       "27      21000  7736880330     4066889007  122167280580  120837098278   \n",
-       "28      21750  7938542197     4171438261  126517258272  135204438642   \n",
-       "29      22500  8077990788     4246616760  130810276693  139077834813   \n",
-       "30      23250  8285521781     4353182638  135105490424  142950262939   \n",
-       "31      24000  8454873634     4442659665  139433968003  146861017464   \n",
-       "32      24750  8590558374     4513368082  143674082582  150679889791   \n",
-       "\n",
-       "      anno_brwt  anno_brwt_relaxed  anno_rbfish  anno_rb_brwt  \\\n",
-       "0    1452939063         1258618962   1069386178     981916827   \n",
-       "1    2669114564         2339253260   2060912922    1718434604   \n",
-       "2    3803000524         3331726412   3163502112    2406339596   \n",
-       "3    4866896980         4204070206   4352412225    3056285478   \n",
-       "4    5756163635         5095047195   5720092738    3604309006   \n",
-       "5    6995844891         6164045164   7327513539    4324783609   \n",
-       "6    8081394693         7058643047   8902006918    5032477995   \n",
-       "7    9062934266         8028452704  10067634234    5570061727   \n",
-       "8    9942224825         8786859184  12106433275    6107492792   \n",
-       "9   11017517019         9729211358  13651203469    6822510201   \n",
-       "10  11784526032        10443111911  15106282398    7290361853   \n",
-       "11  12693407257        11169272073  17598356597    7825914638   \n",
-       "12  13695358937        12097010492  19151641321    8420316987   \n",
-       "13  14522140383        12749433269  20563114468    8995298384   \n",
-       "14  15610919952        13751136393  22314329077    9712647672   \n",
-       "15  16284256030        14577247405  23641228138   10087812789   \n",
-       "16  17219344648        15192629084  25229718280   10642711700   \n",
-       "17  18082369780        16040587337  29302771591   11182331415   \n",
-       "18  18928843383        16700405969  30973064076   11764253798   \n",
-       "19  19867580691        17533688391  32461544225   12276024499   \n",
-       "20  20721155524        18609455464  34005811102   12855713839   \n",
-       "21  21536360234        19169242064  35621328409   13367252849   \n",
-       "22  22415587331        19780201589  37137763582   13859514186   \n",
-       "23  23262022502        20610052847  38744619636   14441643040   \n",
-       "24  24137621426        21306961371  44035130014   14945275059   \n",
-       "25  24900248625        21936882323  45764169308   15386067828   \n",
-       "26  25594970535        22962159673  47585696473   15875376291   \n",
-       "27  26408528700        23615705241  49295986308   16404603055   \n",
-       "28  27165219647        24103456251  51040900568   16857619187   \n",
-       "29  27685076997        24532269013  53543169482   17252285386   \n",
-       "30  28571468192        25425481107  55210298868   17765891998   \n",
-       "31  29302629086        25862996482  56718031383   18201526764   \n",
-       "32  29873379300        26473427889  58233452088   18580300648   \n",
-       "\n",
-       "    anno_brwt_primary  anno_brwt_primary_relaxed  anno_rb_brwt_primary  \n",
-       "0          1368942507                 1198597461             811159748  \n",
-       "1          2527734236                 2176665666            1431942712  \n",
-       "2          3634402819                 3164242999            2020974859  \n",
-       "3          4655137134                 4141684554            2577599110  \n",
-       "4          5579940590                 4879372106            3052699352  \n",
-       "5          6634230155                 5867330389            3664249366  \n",
-       "6          7786719492                 6841964833            4275191738  \n",
-       "7          8590052865                 7606608586            4734489812  \n",
-       "8          9430694882                 8336879752            5206338505  \n",
-       "9         10494946828                 9269793179            5818759893  \n",
-       "10        11375755476                10029880371            6232202959  \n",
-       "11        12247517332                10762079611            6692243494  \n",
-       "12        13092518865                11548076633            7203503404  \n",
-       "13        14037971419                12483899930            7720973386  \n",
-       "14        15056770098                13298903040            8333667443  \n",
-       "15        15777709350                13809234634            8689068889  \n",
-       "16        16583599085                14666962616            9142506260  \n",
-       "17        17298557849                15220445610            9622788429  \n",
-       "18        18179825623                16022851696           10119447307  \n",
-       "19        19126372967                16830472236           10588442172  \n",
-       "20        19942605327                17541676624           11081353503  \n",
-       "21        20880280270                18193671376           11543485705  \n",
-       "22        21571886542                19014999225           11984085789  \n",
-       "23        22417863049                19881351255           12462011528  \n",
-       "24        23250382467                20460930556           12927410755  \n",
-       "25        23820423528                21146381309           13315684087  \n",
-       "26        24634861102                21864282298           13740171758  \n",
-       "27        25506135797                22560293509           14204493149  \n",
-       "28        26326626164                23241111073           14612896625  \n",
-       "29        26792621685                23839527632           14978142643  \n",
-       "30        27558104309                24609005610           15427223528  \n",
-       "31        28305803512                24495427504           15795240703  \n",
-       "32        28949591765                25728392632           16127357752  "
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'results' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m results\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'results' is not defined"
+     ]
     }
    ],
    "source": [
@@ -1064,25 +421,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-13T08:49:31.529328Z",
      "start_time": "2020-08-13T08:49:30.607666Z"
-    },
-    "scrolled": false
+    }
    },
    "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'results' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 35\u001b[0m\n\u001b[1;32m     25\u001b[0m ax \u001b[38;5;241m=\u001b[39m axes\n\u001b[1;32m     27\u001b[0m \u001b[38;5;66;03m# lines = ax.plot(results.n_samples,\u001b[39;00m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m#                 results.mantis_index / 10**9,\u001b[39;00m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m#                 marker='o', ms=4, label='Mantis (CQF only)', linestyle=':', color='g')\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;66;03m#         (results.mantis_index + results.mantis_rrr) / 10**9,\u001b[39;00m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;66;03m#         marker='o', ms=4, label='Mantis', color=lines[0].get_color())\u001b[39;00m\n\u001b[0;32m---> 35\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(results\u001b[38;5;241m.\u001b[39mn_samples, results\u001b[38;5;241m.\u001b[39mbigsi_db \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m10\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m9\u001b[39m,\n\u001b[1;32m     36\u001b[0m         marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, ms\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2.5\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBIGSI\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgrey\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     38\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(results\u001b[38;5;241m.\u001b[39mn_samples,\n\u001b[1;32m     39\u001b[0m         (results\u001b[38;5;241m.\u001b[39mmantis_index \u001b[38;5;241m+\u001b[39m results\u001b[38;5;241m.\u001b[39mmantis_mst) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m10\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m9\u001b[39m,\n\u001b[1;32m     40\u001b[0m         marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m, ms\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2.5\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMantis (MST)\u001b[39m\u001b[38;5;124m'\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mg\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     42\u001b[0m h, fpr \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'results' is not defined"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 414.256x307.235 with 1 Axes>"
+       "<Figure size 575.355x426.716 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1183,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-13T08:50:26.211702Z",
@@ -1209,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-08-13T08:58:37.388236Z",
@@ -1217,40 +582,654 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 489.575x302.58 with 1 Axes>"
+       "<Figure size 679.965x420.25 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n"
+     ]
+    },
     {
      "data": {
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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 489.575x302.58 with 1 Axes>"
+       "<Figure size 679.965x420.25 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n",
+      "/scratch/slurm-job.4609797/ipykernel_3622595/2589563486.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  table = table.append([{'n_samples': int(log_file.split('/')[-1].split('.')[-2]),\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 489.575x302.58 with 1 Axes>"
+       "<Figure size 679.965x420.25 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1397,8 +1376,7 @@
     "ExecuteTime": {
      "end_time": "2020-07-05T18:53:16.072725Z",
      "start_time": "2020-07-05T18:52:51.808008Z"
-    },
-    "scrolled": false
+    }
    },
    "outputs": [
     {
@@ -1521,7 +1499,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1535,7 +1513,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,
@@ -1555,5 +1533,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From 3ed979841c4525376ca37772359b598717fd3689 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 7 May 2024 16:54:28 +0200
Subject: [PATCH 123/410] this works

---
 .../src/graph/annotated_graph_algorithm.cpp   | 157 +++++++++---------
 1 file changed, 80 insertions(+), 77 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 30205661b4..b39e9fa470 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -10,6 +10,7 @@
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/tools/minima.hpp>
+#include <boost/math/tools/roots.hpp>
 
 #include "common/utils/string_utils.hpp"
 #include "common/logger.hpp"
@@ -494,9 +495,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         auto get_a = [&](double k, double k2, uint8_t pick) {
             double mu = k / nelem;
-            auto get_ll = [&](double r) {
-                double p = r / (r + mu);
-                double ll = nelem * r * log(p) - nelem * lgamma(r);
+            auto get_dll = [&](double r) {
+                double lp = log(r) - log(r + mu);
+                double dll = nelem * lp - nelem * boost::math::digamma(r);
                 for (const auto &[si, so] : counts) {
                     double s = 0;
                     if (pick == 0) {
@@ -506,44 +507,64 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     } else {
                         s = si + so;
                     }
-                    ll += lgamma(r + s);
+                    dll += boost::math::digamma(r + s);
                 }
 
-                return -ll;
+                return dll;
             };
 
-            return boost::math::tools::brent_find_minima(
-                get_ll, 0.0, nelem, 64
-            ).first * nelem / (nelem - 1);
+            double var = k2 / nelem - mu * mu;
+            double r_guess = mu * mu / (var - mu);
+
+            long unsigned int max_iter = 100;
+            auto [r_min,r_max] = boost::math::tools::bracket_and_solve_root(
+                get_dll, r_guess, 2.0, false, [&](double r_min, double r_max) {
+                    return abs(r_min - r_max) / r_max < 1e-5;
+                },
+                max_iter
+            );
+            double a = (r_min + r_max) / 2.0 * nelem / (nelem - 1);
+
+            double b = (nelem - 1) * a;
+
+            common::logger->trace("{}:\tmean: {}\tvar: {}\ta: {}\tb: {}\texp: {}\tvar: {}",
+                                  pick, mu, k2 / nelem - mu * mu,
+                                  a, b,
+                                  k * a / (a + b),
+                                  mu + mu * mu * (nelem - 1) / (nelem * a + 1));
+
+            return std::make_pair(a, b);
         };
 
-        double a_in = get_a(in_kmers, sum_of_squares_in, 0);
-        double a_out = get_a(out_kmers, sum_of_squares_out, 1);
-        double a_null = get_a(total_kmers, sum_of_squares_null, 2);
+        auto [a_null, b_null] = get_a(total_kmers, sum_of_squares_null, 2);
+
+        if (a_null <= 1) {
+            throw std::runtime_error("a_null <= 1");
+        }
+
+        auto [a_in, b_in] = get_a(in_kmers, sum_of_squares_in, 0);
+        auto [a_out, b_out] = get_a(out_kmers, sum_of_squares_out, 1);
 
-        double b_in = (nelem - 1) * a_in;
-        double b_out = (nelem - 1) * a_out;
-        double b_null = (nelem - 1) * a_null;
+        if (a_in <= 1) {
+            a_in = a_null;
+            b_in = b_null;
+        }
 
-        common::logger->trace("In: {},{}\tOut: {},{}\tNull: {},{}",
-                              a_in,b_in,a_out,b_out,a_null,b_null);
+        if (a_out <= 1) {
+            a_out = a_null;
+            b_out = b_null;
+        }
 
         auto get_theta = [&](double a, double b, double total, double k_sum) {
             double qb = 2.0 - total - k_sum - a - b;
             double qc = k_sum + a - 1;
-            double common = sqrt(qb * qb - 4 * total * qc);
-            double theta_low = (-qb - common) / 2.0 / total;
-            double theta_high = (-qb + common) / 2.0 / total;
+            double qf = sqrt(qb * qb - 4 * total * qc);
+            double theta_low = (-qb - qf) / 2.0 / total;
+            double theta_high = (-qb + qf) / 2.0 / total;
 
             bool low_valid = theta_low > 0 && theta_low < 1.0;
-            bool high_valid = common != 0 && theta_high > 0 && theta_high < 1.0;
-
-            // double stat_zero = k_sum + a - 1 < 1
-            //     ? std::numeric_limits<double>::infinity()
-            //     : -std::numeric_limits<double>::infinity();
-            // double stat_one = -total;
-            double stat_zero = -std::numeric_limits<double>::infinity();
-            double stat_one = -std::numeric_limits<double>::infinity();
+            bool high_valid = qf != 0 && theta_high > 0 && theta_high < 1.0;
+
             double stat_low = low_valid
                 ? (k_sum + a - 1) * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low
                 : -std::numeric_limits<double>::infinity();
@@ -551,43 +572,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ? (k_sum + a - 1) * log(theta_high) + (b - 1) * log1p(-theta_high) - total * theta_high
                 : -std::numeric_limits<double>::infinity();
 
-            double stat_max = std::max({ stat_zero, stat_one, stat_low, stat_high });
+            double stat_max = std::max({ stat_low, stat_high });
 
-            bool stat_is_zero = stat_max == stat_zero;
-            bool stat_is_one = stat_max == stat_one;
             bool stat_is_low = low_valid && stat_max == stat_low;
             bool stat_is_high = high_valid && stat_max == stat_high;
 
-            if ((stat_is_zero && stat_is_one)
-                    || (stat_is_zero && stat_is_low)
-                    || (stat_is_zero && stat_is_high)
-                    || (stat_is_one && stat_is_low)
-                    || (stat_is_one && stat_is_high)
-                    || (stat_is_low && stat_is_high)) {
+            if (stat_is_low && stat_is_high) {
                 common::logger->error("Multiple valid thetas\t{}", stat_max);
-                common::logger->error("{}\t{}\t{}\t{}", 0.0, 1.0, theta_low, theta_high);
-                common::logger->error("{}\t{}\t{}\t{}", stat_zero, stat_one, stat_low, stat_high);
-                common::logger->error("{}\t{}\t{}\t{}", stat_is_zero, stat_is_one, stat_is_low, stat_is_high);
+                common::logger->error("{}\t{}", theta_low, theta_high);
+                common::logger->error("{}\t{}", stat_low, stat_high);
+                common::logger->error("{}\t{}", stat_is_low, stat_is_high);
                 common::logger->error("Ratio: {} / {}", k_sum, total);
                 throw std::runtime_error("Fail");
             }
 
-            if (stat_is_zero)
-                return 0.0;
-
-            if (stat_is_one)
-                return 1.0;
-
-            if (stat_is_low)
-                return theta_low;
-
-            if (stat_is_high)
-                return theta_high;
+            if (stat_is_low || stat_is_high)
+                return std::make_tuple(theta_low, theta_high, stat_is_low, stat_is_high);
 
             common::logger->error("No valid thetas\t{}", stat_max);
-            common::logger->error("{}\t{}\t{}\t{}", 0.0, 1.0, theta_low, theta_high);
-            common::logger->error("{}\t{}\t{}\t{}", stat_zero, stat_one, stat_low, stat_high);
-            common::logger->error("{}\t{}\t{}\t{}", stat_is_zero, stat_is_one, stat_is_low, stat_is_high);
+            common::logger->error("{}\t{}", theta_low, theta_high);
+            common::logger->error("{}\t{}", stat_low, stat_high);
+            common::logger->error("{}\t{}", stat_is_low, stat_is_high);
+            common::logger->error("Ratio: {} / {}", k_sum, total);
             throw std::runtime_error("Fail");
         };
 
@@ -607,43 +613,40 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double null_sum = in_sum + out_sum;
 
-            double theta_null = get_theta(a_null, b_null, total_kmers, null_sum);
-            double theta_in = get_theta(a_in, b_in, in_kmers, in_sum);
-            double theta_out = get_theta(a_out, b_out, out_kmers, out_sum);
+            auto [theta_null_low, theta_null_high, null_is_low, null_is_high]
+                = get_theta(a_null, b_null, total_kmers, null_sum);
+            auto [theta_in_low, theta_in_high, in_is_low, in_is_high]
+                = get_theta(a_in, b_in, in_kmers, in_sum);
+            auto [theta_out_low, theta_out_high, out_is_low, out_is_high]
+                = get_theta(a_out, b_out, out_kmers, out_sum);
+
+            double theta_null = null_is_low ? theta_null_low : theta_null_high;
+            double theta_in = in_is_low ? theta_in_low : theta_in_high;
+            double theta_out = out_is_low ? theta_out_low : theta_out_high;
 
             auto get_stat_log = [&](double a, double b, double theta, double k, double total) -> double {
                 return (k + a - 1) * log(theta) + (b - 1) * log1p(-theta) - total * theta;
             };
 
-            auto get_stat = [&](double a, double b, double theta, double k, double total) -> double {
-                return pow(theta, k + a - 1) * pow(1.0 - theta, b - 1) * exp(-total * theta);
-            };
-
-            double map_pval_in = theta_in > 0
-                ? exp(get_stat_log(a_in, b_in, theta_null, in_sum, in_kmers)
-                        - get_stat_log(a_in, b_in, theta_in, in_sum, in_kmers))
-                : get_stat(a_in, b_in, theta_null, in_sum, in_kmers);
-
-            double map_pval_out = theta_out > 0
-                ? exp(get_stat_log(a_out, b_out, theta_null, out_sum, out_kmers)
-                        - get_stat_log(a_out, b_out, theta_out, out_sum, out_kmers))
-                : get_stat(a_out, b_out, theta_null, out_sum, out_kmers);
+            double map_pval_in = exp(get_stat_log(a_in, b_in, theta_null, in_sum, in_kmers)
+                                    - get_stat_log(a_in, b_in, theta_in, in_sum, in_kmers));
 
-            // double map_pval_in = theta_in > 0
-            //     ? exp(get_stat_log(theta_in, null_sum, total_kmers)
-            //             - get_stat_log(theta_null, null_sum, total_kmers))
-            //     : exp(-get_stat_log(theta_null, null_sum, total_kmers));
+            double map_pval_out = exp(get_stat_log(a_out, b_out, theta_null, out_sum, out_kmers)
+                                    - get_stat_log(a_out, b_out, theta_out, out_sum, out_kmers));
 
-            // double map_pval_out = theta_out > 0
-            //     ? exp(get_stat_log(theta_out, null_sum, total_kmers)
-            //             - get_stat_log(theta_null, null_sum, total_kmers))
-            //     : exp(-get_stat_log(theta_null, null_sum, total_kmers));
+            // common::logger->trace("{},{},{}\t{},{}", theta_in, theta_out, theta_null, map_pval_in, map_pval_out);
 
             if (map_pval_in > 1.0 || map_pval_in < 0.0
                     || map_pval_out > 1.0 || map_pval_out < 0.0) {
+                return std::make_tuple(1.0, in_sum,
+                                       out_sum / out_kmers * in_kmers);
                 common::logger->error("Failed to compute MAP of thetas");
                 common::logger->error("Theta in: {}\tTheta out: {}\tTheta null: {}",
                                       theta_in, theta_out, theta_null);
+                common::logger->error("Theta in low: {}\tTheta out low: {}\tTheta null low: {}",
+                                      theta_in_low, theta_out_low, theta_null_low);
+                common::logger->error("Theta in high: {}\tTheta out high: {}\tTheta null high: {}",
+                                      theta_in_high, theta_out_high, theta_null_high);
                 common::logger->error("In sum: {}\tOut sum: {}", in_sum, out_sum);
                 common::logger->error("In p-val: {}\tOut p-val: {}", map_pval_in, map_pval_out);
                 throw std::runtime_error("Fail");

From ebef1b8e2d8ccd1d7aea44d24bb2518e9c2358a4 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 8 May 2024 02:48:22 +0200
Subject: [PATCH 124/410] check

---
 .../src/graph/annotated_graph_algorithm.cpp   | 22 ++++++++++---------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b39e9fa470..efe5220b3d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -495,6 +495,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         auto get_a = [&](double k, double k2, uint8_t pick) {
             double mu = k / nelem;
+            // model with a negative binomial, similar to
+            // https://github.com/JBHilton/beta-poisson-epidemics/blob/master/functions.py#L140
             auto get_dll = [&](double r) {
                 double lp = log(r) - log(r + mu);
                 double dll = nelem * lp - nelem * boost::math::digamma(r);
@@ -542,18 +544,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             throw std::runtime_error("a_null <= 1");
         }
 
-        auto [a_in, b_in] = get_a(in_kmers, sum_of_squares_in, 0);
-        auto [a_out, b_out] = get_a(out_kmers, sum_of_squares_out, 1);
+        // auto [a_in, b_in] = get_a(in_kmers, sum_of_squares_in, 0);
+        // auto [a_out, b_out] = get_a(out_kmers, sum_of_squares_out, 1);
 
-        if (a_in <= 1) {
-            a_in = a_null;
-            b_in = b_null;
-        }
+        // if (a_in <= 1) {
+            double a_in = a_null;
+            double b_in = b_null;
+        // }
 
-        if (a_out <= 1) {
-            a_out = a_null;
-            b_out = b_null;
-        }
+        // if (a_out <= 1) {
+            double a_out = a_null;
+            double b_out = b_null;
+        // }
 
         auto get_theta = [&](double a, double b, double total, double k_sum) {
             double qb = 2.0 - total - k_sum - a - b;

From 7f07521e5774164e2c39a7f04c97f28529597b01 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 8 May 2024 15:30:04 +0200
Subject: [PATCH 125/410] tt

---
 metagraph/src/cli/assemble.cpp                | 32 +++++++++++++++----
 .../src/graph/annotated_graph_algorithm.cpp   |  2 ++
 2 files changed, 28 insertions(+), 6 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 4541de7f6d..f0237265e6 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -212,8 +212,34 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                         background_labels,
                                         diff_config, num_threads,
                                         config->parallel_nodes);
+
+            sdsl::bit_vector mask;
             callback(*ingraph, exp_name + "in.");
+            if (auto masked_graph = std::dynamic_pointer_cast<MaskedDeBruijnGraph>(ingraph)) {
+                std::ofstream fout_all(config->outfbase + ".all.pvals", ios::out | ios::app);
+                for (double pval : masked_graph->likelihood_ratios) {
+                    fout_all << pval << "\n";
+                }
+                std::unique_ptr<bitmap_vector> inmask(static_cast<bitmap_vector*>(masked_graph->release_mask()));
+                mask = const_cast<sdsl::bit_vector&&>(inmask->data());
+                inmask.reset();
+            }
+            ingraph.reset();
+
             callback(*outgraph, exp_name + "out.");
+            if (auto masked_graph = std::dynamic_pointer_cast<MaskedDeBruijnGraph>(outgraph)) {
+                std::unique_ptr<bitmap_vector> outmask(static_cast<bitmap_vector*>(masked_graph->release_mask()));
+                outmask->add_to(&mask);
+                outmask.reset();
+                mask.flip();
+                auto masked_graph_null = std::make_shared<MaskedDeBruijnGraph>(
+                    graph_ptr, std::make_unique<bitmap_vector>(std::move(mask)), true,
+                    graph_ptr->get_mode() == DeBruijnGraph::PRIMARY ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+                );
+                masked_graph_null->likelihood_ratios = std::move(masked_graph->likelihood_ratios);
+                callback(*masked_graph_null, exp_name + "shared.");
+            }
+            outgraph.reset();
         }
     }
 }
@@ -261,12 +287,6 @@ int assemble(Config *config) {
                                        num_threads > 1, /* async write */
                                        "a" /* append mode */);
             std::ofstream fout(config->outfbase + ".pvals", ios::out | ios::app);
-            {
-                std::ofstream fout_all(config->outfbase + ".all.pvals", ios::out | ios::app);
-                for (double pval : masked_graph->likelihood_ratios) {
-                    fout_all << pval << "\n";
-                }
-            }
 
             if (config->unitigs || config->min_tip_size > 1) {
                 graph.call_unitigs(
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index efe5220b3d..82508805a4 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -114,6 +114,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     uint8_t max_width = 0;
     uint64_t max_val = 0;
 
+    sdsl::bit_vector singletons(max_index, false);
+
     auto get_count = [&](uint64_t raw_count, uint64_t cutoff, uint64_t row_i) {
         return raw_count <= cutoff ? raw_count : 0;
         // if (raw_count <= cutoff)

From 795b606bc7d062cb115fc330b6fb0efeffc017f9 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 8 May 2024 17:22:49 +0200
Subject: [PATCH 126/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 314 ++++++++++--------
 1 file changed, 174 insertions(+), 140 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 82508805a4..dbe11decbd 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -117,7 +117,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     sdsl::bit_vector singletons(max_index, false);
 
     auto get_count = [&](uint64_t raw_count, uint64_t cutoff, uint64_t row_i) {
-        return raw_count <= cutoff ? raw_count : 0;
+        return raw_count <= cutoff && !singletons[row_i] ? raw_count : 0;
         // if (raw_count <= cutoff)
         //     return raw_count;
 
@@ -133,76 +133,100 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             max_val = sdsl::bits::lo_set[max_width];
             max_vals.push_back(max_val);
 
-            // median calculation modifies the underlying vector, so we have to make a copy
-            std::vector<uint64_t> vals;
-            vals.reserve(column_values.size());
-            std::copy(column_values.begin(), column_values.end(), std::back_inserter(vals));
+            columns_all.emplace_back(std::move(column));
+            column_values_all.push_back(std::make_unique<ValuesContainer>(std::move(column_values)));
+        }
+    );
 
-            uint64_t in_max_obs_val = *std::max_element(vals.begin(), vals.end());
+    if (groups.size()) {
+        uint64_t row_i = 0;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                         std::unique_ptr<ValuesContainer>,
+                         PairContainer>(columns_all, column_values_all, [&](const auto &raw_row) {
+            singletons[row_i++] = raw_row.empty() || (raw_row.size() == 1 && raw_row[0].second == 1);
+        });
+    }
 
-            auto [q1, q3] = quartiles(vals.begin(), vals.end());
-            double q2 = boost::math::statistics::median(vals);
-            double outlier_cutoff = q3 + 1.5 * (q3 - q1);
+    uint64_t num_singletons = sdsl::util::cnt_one_bits(singletons);
 
-            double repeats_mu = 0;
-            double repeats_cutoff = 0;
-            size_t num_large = 0;
-            for (uint64_t c : vals) {
-                if (c >= outlier_cutoff) {
-                    repeats_mu += c;
-                    ++num_large;
-                }
-            }
-            if (num_large) {
-                repeats_mu /= num_large;
+    for (size_t j = 0; j < groups.size(); ++j) {
+        const auto &column = columns_all[j];
+        const auto &column_values = *column_values_all[j];
 
-                repeats_cutoff = boost::math::quantile(boost::math::complement(
-                    boost::math::poisson(repeats_mu), 0.95
-                ));
+        // median calculation modifies the underlying vector, so we have to make a copy
+        std::vector<uint64_t> vals;
+        vals.reserve(column_values.size() - num_singletons);
 
-                if (repeats_cutoff > outlier_cutoff) {
-                    check_cutoff.emplace_back(repeats_cutoff);
-                } else {
-                    check_cutoff.emplace_back(max_val);
-                }
-            } else {
-                check_cutoff.emplace_back(max_val);
-            }
-
-            uint64_t sum = 0;
-            uint64_t sum_of_squares = 0;
-            uint64_t max_obs_val = 0;
+        {
             size_t i = 0;
-            size_t n = 0;
             column->call_ones([&](uint64_t row_i) {
-                if (uint64_t c = get_count(column_values[i], check_cutoff.back(), row_i)) {
-                    sum += c;
-                    sum_of_squares += c * c;
-                    max_obs_val = std::max(max_obs_val, c);
-                    ++n;
-                }
+                if (!singletons[row_i])
+                    vals.emplace_back(column_values[i]);
                 ++i;
             });
+        }
 
-            max_obs_vals.emplace_back(max_obs_val);
+        uint64_t in_max_obs_val = *std::max_element(vals.begin(), vals.end());
 
-            medians.push_back(q2);
+        auto [q1, q3] = quartiles(vals.begin(), vals.end());
+        double q2 = boost::math::statistics::median(vals);
+        double outlier_cutoff = q3 + 1.5 * (q3 - q1);
 
-            sums.push_back(sum);
-            sums_of_squares.push_back(sum_of_squares);
-            n_kmers.push_back(n);
+        double repeats_mu = 0;
+        double repeats_cutoff = 0;
+        size_t num_large = 0;
+        for (uint64_t c : vals) {
+            if (c >= outlier_cutoff) {
+                repeats_mu += c;
+                ++num_large;
+            }
+        }
+        if (num_large) {
+            repeats_mu /= num_large;
 
-            columns_all.emplace_back(std::move(column));
-            column_values_all.push_back(std::make_unique<ValuesContainer>(std::move(column_values)));
+            repeats_cutoff = boost::math::quantile(boost::math::complement(
+                boost::math::poisson(repeats_mu), 0.95
+            ));
 
-            common::logger->trace("{}: {}\tGroup: {}\tTotal: {}\tQ1: {}\tQ2: {}\tQ3: {}\tMax val: {}\tOutlier cutoff: {}\tRepeat mu: {}\tRepeat lb: {}\tCheck cutoff: {}",
-                                  groups.size(), label, groups.back() ? "out": "in", sum,
-                                  q1, q2, q3,
-                                  in_max_obs_val,
-                                  outlier_cutoff,
-                                  repeats_mu, repeats_cutoff, check_cutoff.back());
+            if (repeats_cutoff > outlier_cutoff) {
+                check_cutoff.emplace_back(repeats_cutoff);
+            } else {
+                check_cutoff.emplace_back(max_val);
+            }
+        } else {
+            check_cutoff.emplace_back(max_val);
         }
-    );
+
+        uint64_t sum = 0;
+        uint64_t sum_of_squares = 0;
+        uint64_t max_obs_val = 0;
+        size_t i = 0;
+        size_t n = 0;
+        column->call_ones([&](uint64_t row_i) {
+            if (uint64_t c = get_count(column_values[i], check_cutoff.back(), row_i)) {
+                sum += c;
+                sum_of_squares += c * c;
+                max_obs_val = std::max(max_obs_val, c);
+                ++n;
+            }
+            ++i;
+        });
+
+        max_obs_vals.emplace_back(max_obs_val);
+
+        medians.push_back(q2);
+
+        sums.push_back(sum);
+        sums_of_squares.push_back(sum_of_squares);
+        n_kmers.push_back(n);
+
+        common::logger->trace("{}\tGroup: {}\tTotal: {}\tQ1: {}\tQ2: {}\tQ3: {}\tMax val: {}\tOutlier cutoff: {}\tRepeat mu: {}\tRepeat lb: {}\tCheck cutoff: {}",
+                                j, groups[j] ? "out": "in", sum,
+                                q1, q2, q3,
+                                in_max_obs_val,
+                                outlier_cutoff,
+                                repeats_mu, repeats_cutoff, check_cutoff[j]);
+    }
 
     uint64_t max_observed_value = *std::max_element(max_obs_vals.begin(), max_obs_vals.end());
 
@@ -220,6 +244,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
     double total_kmers = in_kmers + out_kmers;
 
+    double nelem = max_index - num_singletons;
+
     sdsl::bit_vector indicator_in(max_index + 1, false);
     sdsl::bit_vector indicator_out(max_index + 1, false);
 
@@ -309,29 +335,32 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ++i;
             });
         }
-        double mu = static_cast<double>(total_kmers) / groups.size() / max_index;
+        double mu = static_cast<double>(total_kmers) / groups.size() / nelem;
         double mu2 = static_cast<double>(std::accumulate(sums_of_squares.begin(),
-                                                         sums_of_squares.end(), uint64_t(0))) / groups.size() / max_index - mu;
-        double mu3 = static_cast<double>(sum_of_cubes) / groups.size() / max_index;
+                                                         sums_of_squares.end(), uint64_t(0))) / groups.size() / nelem - mu;
+        double mu3 = static_cast<double>(sum_of_cubes) / groups.size() / nelem;
         double a = mu3 * mu + mu * mu * mu2 + 2 * mu2 * mu2;
         double b = 2 * mu3 * mu * mu + mu3 * mu - mu2 * mu3 - mu * mu2 * mu2 + 3 * mu * mu + mu2 - 4 * mu2 * mu2;
         double c = -2 * mu * (mu2 * mu2 - mu * mu2 - mu3 * mu);
-        double r_low = (-b - sqrt(b * b - 4 * a * c)) / 2 / a;
-        double r_high = (-b + sqrt(b * b - 4 * a * c)) / 2 / a;
+
+        auto [r_low, r_high] = boost::math::tools::quadratic_roots(a, b, c);
+
+        common::logger->trace("mu: {}\tmu2: {}\tmu3: {}\tr_low: {}\tr_high: {}",
+                              mu, mu2, mu3, r_low, r_high);
 
         double r_low_var_sqerr = 0;
         double r_high_var_sqerr = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
-            double mu = static_cast<double>(sums[j]) / max_index;
-            double var = static_cast<double>(sums_of_squares[j]) / max_index - mu * mu;
+            double mu = static_cast<double>(sums[j]) / nelem;
+            double var = static_cast<double>(sums_of_squares[j]) / nelem - mu * mu;
             double p_low = r_low / (r_low + mu);
             double p_high = r_high / (r_high + mu);
             double t_var_low = mu / p_low;
             double t_var_high = mu / p_high;
             r_low_var_sqerr += pow(t_var_low - var, 2.0);
             r_high_var_sqerr += pow(t_var_high - var, 2.0);
-            common::logger->trace("label: {}\tvar: {}\tp_low: {}\tvar_low: {}\tp_high: {}\tvar_high: {}",
-                                  j + 1, var, p_low, t_var_low, p_high, t_var_high);
+            common::logger->trace("label: {}\tmean: {}\tvar: {}\tp_low: {}\tvar_low: {}\tp_high: {}\tvar_high: {}",
+                                  j + 1, mu, var, p_low, t_var_low, p_high, t_var_high);
         }
 
         common::logger->trace("r low: {} (sqerr: {})\t r high: {} (sqerr: {})",
@@ -459,9 +488,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         };
     } else if (config.test_type == "bp") {
         double pseudocount = 0;
-        in_kmers += pseudocount * max_index;
-        out_kmers += pseudocount * max_index;
-        total_kmers += pseudocount * 2.0 * max_index;
+        in_kmers += pseudocount * nelem;
+        out_kmers += pseudocount * nelem;
+        total_kmers += pseudocount * 2.0 * nelem;
         uint64_t row_i = 0;
         double sum_of_squares_in = 0;
         double sum_of_squares_out = 0;
@@ -470,23 +499,25 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         utils::call_rows<std::unique_ptr<bit_vector>,
                         std::unique_ptr<ValuesContainer>,
                         PairContainer>(columns_all, column_values_all, [&](const auto &raw_row) {
-            double sum_in = pseudocount;
-            double sum_out = pseudocount;
-            for (const auto &[j, raw_c] : raw_row) {
-                if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i)) {
-                    if (groups[j]) {
-                        sum_in += c;
-                    } else {
-                        sum_out += c;
+            if (!singletons[row_i]) {
+                double sum_in = pseudocount;
+                double sum_out = pseudocount;
+                for (const auto &[j, raw_c] : raw_row) {
+                    if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i)) {
+                        if (groups[j]) {
+                            sum_in += c;
+                        } else {
+                            sum_out += c;
+                        }
                     }
                 }
-            }
 
-            if (sum_in + sum_out > 0) {
-                counts.emplace_back(sum_in, sum_out);
-                sum_of_squares_in += sum_in * sum_in;
-                sum_of_squares_out += sum_out * sum_out;
-                sum_of_squares_null += pow(sum_in + sum_out, 2.0);
+                if (sum_in + sum_out > 0) {
+                    counts.emplace_back(sum_in, sum_out);
+                    sum_of_squares_in += sum_in * sum_in;
+                    sum_of_squares_out += sum_out * sum_out;
+                    sum_of_squares_null += pow(sum_in + sum_out, 2.0);
+                }
             }
             ++row_i;
         });
@@ -561,19 +592,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         auto get_theta = [&](double a, double b, double total, double k_sum) {
             double qb = 2.0 - total - k_sum - a - b;
-            double qc = k_sum + a - 1;
-            double qf = sqrt(qb * qb - 4 * total * qc);
-            double theta_low = (-qb - qf) / 2.0 / total;
-            double theta_high = (-qb + qf) / 2.0 / total;
+            double qc = k_sum + a - 1.0;
+
+            auto [theta_low, theta_high] = boost::math::tools::quadratic_roots(total, qb, qc);
 
             bool low_valid = theta_low > 0 && theta_low < 1.0;
-            bool high_valid = qf != 0 && theta_high > 0 && theta_high < 1.0;
+            bool high_valid = theta_low != theta_high && theta_high > 0 && theta_high < 1.0;
 
             double stat_low = low_valid
-                ? (k_sum + a - 1) * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low
+                ? qc * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low
                 : -std::numeric_limits<double>::infinity();
             double stat_high = high_valid
-                ? (k_sum + a - 1) * log(theta_high) + (b - 1) * log1p(-theta_high) - total * theta_high
+                ? qc * log(theta_high) + (b - 1) * log1p(-theta_high) - total * theta_high
                 : -std::numeric_limits<double>::infinity();
 
             double stat_max = std::max({ stat_low, stat_high });
@@ -587,6 +617,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 common::logger->error("{}\t{}", stat_low, stat_high);
                 common::logger->error("{}\t{}", stat_is_low, stat_is_high);
                 common::logger->error("Ratio: {} / {}", k_sum, total);
+                common::logger->error("{},{}\t{},{},{}", a,b, total, qb, qc);
                 throw std::runtime_error("Fail");
             }
 
@@ -598,10 +629,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->error("{}\t{}", stat_low, stat_high);
             common::logger->error("{}\t{}", stat_is_low, stat_is_high);
             common::logger->error("Ratio: {} / {}", k_sum, total);
+            common::logger->error("{},{}\t{},{},{}", a,b,total, qb, qc);
             throw std::runtime_error("Fail");
         };
 
-        compute_pval = [&](const auto &row) {
+        compute_pval = [&,a_in,a_out,a_null,b_in,b_out,b_null,nelem,get_theta](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0);
 
@@ -774,69 +806,71 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     utils::call_rows<std::unique_ptr<bit_vector>,
                      std::unique_ptr<ValuesContainer>,
                      PairContainer>(columns_all, column_values_all, [&](const auto &raw_row) {
-        PairContainer row;
-        for (const auto &[j, raw_c] : raw_row) {
-            if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i))
-                row.emplace_back(j, c);
-        }
+        if (!singletons[row_i]) {
+            PairContainer row;
+            for (const auto &[j, raw_c] : raw_row) {
+                if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i))
+                    row.emplace_back(j, c);
+            }
 
-        auto [pval, in_stat, out_stat] = compute_pval(row);
-        pvals.emplace_back(pval);
+            auto [pval, in_stat, out_stat] = compute_pval(row);
+            pvals.emplace_back(pval);
 
-        if (pval > 1.0 || in_stat == out_stat) {
-            ++row_i;
-            return;
-        }
+            if (pval > 1.0 || in_stat == out_stat) {
+                ++row_i;
+                return;
+            }
 
-        bool in_kmer = in_stat > out_stat;
-        bool out_kmer = in_stat < out_stat;
-        uint64_t k = std::numeric_limits<uint64_t>::max();
+            bool in_kmer = in_stat > out_stat;
+            bool out_kmer = in_stat < out_stat;
+            uint64_t k = std::numeric_limits<uint64_t>::max();
 
-        if (config.test_type != "nonparametric") {
-            double pval_min = 0;
-            if (config.test_type == "nonparametric_u") {
-                pval_min = 2.0 / exp(lgamma(groups.size() + 1) - lgamma(labels_in.size() + 1) - lgamma(labels_out.size() + 1));
-            } else {
-                PairContainer best;
-                for (const auto &[j, c] : row) {
-                    if ((in_kmer && !groups[j]) || (out_kmer && groups[j])) {
-                        best.emplace_back(j, max_obs_vals[j]);
-                    } else {
-                        best.emplace_back(j, 1);
+            if (config.test_type != "nonparametric") {
+                double pval_min = 0;
+                if (config.test_type == "nonparametric_u") {
+                    pval_min = 2.0 / exp(lgamma(groups.size() + 1) - lgamma(labels_in.size() + 1) - lgamma(labels_out.size() + 1));
+                } else {
+                    PairContainer best;
+                    for (const auto &[j, c] : row) {
+                        if ((in_kmer && !groups[j]) || (out_kmer && groups[j])) {
+                            best.emplace_back(j, max_obs_vals[j]);
+                        } else {
+                            best.emplace_back(j, 1);
+                        }
                     }
-                }
 
-                auto [pm, in_sum_m, out_sum_m] = compute_pval(best);
-                pval_min = pm;
-            }
+                    auto [pm, in_sum_m, out_sum_m] = compute_pval(best);
+                    pval_min = pm;
+                }
 
-            if (pval_min > pval) {
-                common::logger->error("Min p-val estimate too high: min {} > cur {}", pval_min, pval);
-                throw std::runtime_error("Test failed");
-            }
+                if (pval_min > pval) {
+                    common::logger->error("Min p-val estimate too high: min {} > cur {}", pval_min, pval);
+                    throw std::runtime_error("Test failed");
+                }
 
-            if (pval_min >= 0.05) {
-                ++row_i;
-                return;
-            }
+                if (pval_min >= 0.05) {
+                    ++row_i;
+                    return;
+                }
 
-            if (pval_min > 0) {
-                double lkd = log2(0.05) - log2(pval_min);
-                if (lkd <= 64)
-                    k = pow(2.0, lkd);
+                if (pval_min > 0) {
+                    double lkd = log2(0.05) - log2(pval_min);
+                    if (lkd <= 64)
+                        k = pow(2.0, lkd);
 
-                if (k == 0) {
-                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
-                    throw std::runtime_error("Min failed");
+                    if (k == 0) {
+                        common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
+                        throw std::runtime_error("Min failed");
+                    }
                 }
             }
-        }
 
-        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-        m[k].emplace_back(node);
-        auto &indicator = in_kmer ? indicator_in : indicator_out;
-        if (pval < 0.05)
-            indicator[node] = true;
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+            m[k].emplace_back(node);
+            auto &indicator = in_kmer ? indicator_in : indicator_out;
+            if (pval < 0.05)
+                indicator[node] = true;
+        }
 
         ++row_i;
     });

From e8af6559d0990fe177dc7a6f1cc92e12220520ae Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 8 May 2024 17:35:10 +0200
Subject: [PATCH 127/410] add checks

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index dbe11decbd..0446fa6af9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -348,6 +348,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("mu: {}\tmu2: {}\tmu3: {}\tr_low: {}\tr_high: {}",
                               mu, mu2, mu3, r_low, r_high);
 
+        // check for NaNs
+        // https://stackoverflow.com/questions/570669/checking-if-a-double-or-float-is-nan-in-c
+        if (r_low != r_low && r_high != r_high) {
+            common::logger->error("{},{},{}", a, b, c);
+            throw std::runtime_error("Failed to fit, no real roots");
+        }
+
         double r_low_var_sqerr = 0;
         double r_high_var_sqerr = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
@@ -596,8 +603,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             auto [theta_low, theta_high] = boost::math::tools::quadratic_roots(total, qb, qc);
 
-            bool low_valid = theta_low > 0 && theta_low < 1.0;
-            bool high_valid = theta_low != theta_high && theta_high > 0 && theta_high < 1.0;
+            // Note: comparisons of thetas to themselves ensure that they are not NaN
+            // https://stackoverflow.com/questions/570669/checking-if-a-double-or-float-is-nan-in-c
+            bool low_valid = theta_low == theta_low && theta_low > 0 && theta_low < 1.0;
+            bool high_valid = theta_high == theta_high && theta_low != theta_high && theta_high > 0 && theta_high < 1.0;
 
             double stat_low = low_valid
                 ? qc * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low

From a27e0983f30a63e5ef5ce47e03a520d8b4b24ba9 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 9 May 2024 02:00:25 +0200
Subject: [PATCH 128/410] broken

---
 metagraph/src/cli/assemble.cpp                |   1 +
 .../src/graph/annotated_graph_algorithm.cpp   | 426 +++++++++---------
 metagraph/src/graph/graph_cleaning.cpp        |  18 +-
 metagraph/src/graph/graph_cleaning.hpp        |   9 +-
 4 files changed, 221 insertions(+), 233 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index f0237265e6..769271044f 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -72,6 +72,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     logger->trace("count_kmers:\t{}", diff_config.count_kmers);
     logger->trace("Test type:\t{}", diff_config.test_type);
     logger->trace("Filter:\t{}", diff_config.filter);
+    logger->trace("Min count:\t{}", diff_config.min_count);
 
 
     // TODO get the folder with the annotation columns from the .json or command, because it is redundant to have to give all of them as an input.
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0446fa6af9..d3917fb960 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -19,6 +19,7 @@
 #include "common/vectors/transpose.hpp"
 #include "graph/representation/masked_graph.hpp"
 #include "graph/representation/canonical_dbg.hpp"
+#include "graph/graph_cleaning.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 #include "differential_tests.hpp"
 
@@ -32,6 +33,10 @@ typedef AnnotatedDBG::Annotator Annotator;
 typedef AnnotatedDBG::Annotator::Label Label;
 using Column = annot::matrix::BinaryMatrix::Column;
 using PairContainer = std::vector<std::pair<uint64_t, uint64_t>>;
+using ValuesContainer = sdsl::int_vector_buffer<>;
+
+double CDF_CUTOFF = 0.95;
+uint64_t N_BUCKETS_FOR_ESTIMATION = 3;
 
 template <typename ForwardIterator>
 inline auto quartiles(ForwardIterator first, ForwardIterator last)
@@ -98,103 +103,101 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
     common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
 
-    size_t max_index = graph_ptr->max_index();
-    std::vector<double> medians;
-    std::vector<uint64_t> max_vals;
-    std::vector<uint64_t> check_cutoff;
-    std::vector<uint64_t> max_obs_vals;
-    std::vector<size_t> n_kmers;
-    std::vector<uint64_t> sums;
-    std::vector<uint64_t> sums_of_squares;
-    std::vector<std::unique_ptr<bit_vector>> columns_all;
+    std::vector<bool> groups(labels_in.size() + labels_out.size());
+    std::vector<std::unique_ptr<bit_vector>> columns_all(groups.size());
+    std::vector<std::unique_ptr<ValuesContainer>> column_values_all(groups.size());
 
-    using ValuesContainer = sdsl::int_vector_buffer<>;
-    std::vector<std::unique_ptr<ValuesContainer>> column_values_all;
-    std::vector<bool> groups;
     uint8_t max_width = 0;
-    uint64_t max_val = 0;
-
-    sdsl::bit_vector singletons(max_index, false);
 
-    auto get_count = [&](uint64_t raw_count, uint64_t cutoff, uint64_t row_i) {
-        return raw_count <= cutoff && !singletons[row_i] ? raw_count : 0;
-        // if (raw_count <= cutoff)
-        //     return raw_count;
-
-        // node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-        // return is_low_complexity(graph_ptr->get_node_sequence(node)) ? 0 : raw_count;
-    };
+    sdsl::bit_vector ignored(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), false);
 
-    annot::ColumnCompressed<>::load_columns_and_values(files,
+    annot::ColumnCompressed<>::load_columns_and_values(
+        files,
         [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
-            groups.push_back(labels_out.count(label));
-
+            groups[offset] = labels_out.count(label);
             max_width = std::max(max_width, column_values.width());
-            max_val = sdsl::bits::lo_set[max_width];
-            max_vals.push_back(max_val);
-
-            columns_all.emplace_back(std::move(column));
-            column_values_all.push_back(std::make_unique<ValuesContainer>(std::move(column_values)));
-        }
+            std::swap(columns_all[offset], column);
+            column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
+        },
+        num_parallel_files
     );
 
-    if (groups.size()) {
-        uint64_t row_i = 0;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         PairContainer>(columns_all, column_values_all, [&](const auto &raw_row) {
-            singletons[row_i++] = raw_row.empty() || (raw_row.size() == 1 && raw_row[0].second == 1);
-        });
-    }
+    std::vector<double> medians;
+    std::vector<uint64_t> min_counts;
+    std::vector<uint64_t> check_cutoff;
+    std::vector<uint64_t> max_obs_vals;
+    std::vector<size_t> n_kmers;
+    std::vector<uint64_t> sums;
+    std::vector<uint64_t> sums_of_squares;
 
-    uint64_t num_singletons = sdsl::util::cnt_one_bits(singletons);
+    auto adjust_count = [&](uint64_t raw_count, size_t j, uint64_t row_i) {
+        std::ignore = row_i;
+        return raw_count >= min_counts[j] && raw_count <= check_cutoff[j]
+            ? raw_count
+            : 0;
+    };
 
+    common::logger->trace("Cleaning count columns");
+    double in_kmers = 0;
+    double out_kmers = 0;
     for (size_t j = 0; j < groups.size(); ++j) {
-        const auto &column = columns_all[j];
+        const auto &column = *columns_all[j];
         const auto &column_values = *column_values_all[j];
 
         // median calculation modifies the underlying vector, so we have to make a copy
         std::vector<uint64_t> vals;
-        vals.reserve(column_values.size() - num_singletons);
+        vals.reserve(column_values.size());
 
-        {
-            size_t i = 0;
-            column->call_ones([&](uint64_t row_i) {
-                if (!singletons[row_i])
-                    vals.emplace_back(column_values[i]);
-                ++i;
-            });
+        for (size_t i = 0; i < column_values.size(); ++i) {
+            vals.emplace_back(column_values[i]);
         }
 
-        uint64_t in_max_obs_val = *std::max_element(vals.begin(), vals.end());
+        check_cutoff.emplace_back(std::numeric_limits<uint64_t>::max());
+        min_counts.emplace_back(config.min_count);
 
-        auto [q1, q3] = quartiles(vals.begin(), vals.end());
-        double q2 = boost::math::statistics::median(vals);
-        double outlier_cutoff = q3 + 1.5 * (q3 - q1);
+        if (config.clean && !config.min_count) {
+            // set cutoff for lower end of distribution
+            auto [mean_est, nzeros_est] = estimate_ztp_mean(
+                [&](const auto &callback) {
+                    std::for_each(vals.begin(), vals.end(), callback);
+                },
+                max_width,
+                0,
+                N_BUCKETS_FOR_ESTIMATION
+            );
 
-        double repeats_mu = 0;
-        double repeats_cutoff = 0;
-        size_t num_large = 0;
-        for (uint64_t c : vals) {
-            if (c >= outlier_cutoff) {
-                repeats_mu += c;
-                ++num_large;
+            min_counts.back() = boost::math::quantile(
+                boost::math::poisson(mean_est), exp(-mean_est) - CDF_CUTOFF * expm1(-mean_est)
+            );
+
+            vals.clear();
+            for (size_t i = 0; i < column_values.size(); ++i) {
+                if (column_values[i] >= min_counts.back())
+                    vals.emplace_back(column_values[i]);
             }
-        }
-        if (num_large) {
-            repeats_mu /= num_large;
 
-            repeats_cutoff = boost::math::quantile(boost::math::complement(
-                boost::math::poisson(repeats_mu), 0.95
-            ));
+            // set cutoff for upper end of distribution
+            auto [q1, q3] = quartiles(vals.begin(), vals.end());
+            double outlier_cutoff = q3 + 1.5 * (q3 - q1);
+            double repeats_mu = 0;
+            double repeats_cutoff = 0;
+            size_t num_large = 0;
+            for (uint64_t c : vals) {
+                if (c >= outlier_cutoff) {
+                    repeats_mu += c;
+                    ++num_large;
+                }
+            }
+            if (num_large) {
+                repeats_mu /= num_large;
 
-            if (repeats_cutoff > outlier_cutoff) {
-                check_cutoff.emplace_back(repeats_cutoff);
-            } else {
-                check_cutoff.emplace_back(max_val);
+                repeats_cutoff = boost::math::quantile(boost::math::complement(
+                    boost::math::poisson(repeats_mu), CDF_CUTOFF
+                ));
+
+                if (repeats_cutoff > outlier_cutoff)
+                    check_cutoff.back() = repeats_cutoff;
             }
-        } else {
-            check_cutoff.emplace_back(max_val);
         }
 
         uint64_t sum = 0;
@@ -202,8 +205,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         uint64_t max_obs_val = 0;
         size_t i = 0;
         size_t n = 0;
-        column->call_ones([&](uint64_t row_i) {
-            if (uint64_t c = get_count(column_values[i], check_cutoff.back(), row_i)) {
+        column.call_ones([&](uint64_t row_i) {
+            if (uint64_t c = adjust_count(column_values[i], j, row_i)) {
                 sum += c;
                 sum_of_squares += c * c;
                 max_obs_val = std::max(max_obs_val, c);
@@ -213,44 +216,57 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         });
 
         max_obs_vals.emplace_back(max_obs_val);
-
-        medians.push_back(q2);
+        medians.push_back(boost::math::statistics::median(vals));
 
         sums.push_back(sum);
+        if (groups[j]) {
+            out_kmers += sum;
+        } else {
+            in_kmers += sum;
+        }
         sums_of_squares.push_back(sum_of_squares);
         n_kmers.push_back(n);
 
-        common::logger->trace("{}\tGroup: {}\tTotal: {}\tQ1: {}\tQ2: {}\tQ3: {}\tMax val: {}\tOutlier cutoff: {}\tRepeat mu: {}\tRepeat lb: {}\tCheck cutoff: {}",
-                                j, groups[j] ? "out": "in", sum,
-                                q1, q2, q3,
-                                in_max_obs_val,
-                                outlier_cutoff,
-                                repeats_mu, repeats_cutoff, check_cutoff[j]);
+        common::logger->trace("{}: sum: {}\tmedian: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
+                              j, sum, medians[j], max_obs_val, min_counts.back(), check_cutoff.back());
     }
 
-    uint64_t max_observed_value = *std::max_element(max_obs_vals.begin(), max_obs_vals.end());
+    double total_kmers = in_kmers + out_kmers;
 
-    common::logger->trace("Max width: {}\tMax val: {}\tMax obs. val: {}",
-                          max_width, max_val, max_observed_value);
+    common::logger->trace("Marking discarded k-mers");
+    if (groups.size()) {
+        uint64_t row_i = 0;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                         std::unique_ptr<ValuesContainer>,
+                         PairContainer>(columns_all, column_values_all, [&](const auto &row) {
+            if (row.empty()) {
+                ignored[row_i++] = true;
+                return;
+            } else if (!config.clean) {
+                ++row_i;
+                return;
+            }
 
-    double in_kmers = 0;
-    double out_kmers = 0;
-    for (size_t j = 0; j < sums.size(); ++j) {
-        if (groups[j]) {
-            out_kmers += sums[j];
-        } else {
-            in_kmers += sums[j];
-        }
+            bool found = true;
+            for (const auto &[j, raw_c] : row) {
+                if (uint64_t c = adjust_count(raw_c, j, row_i)) {
+                    found = false;
+                    break;
+                }
+            }
+
+            ignored[row_i++] = found;
+        });
     }
-    double total_kmers = in_kmers + out_kmers;
 
-    double nelem = max_index - num_singletons;
+    double nelem = ignored.size() - sdsl::util::cnt_one_bits(ignored);
 
-    sdsl::bit_vector indicator_in(max_index + 1, false);
-    sdsl::bit_vector indicator_out(max_index + 1, false);
+    common::logger->trace("Running differential tests");
+    sdsl::bit_vector indicator_in(graph_ptr->max_index() + 1, false);
+    sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
 
     std::vector<double> pvals;
-    pvals.reserve(max_index + 1);
+    pvals.reserve(graph_ptr->max_index() + 1);
     pvals.emplace_back(1.1);
 
     VectorMap<size_t, std::vector<uint64_t>> m;
@@ -325,13 +341,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         // use moments to fit the r parameter of a beta negative binomial
         uint64_t sum_of_cubes = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
-            const auto &column = columns_all[j];
-            const auto &count_ptr = column_values_all[j];
+            const auto &column = *columns_all[j];
+            const auto &column_values = *column_values_all[j];
             size_t i = 0;
-            column->call_ones([&](uint64_t row_i) {
-                int64_t c = get_count((*count_ptr)[i], check_cutoff[j], row_i);
-                if (c > 0)
-                    sum_of_cubes += c * (c - 1) * (c - 2);
+            column.call_ones([&](uint64_t row_i) {
+                if (!ignored[row_i]) {
+                    if (int64_t c = adjust_count(column_values[i], j, row_i))
+                        sum_of_cubes += c * (c - 1) * (c - 2);
+                }
                 ++i;
             });
         }
@@ -367,7 +384,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             r_low_var_sqerr += pow(t_var_low - var, 2.0);
             r_high_var_sqerr += pow(t_var_high - var, 2.0);
             common::logger->trace("label: {}\tmean: {}\tvar: {}\tp_low: {}\tvar_low: {}\tp_high: {}\tvar_high: {}",
-                                  j + 1, mu, var, p_low, t_var_low, p_high, t_var_high);
+                                  j, mu, var, p_low, t_var_low, p_high, t_var_high);
         }
 
         common::logger->trace("r low: {} (sqerr: {})\t r high: {} (sqerr: {})",
@@ -494,23 +511,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "bp") {
-        double pseudocount = 0;
-        in_kmers += pseudocount * nelem;
-        out_kmers += pseudocount * nelem;
-        total_kmers += pseudocount * 2.0 * nelem;
         uint64_t row_i = 0;
-        double sum_of_squares_in = 0;
-        double sum_of_squares_out = 0;
-        double sum_of_squares_null = 0;
-        std::vector<std::pair<double, double>> counts;
+        double sum_of_squares = 0;
+        std::vector<double> counts;
+        counts.reserve(nelem);
         utils::call_rows<std::unique_ptr<bit_vector>,
-                        std::unique_ptr<ValuesContainer>,
-                        PairContainer>(columns_all, column_values_all, [&](const auto &raw_row) {
-            if (!singletons[row_i]) {
-                double sum_in = pseudocount;
-                double sum_out = pseudocount;
-                for (const auto &[j, raw_c] : raw_row) {
-                    if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i)) {
+                         std::unique_ptr<ValuesContainer>,
+                         PairContainer>(columns_all, column_values_all, [&](const auto &row) {
+            if (!ignored[row_i]) {
+                double sum_in = 0;
+                double sum_out = 0;
+                for (const auto &[j, raw_c] : row) {
+                    if (uint64_t c = adjust_count(raw_c, j, row_i)) {
                         if (groups[j]) {
                             sum_in += c;
                         } else {
@@ -520,36 +532,22 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 if (sum_in + sum_out > 0) {
-                    counts.emplace_back(sum_in, sum_out);
-                    sum_of_squares_in += sum_in * sum_in;
-                    sum_of_squares_out += sum_out * sum_out;
-                    sum_of_squares_null += pow(sum_in + sum_out, 2.0);
+                    counts.emplace_back(sum_in + sum_out);
+                    sum_of_squares += pow(counts.back(), 2.0);
                 }
             }
             ++row_i;
         });
 
-        double nelem = counts.size();
-
-        // double n = total_kmers;
-
-        auto get_a = [&](double k, double k2, uint8_t pick) {
+        auto get_a = [&](double k, double k2) {
             double mu = k / nelem;
             // model with a negative binomial, similar to
             // https://github.com/JBHilton/beta-poisson-epidemics/blob/master/functions.py#L140
             auto get_dll = [&](double r) {
                 double lp = log(r) - log(r + mu);
                 double dll = nelem * lp - nelem * boost::math::digamma(r);
-                for (const auto &[si, so] : counts) {
-                    double s = 0;
-                    if (pick == 0) {
-                        s = si;
-                    } else if (pick == 1) {
-                        s = so;
-                    } else {
-                        s = si + so;
-                    }
-                    dll += boost::math::digamma(r + s);
+                for (double c : counts) {
+                    dll += boost::math::digamma(r + c);
                 }
 
                 return dll;
@@ -569,8 +567,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double b = (nelem - 1) * a;
 
-            common::logger->trace("{}:\tmean: {}\tvar: {}\ta: {}\tb: {}\texp: {}\tvar: {}",
-                                  pick, mu, k2 / nelem - mu * mu,
+            common::logger->trace("mean: {}\tvar: {}\ta: {}\tb: {}\texp: {}\tvar: {}",
+                                  mu, k2 / nelem - mu * mu,
                                   a, b,
                                   k * a / (a + b),
                                   mu + mu * mu * (nelem - 1) / (nelem * a + 1));
@@ -578,26 +576,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_pair(a, b);
         };
 
-        auto [a_null, b_null] = get_a(total_kmers, sum_of_squares_null, 2);
+        auto [a, b] = get_a(total_kmers, sum_of_squares);
 
-        if (a_null <= 1) {
-            throw std::runtime_error("a_null <= 1");
+        if (a <= 1) {
+            common::logger->error("a <= 1");
+            throw std::runtime_error("Fit failed");
         }
 
-        // auto [a_in, b_in] = get_a(in_kmers, sum_of_squares_in, 0);
-        // auto [a_out, b_out] = get_a(out_kmers, sum_of_squares_out, 1);
-
-        // if (a_in <= 1) {
-            double a_in = a_null;
-            double b_in = b_null;
-        // }
-
-        // if (a_out <= 1) {
-            double a_out = a_null;
-            double b_out = b_null;
-        // }
-
-        auto get_theta = [&](double a, double b, double total, double k_sum) {
+        auto get_theta = [&,a,b](double total, double k_sum) {
             double qb = 2.0 - total - k_sum - a - b;
             double qc = k_sum + a - 1.0;
 
@@ -642,12 +628,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             throw std::runtime_error("Fail");
         };
 
-        compute_pval = [&,a_in,a_out,a_null,b_in,b_out,b_null,nelem,get_theta](const auto &row) {
+        compute_pval = [&,a,b,nelem,get_theta](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0);
 
-            double in_sum = pseudocount;
-            double out_sum = pseudocount;
+            double in_sum = 0;
+            double out_sum = 0;
             for (const auto &[j, c] : row) {
                 if (groups[j]) {
                     out_sum += c;
@@ -659,27 +645,25 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double null_sum = in_sum + out_sum;
 
             auto [theta_null_low, theta_null_high, null_is_low, null_is_high]
-                = get_theta(a_null, b_null, total_kmers, null_sum);
+                = get_theta(total_kmers, null_sum);
             auto [theta_in_low, theta_in_high, in_is_low, in_is_high]
-                = get_theta(a_in, b_in, in_kmers, in_sum);
+                = get_theta(in_kmers, in_sum);
             auto [theta_out_low, theta_out_high, out_is_low, out_is_high]
-                = get_theta(a_out, b_out, out_kmers, out_sum);
+                = get_theta(out_kmers, out_sum);
 
             double theta_null = null_is_low ? theta_null_low : theta_null_high;
             double theta_in = in_is_low ? theta_in_low : theta_in_high;
             double theta_out = out_is_low ? theta_out_low : theta_out_high;
 
-            auto get_stat_log = [&](double a, double b, double theta, double k, double total) -> double {
+            auto get_stat_log = [&,a,b](double theta, double k, double total) -> double {
                 return (k + a - 1) * log(theta) + (b - 1) * log1p(-theta) - total * theta;
             };
 
-            double map_pval_in = exp(get_stat_log(a_in, b_in, theta_null, in_sum, in_kmers)
-                                    - get_stat_log(a_in, b_in, theta_in, in_sum, in_kmers));
-
-            double map_pval_out = exp(get_stat_log(a_out, b_out, theta_null, out_sum, out_kmers)
-                                    - get_stat_log(a_out, b_out, theta_out, out_sum, out_kmers));
+            double map_pval_in = exp(get_stat_log(theta_null, in_sum, in_kmers)
+                                    - get_stat_log(theta_in, in_sum, in_kmers));
 
-            // common::logger->trace("{},{},{}\t{},{}", theta_in, theta_out, theta_null, map_pval_in, map_pval_out);
+            double map_pval_out = exp(get_stat_log(theta_null, out_sum, out_kmers)
+                                    - get_stat_log(theta_out, out_sum, out_kmers));
 
             if (map_pval_in > 1.0 || map_pval_in < 0.0
                     || map_pval_out > 1.0 || map_pval_out < 0.0) {
@@ -815,73 +799,71 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     utils::call_rows<std::unique_ptr<bit_vector>,
                      std::unique_ptr<ValuesContainer>,
                      PairContainer>(columns_all, column_values_all, [&](const auto &raw_row) {
-        if (!singletons[row_i]) {
-            PairContainer row;
+        PairContainer row;
+        if (!ignored[row_i]) {
             for (const auto &[j, raw_c] : raw_row) {
-                if (uint64_t c = get_count(raw_c, check_cutoff[j], row_i))
+                if (uint64_t c = adjust_count(raw_c, j, row_i))
                     row.emplace_back(j, c);
             }
+        }
 
-            auto [pval, in_stat, out_stat] = compute_pval(row);
-            pvals.emplace_back(pval);
+        auto [pval, in_stat, out_stat] = compute_pval(row);
+        pvals.emplace_back(pval);
 
-            if (pval > 1.0 || in_stat == out_stat) {
-                ++row_i;
-                return;
-            }
+        if (pval > 1.0 || in_stat == out_stat) {
+            ++row_i;
+            return;
+        }
 
-            bool in_kmer = in_stat > out_stat;
-            bool out_kmer = in_stat < out_stat;
-            uint64_t k = std::numeric_limits<uint64_t>::max();
+        bool in_kmer = in_stat > out_stat;
+        bool out_kmer = in_stat < out_stat;
+        uint64_t k = std::numeric_limits<uint64_t>::max();
 
-            if (config.test_type != "nonparametric") {
-                double pval_min = 0;
-                if (config.test_type == "nonparametric_u") {
-                    pval_min = 2.0 / exp(lgamma(groups.size() + 1) - lgamma(labels_in.size() + 1) - lgamma(labels_out.size() + 1));
-                } else {
-                    PairContainer best;
-                    for (const auto &[j, c] : row) {
-                        if ((in_kmer && !groups[j]) || (out_kmer && groups[j])) {
-                            best.emplace_back(j, max_obs_vals[j]);
-                        } else {
-                            best.emplace_back(j, 1);
-                        }
+        if (config.test_type != "nonparametric") {
+            double pval_min = 0;
+            if (config.test_type == "nonparametric_u") {
+                pval_min = 2.0 / exp(lgamma(groups.size() + 1) - lgamma(labels_in.size() + 1) - lgamma(labels_out.size() + 1));
+            } else {
+                PairContainer best;
+                for (const auto &[j, c] : row) {
+                    if ((in_kmer && !groups[j]) || (out_kmer && groups[j])) {
+                        best.emplace_back(j, max_obs_vals[j]);
+                    } else {
+                        best.emplace_back(j, 1);
                     }
-
-                    auto [pm, in_sum_m, out_sum_m] = compute_pval(best);
-                    pval_min = pm;
                 }
 
-                if (pval_min > pval) {
-                    common::logger->error("Min p-val estimate too high: min {} > cur {}", pval_min, pval);
-                    throw std::runtime_error("Test failed");
-                }
+                auto [pm, in_sum_m, out_sum_m] = compute_pval(best);
+                pval_min = pm;
+            }
 
-                if (pval_min >= 0.05) {
-                    ++row_i;
-                    return;
-                }
+            if (pval_min > pval) {
+                common::logger->error("Min p-val estimate too high: min {} > cur {}", pval_min, pval);
+                throw std::runtime_error("Test failed");
+            }
 
-                if (pval_min > 0) {
-                    double lkd = log2(0.05) - log2(pval_min);
-                    if (lkd <= 64)
-                        k = pow(2.0, lkd);
+            if (pval_min >= 0.05) {
+                ++row_i;
+                return;
+            }
 
-                    if (k == 0) {
-                        common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
-                        throw std::runtime_error("Min failed");
-                    }
+            if (pval_min > 0) {
+                double lkd = log2(0.05) - log2(pval_min);
+                if (lkd <= 64)
+                    k = pow(2.0, lkd);
+
+                if (k == 0) {
+                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
+                    throw std::runtime_error("Min failed");
                 }
             }
-
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            m[k].emplace_back(node);
-            auto &indicator = in_kmer ? indicator_in : indicator_out;
-            if (pval < 0.05)
-                indicator[node] = true;
         }
 
-        ++row_i;
+        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+        m[k].emplace_back(node);
+        auto &indicator = in_kmer ? indicator_in : indicator_out;
+        if (pval < 0.05)
+            indicator[node] = true;
     });
 
     for (auto &col : columns_all) {
diff --git a/metagraph/src/graph/graph_cleaning.cpp b/metagraph/src/graph/graph_cleaning.cpp
index 357e5e2f6a..9d2081b42e 100644
--- a/metagraph/src/graph/graph_cleaning.cpp
+++ b/metagraph/src/graph/graph_cleaning.cpp
@@ -155,10 +155,11 @@ std::pair<double, double> estimate_ztp_mean(const std::function<void(const std::
     return std::make_pair(mean_est, total_est - nupto);
 }
 
-std::tuple<int64_t,double,double> estimate_min_kmer_abundance(const bit_vector &idx,
-                                     const sdsl::int_vector<> &values,
-                                     uint64_t num_singleton_kmers,
-                                     bool discard_last_count) {
+std::tuple<int64_t,double,double>
+estimate_min_kmer_abundance(const bit_vector &idx,
+                            const sdsl::int_vector<> &values,
+                            uint64_t num_singleton_kmers,
+                            bool discard_last_count) {
     std::vector<uint64_t> hist;
     idx.call_ones([&](auto i) {
         uint64_t kmer_count = values[idx.rank1(i) - 1];
@@ -181,9 +182,12 @@ std::tuple<int64_t,double,double> estimate_min_kmer_abundance(const bit_vector &
     }
 
     double alpha_est_ptr, beta_est_ptr, false_pos_ptr, false_neg_ptr;
-    int64_t threshold = cleaning_pick_kmer_threshold(hist.data(), hist.size(),
-                                        &alpha_est_ptr, &beta_est_ptr,
-                                        &false_pos_ptr, &false_neg_ptr);
+    int64_t threshold = cleaning_pick_kmer_threshold(
+        hist.data(), hist.size(),
+        &alpha_est_ptr, &beta_est_ptr,
+        &false_pos_ptr, &false_neg_ptr
+    );
+
     return std::make_tuple(threshold,alpha_est_ptr,beta_est_ptr);
 }
 
diff --git a/metagraph/src/graph/graph_cleaning.hpp b/metagraph/src/graph/graph_cleaning.hpp
index 6d5771fefb..a0d20aab15 100644
--- a/metagraph/src/graph/graph_cleaning.hpp
+++ b/metagraph/src/graph/graph_cleaning.hpp
@@ -26,10 +26,11 @@ uint64_t estimate_min_kmer_abundance_ztp(const DeBruijnGraph &graph,
                                          const NodeWeights &node_weights,
                                          uint64_t num_singleton_kmers = 0);
 
-std::tuple<int64_t,double,double> estimate_min_kmer_abundance(const bit_vector &idx,
-                                     const sdsl::int_vector<> &values,
-                                     uint64_t num_singleton_kmers = 0,
-                                     bool discard_last_count = false);
+std::tuple<int64_t,double,double>
+estimate_min_kmer_abundance(const bit_vector &idx,
+                            const sdsl::int_vector<> &values,
+                            uint64_t num_singleton_kmers = 0,
+                            bool discard_last_count = false);
 
 } // namespace graph
 } // namespace mtg

From 36f411ba4bcb3cc15494147b7750b61022a2a0aa Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 9 May 2024 10:37:50 +0200
Subject: [PATCH 129/410] cleanup

---
 metagraph/src/common/vectors/transpose.hpp    |  12 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 171 ++++++++----------
 2 files changed, 86 insertions(+), 97 deletions(-)

diff --git a/metagraph/src/common/vectors/transpose.hpp b/metagraph/src/common/vectors/transpose.hpp
index 2128a8e1e8..21f5729505 100644
--- a/metagraph/src/common/vectors/transpose.hpp
+++ b/metagraph/src/common/vectors/transpose.hpp
@@ -63,14 +63,14 @@ void call_rows(const std::vector<BitmapPtr> &columns,
 template <class BitmapPtr, class ValuesPtr, typename PairVector = std::vector<std::pair<uint64_t, uint8_t>>>
 void call_rows(const std::vector<BitmapPtr> &columns,
                const std::vector<ValuesPtr> &column_values,
-               const std::function<void(const PairVector &)> &callback,
+               const std::function<void(uint64_t, const PairVector &)> &callback,
                uint64_t num_rows = 0,
                bool show_progress = mtg::common::get_verbose()) {
     if (!columns.size()) {
         ProgressBar progress_bar(num_rows, "Constructed rows", std::cerr, !show_progress);
         PairVector empty;
-        while (num_rows--) {
-            callback(empty);
+        for (uint64_t i = 0; i < num_rows; ++i) {
+            callback(i, empty);
         }
         return;
     }
@@ -97,10 +97,10 @@ void call_rows(const std::vector<BitmapPtr> &columns,
         }
         #pragma omp ordered
         {
-            for (auto &row : rows) {
-                callback(row);
+            for (uint64_t j = 0; j < rows.size(); ++j) {
+                callback(j + begin, rows[j]);
                 ++progress_bar;
-                row.resize(0);
+                rows[j].resize(0);
             }
         }
     }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index d3917fb960..b25daa29d4 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -122,13 +122,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         num_parallel_files
     );
 
-    std::vector<double> medians;
-    std::vector<uint64_t> min_counts;
-    std::vector<uint64_t> check_cutoff;
-    std::vector<uint64_t> max_obs_vals;
-    std::vector<size_t> n_kmers;
-    std::vector<uint64_t> sums;
-    std::vector<uint64_t> sums_of_squares;
+
+    std::vector<uint64_t> min_counts(groups.size(), 0);
+    std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
 
     auto adjust_count = [&](uint64_t raw_count, size_t j, uint64_t row_i) {
         std::ignore = row_i;
@@ -137,42 +133,32 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             : 0;
     };
 
-    common::logger->trace("Cleaning count columns");
-    double in_kmers = 0;
-    double out_kmers = 0;
-    for (size_t j = 0; j < groups.size(); ++j) {
-        const auto &column = *columns_all[j];
-        const auto &column_values = *column_values_all[j];
-
-        // median calculation modifies the underlying vector, so we have to make a copy
-        std::vector<uint64_t> vals;
-        vals.reserve(column_values.size());
-
-        for (size_t i = 0; i < column_values.size(); ++i) {
-            vals.emplace_back(column_values[i]);
-        }
-
-        check_cutoff.emplace_back(std::numeric_limits<uint64_t>::max());
-        min_counts.emplace_back(config.min_count);
+    if (config.clean && !config.min_count) {
+        common::logger->trace("Cleaning count columns");
+        for (size_t j = 0; j < groups.size(); ++j) {
+            const auto &column_values = *column_values_all[j];
 
-        if (config.clean && !config.min_count) {
             // set cutoff for lower end of distribution
             auto [mean_est, nzeros_est] = estimate_ztp_mean(
                 [&](const auto &callback) {
-                    std::for_each(vals.begin(), vals.end(), callback);
+                    for (size_t i = 0; i < column_values.size(); ++i) {
+                        callback(column_values[i]);
+                    }
                 },
                 max_width,
                 0,
                 N_BUCKETS_FOR_ESTIMATION
             );
 
-            min_counts.back() = boost::math::quantile(
+            min_counts[j] = boost::math::quantile(
                 boost::math::poisson(mean_est), exp(-mean_est) - CDF_CUTOFF * expm1(-mean_est)
             );
 
-            vals.clear();
+            // quartile calculation modifies the underlying vector, so we have to make a copy
+            std::vector<uint64_t> vals;
+            vals.reserve(column_values.size());
             for (size_t i = 0; i < column_values.size(); ++i) {
-                if (column_values[i] >= min_counts.back())
+                if (column_values[i] >= min_counts[j])
                     vals.emplace_back(column_values[i]);
             }
 
@@ -196,26 +182,60 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ));
 
                 if (repeats_cutoff > outlier_cutoff)
-                    check_cutoff.back() = repeats_cutoff;
+                    check_cutoff[j] = repeats_cutoff;
             }
         }
+    }
+
+    common::logger->trace("Marking discarded k-mers");
+    if (groups.size()) {
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                         std::unique_ptr<ValuesContainer>,
+                         PairContainer>(columns_all, column_values_all,
+                                        [&](uint64_t row_i, const auto &row) {
+            if (row.empty()) {
+                ignored[row_i] = true;
+            } else if (config.clean) {
+                uint64_t sum = 0;
+                for (const auto &[j, raw_c] : row) {
+                    if (uint64_t c = adjust_count(raw_c, j, row_i))
+                        sum += c;
+                }
 
+                ignored[row_i] = sum < config.min_count;
+            }
+        });
+    }
+
+    common::logger->trace("Computing aggregate statistics");
+    double in_kmers = 0;
+    double out_kmers = 0;
+    std::vector<double> medians;
+    std::vector<uint64_t> max_obs_vals;
+    std::vector<size_t> n_kmers;
+    std::vector<uint64_t> sums;
+    std::vector<uint64_t> sums_of_squares;
+    for (size_t j = 0; j < groups.size(); ++j) {
+        const auto &column = *columns_all[j];
+        const auto &column_values = *column_values_all[j];
         uint64_t sum = 0;
         uint64_t sum_of_squares = 0;
-        uint64_t max_obs_val = 0;
         size_t i = 0;
         size_t n = 0;
+
+        std::vector<uint64_t> vals;
+        vals.reserve(column_values.size());
         column.call_ones([&](uint64_t row_i) {
             if (uint64_t c = adjust_count(column_values[i], j, row_i)) {
+                vals.emplace_back(c);
                 sum += c;
                 sum_of_squares += c * c;
-                max_obs_val = std::max(max_obs_val, c);
                 ++n;
             }
             ++i;
         });
 
-        max_obs_vals.emplace_back(max_obs_val);
+        max_obs_vals.emplace_back(*std::max_element(vals.begin(), vals.end()));
         medians.push_back(boost::math::statistics::median(vals));
 
         sums.push_back(sum);
@@ -228,39 +248,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         n_kmers.push_back(n);
 
         common::logger->trace("{}: sum: {}\tmedian: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
-                              j, sum, medians[j], max_obs_val, min_counts.back(), check_cutoff.back());
+                              j, sum, medians[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
     }
 
     double total_kmers = in_kmers + out_kmers;
-
-    common::logger->trace("Marking discarded k-mers");
-    if (groups.size()) {
-        uint64_t row_i = 0;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         PairContainer>(columns_all, column_values_all, [&](const auto &row) {
-            if (row.empty()) {
-                ignored[row_i++] = true;
-                return;
-            } else if (!config.clean) {
-                ++row_i;
-                return;
-            }
-
-            bool found = true;
-            for (const auto &[j, raw_c] : row) {
-                if (uint64_t c = adjust_count(raw_c, j, row_i)) {
-                    found = false;
-                    break;
-                }
-            }
-
-            ignored[row_i++] = found;
-        });
-    }
-
     double nelem = ignored.size() - sdsl::util::cnt_one_bits(ignored);
 
+    common::logger->trace("Number of kept unique k-mers: {}\tNumber of kept k-mers: {}",
+                          nelem, total_kmers);
+
     common::logger->trace("Running differential tests");
     sdsl::bit_vector indicator_in(graph_ptr->max_index() + 1, false);
     sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
@@ -511,32 +507,32 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "bp") {
-        uint64_t row_i = 0;
         double sum_of_squares = 0;
         std::vector<double> counts;
         counts.reserve(nelem);
         utils::call_rows<std::unique_ptr<bit_vector>,
                          std::unique_ptr<ValuesContainer>,
-                         PairContainer>(columns_all, column_values_all, [&](const auto &row) {
-            if (!ignored[row_i]) {
-                double sum_in = 0;
-                double sum_out = 0;
-                for (const auto &[j, raw_c] : row) {
-                    if (uint64_t c = adjust_count(raw_c, j, row_i)) {
-                        if (groups[j]) {
-                            sum_in += c;
-                        } else {
-                            sum_out += c;
-                        }
+                         PairContainer>(columns_all, column_values_all,
+                                        [&](uint64_t row_i, const auto &row) {
+            if (ignored[row_i])
+                return;
+
+            double sum_in = 0;
+            double sum_out = 0;
+            for (const auto &[j, raw_c] : row) {
+                if (uint64_t c = adjust_count(raw_c, j, row_i)) {
+                    if (groups[j]) {
+                        sum_in += c;
+                    } else {
+                        sum_out += c;
                     }
                 }
+            }
 
-                if (sum_in + sum_out > 0) {
-                    counts.emplace_back(sum_in + sum_out);
-                    sum_of_squares += pow(counts.back(), 2.0);
-                }
+            if (sum_in + sum_out > 0) {
+                counts.emplace_back(sum_in + sum_out);
+                sum_of_squares += pow(counts.back(), 2.0);
             }
-            ++row_i;
         });
 
         auto get_a = [&](double k, double k2) {
@@ -795,10 +791,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         throw std::runtime_error("Test not implemented");
     }
 
-    uint64_t row_i = 0;
     utils::call_rows<std::unique_ptr<bit_vector>,
                      std::unique_ptr<ValuesContainer>,
-                     PairContainer>(columns_all, column_values_all, [&](const auto &raw_row) {
+                     PairContainer>(columns_all, column_values_all,
+                                    [&](uint64_t row_i, const auto &raw_row) {
         PairContainer row;
         if (!ignored[row_i]) {
             for (const auto &[j, raw_c] : raw_row) {
@@ -810,10 +806,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         auto [pval, in_stat, out_stat] = compute_pval(row);
         pvals.emplace_back(pval);
 
-        if (pval > 1.0 || in_stat == out_stat) {
-            ++row_i;
+        if (pval > 1.1 || in_stat == out_stat)
             return;
-        }
 
         bool in_kmer = in_stat > out_stat;
         bool out_kmer = in_stat < out_stat;
@@ -842,10 +836,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 throw std::runtime_error("Test failed");
             }
 
-            if (pval_min >= 0.05) {
-                ++row_i;
+            if (pval_min >= 0.05)
                 return;
-            }
 
             if (pval_min > 0) {
                 double lkd = log2(0.05) - log2(pval_min);
@@ -1141,18 +1133,17 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
             else throw std::runtime_error("Label not found in labels_in or labels_out");
         }
     );
-    int64_t row_id = 0;
     int64_t num_tests = 0;
     int64_t kept_nodes = 0;
     // calculate the p-value for the brunner munzel test
     auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0);
     utils::call_rows<std::unique_ptr<bit_vector>,
                     std::unique_ptr<sdsl::int_vector_buffer<>>,
-                    std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all, [&](const auto &row) {
+                    std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
+                                                               [&](uint64_t row_id, const auto &row) {
                         // auto kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
                         if (row.size() == 0) { // if the kmer is not present in any sample
                             mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
-                            row_id++;
                             return;
                         }
                         else{
@@ -1181,11 +1172,9 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
                                 else{
                                     kept_nodes++;
                                 }
-                                row_id++;
                             }
                             else{
                                 mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
-                                row_id++;
                             }
 
                         }

From 7b38dfa21446edda2dbe623eda4a90339863570d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 9 May 2024 20:06:27 +0200
Subject: [PATCH 130/410] fixes

---
 metagraph/src/cli/clean.cpp                   |  3 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 64 ++++++++++---------
 2 files changed, 35 insertions(+), 32 deletions(-)

diff --git a/metagraph/src/cli/clean.cpp b/metagraph/src/cli/clean.cpp
index cf923abee9..26d877c6aa 100644
--- a/metagraph/src/cli/clean.cpp
+++ b/metagraph/src/cli/clean.cpp
@@ -148,8 +148,7 @@ int clean_graph(Config *config) {
             graph->call_unitigs(callback,
                                 num_threads,
                                 config->min_tip_size,
-                                config->kmers_in_single_form);
-                                // graph->get_mode() == graph::DeBruijnGraph::CANONICAL);
+                                graph->get_mode() == graph::DeBruijnGraph::CANONICAL);
 
         } else {
             graph->call_sequences(callback, num_threads,
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b25daa29d4..3d17732b0a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -33,7 +33,8 @@ typedef AnnotatedDBG::Annotator Annotator;
 typedef AnnotatedDBG::Annotator::Label Label;
 using Column = annot::matrix::BinaryMatrix::Column;
 using PairContainer = std::vector<std::pair<uint64_t, uint64_t>>;
-using ValuesContainer = sdsl::int_vector_buffer<>;
+// using ValuesContainer = sdsl::int_vector_buffer<>;
+using ValuesContainer = sdsl::int_vector<>;
 
 double CDF_CUTOFF = 0.95;
 uint64_t N_BUCKETS_FOR_ESTIMATION = 3;
@@ -123,17 +124,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     );
 
 
-    std::vector<uint64_t> min_counts(groups.size(), 0);
+    std::vector<uint64_t> min_counts(groups.size(), config.min_count);
     std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
 
-    auto adjust_count = [&](uint64_t raw_count, size_t j, uint64_t row_i) {
+    auto adjust_count = [&](uint64_t raw_count, size_t j, uint64_t row_i) -> uint64_t {
         std::ignore = row_i;
         return raw_count >= min_counts[j] && raw_count <= check_cutoff[j]
             ? raw_count
             : 0;
     };
 
-    if (config.clean && !config.min_count) {
+    if (config.clean) {
         common::logger->trace("Cleaning count columns");
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto &column_values = *column_values_all[j];
@@ -142,7 +143,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto [mean_est, nzeros_est] = estimate_ztp_mean(
                 [&](const auto &callback) {
                     for (size_t i = 0; i < column_values.size(); ++i) {
-                        callback(column_values[i]);
+                        if (column_values[i] <= N_BUCKETS_FOR_ESTIMATION)
+                            callback(column_values[i]);
                     }
                 },
                 max_width,
@@ -195,18 +197,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                         [&](uint64_t row_i, const auto &row) {
             if (row.empty()) {
                 ignored[row_i] = true;
-            } else if (config.clean) {
-                uint64_t sum = 0;
+            } else if (config.clean || config.min_count) {
                 for (const auto &[j, raw_c] : row) {
-                    if (uint64_t c = adjust_count(raw_c, j, row_i))
-                        sum += c;
+                    if (adjust_count(raw_c, j, row_i))
+                        return;
                 }
 
-                ignored[row_i] = sum < config.min_count;
+                ignored[row_i] = true;
             }
         });
     }
 
+    double nelem = ignored.size() - sdsl::util::cnt_one_bits(ignored);
+
     common::logger->trace("Computing aggregate statistics");
     double in_kmers = 0;
     double out_kmers = 0;
@@ -247,12 +250,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         sums_of_squares.push_back(sum_of_squares);
         n_kmers.push_back(n);
 
-        common::logger->trace("{}: sum: {}\tmedian: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
-                              j, sum, medians[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
+        common::logger->trace("{}: kept: {} / {}\tsum: {}\tmedian: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
+                              j, n, i, sum, medians[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
     }
 
     double total_kmers = in_kmers + out_kmers;
-    double nelem = ignored.size() - sdsl::util::cnt_one_bits(ignored);
 
     common::logger->trace("Number of kept unique k-mers: {}\tNumber of kept k-mers: {}",
                           nelem, total_kmers);
@@ -806,8 +808,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         auto [pval, in_stat, out_stat] = compute_pval(row);
         pvals.emplace_back(pval);
 
-        if (pval > 1.1 || in_stat == out_stat)
+        if (pval > 1.1 || in_stat == out_stat) {
+            if (pval <= 1.0 && in_stat == out_stat)
+                throw std::runtime_error("FOO");
             return;
+        }
 
         bool in_kmer = in_stat > out_stat;
         bool out_kmer = in_stat < out_stat;
@@ -836,10 +841,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 throw std::runtime_error("Test failed");
             }
 
-            if (pval_min >= 0.05)
-                return;
-
-            if (pval_min > 0) {
+            if (pval_min >= 0.05) {
+                k = 0;
+            } else if (pval_min > 0) {
                 double lkd = log2(0.05) - log2(pval_min);
                 if (lkd <= 64)
                     k = pow(2.0, lkd);
@@ -878,18 +882,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     if (total_sig) {
         auto begin = m_data.begin();
         size_t k = 0;
-        size_t last_k = 0;
-        for ( ; begin != m_data.end(); ++begin) {
-            const auto &[cur_k, bucket] = *begin;
-            common::logger->trace("k: {}\tm(k): {}", cur_k, acc);
-            if (acc <= cur_k) {
-                k = std::max(acc, last_k + 1);
-                break;
-            }
-
-            last_k = cur_k;
-            acc -= bucket.size();
-        }
+        // size_t last_k = 0;
+        // for ( ; begin != m_data.end(); ++begin) {
+        //     const auto &[cur_k, bucket] = *begin;
+        //     common::logger->trace("k: {}\tm(k): {}", cur_k, acc);
+        //     if (acc <= cur_k) {
+        //         k = std::max(acc, last_k + 1);
+        //         break;
+        //     }
+
+        //     last_k = cur_k;
+        //     acc -= bucket.size();
+        // }
 
         if (k == 0) {
             common::logger->trace("No good k value found, defaulting to Bonferroni cutoff");

From d6ac0f8d395b63397533ed0f77b3de064b7ef510 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 9 May 2024 22:00:05 +0200
Subject: [PATCH 131/410] fallback

---
 .../src/graph/annotated_graph_algorithm.cpp   | 109 +++++++++++-------
 1 file changed, 65 insertions(+), 44 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 3d17732b0a..ec3b7489df 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -765,26 +765,32 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 }
             } else {
-                // https://datatab.net/tutorial/mann-whitney-u-test
-                double rankcx_sum = std::accumulate(rankc.begin(), rankc.begin() + nx, double(0.0));
-                double rankcy_sum = std::accumulate(rankc.begin() + nx, rankc.end(), double(0.0));
-                double u1 = nx * ny + static_cast<double>(nx * (nx + 1))/2 - rankcx_sum;
-                double u2 = nx * ny + static_cast<double>(ny * (ny + 1))/2 - rankcy_sum;
-                double u = std::min(u1, u2);
-
-                double n = nx + ny;
-                // normal approximation
-                double mu = static_cast<double>(nx * ny) / 2;
-                double corr = 0;
-                for (const auto &[val,rks] : countsc) {
-                    const auto &[rk, c] = rks;
-                    if (c > 1)
-                        corr += c * c * c - c;
-                }
-                double sd = sqrt(static_cast<double>(nx * ny) / n / (n - 1)) * sqrt((n * n * n - n)/12 - corr / 12);
+                if (!nx || !ny) {
+                    p = 1.0;
+                } else {
+                    // https://datatab.net/tutorial/mann-whitney-u-test
+                    double rankcx_sum = std::accumulate(rankc.begin(), rankc.begin() + nx, double(0.0));
+                    double rankcy_sum = std::accumulate(rankc.begin() + nx, rankc.end(), double(0.0));
+                    double u1 = nx * ny + static_cast<double>(nx * (nx + 1))/2 - rankcx_sum;
+                    double u2 = nx * ny + static_cast<double>(ny * (ny + 1))/2 - rankcy_sum;
+                    double u = std::min(u1, u2);
+
+                    double n = nx + ny;
+                    // normal approximation
+                    double mu = static_cast<double>(nx * ny) / 2;
+                    double corr = 0;
+                    for (const auto &[val,rks] : countsc) {
+                        const auto &[rk, c] = rks;
+                        if (c > 1)
+                            corr += c * c * c - c;
+                    }
+                    double sd = sqrt(static_cast<double>(nx * ny) / n / (n - 1)) * sqrt((n * n * n - n)/12 - corr / 12);
 
-                double z = abs((u - mu) / sd);
-                p = boost::math::cdf(boost::math::complement(norm, z)) * 2;
+                    double z = abs((u - mu) / sd);
+
+                    // hack just in case sd == 0
+                    p = u != mu ? boost::math::cdf(boost::math::complement(norm, z)) * 2 : 1.0;
+                }
             }
 
             return std::make_tuple(p, rankcx_mean, rankcy_mean);
@@ -808,11 +814,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         auto [pval, in_stat, out_stat] = compute_pval(row);
         pvals.emplace_back(pval);
 
-        if (pval > 1.1 || in_stat == out_stat) {
-            if (pval <= 1.0 && in_stat == out_stat)
-                throw std::runtime_error("FOO");
+        if (pval > 1.1)
             return;
-        }
 
         bool in_kmer = in_stat > out_stat;
         bool out_kmer = in_stat < out_stat;
@@ -857,6 +860,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
         m[k].emplace_back(node);
+        if (in_stat == out_stat)
+            return;
+
         auto &indicator = in_kmer ? indicator_in : indicator_out;
         if (pval < 0.05)
             indicator[node] = true;
@@ -882,30 +888,45 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     if (total_sig) {
         auto begin = m_data.begin();
         size_t k = 0;
-        // size_t last_k = 0;
-        // for ( ; begin != m_data.end(); ++begin) {
-        //     const auto &[cur_k, bucket] = *begin;
-        //     common::logger->trace("k: {}\tm(k): {}", cur_k, acc);
-        //     if (acc <= cur_k) {
-        //         k = std::max(acc, last_k + 1);
-        //         break;
-        //     }
-
-        //     last_k = cur_k;
-        //     acc -= bucket.size();
-        // }
+        size_t last_k = 0;
+        for ( ; begin != m_data.end(); ++begin) {
+            const auto &[cur_k, bucket] = *begin;
+            common::logger->trace("k: {}\tm(k): {}", cur_k, acc);
+            if (acc <= cur_k) {
+                k = std::max(acc, last_k + 1);
+                break;
+            }
+
+            last_k = cur_k;
+            acc -= bucket.size();
+        }
 
         if (k == 0) {
-            common::logger->trace("No good k value found, defaulting to Bonferroni cutoff");
-            k = total_tests;
+            common::logger->trace("No good k value found, falling back to Benjamini–Yekutieli");
+            m_data = decltype(m_data)();
+
+            VectorMap<double, size_t> pval_map;
+            for (double p : pvals) {
+                if (p <= 1.0)
+                    ++pval_map[p];
+            }
+            auto p_data = const_cast<std::vector<std::pair<double, size_t>>&&>(pval_map.values_container());
+            std::sort(p_data.begin(), p_data.end(), utils::LessFirst());
+
+            double factor = 0.05 / total_tests / (boost::math::digamma(total_tests) + 0.5772156649015329); // std::numbers::e_gamma_v<double>
+            for (const auto &[p, c] : p_data) {
+                k += c;
+                if (p > factor * k) {
+                    k = p / factor;
+                    break;
+                }
+            }
+
             common::logger->trace("k: {}\talpha_corr: {}", k, 0.05 / k);
-            for (auto jt = m_data.begin(); jt != m_data.end(); ++jt) {
-                const auto &[cur_k, bucket] = *jt;
-                for (uint64_t i : bucket) {
-                    if (pvals[i] < 0.05 && pvals[i] * k >= 0.05) {
-                        indicator_in[i] = false;
-                        indicator_out[i] = false;
-                    }
+            for (size_t i = 0; i < pvals.size(); ++i) {
+                if (pvals[i] < 0.05 && pvals[i] * k >= 0.05) {
+                    indicator_in[i] = false;
+                    indicator_out[i] = false;
                 }
             }
         } else {

From 43c824695bb655e4736e0c9e0bad481f76e05afb Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 10 May 2024 13:15:16 +0200
Subject: [PATCH 132/410] added logit-normal

---
 .../src/graph/annotated_graph_algorithm.cpp   | 277 ++++++++++++------
 1 file changed, 182 insertions(+), 95 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ec3b7489df..e3c66cfe8b 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -9,6 +9,7 @@
 #include <boost/math/statistics/univariate_statistics.hpp>
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/special_functions/digamma.hpp>
+#include <boost/math/special_functions/trigamma.hpp>
 #include <boost/math/tools/minima.hpp>
 #include <boost/math/tools/roots.hpp>
 
@@ -508,9 +509,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
-    } else if (config.test_type == "bp") {
+    } else if (config.test_type == "bp" || config.test_type == "lnp") {
         double sum_of_squares = 0;
-        std::vector<double> counts;
+        double sum_of_squares_in = 0;
+        double sum_of_squares_out = 0;
+        std::vector<std::pair<double, double>> counts;
         counts.reserve(nelem);
         utils::call_rows<std::unique_ptr<bit_vector>,
                          std::unique_ptr<ValuesContainer>,
@@ -532,19 +535,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             if (sum_in + sum_out > 0) {
-                counts.emplace_back(sum_in + sum_out);
-                sum_of_squares += pow(counts.back(), 2.0);
+                counts.emplace_back(sum_in, sum_out);
+                sum_of_squares_in += sum_in * sum_in;
+                sum_of_squares_out += sum_out * sum_out;
+                sum_of_squares += pow(sum_in + sum_out, 2.0);
             }
         });
 
-        auto get_a = [&](double k, double k2) {
+        auto get_a = [&](double k, double k2, uint8_t pick = 0) {
             double mu = k / nelem;
             // model with a negative binomial, similar to
             // https://github.com/JBHilton/beta-poisson-epidemics/blob/master/functions.py#L140
             auto get_dll = [&](double r) {
                 double lp = log(r) - log(r + mu);
                 double dll = nelem * lp - nelem * boost::math::digamma(r);
-                for (double c : counts) {
+                for (const auto &[c_in, c_out] : counts) {
+                    double c;
+                    switch (pick) {
+                        case 1: { c = c_in; } break;
+                        case 2: { c = c_out; } break;
+                        default: { c = c_in + c_out; }
+                    }
+
                     dll += boost::math::digamma(r + c);
                 }
 
@@ -576,57 +588,46 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         auto [a, b] = get_a(total_kmers, sum_of_squares);
 
-        if (a <= 1) {
+        if (config.test_type == "bp" && a <= 1) {
             common::logger->error("a <= 1");
             throw std::runtime_error("Fit failed");
         }
 
-        auto get_theta = [&,a,b](double total, double k_sum) {
-            double qb = 2.0 - total - k_sum - a - b;
-            double qc = k_sum + a - 1.0;
-
-            auto [theta_low, theta_high] = boost::math::tools::quadratic_roots(total, qb, qc);
-
-            // Note: comparisons of thetas to themselves ensure that they are not NaN
-            // https://stackoverflow.com/questions/570669/checking-if-a-double-or-float-is-nan-in-c
-            bool low_valid = theta_low == theta_low && theta_low > 0 && theta_low < 1.0;
-            bool high_valid = theta_high == theta_high && theta_low != theta_high && theta_high > 0 && theta_high < 1.0;
-
-            double stat_low = low_valid
-                ? qc * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low
-                : -std::numeric_limits<double>::infinity();
-            double stat_high = high_valid
-                ? qc * log(theta_high) + (b - 1) * log1p(-theta_high) - total * theta_high
-                : -std::numeric_limits<double>::infinity();
-
-            double stat_max = std::max({ stat_low, stat_high });
-
-            bool stat_is_low = low_valid && stat_max == stat_low;
-            bool stat_is_high = high_valid && stat_max == stat_high;
-
-            if (stat_is_low && stat_is_high) {
-                common::logger->error("Multiple valid thetas\t{}", stat_max);
-                common::logger->error("{}\t{}", theta_low, theta_high);
-                common::logger->error("{}\t{}", stat_low, stat_high);
-                common::logger->error("{}\t{}", stat_is_low, stat_is_high);
-                common::logger->error("Ratio: {} / {}", k_sum, total);
-                common::logger->error("{},{}\t{},{},{}", a,b, total, qb, qc);
-                throw std::runtime_error("Fail");
-            }
+        auto [a_in, b_in] = get_a(in_kmers, sum_of_squares_in, 1);
+        if (config.test_type == "bp" && a_in <= 1) {
+            common::logger->warn("a_in: {} <= 1, resetting to a_null: {}", a_in, a);
+            a_in = a;
+            b_in = b;
+        }
 
-            if (stat_is_low || stat_is_high)
-                return std::make_tuple(theta_low, theta_high, stat_is_low, stat_is_high);
+        auto [a_out, b_out] = get_a(out_kmers, sum_of_squares_out, 2);
+        if (config.test_type == "bp" && a_out <= 1) {
+            common::logger->warn("a_out: {} <= 1, resetting to a_null: {}", a_out, a);
+            a_out = a;
+            b_out = b;
+        }
 
-            common::logger->error("No valid thetas\t{}", stat_max);
-            common::logger->error("{}\t{}", theta_low, theta_high);
-            common::logger->error("{}\t{}", stat_low, stat_high);
-            common::logger->error("{}\t{}", stat_is_low, stat_is_high);
-            common::logger->error("Ratio: {} / {}", k_sum, total);
-            common::logger->error("{},{}\t{},{},{}", a,b,total, qb, qc);
-            throw std::runtime_error("Fail");
-        };
+        double mu = 0;
+        double mu_in = 0;
+        double mu_out = 0;
+        double var = 0;
+        double var_in = 0;
+        double var_out = 0;
+        if (config.test_type == "lnp") {
+            mu = boost::math::digamma(a) - boost::math::digamma(b);
+            mu_in = boost::math::digamma(a_in) - boost::math::digamma(b_in);
+            mu_out = boost::math::digamma(a_out) - boost::math::digamma(b_out);
+
+            var = boost::math::trigamma(a) + boost::math::trigamma(b);
+            var_in = boost::math::trigamma(a_in) + boost::math::trigamma(b_in);
+            var_out = boost::math::trigamma(a_out) + boost::math::trigamma(b_out);
+
+            common::logger->trace("LN null:\tmu: {}\tvar: {}", mu, var);
+            common::logger->trace("LN in:\tmu: {}\tvar: {}", mu_in, var_in);
+            common::logger->trace("LN out:\tmu: {}\tvar: {}", mu_out, var_out);
+        }
 
-        compute_pval = [&,a,b,nelem,get_theta](const auto &row) {
+        compute_pval = [&,a,b,a_in,b_in,a_out,b_out,mu,mu_in,mu_out,var,var_in,var_out,nelem](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0);
 
@@ -642,40 +643,128 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double null_sum = in_sum + out_sum;
 
-            auto [theta_null_low, theta_null_high, null_is_low, null_is_high]
-                = get_theta(total_kmers, null_sum);
-            auto [theta_in_low, theta_in_high, in_is_low, in_is_high]
-                = get_theta(in_kmers, in_sum);
-            auto [theta_out_low, theta_out_high, out_is_low, out_is_high]
-                = get_theta(out_kmers, out_sum);
+            double map_pval_in = 1.0;
+            double map_pval_out = 1.0;
+
+            if (config.test_type == "bp") {
+                auto get_theta = [&](double a, double b, double total, double k_sum) {
+                    double qb = 2.0 - total - k_sum - a - b;
+                    double qc = k_sum + a - 1.0;
+
+                    auto [theta_low, theta_high] = boost::math::tools::quadratic_roots(total, qb, qc);
+
+                    // Note: comparisons of thetas to themselves ensure that they are not NaN
+                    // https://stackoverflow.com/questions/570669/checking-if-a-double-or-float-is-nan-in-c
+                    bool low_valid = theta_low == theta_low && theta_low > 0 && theta_low < 1.0;
+                    bool high_valid = theta_high == theta_high && theta_low != theta_high && theta_high > 0 && theta_high < 1.0;
+
+                    double stat_low = low_valid
+                        ? qc * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low
+                        : -std::numeric_limits<double>::infinity();
+                    double stat_high = high_valid
+                        ? qc * log(theta_high) + (b - 1) * log1p(-theta_high) - total * theta_high
+                        : -std::numeric_limits<double>::infinity();
+
+                    double stat_max = std::max({ stat_low, stat_high });
+
+                    bool stat_is_low = low_valid && stat_max == stat_low;
+                    bool stat_is_high = high_valid && stat_max == stat_high;
+
+                    if (stat_is_low && stat_is_high) {
+                        common::logger->error("Multiple valid thetas\t{}", stat_max);
+                        common::logger->error("{}\t{}", theta_low, theta_high);
+                        common::logger->error("{}\t{}", stat_low, stat_high);
+                        common::logger->error("{}\t{}", stat_is_low, stat_is_high);
+                        common::logger->error("Ratio: {} / {}", k_sum, total);
+                        common::logger->error("{},{}\t{},{},{}", a,b, total, qb, qc);
+                        throw std::runtime_error("Fail");
+                    }
 
-            double theta_null = null_is_low ? theta_null_low : theta_null_high;
-            double theta_in = in_is_low ? theta_in_low : theta_in_high;
-            double theta_out = out_is_low ? theta_out_low : theta_out_high;
+                    if (stat_is_low || stat_is_high)
+                        return std::make_tuple(theta_low, theta_high, stat_is_low, stat_is_high);
 
-            auto get_stat_log = [&,a,b](double theta, double k, double total) -> double {
-                return (k + a - 1) * log(theta) + (b - 1) * log1p(-theta) - total * theta;
-            };
+                    common::logger->error("No valid thetas\t{}", stat_max);
+                    common::logger->error("{}\t{}", theta_low, theta_high);
+                    common::logger->error("{}\t{}", stat_low, stat_high);
+                    common::logger->error("{}\t{}", stat_is_low, stat_is_high);
+                    common::logger->error("Ratio: {} / {}", k_sum, total);
+                    common::logger->error("{},{}\t{},{},{}", a,b,total, qb, qc);
+                    throw std::runtime_error("Fail");
+                };
+
+                auto get_stat_log = [&](double a, double b, double theta, double k, double total) -> double {
+                    return (k + a - 1) * log(theta) + (b - 1) * log1p(-theta) - total * theta;
+                };
+
+                auto [theta_null_low, theta_null_high, null_is_low, null_is_high]
+                    = get_theta(a, b, total_kmers, null_sum);
+                auto [theta_in_low, theta_in_high, in_is_low, in_is_high]
+                    = get_theta(a_in, b_in, in_kmers, in_sum);
+                auto [theta_out_low, theta_out_high, out_is_low, out_is_high]
+                    = get_theta(a_out, b_out, out_kmers, out_sum);
+
+                double theta_null = null_is_low ? theta_null_low : theta_null_high;
+                double theta_in = in_is_low ? theta_in_low : theta_in_high;
+                double theta_out = out_is_low ? theta_out_low : theta_out_high;
+
+
+
+                map_pval_in = exp(get_stat_log(a_in, b_in, theta_null, in_sum, in_kmers)
+                                - get_stat_log(a_in, b_in, theta_in, in_sum, in_kmers));
+
+                map_pval_out = exp(get_stat_log(a_out, b_out, theta_null, out_sum, out_kmers)
+                                - get_stat_log(a_out, b_out, theta_out, out_sum, out_kmers));
+            } else if (config.test_type == "lnp") {
+                auto get_theta = [&](double mu, double var, double total, double k_sum) {
+                    size_t ndigits = 40;
+                    return boost::math::tools::newton_raphson_iterate(
+                        [&](double theta) -> std::pair<double, double> {
+                            double norm_factor = (log(theta) - log1p(-theta) - mu) / var;
+                            return std::make_pair(
+                                (k_sum - total * theta - 1) * (1 - theta) - (1 - 2.0 * theta) * norm_factor,
+                                1.0 - k_sum + 2.0 * norm_factor - (1.0 - 2.0 * theta) * ((1.0 - 2.0 * theta) / (var * theta * (1.0 - theta)) + total)
+                            );
+                        },
+                        k_sum > 0 ? k_sum / total : 0.01, 0.0, 1.0, ndigits
+                    );
+                };
+
+                auto get_stat_log = [&](double mu, double var, double theta, double k, double total) -> double {
+                    return (k - 1) * log(theta) - log1p(-theta) - total * theta - pow(log(theta) - log1p(-theta) - mu, 2.0) / 2.0 / var;
+                };
+
+                auto theta_null = get_theta(mu, var, total_kmers, null_sum);
+                auto theta_in = get_theta(mu_in, var_in, in_kmers, in_sum);
+                auto theta_out = get_theta(mu_out, var_out, out_kmers, out_sum);
+
+                if (theta_null != theta_null || theta_in != theta_in || theta_out != theta_out
+                        || theta_null == 0 || theta_null == 1.0
+                        || theta_in == 0 || theta_in == 1.0
+                        || theta_out == 0 || theta_out == 1.0) {
+                    common::logger->error("{}\t{}\t{}", theta_null, theta_in, theta_out);
+                    throw std::runtime_error("Failed to find theta");
+                }
 
-            double map_pval_in = exp(get_stat_log(theta_null, in_sum, in_kmers)
-                                    - get_stat_log(theta_in, in_sum, in_kmers));
+                map_pval_in = exp(get_stat_log(mu_in, var_in, theta_null, in_sum, in_kmers)
+                                - get_stat_log(mu_in, var_in, theta_in, in_sum, in_kmers));
 
-            double map_pval_out = exp(get_stat_log(theta_null, out_sum, out_kmers)
-                                    - get_stat_log(theta_out, out_sum, out_kmers));
+                map_pval_out = exp(get_stat_log(mu_out, var_out, theta_null, out_sum, out_kmers)
+                                - get_stat_log(mu_out, var_out, theta_out, out_sum, out_kmers));
+            }
 
             if (map_pval_in > 1.0 || map_pval_in < 0.0
                     || map_pval_out > 1.0 || map_pval_out < 0.0) {
                 return std::make_tuple(1.0, in_sum,
                                        out_sum / out_kmers * in_kmers);
                 common::logger->error("Failed to compute MAP of thetas");
-                common::logger->error("Theta in: {}\tTheta out: {}\tTheta null: {}",
-                                      theta_in, theta_out, theta_null);
-                common::logger->error("Theta in low: {}\tTheta out low: {}\tTheta null low: {}",
-                                      theta_in_low, theta_out_low, theta_null_low);
-                common::logger->error("Theta in high: {}\tTheta out high: {}\tTheta null high: {}",
-                                      theta_in_high, theta_out_high, theta_null_high);
-                common::logger->error("In sum: {}\tOut sum: {}", in_sum, out_sum);
-                common::logger->error("In p-val: {}\tOut p-val: {}", map_pval_in, map_pval_out);
+                // common::logger->error("Theta in: {}\tTheta out: {}\tTheta null: {}",
+                //                       theta_in, theta_out, theta_null);
+                // common::logger->error("Theta in low: {}\tTheta out low: {}\tTheta null low: {}",
+                //                       theta_in_low, theta_out_low, theta_null_low);
+                // common::logger->error("Theta in high: {}\tTheta out high: {}\tTheta null high: {}",
+                //                       theta_in_high, theta_out_high, theta_null_high);
+                // common::logger->error("In sum: {}\tOut sum: {}", in_sum, out_sum);
+                // common::logger->error("In p-val: {}\tOut p-val: {}", map_pval_in, map_pval_out);
                 throw std::runtime_error("Fail");
             }
 
@@ -901,30 +990,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             acc -= bucket.size();
         }
 
-        if (k == 0) {
-            common::logger->trace("No good k value found, falling back to Benjamini–Yekutieli");
-            m_data = decltype(m_data)();
-
-            VectorMap<double, size_t> pval_map;
-            for (double p : pvals) {
-                if (p <= 1.0)
-                    ++pval_map[p];
-            }
-            auto p_data = const_cast<std::vector<std::pair<double, size_t>>&&>(pval_map.values_container());
-            std::sort(p_data.begin(), p_data.end(), utils::LessFirst());
-
-            double factor = 0.05 / total_tests / (boost::math::digamma(total_tests) + 0.5772156649015329); // std::numbers::e_gamma_v<double>
-            for (const auto &[p, c] : p_data) {
-                k += c;
-                if (p > factor * k) {
-                    k = p / factor;
-                    break;
-                }
-            }
+        VectorMap<double, double> pval_map;
+        for (double p : pvals) {
+            if (p <= 1.0)
+                ++pval_map[p];
+        }
+        auto p_data = const_cast<std::vector<std::pair<double, double>>&&>(pval_map.values_container());
+        std::sort(p_data.begin(), p_data.end(), utils::GreaterFirst());
+
+        double cm = boost::math::digamma(total_tests) + 0.5772156649015329; // std::numbers::e_gamma_v<double>
+        pval_map = decltype(pval_map)();
+        size_t cur_rank = 0;
+        for (const auto &[p, c] : p_data) {
+            cur_rank += c;
+            pval_map[p] = cm * cur_rank;
+        }
 
+        if (k == 0) {
+            common::logger->trace("Falling back to Benjamini-Yekutieli");
+            double k_b = total_tests;
             common::logger->trace("k: {}\talpha_corr: {}", k, 0.05 / k);
             for (size_t i = 0; i < pvals.size(); ++i) {
-                if (pvals[i] < 0.05 && pvals[i] * k >= 0.05) {
+                if (pvals[i] < 0.05 && pvals[i] * std::min(k_b, pval_map[pvals[i]]) >= 0.05) {
                     indicator_in[i] = false;
                     indicator_out[i] = false;
                 }

From 08acafeecbd6091102b063caddbb45780d62f231 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 10 May 2024 13:41:50 +0200
Subject: [PATCH 133/410] min recurrence

---
 metagraph/src/cli/assemble.cpp                |  1 +
 .../src/graph/annotated_graph_algorithm.cpp   | 41 ++++++++++---------
 .../src/graph/annotated_graph_algorithm.hpp   |  1 +
 3 files changed, 23 insertions(+), 20 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 769271044f..f04578676c 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -58,6 +58,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.filter = experiment.get("filter", true).asBool();
     diff_config.clean = experiment.get("clean", false).asBool();
     diff_config.min_count = experiment.get("min_count", 0).asInt();
+    diff_config.min_recurrence = experiment.get("min_recurrence", 1).asInt();
     diff_config.num_tests = experiment.get("num_tests", 0).asInt();
 
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index e3c66cfe8b..db1ec81b98 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -199,9 +199,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (row.empty()) {
                 ignored[row_i] = true;
             } else if (config.clean || config.min_count) {
+                size_t count = 0;
                 for (const auto &[j, raw_c] : row) {
-                    if (adjust_count(raw_c, j, row_i))
-                        return;
+                    if (adjust_count(raw_c, j, row_i)) {
+                        ++count;
+                        if (count >= config.min_recurrence)
+                            return;
+                    }
                 }
 
                 ignored[row_i] = true;
@@ -230,6 +234,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::vector<uint64_t> vals;
         vals.reserve(column_values.size());
         column.call_ones([&](uint64_t row_i) {
+            if (!ignored[row_i])
+                return;
+
             if (uint64_t c = adjust_count(column_values[i], j, row_i)) {
                 vals.emplace_back(c);
                 sum += c;
@@ -594,18 +601,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         auto [a_in, b_in] = get_a(in_kmers, sum_of_squares_in, 1);
-        if (config.test_type == "bp" && a_in <= 1) {
-            common::logger->warn("a_in: {} <= 1, resetting to a_null: {}", a_in, a);
-            a_in = a;
-            b_in = b;
-        }
-
         auto [a_out, b_out] = get_a(out_kmers, sum_of_squares_out, 2);
-        if (config.test_type == "bp" && a_out <= 1) {
-            common::logger->warn("a_out: {} <= 1, resetting to a_null: {}", a_out, a);
-            a_out = a;
-            b_out = b;
-        }
 
         double mu = 0;
         double mu_in = 0;
@@ -648,6 +644,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             if (config.test_type == "bp") {
                 auto get_theta = [&](double a, double b, double total, double k_sum) {
+                    if (a + k_sum <= 1)
+                        return std::make_tuple(0.0, 0.0, true, false);
+
                     double qb = 2.0 - total - k_sum - a - b;
                     double qc = k_sum + a - 1.0;
 
@@ -707,13 +706,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double theta_in = in_is_low ? theta_in_low : theta_in_high;
                 double theta_out = out_is_low ? theta_out_low : theta_out_high;
 
+                map_pval_in = theta_in > 0
+                    ? exp(get_stat_log(a_in, b_in, theta_null, in_sum, in_kmers)
+                                - get_stat_log(a_in, b_in, theta_in, in_sum, in_kmers))
+                    : 0.0;
 
-
-                map_pval_in = exp(get_stat_log(a_in, b_in, theta_null, in_sum, in_kmers)
-                                - get_stat_log(a_in, b_in, theta_in, in_sum, in_kmers));
-
-                map_pval_out = exp(get_stat_log(a_out, b_out, theta_null, out_sum, out_kmers)
-                                - get_stat_log(a_out, b_out, theta_out, out_sum, out_kmers));
+                map_pval_out = theta_out > 0
+                    ? exp(get_stat_log(a_out, b_out, theta_null, out_sum, out_kmers)
+                                - get_stat_log(a_out, b_out, theta_out, out_sum, out_kmers))
+                    : 0.0;
             } else if (config.test_type == "lnp") {
                 auto get_theta = [&](double mu, double var, double total, double k_sum) {
                     size_t ndigits = 40;
@@ -903,7 +904,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         auto [pval, in_stat, out_stat] = compute_pval(row);
         pvals.emplace_back(pval);
 
-        if (pval > 1.1)
+        if (pval >= 1.1)
             return;
 
         bool in_kmer = in_stat > out_stat;
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 12fb8e066b..184beb52b4 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -41,6 +41,7 @@ struct DifferentialAssemblyConfig {
     bool evaluate_assembly = false; // Myrthe temporary : evaluate an assembly
     bool filter = true;
     uint64_t min_count = 1;
+    uint64_t min_recurrence = 1;
     uint64_t num_tests = 0;
     std::string outfbase;
 };

From 862e8608c11160e34c81498d6b8acb99981b8045 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 10 May 2024 18:20:43 +0200
Subject: [PATCH 134/410] stick to moment estimate

---
 .../src/graph/annotated_graph_algorithm.cpp   | 30 +++++++++++--------
 1 file changed, 18 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index db1ec81b98..d7dacc6ad9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -554,34 +554,40 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             // model with a negative binomial, similar to
             // https://github.com/JBHilton/beta-poisson-epidemics/blob/master/functions.py#L140
             auto get_dll = [&](double r) {
-                double lp = log(r) - log(r + mu);
-                double dll = nelem * lp - nelem * boost::math::digamma(r);
+                double dll = nelem * (log(r) - log(r + mu)) - nelem * boost::math::digamma(r);
+                double ddll = nelem / r - nelem / (r + mu) - nelem * boost::math::trigamma(r);
                 for (const auto &[c_in, c_out] : counts) {
                     double c;
                     switch (pick) {
                         case 1: { c = c_in; } break;
                         case 2: { c = c_out; } break;
-                        default: { c = c_in + c_out; }
+                        default: { c = c_in + c_out; } break;
                     }
 
                     dll += boost::math::digamma(r + c);
+                    ddll += boost::math::trigamma(r + c);
                 }
 
-                return dll;
+                return std::make_pair(dll, ddll);
             };
 
             double var = k2 / nelem - mu * mu;
             double r_guess = mu * mu / (var - mu);
 
-            long unsigned int max_iter = 100;
-            auto [r_min,r_max] = boost::math::tools::bracket_and_solve_root(
-                get_dll, r_guess, 2.0, false, [&](double r_min, double r_max) {
-                    return abs(r_min - r_max) / r_max < 1e-5;
-                },
-                max_iter
-            );
-            double a = (r_min + r_max) / 2.0 * nelem / (nelem - 1);
+            // double a = boost::math::tools::newton_raphson_iterate(
+            //     get_dll, r_guess, 0.0, total_kmers, 30
+            // ) * nelem / (nelem - 1);
+
+            // long unsigned int max_iter = 100;
+            // auto [r_min,r_max] = boost::math::tools::bracket_and_solve_root(
+            //     get_dll, r_guess, 2.0, false, [&](double r_min, double r_max) {
+            //         return abs(r_min - r_max) / r_max < 1e-5;
+            //     },
+            //     max_iter
+            // );
+            // double a = (r_min + r_max) / 2.0 * nelem / (nelem - 1);
 
+            double a = r_guess * nelem / (nelem - 1);
             double b = (nelem - 1) * a;
 
             common::logger->trace("mean: {}\tvar: {}\ta: {}\tb: {}\texp: {}\tvar: {}",

From 52651377c86a6d77ef7a2575c3bb456627ee91ec Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 12 May 2024 11:14:42 +0200
Subject: [PATCH 135/410] check

---
 .../src/graph/annotated_graph_algorithm.cpp   | 99 ++++---------------
 1 file changed, 19 insertions(+), 80 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index d7dacc6ad9..48abd79d6e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -237,8 +237,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (!ignored[row_i])
                 return;
 
-            if (uint64_t c = adjust_count(column_values[i], j, row_i)) {
-                vals.emplace_back(c);
+            uint64_t c = adjust_count(column_values[i], j, row_i);
+            vals.emplace_back(c);
+            if (c) {
                 sum += c;
                 sum_of_squares += c * c;
                 ++n;
@@ -517,97 +518,35 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "bp" || config.test_type == "lnp") {
-        double sum_of_squares = 0;
-        double sum_of_squares_in = 0;
-        double sum_of_squares_out = 0;
-        std::vector<std::pair<double, double>> counts;
-        counts.reserve(nelem);
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         PairContainer>(columns_all, column_values_all,
-                                        [&](uint64_t row_i, const auto &row) {
-            if (ignored[row_i])
-                return;
-
-            double sum_in = 0;
-            double sum_out = 0;
-            for (const auto &[j, raw_c] : row) {
-                if (uint64_t c = adjust_count(raw_c, j, row_i)) {
-                    if (groups[j]) {
-                        sum_in += c;
-                    } else {
-                        sum_out += c;
-                    }
-                }
-            }
-
-            if (sum_in + sum_out > 0) {
-                counts.emplace_back(sum_in, sum_out);
-                sum_of_squares_in += sum_in * sum_in;
-                sum_of_squares_out += sum_out * sum_out;
-                sum_of_squares += pow(sum_in + sum_out, 2.0);
-            }
-        });
-
-        auto get_a = [&](double k, double k2, uint8_t pick = 0) {
-            double mu = k / nelem;
+        auto get_a = [&](double n, double k, double k2, uint8_t pick = 0) {
+            double mu = k / n;
             // model with a negative binomial, similar to
             // https://github.com/JBHilton/beta-poisson-epidemics/blob/master/functions.py#L140
-            auto get_dll = [&](double r) {
-                double dll = nelem * (log(r) - log(r + mu)) - nelem * boost::math::digamma(r);
-                double ddll = nelem / r - nelem / (r + mu) - nelem * boost::math::trigamma(r);
-                for (const auto &[c_in, c_out] : counts) {
-                    double c;
-                    switch (pick) {
-                        case 1: { c = c_in; } break;
-                        case 2: { c = c_out; } break;
-                        default: { c = c_in + c_out; } break;
-                    }
-
-                    dll += boost::math::digamma(r + c);
-                    ddll += boost::math::trigamma(r + c);
-                }
-
-                return std::make_pair(dll, ddll);
-            };
-
-            double var = k2 / nelem - mu * mu;
+            double var = k2 / n - mu * mu;
             double r_guess = mu * mu / (var - mu);
 
-            // double a = boost::math::tools::newton_raphson_iterate(
-            //     get_dll, r_guess, 0.0, total_kmers, 30
-            // ) * nelem / (nelem - 1);
-
-            // long unsigned int max_iter = 100;
-            // auto [r_min,r_max] = boost::math::tools::bracket_and_solve_root(
-            //     get_dll, r_guess, 2.0, false, [&](double r_min, double r_max) {
-            //         return abs(r_min - r_max) / r_max < 1e-5;
-            //     },
-            //     max_iter
-            // );
-            // double a = (r_min + r_max) / 2.0 * nelem / (nelem - 1);
-
-            double a = r_guess * nelem / (nelem - 1);
-            double b = (nelem - 1) * a;
+            double a = r_guess * n / (n - 1);
+            double b = (n - 1) * a;
 
             common::logger->trace("mean: {}\tvar: {}\ta: {}\tb: {}\texp: {}\tvar: {}",
-                                  mu, k2 / nelem - mu * mu,
+                                  mu, var,
                                   a, b,
                                   k * a / (a + b),
-                                  mu + mu * mu * (nelem - 1) / (nelem * a + 1));
+                                  mu + mu * mu * (n - 1) / (n * a + 1));
 
             return std::make_pair(a, b);
         };
 
-        auto [a, b] = get_a(total_kmers, sum_of_squares);
-
-        if (config.test_type == "bp" && a <= 1) {
-            common::logger->error("a <= 1");
-            throw std::runtime_error("Fit failed");
-        }
+        uint64_t total_sq_kmers = std::accumulate(sums_of_squares.begin(),
+                                                  sums_of_squares.end(),
+                                                  uint64_t(0));
+        common::logger->trace("Sum: {}\tSum of squares: {}", total_kmers, total_sq_kmers);
+        auto [a, b] = get_a(nelem * groups.size(), total_kmers, total_sq_kmers);
 
-        auto [a_in, b_in] = get_a(in_kmers, sum_of_squares_in, 1);
-        auto [a_out, b_out] = get_a(out_kmers, sum_of_squares_out, 2);
+        double a_in = a;
+        double a_out = a;
+        double b_in = b;
+        double b_out = b;
 
         double mu = 0;
         double mu_in = 0;

From 774d66f1a616a92216b4b264da51058c815a5bc3 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 12 May 2024 20:50:41 +0200
Subject: [PATCH 136/410] fix

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 48abd79d6e..a17dfc5e36 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -238,8 +238,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return;
 
             uint64_t c = adjust_count(column_values[i], j, row_i);
-            vals.emplace_back(c);
             if (c) {
+                vals.emplace_back(c);
                 sum += c;
                 sum_of_squares += c * c;
                 ++n;

From 4e6486a8f60863613d20f36ecd3b2b30e01b1588 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 13 May 2024 19:02:13 +0200
Subject: [PATCH 137/410] glm_try1

---
 .gitmodules                                   |   2 +-
 metagraph/CMakeListsHelpers.txt               |  48 +--
 metagraph/external-libraries/eigen            |   2 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 309 ++++++++++++++----
 4 files changed, 269 insertions(+), 92 deletions(-)

diff --git a/.gitmodules b/.gitmodules
index 4281e365f7..f058dbe7ce 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -13,7 +13,7 @@
 	url = https://github.com/karasikov/caches.git
 [submodule "metagraph/external-libraries/eigen"]
 	path = metagraph/external-libraries/eigen
-	url = https://github.com/eigenteam/eigen-git-mirror.git
+	url = https://gitlab.com/libeigen/eigen.git
 [submodule "metagraph/external-libraries/KMC"]
 	path = metagraph/external-libraries/KMC
 	url = https://github.com/karasikov/KMC.git
diff --git a/metagraph/CMakeListsHelpers.txt b/metagraph/CMakeListsHelpers.txt
index 973021c5b4..fb955bd82c 100644
--- a/metagraph/CMakeListsHelpers.txt
+++ b/metagraph/CMakeListsHelpers.txt
@@ -3,30 +3,30 @@
 
 
 function (findOpenMP)
-# unset OpenMP_CXX_FLAGS. If OpenMP loading fails once, it will be set to NOT_FOUND
-# and happily cached, thus messing up the compilation flags forever
-unset(OpenMP_C_FLAGS CACHE)
-unset(OpenMP_CXX_FLAGS CACHE)
-if(APPLE AND NOT CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
-  execute_process(
-      COMMAND brew --prefix libomp
-      OUTPUT_VARIABLE BREW_OMP_PREFIX
-      OUTPUT_STRIP_TRAILING_WHITESPACE
-  )
-  set(OpenMP_C_FLAGS "-Xclang -fopenmp -I${BREW_OMP_PREFIX}/include")
-  set(OpenMP_CXX_FLAGS "-Xclang -fopenmp -I${BREW_OMP_PREFIX}/include")
-  set(OpenMP_C_LIB_NAMES libomp)
-  set(OpenMP_CXX_LIB_NAMES libomp)
-  set(OpenMP_libomp_LIBRARY ${BREW_OMP_PREFIX}/lib/libomp.dylib)
-endif()
-find_package(OpenMP REQUIRED)
-set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}" PARENT_SCOPE)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}" PARENT_SCOPE)
-set(OpenMP_CXX_LIBRARIES "${OpenMP_CXX_LIBRARIES}" PARENT_SCOPE)
-set(OpenMP_CXX_VERSION "${OpenMP_CXX_VERSION}" PARENT_SCOPE)
-if(CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
-set(OpenMP_CXX_LIBRARIES ${OpenMP_CXX_LIBRARIES} -latomic PARENT_SCOPE)
-endif()
+    # unset OpenMP_CXX_FLAGS. If OpenMP loading fails once, it will be set to NOT_FOUND
+    # and happily cached, thus messing up the compilation flags forever
+    unset(OpenMP_C_FLAGS CACHE)
+    unset(OpenMP_CXX_FLAGS CACHE)
+    if(APPLE AND NOT CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+        execute_process(
+            COMMAND brew --prefix libomp
+            OUTPUT_VARIABLE BREW_OMP_PREFIX
+            OUTPUT_STRIP_TRAILING_WHITESPACE
+        )
+        set(OpenMP_C_FLAGS "-Xclang -fopenmp -I${BREW_OMP_PREFIX}/include")
+        set(OpenMP_CXX_FLAGS "-Xclang -fopenmp -I${BREW_OMP_PREFIX}/include")
+        set(OpenMP_C_LIB_NAMES libomp)
+        set(OpenMP_CXX_LIB_NAMES libomp)
+        set(OpenMP_libomp_LIBRARY ${BREW_OMP_PREFIX}/lib/libomp.dylib)
+    endif()
+    find_package(OpenMP REQUIRED)
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}" PARENT_SCOPE)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}" PARENT_SCOPE)
+    set(OpenMP_CXX_LIBRARIES "${OpenMP_CXX_LIBRARIES}" PARENT_SCOPE)
+    set(OpenMP_CXX_VERSION "${OpenMP_CXX_VERSION}" PARENT_SCOPE)
+    if(CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+        set(OpenMP_CXX_LIBRARIES ${OpenMP_CXX_LIBRARIES} -latomic PARENT_SCOPE)
+    endif()
 endfunction()
 
 # check for std::filesystem::temp_directory_path
diff --git a/metagraph/external-libraries/eigen b/metagraph/external-libraries/eigen
index 36b9596275..3147391d94 160000
--- a/metagraph/external-libraries/eigen
+++ b/metagraph/external-libraries/eigen
@@ -1 +1 @@
-Subproject commit 36b95962756c1fce8e29b1f8bc45967f30773c00
+Subproject commit 3147391d946bb4b6c68edd901f2add6ac1f31f8c
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a17dfc5e36..567ae3f617 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -12,6 +12,10 @@
 #include <boost/math/special_functions/trigamma.hpp>
 #include <boost/math/tools/minima.hpp>
 #include <boost/math/tools/roots.hpp>
+#include <boost/math/distributions/fisher_f.hpp>
+
+#include <Eigen/Dense>
+#include <unsupported/Eigen/MatrixFunctions>
 
 #include "common/utils/string_utils.hpp"
 #include "common/logger.hpp"
@@ -588,78 +592,251 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double map_pval_out = 1.0;
 
             if (config.test_type == "bp") {
-                auto get_theta = [&](double a, double b, double total, double k_sum) {
-                    if (a + k_sum <= 1)
-                        return std::make_tuple(0.0, 0.0, true, false);
-
-                    double qb = 2.0 - total - k_sum - a - b;
-                    double qc = k_sum + a - 1.0;
-
-                    auto [theta_low, theta_high] = boost::math::tools::quadratic_roots(total, qb, qc);
-
-                    // Note: comparisons of thetas to themselves ensure that they are not NaN
-                    // https://stackoverflow.com/questions/570669/checking-if-a-double-or-float-is-nan-in-c
-                    bool low_valid = theta_low == theta_low && theta_low > 0 && theta_low < 1.0;
-                    bool high_valid = theta_high == theta_high && theta_low != theta_high && theta_high > 0 && theta_high < 1.0;
-
-                    double stat_low = low_valid
-                        ? qc * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low
-                        : -std::numeric_limits<double>::infinity();
-                    double stat_high = high_valid
-                        ? qc * log(theta_high) + (b - 1) * log1p(-theta_high) - total * theta_high
-                        : -std::numeric_limits<double>::infinity();
-
-                    double stat_max = std::max({ stat_low, stat_high });
-
-                    bool stat_is_low = low_valid && stat_max == stat_low;
-                    bool stat_is_high = high_valid && stat_max == stat_high;
-
-                    if (stat_is_low && stat_is_high) {
-                        common::logger->error("Multiple valid thetas\t{}", stat_max);
-                        common::logger->error("{}\t{}", theta_low, theta_high);
-                        common::logger->error("{}\t{}", stat_low, stat_high);
-                        common::logger->error("{}\t{}", stat_is_low, stat_is_high);
-                        common::logger->error("Ratio: {} / {}", k_sum, total);
-                        common::logger->error("{},{}\t{},{},{}", a,b, total, qb, qc);
-                        throw std::runtime_error("Fail");
+                // fit to a GLM
+                auto get_glm = [&](double a, double b, size_t m, const auto &picker) -> double {
+                    typedef Eigen::Matrix<long double, Eigen::Dynamic, 2> MatrixRd;
+                    typedef Eigen::Matrix<long double, Eigen::Dynamic, 1> Vectord;
+                    typedef Eigen::Matrix<long double, 2, 1> VectorS;
+                    typedef Eigen::Matrix<long double, 2, 2> MatrixS;
+                    typedef Eigen::DiagonalMatrix<long double, Eigen::Dynamic> Diag;
+
+                    MatrixRd A(m, 2);
+                    A.col(0).setOnes();
+                    Vectord totals(m);
+                    size_t im = 0;
+                    for (size_t j = 0; j < groups.size(); ++j) {
+                        if (picker(j)) {
+                            totals(im, 0) = sums[j];
+                            ++im;
+                        }
                     }
+                    assert(im == m);
 
-                    if (stat_is_low || stat_is_high)
-                        return std::make_tuple(theta_low, theta_high, stat_is_low, stat_is_high);
+                    Vectord y(m);
+                    y.array() = totals.array() * a / (a + b);
 
-                    common::logger->error("No valid thetas\t{}", stat_max);
-                    common::logger->error("{}\t{}", theta_low, theta_high);
-                    common::logger->error("{}\t{}", stat_low, stat_high);
-                    common::logger->error("{}\t{}", stat_is_low, stat_is_high);
-                    common::logger->error("Ratio: {} / {}", k_sum, total);
-                    common::logger->error("{},{}\t{},{},{}", a,b,total, qb, qc);
-                    throw std::runtime_error("Fail");
-                };
+                    size_t i = 0;
+                    for (const auto &[j, c] : row) {
+                        if (picker(j)) {
+                            A(i, 1) = c;
+                            ++i;
+                        }
+                    }
+
+                    VectorS beta = { 0, 0 };
+                    Vectord nu(m, 1);
+                    Vectord enu(m, 1);
+
+                    // if all elements are equal (with an easier check if they are all zero)
+                    if (!i || std::equal(A.col(1).begin(), A.col(1).end() - 1, A.col(1).begin() + 1)) {
+                        Vectord c(m);
+                        c.noalias() = totals - y;
+                        c.array() /= a + b + 1;
+                        c.array() += 1.0;
+                        c.array() = y.array() / c.array();
+
+                        double num = y.transpose() * c;
+                        double denom = c.sum();
+
+                        beta(0, 0) = log(num) - log(denom);
+                        nu.noalias() = A * beta;
+                        enu.array() = Eigen::exp(nu.array());
+                    } else {
+                        VectorS beta_next;
+                        Vectord var(m, 1);
+                        Vectord z(m, 1);
+
+                        MatrixS scale;
+
+                        Diag W(m);
+
+                        for (size_t i = 0; i < 100; ++i) {
+                            nu.noalias() = A * beta;
+
+                            // TODO: for some reason, this is fine, but nu.exp() segfaults
+                            enu.array() = Eigen::exp(nu.array());
+
+                            z.noalias() = beta - nu;
+                            z.array() /= enu.array();
+                            z.noalias() += nu;
+
+                            // var.noalias() = totals - enu;
+                            // var.array() /= a + b + 1;
+                            // var.array() += 1.0;
+                            // var.array() *= enu.array();
+
+                            // W.diagonal().noalias() = enu.pow(2.0);
+                            // W.diagonal().array() /= var.array();
+
+                            // compute in log space to avoid underflow
+                            var.array() = Eigen::log((totals - enu).array());
+                            var.array() -= log(a + b + 1);
+                            var.array() += Eigen::log(enu.array());
+                            var.array() = Eigen::exp(var.array());
+                            var.noalias() += enu;
+
+                            W.diagonal().noalias() = nu;
+                            W.diagonal().array() *= 2.0;
+                            W.diagonal().array() -= Eigen::log(var.array());
+                            W.diagonal().array() = Eigen::exp(W.diagonal().array());
+
+                            scale.noalias() = A.transpose() * W * A; // nxm * mxm * mxn -> nxn
+                            if (scale != scale) {
+                                common::logger->error("Scale fail");
+                                std::cerr << A << "\n\n";
+                                std::cerr << totals << "\n\n";
+                                std::cerr << y << "\n\n";
+                                std::cerr << nu << "\n\n";
+                                std::cerr << enu << "\n\n";
+                                std::cerr << z << "\n\n";
+                                std::cerr << var << "\n\n";
+                                std::cerr << W.diagonal() << "\n\n";
+                                std::cerr << scale << std::endl;
+                                throw std::runtime_error("Fail");
+                            }
+                            beta_next.noalias() = scale.inverse() * A.transpose() * W * z; // nxn * nxm * mxm * mx1 -> nx1
+                            if (beta_next != beta_next) {
+                                common::logger->error("beta fail");
+                                std::cerr << A << "\n\n";
+                                std::cerr << totals << "\n\n";
+                                std::cerr << y << "\n\n";
+                                std::cerr << nu << "\n\n";
+                                std::cerr << enu << "\n\n";
+                                std::cerr << z << "\n\n";
+                                std::cerr << var << "\n\n";
+                                std::cerr << W.diagonal() << "\n\n";
+                                std::cerr << scale << "\n\n";
+                                std::cerr << scale.inverse() << "\n\n";
+                                std::cerr << beta_next << std::endl;
+                                throw std::runtime_error("Fail");
+                            }
+
+                            std::swap(beta, beta_next);
+                            beta_next.noalias() -= beta;
 
-                auto get_stat_log = [&](double a, double b, double theta, double k, double total) -> double {
-                    return (k + a - 1) * log(theta) + (b - 1) * log1p(-theta) - total * theta;
+                            if (beta_next.norm() < 1e-5)
+                                break;
+                        }
+                    }
+
+                    enu -= y;
+                    return enu.squaredNorm();
                 };
 
-                auto [theta_null_low, theta_null_high, null_is_low, null_is_high]
-                    = get_theta(a, b, total_kmers, null_sum);
-                auto [theta_in_low, theta_in_high, in_is_low, in_is_high]
-                    = get_theta(a_in, b_in, in_kmers, in_sum);
-                auto [theta_out_low, theta_out_high, out_is_low, out_is_high]
-                    = get_theta(a_out, b_out, out_kmers, out_sum);
-
-                double theta_null = null_is_low ? theta_null_low : theta_null_high;
-                double theta_in = in_is_low ? theta_in_low : theta_in_high;
-                double theta_out = out_is_low ? theta_out_low : theta_out_high;
-
-                map_pval_in = theta_in > 0
-                    ? exp(get_stat_log(a_in, b_in, theta_null, in_sum, in_kmers)
-                                - get_stat_log(a_in, b_in, theta_in, in_sum, in_kmers))
-                    : 0.0;
-
-                map_pval_out = theta_out > 0
-                    ? exp(get_stat_log(a_out, b_out, theta_null, out_sum, out_kmers)
-                                - get_stat_log(a_out, b_out, theta_out, out_sum, out_kmers))
-                    : 0.0;
+                int64_t df_null = groups.size() - 2;
+                int64_t df_in = labels_in.size() - 2;
+                int64_t df_out = labels_in.size() - 2;
+
+                double sse_in = get_glm(a, b, labels_in.size(), [&](size_t j) { return !groups[j]; });
+                double sse_out = get_glm(a, b, labels_out.size(), [&](size_t j) { return groups[j]; });
+                double sse_null = get_glm(a, b, groups.size(), [&](size_t) { return true; });
+
+                double dsse_in = sse_null - sse_in;
+                double dsse_out = sse_null - sse_out;
+
+                if (dsse_in <= 0) {
+                    map_pval_in = 1.0;
+                } else {
+                    double f = dsse_in / sse_in * (df_null - df_in) / df_in;
+                    map_pval_in = boost::math::cdf(boost::math::complement(boost::math::fisher_f(df_null, df_in), f));
+                }
+
+                if (dsse_out <= 0) {
+                    map_pval_out = 1.0;
+                } else {
+                    double f = dsse_out / sse_out * (df_null - df_out) / df_out;
+                    map_pval_out = boost::math::cdf(boost::math::complement(boost::math::fisher_f(df_null, df_out), f));
+                }
+
+                // double sse_alt = sse_in + sse_out;
+                // double dsse = sse_null - sse_alt;
+                // if (dsse <= 0) {
+                //     map_pval_in = 0.5;
+                //     map_pval_out = 0.5;
+                // } else {
+                //     double f = dsse / sse_alt * d_df / df_alt;
+
+                //     if (f != f) {
+                //         common::logger->error("sse_in: {}\tsse_out: {}\tsse_null: {}",
+                //                             sse_in, sse_out, sse_alt);
+                //     }
+                //     map_pval_in = boost::math::cdf(boost::math::complement(boost::math::fisher_f(df_null, df_alt), f)) / 2;
+                //     map_pval_out = map_pval_out;
+                // }
+
+
+                // auto get_theta = [&](double a, double b, double total, double k_sum) {
+                //     if (a + k_sum <= 1)
+                //         return std::make_tuple(0.0, 0.0, true, false);
+
+                //     double qb = 2.0 - total - k_sum - a - b;
+                //     double qc = k_sum + a - 1.0;
+
+                //     auto [theta_low, theta_high] = boost::math::tools::quadratic_roots(total, qb, qc);
+
+                //     // Note: comparisons of thetas to themselves ensure that they are not NaN
+                //     // https://stackoverflow.com/questions/570669/checking-if-a-double-or-float-is-nan-in-c
+                //     bool low_valid = theta_low == theta_low && theta_low > 0 && theta_low < 1.0;
+                //     bool high_valid = theta_high == theta_high && theta_low != theta_high && theta_high > 0 && theta_high < 1.0;
+
+                //     double stat_low = low_valid
+                //         ? qc * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low
+                //         : -std::numeric_limits<double>::infinity();
+                //     double stat_high = high_valid
+                //         ? qc * log(theta_high) + (b - 1) * log1p(-theta_high) - total * theta_high
+                //         : -std::numeric_limits<double>::infinity();
+
+                //     double stat_max = std::max({ stat_low, stat_high });
+
+                //     bool stat_is_low = low_valid && stat_max == stat_low;
+                //     bool stat_is_high = high_valid && stat_max == stat_high;
+
+                //     if (stat_is_low && stat_is_high) {
+                //         common::logger->error("Multiple valid thetas\t{}", stat_max);
+                //         common::logger->error("{}\t{}", theta_low, theta_high);
+                //         common::logger->error("{}\t{}", stat_low, stat_high);
+                //         common::logger->error("{}\t{}", stat_is_low, stat_is_high);
+                //         common::logger->error("Ratio: {} / {}", k_sum, total);
+                //         common::logger->error("{},{}\t{},{},{}", a,b, total, qb, qc);
+                //         throw std::runtime_error("Fail");
+                //     }
+
+                //     if (stat_is_low || stat_is_high)
+                //         return std::make_tuple(theta_low, theta_high, stat_is_low, stat_is_high);
+
+                //     common::logger->error("No valid thetas\t{}", stat_max);
+                //     common::logger->error("{}\t{}", theta_low, theta_high);
+                //     common::logger->error("{}\t{}", stat_low, stat_high);
+                //     common::logger->error("{}\t{}", stat_is_low, stat_is_high);
+                //     common::logger->error("Ratio: {} / {}", k_sum, total);
+                //     common::logger->error("{},{}\t{},{},{}", a,b,total, qb, qc);
+                //     throw std::runtime_error("Fail");
+                // };
+
+                // auto get_stat_log = [&](double a, double b, double theta, double k, double total) -> double {
+                //     return (k + a - 1) * log(theta) + (b - 1) * log1p(-theta) - total * theta;
+                // };
+
+                // auto [theta_null_low, theta_null_high, null_is_low, null_is_high]
+                //     = get_theta(a, b, total_kmers, null_sum);
+                // auto [theta_in_low, theta_in_high, in_is_low, in_is_high]
+                //     = get_theta(a_in, b_in, in_kmers, in_sum);
+                // auto [theta_out_low, theta_out_high, out_is_low, out_is_high]
+                //     = get_theta(a_out, b_out, out_kmers, out_sum);
+
+                // double theta_null = null_is_low ? theta_null_low : theta_null_high;
+                // double theta_in = in_is_low ? theta_in_low : theta_in_high;
+                // double theta_out = out_is_low ? theta_out_low : theta_out_high;
+
+                // map_pval_in = theta_in > 0
+                //     ? exp(get_stat_log(a_in, b_in, theta_null, in_sum, in_kmers)
+                //                 - get_stat_log(a_in, b_in, theta_in, in_sum, in_kmers))
+                //     : 0.0;
+
+                // map_pval_out = theta_out > 0
+                //     ? exp(get_stat_log(a_out, b_out, theta_null, out_sum, out_kmers)
+                //                 - get_stat_log(a_out, b_out, theta_out, out_sum, out_kmers))
+                //     : 0.0;
             } else if (config.test_type == "lnp") {
                 auto get_theta = [&](double mu, double var, double total, double k_sum) {
                     size_t ndigits = 40;

From 008e9abc922f5fa2a05354218d0ada41bfbfeb0b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 13 May 2024 19:07:34 +0200
Subject: [PATCH 138/410] fix

---
 .../src/graph/annotated_graph_algorithm.cpp   | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 567ae3f617..c04380b16c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -600,12 +600,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     typedef Eigen::Matrix<long double, 2, 2> MatrixS;
                     typedef Eigen::DiagonalMatrix<long double, Eigen::Dynamic> Diag;
 
+                    std::vector<uint64_t> k(groups.size());
+                    for (const auto &[j, c] : row) {
+                        k[j] = c;
+                    }
+
                     MatrixRd A(m, 2);
                     A.col(0).setOnes();
+                    A.col(1).setZero();
                     Vectord totals(m);
                     size_t im = 0;
                     for (size_t j = 0; j < groups.size(); ++j) {
                         if (picker(j)) {
+                            A(im, 1) = k[j];
                             totals(im, 0) = sums[j];
                             ++im;
                         }
@@ -615,20 +622,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     Vectord y(m);
                     y.array() = totals.array() * a / (a + b);
 
-                    size_t i = 0;
-                    for (const auto &[j, c] : row) {
-                        if (picker(j)) {
-                            A(i, 1) = c;
-                            ++i;
-                        }
-                    }
-
                     VectorS beta = { 0, 0 };
                     Vectord nu(m, 1);
                     Vectord enu(m, 1);
 
-                    // if all elements are equal (with an easier check if they are all zero)
-                    if (!i || std::equal(A.col(1).begin(), A.col(1).end() - 1, A.col(1).begin() + 1)) {
+                    // if all elements are equal
+                    if (std::equal(A.col(1).begin(), A.col(1).end() - 1, A.col(1).begin() + 1)) {
                         Vectord c(m);
                         c.noalias() = totals - y;
                         c.array() /= a + b + 1;

From 8feb4ade07a39463b0d8133e634af209b267a024 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 14 May 2024 04:17:24 +0200
Subject: [PATCH 139/410] t

---
 .../src/graph/annotated_graph_algorithm.cpp   | 171 +++++++++++++-----
 1 file changed, 129 insertions(+), 42 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c04380b16c..a67ae278f4 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -593,7 +593,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             if (config.test_type == "bp") {
                 // fit to a GLM
-                auto get_glm = [&](double a, double b, size_t m, const auto &picker) -> double {
+                auto get_glm_joint = [&](double a, double b, size_t m, const auto &picker) -> double {
                     typedef Eigen::Matrix<long double, Eigen::Dynamic, 2> MatrixRd;
                     typedef Eigen::Matrix<long double, Eigen::Dynamic, 1> Vectord;
                     typedef Eigen::Matrix<long double, 2, 1> VectorS;
@@ -609,15 +609,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     A.col(0).setOnes();
                     A.col(1).setZero();
                     Vectord totals(m);
-                    size_t im = 0;
                     for (size_t j = 0; j < groups.size(); ++j) {
-                        if (picker(j)) {
-                            A(im, 1) = k[j];
-                            totals(im, 0) = sums[j];
-                            ++im;
-                        }
+                        if (picker(j))
+                            A(j, 1) = k[j];
+
+                        totals(j, 0) = sums[j];
                     }
-                    assert(im == m);
 
                     Vectord y(m);
                     y.array() = totals.array() * a / (a + b);
@@ -722,47 +719,137 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return enu.squaredNorm();
                 };
 
-                int64_t df_null = groups.size() - 2;
-                int64_t df_in = labels_in.size() - 2;
-                int64_t df_out = labels_in.size() - 2;
+                auto get_glm_sep = [&](double a, double b, size_t m) -> double {
+                    typedef Eigen::Matrix<long double, Eigen::Dynamic, 3> MatrixRd;
+                    typedef Eigen::Matrix<long double, Eigen::Dynamic, 1> Vectord;
+                    typedef Eigen::Matrix<long double, 3, 1> VectorS;
+                    typedef Eigen::Matrix<long double, 3, 3> MatrixS;
+                    typedef Eigen::DiagonalMatrix<long double, Eigen::Dynamic> Diag;
 
-                double sse_in = get_glm(a, b, labels_in.size(), [&](size_t j) { return !groups[j]; });
-                double sse_out = get_glm(a, b, labels_out.size(), [&](size_t j) { return groups[j]; });
-                double sse_null = get_glm(a, b, groups.size(), [&](size_t) { return true; });
+                    std::vector<uint64_t> k(groups.size());
+                    for (const auto &[j, c] : row) {
+                        k[j] = c;
+                    }
 
-                double dsse_in = sse_null - sse_in;
-                double dsse_out = sse_null - sse_out;
+                    MatrixRd A(m, 3);
+                    A.col(0).setOnes();
+                    A.col(1).setZero();
+                    A.col(2).setZero();
+                    Vectord totals(m);
+                    for (size_t j = 0; j < groups.size(); ++j) {
+                        A(j, 1 + groups[j]) = k[j];
+                        totals(j, 0) = sums[j];
+                    }
 
-                if (dsse_in <= 0) {
-                    map_pval_in = 1.0;
-                } else {
-                    double f = dsse_in / sse_in * (df_null - df_in) / df_in;
-                    map_pval_in = boost::math::cdf(boost::math::complement(boost::math::fisher_f(df_null, df_in), f));
-                }
+                    Vectord y(m);
+                    y.array() = totals.array() * a / (a + b);
+
+                    VectorS beta = { 0, 0, 0 };
+                    Vectord nu(m, 1);
+                    Vectord enu(m, 1);
+
+                    // if all elements are equal
+                    if (std::equal(A.col(1).begin(), A.col(1).end() - 1, A.col(1).begin() + 1)) {
+                        return get_glm_joint(a, b, m, [&](size_t j) { return !groups[j]; });
+                    } else if (std::equal(A.col(2).begin(), A.col(2).end() - 1, A.col(2).begin() + 1)) {
+                        return get_glm_joint(a, b, m, [&](size_t j) { return groups[j]; });
+                    } else {
+                        VectorS beta_next;
+                        Vectord var(m, 1);
+                        Vectord z(m, 1);
 
-                if (dsse_out <= 0) {
-                    map_pval_out = 1.0;
+                        MatrixS scale;
+
+                        Diag W(m);
+
+                        for (size_t i = 0; i < 100; ++i) {
+                            nu.noalias() = A * beta;
+
+                            // TODO: for some reason, this is fine, but nu.exp() segfaults
+                            enu.array() = Eigen::exp(nu.array());
+
+                            z.noalias() = beta - nu;
+                            z.array() /= enu.array();
+                            z.noalias() += nu;
+
+                            // var.noalias() = totals - enu;
+                            // var.array() /= a + b + 1;
+                            // var.array() += 1.0;
+                            // var.array() *= enu.array();
+
+                            // W.diagonal().noalias() = enu.pow(2.0);
+                            // W.diagonal().array() /= var.array();
+
+                            // compute in log space to avoid underflow
+                            var.array() = Eigen::log((totals - enu).array());
+                            var.array() -= log(a + b + 1);
+                            var.array() += Eigen::log(enu.array());
+                            var.array() = Eigen::exp(var.array());
+                            var.noalias() += enu;
+
+                            W.diagonal().noalias() = nu;
+                            W.diagonal().array() *= 2.0;
+                            W.diagonal().array() -= Eigen::log(var.array());
+                            W.diagonal().array() = Eigen::exp(W.diagonal().array());
+
+                            scale.noalias() = A.transpose() * W * A; // nxm * mxm * mxn -> nxn
+                            if (scale != scale) {
+                                common::logger->error("Scale fail");
+                                std::cerr << A << "\n\n";
+                                std::cerr << totals << "\n\n";
+                                std::cerr << y << "\n\n";
+                                std::cerr << nu << "\n\n";
+                                std::cerr << enu << "\n\n";
+                                std::cerr << z << "\n\n";
+                                std::cerr << var << "\n\n";
+                                std::cerr << W.diagonal() << "\n\n";
+                                std::cerr << scale << std::endl;
+                                throw std::runtime_error("Fail");
+                            }
+                            beta_next.noalias() = scale.inverse() * A.transpose() * W * z; // nxn * nxm * mxm * mx1 -> nx1
+                            if (beta_next != beta_next) {
+                                common::logger->error("beta fail");
+                                std::cerr << A << "\n\n";
+                                std::cerr << totals << "\n\n";
+                                std::cerr << y << "\n\n";
+                                std::cerr << nu << "\n\n";
+                                std::cerr << enu << "\n\n";
+                                std::cerr << z << "\n\n";
+                                std::cerr << var << "\n\n";
+                                std::cerr << W.diagonal() << "\n\n";
+                                std::cerr << scale << "\n\n";
+                                std::cerr << scale.inverse() << "\n\n";
+                                std::cerr << beta_next << std::endl;
+                                throw std::runtime_error("Fail");
+                            }
+
+                            std::swap(beta, beta_next);
+                            beta_next.noalias() -= beta;
+
+                            if (beta_next.norm() < 1e-5)
+                                break;
+                        }
+                    }
+
+                    enu -= y;
+                    return enu.squaredNorm();
+                };
+
+                int64_t df_alt = groups.size() - 3;
+
+                double sse_alt = get_glm_sep(a, b, groups.size());
+                double sse_null = get_glm_joint(a, b, groups.size(), [&](size_t) { return true; });
+
+                double dsse_alt = sse_null - sse_alt;
+
+                if (dsse_alt <= 0) {
+                    map_pval_in = 1.0;
                 } else {
-                    double f = dsse_out / sse_out * (df_null - df_out) / df_out;
-                    map_pval_out = boost::math::cdf(boost::math::complement(boost::math::fisher_f(df_null, df_out), f));
+                    double f = dsse_alt / sse_alt * df_alt;
+                    map_pval_in = boost::math::cdf(boost::math::complement(boost::math::fisher_f(1, df_alt), f)) / 2;
+                    map_pval_out = map_pval_in;
                 }
 
-                // double sse_alt = sse_in + sse_out;
-                // double dsse = sse_null - sse_alt;
-                // if (dsse <= 0) {
-                //     map_pval_in = 0.5;
-                //     map_pval_out = 0.5;
-                // } else {
-                //     double f = dsse / sse_alt * d_df / df_alt;
-
-                //     if (f != f) {
-                //         common::logger->error("sse_in: {}\tsse_out: {}\tsse_null: {}",
-                //                             sse_in, sse_out, sse_alt);
-                //     }
-                //     map_pval_in = boost::math::cdf(boost::math::complement(boost::math::fisher_f(df_null, df_alt), f)) / 2;
-                //     map_pval_out = map_pval_out;
-                // }
-
 
                 // auto get_theta = [&](double a, double b, double total, double k_sum) {
                 //     if (a + k_sum <= 1)

From 3969ab8e275d5862ce06449a7c0af0e286cfd10e Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 20 May 2024 23:16:27 +0200
Subject: [PATCH 140/410] nbinom pval

---
 .../src/graph/annotated_graph_algorithm.cpp   | 428 ++++++++++++------
 1 file changed, 280 insertions(+), 148 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a67ae278f4..a0450eca72 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -349,175 +349,307 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "nbinom") {
-        // use moments to fit the r parameter of a beta negative binomial
-        uint64_t sum_of_cubes = 0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            const auto &column = *columns_all[j];
-            const auto &column_values = *column_values_all[j];
-            size_t i = 0;
-            column.call_ones([&](uint64_t row_i) {
-                if (!ignored[row_i]) {
-                    if (int64_t c = adjust_count(column_values[i], j, row_i))
-                        sum_of_cubes += c * (c - 1) * (c - 2);
-                }
-                ++i;
-            });
-        }
-        double mu = static_cast<double>(total_kmers) / groups.size() / nelem;
-        double mu2 = static_cast<double>(std::accumulate(sums_of_squares.begin(),
-                                                         sums_of_squares.end(), uint64_t(0))) / groups.size() / nelem - mu;
-        double mu3 = static_cast<double>(sum_of_cubes) / groups.size() / nelem;
-        double a = mu3 * mu + mu * mu * mu2 + 2 * mu2 * mu2;
-        double b = 2 * mu3 * mu * mu + mu3 * mu - mu2 * mu3 - mu * mu2 * mu2 + 3 * mu * mu + mu2 - 4 * mu2 * mu2;
-        double c = -2 * mu * (mu2 * mu2 - mu * mu2 - mu3 * mu);
-
-        auto [r_low, r_high] = boost::math::tools::quadratic_roots(a, b, c);
-
-        common::logger->trace("mu: {}\tmu2: {}\tmu3: {}\tr_low: {}\tr_high: {}",
-                              mu, mu2, mu3, r_low, r_high);
-
-        // check for NaNs
-        // https://stackoverflow.com/questions/570669/checking-if-a-double-or-float-is-nan-in-c
-        if (r_low != r_low && r_high != r_high) {
-            common::logger->error("{},{},{}", a, b, c);
-            throw std::runtime_error("Failed to fit, no real roots");
-        }
-
-        double r_low_var_sqerr = 0;
-        double r_high_var_sqerr = 0;
+        double in_num = 0;
+        double out_num = 0;
+        double in_denom = 0;
+        double out_denom = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
             double mu = static_cast<double>(sums[j]) / nelem;
             double var = static_cast<double>(sums_of_squares[j]) / nelem - mu * mu;
-            double p_low = r_low / (r_low + mu);
-            double p_high = r_high / (r_high + mu);
-            double t_var_low = mu / p_low;
-            double t_var_high = mu / p_high;
-            r_low_var_sqerr += pow(t_var_low - var, 2.0);
-            r_high_var_sqerr += pow(t_var_high - var, 2.0);
-            common::logger->trace("label: {}\tmean: {}\tvar: {}\tp_low: {}\tvar_low: {}\tp_high: {}\tvar_high: {}",
-                                  j, mu, var, p_low, t_var_low, p_high, t_var_high);
-        }
-
-        common::logger->trace("r low: {} (sqerr: {})\t r high: {} (sqerr: {})",
-                              r_low, r_low_var_sqerr, r_high, r_high_var_sqerr);
-
-        double r = r_low_var_sqerr < r_high_var_sqerr ? r_low : r_high;
-        common::logger->trace("r: {}", r);
-
-        boost::math::chi_squared dist(1);
 
-        compute_pval = [&,r,dist](const auto &row) {
+            double r = mu * mu / (var - mu);
+            double p = mu / var;
+            double p_frac = (1.0 - p) / p;
+            double ab = r * p_frac;
+            double abb = ab * p_frac;
+            if (groups[j]) {
+                out_num += ab;
+                out_denom += abb;
+            } else {
+                in_num += ab;
+                in_denom += abb;
+            }
+        }
+        double b_in = in_denom / in_num;
+        double r_in = in_num / b_in;
+
+        double b_out = out_denom / out_num;
+        double r_out = out_num / b_out;
+
+        double b_null = (in_denom + out_denom) / (in_num + out_num);
+        double r_null = (in_num + out_num) / b_null;
+        // tsl::hopscotch_map<uint64_t, size_t> histogram;
+        // for (size_t j = 0; j < groups.size(); ++j) {
+        //     const auto &column = *columns_all[j];
+        //     const auto &column_values = *column_values_all[j];
+        //     size_t i = 0;
+        //     column.call_ones([&](uint64_t row_i) {
+        //         if (!ignored[row_i])
+        //             ++histogram[adjust_count(column_values[i], j, row_i)];
+
+        //         ++i;
+        //     });
+        // }
+
+        // double r = boost::math::tools::newton_raphson_iterate([&](double r) {
+        //     double size = nelem * groups.size();
+        //     double mu = total_kmers / size;
+        //     double dl = size * (log(r) - log(r + mu) - boost::math::digamma(r));
+        //     double ddl = size * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
+        //     for (const auto &[k, c] : histogram) {
+        //         dl += boost::math::digamma(k + r) * c;
+        //         ddl += boost::math::trigamma(k + r) * c;
+        //     }
+        //     return std::make_pair(dl, ddl);
+        // }, 1.0, 0.0, static_cast<double>(total_kmers), 30);
+
+        // common::logger->trace("r: {}", r);
+
+        // // use moments to fit the r parameter of a beta negative binomial
+        // uint64_t sum_of_cubes = 0;
+        // for (size_t j = 0; j < groups.size(); ++j) {
+        //     const auto &column = *columns_all[j];
+        //     const auto &column_values = *column_values_all[j];
+        //     size_t i = 0;
+        //     column.call_ones([&](uint64_t row_i) {
+        //         if (!ignored[row_i]) {
+        //             if (int64_t c = adjust_count(column_values[i], j, row_i))
+        //                 sum_of_cubes += c * (c - 1) * (c - 2);
+        //         }
+        //         ++i;
+        //     });
+        // }
+        // double mu = static_cast<double>(total_kmers) / groups.size() / nelem;
+        // double mu2 = static_cast<double>(std::accumulate(sums_of_squares.begin(),
+        //                                                  sums_of_squares.end(), uint64_t(0))) / groups.size() / nelem - mu;
+        // double mu3 = static_cast<double>(sum_of_cubes) / groups.size() / nelem;
+        // double a = mu3 * mu + mu * mu * mu2 + 2 * mu2 * mu2;
+        // double b = 2 * mu3 * mu * mu + mu3 * mu - mu2 * mu3 - mu * mu2 * mu2 + 3 * mu * mu + mu2 - 4 * mu2 * mu2;
+        // double c = -2 * mu * (mu2 * mu2 - mu * mu2 - mu3 * mu);
+
+        // auto [r_low, r_high] = boost::math::tools::quadratic_roots(a, b, c);
+
+        // common::logger->trace("mu: {}\tmu2: {}\tmu3: {}\tr_low: {}\tr_high: {}",
+        //                       mu, mu2, mu3, r_low, r_high);
+
+        // // check for NaNs
+        // // https://stackoverflow.com/questions/570669/checking-if-a-double-or-float-is-nan-in-c
+        // if (r_low != r_low && r_high != r_high) {
+        //     common::logger->error("{},{},{}", a, b, c);
+        //     throw std::runtime_error("Failed to fit, no real roots");
+        // }
+
+        // double r_low_var_sqerr = 0;
+        // double r_high_var_sqerr = 0;
+        // for (size_t j = 0; j < groups.size(); ++j) {
+        //     double mu = static_cast<double>(sums[j]) / nelem;
+        //     double var = static_cast<double>(sums_of_squares[j]) / nelem - mu * mu;
+        //     double p_low = r_low / (r_low + mu);
+        //     double p_high = r_high / (r_high + mu);
+        //     double t_var_low = mu / p_low;
+        //     double t_var_high = mu / p_high;
+        //     r_low_var_sqerr += pow(t_var_low - var, 2.0);
+        //     r_high_var_sqerr += pow(t_var_high - var, 2.0);
+        //     common::logger->trace("label: {}\tmean: {}\tvar: {}\tp_low: {}\tvar_low: {}\tp_high: {}\tvar_high: {}",
+        //                           j, mu, var, p_low, t_var_low, p_high, t_var_high);
+        // }
+
+        // common::logger->trace("r low: {} (sqerr: {})\t r high: {} (sqerr: {})",
+        //                       r_low, r_low_var_sqerr, r_high, r_high_var_sqerr);
+
+        // double r = r_low_var_sqerr < r_high_var_sqerr ? r_low : r_high;
+        // common::logger->trace("r: {}", r);
+
+        boost::math::chi_squared dist(2);
+
+        compute_pval = [&,r_in,r_out,r_null,b_in,b_out,b_null,dist](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0);
 
             double in_sum = 0;
             double out_sum = 0;
-            sdsl::bit_vector found(groups.size());
+            // std::vector<uint64_t> counts(groups.size());
             for (const auto &[j, c] : row) {
-                found[j] = true;
                 if (groups[j]) {
                     out_sum += c;
                 } else {
                     in_sum += c;
                 }
+                // counts[j] = c;
             }
 
-            std::vector<size_t> unfound;
-            for (size_t i = 0; i < found.size(); ++i) {
-                if (!found[i])
-                    unfound.emplace_back(i);
-            }
-
-            double total_sum = in_sum + out_sum;
-            size_t ndigits = 30;
-
-            auto ll_dx_ddx_pick = [&](const auto &picker) {
-                return [&](double lambda) {
-                    double val = 0;
-                    double dval = 0;
-                    for (const auto &[j, c] : row) {
-                        if (picker(j)) {
-                            double prop = static_cast<double>(sums[j]) * lambda;
-                            double shift_prop = r + prop;
-                            val += (c - prop) / shift_prop;
-                            dval -= (r + c) / shift_prop / shift_prop * sums[j];
-                        }
-                    }
-
-                    for (size_t j : unfound) {
-                        if (picker(j)) {
-                            double prop = static_cast<double>(sums[j]) * lambda;
-                            double shift_prop = r + prop;
-                            val -= prop / shift_prop;
-                            dval -= r / shift_prop / shift_prop * sums[j];
-                        }
-                    }
-
-                    return std::make_pair(val, dval);
-                };
-            };
-
-            double lambda_null = boost::math::tools::newton_raphson_iterate(
-                ll_dx_ddx_pick([&](size_t) { return true; }),
-                total_sum / total_kmers, -0.01, 1.01, ndigits
-            );
-
-            double chi_stat = 0;
-            double loglikelihood_null = 0;
-            // if (false) {
-            //     // score test
-            //     auto [val_in, dval_in] = ll_dx_ddx_in(lambda_null);
-            //     auto [val_out, dval_out] = ll_dx_ddx_out(lambda_null);
-
-            //     val_in *= r / lambda_null;
-            //     val_out *= r / lambda_null;
-
-            //     dval_in = (val_in / lambda_null - dval_in * r / lambda_null) / labels_in.size();
-            //     dval_out = (val_out / lambda_null - dval_out * r / lambda_null) / labels_out.size();
-            //     chi_stat = val_in * val_in / dval_in + val_out * val_out / dval_out;
-            // } else {
-                // log likelihood test
-                double lambda_in = boost::math::tools::newton_raphson_iterate(
-                    ll_dx_ddx_pick([&](size_t j) { return !groups[j]; }),
-                    in_sum / in_kmers, -0.01, 1.01, ndigits
-                );
-
-                double lambda_out = boost::math::tools::newton_raphson_iterate(
-                    ll_dx_ddx_pick([&](size_t j) { return groups[j]; }),
-                    out_sum / out_kmers, -0.01, 1.01, ndigits
-                );
-
-                double loglikelihood_alt = 0;
-
-                for (const auto &[j, c] : row) {
-                    double lambda = groups[j] ? lambda_out : lambda_in;
-                    double prop = lambda * sums[j];
-                    loglikelihood_alt += log(lambda) * c - log(r + prop) * (r + c);
-
-                    double prop_null = lambda_null * sums[j];
-                    loglikelihood_null += log(lambda_null) * c - log(r + prop_null) * (r + c);
-                }
-
-                for (size_t j : unfound) {
-                    double lambda = groups[j] ? lambda_out : lambda_in;
-                    double prop = lambda * sums[j];
-                    loglikelihood_alt -= log(r + prop) * r;
-
-                    double prop_null = lambda_null * sums[j];
-                    loglikelihood_null -= log(r + prop_null) * r;
-                }
+            double theta_in = (in_sum + r_in - 1) / in_kmers / (b_in + 1);
+            double theta_out = (out_sum + r_out - 1) / out_kmers / (b_out + 1);
+            double theta_null = (in_sum + out_sum + r_null - 1) / total_kmers / (b_null + 1);
+
+            double ll_in = theta_in > 0 ? in_sum * log(theta_in) - in_kmers * theta_in : 0;
+            double ll_out = theta_out > 0 ? out_sum * log(theta_out) - out_kmers * theta_out : 0;
+            double ll_null = (in_sum + out_sum) * log(theta_null) - total_kmers * theta_null;
+
+            double stat = 2 * (ll_in + ll_out - ll_null);
+
+            // auto make_ll_getter = [&](const auto &picker) {
+            //     double sum = 0;
+            //     for (const auto &[j, c] : row) {
+            //         if (picker(j))
+            //             sum += c;
+            //     }
+
+            //     return [&,sum](double theta) {
+            //         double dl = sum / theta;
+            //         double ddl = -sum / theta / theta;
+            //         for (size_t j = 0; j < groups.size(); ++j) {
+            //             if (picker(j)) {
+            //                 double r_shift = (r + counts[j]) * sums[j];
+            //                 double denom = r + theta * sums[j];
+            //                 dl -= r_shift / denom;
+            //                 ddl += r_shift * sums[j] / denom / denom;
+            //             }
+            //         }
+
+            //         return std::make_pair(dl, ddl);
+            //     };
+            // };
+
+            // double theta_in = boost::math::tools::newton_raphson_iterate(
+            //     make_ll_getter([&](size_t j) { return !groups[j]; }),
+            //     0.5, 0.0, 1.0, 30
+            // );
+            // double theta_out = boost::math::tools::newton_raphson_iterate(
+            //     make_ll_getter([&](size_t j) { return groups[j]; }),
+            //     0.5, 0.0, 1.0, 30
+            // );
+            // double theta_null = boost::math::tools::newton_raphson_iterate(
+            //     make_ll_getter([&](size_t) { return true; }),
+            //     0.5, 0.0, 1.0, 30
+            // );
+
+            // double ll_alt = 0;
+            // double ll_null = 0;
+
+            // for (size_t j = 0; j < groups.size(); ++j) {
+            //     double theta = groups[j] ? theta_out : theta_in;
+            //     double denom = log(r + sums[j] * theta);
+            //     ll_alt += - r * denom + counts[j] * (log(theta) - denom);
+
+            //     double denom_null = log(r + sums[j] * theta_null);
+            //     ll_null += - r * denom_null + counts[j] * (log(theta_null) - denom_null);
+            // }
 
-                chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
+            // double stat = 2 * (ll_alt - ll_null);
+            // double pval = boost::math::cdf(boost::math::complement(dist, stat));
+            double pval = stat > 0
+                ? boost::math::cdf(boost::math::complement(dist, stat))
+                : 1.0;
+
+            // double in_sum = 0;
+            // double out_sum = 0;
+            // sdsl::bit_vector found(groups.size());
+            // for (const auto &[j, c] : row) {
+            //     found[j] = true;
+            //     if (groups[j]) {
+            //         out_sum += c;
+            //     } else {
+            //         in_sum += c;
+            //     }
+            // }
 
-                if (chi_stat < 0) {
-                    common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
-                    common::logger->error("Theta null: {}\ttheta in: {}\ttheta out: {}",
-                                          lambda_null, lambda_in, lambda_out);
-                    throw std::runtime_error("Test failed");
-                }
+            // std::vector<size_t> unfound;
+            // for (size_t i = 0; i < found.size(); ++i) {
+            //     if (!found[i])
+            //         unfound.emplace_back(i);
             // }
 
-            double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
+            // double total_sum = in_sum + out_sum;
+            // size_t ndigits = 30;
+
+            // auto ll_dx_ddx_pick = [&](const auto &picker) {
+            //     return [&](double lambda) {
+            //         double val = 0;
+            //         double dval = 0;
+            //         for (const auto &[j, c] : row) {
+            //             if (picker(j)) {
+            //                 double prop = static_cast<double>(sums[j]) * lambda;
+            //                 double shift_prop = r + prop;
+            //                 val += (c - prop) / shift_prop;
+            //                 dval -= (r + c) / shift_prop / shift_prop * sums[j];
+            //             }
+            //         }
+
+            //         for (size_t j : unfound) {
+            //             if (picker(j)) {
+            //                 double prop = static_cast<double>(sums[j]) * lambda;
+            //                 double shift_prop = r + prop;
+            //                 val -= prop / shift_prop;
+            //                 dval -= r / shift_prop / shift_prop * sums[j];
+            //             }
+            //         }
+
+            //         return std::make_pair(val, dval);
+            //     };
+            // };
+
+            // double lambda_null = boost::math::tools::newton_raphson_iterate(
+            //     ll_dx_ddx_pick([&](size_t) { return true; }),
+            //     total_sum / total_kmers, -0.01, 1.01, ndigits
+            // );
+
+            // double chi_stat = 0;
+            // double loglikelihood_null = 0;
+            // // if (false) {
+            // //     // score test
+            // //     auto [val_in, dval_in] = ll_dx_ddx_in(lambda_null);
+            // //     auto [val_out, dval_out] = ll_dx_ddx_out(lambda_null);
+
+            // //     val_in *= r / lambda_null;
+            // //     val_out *= r / lambda_null;
+
+            // //     dval_in = (val_in / lambda_null - dval_in * r / lambda_null) / labels_in.size();
+            // //     dval_out = (val_out / lambda_null - dval_out * r / lambda_null) / labels_out.size();
+            // //     chi_stat = val_in * val_in / dval_in + val_out * val_out / dval_out;
+            // // } else {
+            //     // log likelihood test
+            //     double lambda_in = boost::math::tools::newton_raphson_iterate(
+            //         ll_dx_ddx_pick([&](size_t j) { return !groups[j]; }),
+            //         in_sum / in_kmers, -0.01, 1.01, ndigits
+            //     );
+
+            //     double lambda_out = boost::math::tools::newton_raphson_iterate(
+            //         ll_dx_ddx_pick([&](size_t j) { return groups[j]; }),
+            //         out_sum / out_kmers, -0.01, 1.01, ndigits
+            //     );
+
+            //     double loglikelihood_alt = 0;
+
+            //     for (const auto &[j, c] : row) {
+            //         double lambda = groups[j] ? lambda_out : lambda_in;
+            //         double prop = lambda * sums[j];
+            //         loglikelihood_alt += log(lambda) * c - log(r + prop) * (r + c);
+
+            //         double prop_null = lambda_null * sums[j];
+            //         loglikelihood_null += log(lambda_null) * c - log(r + prop_null) * (r + c);
+            //     }
+
+            //     for (size_t j : unfound) {
+            //         double lambda = groups[j] ? lambda_out : lambda_in;
+            //         double prop = lambda * sums[j];
+            //         loglikelihood_alt -= log(r + prop) * r;
+
+            //         double prop_null = lambda_null * sums[j];
+            //         loglikelihood_null -= log(r + prop_null) * r;
+            //     }
+
+            //     chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
+
+            //     if (chi_stat < 0) {
+            //         common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+            //         common::logger->error("Theta null: {}\ttheta in: {}\ttheta out: {}",
+            //                               lambda_null, lambda_in, lambda_out);
+            //         throw std::runtime_error("Test failed");
+            //     }
+            // // }
+
+            // double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
 
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };

From 927f2e64e9f44fadbdb2aedcd73476c4dab0dfa2 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 21 May 2024 09:09:29 +0200
Subject: [PATCH 141/410] nbinom bayes try1

---
 .../src/graph/annotated_graph_algorithm.cpp   | 133 ++++++++++--------
 1 file changed, 71 insertions(+), 62 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a0450eca72..f8d92a94f6 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -378,6 +378,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         double b_null = (in_denom + out_denom) / (in_num + out_num);
         double r_null = (in_num + out_num) / b_null;
+
+
         // tsl::hopscotch_map<uint64_t, size_t> histogram;
         // for (size_t j = 0; j < groups.size(); ++j) {
         //     const auto &column = *columns_all[j];
@@ -478,69 +480,76 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 // counts[j] = c;
             }
 
-            double theta_in = (in_sum + r_in - 1) / in_kmers / (b_in + 1);
-            double theta_out = (out_sum + r_out - 1) / out_kmers / (b_out + 1);
-            double theta_null = (in_sum + out_sum + r_null - 1) / total_kmers / (b_null + 1);
-
-            double ll_in = theta_in > 0 ? in_sum * log(theta_in) - in_kmers * theta_in : 0;
-            double ll_out = theta_out > 0 ? out_sum * log(theta_out) - out_kmers * theta_out : 0;
-            double ll_null = (in_sum + out_sum) * log(theta_null) - total_kmers * theta_null;
-
-            double stat = 2 * (ll_in + ll_out - ll_null);
-
-            // auto make_ll_getter = [&](const auto &picker) {
-            //     double sum = 0;
-            //     for (const auto &[j, c] : row) {
-            //         if (picker(j))
-            //             sum += c;
-            //     }
-
-            //     return [&,sum](double theta) {
-            //         double dl = sum / theta;
-            //         double ddl = -sum / theta / theta;
-            //         for (size_t j = 0; j < groups.size(); ++j) {
-            //             if (picker(j)) {
-            //                 double r_shift = (r + counts[j]) * sums[j];
-            //                 double denom = r + theta * sums[j];
-            //                 dl -= r_shift / denom;
-            //                 ddl += r_shift * sums[j] / denom / denom;
-            //             }
-            //         }
-
-            //         return std::make_pair(dl, ddl);
-            //     };
-            // };
-
-            // double theta_in = boost::math::tools::newton_raphson_iterate(
-            //     make_ll_getter([&](size_t j) { return !groups[j]; }),
-            //     0.5, 0.0, 1.0, 30
-            // );
-            // double theta_out = boost::math::tools::newton_raphson_iterate(
-            //     make_ll_getter([&](size_t j) { return groups[j]; }),
-            //     0.5, 0.0, 1.0, 30
-            // );
-            // double theta_null = boost::math::tools::newton_raphson_iterate(
-            //     make_ll_getter([&](size_t) { return true; }),
-            //     0.5, 0.0, 1.0, 30
-            // );
-
-            // double ll_alt = 0;
-            // double ll_null = 0;
-
-            // for (size_t j = 0; j < groups.size(); ++j) {
-            //     double theta = groups[j] ? theta_out : theta_in;
-            //     double denom = log(r + sums[j] * theta);
-            //     ll_alt += - r * denom + counts[j] * (log(theta) - denom);
-
-            //     double denom_null = log(r + sums[j] * theta_null);
-            //     ll_null += - r * denom_null + counts[j] * (log(theta_null) - denom_null);
-            // }
+            double in_sum_shift = in_sum + r_in;
+            double out_sum_shift = out_sum + r_out;
+            double crit_val = out_sum_shift * in_kmers * (b_in + 1) / in_sum_shift / out_kmers / (b_out + 1);
+            auto f_dist = boost::math::fisher_f(in_sum_shift * 2, out_sum_shift * 2);
+            double pval = std::min(boost::math::cdf(f_dist, crit_val),
+                                   boost::math::cdf(boost::math::complement(f_dist, crit_val))) * 2.0;
+
+            // double theta_in = (in_sum + r_in - 1) / in_kmers / (b_in + 1);
+            // double theta_out = (out_sum + r_out - 1) / out_kmers / (b_out + 1);
+            // double theta_null = (in_sum + out_sum + r_null - 1) / total_kmers / (b_null + 1);
+
+            // double ll_in = theta_in > 0 ? in_sum * log(theta_in) - in_kmers * theta_in : 0;
+            // double ll_out = theta_out > 0 ? out_sum * log(theta_out) - out_kmers * theta_out : 0;
+            // double ll_null = (in_sum + out_sum) * log(theta_null) - total_kmers * theta_null;
+
+            // double stat = 2 * (ll_in + ll_out - ll_null);
+
+            // // auto make_ll_getter = [&](const auto &picker) {
+            // //     double sum = 0;
+            // //     for (const auto &[j, c] : row) {
+            // //         if (picker(j))
+            // //             sum += c;
+            // //     }
+
+            // //     return [&,sum](double theta) {
+            // //         double dl = sum / theta;
+            // //         double ddl = -sum / theta / theta;
+            // //         for (size_t j = 0; j < groups.size(); ++j) {
+            // //             if (picker(j)) {
+            // //                 double r_shift = (r + counts[j]) * sums[j];
+            // //                 double denom = r + theta * sums[j];
+            // //                 dl -= r_shift / denom;
+            // //                 ddl += r_shift * sums[j] / denom / denom;
+            // //             }
+            // //         }
+
+            // //         return std::make_pair(dl, ddl);
+            // //     };
+            // // };
+
+            // // double theta_in = boost::math::tools::newton_raphson_iterate(
+            // //     make_ll_getter([&](size_t j) { return !groups[j]; }),
+            // //     0.5, 0.0, 1.0, 30
+            // // );
+            // // double theta_out = boost::math::tools::newton_raphson_iterate(
+            // //     make_ll_getter([&](size_t j) { return groups[j]; }),
+            // //     0.5, 0.0, 1.0, 30
+            // // );
+            // // double theta_null = boost::math::tools::newton_raphson_iterate(
+            // //     make_ll_getter([&](size_t) { return true; }),
+            // //     0.5, 0.0, 1.0, 30
+            // // );
+
+            // // double ll_alt = 0;
+            // // double ll_null = 0;
+
+            // // for (size_t j = 0; j < groups.size(); ++j) {
+            // //     double theta = groups[j] ? theta_out : theta_in;
+            // //     double denom = log(r + sums[j] * theta);
+            // //     ll_alt += - r * denom + counts[j] * (log(theta) - denom);
+
+            // //     double denom_null = log(r + sums[j] * theta_null);
+            // //     ll_null += - r * denom_null + counts[j] * (log(theta_null) - denom_null);
+            // // }
 
-            // double stat = 2 * (ll_alt - ll_null);
-            // double pval = boost::math::cdf(boost::math::complement(dist, stat));
-            double pval = stat > 0
-                ? boost::math::cdf(boost::math::complement(dist, stat))
-                : 1.0;
+            // // double stat = 2 * (ll_alt - ll_null);
+            // // double pval = boost::math::cdf(boost::math::complement(dist, stat));
+            // double pval = stat > 0
+            //     ? boost::math::cdf(boost::math::complement(dist, stat))
+            //     : 1.0;
 
             // double in_sum = 0;
             // double out_sum = 0;

From 704ff5285747827ca4c21a7230317d66c4e8fb89 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 21 May 2024 11:29:06 +0200
Subject: [PATCH 142/410] nbinom meth moments

---
 .../src/graph/annotated_graph_algorithm.cpp   | 98 +++++++++++++------
 1 file changed, 69 insertions(+), 29 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index f8d92a94f6..cbac3838b4 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -349,35 +349,74 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "nbinom") {
-        double in_num = 0;
-        double out_num = 0;
-        double in_denom = 0;
-        double out_denom = 0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            double mu = static_cast<double>(sums[j]) / nelem;
-            double var = static_cast<double>(sums_of_squares[j]) / nelem - mu * mu;
-
-            double r = mu * mu / (var - mu);
-            double p = mu / var;
-            double p_frac = (1.0 - p) / p;
-            double ab = r * p_frac;
-            double abb = ab * p_frac;
-            if (groups[j]) {
-                out_num += ab;
-                out_denom += abb;
-            } else {
-                in_num += ab;
-                in_denom += abb;
+        double in_sum_mu = in_kmers / nelem;
+        double out_sum_mu = out_kmers / nelem;
+
+        double in_sum_var = 0;
+        double out_sum_var = 0;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                     std::unique_ptr<ValuesContainer>,
+                     PairContainer>(columns_all, column_values_all,
+                                    [&](uint64_t row_i, const auto &raw_row) {
+            if (ignored[row_i])
+                return;
+
+            double in_sum = 0;
+            double out_sum = 0;
+            for (const auto &[j, raw_c] : raw_row) {
+                if (uint64_t c = adjust_count(raw_c, j, row_i)) {
+                    if (groups[j]) {
+                        out_sum += c;
+                    } else {
+                        in_sum += c;
+                    }
+                }
             }
-        }
-        double b_in = in_denom / in_num;
-        double r_in = in_num / b_in;
 
-        double b_out = out_denom / out_num;
-        double r_out = out_num / b_out;
+            in_sum_var += in_sum * in_sum;
+            out_sum_var += out_sum * out_sum;
+        });
+
+        double in_sum_mu2 = in_sum_mu * in_sum_mu;
+        double out_sum_mu2 = out_sum_mu * out_sum_mu;
+
+        in_sum_var = in_sum_var / nelem - in_sum_mu2;
+        out_sum_var = out_sum_var / nelem - out_sum_mu2;
+
+        double r_in = in_sum_mu2 / (in_sum_var - in_sum_mu);
+        double r_out = out_sum_mu2 / (out_sum_var - out_sum_mu);
+
+        double p_in = in_sum_mu / in_sum_var;
+        double p_out = out_sum_mu / out_sum_var;
+        // double in_num = 0;
+        // double out_num = 0;
+        // double in_denom = 0;
+        // double out_denom = 0;
+        // for (size_t j = 0; j < groups.size(); ++j) {
+        //     double mu = static_cast<double>(sums[j]) / nelem;
+        //     double var = static_cast<double>(sums_of_squares[j]) / nelem - mu * mu;
+
+        //     double r = mu * mu / (var - mu);
+        //     double p = mu / var;
+        //     double p_frac = (1.0 - p) / p;
+        //     double ab = r * p_frac;
+        //     double abb = ab * p_frac;
+        //     if (groups[j]) {
+        //         out_num += ab;
+        //         out_denom += abb;
+        //     } else {
+        //         in_num += ab;
+        //         in_denom += abb;
+        //     }
+        // }
+        // double b_in = in_denom / in_num;
+        // double r_in = in_num / b_in;
+
+        // double b_out = out_denom / out_num;
+        // double r_out = out_num / b_out;
 
-        double b_null = (in_denom + out_denom) / (in_num + out_num);
-        double r_null = (in_num + out_num) / b_null;
+        // double b_null = (in_denom + out_denom) / (in_num + out_num);
+        // double r_null = (in_num + out_num) / b_null;
 
 
         // tsl::hopscotch_map<uint64_t, size_t> histogram;
@@ -462,9 +501,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         // double r = r_low_var_sqerr < r_high_var_sqerr ? r_low : r_high;
         // common::logger->trace("r: {}", r);
 
-        boost::math::chi_squared dist(2);
+        // boost::math::chi_squared dist(2);
 
-        compute_pval = [&,r_in,r_out,r_null,b_in,b_out,b_null,dist](const auto &row) {
+        // compute_pval = [&,r_in,r_out,r_null,b_in,b_out,b_null,dist](const auto &row) {
+        compute_pval = [&,r_in,r_out,p_in,p_out](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0);
 
@@ -482,7 +522,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double in_sum_shift = in_sum + r_in;
             double out_sum_shift = out_sum + r_out;
-            double crit_val = out_sum_shift * in_kmers * (b_in + 1) / in_sum_shift / out_kmers / (b_out + 1);
+            double crit_val = out_sum_shift * (1.0 - p_out) / in_sum_shift / (1.0 - p_in);
             auto f_dist = boost::math::fisher_f(in_sum_shift * 2, out_sum_shift * 2);
             double pval = std::min(boost::math::cdf(f_dist, crit_val),
                                    boost::math::cdf(boost::math::complement(f_dist, crit_val))) * 2.0;

From b25efe902f3da369b9ffcd0ca8f4b8b4dd35d291 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 22 May 2024 20:04:33 +0200
Subject: [PATCH 143/410] fail

---
 .../src/graph/annotated_graph_algorithm.cpp   | 195 ++++++++++++++++--
 1 file changed, 173 insertions(+), 22 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index cbac3838b4..77f0ee51bc 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -13,6 +13,8 @@
 #include <boost/math/tools/minima.hpp>
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/distributions/fisher_f.hpp>
+#include <boost/math/special_functions/polygamma.hpp>
+#include <boost/math/special_functions/gamma.hpp>
 
 #include <Eigen/Dense>
 #include <unsupported/Eigen/MatrixFunctions>
@@ -349,11 +351,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "nbinom") {
-        double in_sum_mu = in_kmers / nelem;
-        double out_sum_mu = out_kmers / nelem;
-
+        double in_sum_mu = 0;
         double in_sum_var = 0;
+        double out_sum_mu = 0;
         double out_sum_var = 0;
+        double null_sum_var = 0;
+        VectorMap<uint64_t, size_t> in_hist;
+        VectorMap<uint64_t, size_t> out_hist;
+        VectorMap<uint64_t, size_t> null_hist;
         utils::call_rows<std::unique_ptr<bit_vector>,
                      std::unique_ptr<ValuesContainer>,
                      PairContainer>(columns_all, column_values_all,
@@ -361,33 +366,78 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (ignored[row_i])
                 return;
 
-            double in_sum = 0;
-            double out_sum = 0;
+            uint64_t in_sum = 0;
+            uint64_t out_sum = 0;
             for (const auto &[j, raw_c] : raw_row) {
-                if (uint64_t c = adjust_count(raw_c, j, row_i)) {
-                    if (groups[j]) {
-                        out_sum += c;
-                    } else {
-                        in_sum += c;
-                    }
+                if (groups[j]) {
+                    out_sum += adjust_count(raw_c, j, row_i);
+                } else {
+                    in_sum += adjust_count(raw_c, j, row_i);
                 }
             }
 
+            ++in_hist[in_sum];
+            ++out_hist[out_sum];
+            ++null_hist[in_sum + out_sum];
+            in_sum_mu += in_sum;
+            out_sum_mu += out_sum;
             in_sum_var += in_sum * in_sum;
             out_sum_var += out_sum * out_sum;
+            null_sum_var += pow(in_sum + out_sum, 2.0);
         });
 
+        double null_sum_mu = in_sum_mu + out_sum_mu;
+
+        in_sum_mu /= nelem;
+        out_sum_mu /= nelem;
+        null_sum_mu /= nelem;
+
         double in_sum_mu2 = in_sum_mu * in_sum_mu;
         double out_sum_mu2 = out_sum_mu * out_sum_mu;
+        double null_sum_mu2 = null_sum_mu * null_sum_mu;
 
         in_sum_var = in_sum_var / nelem - in_sum_mu2;
         out_sum_var = out_sum_var / nelem - out_sum_mu2;
+        null_sum_var = null_sum_var / nelem - null_sum_mu2;
+
+        // double r_in = in_sum_mu2 / (in_sum_var - in_sum_mu);
+        // double r_out = out_sum_mu2 / (out_sum_var - out_sum_mu);
+
+        auto get_params = [&](const auto &hist, double mu, double mu2, double var) {
+            double tr = mu2 / (var - mu);
+            common::logger->trace("Mean: {}\tVar: {}", mu, var);
+            common::logger->trace("Initial guess r: {}", tr);
+            tr = boost::math::tools::schroder_iterate([&](double r) {
+                double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
+                double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
+                double dddl = nelem * (-1.0 / r / r + 1.0 / pow(r + mu, 2.0) - boost::math::polygamma(2, r));
+                for (const auto &[k, c] : hist) {
+                    dl += boost::math::digamma(k + r) * c;
+                    ddl += boost::math::trigamma(k + r) * c;
+                    dddl += boost::math::polygamma(2, k + r) * c;
+                }
+                return std::make_tuple(dl, ddl, dddl);
+            }, tr, 0.0, total_kmers, 10);
+
+            double p = tr / (tr + mu);
+            common::logger->trace("MLE r: {}\tp: {}", tr, p);
+            common::logger->trace("Fit mean: {}\tFit var: {}", tr * (1.0 - p) / p, tr * (1.0 - p) / p / p);
+            return std::make_pair(tr, p);
+        };
+
+        auto [tr_in, p_in] = get_params(in_hist, in_sum_mu, in_sum_mu2, in_sum_var);
+        auto [tr_out, p_out] = get_params(out_hist, out_sum_mu, out_sum_mu2, out_sum_var);
+
+        double tr_null = null_sum_mu2 / (null_sum_var - null_sum_mu);
+        double p_null = tr_null / (tr_null + null_sum_mu);
+
+        double r_in = tr_in / in_kmers;
+        double r_out = tr_out / out_kmers;
+        double r_null = tr_null / total_kmers;
 
-        double r_in = in_sum_mu2 / (in_sum_var - in_sum_mu);
-        double r_out = out_sum_mu2 / (out_sum_var - out_sum_mu);
+        // double p_in = in_sum_mu / in_sum_var;
+        // double p_out = out_sum_mu / out_sum_var;
 
-        double p_in = in_sum_mu / in_sum_var;
-        double p_out = out_sum_mu / out_sum_var;
         // double in_num = 0;
         // double out_num = 0;
         // double in_denom = 0;
@@ -504,10 +554,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         // boost::math::chi_squared dist(2);
 
         // compute_pval = [&,r_in,r_out,r_null,b_in,b_out,b_null,dist](const auto &row) {
-        compute_pval = [&,r_in,r_out,p_in,p_out](const auto &row) {
+        p_in = p_null;
+        p_out = p_null;
+        compute_pval = [&,r_in,r_out,r_null,p_in,p_out,p_null](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0);
 
+
             double in_sum = 0;
             double out_sum = 0;
             // std::vector<uint64_t> counts(groups.size());
@@ -520,12 +573,110 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 // counts[j] = c;
             }
 
-            double in_sum_shift = in_sum + r_in;
-            double out_sum_shift = out_sum + r_out;
-            double crit_val = out_sum_shift * (1.0 - p_out) / in_sum_shift / (1.0 - p_in);
-            auto f_dist = boost::math::fisher_f(in_sum_shift * 2, out_sum_shift * 2);
-            double pval = std::min(boost::math::cdf(f_dist, crit_val),
-                                   boost::math::cdf(boost::math::complement(f_dist, crit_val))) * 2.0;
+            auto get_llike = [&](double p, double total, double k) {
+                if (k == 0)
+                    return 0.0;
+
+                double factor = exp(log(p) + log(total) - log1p(-p));
+                double factor2 = factor * factor;
+                double th = boost::math::tools::schroder_iterate([&,factor,factor2,p,k](double th) {
+                    double base = factor * th;
+                    double dl = boost::math::digamma(base + k) - boost::math::digamma(base) + log(p);
+                    double ddl = factor * (boost::math::trigamma(base + k) - boost::math::trigamma(base));
+                    double dddl = factor2 * (boost::math::polygamma(2, base + k) - boost::math::polygamma(2, base));
+                    return std::make_tuple(dl, ddl, dddl);
+                }, k / total, std::numeric_limits<double>::min(), 1.0, 30);
+                factor *= th;
+                return lgamma(factor + k) - lgamma(factor) + factor * log(p);
+            };
+
+            auto get_llike_null = [&]() {
+                double k_in = in_sum;
+                double k_out = out_sum;
+                double factor_in = exp(log(p_in) + log(in_kmers) - log1p(-p_in));
+                double factor_out = exp(log(p_out) + log(out_kmers) - log1p(-p_out));
+                double factor2_in = factor_in * factor_in;
+                double factor2_out = factor_out * factor_out;
+                double factor3_in = factor2_in * factor_in;
+                double factor3_out = factor2_out * factor_out;
+                double th = boost::math::tools::schroder_iterate([&](double th) {
+                    double base_in = factor_in * th;
+                    double base_out = factor_out * th;
+                    double dl = factor_in * log(p_in) + factor_out * log(p_out);
+                    double ddl = 0;
+                    double dddl = 0;
+                    if (k_in > 0) {
+                        dl += factor_in * (boost::math::digamma(base_in + k_in) - boost::math::digamma(base_in));
+                        ddl += factor2_in * (boost::math::trigamma(base_in + k_in) - boost::math::trigamma(base_in));
+                        dddl += factor3_in * (boost::math::polygamma(2, base_in + k_in) - boost::math::polygamma(2, base_in));
+                    }
+
+                    if (k_out > 0) {
+                        dl += factor_out * (boost::math::digamma(base_out + k_out) - boost::math::digamma(base_out));
+                        ddl += factor2_out * (boost::math::trigamma(base_out + k_out) - boost::math::trigamma(base_out));
+                        dddl += factor3_out * (boost::math::polygamma(2, base_out + k_out) - boost::math::polygamma(2, base_out));
+                    }
+
+                    return std::make_tuple(dl, ddl, dddl);
+                }, (k_in + k_out) / total_kmers, std::numeric_limits<double>::min(), 1.0, 30);
+                factor_in *= th;
+                factor_out *= th;
+                return lgamma(factor_in + k_in) - lgamma(factor_in) + factor_in * log(p_in)
+                        + lgamma(factor_out + k_out) - lgamma(factor_out) + factor_out * log(p_out);
+            };
+
+            double llik_in = get_llike(p_in, in_kmers, in_sum);
+            double llik_out = get_llike(p_out, out_kmers, out_sum);
+            double llik_null = get_llike_null();
+
+            double pval = boost::math::cdf(boost::math::complement(
+                boost::math::chi_squared(1), 2 * (llik_in + llik_out - llik_null)
+            ));
+
+            // double in_sum_shift = in_sum + in_kmers * r_in;
+            // double out_sum_shift = out_sum + out_kmers * r_out;
+
+            // double pval;
+            // if (in_sum_shift < 1 && out_sum_shift < 1) {
+            //     pval = 1.0;
+            // } else if (in_sum_shift < 1 || out_sum_shift < 1) {
+            //     pval = 0.0;
+            // } else {
+            //     double in_sum_shift_null = in_sum + in_kmers * r_null;
+            //     double out_sum_shift_null = out_sum + out_kmers * r_null;
+
+            //     auto dist = boost::math::fisher_f(in_sum_shift_null, out_sum_shift_null);
+            //     double f_stat = exp(log(out_sum_shift) - log(in_sum_shift) + log(in_sum_shift - 1) - log(out_sum_shift - 1));
+            //     pval = std::min(boost::math::cdf(dist, f_stat),
+            //                     boost::math::cdf(boost::math::complement(dist, f_stat))) * 2.0;
+            // }
+
+            // // KS-test
+            // double sum_shift_max;
+            // double sum_shift_min;
+            // double b_max;
+            // double b_min;
+            // if (in_sum_shift > out_sum_shift) {
+            //     sum_shift_max = in_sum_shift;
+            //     b_max = exp(log(in_kmers) - log1p(-p_in));
+            //     sum_shift_min = out_sum_shift;
+            //     b_min = exp(log(out_kmers) - log1p(-p_out));
+            // } else {
+            //     sum_shift_min = in_sum_shift;
+            //     b_min = exp(log(in_kmers) - log1p(-p_in));
+            //     sum_shift_max = out_sum_shift;
+            //     b_max = exp(log(out_kmers) - log1p(-p_out));
+            // }
+
+            // double crit_x = exp((lgamma(sum_shift_max) - lgamma(sum_shift_min)) / (sum_shift_max - sum_shift_min));
+            // double ks_stat = boost::math::gamma_p(sum_shift_max, b_max * crit_x) - boost::math::gamma_p(sum_shift_min, b_min * crit_x);
+
+            // OLD: check values on F distribution
+            // double crit_val = exp(log(out_sum_shift) + log(out_kmers) - log1p(-p_out) - log(in_sum_shift) - log(in_kmers) + log1p(-p_in));
+            // auto f_dist = boost::math::fisher_f(in_sum_shift * 2, out_sum_shift * 2);
+            // // double pval = std::min(boost::math::cdf(f_dist, crit_val),
+            // //                        boost::math::cdf(boost::math::complement(f_dist, crit_val))) * 2;
+            // double pval = boost::math::pdf(f_dist, crit_val);
 
             // double theta_in = (in_sum + r_in - 1) / in_kmers / (b_in + 1);
             // double theta_out = (out_sum + r_out - 1) / out_kmers / (b_out + 1);

From b9539284960d33db4629dc85e23d34a08cebd3e2 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 22 May 2024 23:29:27 +0200
Subject: [PATCH 144/410] complicated

---
 .../src/graph/annotated_graph_algorithm.cpp   | 631 +++++-------------
 1 file changed, 154 insertions(+), 477 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 77f0ee51bc..34f6328501 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -351,506 +351,183 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "nbinom") {
-        double in_sum_mu = 0;
-        double in_sum_var = 0;
-        double out_sum_mu = 0;
-        double out_sum_var = 0;
-        double null_sum_var = 0;
-        VectorMap<uint64_t, size_t> in_hist;
-        VectorMap<uint64_t, size_t> out_hist;
-        VectorMap<uint64_t, size_t> null_hist;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                     std::unique_ptr<ValuesContainer>,
-                     PairContainer>(columns_all, column_values_all,
-                                    [&](uint64_t row_i, const auto &raw_row) {
-            if (ignored[row_i])
-                return;
+        boost::math::chi_squared dist(1);
+        compute_pval = [&,dist](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0);
 
-            uint64_t in_sum = 0;
-            uint64_t out_sum = 0;
-            for (const auto &[j, raw_c] : raw_row) {
+            std::vector<uint64_t> counts(groups.size());
+            double in_sum = 0;
+            double out_sum = 0;
+            for (const auto &[j, c] : row) {
+                counts[j] = c;
                 if (groups[j]) {
-                    out_sum += adjust_count(raw_c, j, row_i);
+                    out_sum += c;
                 } else {
-                    in_sum += adjust_count(raw_c, j, row_i);
+                    in_sum += c;
                 }
             }
 
-            ++in_hist[in_sum];
-            ++out_hist[out_sum];
-            ++null_hist[in_sum + out_sum];
-            in_sum_mu += in_sum;
-            out_sum_mu += out_sum;
-            in_sum_var += in_sum * in_sum;
-            out_sum_var += out_sum * out_sum;
-            null_sum_var += pow(in_sum + out_sum, 2.0);
-        });
+            size_t n_iter = 1000;
+
+            auto get_llik_alt = [&]() {
+                double r = 10;
+                double th[2] = {
+                    in_sum > 0 ? in_sum / in_kmers : 0.0,
+                    out_sum > 0 ? out_sum / out_kmers : 0.0
+                };
+                for (size_t i = 0; i < n_iter; ++i) {
+                    double r_next = boost::math::tools::newton_raphson_iterate([&](double r) {
+                        double dl = 0;
+                        double ddl = 0;
+
+                        double dl_base = log(r) + 1 - boost::math::digamma(r);
+                        double ddl_base = 1.0 / r - boost::math::trigamma(r);
+                        for (size_t j = 0; j < groups.size(); ++j) {
+                            if (th[groups[j]]) {
+                                double r_shift = r + sums[j] * th[groups[j]];
+                                double c_shift = r + counts[j];
+                                dl += dl_base + boost::math::digamma(c_shift) - c_shift / r_shift - log(r_shift);
+                                ddl += ddl_base + boost::math::trigamma(c_shift) - 2.0 / r_shift + c_shift / r_shift / r_shift;
+                            }
+                        }
+
+                        return std::make_pair(dl, ddl);
+                    }, r, 0.0, total_kmers, 30);
+
+                    double th_0_next = in_sum > 0 ? boost::math::tools::newton_raphson_iterate([&](double th) {
+                        double dl = in_sum / th;
+                        double ddl = -in_sum / th / th;
+                        for (size_t j = 0; j < groups.size(); ++j) {
+                            if (!groups[j]) {
+                                double c_shift = r_next + counts[j];
+                                double r_shift = static_cast<double>(sums[j]) / (r_next + th * sums[j]);
+                                dl -= c_shift * r_shift;
+                                ddl += c_shift * r_shift * r_shift;
+                            }
+                        }
 
-        double null_sum_mu = in_sum_mu + out_sum_mu;
-
-        in_sum_mu /= nelem;
-        out_sum_mu /= nelem;
-        null_sum_mu /= nelem;
-
-        double in_sum_mu2 = in_sum_mu * in_sum_mu;
-        double out_sum_mu2 = out_sum_mu * out_sum_mu;
-        double null_sum_mu2 = null_sum_mu * null_sum_mu;
-
-        in_sum_var = in_sum_var / nelem - in_sum_mu2;
-        out_sum_var = out_sum_var / nelem - out_sum_mu2;
-        null_sum_var = null_sum_var / nelem - null_sum_mu2;
-
-        // double r_in = in_sum_mu2 / (in_sum_var - in_sum_mu);
-        // double r_out = out_sum_mu2 / (out_sum_var - out_sum_mu);
-
-        auto get_params = [&](const auto &hist, double mu, double mu2, double var) {
-            double tr = mu2 / (var - mu);
-            common::logger->trace("Mean: {}\tVar: {}", mu, var);
-            common::logger->trace("Initial guess r: {}", tr);
-            tr = boost::math::tools::schroder_iterate([&](double r) {
-                double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
-                double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
-                double dddl = nelem * (-1.0 / r / r + 1.0 / pow(r + mu, 2.0) - boost::math::polygamma(2, r));
-                for (const auto &[k, c] : hist) {
-                    dl += boost::math::digamma(k + r) * c;
-                    ddl += boost::math::trigamma(k + r) * c;
-                    dddl += boost::math::polygamma(2, k + r) * c;
+                        return std::make_pair(dl, ddl);
+                    }, th[0], std::numeric_limits<double>::min(), 1.0, 30) : 0.0;
+
+                    double th_1_next = out_sum > 0 ? boost::math::tools::newton_raphson_iterate([&](double th) {
+                        double dl = out_sum / th;
+                        double ddl = -out_sum / th / th;
+                        for (size_t j = 0; j < groups.size(); ++j) {
+                            if (groups[j]) {
+                                double c_shift = r_next + counts[j];
+                                double r_shift = static_cast<double>(sums[j]) / (r_next + th * sums[j]);
+                                dl -= c_shift * r_shift;
+                                ddl += c_shift * r_shift * r_shift;
+                            }
+                        }
+
+                        return std::make_pair(dl, ddl);
+                    }, th[1], std::numeric_limits<double>::min(), 1.0, 30) : 0.0;
+
+                    if (abs(r_next - r) / r_next < 1e-5
+                            && (th_0_next == th[0] || abs(th_0_next - th[0]) / th_0_next < 1e-5)
+                            && (th_1_next == th[1] || abs(th_1_next - th[1]) / th_1_next < 1e-5)) {
+                        std::swap(r, r_next);
+                        std::swap(th_0_next, th[0]);
+                        std::swap(th_1_next, th[1]);
+                        break;
+                    }
+
+                    std::swap(r, r_next);
+                    std::swap(th_0_next, th[0]);
+                    std::swap(th_1_next, th[1]);
+
+                    if (i + 1 == n_iter) {
+                        common::logger->error("Failed to converge: {}, {}, {}", r, th[0], th[1]);
+                        throw std::runtime_error("Fail");
+                    }
                 }
-                return std::make_tuple(dl, ddl, dddl);
-            }, tr, 0.0, total_kmers, 10);
 
-            double p = tr / (tr + mu);
-            common::logger->trace("MLE r: {}\tp: {}", tr, p);
-            common::logger->trace("Fit mean: {}\tFit var: {}", tr * (1.0 - p) / p, tr * (1.0 - p) / p / p);
-            return std::make_pair(tr, p);
-        };
+                double llik = r * log(r) - lgamma(r);
+                if (th[0] != 0.0)
+                    llik += in_sum * log(th[0]);
 
-        auto [tr_in, p_in] = get_params(in_hist, in_sum_mu, in_sum_mu2, in_sum_var);
-        auto [tr_out, p_out] = get_params(out_hist, out_sum_mu, out_sum_mu2, out_sum_var);
-
-        double tr_null = null_sum_mu2 / (null_sum_var - null_sum_mu);
-        double p_null = tr_null / (tr_null + null_sum_mu);
-
-        double r_in = tr_in / in_kmers;
-        double r_out = tr_out / out_kmers;
-        double r_null = tr_null / total_kmers;
-
-        // double p_in = in_sum_mu / in_sum_var;
-        // double p_out = out_sum_mu / out_sum_var;
-
-        // double in_num = 0;
-        // double out_num = 0;
-        // double in_denom = 0;
-        // double out_denom = 0;
-        // for (size_t j = 0; j < groups.size(); ++j) {
-        //     double mu = static_cast<double>(sums[j]) / nelem;
-        //     double var = static_cast<double>(sums_of_squares[j]) / nelem - mu * mu;
-
-        //     double r = mu * mu / (var - mu);
-        //     double p = mu / var;
-        //     double p_frac = (1.0 - p) / p;
-        //     double ab = r * p_frac;
-        //     double abb = ab * p_frac;
-        //     if (groups[j]) {
-        //         out_num += ab;
-        //         out_denom += abb;
-        //     } else {
-        //         in_num += ab;
-        //         in_denom += abb;
-        //     }
-        // }
-        // double b_in = in_denom / in_num;
-        // double r_in = in_num / b_in;
-
-        // double b_out = out_denom / out_num;
-        // double r_out = out_num / b_out;
-
-        // double b_null = (in_denom + out_denom) / (in_num + out_num);
-        // double r_null = (in_num + out_num) / b_null;
-
-
-        // tsl::hopscotch_map<uint64_t, size_t> histogram;
-        // for (size_t j = 0; j < groups.size(); ++j) {
-        //     const auto &column = *columns_all[j];
-        //     const auto &column_values = *column_values_all[j];
-        //     size_t i = 0;
-        //     column.call_ones([&](uint64_t row_i) {
-        //         if (!ignored[row_i])
-        //             ++histogram[adjust_count(column_values[i], j, row_i)];
-
-        //         ++i;
-        //     });
-        // }
-
-        // double r = boost::math::tools::newton_raphson_iterate([&](double r) {
-        //     double size = nelem * groups.size();
-        //     double mu = total_kmers / size;
-        //     double dl = size * (log(r) - log(r + mu) - boost::math::digamma(r));
-        //     double ddl = size * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
-        //     for (const auto &[k, c] : histogram) {
-        //         dl += boost::math::digamma(k + r) * c;
-        //         ddl += boost::math::trigamma(k + r) * c;
-        //     }
-        //     return std::make_pair(dl, ddl);
-        // }, 1.0, 0.0, static_cast<double>(total_kmers), 30);
-
-        // common::logger->trace("r: {}", r);
-
-        // // use moments to fit the r parameter of a beta negative binomial
-        // uint64_t sum_of_cubes = 0;
-        // for (size_t j = 0; j < groups.size(); ++j) {
-        //     const auto &column = *columns_all[j];
-        //     const auto &column_values = *column_values_all[j];
-        //     size_t i = 0;
-        //     column.call_ones([&](uint64_t row_i) {
-        //         if (!ignored[row_i]) {
-        //             if (int64_t c = adjust_count(column_values[i], j, row_i))
-        //                 sum_of_cubes += c * (c - 1) * (c - 2);
-        //         }
-        //         ++i;
-        //     });
-        // }
-        // double mu = static_cast<double>(total_kmers) / groups.size() / nelem;
-        // double mu2 = static_cast<double>(std::accumulate(sums_of_squares.begin(),
-        //                                                  sums_of_squares.end(), uint64_t(0))) / groups.size() / nelem - mu;
-        // double mu3 = static_cast<double>(sum_of_cubes) / groups.size() / nelem;
-        // double a = mu3 * mu + mu * mu * mu2 + 2 * mu2 * mu2;
-        // double b = 2 * mu3 * mu * mu + mu3 * mu - mu2 * mu3 - mu * mu2 * mu2 + 3 * mu * mu + mu2 - 4 * mu2 * mu2;
-        // double c = -2 * mu * (mu2 * mu2 - mu * mu2 - mu3 * mu);
-
-        // auto [r_low, r_high] = boost::math::tools::quadratic_roots(a, b, c);
-
-        // common::logger->trace("mu: {}\tmu2: {}\tmu3: {}\tr_low: {}\tr_high: {}",
-        //                       mu, mu2, mu3, r_low, r_high);
-
-        // // check for NaNs
-        // // https://stackoverflow.com/questions/570669/checking-if-a-double-or-float-is-nan-in-c
-        // if (r_low != r_low && r_high != r_high) {
-        //     common::logger->error("{},{},{}", a, b, c);
-        //     throw std::runtime_error("Failed to fit, no real roots");
-        // }
-
-        // double r_low_var_sqerr = 0;
-        // double r_high_var_sqerr = 0;
-        // for (size_t j = 0; j < groups.size(); ++j) {
-        //     double mu = static_cast<double>(sums[j]) / nelem;
-        //     double var = static_cast<double>(sums_of_squares[j]) / nelem - mu * mu;
-        //     double p_low = r_low / (r_low + mu);
-        //     double p_high = r_high / (r_high + mu);
-        //     double t_var_low = mu / p_low;
-        //     double t_var_high = mu / p_high;
-        //     r_low_var_sqerr += pow(t_var_low - var, 2.0);
-        //     r_high_var_sqerr += pow(t_var_high - var, 2.0);
-        //     common::logger->trace("label: {}\tmean: {}\tvar: {}\tp_low: {}\tvar_low: {}\tp_high: {}\tvar_high: {}",
-        //                           j, mu, var, p_low, t_var_low, p_high, t_var_high);
-        // }
-
-        // common::logger->trace("r low: {} (sqerr: {})\t r high: {} (sqerr: {})",
-        //                       r_low, r_low_var_sqerr, r_high, r_high_var_sqerr);
-
-        // double r = r_low_var_sqerr < r_high_var_sqerr ? r_low : r_high;
-        // common::logger->trace("r: {}", r);
-
-        // boost::math::chi_squared dist(2);
-
-        // compute_pval = [&,r_in,r_out,r_null,b_in,b_out,b_null,dist](const auto &row) {
-        p_in = p_null;
-        p_out = p_null;
-        compute_pval = [&,r_in,r_out,r_null,p_in,p_out,p_null](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0);
+                if (th[1] != 0.0)
+                    llik += out_sum * log(th[1]);
 
+                llik *= groups.size();
 
-            double in_sum = 0;
-            double out_sum = 0;
-            // std::vector<uint64_t> counts(groups.size());
-            for (const auto &[j, c] : row) {
-                if (groups[j]) {
-                    out_sum += c;
-                } else {
-                    in_sum += c;
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    llik += lgamma(r + counts[j]) - (r + counts[j]) * log(r + th[groups[j]] * sums[j]);
+
+                    if (th[groups[j]] != 0.0)
+                        llik += log(th[groups[j]]) * counts[j];
                 }
-                // counts[j] = c;
-            }
 
-            auto get_llike = [&](double p, double total, double k) {
-                if (k == 0)
-                    return 0.0;
-
-                double factor = exp(log(p) + log(total) - log1p(-p));
-                double factor2 = factor * factor;
-                double th = boost::math::tools::schroder_iterate([&,factor,factor2,p,k](double th) {
-                    double base = factor * th;
-                    double dl = boost::math::digamma(base + k) - boost::math::digamma(base) + log(p);
-                    double ddl = factor * (boost::math::trigamma(base + k) - boost::math::trigamma(base));
-                    double dddl = factor2 * (boost::math::polygamma(2, base + k) - boost::math::polygamma(2, base));
-                    return std::make_tuple(dl, ddl, dddl);
-                }, k / total, std::numeric_limits<double>::min(), 1.0, 30);
-                factor *= th;
-                return lgamma(factor + k) - lgamma(factor) + factor * log(p);
+                return std::make_tuple(llik, r, th[0], th[1]);
             };
 
-            auto get_llike_null = [&]() {
-                double k_in = in_sum;
-                double k_out = out_sum;
-                double factor_in = exp(log(p_in) + log(in_kmers) - log1p(-p_in));
-                double factor_out = exp(log(p_out) + log(out_kmers) - log1p(-p_out));
-                double factor2_in = factor_in * factor_in;
-                double factor2_out = factor_out * factor_out;
-                double factor3_in = factor2_in * factor_in;
-                double factor3_out = factor2_out * factor_out;
-                double th = boost::math::tools::schroder_iterate([&](double th) {
-                    double base_in = factor_in * th;
-                    double base_out = factor_out * th;
-                    double dl = factor_in * log(p_in) + factor_out * log(p_out);
-                    double ddl = 0;
-                    double dddl = 0;
-                    if (k_in > 0) {
-                        dl += factor_in * (boost::math::digamma(base_in + k_in) - boost::math::digamma(base_in));
-                        ddl += factor2_in * (boost::math::trigamma(base_in + k_in) - boost::math::trigamma(base_in));
-                        dddl += factor3_in * (boost::math::polygamma(2, base_in + k_in) - boost::math::polygamma(2, base_in));
+            auto get_llik_null = [&]() {
+                double r = 10;
+                double th = (in_sum + out_sum) / total_kmers;
+                for (size_t i = 0; i < n_iter; ++i) {
+                    double r_next = boost::math::tools::newton_raphson_iterate([&](double r) {
+                        double dl = (log(r) + 1 - boost::math::digamma(r)) * groups.size();
+                        double ddl = (1.0 / r - boost::math::trigamma(r)) * groups.size();
+                        for (size_t j = 0; j < groups.size(); ++j) {
+                            double r_shift = r + sums[j] * th;
+                            double c_shift = r + counts[j];
+                            dl += boost::math::digamma(c_shift) - c_shift / r_shift - log(r_shift);
+                            ddl += boost::math::trigamma(c_shift) - 2.0 / r_shift + c_shift / r_shift / r_shift;
+                        }
+
+                        return std::make_pair(dl, ddl);
+                    }, r, 0.0, total_kmers, 30);
+
+                    double th_next = boost::math::tools::newton_raphson_iterate([&](double th) {
+                        double dl = (in_sum + out_sum) / th;
+                        double ddl = -(in_sum + out_sum) / th / th;
+                        for (size_t j = 0; j < groups.size(); ++j) {
+                            double c_shift = r_next + counts[j];
+                            double r_shift = static_cast<double>(sums[j]) / (r_next + th * sums[j]);
+                            dl -= c_shift * r_shift;
+                            ddl += c_shift * r_shift * r_shift;
+                        }
+
+                        return std::make_pair(dl, ddl);
+                    }, th, std::numeric_limits<double>::min(), 1.0, 30);
+
+                    if (abs(r_next - r) / r_next < 1e-5
+                            && abs(th_next - th) / th_next < 1e-5) {
+                        std::swap(r, r_next);
+                        std::swap(th_next, th);
+                        break;
                     }
 
-                    if (k_out > 0) {
-                        dl += factor_out * (boost::math::digamma(base_out + k_out) - boost::math::digamma(base_out));
-                        ddl += factor2_out * (boost::math::trigamma(base_out + k_out) - boost::math::trigamma(base_out));
-                        dddl += factor3_out * (boost::math::polygamma(2, base_out + k_out) - boost::math::polygamma(2, base_out));
+                    std::swap(r, r_next);
+                    std::swap(th_next, th);
+
+                    if (i + 1 == n_iter) {
+                        common::logger->error("Failed to converge: {}, {}", r, th);
+                        throw std::runtime_error("Fail");
                     }
+                }
+
+                double llik = ((in_sum + out_sum) * log(th) + r * log(r) - lgamma(r)) * groups.size();
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    llik += lgamma(r + counts[j]) + log(th) * counts[j] - (r + counts[j]) * log(r + th * sums[j]);
+                }
 
-                    return std::make_tuple(dl, ddl, dddl);
-                }, (k_in + k_out) / total_kmers, std::numeric_limits<double>::min(), 1.0, 30);
-                factor_in *= th;
-                factor_out *= th;
-                return lgamma(factor_in + k_in) - lgamma(factor_in) + factor_in * log(p_in)
-                        + lgamma(factor_out + k_out) - lgamma(factor_out) + factor_out * log(p_out);
+                return std::make_tuple(llik, r, th);
             };
 
-            double llik_in = get_llike(p_in, in_kmers, in_sum);
-            double llik_out = get_llike(p_out, out_kmers, out_sum);
-            double llik_null = get_llike_null();
-
-            double pval = boost::math::cdf(boost::math::complement(
-                boost::math::chi_squared(1), 2 * (llik_in + llik_out - llik_null)
-            ));
-
-            // double in_sum_shift = in_sum + in_kmers * r_in;
-            // double out_sum_shift = out_sum + out_kmers * r_out;
-
-            // double pval;
-            // if (in_sum_shift < 1 && out_sum_shift < 1) {
-            //     pval = 1.0;
-            // } else if (in_sum_shift < 1 || out_sum_shift < 1) {
-            //     pval = 0.0;
-            // } else {
-            //     double in_sum_shift_null = in_sum + in_kmers * r_null;
-            //     double out_sum_shift_null = out_sum + out_kmers * r_null;
-
-            //     auto dist = boost::math::fisher_f(in_sum_shift_null, out_sum_shift_null);
-            //     double f_stat = exp(log(out_sum_shift) - log(in_sum_shift) + log(in_sum_shift - 1) - log(out_sum_shift - 1));
-            //     pval = std::min(boost::math::cdf(dist, f_stat),
-            //                     boost::math::cdf(boost::math::complement(dist, f_stat))) * 2.0;
-            // }
-
-            // // KS-test
-            // double sum_shift_max;
-            // double sum_shift_min;
-            // double b_max;
-            // double b_min;
-            // if (in_sum_shift > out_sum_shift) {
-            //     sum_shift_max = in_sum_shift;
-            //     b_max = exp(log(in_kmers) - log1p(-p_in));
-            //     sum_shift_min = out_sum_shift;
-            //     b_min = exp(log(out_kmers) - log1p(-p_out));
-            // } else {
-            //     sum_shift_min = in_sum_shift;
-            //     b_min = exp(log(in_kmers) - log1p(-p_in));
-            //     sum_shift_max = out_sum_shift;
-            //     b_max = exp(log(out_kmers) - log1p(-p_out));
-            // }
-
-            // double crit_x = exp((lgamma(sum_shift_max) - lgamma(sum_shift_min)) / (sum_shift_max - sum_shift_min));
-            // double ks_stat = boost::math::gamma_p(sum_shift_max, b_max * crit_x) - boost::math::gamma_p(sum_shift_min, b_min * crit_x);
-
-            // OLD: check values on F distribution
-            // double crit_val = exp(log(out_sum_shift) + log(out_kmers) - log1p(-p_out) - log(in_sum_shift) - log(in_kmers) + log1p(-p_in));
-            // auto f_dist = boost::math::fisher_f(in_sum_shift * 2, out_sum_shift * 2);
-            // // double pval = std::min(boost::math::cdf(f_dist, crit_val),
-            // //                        boost::math::cdf(boost::math::complement(f_dist, crit_val))) * 2;
-            // double pval = boost::math::pdf(f_dist, crit_val);
-
-            // double theta_in = (in_sum + r_in - 1) / in_kmers / (b_in + 1);
-            // double theta_out = (out_sum + r_out - 1) / out_kmers / (b_out + 1);
-            // double theta_null = (in_sum + out_sum + r_null - 1) / total_kmers / (b_null + 1);
-
-            // double ll_in = theta_in > 0 ? in_sum * log(theta_in) - in_kmers * theta_in : 0;
-            // double ll_out = theta_out > 0 ? out_sum * log(theta_out) - out_kmers * theta_out : 0;
-            // double ll_null = (in_sum + out_sum) * log(theta_null) - total_kmers * theta_null;
-
-            // double stat = 2 * (ll_in + ll_out - ll_null);
-
-            // // auto make_ll_getter = [&](const auto &picker) {
-            // //     double sum = 0;
-            // //     for (const auto &[j, c] : row) {
-            // //         if (picker(j))
-            // //             sum += c;
-            // //     }
-
-            // //     return [&,sum](double theta) {
-            // //         double dl = sum / theta;
-            // //         double ddl = -sum / theta / theta;
-            // //         for (size_t j = 0; j < groups.size(); ++j) {
-            // //             if (picker(j)) {
-            // //                 double r_shift = (r + counts[j]) * sums[j];
-            // //                 double denom = r + theta * sums[j];
-            // //                 dl -= r_shift / denom;
-            // //                 ddl += r_shift * sums[j] / denom / denom;
-            // //             }
-            // //         }
-
-            // //         return std::make_pair(dl, ddl);
-            // //     };
-            // // };
-
-            // // double theta_in = boost::math::tools::newton_raphson_iterate(
-            // //     make_ll_getter([&](size_t j) { return !groups[j]; }),
-            // //     0.5, 0.0, 1.0, 30
-            // // );
-            // // double theta_out = boost::math::tools::newton_raphson_iterate(
-            // //     make_ll_getter([&](size_t j) { return groups[j]; }),
-            // //     0.5, 0.0, 1.0, 30
-            // // );
-            // // double theta_null = boost::math::tools::newton_raphson_iterate(
-            // //     make_ll_getter([&](size_t) { return true; }),
-            // //     0.5, 0.0, 1.0, 30
-            // // );
-
-            // // double ll_alt = 0;
-            // // double ll_null = 0;
-
-            // // for (size_t j = 0; j < groups.size(); ++j) {
-            // //     double theta = groups[j] ? theta_out : theta_in;
-            // //     double denom = log(r + sums[j] * theta);
-            // //     ll_alt += - r * denom + counts[j] * (log(theta) - denom);
-
-            // //     double denom_null = log(r + sums[j] * theta_null);
-            // //     ll_null += - r * denom_null + counts[j] * (log(theta_null) - denom_null);
-            // // }
-
-            // // double stat = 2 * (ll_alt - ll_null);
-            // // double pval = boost::math::cdf(boost::math::complement(dist, stat));
-            // double pval = stat > 0
-            //     ? boost::math::cdf(boost::math::complement(dist, stat))
-            //     : 1.0;
-
-            // double in_sum = 0;
-            // double out_sum = 0;
-            // sdsl::bit_vector found(groups.size());
-            // for (const auto &[j, c] : row) {
-            //     found[j] = true;
-            //     if (groups[j]) {
-            //         out_sum += c;
-            //     } else {
-            //         in_sum += c;
-            //     }
-            // }
-
-            // std::vector<size_t> unfound;
-            // for (size_t i = 0; i < found.size(); ++i) {
-            //     if (!found[i])
-            //         unfound.emplace_back(i);
-            // }
-
-            // double total_sum = in_sum + out_sum;
-            // size_t ndigits = 30;
-
-            // auto ll_dx_ddx_pick = [&](const auto &picker) {
-            //     return [&](double lambda) {
-            //         double val = 0;
-            //         double dval = 0;
-            //         for (const auto &[j, c] : row) {
-            //             if (picker(j)) {
-            //                 double prop = static_cast<double>(sums[j]) * lambda;
-            //                 double shift_prop = r + prop;
-            //                 val += (c - prop) / shift_prop;
-            //                 dval -= (r + c) / shift_prop / shift_prop * sums[j];
-            //             }
-            //         }
-
-            //         for (size_t j : unfound) {
-            //             if (picker(j)) {
-            //                 double prop = static_cast<double>(sums[j]) * lambda;
-            //                 double shift_prop = r + prop;
-            //                 val -= prop / shift_prop;
-            //                 dval -= r / shift_prop / shift_prop * sums[j];
-            //             }
-            //         }
-
-            //         return std::make_pair(val, dval);
-            //     };
-            // };
-
-            // double lambda_null = boost::math::tools::newton_raphson_iterate(
-            //     ll_dx_ddx_pick([&](size_t) { return true; }),
-            //     total_sum / total_kmers, -0.01, 1.01, ndigits
-            // );
-
-            // double chi_stat = 0;
-            // double loglikelihood_null = 0;
-            // // if (false) {
-            // //     // score test
-            // //     auto [val_in, dval_in] = ll_dx_ddx_in(lambda_null);
-            // //     auto [val_out, dval_out] = ll_dx_ddx_out(lambda_null);
-
-            // //     val_in *= r / lambda_null;
-            // //     val_out *= r / lambda_null;
-
-            // //     dval_in = (val_in / lambda_null - dval_in * r / lambda_null) / labels_in.size();
-            // //     dval_out = (val_out / lambda_null - dval_out * r / lambda_null) / labels_out.size();
-            // //     chi_stat = val_in * val_in / dval_in + val_out * val_out / dval_out;
-            // // } else {
-            //     // log likelihood test
-            //     double lambda_in = boost::math::tools::newton_raphson_iterate(
-            //         ll_dx_ddx_pick([&](size_t j) { return !groups[j]; }),
-            //         in_sum / in_kmers, -0.01, 1.01, ndigits
-            //     );
-
-            //     double lambda_out = boost::math::tools::newton_raphson_iterate(
-            //         ll_dx_ddx_pick([&](size_t j) { return groups[j]; }),
-            //         out_sum / out_kmers, -0.01, 1.01, ndigits
-            //     );
-
-            //     double loglikelihood_alt = 0;
-
-            //     for (const auto &[j, c] : row) {
-            //         double lambda = groups[j] ? lambda_out : lambda_in;
-            //         double prop = lambda * sums[j];
-            //         loglikelihood_alt += log(lambda) * c - log(r + prop) * (r + c);
-
-            //         double prop_null = lambda_null * sums[j];
-            //         loglikelihood_null += log(lambda_null) * c - log(r + prop_null) * (r + c);
-            //     }
-
-            //     for (size_t j : unfound) {
-            //         double lambda = groups[j] ? lambda_out : lambda_in;
-            //         double prop = lambda * sums[j];
-            //         loglikelihood_alt -= log(r + prop) * r;
-
-            //         double prop_null = lambda_null * sums[j];
-            //         loglikelihood_null -= log(r + prop_null) * r;
-            //     }
-
-            //     chi_stat = (loglikelihood_alt - loglikelihood_null) * 2;
-
-            //     if (chi_stat < 0) {
-            //         common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
-            //         common::logger->error("Theta null: {}\ttheta in: {}\ttheta out: {}",
-            //                               lambda_null, lambda_in, lambda_out);
-            //         throw std::runtime_error("Test failed");
-            //     }
-            // // }
-
-            // double pval = chi_stat > 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
+            auto [llik_alt, r_alt, th_0_alt, th_1_alt] = get_llik_alt();
+            auto [llik_null, r_null, th_null] = get_llik_null();
+            double lstat = 2 * (llik_alt - llik_null);
+
+            if (lstat <= 0) {
+                common::logger->error("Failed to optimize:\tcounts: {}\talt: {},{},{}\tnull: {},{}",
+                                      fmt::join(counts, ","), r_alt,th_0_alt,th_1_alt,r_null,th_null);
+                throw std::runtime_error("Fail");
+            }
 
+            double pval = boost::math::cdf(boost::math::complement(dist, lstat));
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "bp" || config.test_type == "lnp") {

From cbb332fbeab1426d60623eeaccc08bd69d92bc3a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 23 May 2024 12:17:37 +0200
Subject: [PATCH 145/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 177 +++++++-----------
 1 file changed, 69 insertions(+), 108 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 34f6328501..7f145aa549 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -370,115 +370,24 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             size_t n_iter = 1000;
 
-            auto get_llik_alt = [&]() {
-                double r = 10;
-                double th[2] = {
-                    in_sum > 0 ? in_sum / in_kmers : 0.0,
-                    out_sum > 0 ? out_sum / out_kmers : 0.0
-                };
-                for (size_t i = 0; i < n_iter; ++i) {
-                    double r_next = boost::math::tools::newton_raphson_iterate([&](double r) {
-                        double dl = 0;
-                        double ddl = 0;
-
-                        double dl_base = log(r) + 1 - boost::math::digamma(r);
-                        double ddl_base = 1.0 / r - boost::math::trigamma(r);
-                        for (size_t j = 0; j < groups.size(); ++j) {
-                            if (th[groups[j]]) {
-                                double r_shift = r + sums[j] * th[groups[j]];
-                                double c_shift = r + counts[j];
-                                dl += dl_base + boost::math::digamma(c_shift) - c_shift / r_shift - log(r_shift);
-                                ddl += ddl_base + boost::math::trigamma(c_shift) - 2.0 / r_shift + c_shift / r_shift / r_shift;
-                            }
-                        }
-
-                        return std::make_pair(dl, ddl);
-                    }, r, 0.0, total_kmers, 30);
-
-                    double th_0_next = in_sum > 0 ? boost::math::tools::newton_raphson_iterate([&](double th) {
-                        double dl = in_sum / th;
-                        double ddl = -in_sum / th / th;
-                        for (size_t j = 0; j < groups.size(); ++j) {
-                            if (!groups[j]) {
-                                double c_shift = r_next + counts[j];
-                                double r_shift = static_cast<double>(sums[j]) / (r_next + th * sums[j]);
-                                dl -= c_shift * r_shift;
-                                ddl += c_shift * r_shift * r_shift;
-                            }
-                        }
-
-                        return std::make_pair(dl, ddl);
-                    }, th[0], std::numeric_limits<double>::min(), 1.0, 30) : 0.0;
-
-                    double th_1_next = out_sum > 0 ? boost::math::tools::newton_raphson_iterate([&](double th) {
-                        double dl = out_sum / th;
-                        double ddl = -out_sum / th / th;
-                        for (size_t j = 0; j < groups.size(); ++j) {
-                            if (groups[j]) {
-                                double c_shift = r_next + counts[j];
-                                double r_shift = static_cast<double>(sums[j]) / (r_next + th * sums[j]);
-                                dl -= c_shift * r_shift;
-                                ddl += c_shift * r_shift * r_shift;
-                            }
-                        }
-
-                        return std::make_pair(dl, ddl);
-                    }, th[1], std::numeric_limits<double>::min(), 1.0, 30) : 0.0;
-
-                    if (abs(r_next - r) / r_next < 1e-5
-                            && (th_0_next == th[0] || abs(th_0_next - th[0]) / th_0_next < 1e-5)
-                            && (th_1_next == th[1] || abs(th_1_next - th[1]) / th_1_next < 1e-5)) {
-                        std::swap(r, r_next);
-                        std::swap(th_0_next, th[0]);
-                        std::swap(th_1_next, th[1]);
-                        break;
-                    }
-
-                    std::swap(r, r_next);
-                    std::swap(th_0_next, th[0]);
-                    std::swap(th_1_next, th[1]);
-
-                    if (i + 1 == n_iter) {
-                        common::logger->error("Failed to converge: {}, {}, {}", r, th[0], th[1]);
-                        throw std::runtime_error("Fail");
-                    }
-                }
-
-                double llik = r * log(r) - lgamma(r);
-                if (th[0] != 0.0)
-                    llik += in_sum * log(th[0]);
-
-                if (th[1] != 0.0)
-                    llik += out_sum * log(th[1]);
-
-                llik *= groups.size();
-
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    llik += lgamma(r + counts[j]) - (r + counts[j]) * log(r + th[groups[j]] * sums[j]);
-
-                    if (th[groups[j]] != 0.0)
-                        llik += log(th[groups[j]]) * counts[j];
-                }
-
-                return std::make_tuple(llik, r, th[0], th[1]);
-            };
-
             auto get_llik_null = [&]() {
                 double r = 10;
                 double th = (in_sum + out_sum) / total_kmers;
                 for (size_t i = 0; i < n_iter; ++i) {
-                    double r_next = boost::math::tools::newton_raphson_iterate([&](double r) {
+                    double r_next = boost::math::tools::schroder_iterate([&](double r) {
                         double dl = (log(r) + 1 - boost::math::digamma(r)) * groups.size();
                         double ddl = (1.0 / r - boost::math::trigamma(r)) * groups.size();
+                        double dddl = (-1.0 / r / r - boost::math::polygamma(2, r)) * groups.size();
                         for (size_t j = 0; j < groups.size(); ++j) {
                             double r_shift = r + sums[j] * th;
                             double c_shift = r + counts[j];
                             dl += boost::math::digamma(c_shift) - c_shift / r_shift - log(r_shift);
                             ddl += boost::math::trigamma(c_shift) - 2.0 / r_shift + c_shift / r_shift / r_shift;
+                            dddl += boost::math::polygamma(2, c_shift) + 2.0 / r_shift / r_shift + 1.0 / r_shift / r_shift - 2.0 * c_shift / r_shift / r_shift / r_shift;
                         }
 
-                        return std::make_pair(dl, ddl);
-                    }, r, 0.0, total_kmers, 30);
+                        return std::make_tuple(dl, ddl, dddl);
+                    }, r, std::numeric_limits<double>::min(), total_kmers, 30);
 
                     double th_next = boost::math::tools::newton_raphson_iterate([&](double th) {
                         double dl = (in_sum + out_sum) / th;
@@ -509,25 +418,77 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 }
 
-                double llik = ((in_sum + out_sum) * log(th) + r * log(r) - lgamma(r)) * groups.size();
+                return std::make_pair(r, th);
+            };
+
+            auto [r_null, th_null] = get_llik_null();
+
+            auto eval_r_d = [&](double th0, double th1, double r) {
+                double dl = 0;
+                double ddl = 0;
+                double th[2] = { th0, th1 };
+
+                double dl_base = log(r) + 1 - boost::math::digamma(r);
+                double ddl_base = 1.0 / r - boost::math::trigamma(r);
                 for (size_t j = 0; j < groups.size(); ++j) {
-                    llik += lgamma(r + counts[j]) + log(th) * counts[j] - (r + counts[j]) * log(r + th * sums[j]);
+                    if (th[groups[j]]) {
+                        double r_shift = r + sums[j] * th[groups[j]];
+                        double c_shift = r + counts[j];
+                        dl += dl_base + boost::math::digamma(c_shift) - c_shift / r_shift - log(r_shift);
+                        ddl += ddl_base + boost::math::trigamma(c_shift) - 2.0 / r_shift + c_shift / r_shift / r_shift;
+                    }
                 }
 
-                return std::make_tuple(llik, r, th);
+                return std::make_pair(dl, ddl);
             };
 
-            auto [llik_alt, r_alt, th_0_alt, th_1_alt] = get_llik_alt();
-            auto [llik_null, r_null, th_null] = get_llik_null();
-            double lstat = 2 * (llik_alt - llik_null);
+            auto eval_th0_d = [&](double r_next, double th) {
+                double dl = in_sum / th;
+                double ddl = -in_sum / th / th;
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    if (!groups[j]) {
+                        double c_shift = r_next + counts[j];
+                        double r_shift = static_cast<double>(sums[j]) / (r_next + th * sums[j]);
+                        dl -= c_shift * r_shift;
+                        ddl += c_shift * r_shift * r_shift;
+                    }
+                }
 
-            if (lstat <= 0) {
-                common::logger->error("Failed to optimize:\tcounts: {}\talt: {},{},{}\tnull: {},{}",
-                                      fmt::join(counts, ","), r_alt,th_0_alt,th_1_alt,r_null,th_null);
-                throw std::runtime_error("Fail");
-            }
+                return std::make_pair(dl, ddl);
+            };
+            auto eval_th1_d = [&](double r_next, double th) {
+                double dl = out_sum / th;
+                double ddl = -out_sum / th / th;
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    if (groups[j]) {
+                        double c_shift = r_next + counts[j];
+                        double r_shift = static_cast<double>(sums[j]) / (r_next + th * sums[j]);
+                        dl -= c_shift * r_shift;
+                        ddl += c_shift * r_shift * r_shift;
+                    }
+                }
+
+                return std::make_pair(dl, ddl);
+            };
+            auto [dl_r, ddl_r] = eval_r_d(th_null, th_null, r_null);
+            auto [dl_th0, ddl_th0] = eval_th0_d(r_null, th_null);
+            auto [dl_th1, ddl_th1] = eval_th1_d(r_null, th_null);
+            double nscore = dl_r * dl_r / ddl_r + dl_th0 * dl_th0 / ddl_th0 + dl_th1 * dl_th1 / ddl_th1;
+            double score = -nscore * groups.size();
+
+            // if (score <= 0) {
+            //     common::logger->error("{}\t{}\t{},{}\t{},{},{},{},{},{}",
+            //                           fmt::join(counts, ","),
+            //                           score,
+            //                           r_null,th_null,
+            //                           dl_r, ddl_r,
+            //                           dl_th0, ddl_th0,
+            //                           dl_th1, ddl_th1);
+            //     throw std::runtime_error("Fail");
+            // }
+
+            double pval = score > 0 ? boost::math::cdf(boost::math::complement(dist, score)) : 1.0;
 
-            double pval = boost::math::cdf(boost::math::complement(dist, lstat));
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "bp" || config.test_type == "lnp") {

From f4a20a04db38854919cee1dae54c31581e753ef7 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 23 May 2024 17:01:41 +0200
Subject: [PATCH 146/410] fix

---
 .../src/graph/annotated_graph_algorithm.cpp     | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 7f145aa549..3c9bf4380d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -240,15 +240,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::vector<uint64_t> vals;
         vals.reserve(column_values.size());
         column.call_ones([&](uint64_t row_i) {
-            if (!ignored[row_i])
-                return;
-
-            uint64_t c = adjust_count(column_values[i], j, row_i);
-            if (c) {
-                vals.emplace_back(c);
-                sum += c;
-                sum_of_squares += c * c;
-                ++n;
+            if (!ignored[row_i]) {
+                uint64_t c = adjust_count(column_values[i], j, row_i);
+                if (c) {
+                    vals.emplace_back(c);
+                    sum += c;
+                    sum_of_squares += c * c;
+                    ++n;
+                }
             }
             ++i;
         });

From cf7e13b9310b42df9a1dad60d5d26e83b0ab6576 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 23 May 2024 19:21:00 +0200
Subject: [PATCH 147/410] try common rp

---
 .../src/graph/annotated_graph_algorithm.cpp   | 194 +++++++-----------
 1 file changed, 76 insertions(+), 118 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 3c9bf4380d..b85fe1afe0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -350,16 +350,79 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
         };
     } else if (config.test_type == "nbinom") {
+        VectorMap<uint64_t, size_t> in_hist;
+        VectorMap<uint64_t, size_t> out_hist;
+        utils::call_rows<std::unique_ptr<bit_vector>,
+                         std::unique_ptr<ValuesContainer>,
+                         PairContainer>(columns_all, column_values_all,
+                                        [&](uint64_t row_i, const auto &raw_row) {
+            if (!ignored[row_i]) {
+                uint64_t in_sum = 0;
+                uint64_t out_sum = 0;
+                for (const auto &[j, raw_c] : raw_row) {
+                    uint64_t c = adjust_count(raw_c, j, row_i);
+                    if (groups[j]) {
+                        out_sum += c;
+                    } else {
+                        in_sum += c;
+                    }
+                }
+
+                ++in_hist[in_sum];
+                ++out_hist[out_sum];
+            }
+        });
+
+        auto hist = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(in_hist.values_container());
+        size_t in_size = hist.size();
+        auto out_hist_v = const_cast<std::vector<std::pair<uint64_t, size_t>>&>(out_hist.values_container());
+        std::copy(std::make_move_iterator(out_hist_v.begin()),
+                  std::make_move_iterator(out_hist_v.end()),
+                  std::back_inserter(hist));
+        auto in_end = hist.begin() + in_size;
+
+        auto get_rp = [&](auto begin, auto end, double total, double nelem) {
+            uint64_t sum = std::accumulate(begin, end, uint64_t(0),
+                                           [&](uint64_t s, const auto &a) { return s + a.first * a.second; });
+            if (sum != total) {
+                common::logger->error("{} != {}", sum, total);
+                throw std::runtime_error("Incorrect sum");
+            }
+            uint64_t sqsum = std::accumulate(begin, end, uint64_t(0),
+                                             [&](uint64_t s, const auto &a) { return s + a.first * a.first * a.second; });
+            double mu = sum / nelem;
+            double mu2 = mu * mu;
+            double var = sqsum / nelem - mu2;
+
+            double r = mu2 / (var - mu);
+            r = boost::math::tools::newton_raphson_iterate([&](double r) {
+                double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
+                double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
+                std::for_each(begin, end, [&](const auto &a) {
+                    const auto &[k, c] = a;
+                    dl += boost::math::digamma(k + r) * c;
+                    ddl += boost::math::trigamma(k + r) * c;
+                });
+                return std::make_pair(dl, ddl);
+            }, r, std::numeric_limits<double>::min(), total_kmers, 30);
+
+            double p = r / (r + mu);
+            common::logger->trace("Fit: {},{}", r, p);
+            return std::make_pair(r, p);
+        };
+
+        auto [r_in, p_in] = get_rp(hist.begin(), in_end, in_kmers, nelem);
+        auto [r_out, p_out] = get_rp(in_end, hist.end(), out_kmers, nelem);
+        auto [r_null, p_null] = get_rp(hist.begin(), hist.end(), total_kmers, nelem * 2.0);
+
         boost::math::chi_squared dist(1);
-        compute_pval = [&,dist](const auto &row) {
+        compute_pval = [&,dist,r_in,p_in,r_out,p_out,r_null,p_null](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0);
 
-            std::vector<uint64_t> counts(groups.size());
             double in_sum = 0;
             double out_sum = 0;
             for (const auto &[j, c] : row) {
-                counts[j] = c;
                 if (groups[j]) {
                     out_sum += c;
                 } else {
@@ -367,124 +430,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            size_t n_iter = 1000;
-
-            auto get_llik_null = [&]() {
-                double r = 10;
-                double th = (in_sum + out_sum) / total_kmers;
-                for (size_t i = 0; i < n_iter; ++i) {
-                    double r_next = boost::math::tools::schroder_iterate([&](double r) {
-                        double dl = (log(r) + 1 - boost::math::digamma(r)) * groups.size();
-                        double ddl = (1.0 / r - boost::math::trigamma(r)) * groups.size();
-                        double dddl = (-1.0 / r / r - boost::math::polygamma(2, r)) * groups.size();
-                        for (size_t j = 0; j < groups.size(); ++j) {
-                            double r_shift = r + sums[j] * th;
-                            double c_shift = r + counts[j];
-                            dl += boost::math::digamma(c_shift) - c_shift / r_shift - log(r_shift);
-                            ddl += boost::math::trigamma(c_shift) - 2.0 / r_shift + c_shift / r_shift / r_shift;
-                            dddl += boost::math::polygamma(2, c_shift) + 2.0 / r_shift / r_shift + 1.0 / r_shift / r_shift - 2.0 * c_shift / r_shift / r_shift / r_shift;
-                        }
-
-                        return std::make_tuple(dl, ddl, dddl);
-                    }, r, std::numeric_limits<double>::min(), total_kmers, 30);
-
-                    double th_next = boost::math::tools::newton_raphson_iterate([&](double th) {
-                        double dl = (in_sum + out_sum) / th;
-                        double ddl = -(in_sum + out_sum) / th / th;
-                        for (size_t j = 0; j < groups.size(); ++j) {
-                            double c_shift = r_next + counts[j];
-                            double r_shift = static_cast<double>(sums[j]) / (r_next + th * sums[j]);
-                            dl -= c_shift * r_shift;
-                            ddl += c_shift * r_shift * r_shift;
-                        }
-
-                        return std::make_pair(dl, ddl);
-                    }, th, std::numeric_limits<double>::min(), 1.0, 30);
-
-                    if (abs(r_next - r) / r_next < 1e-5
-                            && abs(th_next - th) / th_next < 1e-5) {
-                        std::swap(r, r_next);
-                        std::swap(th_next, th);
-                        break;
-                    }
-
-                    std::swap(r, r_next);
-                    std::swap(th_next, th);
-
-                    if (i + 1 == n_iter) {
-                        common::logger->error("Failed to converge: {}, {}", r, th);
-                        throw std::runtime_error("Fail");
-                    }
-                }
-
-                return std::make_pair(r, th);
-            };
-
-            auto [r_null, th_null] = get_llik_null();
-
-            auto eval_r_d = [&](double th0, double th1, double r) {
-                double dl = 0;
-                double ddl = 0;
-                double th[2] = { th0, th1 };
-
-                double dl_base = log(r) + 1 - boost::math::digamma(r);
-                double ddl_base = 1.0 / r - boost::math::trigamma(r);
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    if (th[groups[j]]) {
-                        double r_shift = r + sums[j] * th[groups[j]];
-                        double c_shift = r + counts[j];
-                        dl += dl_base + boost::math::digamma(c_shift) - c_shift / r_shift - log(r_shift);
-                        ddl += ddl_base + boost::math::trigamma(c_shift) - 2.0 / r_shift + c_shift / r_shift / r_shift;
-                    }
-                }
-
-                return std::make_pair(dl, ddl);
+            auto get_llik = [&](double sum, double r, double p) {
+                return lgamma(sum + r) - lgamma(r) + sum * log1p(-p) + r * log(p);
             };
 
-            auto eval_th0_d = [&](double r_next, double th) {
-                double dl = in_sum / th;
-                double ddl = -in_sum / th / th;
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    if (!groups[j]) {
-                        double c_shift = r_next + counts[j];
-                        double r_shift = static_cast<double>(sums[j]) / (r_next + th * sums[j]);
-                        dl -= c_shift * r_shift;
-                        ddl += c_shift * r_shift * r_shift;
-                    }
-                }
-
-                return std::make_pair(dl, ddl);
-            };
-            auto eval_th1_d = [&](double r_next, double th) {
-                double dl = out_sum / th;
-                double ddl = -out_sum / th / th;
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    if (groups[j]) {
-                        double c_shift = r_next + counts[j];
-                        double r_shift = static_cast<double>(sums[j]) / (r_next + th * sums[j]);
-                        dl -= c_shift * r_shift;
-                        ddl += c_shift * r_shift * r_shift;
-                    }
-                }
+            double llik_in = get_llik(in_sum, r_in, p_in);
+            double llik_out = get_llik(out_sum, r_out, p_out);
+            double llik_null = get_llik(in_sum, r_null, p_null) + get_llik(out_sum, r_null, p_null);
+            double score = (llik_in + llik_out - llik_null) * 2.0;
 
-                return std::make_pair(dl, ddl);
-            };
-            auto [dl_r, ddl_r] = eval_r_d(th_null, th_null, r_null);
-            auto [dl_th0, ddl_th0] = eval_th0_d(r_null, th_null);
-            auto [dl_th1, ddl_th1] = eval_th1_d(r_null, th_null);
-            double nscore = dl_r * dl_r / ddl_r + dl_th0 * dl_th0 / ddl_th0 + dl_th1 * dl_th1 / ddl_th1;
-            double score = -nscore * groups.size();
-
-            // if (score <= 0) {
-            //     common::logger->error("{}\t{}\t{},{}\t{},{},{},{},{},{}",
-            //                           fmt::join(counts, ","),
-            //                           score,
-            //                           r_null,th_null,
-            //                           dl_r, ddl_r,
-            //                           dl_th0, ddl_th0,
-            //                           dl_th1, ddl_th1);
-            //     throw std::runtime_error("Fail");
-            // }
+            if (score < 0) {
+                common::logger->error("llik < 0: {}", score);
+                throw std::runtime_error("Fail");
+            }
 
             double pval = score > 0 ? boost::math::cdf(boost::math::complement(dist, score)) : 1.0;
 

From ad87b3a3300b7f0d533b599fb38b3d3d58500cfc Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 24 May 2024 12:03:32 +0200
Subject: [PATCH 148/410] this works

---
 .../src/graph/annotated_graph_algorithm.cpp   | 234 +++++++++++-------
 1 file changed, 142 insertions(+), 92 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b85fe1afe0..7ba48777db 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -282,7 +282,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     pvals.emplace_back(1.1);
 
     VectorMap<size_t, std::vector<uint64_t>> m;
-    using RowStats = std::tuple<double, double, double>;
+    using RowStats = std::tuple<double, double, double, double>;
     std::function<RowStats(const PairContainer&)> compute_pval;
 
     if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig"
@@ -291,7 +291,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         boost::math::chi_squared dist(1);
         compute_pval = [&,dist](const auto &row) {
             if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0);
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
             double in_sum = 0;
             double out_sum = 0;
@@ -337,7 +337,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             if (chi_stat == 0)
-                return std::make_tuple(1.0, in_sum, out_sum);
+                return std::make_tuple(1.0, in_sum, out_sum, 1.1);
 
             if (chi_stat < 0) {
                 common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
@@ -347,9 +347,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (pval == -1)
                 pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
 
-            return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
+            return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers, 1.1);
         };
-    } else if (config.test_type == "nbinom") {
+    } else if (config.test_type == "dmn") {
+        double row_sqsum_in = 0;
+        double row_sqsum_out = 0;
         VectorMap<uint64_t, size_t> in_hist;
         VectorMap<uint64_t, size_t> out_hist;
         utils::call_rows<std::unique_ptr<bit_vector>,
@@ -357,68 +359,97 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          PairContainer>(columns_all, column_values_all,
                                         [&](uint64_t row_i, const auto &raw_row) {
             if (!ignored[row_i]) {
-                uint64_t in_sum = 0;
-                uint64_t out_sum = 0;
+                double sum_in = 0;
+                double sum_out = 0;
                 for (const auto &[j, raw_c] : raw_row) {
-                    uint64_t c = adjust_count(raw_c, j, row_i);
-                    if (groups[j]) {
-                        out_sum += c;
-                    } else {
-                        in_sum += c;
+                    if (uint64_t c = adjust_count(raw_c, j, row_i)) {
+                        if (groups[j]) {
+                            sum_out += c;
+                        } else {
+                            sum_in += c;
+                        }
                     }
                 }
-
-                ++in_hist[in_sum];
-                ++out_hist[out_sum];
+                ++in_hist[sum_in];
+                ++out_hist[sum_out];
+                row_sqsum_in += sum_in * sum_in;
+                row_sqsum_out += sum_out * sum_out;
             }
         });
 
-        auto hist = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(in_hist.values_container());
-        size_t in_size = hist.size();
-        auto out_hist_v = const_cast<std::vector<std::pair<uint64_t, size_t>>&>(out_hist.values_container());
-        std::copy(std::make_move_iterator(out_hist_v.begin()),
-                  std::make_move_iterator(out_hist_v.end()),
-                  std::back_inserter(hist));
-        auto in_end = hist.begin() + in_size;
-
-        auto get_rp = [&](auto begin, auto end, double total, double nelem) {
-            uint64_t sum = std::accumulate(begin, end, uint64_t(0),
-                                           [&](uint64_t s, const auto &a) { return s + a.first * a.second; });
-            if (sum != total) {
-                common::logger->error("{} != {}", sum, total);
-                throw std::runtime_error("Incorrect sum");
-            }
-            uint64_t sqsum = std::accumulate(begin, end, uint64_t(0),
-                                             [&](uint64_t s, const auto &a) { return s + a.first * a.first * a.second; });
+        auto get_r = [&](double sum, double sqsum, const auto &hist) {
             double mu = sum / nelem;
             double mu2 = mu * mu;
             double var = sqsum / nelem - mu2;
 
-            double r = mu2 / (var - mu);
-            r = boost::math::tools::newton_raphson_iterate([&](double r) {
-                double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
-                double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
-                std::for_each(begin, end, [&](const auto &a) {
-                    const auto &[k, c] = a;
-                    dl += boost::math::digamma(k + r) * c;
-                    ddl += boost::math::trigamma(k + r) * c;
-                });
+            double r_est = mu2 / (var - mu);
+            double p_est = mu / var;
+
+            double r = boost::math::tools::newton_raphson_iterate([&](double r) {
+                double dl = -nelem * (log(2.0) + boost::math::digamma(r));
+                double ddl = -nelem * boost::math::trigamma(r);
+                for (const auto &[k, c] : hist) {
+                    dl += boost::math::digamma(r + k) * c;
+                    ddl += boost::math::trigamma(r + k) * c;
+                }
                 return std::make_pair(dl, ddl);
-            }, r, std::numeric_limits<double>::min(), total_kmers, 30);
+            }, p_est / (1.0 - p_est) * r_est, std::numeric_limits<double>::min(), total_kmers, 30);
 
-            double p = r / (r + mu);
-            common::logger->trace("Fit: {},{}", r, p);
-            return std::make_pair(r, p);
+            return r;
         };
 
-        auto [r_in, p_in] = get_rp(hist.begin(), in_end, in_kmers, nelem);
-        auto [r_out, p_out] = get_rp(in_end, hist.end(), out_kmers, nelem);
-        auto [r_null, p_null] = get_rp(hist.begin(), hist.end(), total_kmers, nelem * 2.0);
+        double r_in = get_r(in_kmers, row_sqsum_in, in_hist);
+        double r_out = get_r(out_kmers, row_sqsum_out, out_hist);
+        common::logger->trace("Fits: in: {}\tout: {}", r_in, r_out);
+
+        compute_pval = [&,r_in,r_out](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+
+            double in_sum = 0;
+            double out_sum = 0;
+            for (const auto &[j, c] : row) {
+                if (groups[j]) {
+                    out_sum += c;
+                } else {
+                    in_sum += c;
+                }
+            }
+
+            double scaled_outsum = out_sum / out_kmers * in_kmers;
 
+            double a[2] = { r_in, r_out };
+            double a0 = a[0] + a[1];
+            int64_t n = in_sum + out_sum;
+            int64_t d = abs(in_sum - scaled_outsum);
+
+            double lscaling = lgamma(a0) + lgamma(n + 1) - lgamma(a0 + n);
+            double pval = 0;
+            double pval_min = 0;
+            for (int64_t s = 0; s <= n; ++s) {
+                double cur_in_sum = s;
+                double cur_scaled_out_sum = static_cast<double>(n - s) / out_kmers * in_kmers;
+
+                if (abs(cur_in_sum - cur_scaled_out_sum) >= d) {
+                    double pmf = exp(lscaling + lgamma(s + a[0]) - lgamma(a[0]) - lgamma(s + 1) + lgamma(n - s + a[1]) - lgamma(a[1]) - lgamma(n - s + 1));
+                    pval += pmf;
+                    if (s == 0 || s == n)
+                        pval_min += pmf;
+                }
+            }
+
+            // if (pval > 1.0) {
+            //     common::logger->error("{} > 1.0\t{},{}", pval, in_sum, out_sum);
+            //     throw std::runtime_error("Fail");
+            // }
+
+            return std::make_tuple(std::min(pval, 1.0), in_sum, scaled_outsum, std::min(pval_min, 1.0));
+        };
+    } else if (config.test_type == "nbinom") {
         boost::math::chi_squared dist(1);
-        compute_pval = [&,dist,r_in,p_in,r_out,p_out,r_null,p_null](const auto &row) {
+        compute_pval = [&,dist](const auto &row) {
             if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0);
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
             double in_sum = 0;
             double out_sum = 0;
@@ -430,23 +461,42 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            auto get_llik = [&](double sum, double r, double p) {
-                return lgamma(sum + r) - lgamma(r) + sum * log1p(-p) + r * log(p);
+            auto get_dls = [&](double r1, double r2) {
+                double dl1 = boost::math::digamma(r1 + in_sum) - boost::math::digamma(r1) - log(2.0);
+                double dl2 = boost::math::digamma(r2 + out_sum) - boost::math::digamma(r2) - log(2.0);
+                double ddl1 = boost::math::trigamma(r1 + in_sum) - boost::math::trigamma(r1);
+                double ddl2 = boost::math::trigamma(r2 + out_sum) - boost::math::trigamma(r2);
+                return std::make_tuple(dl1, dl2, ddl1, ddl2);
             };
 
-            double llik_in = get_llik(in_sum, r_in, p_in);
-            double llik_out = get_llik(out_sum, r_out, p_out);
-            double llik_null = get_llik(in_sum, r_null, p_null) + get_llik(out_sum, r_null, p_null);
-            double score = (llik_in + llik_out - llik_null) * 2.0;
-
-            if (score < 0) {
-                common::logger->error("llik < 0: {}", score);
+            double r_null = boost::math::tools::newton_raphson_iterate([&](double r) {
+                auto [dl1, dl2, ddl1, ddl2] = get_dls(r, r);
+                return std::make_pair(dl1 + dl2, ddl1 + ddl2);
+            }, 1.0, std::numeric_limits<double>::min(), total_kmers, 30);
+
+            double r_1 = in_sum > 0 ? boost::math::tools::newton_raphson_iterate([&](double r) {
+                auto [dl1, dl2, ddl1, ddl2] = get_dls(r, r);
+                return std::make_pair(dl1, ddl1);
+            }, 1.0, std::numeric_limits<double>::min(), total_kmers, 30) : 0.0;
+            double r_2 = out_sum > 0 ? boost::math::tools::newton_raphson_iterate([&](double r) {
+                auto [dl1, dl2, ddl1, ddl2] = get_dls(r, r);
+                return std::make_pair(dl2, ddl2);
+            }, 1.0, std::numeric_limits<double>::min(), total_kmers, 30) : 0.0;
+            double score = (lgamma(r_1 + in_sum) + lgamma(r_2 + out_sum) - lgamma(r_null + in_sum) - lgamma(r_null + out_sum)
+                            - lgamma(r_1) - lgamma(r_2) + lgamma(r_null) * 2.0
+                            - (r_1 + r_2 - r_null * 2.0) * log(2.0)) * 2.0;
+
+            // auto [dl1, dl2, ddl1, ddl2] = get_dls(r_null, r_null);
+            // double score = -(dl1 * dl1 / ddl1 + dl2 * dl2 / ddl2) * 2.0;
+
+            if (score <= 0) {
+                common::logger->error("score <= 0: {}", score);
                 throw std::runtime_error("Fail");
             }
 
-            double pval = score > 0 ? boost::math::cdf(boost::math::complement(dist, score)) : 1.0;
+            double pval = boost::math::cdf(boost::math::complement(dist, score));
 
-            return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers);
+            return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers, 1.1);
         };
     } else if (config.test_type == "bp" || config.test_type == "lnp") {
         auto get_a = [&](double n, double k, double k2, uint8_t pick = 0) {
@@ -501,7 +551,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         compute_pval = [&,a,b,a_in,b_in,a_out,b_out,mu,mu_in,mu_out,var,var_in,var_out,nelem](const auto &row) {
             if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0);
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
             double in_sum = 0;
             double out_sum = 0;
@@ -891,7 +941,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (map_pval_in > 1.0 || map_pval_in < 0.0
                     || map_pval_out > 1.0 || map_pval_out < 0.0) {
                 return std::make_tuple(1.0, in_sum,
-                                       out_sum / out_kmers * in_kmers);
+                                       out_sum / out_kmers * in_kmers,
+                                       1.1);
                 common::logger->error("Failed to compute MAP of thetas");
                 // common::logger->error("Theta in: {}\tTheta out: {}\tTheta null: {}",
                 //                       theta_in, theta_out, theta_null);
@@ -906,12 +957,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return std::make_tuple(std::min(std::min(map_pval_in, map_pval_out) * 2.0, 1.0),
                                    in_sum,
-                                   out_sum / out_kmers * in_kmers);
+                                   out_sum / out_kmers * in_kmers,
+                                   1.1);
         };
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u" || config.test_type == "quantile") {
         compute_pval = [&](const auto &row) {
             if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0);
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
             auto norm = boost::math::normal_distribution();
             auto rankdata = [&](const std::vector<double> &c) {
@@ -1018,7 +1070,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            return std::make_tuple(p, rankcx_mean, rankcy_mean);
+            return std::make_tuple(p, rankcx_mean, rankcy_mean,
+                                   config.test_type == "nonparametric_u"
+                                       ? 2.0 / exp(lgamma(groups.size() + 1) - lgamma(labels_in.size() + 1) - lgamma(labels_out.size() + 1))
+                                       : 1.1);
         };
     } else {
         throw std::runtime_error("Test not implemented");
@@ -1036,7 +1091,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        auto [pval, in_stat, out_stat] = compute_pval(row);
+        auto [pval, in_stat, out_stat, pval_min] = compute_pval(row);
         pvals.emplace_back(pval);
 
         if (pval >= 1.1)
@@ -1046,40 +1101,35 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         bool out_kmer = in_stat < out_stat;
         uint64_t k = std::numeric_limits<uint64_t>::max();
 
-        if (config.test_type != "nonparametric") {
-            double pval_min = 0;
-            if (config.test_type == "nonparametric_u") {
-                pval_min = 2.0 / exp(lgamma(groups.size() + 1) - lgamma(labels_in.size() + 1) - lgamma(labels_out.size() + 1));
-            } else {
-                PairContainer best;
-                for (const auto &[j, c] : row) {
-                    if ((in_kmer && !groups[j]) || (out_kmer && groups[j])) {
-                        best.emplace_back(j, max_obs_vals[j]);
-                    } else {
-                        best.emplace_back(j, 1);
-                    }
+        if (pval_min > 1.0) {
+            PairContainer best;
+            for (const auto &[j, c] : row) {
+                if ((in_kmer && !groups[j]) || (out_kmer && groups[j])) {
+                    best.emplace_back(j, max_obs_vals[j]);
+                } else {
+                    best.emplace_back(j, 1);
                 }
-
-                auto [pm, in_sum_m, out_sum_m] = compute_pval(best);
-                pval_min = pm;
             }
 
+            auto [pm, in_sum_m, out_sum_m, pm_m] = compute_pval(best);
+            pval_min = pm;
+
             if (pval_min > pval) {
                 common::logger->error("Min p-val estimate too high: min {} > cur {}", pval_min, pval);
                 throw std::runtime_error("Test failed");
             }
+        }
 
-            if (pval_min >= 0.05) {
-                k = 0;
-            } else if (pval_min > 0) {
-                double lkd = log2(0.05) - log2(pval_min);
-                if (lkd <= 64)
-                    k = pow(2.0, lkd);
+        if (pval_min >= 0.05) {
+            k = 0;
+        } else if (pval_min > 0) {
+            double lkd = log2(0.05) - log2(pval_min);
+            if (lkd <= 64)
+                k = pow(2.0, lkd);
 
-                if (k == 0) {
-                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
-                    throw std::runtime_error("Min failed");
-                }
+            if (k == 0) {
+                common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
+                throw std::runtime_error("Min failed");
             }
         }
 

From a57a148d1cb8513db8bb433101a95a9d0fe4ed55 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 24 May 2024 13:16:06 +0200
Subject: [PATCH 149/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 40 +++++++++----------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 7ba48777db..057cceb436 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -352,8 +352,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     } else if (config.test_type == "dmn") {
         double row_sqsum_in = 0;
         double row_sqsum_out = 0;
+        double row_sqsum_null = 0;
         VectorMap<uint64_t, size_t> in_hist;
         VectorMap<uint64_t, size_t> out_hist;
+        VectorMap<uint64_t, size_t> null_hist;
         utils::call_rows<std::unique_ptr<bit_vector>,
                          std::unique_ptr<ValuesContainer>,
                          PairContainer>(columns_all, column_values_all,
@@ -372,35 +374,33 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
                 ++in_hist[sum_in];
                 ++out_hist[sum_out];
+                ++null_hist[sum_in + sum_out];
                 row_sqsum_in += sum_in * sum_in;
                 row_sqsum_out += sum_out * sum_out;
+                row_sqsum_null += pow(sum_in + sum_out, 2.0);
             }
         });
 
-        auto get_r = [&](double sum, double sqsum, const auto &hist) {
-            double mu = sum / nelem;
-            double mu2 = mu * mu;
-            double var = sqsum / nelem - mu2;
+        double mu = (in_kmers + out_kmers) / nelem;
+        double mu2 = mu * mu;
+        double var = row_sqsum_null / nelem - mu2;
 
-            double r_est = mu2 / (var - mu);
-            double p_est = mu / var;
+        double r_null = boost::math::tools::newton_raphson_iterate([&](double r) {
+            double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
+            double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
+            for (const auto &[k, c] : null_hist) {
+                dl += boost::math::digamma(r + k) * c;
+                ddl += boost::math::trigamma(r + k) * c;
+            }
+            return std::make_pair(dl, ddl);
+        }, mu2 / (var - mu), std::numeric_limits<double>::min(), total_kmers, 30);
+        double log_p_est = log(r_null) - log(r_null + mu);
 
-            double r = boost::math::tools::newton_raphson_iterate([&](double r) {
-                double dl = -nelem * (log(2.0) + boost::math::digamma(r));
-                double ddl = -nelem * boost::math::trigamma(r);
-                for (const auto &[k, c] : hist) {
-                    dl += boost::math::digamma(r + k) * c;
-                    ddl += boost::math::trigamma(r + k) * c;
-                }
-                return std::make_pair(dl, ddl);
-            }, p_est / (1.0 - p_est) * r_est, std::numeric_limits<double>::min(), total_kmers, 30);
 
-            return r;
-        };
+        double r_in = exp(log(in_kmers) - log(nelem) + log_p_est - log1p(-exp(log_p_est)));
+        double r_out = exp(log(out_kmers) - log(nelem) + log_p_est - log1p(-exp(log_p_est)));
 
-        double r_in = get_r(in_kmers, row_sqsum_in, in_hist);
-        double r_out = get_r(out_kmers, row_sqsum_out, out_hist);
-        common::logger->trace("Fits: in: {}\tout: {}", r_in, r_out);
+        common::logger->trace("Fits: in: {}\tout: {}\tp: {}\t{} vs. {}", r_in, r_out, exp(log_p_est), r_in + r_out, r_null);
 
         compute_pval = [&,r_in,r_out](const auto &row) {
             if (row.empty())

From a2f612432748fdccaba419f5213ced34b1a9001c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 24 May 2024 14:59:23 +0200
Subject: [PATCH 150/410] this works

---
 .../src/graph/annotated_graph_algorithm.cpp   | 88 +++++++++++++++++--
 1 file changed, 79 insertions(+), 9 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 057cceb436..ff899e9810 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -6,6 +6,8 @@
 
 #include <boost/math/distributions/chi_squared.hpp>
 #include <boost/math/distributions/poisson.hpp>
+#include <boost/math/distributions/negative_binomial.hpp>
+#include <boost/math/distributions/gamma.hpp>
 #include <boost/math/statistics/univariate_statistics.hpp>
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/special_functions/digamma.hpp>
@@ -350,6 +352,72 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers, 1.1);
         };
     } else if (config.test_type == "dmn") {
+        double target_size = 0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            double s = static_cast<double>(sums[j]);
+            target_size += log(s);
+        }
+        target_size = exp(target_size / groups.size());
+
+        std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
+        for (size_t j = 0; j < groups.size(); ++j) {
+            double s = static_cast<double>(sums[j]);
+            double mu = s / nelem;
+            double mu2 = mu * mu;
+            double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
+
+            // approx nb params
+            double r = mu2 / (var - mu);
+            double p = mu / var;
+            boost::math::negative_binomial nb(r, p);
+
+            // approx gamma params
+            double th = var / mu;
+            double k = mu2 / var;
+            boost::math::gamma_distribution<> gamma(k, th * target_size / s);
+
+            common::logger->trace("{}\tApproximating NB({}, {}) with Gamma({}, {})",
+                                  j, r, p, k, th);
+
+            const auto &column = *columns_all[j];
+            const auto &column_values = *column_values_all[j];
+            size_t i = 0;
+            column.call_ones([&](uint64_t row_i) {
+                if (!ignored[row_i]) {
+                    uint64_t c = adjust_count(column_values[i], j, row_i);
+                    if (!count_maps[j].count(c)) {
+                        if (c == 0) {
+                            count_maps[j][c] = std::make_pair(0, 1);
+                        } else {
+                            double cdf = boost::math::cdf(nb, c);
+                            if (cdf == 1.0 || cdf == 0.0) {
+                                double ccdf = boost::math::cdf(boost::math::complement(nb, c));
+                                count_maps[j][c] = std::make_pair(round(boost::math::quantile(boost::math::complement(gamma, ccdf))), 1);
+                            } else {
+                                count_maps[j][c] = std::make_pair(round(boost::math::quantile(gamma, cdf)), 1);
+                            }
+                        }
+                    } else {
+                        ++count_maps[j][c].second;
+                    }
+                }
+                ++i;
+            });
+        }
+
+        double in_kmers_adj = 0;
+        double out_kmers_adj = 0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            for (const auto &[k, m] : count_maps[j]) {
+                const auto &[v, c] = m;
+                if (groups[j]) {
+                    out_kmers_adj += v * c;
+                } else {
+                    in_kmers_adj += v * c;
+                }
+            }
+        }
+
         double row_sqsum_in = 0;
         double row_sqsum_out = 0;
         double row_sqsum_null = 0;
@@ -365,6 +433,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double sum_out = 0;
                 for (const auto &[j, raw_c] : raw_row) {
                     if (uint64_t c = adjust_count(raw_c, j, row_i)) {
+                        c = count_maps[j].find(c)->second.first;
                         if (groups[j]) {
                             sum_out += c;
                         } else {
@@ -381,7 +450,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         });
 
-        double mu = (in_kmers + out_kmers) / nelem;
+        double mu = (in_kmers_adj + out_kmers_adj) / nelem;
         double mu2 = mu * mu;
         double var = row_sqsum_null / nelem - mu2;
 
@@ -393,16 +462,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ddl += boost::math::trigamma(r + k) * c;
             }
             return std::make_pair(dl, ddl);
-        }, mu2 / (var - mu), std::numeric_limits<double>::min(), total_kmers, 30);
+        }, mu2 / (var - mu), std::numeric_limits<double>::min(), in_kmers_adj + out_kmers_adj, 30);
         double log_p_est = log(r_null) - log(r_null + mu);
 
 
-        double r_in = exp(log(in_kmers) - log(nelem) + log_p_est - log1p(-exp(log_p_est)));
-        double r_out = exp(log(out_kmers) - log(nelem) + log_p_est - log1p(-exp(log_p_est)));
+        double r_in = exp(log(in_kmers_adj) - log(nelem) + log_p_est - log1p(-exp(log_p_est)));
+        double r_out = exp(log(out_kmers_adj) - log(nelem) + log_p_est - log1p(-exp(log_p_est)));
 
         common::logger->trace("Fits: in: {}\tout: {}\tp: {}\t{} vs. {}", r_in, r_out, exp(log_p_est), r_in + r_out, r_null);
 
-        compute_pval = [&,r_in,r_out](const auto &row) {
+        compute_pval = [&,r_in,r_out,count_maps](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
@@ -410,13 +479,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double out_sum = 0;
             for (const auto &[j, c] : row) {
                 if (groups[j]) {
-                    out_sum += c;
+                    out_sum += count_maps[j].find(c)->second.first;
                 } else {
-                    in_sum += c;
+                    in_sum += count_maps[j].find(c)->second.first;
                 }
             }
 
-            double scaled_outsum = out_sum / out_kmers * in_kmers;
+            double scaled_outsum = out_sum;
 
             double a[2] = { r_in, r_out };
             double a0 = a[0] + a[1];
@@ -428,7 +497,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double pval_min = 0;
             for (int64_t s = 0; s <= n; ++s) {
                 double cur_in_sum = s;
-                double cur_scaled_out_sum = static_cast<double>(n - s) / out_kmers * in_kmers;
+                double cur_scaled_out_sum = n - s;
+                // double cur_scaled_out_sum = static_cast<double>(n - s) / out_kmers * in_kmers;
 
                 if (abs(cur_in_sum - cur_scaled_out_sum) >= d) {
                     double pmf = exp(lscaling + lgamma(s + a[0]) - lgamma(a[0]) - lgamma(s + 1) + lgamma(n - s + a[1]) - lgamma(a[1]) - lgamma(n - s + 1));

From 126085a57133f1fa277ec1bd7d5146b3ef989c0d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 24 May 2024 18:12:37 +0200
Subject: [PATCH 151/410] sort of works

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ff899e9810..6e3b6e4e90 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -491,6 +491,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double a0 = a[0] + a[1];
             int64_t n = in_sum + out_sum;
             int64_t d = abs(in_sum - scaled_outsum);
+            double max_diff = d;
 
             double lscaling = lgamma(a0) + lgamma(n + 1) - lgamma(a0 + n);
             double pval = 0;
@@ -499,12 +500,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double cur_in_sum = s;
                 double cur_scaled_out_sum = n - s;
                 // double cur_scaled_out_sum = static_cast<double>(n - s) / out_kmers * in_kmers;
+                double cur_diff = abs(cur_in_sum - cur_scaled_out_sum);
 
-                if (abs(cur_in_sum - cur_scaled_out_sum) >= d) {
+                if (cur_diff >= d) {
                     double pmf = exp(lscaling + lgamma(s + a[0]) - lgamma(a[0]) - lgamma(s + 1) + lgamma(n - s + a[1]) - lgamma(a[1]) - lgamma(n - s + 1));
                     pval += pmf;
-                    if (s == 0 || s == n)
+                    if (cur_diff > max_diff) {
+                        max_diff = cur_diff;
+                        pval_min = pmf;
+                    } else if (cur_diff == max_diff) {
                         pval_min += pmf;
+                    }
                 }
             }
 

From 6c38729472e3221bbf7f0cf7c3edfb9ea1492891 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 24 May 2024 18:53:57 +0200
Subject: [PATCH 152/410] better

---
 .../src/graph/annotated_graph_algorithm.cpp   | 81 +++++++------------
 1 file changed, 28 insertions(+), 53 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6e3b6e4e90..6e3cecfe38 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -352,32 +352,37 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers, 1.1);
         };
     } else if (config.test_type == "dmn") {
-        double target_size = 0;
+        std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
+
+        double target_p = 0.0;
         for (size_t j = 0; j < groups.size(); ++j) {
             double s = static_cast<double>(sums[j]);
-            target_size += log(s);
+            double mu = s / nelem;
+            double mu2 = mu * mu;
+            double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
+            target_p += log(s) - log(nelem) - log(var);
         }
-        target_size = exp(target_size / groups.size());
+        double log_p_est = target_p / groups.size();
+        target_p = exp(log_p_est);
 
-        std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
         for (size_t j = 0; j < groups.size(); ++j) {
             double s = static_cast<double>(sums[j]);
             double mu = s / nelem;
             double mu2 = mu * mu;
             double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
 
-            // approx nb params
+            // approx in nb params
             double r = mu2 / (var - mu);
             double p = mu / var;
-            boost::math::negative_binomial nb(r, p);
 
-            // approx gamma params
-            double th = var / mu;
-            double k = mu2 / var;
-            boost::math::gamma_distribution<> gamma(k, th * target_size / s);
+            // approx out nb params
+            double r_out = exp(log(mu) + log_p_est - log1p(-target_p));
+
+            common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
+                                  j, r, p, r_out, target_p);
 
-            common::logger->trace("{}\tApproximating NB({}, {}) with Gamma({}, {})",
-                                  j, r, p, k, th);
+            boost::math::negative_binomial nb(r, p);
+            boost::math::negative_binomial nb_out(r_out, target_p);
 
             const auto &column = *columns_all[j];
             const auto &column_values = *column_values_all[j];
@@ -386,16 +391,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (!ignored[row_i]) {
                     uint64_t c = adjust_count(column_values[i], j, row_i);
                     if (!count_maps[j].count(c)) {
-                        if (c == 0) {
-                            count_maps[j][c] = std::make_pair(0, 1);
+                        double cdf = boost::math::cdf(nb, c);
+                        if (cdf == 1.0 || cdf == 0.0) {
+                            double ccdf = boost::math::cdf(boost::math::complement(nb, c));
+                            count_maps[j][c] = std::make_pair(round(boost::math::quantile(boost::math::complement(nb_out, ccdf))), 1);
                         } else {
-                            double cdf = boost::math::cdf(nb, c);
-                            if (cdf == 1.0 || cdf == 0.0) {
-                                double ccdf = boost::math::cdf(boost::math::complement(nb, c));
-                                count_maps[j][c] = std::make_pair(round(boost::math::quantile(boost::math::complement(gamma, ccdf))), 1);
-                            } else {
-                                count_maps[j][c] = std::make_pair(round(boost::math::quantile(gamma, cdf)), 1);
-                            }
+                            count_maps[j][c] = std::make_pair(round(boost::math::quantile(nb_out, cdf)), 1);
                         }
                     } else {
                         ++count_maps[j][c].second;
@@ -450,26 +451,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         });
 
-        double mu = (in_kmers_adj + out_kmers_adj) / nelem;
-        double mu2 = mu * mu;
-        double var = row_sqsum_null / nelem - mu2;
-
-        double r_null = boost::math::tools::newton_raphson_iterate([&](double r) {
-            double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
-            double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
-            for (const auto &[k, c] : null_hist) {
-                dl += boost::math::digamma(r + k) * c;
-                ddl += boost::math::trigamma(r + k) * c;
-            }
-            return std::make_pair(dl, ddl);
-        }, mu2 / (var - mu), std::numeric_limits<double>::min(), in_kmers_adj + out_kmers_adj, 30);
-        double log_p_est = log(r_null) - log(r_null + mu);
-
-
-        double r_in = exp(log(in_kmers_adj) - log(nelem) + log_p_est - log1p(-exp(log_p_est)));
-        double r_out = exp(log(out_kmers_adj) - log(nelem) + log_p_est - log1p(-exp(log_p_est)));
+        double r_in = exp(log(in_kmers_adj) - log(nelem) + log_p_est - log1p(-target_p));
+        double r_out = exp(log(out_kmers_adj) - log(nelem) + log_p_est - log1p(-target_p));
 
-        common::logger->trace("Fits: in: {}\tout: {}\tp: {}\t{} vs. {}", r_in, r_out, exp(log_p_est), r_in + r_out, r_null);
+        common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
 
         compute_pval = [&,r_in,r_out,count_maps](const auto &row) {
             if (row.empty())
@@ -485,22 +470,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            double scaled_outsum = out_sum;
-
             double a[2] = { r_in, r_out };
             double a0 = a[0] + a[1];
             int64_t n = in_sum + out_sum;
-            int64_t d = abs(in_sum - scaled_outsum);
+            int64_t d = abs(in_sum - out_sum);
             double max_diff = d;
 
             double lscaling = lgamma(a0) + lgamma(n + 1) - lgamma(a0 + n);
             double pval = 0;
             double pval_min = 0;
             for (int64_t s = 0; s <= n; ++s) {
-                double cur_in_sum = s;
-                double cur_scaled_out_sum = n - s;
-                // double cur_scaled_out_sum = static_cast<double>(n - s) / out_kmers * in_kmers;
-                double cur_diff = abs(cur_in_sum - cur_scaled_out_sum);
+                double cur_diff = abs(2 * s - n);
 
                 if (cur_diff >= d) {
                     double pmf = exp(lscaling + lgamma(s + a[0]) - lgamma(a[0]) - lgamma(s + 1) + lgamma(n - s + a[1]) - lgamma(a[1]) - lgamma(n - s + 1));
@@ -514,12 +494,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            // if (pval > 1.0) {
-            //     common::logger->error("{} > 1.0\t{},{}", pval, in_sum, out_sum);
-            //     throw std::runtime_error("Fail");
-            // }
-
-            return std::make_tuple(std::min(pval, 1.0), in_sum, scaled_outsum, std::min(pval_min, 1.0));
+            return std::make_tuple(std::min(pval, 1.0), in_sum, out_sum / out_kmers * in_kmers, std::min(pval_min, 1.0));
         };
     } else if (config.test_type == "nbinom") {
         boost::math::chi_squared dist(1);

From 8d801ea548b7ba3107ef6593802db50dfd376d58 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 26 May 2024 12:16:17 +0200
Subject: [PATCH 153/410] fixed

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6e3cecfe38..5b39897a7a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -376,7 +376,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double p = mu / var;
 
             // approx out nb params
-            double r_out = exp(log(mu) + log_p_est - log1p(-target_p));
+            double r_out = exp(log(mu) + log(p) - log1p(-target_p));
 
             common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
                                   j, r, p, r_out, target_p);

From da14f5deec48056a3c336b99b6307ce197e73505 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 26 May 2024 13:47:19 +0200
Subject: [PATCH 154/410] fix

---
 .../src/graph/annotated_graph_algorithm.cpp   | 124 ++++++++----------
 1 file changed, 57 insertions(+), 67 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5b39897a7a..c42e0f6308 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -352,58 +352,83 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers, 1.1);
         };
     } else if (config.test_type == "dmn") {
-        std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
+        auto get_rp = [&](double mu, double var, const auto &hist) {
+            double r = boost::math::tools::newton_raphson_iterate([&](double r) {
+                double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
+                double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
+                for (const auto &[k, c] : hist) {
+                    dl += boost::math::digamma(k + r) * c;
+                    ddl += boost::math::trigamma(k + r) * c;
+                }
+                return std::make_pair(dl, ddl);
+            }, mu * mu / (var - mu), std::numeric_limits<double>::min(), mu * nelem, 30);
+
+            return std::make_pair(r, r / (r + mu));
+        };
 
         double target_p = 0.0;
+        std::vector<std::pair<double, double>> nb_params;
+        std::vector<VectorMap<uint64_t, size_t>> hists;
+        nb_params.reserve(groups.size());
+        hists.reserve(groups.size());
         for (size_t j = 0; j < groups.size(); ++j) {
+            auto &hist = hists.emplace_back();
+            size_t i = 0;
+            const auto &column = *columns_all[j];
+            const auto &column_values = *column_values_all[j];
+            column.call_ones([&](uint64_t row_i) {
+                if (!ignored[row_i]) {
+                    uint64_t c = adjust_count(column_values[i], j, row_i);
+                    ++hist[c];
+                }
+                ++i;
+            });
+
             double s = static_cast<double>(sums[j]);
             double mu = s / nelem;
             double mu2 = mu * mu;
             double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
-            target_p += log(s) - log(nelem) - log(var);
+            auto [r, p] = get_rp(mu, var, hist);
+            target_p += log(p);
+            nb_params.emplace_back(r, p);
         }
         double log_p_est = target_p / groups.size();
         target_p = exp(log_p_est);
 
+        std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
+        double r_in = 0;
+        double r_out = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
-            double s = static_cast<double>(sums[j]);
-            double mu = s / nelem;
-            double mu2 = mu * mu;
-            double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
-
-            // approx in nb params
-            double r = mu2 / (var - mu);
-            double p = mu / var;
+            const auto &[r, p] = nb_params[j];
 
             // approx out nb params
-            double r_out = exp(log(mu) + log(p) - log1p(-target_p));
+            double r_map = exp(log(r) + log1p(-p) - log1p(-target_p));
 
             common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
-                                  j, r, p, r_out, target_p);
+                                  j, r, p, r_map, target_p);
 
-            boost::math::negative_binomial nb(r, p);
-            boost::math::negative_binomial nb_out(r_out, target_p);
+            if (groups[j]) {
+                r_out += r_map;
+            } else {
+                r_in += r_map;
+            }
 
-            const auto &column = *columns_all[j];
-            const auto &column_values = *column_values_all[j];
-            size_t i = 0;
-            column.call_ones([&](uint64_t row_i) {
-                if (!ignored[row_i]) {
-                    uint64_t c = adjust_count(column_values[i], j, row_i);
-                    if (!count_maps[j].count(c)) {
-                        double cdf = boost::math::cdf(nb, c);
-                        if (cdf == 1.0 || cdf == 0.0) {
-                            double ccdf = boost::math::cdf(boost::math::complement(nb, c));
-                            count_maps[j][c] = std::make_pair(round(boost::math::quantile(boost::math::complement(nb_out, ccdf))), 1);
-                        } else {
-                            count_maps[j][c] = std::make_pair(round(boost::math::quantile(nb_out, cdf)), 1);
-                        }
+            boost::math::negative_binomial nb(r, p);
+            boost::math::negative_binomial nb_out(r_map, target_p);
+
+            for (const auto &[k, c] : hists[j]) {
+                if (!count_maps[j].count(k)) {
+                    double cdf = boost::math::cdf(nb, k);
+                    if (cdf == 1.0 || cdf == 0.0) {
+                        double ccdf = boost::math::cdf(boost::math::complement(nb, k));
+                        count_maps[j][k] = std::make_pair(round(boost::math::quantile(boost::math::complement(nb_out, ccdf))), c);
                     } else {
-                        ++count_maps[j][c].second;
+                        count_maps[j][k] = std::make_pair(round(boost::math::quantile(nb_out, cdf)), c);
                     }
+                } else {
+                    count_maps[j][k].second += c;
                 }
-                ++i;
-            });
+            }
         }
 
         double in_kmers_adj = 0;
@@ -419,41 +444,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        double row_sqsum_in = 0;
-        double row_sqsum_out = 0;
-        double row_sqsum_null = 0;
-        VectorMap<uint64_t, size_t> in_hist;
-        VectorMap<uint64_t, size_t> out_hist;
-        VectorMap<uint64_t, size_t> null_hist;
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         PairContainer>(columns_all, column_values_all,
-                                        [&](uint64_t row_i, const auto &raw_row) {
-            if (!ignored[row_i]) {
-                double sum_in = 0;
-                double sum_out = 0;
-                for (const auto &[j, raw_c] : raw_row) {
-                    if (uint64_t c = adjust_count(raw_c, j, row_i)) {
-                        c = count_maps[j].find(c)->second.first;
-                        if (groups[j]) {
-                            sum_out += c;
-                        } else {
-                            sum_in += c;
-                        }
-                    }
-                }
-                ++in_hist[sum_in];
-                ++out_hist[sum_out];
-                ++null_hist[sum_in + sum_out];
-                row_sqsum_in += sum_in * sum_in;
-                row_sqsum_out += sum_out * sum_out;
-                row_sqsum_null += pow(sum_in + sum_out, 2.0);
-            }
-        });
-
-        double r_in = exp(log(in_kmers_adj) - log(nelem) + log_p_est - log1p(-target_p));
-        double r_out = exp(log(out_kmers_adj) - log(nelem) + log_p_est - log1p(-target_p));
-
         common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
 
         compute_pval = [&,r_in,r_out,count_maps](const auto &row) {
@@ -494,7 +484,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            return std::make_tuple(std::min(pval, 1.0), in_sum, out_sum / out_kmers * in_kmers, std::min(pval_min, 1.0));
+            return std::make_tuple(std::min(pval, 1.0), in_sum, out_sum / out_kmers_adj * in_kmers_adj, std::min(pval_min, 1.0));
         };
     } else if (config.test_type == "nbinom") {
         boost::math::chi_squared dist(1);

From 420e1c7889371ea1d3c37b42d61fa7de121a262c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 3 Jun 2024 15:43:28 +0200
Subject: [PATCH 155/410] more

---
 metagraph/src/cli/assemble.cpp                |   2 +
 .../src/graph/annotated_graph_algorithm.cpp   | 226 +++++++++++++-----
 .../src/graph/annotated_graph_algorithm.hpp   |   2 +
 3 files changed, 169 insertions(+), 61 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index f04578676c..992ad6062f 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -59,6 +59,8 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.clean = experiment.get("clean", false).asBool();
     diff_config.min_count = experiment.get("min_count", 0).asInt();
     diff_config.min_recurrence = experiment.get("min_recurrence", 1).asInt();
+    diff_config.min_recurrence = experiment.get("min_in_recurrence", 0).asInt();
+    diff_config.min_recurrence = experiment.get("min_out_recurrence", 0).asInt();
     diff_config.num_tests = experiment.get("num_tests", 0).asInt();
 
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c42e0f6308..b1fa35f071 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -208,11 +208,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ignored[row_i] = true;
             } else if (config.clean || config.min_count) {
                 size_t count = 0;
+                size_t count_in = 0;
+                size_t count_out = 0;
                 for (const auto &[j, raw_c] : row) {
                     if (adjust_count(raw_c, j, row_i)) {
                         ++count;
-                        if (count >= config.min_recurrence)
+
+                        if (groups[j]) {
+                            ++count_out;
+                        } else {
+                            ++count_in;
+                        }
+
+                        if (count >= config.min_recurrence
+                                && count_in >= config.min_in_recurrence
+                                && count_out >= config.min_out_recurrence) {
                             return;
+                        }
                     }
                 }
 
@@ -289,14 +301,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig"
             || config.test_type == "cmh"
+            || config.test_type == "binomial"
             || config.test_type == "fisher") {
         boost::math::chi_squared dist(1);
         compute_pval = [&,dist](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
-            double in_sum = 0;
-            double out_sum = 0;
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
 
             for (const auto &[j, c] : row) {
                 if (groups[j]) {
@@ -306,50 +319,78 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            double chi_stat = 0;
-            double pval = -1;
-            if (config.test_type == "cmh") {
-                if (in_sum + out_sum == total_kmers || total_kmers <= 1 || in_sum + out_sum == 0) {
-                    common::logger->error("Invalid counts: in_sum: {}\tout_sum: {}\ttotal_kmers: {}",
-                                          in_sum, out_sum, total_kmers);
+            if (config.test_type == "binomial") {
+                int64_t n = in_sum + out_sum;
+                double p = static_cast<double>(in_kmers) / total_kmers;
+                double pval = 0;
+                double p_min = 0;
+                double d = abs(static_cast<double>(in_sum) / in_kmers - static_cast<double>(out_sum) / out_kmers);
+                double max_d = d;
+                for (int64_t s = 0; s <= n; ++s) {
+                    int64_t t = n - s;
+                    double cur_d = abs(static_cast<double>(s) / in_kmers - static_cast<double>(t) / out_kmers);
+                    double pdf = exp(lgamma(n) - lgamma(s + 1) - lgamma(n - s + 1) + log(p) * in_sum + log1p(-p) * out_sum);
+                    if (cur_d > max_d) {
+                        max_d = cur_d;
+                        p_min = pdf;
+                    } else if (cur_d == max_d) {
+                        p_min += pdf;
+                    }
+                    if (cur_d >= d) {
+                        pval += pdf;
+                    }
                 }
 
-                double xi = in_sum * out_kmers - out_sum * in_kmers;
-                xi *= xi * (total_kmers - 1) / in_kmers / out_kmers / (in_sum + out_sum) / (total_kmers - in_sum - out_sum);
-                chi_stat = xi;
-            } else if (config.test_type == "fisher") {
-                double lp_base = lgamma(in_kmers + 1) - lgamma(in_kmers - in_sum + 1) - lgamma(in_sum + 1);
-                lp_base += lgamma(out_kmers + 1) - lgamma(out_kmers - out_sum + 1) - lgamma(out_sum + 1);
+                return std::make_tuple(pval,
+                                       static_cast<double>(in_sum),
+                                       static_cast<double>(out_sum) / out_kmers * in_kmers,
+                                       p_min);
+            } else {
+                double chi_stat = 0;
+                double pval = -1;
+                if (config.test_type == "cmh") {
+                    if (in_sum + out_sum == total_kmers || total_kmers <= 1 || in_sum + out_sum == 0) {
+                        common::logger->error("Invalid counts: in_sum: {}\tout_sum: {}\ttotal_kmers: {}",
+                                            in_sum, out_sum, total_kmers);
+                    }
 
-                double denom_in = lgamma(total_kmers + 1) - lgamma(total_kmers - in_sum - out_sum + 1) - lgamma(in_sum + out_sum + 1);
+                    double xi = in_sum * out_kmers - out_sum * in_kmers;
+                    xi *= xi * (total_kmers - 1) / in_kmers / out_kmers / (in_sum + out_sum) / (total_kmers - in_sum - out_sum);
+                    chi_stat = xi;
+                } else if (config.test_type == "fisher") {
+                    double lp_base = lgamma(in_kmers + 1) - lgamma(in_kmers - in_sum + 1) - lgamma(in_sum + 1);
+                    lp_base += lgamma(out_kmers + 1) - lgamma(out_kmers - out_sum + 1) - lgamma(out_sum + 1);
 
-                pval = exp(lp_base - denom_in);
-                chi_stat = boost::math::quantile(boost::math::complement(dist, pval));
-            } else {
-                double log_likelihood_in = in_sum > 0 ? -in_sum + in_sum * log(in_sum) : 0.0;
-                double log_likelihood_out = out_sum > 0 ? -out_sum + out_sum * log(out_sum) : 0.0;
+                    double denom_in = lgamma(total_kmers + 1) - lgamma(total_kmers - in_sum - out_sum + 1) - lgamma(in_sum + out_sum + 1);
 
-                double theta = (in_sum + out_sum) / total_kmers;
-                double mean_denom_in = theta * in_kmers;
-                double mean_denom_out = theta * out_kmers;
-                double log_likelihood_null = mean_denom_in > 0 && in_sum >= 0 ? -mean_denom_in + in_sum * log(mean_denom_in) : 0.0;
-                log_likelihood_null += mean_denom_out > 0 && out_sum >= 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
+                    pval = exp(lp_base - denom_in);
+                    chi_stat = boost::math::quantile(boost::math::complement(dist, pval));
+                } else {
+                    double log_likelihood_in = in_sum > 0 ? -in_sum + in_sum * log(static_cast<double>(in_sum)) * in_sum : 0.0;
+                    double log_likelihood_out = out_sum > 0 ? -out_sum + log(static_cast<double>(out_sum)) * out_sum : 0.0;
 
-                chi_stat = (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
-            }
+                    double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
+                    double mean_denom_in = theta * in_kmers;
+                    double mean_denom_out = theta * out_kmers;
+                    double log_likelihood_null = mean_denom_in > 0 && in_sum >= 0 ? -mean_denom_in + in_sum * log(mean_denom_in) : 0.0;
+                    log_likelihood_null += mean_denom_out > 0 && out_sum >= 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
 
-            if (chi_stat == 0)
-                return std::make_tuple(1.0, in_sum, out_sum, 1.1);
+                    chi_stat = (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
+                }
 
-            if (chi_stat < 0) {
-                common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
-                throw std::runtime_error("Test failed");
-            }
+                if (chi_stat == 0)
+                    return std::make_tuple(1.0, static_cast<double>(in_sum), static_cast<double>(out_sum) / out_kmers * in_kmers, 1.1);
 
-            if (pval == -1)
-                pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
+                if (chi_stat < 0) {
+                    common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+                    throw std::runtime_error("Test failed");
+                }
 
-            return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers, 1.1);
+                if (pval == -1)
+                    pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
+
+                return std::make_tuple(pval, static_cast<double>(in_sum), static_cast<double>(out_sum) / out_kmers * in_kmers, 1.1);
+            }
         };
     } else if (config.test_type == "dmn") {
         auto get_rp = [&](double mu, double var, const auto &hist) {
@@ -366,7 +407,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_pair(r, r / (r + mu));
         };
 
-        double target_p = 0.0;
+        double target_size = 0.0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            target_size += log(static_cast<double>(sums[j]));
+        }
+        target_size = exp(target_size / groups.size());
+
+        double target_p_num = 0.0;
+        double target_p_denom = 0.0;
         std::vector<std::pair<double, double>> nb_params;
         std::vector<VectorMap<uint64_t, size_t>> hists;
         nb_params.reserve(groups.size());
@@ -389,11 +437,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double mu2 = mu * mu;
             double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
             auto [r, p] = get_rp(mu, var, hist);
-            target_p += log(p);
             nb_params.emplace_back(r, p);
+
+            target_p_num += p * p;
+            target_p_denom += p / s;
         }
-        double log_p_est = target_p / groups.size();
-        target_p = exp(log_p_est);
+        double target_p = target_p_num / target_size / target_p_denom;
 
         std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
         double r_in = 0;
@@ -416,6 +465,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             boost::math::negative_binomial nb(r, p);
             boost::math::negative_binomial nb_out(r_map, target_p);
 
+            // ensure that 0 maps to 0
+            count_maps[j][0] = std::make_pair(0, 0);
+
             for (const auto &[k, c] : hists[j]) {
                 if (!count_maps[j].count(k)) {
                     double cdf = boost::math::cdf(nb, k);
@@ -431,9 +483,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
+        common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
+
+        common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
+
         double in_kmers_adj = 0;
         double out_kmers_adj = 0;
+        double in_kmers_adj_exp = 0;
+        double out_kmers_adj_exp = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
+            double exp_size = sums[j] * nb_params[j].second / target_p;
+            if (groups[j]) {
+                out_kmers_adj_exp += exp_size;
+            } else {
+                in_kmers_adj_exp += exp_size;
+            }
             for (const auto &[k, m] : count_maps[j]) {
                 const auto &[v, c] = m;
                 if (groups[j]) {
@@ -443,15 +507,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
         }
+        common::logger->trace("Expected Scaled Totals: in: {}\tout: {}", in_kmers_adj_exp, out_kmers_adj_exp);
+        common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers_adj, out_kmers_adj);
 
-        common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
-
-        compute_pval = [&,r_in,r_out,count_maps](const auto &row) {
+        compute_pval = [&,r_in,r_out,count_maps,in_kmers_adj,out_kmers_adj](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
-            double in_sum = 0;
-            double out_sum = 0;
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
             for (const auto &[j, c] : row) {
                 if (groups[j]) {
                     out_sum += count_maps[j].find(c)->second.first;
@@ -463,28 +527,70 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double a[2] = { r_in, r_out };
             double a0 = a[0] + a[1];
             int64_t n = in_sum + out_sum;
-            int64_t d = abs(in_sum - out_sum);
-            double max_diff = d;
 
-            double lscaling = lgamma(a0) + lgamma(n + 1) - lgamma(a0 + n);
-            double pval = 0;
+            double lscaling = lgamma(a0) + lgamma(n + 1) - lgamma(a0 + static_cast<double>(n));
+            auto get_pmf = [&](int64_t s) {
+                int64_t t = n - s;
+                double sd = s;
+                double td = t;
+                return exp(lscaling + lgamma(sd + a[0]) - lgamma(a[0]) - lgamma(s + 1) + lgamma(td + a[1]) - lgamma(a[1]) - lgamma(t + 1));
+            };
             double pval_min = 0;
-            for (int64_t s = 0; s <= n; ++s) {
-                double cur_diff = abs(2 * s - n);
 
-                if (cur_diff >= d) {
-                    double pmf = exp(lscaling + lgamma(s + a[0]) - lgamma(a[0]) - lgamma(s + 1) + lgamma(n - s + a[1]) - lgamma(a[1]) - lgamma(n - s + 1));
+            double pval = 0;
+            double d = abs(static_cast<double>(in_sum) / in_kmers_adj - static_cast<double>(out_sum) / out_kmers_adj);
+            double max_d = d;
+            for (int64_t s = 0; s <= n; ++s) {
+                int64_t t = n - s;
+                double cur_d = abs(static_cast<double>(s) / in_kmers_adj - static_cast<double>(t) / out_kmers_adj);
+                if (cur_d >= d) {
+                    double pmf = get_pmf(s);
                     pval += pmf;
-                    if (cur_diff > max_diff) {
-                        max_diff = cur_diff;
+                    if (cur_d > max_d) {
                         pval_min = pmf;
-                    } else if (cur_diff == max_diff) {
+                        max_d = cur_d;
+                    } else if (cur_d == max_d) {
                         pval_min += pmf;
                     }
                 }
             }
 
-            return std::make_tuple(std::min(pval, 1.0), in_sum, out_sum / out_kmers_adj * in_kmers_adj, std::min(pval_min, 1.0));
+            // double pval_in = 0;
+            // for (int64_t s = 0; s < in_sum; ++s) {
+            //     pval_in += get_pmf(s);
+            // }
+            // pval_in = std::min(pval_in, 1.0 - pval_in);
+
+            // double pval_out = 0;
+            // for (int64_t t = 0; t < out_sum; ++t) {
+            //     pval_out += get_pmf(n - t);
+            // }
+            // pval_out = std::min(pval_out, 1.0 - pval_out);
+
+            // double pmf = get_pmf(in_sum);
+
+            // double pval = pval_in + pval_out + pmf;
+            // double pval = 0;
+            // double pval_min = 0;
+            // for (int64_t s = 0; s <= n; ++s) {
+            //     double cur_diff = abs(2 * s - n);
+
+            //     if (cur_diff >= d) {
+            //         double pmf = exp(lscaling + lgamma(s + a[0]) - lgamma(a[0]) - lgamma(s + 1) + lgamma(n - s + a[1]) - lgamma(a[1]) - lgamma(n - s + 1));
+            //         pval += pmf;
+            //         if (cur_diff > max_diff) {
+            //             max_diff = cur_diff;
+            //             pval_min = pmf;
+            //         } else if (cur_diff == max_diff) {
+            //             pval_min += pmf;
+            //         }
+            //     }
+            // }
+
+            return std::make_tuple(std::min(pval, 1.0),
+                                   static_cast<double>(in_sum),
+                                   static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj,
+                                   std::min(pval_min, 1.0));
         };
     } else if (config.test_type == "nbinom") {
         boost::math::chi_squared dist(1);
@@ -1235,10 +1341,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         if (k == 0) {
             common::logger->trace("Falling back to Benjamini-Yekutieli");
-            double k_b = total_tests;
-            common::logger->trace("k: {}\talpha_corr: {}", k, 0.05 / k);
             for (size_t i = 0; i < pvals.size(); ++i) {
-                if (pvals[i] < 0.05 && pvals[i] * std::min(k_b, pval_map[pvals[i]]) >= 0.05) {
+                if (pvals[i] < 0.05 && pvals[i] * pval_map[pvals[i]] >= 0.05) {
                     indicator_in[i] = false;
                     indicator_out[i] = false;
                 }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 184beb52b4..937be124a5 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -42,6 +42,8 @@ struct DifferentialAssemblyConfig {
     bool filter = true;
     uint64_t min_count = 1;
     uint64_t min_recurrence = 1;
+    uint64_t min_in_recurrence = 0;
+    uint64_t min_out_recurrence = 0;
     uint64_t num_tests = 0;
     std::string outfbase;
 };

From 1cb6bbd57f4d550024f180c93a3f64ecd9cd28d7 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 3 Jun 2024 15:47:14 +0200
Subject: [PATCH 156/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 474 +-----------------
 1 file changed, 1 insertion(+), 473 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b1fa35f071..4ea45737b5 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -7,21 +7,11 @@
 #include <boost/math/distributions/chi_squared.hpp>
 #include <boost/math/distributions/poisson.hpp>
 #include <boost/math/distributions/negative_binomial.hpp>
-#include <boost/math/distributions/gamma.hpp>
 #include <boost/math/statistics/univariate_statistics.hpp>
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
-#include <boost/math/tools/minima.hpp>
-#include <boost/math/tools/roots.hpp>
-#include <boost/math/distributions/fisher_f.hpp>
-#include <boost/math/special_functions/polygamma.hpp>
-#include <boost/math/special_functions/gamma.hpp>
-
-#include <Eigen/Dense>
-#include <unsupported/Eigen/MatrixFunctions>
 
-#include "common/utils/string_utils.hpp"
 #include "common/logger.hpp"
 #include "common/vectors/bitmap.hpp"
 #include "common/vector_map.hpp"
@@ -645,469 +635,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers, 1.1);
         };
-    } else if (config.test_type == "bp" || config.test_type == "lnp") {
-        auto get_a = [&](double n, double k, double k2, uint8_t pick = 0) {
-            double mu = k / n;
-            // model with a negative binomial, similar to
-            // https://github.com/JBHilton/beta-poisson-epidemics/blob/master/functions.py#L140
-            double var = k2 / n - mu * mu;
-            double r_guess = mu * mu / (var - mu);
-
-            double a = r_guess * n / (n - 1);
-            double b = (n - 1) * a;
-
-            common::logger->trace("mean: {}\tvar: {}\ta: {}\tb: {}\texp: {}\tvar: {}",
-                                  mu, var,
-                                  a, b,
-                                  k * a / (a + b),
-                                  mu + mu * mu * (n - 1) / (n * a + 1));
-
-            return std::make_pair(a, b);
-        };
-
-        uint64_t total_sq_kmers = std::accumulate(sums_of_squares.begin(),
-                                                  sums_of_squares.end(),
-                                                  uint64_t(0));
-        common::logger->trace("Sum: {}\tSum of squares: {}", total_kmers, total_sq_kmers);
-        auto [a, b] = get_a(nelem * groups.size(), total_kmers, total_sq_kmers);
-
-        double a_in = a;
-        double a_out = a;
-        double b_in = b;
-        double b_out = b;
-
-        double mu = 0;
-        double mu_in = 0;
-        double mu_out = 0;
-        double var = 0;
-        double var_in = 0;
-        double var_out = 0;
-        if (config.test_type == "lnp") {
-            mu = boost::math::digamma(a) - boost::math::digamma(b);
-            mu_in = boost::math::digamma(a_in) - boost::math::digamma(b_in);
-            mu_out = boost::math::digamma(a_out) - boost::math::digamma(b_out);
-
-            var = boost::math::trigamma(a) + boost::math::trigamma(b);
-            var_in = boost::math::trigamma(a_in) + boost::math::trigamma(b_in);
-            var_out = boost::math::trigamma(a_out) + boost::math::trigamma(b_out);
-
-            common::logger->trace("LN null:\tmu: {}\tvar: {}", mu, var);
-            common::logger->trace("LN in:\tmu: {}\tvar: {}", mu_in, var_in);
-            common::logger->trace("LN out:\tmu: {}\tvar: {}", mu_out, var_out);
-        }
-
-        compute_pval = [&,a,b,a_in,b_in,a_out,b_out,mu,mu_in,mu_out,var,var_in,var_out,nelem](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
-
-            double in_sum = 0;
-            double out_sum = 0;
-            for (const auto &[j, c] : row) {
-                if (groups[j]) {
-                    out_sum += c;
-                } else {
-                    in_sum += c;
-                }
-            }
-
-            double null_sum = in_sum + out_sum;
-
-            double map_pval_in = 1.0;
-            double map_pval_out = 1.0;
-
-            if (config.test_type == "bp") {
-                // fit to a GLM
-                auto get_glm_joint = [&](double a, double b, size_t m, const auto &picker) -> double {
-                    typedef Eigen::Matrix<long double, Eigen::Dynamic, 2> MatrixRd;
-                    typedef Eigen::Matrix<long double, Eigen::Dynamic, 1> Vectord;
-                    typedef Eigen::Matrix<long double, 2, 1> VectorS;
-                    typedef Eigen::Matrix<long double, 2, 2> MatrixS;
-                    typedef Eigen::DiagonalMatrix<long double, Eigen::Dynamic> Diag;
-
-                    std::vector<uint64_t> k(groups.size());
-                    for (const auto &[j, c] : row) {
-                        k[j] = c;
-                    }
-
-                    MatrixRd A(m, 2);
-                    A.col(0).setOnes();
-                    A.col(1).setZero();
-                    Vectord totals(m);
-                    for (size_t j = 0; j < groups.size(); ++j) {
-                        if (picker(j))
-                            A(j, 1) = k[j];
-
-                        totals(j, 0) = sums[j];
-                    }
-
-                    Vectord y(m);
-                    y.array() = totals.array() * a / (a + b);
-
-                    VectorS beta = { 0, 0 };
-                    Vectord nu(m, 1);
-                    Vectord enu(m, 1);
-
-                    // if all elements are equal
-                    if (std::equal(A.col(1).begin(), A.col(1).end() - 1, A.col(1).begin() + 1)) {
-                        Vectord c(m);
-                        c.noalias() = totals - y;
-                        c.array() /= a + b + 1;
-                        c.array() += 1.0;
-                        c.array() = y.array() / c.array();
-
-                        double num = y.transpose() * c;
-                        double denom = c.sum();
-
-                        beta(0, 0) = log(num) - log(denom);
-                        nu.noalias() = A * beta;
-                        enu.array() = Eigen::exp(nu.array());
-                    } else {
-                        VectorS beta_next;
-                        Vectord var(m, 1);
-                        Vectord z(m, 1);
-
-                        MatrixS scale;
-
-                        Diag W(m);
-
-                        for (size_t i = 0; i < 100; ++i) {
-                            nu.noalias() = A * beta;
-
-                            // TODO: for some reason, this is fine, but nu.exp() segfaults
-                            enu.array() = Eigen::exp(nu.array());
-
-                            z.noalias() = beta - nu;
-                            z.array() /= enu.array();
-                            z.noalias() += nu;
-
-                            // var.noalias() = totals - enu;
-                            // var.array() /= a + b + 1;
-                            // var.array() += 1.0;
-                            // var.array() *= enu.array();
-
-                            // W.diagonal().noalias() = enu.pow(2.0);
-                            // W.diagonal().array() /= var.array();
-
-                            // compute in log space to avoid underflow
-                            var.array() = Eigen::log((totals - enu).array());
-                            var.array() -= log(a + b + 1);
-                            var.array() += Eigen::log(enu.array());
-                            var.array() = Eigen::exp(var.array());
-                            var.noalias() += enu;
-
-                            W.diagonal().noalias() = nu;
-                            W.diagonal().array() *= 2.0;
-                            W.diagonal().array() -= Eigen::log(var.array());
-                            W.diagonal().array() = Eigen::exp(W.diagonal().array());
-
-                            scale.noalias() = A.transpose() * W * A; // nxm * mxm * mxn -> nxn
-                            if (scale != scale) {
-                                common::logger->error("Scale fail");
-                                std::cerr << A << "\n\n";
-                                std::cerr << totals << "\n\n";
-                                std::cerr << y << "\n\n";
-                                std::cerr << nu << "\n\n";
-                                std::cerr << enu << "\n\n";
-                                std::cerr << z << "\n\n";
-                                std::cerr << var << "\n\n";
-                                std::cerr << W.diagonal() << "\n\n";
-                                std::cerr << scale << std::endl;
-                                throw std::runtime_error("Fail");
-                            }
-                            beta_next.noalias() = scale.inverse() * A.transpose() * W * z; // nxn * nxm * mxm * mx1 -> nx1
-                            if (beta_next != beta_next) {
-                                common::logger->error("beta fail");
-                                std::cerr << A << "\n\n";
-                                std::cerr << totals << "\n\n";
-                                std::cerr << y << "\n\n";
-                                std::cerr << nu << "\n\n";
-                                std::cerr << enu << "\n\n";
-                                std::cerr << z << "\n\n";
-                                std::cerr << var << "\n\n";
-                                std::cerr << W.diagonal() << "\n\n";
-                                std::cerr << scale << "\n\n";
-                                std::cerr << scale.inverse() << "\n\n";
-                                std::cerr << beta_next << std::endl;
-                                throw std::runtime_error("Fail");
-                            }
-
-                            std::swap(beta, beta_next);
-                            beta_next.noalias() -= beta;
-
-                            if (beta_next.norm() < 1e-5)
-                                break;
-                        }
-                    }
-
-                    enu -= y;
-                    return enu.squaredNorm();
-                };
-
-                auto get_glm_sep = [&](double a, double b, size_t m) -> double {
-                    typedef Eigen::Matrix<long double, Eigen::Dynamic, 3> MatrixRd;
-                    typedef Eigen::Matrix<long double, Eigen::Dynamic, 1> Vectord;
-                    typedef Eigen::Matrix<long double, 3, 1> VectorS;
-                    typedef Eigen::Matrix<long double, 3, 3> MatrixS;
-                    typedef Eigen::DiagonalMatrix<long double, Eigen::Dynamic> Diag;
-
-                    std::vector<uint64_t> k(groups.size());
-                    for (const auto &[j, c] : row) {
-                        k[j] = c;
-                    }
-
-                    MatrixRd A(m, 3);
-                    A.col(0).setOnes();
-                    A.col(1).setZero();
-                    A.col(2).setZero();
-                    Vectord totals(m);
-                    for (size_t j = 0; j < groups.size(); ++j) {
-                        A(j, 1 + groups[j]) = k[j];
-                        totals(j, 0) = sums[j];
-                    }
-
-                    Vectord y(m);
-                    y.array() = totals.array() * a / (a + b);
-
-                    VectorS beta = { 0, 0, 0 };
-                    Vectord nu(m, 1);
-                    Vectord enu(m, 1);
-
-                    // if all elements are equal
-                    if (std::equal(A.col(1).begin(), A.col(1).end() - 1, A.col(1).begin() + 1)) {
-                        return get_glm_joint(a, b, m, [&](size_t j) { return !groups[j]; });
-                    } else if (std::equal(A.col(2).begin(), A.col(2).end() - 1, A.col(2).begin() + 1)) {
-                        return get_glm_joint(a, b, m, [&](size_t j) { return groups[j]; });
-                    } else {
-                        VectorS beta_next;
-                        Vectord var(m, 1);
-                        Vectord z(m, 1);
-
-                        MatrixS scale;
-
-                        Diag W(m);
-
-                        for (size_t i = 0; i < 100; ++i) {
-                            nu.noalias() = A * beta;
-
-                            // TODO: for some reason, this is fine, but nu.exp() segfaults
-                            enu.array() = Eigen::exp(nu.array());
-
-                            z.noalias() = beta - nu;
-                            z.array() /= enu.array();
-                            z.noalias() += nu;
-
-                            // var.noalias() = totals - enu;
-                            // var.array() /= a + b + 1;
-                            // var.array() += 1.0;
-                            // var.array() *= enu.array();
-
-                            // W.diagonal().noalias() = enu.pow(2.0);
-                            // W.diagonal().array() /= var.array();
-
-                            // compute in log space to avoid underflow
-                            var.array() = Eigen::log((totals - enu).array());
-                            var.array() -= log(a + b + 1);
-                            var.array() += Eigen::log(enu.array());
-                            var.array() = Eigen::exp(var.array());
-                            var.noalias() += enu;
-
-                            W.diagonal().noalias() = nu;
-                            W.diagonal().array() *= 2.0;
-                            W.diagonal().array() -= Eigen::log(var.array());
-                            W.diagonal().array() = Eigen::exp(W.diagonal().array());
-
-                            scale.noalias() = A.transpose() * W * A; // nxm * mxm * mxn -> nxn
-                            if (scale != scale) {
-                                common::logger->error("Scale fail");
-                                std::cerr << A << "\n\n";
-                                std::cerr << totals << "\n\n";
-                                std::cerr << y << "\n\n";
-                                std::cerr << nu << "\n\n";
-                                std::cerr << enu << "\n\n";
-                                std::cerr << z << "\n\n";
-                                std::cerr << var << "\n\n";
-                                std::cerr << W.diagonal() << "\n\n";
-                                std::cerr << scale << std::endl;
-                                throw std::runtime_error("Fail");
-                            }
-                            beta_next.noalias() = scale.inverse() * A.transpose() * W * z; // nxn * nxm * mxm * mx1 -> nx1
-                            if (beta_next != beta_next) {
-                                common::logger->error("beta fail");
-                                std::cerr << A << "\n\n";
-                                std::cerr << totals << "\n\n";
-                                std::cerr << y << "\n\n";
-                                std::cerr << nu << "\n\n";
-                                std::cerr << enu << "\n\n";
-                                std::cerr << z << "\n\n";
-                                std::cerr << var << "\n\n";
-                                std::cerr << W.diagonal() << "\n\n";
-                                std::cerr << scale << "\n\n";
-                                std::cerr << scale.inverse() << "\n\n";
-                                std::cerr << beta_next << std::endl;
-                                throw std::runtime_error("Fail");
-                            }
-
-                            std::swap(beta, beta_next);
-                            beta_next.noalias() -= beta;
-
-                            if (beta_next.norm() < 1e-5)
-                                break;
-                        }
-                    }
-
-                    enu -= y;
-                    return enu.squaredNorm();
-                };
-
-                int64_t df_alt = groups.size() - 3;
-
-                double sse_alt = get_glm_sep(a, b, groups.size());
-                double sse_null = get_glm_joint(a, b, groups.size(), [&](size_t) { return true; });
-
-                double dsse_alt = sse_null - sse_alt;
-
-                if (dsse_alt <= 0) {
-                    map_pval_in = 1.0;
-                } else {
-                    double f = dsse_alt / sse_alt * df_alt;
-                    map_pval_in = boost::math::cdf(boost::math::complement(boost::math::fisher_f(1, df_alt), f)) / 2;
-                    map_pval_out = map_pval_in;
-                }
-
-
-                // auto get_theta = [&](double a, double b, double total, double k_sum) {
-                //     if (a + k_sum <= 1)
-                //         return std::make_tuple(0.0, 0.0, true, false);
-
-                //     double qb = 2.0 - total - k_sum - a - b;
-                //     double qc = k_sum + a - 1.0;
-
-                //     auto [theta_low, theta_high] = boost::math::tools::quadratic_roots(total, qb, qc);
-
-                //     // Note: comparisons of thetas to themselves ensure that they are not NaN
-                //     // https://stackoverflow.com/questions/570669/checking-if-a-double-or-float-is-nan-in-c
-                //     bool low_valid = theta_low == theta_low && theta_low > 0 && theta_low < 1.0;
-                //     bool high_valid = theta_high == theta_high && theta_low != theta_high && theta_high > 0 && theta_high < 1.0;
-
-                //     double stat_low = low_valid
-                //         ? qc * log(theta_low) + (b - 1) * log1p(-theta_low) - total * theta_low
-                //         : -std::numeric_limits<double>::infinity();
-                //     double stat_high = high_valid
-                //         ? qc * log(theta_high) + (b - 1) * log1p(-theta_high) - total * theta_high
-                //         : -std::numeric_limits<double>::infinity();
-
-                //     double stat_max = std::max({ stat_low, stat_high });
-
-                //     bool stat_is_low = low_valid && stat_max == stat_low;
-                //     bool stat_is_high = high_valid && stat_max == stat_high;
-
-                //     if (stat_is_low && stat_is_high) {
-                //         common::logger->error("Multiple valid thetas\t{}", stat_max);
-                //         common::logger->error("{}\t{}", theta_low, theta_high);
-                //         common::logger->error("{}\t{}", stat_low, stat_high);
-                //         common::logger->error("{}\t{}", stat_is_low, stat_is_high);
-                //         common::logger->error("Ratio: {} / {}", k_sum, total);
-                //         common::logger->error("{},{}\t{},{},{}", a,b, total, qb, qc);
-                //         throw std::runtime_error("Fail");
-                //     }
-
-                //     if (stat_is_low || stat_is_high)
-                //         return std::make_tuple(theta_low, theta_high, stat_is_low, stat_is_high);
-
-                //     common::logger->error("No valid thetas\t{}", stat_max);
-                //     common::logger->error("{}\t{}", theta_low, theta_high);
-                //     common::logger->error("{}\t{}", stat_low, stat_high);
-                //     common::logger->error("{}\t{}", stat_is_low, stat_is_high);
-                //     common::logger->error("Ratio: {} / {}", k_sum, total);
-                //     common::logger->error("{},{}\t{},{},{}", a,b,total, qb, qc);
-                //     throw std::runtime_error("Fail");
-                // };
-
-                // auto get_stat_log = [&](double a, double b, double theta, double k, double total) -> double {
-                //     return (k + a - 1) * log(theta) + (b - 1) * log1p(-theta) - total * theta;
-                // };
-
-                // auto [theta_null_low, theta_null_high, null_is_low, null_is_high]
-                //     = get_theta(a, b, total_kmers, null_sum);
-                // auto [theta_in_low, theta_in_high, in_is_low, in_is_high]
-                //     = get_theta(a_in, b_in, in_kmers, in_sum);
-                // auto [theta_out_low, theta_out_high, out_is_low, out_is_high]
-                //     = get_theta(a_out, b_out, out_kmers, out_sum);
-
-                // double theta_null = null_is_low ? theta_null_low : theta_null_high;
-                // double theta_in = in_is_low ? theta_in_low : theta_in_high;
-                // double theta_out = out_is_low ? theta_out_low : theta_out_high;
-
-                // map_pval_in = theta_in > 0
-                //     ? exp(get_stat_log(a_in, b_in, theta_null, in_sum, in_kmers)
-                //                 - get_stat_log(a_in, b_in, theta_in, in_sum, in_kmers))
-                //     : 0.0;
-
-                // map_pval_out = theta_out > 0
-                //     ? exp(get_stat_log(a_out, b_out, theta_null, out_sum, out_kmers)
-                //                 - get_stat_log(a_out, b_out, theta_out, out_sum, out_kmers))
-                //     : 0.0;
-            } else if (config.test_type == "lnp") {
-                auto get_theta = [&](double mu, double var, double total, double k_sum) {
-                    size_t ndigits = 40;
-                    return boost::math::tools::newton_raphson_iterate(
-                        [&](double theta) -> std::pair<double, double> {
-                            double norm_factor = (log(theta) - log1p(-theta) - mu) / var;
-                            return std::make_pair(
-                                (k_sum - total * theta - 1) * (1 - theta) - (1 - 2.0 * theta) * norm_factor,
-                                1.0 - k_sum + 2.0 * norm_factor - (1.0 - 2.0 * theta) * ((1.0 - 2.0 * theta) / (var * theta * (1.0 - theta)) + total)
-                            );
-                        },
-                        k_sum > 0 ? k_sum / total : 0.01, 0.0, 1.0, ndigits
-                    );
-                };
-
-                auto get_stat_log = [&](double mu, double var, double theta, double k, double total) -> double {
-                    return (k - 1) * log(theta) - log1p(-theta) - total * theta - pow(log(theta) - log1p(-theta) - mu, 2.0) / 2.0 / var;
-                };
-
-                auto theta_null = get_theta(mu, var, total_kmers, null_sum);
-                auto theta_in = get_theta(mu_in, var_in, in_kmers, in_sum);
-                auto theta_out = get_theta(mu_out, var_out, out_kmers, out_sum);
-
-                if (theta_null != theta_null || theta_in != theta_in || theta_out != theta_out
-                        || theta_null == 0 || theta_null == 1.0
-                        || theta_in == 0 || theta_in == 1.0
-                        || theta_out == 0 || theta_out == 1.0) {
-                    common::logger->error("{}\t{}\t{}", theta_null, theta_in, theta_out);
-                    throw std::runtime_error("Failed to find theta");
-                }
-
-                map_pval_in = exp(get_stat_log(mu_in, var_in, theta_null, in_sum, in_kmers)
-                                - get_stat_log(mu_in, var_in, theta_in, in_sum, in_kmers));
-
-                map_pval_out = exp(get_stat_log(mu_out, var_out, theta_null, out_sum, out_kmers)
-                                - get_stat_log(mu_out, var_out, theta_out, out_sum, out_kmers));
-            }
-
-            if (map_pval_in > 1.0 || map_pval_in < 0.0
-                    || map_pval_out > 1.0 || map_pval_out < 0.0) {
-                return std::make_tuple(1.0, in_sum,
-                                       out_sum / out_kmers * in_kmers,
-                                       1.1);
-                common::logger->error("Failed to compute MAP of thetas");
-                // common::logger->error("Theta in: {}\tTheta out: {}\tTheta null: {}",
-                //                       theta_in, theta_out, theta_null);
-                // common::logger->error("Theta in low: {}\tTheta out low: {}\tTheta null low: {}",
-                //                       theta_in_low, theta_out_low, theta_null_low);
-                // common::logger->error("Theta in high: {}\tTheta out high: {}\tTheta null high: {}",
-                //                       theta_in_high, theta_out_high, theta_null_high);
-                // common::logger->error("In sum: {}\tOut sum: {}", in_sum, out_sum);
-                // common::logger->error("In p-val: {}\tOut p-val: {}", map_pval_in, map_pval_out);
-                throw std::runtime_error("Fail");
-            }
-
-            return std::make_tuple(std::min(std::min(map_pval_in, map_pval_out) * 2.0, 1.0),
-                                   in_sum,
-                                   out_sum / out_kmers * in_kmers,
-                                   1.1);
-        };
-    } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u" || config.test_type == "quantile") {
+    } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u") {
         compute_pval = [&](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0, 1.1);

From 161c1538eb5f5f7a23c76b5f9d10f0976dedd7c4 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 3 Jun 2024 16:43:21 +0200
Subject: [PATCH 157/410] assembling shared sequences is optional

---
 metagraph/src/cli/assemble.cpp                | 25 +++++++++++--------
 .../src/graph/annotated_graph_algorithm.hpp   |  1 +
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 992ad6062f..4b55409ab0 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -62,6 +62,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.min_recurrence = experiment.get("min_in_recurrence", 0).asInt();
     diff_config.min_recurrence = experiment.get("min_out_recurrence", 0).asInt();
     diff_config.num_tests = experiment.get("num_tests", 0).asInt();
+    diff_config.assemble_shared = experiment.get("assemble_shared", false).asBool();
 
 
     logger->trace("Per-kmer mask in fraction:\t\t{}", diff_config.label_mask_in_kmer_fraction);
@@ -231,17 +232,19 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             ingraph.reset();
 
             callback(*outgraph, exp_name + "out.");
-            if (auto masked_graph = std::dynamic_pointer_cast<MaskedDeBruijnGraph>(outgraph)) {
-                std::unique_ptr<bitmap_vector> outmask(static_cast<bitmap_vector*>(masked_graph->release_mask()));
-                outmask->add_to(&mask);
-                outmask.reset();
-                mask.flip();
-                auto masked_graph_null = std::make_shared<MaskedDeBruijnGraph>(
-                    graph_ptr, std::make_unique<bitmap_vector>(std::move(mask)), true,
-                    graph_ptr->get_mode() == DeBruijnGraph::PRIMARY ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-                );
-                masked_graph_null->likelihood_ratios = std::move(masked_graph->likelihood_ratios);
-                callback(*masked_graph_null, exp_name + "shared.");
+            if (diff_config.assemble_shared) {
+                if (auto masked_graph = std::dynamic_pointer_cast<MaskedDeBruijnGraph>(outgraph)) {
+                    std::unique_ptr<bitmap_vector> outmask(static_cast<bitmap_vector*>(masked_graph->release_mask()));
+                    outmask->add_to(&mask);
+                    outmask.reset();
+                    mask.flip();
+                    auto masked_graph_null = std::make_shared<MaskedDeBruijnGraph>(
+                        graph_ptr, std::make_unique<bitmap_vector>(std::move(mask)), true,
+                        graph_ptr->get_mode() == DeBruijnGraph::PRIMARY ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+                    );
+                    masked_graph_null->likelihood_ratios = std::move(masked_graph->likelihood_ratios);
+                    callback(*masked_graph_null, exp_name + "shared.");
+                }
             }
             outgraph.reset();
         }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 937be124a5..cdd505c457 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -40,6 +40,7 @@ struct DifferentialAssemblyConfig {
     std::string test_type = "brunner_munzel"; // default is "brunner_munzel", alternatives are: "likelihoodratio", "binary"
     bool evaluate_assembly = false; // Myrthe temporary : evaluate an assembly
     bool filter = true;
+    bool assemble_shared = false;
     uint64_t min_count = 1;
     uint64_t min_recurrence = 1;
     uint64_t min_in_recurrence = 0;

From 2177ed72c7e8e34257c4ff5d8a8731180167f779 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 3 Jun 2024 17:35:00 +0200
Subject: [PATCH 158/410] fixes

---
 metagraph/src/cli/assemble.cpp                |   4 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 102 +++---------------
 2 files changed, 18 insertions(+), 88 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 4b55409ab0..bcfed7b15d 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -59,8 +59,8 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.clean = experiment.get("clean", false).asBool();
     diff_config.min_count = experiment.get("min_count", 0).asInt();
     diff_config.min_recurrence = experiment.get("min_recurrence", 1).asInt();
-    diff_config.min_recurrence = experiment.get("min_in_recurrence", 0).asInt();
-    diff_config.min_recurrence = experiment.get("min_out_recurrence", 0).asInt();
+    diff_config.min_in_recurrence = experiment.get("min_in_recurrence", 0).asInt();
+    diff_config.min_out_recurrence = experiment.get("min_out_recurrence", 0).asInt();
     diff_config.num_tests = experiment.get("num_tests", 0).asInt();
     diff_config.assemble_shared = experiment.get("assemble_shared", false).asBool();
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 4ea45737b5..f88b0b050c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -6,6 +6,7 @@
 
 #include <boost/math/distributions/chi_squared.hpp>
 #include <boost/math/distributions/poisson.hpp>
+#include <boost/math/distributions/binomial.hpp>
 #include <boost/math/distributions/negative_binomial.hpp>
 #include <boost/math/statistics/univariate_statistics.hpp>
 #include <boost/math/tools/roots.hpp>
@@ -311,7 +312,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             if (config.test_type == "binomial") {
                 int64_t n = in_sum + out_sum;
-                double p = static_cast<double>(in_kmers) / total_kmers;
+                auto bdist = boost::math::binomial(n, static_cast<double>(in_kmers) / total_kmers);
                 double pval = 0;
                 double p_min = 0;
                 double d = abs(static_cast<double>(in_sum) / in_kmers - static_cast<double>(out_sum) / out_kmers);
@@ -319,7 +320,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 for (int64_t s = 0; s <= n; ++s) {
                     int64_t t = n - s;
                     double cur_d = abs(static_cast<double>(s) / in_kmers - static_cast<double>(t) / out_kmers);
-                    double pdf = exp(lgamma(n) - lgamma(s + 1) - lgamma(n - s + 1) + log(p) * in_sum + log1p(-p) * out_sum);
+                    double pdf = boost::math::pdf(bdist, s);
                     if (cur_d > max_d) {
                         max_d = cur_d;
                         p_min = pdf;
@@ -331,6 +332,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 }
 
+                if (pval >= 1.1) {
+                    common::logger->error("{} >= 1.1\t{}\t{},{}", pval, p_min, in_sum, out_sum);
+                    throw std::runtime_error("pval fail");
+                }
+
                 return std::make_tuple(pval,
                                        static_cast<double>(in_sum),
                                        static_cast<double>(out_sum) / out_kmers * in_kmers,
@@ -545,96 +551,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            // double pval_in = 0;
-            // for (int64_t s = 0; s < in_sum; ++s) {
-            //     pval_in += get_pmf(s);
-            // }
-            // pval_in = std::min(pval_in, 1.0 - pval_in);
-
-            // double pval_out = 0;
-            // for (int64_t t = 0; t < out_sum; ++t) {
-            //     pval_out += get_pmf(n - t);
-            // }
-            // pval_out = std::min(pval_out, 1.0 - pval_out);
-
-            // double pmf = get_pmf(in_sum);
-
-            // double pval = pval_in + pval_out + pmf;
-            // double pval = 0;
-            // double pval_min = 0;
-            // for (int64_t s = 0; s <= n; ++s) {
-            //     double cur_diff = abs(2 * s - n);
-
-            //     if (cur_diff >= d) {
-            //         double pmf = exp(lscaling + lgamma(s + a[0]) - lgamma(a[0]) - lgamma(s + 1) + lgamma(n - s + a[1]) - lgamma(a[1]) - lgamma(n - s + 1));
-            //         pval += pmf;
-            //         if (cur_diff > max_diff) {
-            //             max_diff = cur_diff;
-            //             pval_min = pmf;
-            //         } else if (cur_diff == max_diff) {
-            //             pval_min += pmf;
-            //         }
-            //     }
-            // }
+            if (pval >= 1.1) {
+                common::logger->error("{} >= 1.1\t{}\t{},{}", pval, pval_min, in_sum, out_sum);
+                throw std::runtime_error("pval fail");
+            }
 
             return std::make_tuple(std::min(pval, 1.0),
                                    static_cast<double>(in_sum),
                                    static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj,
                                    std::min(pval_min, 1.0));
         };
-    } else if (config.test_type == "nbinom") {
-        boost::math::chi_squared dist(1);
-        compute_pval = [&,dist](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
-
-            double in_sum = 0;
-            double out_sum = 0;
-            for (const auto &[j, c] : row) {
-                if (groups[j]) {
-                    out_sum += c;
-                } else {
-                    in_sum += c;
-                }
-            }
-
-            auto get_dls = [&](double r1, double r2) {
-                double dl1 = boost::math::digamma(r1 + in_sum) - boost::math::digamma(r1) - log(2.0);
-                double dl2 = boost::math::digamma(r2 + out_sum) - boost::math::digamma(r2) - log(2.0);
-                double ddl1 = boost::math::trigamma(r1 + in_sum) - boost::math::trigamma(r1);
-                double ddl2 = boost::math::trigamma(r2 + out_sum) - boost::math::trigamma(r2);
-                return std::make_tuple(dl1, dl2, ddl1, ddl2);
-            };
-
-            double r_null = boost::math::tools::newton_raphson_iterate([&](double r) {
-                auto [dl1, dl2, ddl1, ddl2] = get_dls(r, r);
-                return std::make_pair(dl1 + dl2, ddl1 + ddl2);
-            }, 1.0, std::numeric_limits<double>::min(), total_kmers, 30);
-
-            double r_1 = in_sum > 0 ? boost::math::tools::newton_raphson_iterate([&](double r) {
-                auto [dl1, dl2, ddl1, ddl2] = get_dls(r, r);
-                return std::make_pair(dl1, ddl1);
-            }, 1.0, std::numeric_limits<double>::min(), total_kmers, 30) : 0.0;
-            double r_2 = out_sum > 0 ? boost::math::tools::newton_raphson_iterate([&](double r) {
-                auto [dl1, dl2, ddl1, ddl2] = get_dls(r, r);
-                return std::make_pair(dl2, ddl2);
-            }, 1.0, std::numeric_limits<double>::min(), total_kmers, 30) : 0.0;
-            double score = (lgamma(r_1 + in_sum) + lgamma(r_2 + out_sum) - lgamma(r_null + in_sum) - lgamma(r_null + out_sum)
-                            - lgamma(r_1) - lgamma(r_2) + lgamma(r_null) * 2.0
-                            - (r_1 + r_2 - r_null * 2.0) * log(2.0)) * 2.0;
-
-            // auto [dl1, dl2, ddl1, ddl2] = get_dls(r_null, r_null);
-            // double score = -(dl1 * dl1 / ddl1 + dl2 * dl2 / ddl2) * 2.0;
-
-            if (score <= 0) {
-                common::logger->error("score <= 0: {}", score);
-                throw std::runtime_error("Fail");
-            }
-
-            double pval = boost::math::cdf(boost::math::complement(dist, score));
-
-            return std::make_tuple(pval, in_sum, out_sum / out_kmers * in_kmers, 1.1);
-        };
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u") {
         compute_pval = [&](const auto &row) {
             if (row.empty())
@@ -832,6 +758,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
     size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
                                  [](size_t sum, const auto &a) { return sum + a.second.size(); });
+    if (acc != nelem) {
+        common::logger->error("acc != nelem: {} != {}", acc, nelem);
+        throw std::runtime_error("internal failure");
+    }
     size_t total_tests = acc;
     common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
 

From 9a749f3636c97fe1ca72784437fbef7d549772d5 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 3 Jun 2024 18:48:34 +0200
Subject: [PATCH 159/410] minor

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index f88b0b050c..6d73a61ab6 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -403,12 +403,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_pair(r, r / (r + mu));
         };
 
-        double target_size = 0.0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            target_size += log(static_cast<double>(sums[j]));
-        }
-        target_size = exp(target_size / groups.size());
-
         double target_p_num = 0.0;
         double target_p_denom = 0.0;
         std::vector<std::pair<double, double>> nb_params;
@@ -436,9 +430,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             nb_params.emplace_back(r, p);
 
             target_p_num += p * p;
-            target_p_denom += p / s;
+            target_p_denom += p;
         }
-        double target_p = target_p_num / target_size / target_p_denom;
+        double target_p = target_p_num / target_p_denom;
 
         std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
         double r_in = 0;

From 86137fae51605177a14b93cd18e5d8eedb36668a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 4 Jun 2024 10:32:35 +0200
Subject: [PATCH 160/410] multithreading

---
 .../src/graph/annotated_graph_algorithm.cpp   | 96 +++++++++++--------
 1 file changed, 55 insertions(+), 41 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6d73a61ab6..c7a7ad5fdb 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1,6 +1,7 @@
 #include "annotated_graph_algorithm.hpp"
 
 #include <typeinfo>
+#include <mutex>
 
 #include <sdust.h>
 
@@ -33,8 +34,8 @@ typedef AnnotatedDBG::Annotator Annotator;
 typedef AnnotatedDBG::Annotator::Label Label;
 using Column = annot::matrix::BinaryMatrix::Column;
 using PairContainer = std::vector<std::pair<uint64_t, uint64_t>>;
-// using ValuesContainer = sdsl::int_vector_buffer<>;
-using ValuesContainer = sdsl::int_vector<>;
+using ValuesContainer = sdsl::int_vector_buffer<>;
+constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
 
 double CDF_CUTOFF = 0.95;
 uint64_t N_BUCKETS_FOR_ESTIMATION = 3;
@@ -100,6 +101,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          size_t num_threads,
                          size_t num_parallel_files) {
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
+    bool parallel = num_parallel_files > 1;
     common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
     common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
     common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
@@ -136,6 +138,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     if (config.clean) {
         common::logger->trace("Cleaning count columns");
+
+        #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto &column_values = *column_values_all[j];
 
@@ -191,12 +195,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Marking discarded k-mers");
     if (groups.size()) {
+        std::atomic_thread_fence(std::memory_order_release);
         utils::call_rows<std::unique_ptr<bit_vector>,
                          std::unique_ptr<ValuesContainer>,
                          PairContainer>(columns_all, column_values_all,
                                         [&](uint64_t row_i, const auto &row) {
             if (row.empty()) {
-                ignored[row_i] = true;
+                set_bit(ignored.data(), row_i, parallel, MO_RELAXED);
             } else if (config.clean || config.min_count) {
                 size_t count = 0;
                 size_t count_in = 0;
@@ -219,9 +224,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 }
 
-                ignored[row_i] = true;
+                set_bit(ignored.data(), row_i, parallel, MO_RELAXED);
             }
         });
+        std::atomic_thread_fence(std::memory_order_acquire);
     }
 
     double nelem = ignored.size() - sdsl::util::cnt_one_bits(ignored);
@@ -229,11 +235,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Computing aggregate statistics");
     double in_kmers = 0;
     double out_kmers = 0;
-    std::vector<double> medians;
-    std::vector<uint64_t> max_obs_vals;
-    std::vector<size_t> n_kmers;
-    std::vector<uint64_t> sums;
-    std::vector<uint64_t> sums_of_squares;
+    std::vector<double> medians(groups.size());
+    std::vector<uint64_t> max_obs_vals(groups.size());
+    std::vector<size_t> n_kmers(groups.size());
+    std::vector<uint64_t> sums(groups.size());
+    std::vector<uint64_t> sums_of_squares(groups.size());
+
+    #pragma omp parallel for num_threads(num_parallel_files)
     for (size_t j = 0; j < groups.size(); ++j) {
         const auto &column = *columns_all[j];
         const auto &column_values = *column_values_all[j];
@@ -257,17 +265,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             ++i;
         });
 
-        max_obs_vals.emplace_back(*std::max_element(vals.begin(), vals.end()));
-        medians.push_back(boost::math::statistics::median(vals));
+        max_obs_vals[j] = *std::max_element(vals.begin(), vals.end());
+        medians[j] = boost::math::statistics::median(vals);
 
-        sums.push_back(sum);
+        sums[j] = sum;
         if (groups[j]) {
             out_kmers += sum;
         } else {
             in_kmers += sum;
         }
-        sums_of_squares.push_back(sum_of_squares);
-        n_kmers.push_back(n);
+        sums_of_squares[j] = sum_of_squares;
+        n_kmers[j] = n;
 
         common::logger->trace("{}: kept: {} / {}\tsum: {}\tmedian: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
                               j, n, i, sum, medians[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
@@ -282,15 +290,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     sdsl::bit_vector indicator_in(graph_ptr->max_index() + 1, false);
     sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
 
-    std::vector<double> pvals;
-    pvals.reserve(graph_ptr->max_index() + 1);
-    pvals.emplace_back(1.1);
+    std::vector<double> pvals(graph_ptr->max_index() + 1, 1.1);
 
     VectorMap<size_t, std::vector<uint64_t>> m;
     using RowStats = std::tuple<double, double, double, double>;
     std::function<RowStats(const PairContainer&)> compute_pval;
 
-    if (config.test_type == "likelihoodratio" || config.test_type == "likelihoodratio_unitig"
+    if (config.test_type == "likelihoodratio_unitig"
             || config.test_type == "cmh"
             || config.test_type == "binomial"
             || config.test_type == "fisher") {
@@ -389,6 +395,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         };
     } else if (config.test_type == "dmn") {
+        common::logger->trace("Fitting negative binomial distributions");
         auto get_rp = [&](double mu, double var, const auto &hist) {
             double r = boost::math::tools::newton_raphson_iterate([&](double r) {
                 double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
@@ -405,12 +412,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         double target_p_num = 0.0;
         double target_p_denom = 0.0;
-        std::vector<std::pair<double, double>> nb_params;
-        std::vector<VectorMap<uint64_t, size_t>> hists;
-        nb_params.reserve(groups.size());
-        hists.reserve(groups.size());
+        std::vector<std::pair<double, double>> nb_params(groups.size());
+        std::vector<VectorMap<uint64_t, size_t>> hists(groups.size());
+
+        #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
-            auto &hist = hists.emplace_back();
+            auto &hist = hists[j];
             size_t i = 0;
             const auto &column = *columns_all[j];
             const auto &column_values = *column_values_all[j];
@@ -426,8 +433,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double mu = s / nelem;
             double mu2 = mu * mu;
             double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
-            auto [r, p] = get_rp(mu, var, hist);
-            nb_params.emplace_back(r, p);
+            nb_params[j] = get_rp(mu, var, hist);
+            double p = nb_params[j].second;
 
             target_p_num += p * p;
             target_p_denom += p;
@@ -437,6 +444,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
         double r_in = 0;
         double r_out = 0;
+        std::mutex r_mu;
+
+        #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto &[r, p] = nb_params[j];
 
@@ -446,12 +456,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
                                   j, r, p, r_map, target_p);
 
-            if (groups[j]) {
-                r_out += r_map;
-            } else {
-                r_in += r_map;
-            }
-
             boost::math::negative_binomial nb(r, p);
             boost::math::negative_binomial nb_out(r_map, target_p);
 
@@ -471,6 +475,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     count_maps[j][k].second += c;
                 }
             }
+
+            std::lock_guard<std::mutex> lock(r_mu);
+            if (groups[j]) {
+                r_out += r_map;
+            } else {
+                r_in += r_map;
+            }
         }
 
         common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
@@ -497,6 +508,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
         }
+
         common::logger->trace("Expected Scaled Totals: in: {}\tout: {}", in_kmers_adj_exp, out_kmers_adj_exp);
         common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers_adj, out_kmers_adj);
 
@@ -674,6 +686,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         throw std::runtime_error("Test not implemented");
     }
 
+    std::mutex pval_mu;
+    std::atomic_thread_fence(std::memory_order_release);
+
     utils::call_rows<std::unique_ptr<bit_vector>,
                      std::unique_ptr<ValuesContainer>,
                      PairContainer>(columns_all, column_values_all,
@@ -686,8 +701,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
+        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+
         auto [pval, in_stat, out_stat, pval_min] = compute_pval(row);
-        pvals.emplace_back(pval);
+        pvals[node] = pval;
 
         if (pval >= 1.1)
             return;
@@ -728,16 +745,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-        m[k].emplace_back(node);
-        if (in_stat == out_stat)
-            return;
+        if (in_stat != out_stat && pval < 0.05)
+            set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
 
-        auto &indicator = in_kmer ? indicator_in : indicator_out;
-        if (pval < 0.05)
-            indicator[node] = true;
+        std::lock_guard<std::mutex> lock(pval_mu);
+        m[k].emplace_back(node);
     });
 
+    std::atomic_thread_fence(std::memory_order_acquire);
+
     for (auto &col : columns_all) {
         col.reset();
     }
@@ -832,8 +848,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
 typedef std::function<size_t()> LabelCountCallback;
 
-constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
-
 uint64_t atomic_fetch(const sdsl::int_vector<> &vector,
                       uint64_t i,
                       std::mutex &backup_mutex,

From de82b05f9f28e127c7fb2918a27d51ea2f287a67 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 4 Jun 2024 12:26:13 +0200
Subject: [PATCH 161/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 198 +++++++++++-------
 1 file changed, 117 insertions(+), 81 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c7a7ad5fdb..edcac9413a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -296,12 +296,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     using RowStats = std::tuple<double, double, double, double>;
     std::function<RowStats(const PairContainer&)> compute_pval;
 
-    if (config.test_type == "likelihoodratio_unitig"
-            || config.test_type == "cmh"
-            || config.test_type == "binomial"
-            || config.test_type == "fisher") {
-        boost::math::chi_squared dist(1);
-        compute_pval = [&,dist](const auto &row) {
+    if (config.test_type == "binomial") {
+        compute_pval = [&](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
@@ -316,83 +312,122 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            if (config.test_type == "binomial") {
-                int64_t n = in_sum + out_sum;
-                auto bdist = boost::math::binomial(n, static_cast<double>(in_kmers) / total_kmers);
-                double pval = 0;
-                double p_min = 0;
-                double d = abs(static_cast<double>(in_sum) / in_kmers - static_cast<double>(out_sum) / out_kmers);
-                double max_d = d;
-                for (int64_t s = 0; s <= n; ++s) {
-                    int64_t t = n - s;
-                    double cur_d = abs(static_cast<double>(s) / in_kmers - static_cast<double>(t) / out_kmers);
-                    double pdf = boost::math::pdf(bdist, s);
-                    if (cur_d > max_d) {
-                        max_d = cur_d;
-                        p_min = pdf;
-                    } else if (cur_d == max_d) {
-                        p_min += pdf;
-                    }
-                    if (cur_d >= d) {
-                        pval += pdf;
-                    }
-                }
+            int64_t n = in_sum + out_sum;
+            auto bdist = boost::math::binomial(n, static_cast<double>(in_kmers) / total_kmers);
+            double pval = 0;
 
-                if (pval >= 1.1) {
-                    common::logger->error("{} >= 1.1\t{}\t{},{}", pval, p_min, in_sum, out_sum);
-                    throw std::runtime_error("pval fail");
-                }
+            double d_0 = static_cast<double>(n) / out_kmers;
+            double d_n = static_cast<double>(n) / in_kmers;
+            double p_min = 0;
+            if (d_0 > d_n) {
+                p_min = boost::math::pdf(bdist, 0);
+            } else if (d_0 < d_n) {
+                p_min = boost::math::pdf(bdist, n);
+            } else {
+                p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
+            }
+
+            double d = abs(static_cast<double>(in_sum) / in_kmers - static_cast<double>(out_sum) / out_kmers);
+
+            // lower tail
+            int64_t s = 0;
+            for ( ; s <= n; ++s) {
+                int64_t t = n - s;
+                double cur_d = abs(static_cast<double>(s) / in_kmers - static_cast<double>(t) / out_kmers);
+                if (cur_d < d)
+                    break;
+            }
 
-                return std::make_tuple(pval,
-                                       static_cast<double>(in_sum),
-                                       static_cast<double>(out_sum) / out_kmers * in_kmers,
-                                       p_min);
+            if (s == n + 1) {
+                pval = 1.0;
             } else {
-                double chi_stat = 0;
-                double pval = -1;
-                if (config.test_type == "cmh") {
-                    if (in_sum + out_sum == total_kmers || total_kmers <= 1 || in_sum + out_sum == 0) {
-                        common::logger->error("Invalid counts: in_sum: {}\tout_sum: {}\ttotal_kmers: {}",
-                                            in_sum, out_sum, total_kmers);
-                    }
+                if (s > 0)
+                    pval = boost::math::cdf(bdist, s - 1);
+
+                // upper tail
+                int64_t s_u = n;
+                for ( ; s_u >= s; --s_u) {
+                    int64_t t = n - s_u;
+                    double cur_d = abs(static_cast<double>(s_u) / in_kmers - static_cast<double>(t) / out_kmers);
+                    if (cur_d < d)
+                        break;
+                }
 
-                    double xi = in_sum * out_kmers - out_sum * in_kmers;
-                    xi *= xi * (total_kmers - 1) / in_kmers / out_kmers / (in_sum + out_sum) / (total_kmers - in_sum - out_sum);
-                    chi_stat = xi;
-                } else if (config.test_type == "fisher") {
-                    double lp_base = lgamma(in_kmers + 1) - lgamma(in_kmers - in_sum + 1) - lgamma(in_sum + 1);
-                    lp_base += lgamma(out_kmers + 1) - lgamma(out_kmers - out_sum + 1) - lgamma(out_sum + 1);
+                assert(s_u >= s - 1);
 
-                    double denom_in = lgamma(total_kmers + 1) - lgamma(total_kmers - in_sum - out_sum + 1) - lgamma(in_sum + out_sum + 1);
+                if (s_u + 1 <= n)
+                    pval += boost::math::cdf(boost::math::complement(bdist, s_u + 1));
+            }
 
-                    pval = exp(lp_base - denom_in);
-                    chi_stat = boost::math::quantile(boost::math::complement(dist, pval));
-                } else {
-                    double log_likelihood_in = in_sum > 0 ? -in_sum + in_sum * log(static_cast<double>(in_sum)) * in_sum : 0.0;
-                    double log_likelihood_out = out_sum > 0 ? -out_sum + log(static_cast<double>(out_sum)) * out_sum : 0.0;
+            if (pval > 1.0) {
+                common::logger->error("{} > 1.0\t{}\t{},{}", pval, p_min, in_sum, out_sum);
+                throw std::runtime_error("pval fail");
+            }
+
+            return std::make_tuple(pval,
+                                    static_cast<double>(in_sum),
+                                    static_cast<double>(out_sum) / out_kmers * in_kmers,
+                                    p_min);
+        };
+    } else if (config.test_type == "poisson_likelihoodratio" || config.test_type == "cmh") {
+        compute_pval = [&,dist=boost::math::chi_squared(1)](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
 
-                    double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
-                    double mean_denom_in = theta * in_kmers;
-                    double mean_denom_out = theta * out_kmers;
-                    double log_likelihood_null = mean_denom_in > 0 && in_sum >= 0 ? -mean_denom_in + in_sum * log(mean_denom_in) : 0.0;
-                    log_likelihood_null += mean_denom_out > 0 && out_sum >= 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
+            for (const auto &[j, c] : row) {
+                if (groups[j]) {
+                    out_sum += c;
+                } else {
+                    in_sum += c;
+                }
+            }
 
-                    chi_stat = (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
+            double chi_stat = 0;
+            if (config.test_type == "cmh") {
+                double xi = in_sum * out_kmers - out_sum * in_kmers;
+                xi *= xi * (total_kmers - 1) / in_kmers / out_kmers / (in_sum + out_sum) / (total_kmers - in_sum - out_sum);
+                chi_stat = xi;
+            } else {
+                if (in_sum == 0 || out_sum == 0 || in_kmers == 0 || out_kmers == 0) {
+                    common::logger->error("Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'binomial' instead");
+                    throw std::runtime_error("Test failed");
                 }
 
-                if (chi_stat == 0)
-                    return std::make_tuple(1.0, static_cast<double>(in_sum), static_cast<double>(out_sum) / out_kmers * in_kmers, 1.1);
+                double log_likelihood_in = -in_sum + log(static_cast<double>(in_sum)) * in_sum;
+                double log_likelihood_out = -out_sum + log(static_cast<double>(out_sum)) * out_sum;
+
+                double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
+                double mean_denom_in = theta * in_kmers;
+                double mean_denom_out = theta * out_kmers;
 
-                if (chi_stat < 0) {
-                    common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+                if (mean_denom_in == 0 || mean_denom_out == 0) {
+                    common::logger->error("Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'binomial' instead");
                     throw std::runtime_error("Test failed");
                 }
 
-                if (pval == -1)
-                    pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
+                double log_likelihood_null = -mean_denom_in + in_sum * log(mean_denom_in);
+                log_likelihood_null += -mean_denom_out + out_sum * log(mean_denom_out);
 
-                return std::make_tuple(pval, static_cast<double>(in_sum), static_cast<double>(out_sum) / out_kmers * in_kmers, 1.1);
+                chi_stat = (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
             }
+
+            if (chi_stat == 0)
+                return std::make_tuple(1.0, static_cast<double>(in_sum), static_cast<double>(out_sum) / out_kmers * in_kmers, 1.1);
+
+            if (chi_stat < 0) {
+                common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+                throw std::runtime_error("Test failed");
+            }
+
+            double pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
+
+            return std::make_tuple(pval,
+                                   static_cast<double>(in_sum),
+                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
+                                   1.1);
         };
     } else if (config.test_type == "dmn") {
         common::logger->trace("Fitting negative binomial distributions");
@@ -537,35 +572,36 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double td = t;
                 return exp(lscaling + lgamma(sd + a[0]) - lgamma(a[0]) - lgamma(s + 1) + lgamma(td + a[1]) - lgamma(a[1]) - lgamma(t + 1));
             };
-            double pval_min = 0;
+
+            double d_0 = static_cast<double>(n) / out_kmers_adj;
+            double d_n = static_cast<double>(n) / in_kmers_adj;
+            double p_min = 0;
+            if (d_0 > d_n) {
+                p_min = get_pmf(0);
+            } else if (d_0 < d_n) {
+                p_min = get_pmf(n);
+            } else {
+                p_min = get_pmf(0) + get_pmf(n);
+            }
 
             double pval = 0;
             double d = abs(static_cast<double>(in_sum) / in_kmers_adj - static_cast<double>(out_sum) / out_kmers_adj);
-            double max_d = d;
             for (int64_t s = 0; s <= n; ++s) {
                 int64_t t = n - s;
                 double cur_d = abs(static_cast<double>(s) / in_kmers_adj - static_cast<double>(t) / out_kmers_adj);
-                if (cur_d >= d) {
-                    double pmf = get_pmf(s);
-                    pval += pmf;
-                    if (cur_d > max_d) {
-                        pval_min = pmf;
-                        max_d = cur_d;
-                    } else if (cur_d == max_d) {
-                        pval_min += pmf;
-                    }
-                }
+                if (cur_d >= d)
+                    pval += get_pmf(s);
             }
 
             if (pval >= 1.1) {
-                common::logger->error("{} >= 1.1\t{}\t{},{}", pval, pval_min, in_sum, out_sum);
+                common::logger->error("{} >= 1.1\t{}\t{},{}", pval, p_min, in_sum, out_sum);
                 throw std::runtime_error("pval fail");
             }
 
             return std::make_tuple(std::min(pval, 1.0),
                                    static_cast<double>(in_sum),
                                    static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj,
-                                   std::min(pval_min, 1.0));
+                                   std::min(p_min, 1.0));
         };
     } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u") {
         compute_pval = [&](const auto &row) {

From 4232f9f16ee294a8af5d5cbc8426fc9880d072ed Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 4 Jun 2024 16:41:34 +0200
Subject: [PATCH 162/410] t

---
 metagraph/src/common/vectors/transpose.hpp       | 16 +++++++++++-----
 .../src/graph/annotated_graph_algorithm.cpp      | 13 +++++--------
 2 files changed, 16 insertions(+), 13 deletions(-)

diff --git a/metagraph/src/common/vectors/transpose.hpp b/metagraph/src/common/vectors/transpose.hpp
index 21f5729505..c3e68ca0c1 100644
--- a/metagraph/src/common/vectors/transpose.hpp
+++ b/metagraph/src/common/vectors/transpose.hpp
@@ -5,6 +5,7 @@
 #include <memory>
 
 #include <progress_bar.hpp>
+#include <sdsl/int_vector_buffer.hpp>
 
 #include "bit_vector_sdsl.hpp"
 
@@ -89,11 +90,16 @@ void call_rows(const std::vector<BitmapPtr> &columns,
         rows.resize(end - begin);
 
         for (size_t j = 0; j < columns.size(); ++j) {
-            columns[j]->call_ones_in_range(begin, end,
-                [&](uint64_t i) {
-                    rows[i - begin].emplace_back(j, (*column_values[j])[columns[j]->rank1(i) - 1]);
-                }
-            );
+            const sdsl::int_vector_buffer<> *col = column_values[j].get();
+            std::unique_ptr<sdsl::int_vector_buffer<>> in_col;
+            if constexpr(std::is_same_v<ValuesPtr, std::unique_ptr<sdsl::int_vector_buffer<>>>) {
+                in_col = std::make_unique<sdsl::int_vector_buffer<>>(in_col->filename(), std::ios::in, in_col->buffersize());
+                col = in_col.get();
+            }
+
+            columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
+                rows[i - begin].emplace_back(j, (*col)[columns[j]->rank1(i) - 1]);
+            });
         }
         #pragma omp ordered
         {
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index edcac9413a..424b08aa1f 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -561,16 +561,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            double a[2] = { r_in, r_out };
-            double a0 = a[0] + a[1];
             int64_t n = in_sum + out_sum;
 
-            double lscaling = lgamma(a0) + lgamma(n + 1) - lgamma(a0 + static_cast<double>(n));
+            double lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
             auto get_pmf = [&](int64_t s) {
                 int64_t t = n - s;
-                double sd = s;
-                double td = t;
-                return exp(lscaling + lgamma(sd + a[0]) - lgamma(a[0]) - lgamma(s + 1) + lgamma(td + a[1]) - lgamma(a[1]) - lgamma(t + 1));
+                double rs = r_in + s;
+                double rt = r_out + t;
+                return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
             };
 
             double d_0 = static_cast<double>(n) / out_kmers_adj;
@@ -587,8 +585,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double pval = 0;
             double d = abs(static_cast<double>(in_sum) / in_kmers_adj - static_cast<double>(out_sum) / out_kmers_adj);
             for (int64_t s = 0; s <= n; ++s) {
-                int64_t t = n - s;
-                double cur_d = abs(static_cast<double>(s) / in_kmers_adj - static_cast<double>(t) / out_kmers_adj);
+                double cur_d = abs(static_cast<double>(s) / in_kmers_adj - static_cast<double>(n - s) / out_kmers_adj);
                 if (cur_d >= d)
                     pval += get_pmf(s);
             }

From 1a4c83cc9f7b282f8ead468991372f09deac4359 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 4 Jun 2024 17:18:03 +0200
Subject: [PATCH 163/410] cleanup

---
 metagraph/src/common/vectors/transpose.hpp    | 33 ++++++++++++-------
 .../representation/base/sequence_graph.cpp    |  7 ----
 2 files changed, 22 insertions(+), 18 deletions(-)

diff --git a/metagraph/src/common/vectors/transpose.hpp b/metagraph/src/common/vectors/transpose.hpp
index c3e68ca0c1..41d9cd1efa 100644
--- a/metagraph/src/common/vectors/transpose.hpp
+++ b/metagraph/src/common/vectors/transpose.hpp
@@ -61,7 +61,7 @@ void call_rows(const std::vector<BitmapPtr> &columns,
 
 // Transpose all columns and call all rows.
 // If there are no columns, call `num_rows` empty rows.
-template <class BitmapPtr, class ValuesPtr, typename PairVector = std::vector<std::pair<uint64_t, uint8_t>>>
+template <class BitmapPtr, class ValuesPtr, typename PairVector = std::vector<std::pair<uint64_t, uint8_t>>, bool ordered = true>
 void call_rows(const std::vector<BitmapPtr> &columns,
                const std::vector<ValuesPtr> &column_values,
                const std::function<void(uint64_t, const PairVector &)> &callback,
@@ -90,25 +90,36 @@ void call_rows(const std::vector<BitmapPtr> &columns,
         rows.resize(end - begin);
 
         for (size_t j = 0; j < columns.size(); ++j) {
-            const sdsl::int_vector_buffer<> *col = column_values[j].get();
-            std::unique_ptr<sdsl::int_vector_buffer<>> in_col;
+            const auto &col = *column_values[j];
             if constexpr(std::is_same_v<ValuesPtr, std::unique_ptr<sdsl::int_vector_buffer<>>>) {
-                in_col = std::make_unique<sdsl::int_vector_buffer<>>(in_col->filename(), std::ios::in, in_col->buffersize());
-                col = in_col.get();
+                sdsl::int_vector_buffer<> cur_col(col.filename(), std::ios::in, col.buffersize());
+                columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
+                    rows[i - begin].emplace_back(j, cur_col[columns[j]->rank1(i) - 1]);
+                });
+            } else {
+                columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
+                    rows[i - begin].emplace_back(j, col[columns[j]->rank1(i) - 1]);
+                });
             }
-
-            columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
-                rows[i - begin].emplace_back(j, (*col)[columns[j]->rank1(i) - 1]);
-            });
         }
-        #pragma omp ordered
-        {
+
+        if constexpr(ordered) {
+            #pragma omp ordered
+            {
+                for (uint64_t j = 0; j < rows.size(); ++j) {
+                    callback(j + begin, rows[j]);
+                    ++progress_bar;
+                    rows[j].resize(0);
+                }
+            }
+        } else {
             for (uint64_t j = 0; j < rows.size(); ++j) {
                 callback(j + begin, rows[j]);
                 ++progress_bar;
                 rows[j].resize(0);
             }
         }
+
     }
 }
 
diff --git a/metagraph/src/graph/representation/base/sequence_graph.cpp b/metagraph/src/graph/representation/base/sequence_graph.cpp
index c366d742fe..26006cbd4d 100644
--- a/metagraph/src/graph/representation/base/sequence_graph.cpp
+++ b/metagraph/src/graph/representation/base/sequence_graph.cpp
@@ -505,13 +505,6 @@ ::call_source_nodes(const std::function<void(node_index)> &callback) const {
 }
 
 
-void DeBruijnGraph::lrt_resize(size_t size){
-    lrs.reserve(size);
-}; // Myrthe
-void DeBruijnGraph::lrt_set_value(double value, size_t index){
-    lrs[index] = value;
-};
-
 std::ostream& operator<<(std::ostream &out, const DeBruijnGraph &graph) {
     graph.print(out);
     return out;

From 0eccbd4a07142c23f4f5fdf2b2c3acdd707a36fc Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 4 Jun 2024 18:05:00 +0200
Subject: [PATCH 164/410] rearrangement

---
 metagraph/src/common/vectors/transpose.hpp    |  24 ++--
 .../src/graph/annotated_graph_algorithm.cpp   | 107 ++++++++++++------
 .../src/graph/annotated_graph_algorithm.hpp   |  11 ++
 3 files changed, 101 insertions(+), 41 deletions(-)

diff --git a/metagraph/src/common/vectors/transpose.hpp b/metagraph/src/common/vectors/transpose.hpp
index 41d9cd1efa..e98712034e 100644
--- a/metagraph/src/common/vectors/transpose.hpp
+++ b/metagraph/src/common/vectors/transpose.hpp
@@ -8,6 +8,7 @@
 #include <sdsl/int_vector_buffer.hpp>
 
 #include "bit_vector_sdsl.hpp"
+#include "common/logger.hpp"
 
 
 namespace utils {
@@ -76,6 +77,9 @@ void call_rows(const std::vector<BitmapPtr> &columns,
         return;
     }
 
+    if (column_values.empty())
+        mtg::common::logger->warn("No column values provided, using default value of 1");
+
     num_rows = columns[0]->size();
     std::vector<PairVector> rows;
 
@@ -90,16 +94,22 @@ void call_rows(const std::vector<BitmapPtr> &columns,
         rows.resize(end - begin);
 
         for (size_t j = 0; j < columns.size(); ++j) {
-            const auto &col = *column_values[j];
-            if constexpr(std::is_same_v<ValuesPtr, std::unique_ptr<sdsl::int_vector_buffer<>>>) {
-                sdsl::int_vector_buffer<> cur_col(col.filename(), std::ios::in, col.buffersize());
+            if (column_values.empty()) {
                 columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
-                    rows[i - begin].emplace_back(j, cur_col[columns[j]->rank1(i) - 1]);
+                    rows[i - begin].emplace_back(j, 1);
                 });
             } else {
-                columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
-                    rows[i - begin].emplace_back(j, col[columns[j]->rank1(i) - 1]);
-                });
+                const auto &col = *column_values[j];
+                if constexpr(std::is_same_v<ValuesPtr, std::unique_ptr<sdsl::int_vector_buffer<>>>) {
+                    sdsl::int_vector_buffer<> cur_col(col.filename(), std::ios::in, col.buffersize());
+                    columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
+                        rows[i - begin].emplace_back(j, cur_col[columns[j]->rank1(i) - 1]);
+                    });
+                } else {
+                    columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
+                        rows[i - begin].emplace_back(j, col[columns[j]->rank1(i) - 1]);
+                    });
+                }
             }
         }
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 424b08aa1f..0c4e9a0b38 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -34,7 +34,6 @@ typedef AnnotatedDBG::Annotator Annotator;
 typedef AnnotatedDBG::Annotator::Label Label;
 using Column = annot::matrix::BinaryMatrix::Column;
 using PairContainer = std::vector<std::pair<uint64_t, uint64_t>>;
-using ValuesContainer = sdsl::int_vector_buffer<>;
 constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
 
 double CDF_CUTOFF = 0.95;
@@ -91,7 +90,6 @@ inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
     return n > 0;
 }
 
-
 std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const std::vector<std::string> &files,
@@ -100,31 +98,55 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads,
                          size_t num_parallel_files) {
-    bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
-    bool parallel = num_parallel_files > 1;
-    common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
+    using ValuesContainer = sdsl::int_vector_buffer<>;
+
     common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
     common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
 
-    std::vector<bool> groups(labels_in.size() + labels_out.size());
-    std::vector<std::unique_ptr<bit_vector>> columns_all(groups.size());
-    std::vector<std::unique_ptr<ValuesContainer>> column_values_all(groups.size());
-
-    uint8_t max_width = 0;
-
-    sdsl::bit_vector ignored(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), false);
+    size_t total_labels = labels_in.size() + labels_out.size();
+    std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
+    std::vector<std::unique_ptr<const ValuesContainer>> column_values_all(total_labels);
+    std::vector<bool> groups(total_labels);
 
     annot::ColumnCompressed<>::load_columns_and_values(
         files,
         [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
             groups[offset] = labels_out.count(label);
-            max_width = std::max(max_width, column_values.width());
-            std::swap(columns_all[offset], column);
+            columns_all[offset].reset(column.release());
             column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
         },
         num_parallel_files
     );
 
+    return mask_nodes_by_label_dual<ValuesContainer>(
+        graph_ptr, columns_all, column_values_all, groups,
+        config, num_threads, num_parallel_files
+    );
+}
+
+template <class ValuesContainer>
+std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
+mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                         std::vector<std::unique_ptr<const bit_vector>> &columns_all,
+                         std::vector<std::unique_ptr<const ValuesContainer>> &column_values_all,
+                         const std::vector<bool> &groups,
+                         const DifferentialAssemblyConfig &config,
+                         size_t num_threads,
+                         size_t num_parallel_files) {
+    bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
+    bool parallel = num_parallel_files > 1;
+
+    size_t num_labels_out = std::count(groups.begin(), groups.end(), true);
+    size_t num_labels_in = groups.size() - num_labels_out;
+
+    common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
+
+    uint8_t max_width = 0;
+    for (const auto &col_vals : column_values_all) {
+        max_width = std::max(max_width, col_vals->width());
+    }
+
+    sdsl::bit_vector ignored(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), false);
 
     std::vector<uint64_t> min_counts(groups.size(), config.min_count);
     std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
@@ -196,10 +218,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Marking discarded k-mers");
     if (groups.size()) {
         std::atomic_thread_fence(std::memory_order_release);
-        utils::call_rows<std::unique_ptr<bit_vector>,
-                         std::unique_ptr<ValuesContainer>,
-                         PairContainer>(columns_all, column_values_all,
-                                        [&](uint64_t row_i, const auto &row) {
+        utils::call_rows<std::unique_ptr<const bit_vector>,
+                         std::unique_ptr<const ValuesContainer>,
+                         PairContainer, false>(columns_all, column_values_all,
+                                               [&](uint64_t row_i, const auto &row) {
             if (row.empty()) {
                 set_bit(ignored.data(), row_i, parallel, MO_RELAXED);
             } else if (config.clean || config.min_count) {
@@ -365,9 +387,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             return std::make_tuple(pval,
-                                    static_cast<double>(in_sum),
-                                    static_cast<double>(out_sum) / out_kmers * in_kmers,
-                                    p_min);
+                                   static_cast<double>(in_sum),
+                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
+                                   p_min);
         };
     } else if (config.test_type == "poisson_likelihoodratio" || config.test_type == "cmh") {
         compute_pval = [&,dist=boost::math::chi_squared(1)](const auto &row) {
@@ -626,17 +648,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             };
 
             std::vector<double> in_counts;
-            in_counts.reserve(labels_in.size());
+            in_counts.reserve(num_labels_in);
             std::vector<double> out_counts;
-            out_counts.reserve(labels_out.size());
+            out_counts.reserve(num_labels_out);
 
             for (const auto &[j, c] : row) {
                 auto &bucket = groups[j] ? out_counts : in_counts;
                 bucket.emplace_back(static_cast<double>(c) / medians[j]);
             }
 
-            in_counts.resize(labels_in.size());
-            out_counts.resize(labels_out.size());
+            in_counts.resize(num_labels_in);
+            out_counts.resize(num_labels_out);
 
             double p = 0.0;
             size_t nx = in_counts.size();
@@ -712,7 +734,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return std::make_tuple(p, rankcx_mean, rankcy_mean,
                                    config.test_type == "nonparametric_u"
-                                       ? 2.0 / exp(lgamma(groups.size() + 1) - lgamma(labels_in.size() + 1) - lgamma(labels_out.size() + 1))
+                                       ? 2.0 / exp(lgamma(groups.size() + 1) - lgamma(num_labels_in + 1) - lgamma(num_labels_out + 1))
                                        : 1.1);
         };
     } else {
@@ -722,10 +744,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::mutex pval_mu;
     std::atomic_thread_fence(std::memory_order_release);
 
-    utils::call_rows<std::unique_ptr<bit_vector>,
-                     std::unique_ptr<ValuesContainer>,
-                     PairContainer>(columns_all, column_values_all,
-                                    [&](uint64_t row_i, const auto &raw_row) {
+    utils::call_rows<std::unique_ptr<const bit_vector>,
+                     std::unique_ptr<const ValuesContainer>,
+                     PairContainer, false>(columns_all, column_values_all,
+                                           [&](uint64_t row_i, const auto &raw_row) {
         PairContainer row;
         if (!ignored[row_i]) {
             for (const auto &[j, raw_c] : raw_row) {
@@ -877,7 +899,24 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     return std::make_pair(masked_graph_in, masked_graph_out);
 }
 
-
+template
+std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
+mask_nodes_by_label_dual<sdsl::int_vector_buffer<>>(std::shared_ptr<const DeBruijnGraph>,
+                         std::vector<std::unique_ptr<const bit_vector>> &,
+                         std::vector<std::unique_ptr<const sdsl::int_vector_buffer<>>> &,
+                         const std::vector<bool> &,
+                         const DifferentialAssemblyConfig &,
+                         size_t,
+                         size_t);
+template
+std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
+mask_nodes_by_label_dual<sdsl::int_vector<>>(std::shared_ptr<const DeBruijnGraph>,
+                         std::vector<std::unique_ptr<const bit_vector>> &,
+                         std::vector<std::unique_ptr<const sdsl::int_vector<>>> &,
+                         const std::vector<bool> &,
+                         const DifferentialAssemblyConfig &,
+                         size_t,
+                         size_t);
 
 typedef std::function<size_t()> LabelCountCallback;
 
@@ -1080,9 +1119,9 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
     // calculate the p-value for the brunner munzel test
     auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0);
     utils::call_rows<std::unique_ptr<bit_vector>,
-                    std::unique_ptr<sdsl::int_vector_buffer<>>,
-                    std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
-                                                               [&](uint64_t row_id, const auto &row) {
+                     std::unique_ptr<sdsl::int_vector_buffer<>>,
+                     std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
+                                                                [&](uint64_t row_id, const auto &row) {
                         // auto kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
                         if (row.size() == 0) { // if the kmer is not present in any sample
                             mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index cdd505c457..916bd9e26a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -4,6 +4,7 @@
 #include <tsl/hopscotch_set.h>
 
 #include "graph/annotated_dbg.hpp"
+#include "common/vectors/bit_vector.hpp"
 
 
 namespace mtg {
@@ -91,6 +92,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          size_t num_threads = 1,
                          size_t num_parallel_files = std::numeric_limits<size_t>::max());
 
+template <class ValuesContainer>
+std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
+mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                         std::vector<std::unique_ptr<const bit_vector>> &columns_all,
+                         std::vector<std::unique_ptr<const ValuesContainer>> &column_values_all,
+                         const std::vector<bool> &groups,
+                         const DifferentialAssemblyConfig &config,
+                         size_t num_threads = 1,
+                         size_t num_parallel_files = std::numeric_limits<size_t>::max());
+
 } // namespace graph
 } // namespace mtg
 

From 48ec951f26a951a68ab09c23f07188436660ab1a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 4 Jun 2024 19:06:52 +0200
Subject: [PATCH 165/410] ttt

---
 .../src/graph/annotated_graph_algorithm.cpp   | 46 +++++++++++++------
 .../test_annotated_graph_algorithm.cpp        | 44 +++++++++++-------
 2 files changed, 61 insertions(+), 29 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0c4e9a0b38..704d98ec17 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -133,6 +133,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads,
                          size_t num_parallel_files) {
+    num_parallel_files = std::min(num_parallel_files, num_threads);
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
     bool parallel = num_parallel_files > 1;
 
@@ -158,7 +159,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             : 0;
     };
 
-    if (config.clean) {
+    if (config.clean && column_values_all.empty())
+        common::logger->warn("Can't clean when no counts provided, skipping cleaning");
+
+    if (config.clean && column_values_all.size()) {
         common::logger->trace("Cleaning count columns");
 
         #pragma omp parallel for num_threads(num_parallel_files)
@@ -266,18 +270,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     #pragma omp parallel for num_threads(num_parallel_files)
     for (size_t j = 0; j < groups.size(); ++j) {
         const auto &column = *columns_all[j];
-        const auto &column_values = *column_values_all[j];
+        size_t num_vals = column_values_all.size()
+            ? column_values_all[j]->size()
+            : column.num_set_bits();
+
         uint64_t sum = 0;
         uint64_t sum_of_squares = 0;
         size_t i = 0;
         size_t n = 0;
 
         std::vector<uint64_t> vals;
-        vals.reserve(column_values.size());
+        vals.reserve(num_vals);
         column.call_ones([&](uint64_t row_i) {
             if (!ignored[row_i]) {
-                uint64_t c = adjust_count(column_values[i], j, row_i);
-                if (c) {
+                if (uint64_t c = adjust_count(column_values_all.size() ? (*column_values_all[j])[i] : 1, j, row_i)) {
                     vals.emplace_back(c);
                     sum += c;
                     sum_of_squares += c * c;
@@ -356,10 +362,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             for ( ; s <= n; ++s) {
                 int64_t t = n - s;
                 double cur_d = abs(static_cast<double>(s) / in_kmers - static_cast<double>(t) / out_kmers);
+                common::logger->info("bar: {}\t{} vs. {}", s, d, cur_d);
                 if (cur_d < d)
                     break;
             }
 
+            common::logger->info("bars: {} / {}", s, n);
+
             if (s == n + 1) {
                 pval = 1.0;
             } else {
@@ -371,6 +380,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 for ( ; s_u >= s; --s_u) {
                     int64_t t = n - s_u;
                     double cur_d = abs(static_cast<double>(s_u) / in_kmers - static_cast<double>(t) / out_kmers);
+                    common::logger->info("bar: {}\t{} vs. {}", s_u, d, cur_d);
                     if (cur_d < d)
                         break;
                 }
@@ -386,6 +396,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 throw std::runtime_error("pval fail");
             }
 
+            common::logger->info("foo: {}/{}\t{}/{}\t{}\t{}", in_sum, in_kmers, out_sum, out_kmers, pval, p_min);
+
             return std::make_tuple(pval,
                                    static_cast<double>(in_sum),
                                    static_cast<double>(out_sum) / out_kmers * in_kmers,
@@ -477,14 +489,22 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto &hist = hists[j];
             size_t i = 0;
             const auto &column = *columns_all[j];
-            const auto &column_values = *column_values_all[j];
-            column.call_ones([&](uint64_t row_i) {
-                if (!ignored[row_i]) {
-                    uint64_t c = adjust_count(column_values[i], j, row_i);
-                    ++hist[c];
-                }
-                ++i;
-            });
+            if (column_values_all.size()) {
+                const auto &column_values = *column_values_all[j];
+                column.call_ones([&](uint64_t row_i) {
+                    if (!ignored[row_i])
+                        ++hist[adjust_count(column_values[i], j, row_i)];
+
+                    ++i;
+                });
+            } else {
+                column.call_ones([&](uint64_t row_i) {
+                    if (!ignored[row_i])
+                        ++hist[adjust_count(1, j, row_i)];
+
+                    ++i;
+                });
+            }
 
             double s = static_cast<double>(sums[j]);
             double mu = s / nelem;
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index 069db76059..8628a32dd6 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -9,6 +9,7 @@
 #include "graph/representation/masked_graph.hpp"
 #include "annotation/representation/annotation_matrix/static_annotators_def.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
+#include "common/vectors/bit_vector_sdsl.hpp"
 
 
 namespace {
@@ -23,9 +24,7 @@ template <typename GraphAnnotationPair>
 class MaskedDeBruijnGraphAlgorithm : public ::testing::Test {};
 // test with DBGBitmap and DBGHashFast to have k-mers in different order
 typedef ::testing::Types<std::pair<DBGBitmap, ColumnCompressed<>>,
-                         std::pair<DBGHashFast, ColumnCompressed<>>,
-                         std::pair<DBGBitmap, RowFlatAnnotator>,
-                         std::pair<DBGHashFast, RowFlatAnnotator>
+                         std::pair<DBGHashFast, ColumnCompressed<>>
                         > GraphAnnoTypes;
 TYPED_TEST_SUITE(MaskedDeBruijnGraphAlgorithm, GraphAnnoTypes);
 
@@ -33,9 +32,6 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
     typedef typename TypeParam::first_type Graph;
     typedef typename TypeParam::second_type Annotation;
     for (size_t num_threads : { 1, 4 }) {
-        const tsl::hopscotch_set<std::string> ingroup { "B", "C" };
-        const tsl::hopscotch_set<std::string> outgroup { "A" };
-
         size_t max_k = std::min(max_test_k<Graph>(), (size_t)15);
         for (size_t k = 3; k < max_k; ++k) {
             const std::vector<std::string> sequences {
@@ -47,7 +43,22 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
             };
             const std::vector<std::string> labels { "A", "B", "C", "D", "E" };
 
+            // ingroup: B, C
+            // outgroup: A
+            std::vector<bool> groups = { true, false, false };
+
             auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels);
+            auto graph_ptr = std::static_pointer_cast<const Graph>(anno_graph->get_graph_ptr());
+
+            const auto &in_columns = static_cast<const Annotation&>(anno_graph->get_annotator()).get_matrix().data();
+            std::vector<std::unique_ptr<const bit_vector>> columns;
+            columns.reserve(in_columns.size());
+            std::transform(in_columns.begin(), in_columns.end(), std::back_inserter(columns),
+                           [&](const auto &a) {
+                               return std::make_unique<const bit_vector_stat>(a->to_vector());
+                           });
+
+            std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values_all;
 
             std::unordered_set<std::string> obs_labels, obs_kmers;
             const std::unordered_set<std::string> ref_kmers {
@@ -58,20 +69,21 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
             };
 
             DifferentialAssemblyConfig config {
-                .label_mask_in_unitig_fraction = 0.0,
-                .label_mask_in_kmer_fraction = 1.0,
-                .label_mask_out_unitig_fraction = 1.0,
-                .label_mask_out_kmer_fraction = 0.0,
-                .label_mask_other_unitig_fraction = 1.0,
+                .test_type = "binomial",
+                .min_count = 1,
+                .min_in_recurrence = 2,
                 .outfbase = "",
             };
 
-            auto masked_dbg = mask_nodes_by_label(*anno_graph,
-                                                  ingroup, outgroup,
-                                                  {}, {},
-                                                  config, num_threads);
+            auto [masked_dbg_in, masked_dbg_out] = mask_nodes_by_label_dual<sdsl::int_vector<>>(
+                graph_ptr,
+                columns,
+                column_values_all,
+                groups,
+                config, num_threads
+            );
 
-            masked_dbg->call_kmers([&](auto, const std::string &kmer) {
+            masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
                 auto cur_labels = anno_graph->get_labels(kmer, 0.0);
                 obs_labels.insert(cur_labels.begin(), cur_labels.end());
                 obs_kmers.insert(kmer);

From 151e1ece6309bbb59c801244609c05289a7ee356 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 4 Jun 2024 21:06:41 +0200
Subject: [PATCH 166/410] implement no test

---
 metagraph/src/cli/assemble.cpp                |  10 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 227 +++++++++---------
 .../src/graph/annotated_graph_algorithm.hpp   |   2 +
 .../test_annotated_graph_algorithm.cpp        |   3 +-
 4 files changed, 127 insertions(+), 115 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index bcfed7b15d..01fd4b2d4b 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -57,10 +57,12 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.test_type = experiment.get("test_type", "brunner_munzel").asString();
     diff_config.filter = experiment.get("filter", true).asBool();
     diff_config.clean = experiment.get("clean", false).asBool();
-    diff_config.min_count = experiment.get("min_count", 0).asInt();
-    diff_config.min_recurrence = experiment.get("min_recurrence", 1).asInt();
-    diff_config.min_in_recurrence = experiment.get("min_in_recurrence", 0).asInt();
-    diff_config.min_out_recurrence = experiment.get("min_out_recurrence", 0).asInt();
+    diff_config.min_count = experiment.get("min_count", 0).asUInt64();
+    diff_config.min_recurrence = experiment.get("min_recurrence", 1).asUInt64();
+    diff_config.min_in_recurrence = experiment.get("min_in_recurrence", 0).asUInt64();
+    diff_config.min_out_recurrence = experiment.get("min_out_recurrence", 0).asUInt64();
+    diff_config.max_in_recurrence = experiment.get("max_in_recurrence", std::numeric_limits<uint64_t>::max()).asUInt64();
+    diff_config.max_out_recurrence = experiment.get("max_out_recurrence", std::numeric_limits<uint64_t>::max()).asUInt64();
     diff_config.num_tests = experiment.get("num_tests", 0).asInt();
     diff_config.assemble_shared = experiment.get("assemble_shared", false).asBool();
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 704d98ec17..72d6c40581 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -13,6 +13,7 @@
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
+#include <boost/math/special_functions/beta.hpp>
 
 #include "common/logger.hpp"
 #include "common/vectors/bitmap.hpp"
@@ -221,6 +222,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Marking discarded k-mers");
     if (groups.size()) {
+        bool filter_rows = config.clean || config.min_count || config.min_recurrence
+                            || config.min_in_recurrence || config.min_out_recurrence
+                            || config.max_in_recurrence || config.max_out_recurrence;
         std::atomic_thread_fence(std::memory_order_release);
         utils::call_rows<std::unique_ptr<const bit_vector>,
                          std::unique_ptr<const ValuesContainer>,
@@ -228,10 +232,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                                [&](uint64_t row_i, const auto &row) {
             if (row.empty()) {
                 set_bit(ignored.data(), row_i, parallel, MO_RELAXED);
-            } else if (config.clean || config.min_count) {
+            } else if (filter_rows) {
                 size_t count = 0;
                 size_t count_in = 0;
                 size_t count_out = 0;
+                bool keep_row = false;
                 for (const auto &[j, raw_c] : row) {
                     if (adjust_count(raw_c, j, row_i)) {
                         ++count;
@@ -245,12 +250,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         if (count >= config.min_recurrence
                                 && count_in >= config.min_in_recurrence
                                 && count_out >= config.min_out_recurrence) {
-                            return;
+                            keep_row = true;;
+                        }
+
+                        if (count_in > config.max_in_recurrence || count_out > config.max_out_recurrence) {
+                            keep_row = false;
+                            break;
                         }
                     }
                 }
 
-                set_bit(ignored.data(), row_i, parallel, MO_RELAXED);
+                if (!keep_row)
+                    set_bit(ignored.data(), row_i, parallel, MO_RELAXED);
             }
         });
         std::atomic_thread_fence(std::memory_order_acquire);
@@ -267,6 +278,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<uint64_t> sums(groups.size());
     std::vector<uint64_t> sums_of_squares(groups.size());
 
+    bool compute_median = (config.test_type.substr(0, 13) == "nonparametric");
+
     #pragma omp parallel for num_threads(num_parallel_files)
     for (size_t j = 0; j < groups.size(); ++j) {
         const auto &column = *columns_all[j];
@@ -294,7 +307,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         });
 
         max_obs_vals[j] = *std::max_element(vals.begin(), vals.end());
-        medians[j] = boost::math::statistics::median(vals);
+
+        if (compute_median)
+            medians[j] = boost::math::statistics::median(vals);
 
         sums[j] = sum;
         if (groups[j]) {
@@ -341,62 +356,29 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             int64_t n = in_sum + out_sum;
-            auto bdist = boost::math::binomial(n, static_cast<double>(in_kmers) / total_kmers);
-            double pval = 0;
-
-            double d_0 = static_cast<double>(n) / out_kmers;
-            double d_n = static_cast<double>(n) / in_kmers;
-            double p_min = 0;
-            if (d_0 > d_n) {
-                p_min = boost::math::pdf(bdist, 0);
-            } else if (d_0 < d_n) {
-                p_min = boost::math::pdf(bdist, n);
-            } else {
-                p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
-            }
+            double p = static_cast<double>(in_kmers) / total_kmers;
 
-            double d = abs(static_cast<double>(in_sum) / in_kmers - static_cast<double>(out_sum) / out_kmers);
+            auto bdist = boost::math::binomial(n, p);
+            double p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
 
-            // lower tail
-            int64_t s = 0;
-            for ( ; s <= n; ++s) {
-                int64_t t = n - s;
-                double cur_d = abs(static_cast<double>(s) / in_kmers - static_cast<double>(t) / out_kmers);
-                common::logger->info("bar: {}\t{} vs. {}", s, d, cur_d);
-                if (cur_d < d)
-                    break;
-            }
-
-            common::logger->info("bars: {} / {}", s, n);
-
-            if (s == n + 1) {
-                pval = 1.0;
-            } else {
-                if (s > 0)
-                    pval = boost::math::cdf(bdist, s - 1);
-
-                // upper tail
-                int64_t s_u = n;
-                for ( ; s_u >= s; --s_u) {
-                    int64_t t = n - s_u;
-                    double cur_d = abs(static_cast<double>(s_u) / in_kmers - static_cast<double>(t) / out_kmers);
-                    common::logger->info("bar: {}\t{} vs. {}", s_u, d, cur_d);
-                    if (cur_d < d)
-                        break;
-                }
-
-                assert(s_u >= s - 1);
-
-                if (s_u + 1 <= n)
-                    pval += boost::math::cdf(boost::math::complement(bdist, s_u + 1));
-            }
+            // we want |2s - n| >= d, so
+            // 2s - n >= d || n - 2s >= d
+            // s >= (d + n) / 2 || (n - d) / 2 >= s
+            uint64_t d = abs(in_sum - out_sum);
+            int64_t lower = (n - d) / 2;
+            int64_t upper = ceil(static_cast<double>(n + d) / 2);
+            double pval = boost::math::cdf(bdist, lower)
+                            + boost::math::cdf(boost::math::complement(bdist, upper - 1));
 
             if (pval > 1.0) {
                 common::logger->error("{} > 1.0\t{}\t{},{}", pval, p_min, in_sum, out_sum);
                 throw std::runtime_error("pval fail");
             }
 
-            common::logger->info("foo: {}/{}\t{}/{}\t{}\t{}", in_sum, in_kmers, out_sum, out_kmers, pval, p_min);
+            if (pval < p_min) {
+                common::logger->error("{} < {}\t{},{}", pval, p_min, in_sum, out_sum);
+                throw std::runtime_error("pval fail");
+            }
 
             return std::make_tuple(pval,
                                    static_cast<double>(in_sum),
@@ -757,6 +739,27 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                        ? 2.0 / exp(lgamma(groups.size() + 1) - lgamma(num_labels_in + 1) - lgamma(num_labels_out + 1))
                                        : 1.1);
         };
+    } else if (config.test_type == "notest") {
+        compute_pval = [&](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+
+            for (const auto &[j, c] : row) {
+                if (groups[j]) {
+                    out_sum += c;
+                } else {
+                    in_sum += c;
+                }
+            }
+
+            return std::make_tuple(0.0,
+                                   static_cast<double>(in_sum),
+                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
+                                   0.0);
+        };
     } else {
         throw std::runtime_error("Test not implemented");
     }
@@ -784,8 +787,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (pval >= 1.1)
             return;
 
-        bool in_kmer = in_stat > out_stat;
-        bool out_kmer = in_stat < out_stat;
+        bool in_kmer = (in_stat > out_stat) || (out_stat != out_stat && in_stat == in_stat);
+        bool out_kmer = (in_stat < out_stat) || (in_stat != in_stat && out_stat == out_stat);
         uint64_t k = std::numeric_limits<uint64_t>::max();
 
         if (pval_min > 1.0) {
@@ -823,8 +826,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (in_stat != out_stat && pval < 0.05)
             set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
 
-        std::lock_guard<std::mutex> lock(pval_mu);
-        m[k].emplace_back(node);
+        if (config.test_type != "notest") {
+            std::lock_guard<std::mutex> lock(pval_mu);
+            m[k].emplace_back(node);
+        }
     });
 
     std::atomic_thread_fence(std::memory_order_acquire);
@@ -837,69 +842,71 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         col_vals.reset();
     }
 
-    uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
-    auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
-
-    std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
-    size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
-                                 [](size_t sum, const auto &a) { return sum + a.second.size(); });
-    if (acc != nelem) {
-        common::logger->error("acc != nelem: {} != {}", acc, nelem);
-        throw std::runtime_error("internal failure");
-    }
-    size_t total_tests = acc;
-    common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
-
-    if (total_sig) {
-        auto begin = m_data.begin();
-        size_t k = 0;
-        size_t last_k = 0;
-        for ( ; begin != m_data.end(); ++begin) {
-            const auto &[cur_k, bucket] = *begin;
-            common::logger->trace("k: {}\tm(k): {}", cur_k, acc);
-            if (acc <= cur_k) {
-                k = std::max(acc, last_k + 1);
-                break;
-            }
+    if (config.test_type != "notest") {
+        uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
+        auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
 
-            last_k = cur_k;
-            acc -= bucket.size();
+        std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
+        size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
+                                    [](size_t sum, const auto &a) { return sum + a.second.size(); });
+        if (acc != nelem) {
+            common::logger->error("acc != nelem: {} != {}", acc, nelem);
+            throw std::runtime_error("internal failure");
         }
+        size_t total_tests = acc;
+        common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
+
+        if (total_sig) {
+            auto begin = m_data.begin();
+            size_t k = 0;
+            size_t last_k = 0;
+            for ( ; begin != m_data.end(); ++begin) {
+                const auto &[cur_k, bucket] = *begin;
+                common::logger->trace("k: {}\tm(k): {}", cur_k, acc);
+                if (acc <= cur_k) {
+                    k = std::max(acc, last_k + 1);
+                    break;
+                }
 
-        VectorMap<double, double> pval_map;
-        for (double p : pvals) {
-            if (p <= 1.0)
-                ++pval_map[p];
-        }
-        auto p_data = const_cast<std::vector<std::pair<double, double>>&&>(pval_map.values_container());
-        std::sort(p_data.begin(), p_data.end(), utils::GreaterFirst());
-
-        double cm = boost::math::digamma(total_tests) + 0.5772156649015329; // std::numbers::e_gamma_v<double>
-        pval_map = decltype(pval_map)();
-        size_t cur_rank = 0;
-        for (const auto &[p, c] : p_data) {
-            cur_rank += c;
-            pval_map[p] = cm * cur_rank;
-        }
+                last_k = cur_k;
+                acc -= bucket.size();
+            }
 
-        if (k == 0) {
-            common::logger->trace("Falling back to Benjamini-Yekutieli");
-            for (size_t i = 0; i < pvals.size(); ++i) {
-                if (pvals[i] < 0.05 && pvals[i] * pval_map[pvals[i]] >= 0.05) {
-                    indicator_in[i] = false;
-                    indicator_out[i] = false;
-                }
+            VectorMap<double, double> pval_map;
+            for (double p : pvals) {
+                if (p <= 1.0)
+                    ++pval_map[p];
             }
-        } else {
-            common::logger->trace("k: {}\talpha_corr: {}", k, 0.05 / k);
-            for (auto jt = m_data.begin(); jt != m_data.end(); ++jt) {
-                const auto &[cur_k, bucket] = *jt;
-                for (uint64_t i : bucket) {
-                    if (jt < begin || pvals[i] * k >= 0.05) {
+            auto p_data = const_cast<std::vector<std::pair<double, double>>&&>(pval_map.values_container());
+            std::sort(p_data.begin(), p_data.end(), utils::GreaterFirst());
+
+            double cm = boost::math::digamma(total_tests) + 0.5772156649015329; // std::numbers::e_gamma_v<double>
+            pval_map = decltype(pval_map)();
+            size_t cur_rank = 0;
+            for (const auto &[p, c] : p_data) {
+                cur_rank += c;
+                pval_map[p] = cm * cur_rank;
+            }
+
+            if (k == 0) {
+                common::logger->trace("Falling back to Benjamini-Yekutieli");
+                for (size_t i = 0; i < pvals.size(); ++i) {
+                    if (pvals[i] < 0.05 && pvals[i] * pval_map[pvals[i]] >= 0.05) {
                         indicator_in[i] = false;
                         indicator_out[i] = false;
                     }
                 }
+            } else {
+                common::logger->trace("k: {}\talpha_corr: {}", k, 0.05 / k);
+                for (auto jt = m_data.begin(); jt != m_data.end(); ++jt) {
+                    const auto &[cur_k, bucket] = *jt;
+                    for (uint64_t i : bucket) {
+                        if (jt < begin || pvals[i] * k >= 0.05) {
+                            indicator_in[i] = false;
+                            indicator_out[i] = false;
+                        }
+                    }
+                }
             }
         }
     }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 916bd9e26a..724ae8d847 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -46,6 +46,8 @@ struct DifferentialAssemblyConfig {
     uint64_t min_recurrence = 1;
     uint64_t min_in_recurrence = 0;
     uint64_t min_out_recurrence = 0;
+    uint64_t max_in_recurrence = std::numeric_limits<uint64_t>::max();
+    uint64_t max_out_recurrence = std::numeric_limits<uint64_t>::max();
     uint64_t num_tests = 0;
     std::string outfbase;
 };
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index 8628a32dd6..bdc1f4528e 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -69,9 +69,10 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
             };
 
             DifferentialAssemblyConfig config {
-                .test_type = "binomial",
+                .test_type = "notest",
                 .min_count = 1,
                 .min_in_recurrence = 2,
+                .max_out_recurrence = 0,
                 .outfbase = "",
             };
 

From 4f0689f46f6103167f0ae15437df5e87d9092a78 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 5 Jun 2024 12:02:07 +0200
Subject: [PATCH 167/410] properly handle exceptions

---
 .../src/graph/annotated_graph_algorithm.cpp   | 120 ++-
 .../src/graph/annotated_graph_algorithm.hpp   |   3 +-
 .../test_annotated_graph_algorithm.cpp        | 690 +++++++++++-------
 3 files changed, 494 insertions(+), 319 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 72d6c40581..dd342c0e1f 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -121,7 +121,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     return mask_nodes_by_label_dual<ValuesContainer>(
         graph_ptr, columns_all, column_values_all, groups,
-        config, num_threads, num_parallel_files
+        config, num_threads, num_parallel_files, true
     );
 }
 
@@ -133,7 +133,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads,
-                         size_t num_parallel_files) {
+                         size_t num_parallel_files,
+                         bool deallocate) {
     num_parallel_files = std::min(num_parallel_files, num_threads);
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
     bool parallel = num_parallel_files > 1;
@@ -278,8 +279,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<uint64_t> sums(groups.size());
     std::vector<uint64_t> sums_of_squares(groups.size());
 
-    bool compute_median = (config.test_type.substr(0, 13) == "nonparametric");
-
     #pragma omp parallel for num_threads(num_parallel_files)
     for (size_t j = 0; j < groups.size(); ++j) {
         const auto &column = *columns_all[j];
@@ -308,7 +307,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         max_obs_vals[j] = *std::max_element(vals.begin(), vals.end());
 
-        if (compute_median)
+        if (vals.size())
             medians[j] = boost::math::statistics::median(vals);
 
         sums[j] = sum;
@@ -375,17 +374,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 throw std::runtime_error("pval fail");
             }
 
-            if (pval < p_min) {
-                common::logger->error("{} < {}\t{},{}", pval, p_min, in_sum, out_sum);
-                throw std::runtime_error("pval fail");
-            }
-
             return std::make_tuple(pval,
                                    static_cast<double>(in_sum),
                                    static_cast<double>(out_sum) / out_kmers * in_kmers,
                                    p_min);
         };
     } else if (config.test_type == "poisson_likelihoodratio" || config.test_type == "cmh") {
+        if (in_kmers == 0 || out_kmers == 0) {
+            common::logger->error("Test invalid, try binomial instead");
+            throw std::domain_error("Test fail");
+        }
         compute_pval = [&,dist=boost::math::chi_squared(1)](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0, 1.1);
@@ -403,13 +401,34 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double chi_stat = 0;
             if (config.test_type == "cmh") {
-                double xi = in_sum * out_kmers - out_sum * in_kmers;
-                xi *= xi * (total_kmers - 1) / in_kmers / out_kmers / (in_sum + out_sum) / (total_kmers - in_sum - out_sum);
-                chi_stat = xi;
+                double a = in_sum;
+                double b = in_kmers - in_sum;
+                double c = out_sum;
+                double d = out_kmers - out_sum;
+                double n1 = a + b;
+                double n2 = c + d;
+                double m1 = a + c;
+                double m2 = b + d;
+                double t = n1 + n2;
+
+                assert(t == total_kmers);
+                assert(n1 == in_kmers);
+                assert(n2 == out_kmers);
+
+                if (m1 == 0 || m2 == 0) {
+                    common::logger->error("Test invalid, try binomial instead");
+                    throw std::domain_error("Test fail");
+                }
+
+                chi_stat = pow(a - n1 * m1 / t, 2.0) * t * t * (t - 1) / (n1 * n2 * m1 * m2);
+                if (chi_stat < 0) {
+                    common::logger->error("Test statistic {} < 0, too few data points. Use binomial instead", chi_stat);
+                    throw std::domain_error("Test failed");
+                }
             } else {
-                if (in_sum == 0 || out_sum == 0 || in_kmers == 0 || out_kmers == 0) {
-                    common::logger->error("Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'binomial' instead");
-                    throw std::runtime_error("Test failed");
+                if (in_sum == 0 || out_sum == 0) {
+                    common::logger->error("Empty row: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'binomial' instead");
+                    throw std::domain_error("Test failed");
                 }
 
                 double log_likelihood_in = -in_sum + log(static_cast<double>(in_sum)) * in_sum;
@@ -420,23 +439,25 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double mean_denom_out = theta * out_kmers;
 
                 if (mean_denom_in == 0 || mean_denom_out == 0) {
-                    common::logger->error("Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'binomial' instead");
-                    throw std::runtime_error("Test failed");
+                    common::logger->error("Empty null hypothesis: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'binomial' instead");
+                    throw std::domain_error("Test failed");
                 }
 
                 double log_likelihood_null = -mean_denom_in + in_sum * log(mean_denom_in);
                 log_likelihood_null += -mean_denom_out + out_sum * log(mean_denom_out);
 
                 chi_stat = (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
+
+                if (chi_stat < 0) {
+                    common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+                    throw std::runtime_error("Test failed");
+                }
             }
 
             if (chi_stat == 0)
                 return std::make_tuple(1.0, static_cast<double>(in_sum), static_cast<double>(out_sum) / out_kmers * in_kmers, 1.1);
 
-            if (chi_stat < 0) {
-                common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
-                throw std::runtime_error("Test failed");
-            }
+            assert(chi_stat > 0);
 
             double pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
 
@@ -674,6 +695,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
             double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
 
+            double p_min = 1.1;
+
             if (config.test_type == "nonparametric") {
                 if (nx > 1 && ny > 1) {
                     auto [rankx, countsx] = rankdata(in_counts);
@@ -706,6 +729,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 }
             } else {
+                p_min = 2.0 / exp(lgamma(groups.size() + 1) - lgamma(num_labels_in + 1) - lgamma(num_labels_out + 1));
                 if (!nx || !ny) {
                     p = 1.0;
                 } else {
@@ -734,10 +758,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            return std::make_tuple(p, rankcx_mean, rankcy_mean,
-                                   config.test_type == "nonparametric_u"
-                                       ? 2.0 / exp(lgamma(groups.size() + 1) - lgamma(num_labels_in + 1) - lgamma(num_labels_out + 1))
-                                       : 1.1);
+            return std::make_tuple(p, rankcx_mean, rankcy_mean, p_min);
         };
     } else if (config.test_type == "notest") {
         compute_pval = [&](const auto &row) {
@@ -764,6 +785,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         throw std::runtime_error("Test not implemented");
     }
 
+    std::exception_ptr ex = nullptr;
+
     std::mutex pval_mu;
     std::atomic_thread_fence(std::memory_order_release);
 
@@ -771,6 +794,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                      std::unique_ptr<const ValuesContainer>,
                      PairContainer, false>(columns_all, column_values_all,
                                            [&](uint64_t row_i, const auto &raw_row) {
+        if (ex)
+            return;
+
         PairContainer row;
         if (!ignored[row_i]) {
             for (const auto &[j, raw_c] : raw_row) {
@@ -781,7 +807,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
 
-        auto [pval, in_stat, out_stat, pval_min] = compute_pval(row);
+        double pval;
+        double in_stat;
+        double out_stat;
+        double pval_min;
+        try {
+            std::tie(pval, in_stat, out_stat, pval_min) = compute_pval(row);
+        } catch (...) {
+            std::lock_guard<std::mutex> lock(pval_mu);
+            ex = std::current_exception();
+            return;
+        }
+
         pvals[node] = pval;
 
         if (pval >= 1.1)
@@ -803,11 +840,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             auto [pm, in_sum_m, out_sum_m, pm_m] = compute_pval(best);
             pval_min = pm;
+        }
 
-            if (pval_min > pval) {
-                common::logger->error("Min p-val estimate too high: min {} > cur {}", pval_min, pval);
-                throw std::runtime_error("Test failed");
-            }
+        if (pval_min - pval > 1e-10) {
+            common::logger->error("Min p-val estimate too high: min {} > cur {}\ttest: {}", pval_min, pval, config.test_type);
+            throw std::runtime_error("Test failed");
         }
 
         if (pval_min >= 0.05) {
@@ -834,14 +871,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     std::atomic_thread_fence(std::memory_order_acquire);
 
-    for (auto &col : columns_all) {
-        col.reset();
-    }
+    if (deallocate) {
+        for (auto &col : columns_all) {
+            col.reset();
+        }
 
-    for (auto &col_vals : column_values_all) {
-        col_vals.reset();
+        for (auto &col_vals : column_values_all) {
+            col_vals.reset();
+        }
     }
 
+    if (ex)
+        std::rethrow_exception(ex);
+
     if (config.test_type != "notest") {
         uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
         auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
@@ -934,7 +976,8 @@ mask_nodes_by_label_dual<sdsl::int_vector_buffer<>>(std::shared_ptr<const DeBrui
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
-                         size_t);
+                         size_t,
+                         bool);
 template
 std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
 mask_nodes_by_label_dual<sdsl::int_vector<>>(std::shared_ptr<const DeBruijnGraph>,
@@ -943,7 +986,8 @@ mask_nodes_by_label_dual<sdsl::int_vector<>>(std::shared_ptr<const DeBruijnGraph
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
-                         size_t);
+                         size_t,
+                         bool);
 
 typedef std::function<size_t()> LabelCountCallback;
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 724ae8d847..70943746a9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -102,7 +102,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads = 1,
-                         size_t num_parallel_files = std::numeric_limits<size_t>::max());
+                         size_t num_parallel_files = std::numeric_limits<size_t>::max(),
+                         bool deallocate = false);
 
 } // namespace graph
 } // namespace mtg
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index bdc1f4528e..f15ff1748e 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -9,6 +9,7 @@
 #include "graph/representation/masked_graph.hpp"
 #include "annotation/representation/annotation_matrix/static_annotators_def.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
+#include "annotation/int_matrix/rank_extended/tuple_csc_matrix.hpp"
 #include "common/vectors/bit_vector_sdsl.hpp"
 
 
@@ -24,7 +25,8 @@ template <typename GraphAnnotationPair>
 class MaskedDeBruijnGraphAlgorithm : public ::testing::Test {};
 // test with DBGBitmap and DBGHashFast to have k-mers in different order
 typedef ::testing::Types<std::pair<DBGBitmap, ColumnCompressed<>>,
-                         std::pair<DBGHashFast, ColumnCompressed<>>
+                         std::pair<DBGHashFast, ColumnCompressed<>>,
+                         std::pair<DBGSuccinct, ColumnCompressed<>>
                         > GraphAnnoTypes;
 TYPED_TEST_SUITE(MaskedDeBruijnGraphAlgorithm, GraphAnnoTypes);
 
@@ -43,14 +45,11 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
             };
             const std::vector<std::string> labels { "A", "B", "C", "D", "E" };
 
-            // ingroup: B, C
-            // outgroup: A
-            std::vector<bool> groups = { true, false, false };
-
             auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels);
             auto graph_ptr = std::static_pointer_cast<const Graph>(anno_graph->get_graph_ptr());
 
-            const auto &in_columns = static_cast<const Annotation&>(anno_graph->get_annotator()).get_matrix().data();
+            const auto &annotation = static_cast<const Annotation&>(anno_graph->get_annotator());
+            const auto &in_columns = annotation.get_matrix().data();
             std::vector<std::unique_ptr<const bit_vector>> columns;
             columns.reserve(in_columns.size());
             std::transform(in_columns.begin(), in_columns.end(), std::back_inserter(columns),
@@ -70,12 +69,16 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
 
             DifferentialAssemblyConfig config {
                 .test_type = "notest",
-                .min_count = 1,
                 .min_in_recurrence = 2,
                 .max_out_recurrence = 0,
                 .outfbase = "",
             };
 
+            // ingroup: B, C
+            // outgroup: A
+            std::vector<bool> groups = { true, false, false };
+            columns.resize(groups.size());
+
             auto [masked_dbg_in, masked_dbg_out] = mask_nodes_by_label_dual<sdsl::int_vector<>>(
                 graph_ptr,
                 columns,
@@ -84,6 +87,8 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
                 config, num_threads
             );
 
+            EXPECT_EQ(0u, masked_dbg_out->num_nodes());
+
             masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
                 auto cur_labels = anno_graph->get_labels(kmer, 0.0);
                 obs_labels.insert(cur_labels.begin(), cur_labels.end());
@@ -96,301 +101,426 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
     }
 }
 
-TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelOverlap) {
+TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
     typedef typename TypeParam::first_type Graph;
     typedef typename TypeParam::second_type Annotation;
     for (size_t num_threads : { 1, 4 }) {
-        size_t k = 5;
-        const tsl::hopscotch_set<std::string> ingroup { "A", "B", "C" };
-        const tsl::hopscotch_set<std::string> outgroup { "A", "B", "C" };
-
-        const std::vector<std::string> sequences {
-            "TTTTTAAAAATTTTTTTTTT",
-            "CCCCCAAAAACCCCCCCCCC",
-            "GGGGGAAAAAGGGGGGGGGG"
-        };
-        const std::vector<std::string> labels { "A", "B", "C" };
-
-        auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels);
-
-        std::unordered_set<std::string> obs_kmers;
-        const std::unordered_set<std::string> ref_kmers {
-            "TTTTT", "TTTTA", "TTTAA", "TTAAA", "TAAAA", "AAAAT", "AAATT", "AATTT", "ATTTT",
-            "CCCCC", "CCCCA", "CCCAA", "CCAAA", "CAAAA", "AAAAC", "AAACC", "AACCC", "ACCCC",
-            "GGGGG", "GGGGA", "GGGAA", "GGAAA", "GAAAA", "AAAAG", "AAAGG", "AAGGG", "AGGGG",
-        };
-        DifferentialAssemblyConfig config {
-            .label_mask_in_unitig_fraction = 0.0,
-            .label_mask_in_kmer_fraction = 0.0,
-            .label_mask_out_unitig_fraction = 1.0,
-            .label_mask_out_kmer_fraction = 0.99,
-            .label_mask_other_unitig_fraction = 1.0,
-            .outfbase = "",
-        };
-
-        auto masked_dbg = mask_nodes_by_label(*anno_graph,
-                                              ingroup, outgroup,
-                                              {}, {},
-                                              config, num_threads);
-
-        masked_dbg->call_kmers([&](auto, const std::string &kmer) {
-            obs_kmers.insert(kmer);
-        });
-
-        EXPECT_EQ(ref_kmers, obs_kmers);
-    }
-}
-
-template <class Graph, class Annotation = ColumnCompressed<>>
-void test_mask_unitigs(double inlabel_fraction,
-                       double outlabel_fraction,
-                       double other_label_fraction,
-                       const std::unordered_set<std::string> &ref_kmers) {
-    for (size_t num_threads : {1, 4}) {
-        const tsl::hopscotch_set<std::string> ingroup { "B", "C" };
-        const tsl::hopscotch_set<std::string> outgroup { "A" };
-        size_t k = 3;
-
-        {
-            /*  B                  AB  AB
-               CGA                 GCC-CCT
-                  \ BC  BC  BC ABC/
-                   GAA-AAT-ATG-TGC
-                 C/               \  C   C
-               GGA                 GCA-CAC
-            */
-            const std::vector<std::string> sequences {
-                    "TGCCT",
-                "CGAATGCCT",
-                "GGAATGCAC",
-                "TTTTTTTTTTTTTT"
-            };
-            const std::vector<std::string> labels { "A", "B", "C", "D" };
-
-            auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels);
-
-            std::unordered_set<std::string> obs_kmers;
+        size_t max_k = std::min(max_test_k<Graph>(), (size_t)15);
+        for (size_t k = 3; k < max_k; ++k) {
+            std::string sig = std::string(k - 1, 'A') + "C$";
+            for (size_t i = 0; i < 7; ++i) {
+                sig += sig;
+            }
 
-            DifferentialAssemblyConfig config {
-                .label_mask_in_unitig_fraction = inlabel_fraction,
-                .label_mask_in_kmer_fraction = 1.0,
-                .label_mask_out_unitig_fraction = outlabel_fraction,
-                .label_mask_out_kmer_fraction = 0.0,
-                .label_mask_other_unitig_fraction = other_label_fraction,
-                .outfbase = "",
+            std::vector<std::string> sequences {
+                std::string("T") + std::string(k - 1, 'A') + std::string(100, 'T'),
+                std::string("T") + sig,
+                std::string("T") + sig,
+                std::string("T") + std::string(k - 1, 'A') + "A",
+                std::string("T") + std::string(k - 1, 'A') + "G"
             };
 
-            auto masked_dbg = mask_nodes_by_label(*anno_graph,
-                                                  ingroup, outgroup,
-                                                  {}, {},
-                                                  config, num_threads);
-
-            masked_dbg->call_kmers([&](auto, const std::string &kmer) { obs_kmers.insert(kmer); });
+            std::vector<std::string> labels { "A", "B", "C", "D", "E" };
 
-            EXPECT_EQ(ref_kmers, obs_kmers)
-                << k << " "
-                << inlabel_fraction << " "
-                << outlabel_fraction << " "
-                << other_label_fraction;
-        }
-    }
-}
-
-TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskUnitigsByLabel) {
-    for (double other_frac : std::vector<double>{ 0.0, 1.0 }) {
-        std::unordered_set<std::string> ref_kmers;
-        test_mask_unitigs<typename TypeParam::first_type,
-                          typename TypeParam::second_type>(1.0, 0.0, other_frac, ref_kmers);
-
-        test_mask_unitigs<typename TypeParam::first_type,
-                          typename TypeParam::second_type>(1.0, 0.24, other_frac, ref_kmers);
-
-        ref_kmers.insert("GAA");
-        ref_kmers.insert("AAT");
-        ref_kmers.insert("ATG");
-        ref_kmers.insert("TGC");
+            auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels, DeBruijnGraph::BASIC, true);
+            auto graph_ptr = std::static_pointer_cast<const Graph>(anno_graph->get_graph_ptr());
 
-        test_mask_unitigs<typename TypeParam::first_type,
-                          typename TypeParam::second_type>(1.0, 0.25, other_frac, ref_kmers);
+            using BinMatType = matrix::ColumnMajor;
+            using CoordMatType = matrix::TupleCSCMatrix<BinMatType>;
+            const auto &annotation = static_cast<const ColumnCoordAnnotator&>(anno_graph->get_annotator());
+            const auto &count_matrix = static_cast<const CoordMatType&>(annotation.get_matrix());
+            const auto &in_binary_columns = static_cast<const BinMatType&>(count_matrix.get_binary_matrix()).data();
+            std::vector<std::unique_ptr<const bit_vector>> columns;
+            columns.reserve(in_binary_columns.size());
+            std::transform(in_binary_columns.begin(), in_binary_columns.end(), std::back_inserter(columns),
+                           [&](const auto &a) {
+                               return std::make_unique<const bit_vector_stat>(a->to_vector());
+                           });
 
-        test_mask_unitigs<typename TypeParam::first_type,
-                          typename TypeParam::second_type>(1.0, 0.50, other_frac, ref_kmers);
+            ASSERT_LT(0u, columns.size());
 
-        test_mask_unitigs<typename TypeParam::first_type,
-                          typename TypeParam::second_type>(1.0, 0.75, other_frac, ref_kmers);
+            std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values_all;
+            std::vector<sdsl::int_vector<>::iterator> pos;
+            column_values_all.reserve(columns.size());
+            for (const auto &bin_col : columns) {
+                column_values_all.emplace_back(std::make_unique<const sdsl::int_vector<>>(bin_col->num_set_bits()));
+                pos.emplace_back(const_cast<sdsl::int_vector<>&>(*column_values_all.back()).begin());
+            }
 
-        test_mask_unitigs<typename TypeParam::first_type,
-                          typename TypeParam::second_type>(1.0, 1.0, other_frac, ref_kmers);
-    }
-}
+            std::vector<uint64_t> row_indices(columns[0]->size());
+            std::iota(row_indices.begin(), row_indices.end(), 0);
+            auto row_values = count_matrix.get_row_values(row_indices);
+            for (size_t i = 0; i < row_indices.size(); ++i) {
+                for (const auto &[j, c] : row_values[i]) {
+                    *pos[j] = c;
+                    ++pos[j];
+                }
+            }
 
-#if ! _PROTEIN_GRAPH
-template <class Graph, class Annotation = ColumnCompressed<>>
-std::unordered_set<std::string>
-test_mask_unitigs_canonical(double inlabel_fraction,
-                            double outlabel_fraction,
-                            double other_label_fraction,
-                            const std::unordered_set<std::string> &ref_kmers,
-                            bool add_complement,
-                            DeBruijnGraph::Mode mode) {
-    for (size_t num_threads : { 1, 4 }) {
-        const tsl::hopscotch_set<std::string> ingroup { "B", "C" };
-        const tsl::hopscotch_set<std::string> outgroup { "A" };
-        size_t k = 5;
-
-        {
-            /*
-                B                AB    AB
-               CGAAT             ATGCC-TGCCT
-                    \ BC   ABC  /
-                     GAATG-AATGC
-                 C  /           \  C     C
-               GGAAT             ATGCA-TGCAC
-                                   C  /  C                B
-                                 GTGCA-TGCAT             ATTCG
-                                            \ABC    BC  /
-                                             GCATT-CATTC
-                                 AB    AB   /           \  C
-                                 AGGCA-GGCAT             ATTCC
-            */
-            const std::vector<std::string> sequences {
-                  "AATGCCT",     // "AGGCATT"
-                "CGAATGCCT",     // "AGGCATTCG"
-                "GGAATGCAC",     // "GTGCATTCC"
-                "TTTTTTTTTTTTTT" // "AAAAAAAAAAAAAA"
+            std::unordered_set<std::string> obs_labels, obs_kmers;
+            const std::unordered_set<std::string> ref_kmers {
+                std::string(k - 1, 'A') + "C"
             };
-            const std::vector<std::string> labels { "A", "B", "C", "D" };
-
-            // if add_complement, then the differential
-            auto anno_graph = build_anno_graph<Graph, Annotation>(
-                k, sequences, labels, mode
-            );
-
-            std::unordered_set<std::string> obs_kmers;
-
-            DifferentialAssemblyConfig config {
-                .label_mask_in_unitig_fraction = inlabel_fraction,
-                .label_mask_in_kmer_fraction = 1.0,
-                .label_mask_out_unitig_fraction = outlabel_fraction,
-                .label_mask_out_kmer_fraction = 0.0,
-                .label_mask_other_unitig_fraction = other_label_fraction,
-                .add_complement = add_complement,
-                .outfbase = "",
+            const std::unordered_set<std::string> ref_labels {
+                "B", "C"
             };
 
-            auto masked_dbg = mask_nodes_by_label(*anno_graph,
-                                                  ingroup, outgroup,
-                                                  {}, {},
-                                                  config, num_threads);
-
-            masked_dbg->call_kmers([&](auto, const std::string &kmer) { obs_kmers.insert(kmer); });
-
-            if (ref_kmers != obs_kmers)
-                return obs_kmers;
-        }
-    }
-
-    return ref_kmers;
-}
-
-TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskUnitigsByLabelAddCanonical) {
-    for (DeBruijnGraph::Mode mode : { DeBruijnGraph::BASIC,
-                                      DeBruijnGraph::CANONICAL,
-                                      DeBruijnGraph::PRIMARY }) {
-        std::string mode_str = mode == DeBruijnGraph::BASIC
-            ? "BASIC"
-            : (mode == DeBruijnGraph::CANONICAL ? "CANONICAL" : "PRIMARY");
-
-        for (double other_frac : std::vector<double>{ 1.0, 0.0 }) {
-            std::unordered_set<std::string> ref_kmers;
-
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 0.0, other_frac, ref_kmers, true, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
-
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 0.25, other_frac, ref_kmers, true, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
-
+            // ingroup: B, C
+            // outgroup: A
+            std::vector<bool> groups { true, false, false };
+            columns.resize(groups.size());
 
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 0.49, other_frac, ref_kmers, true, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
+            ASSERT_GE(column_values_all.size(), groups.size());
+            column_values_all.resize(groups.size());
 
-            ref_kmers.insert("GAATG");
-            ref_kmers.insert("AATGC");
+            std::vector<std::string> test_types = {
+                "binomial",
+                "dmn",
+                "poisson_likelihoodratio",
+            };
 
-            if (mode == DeBruijnGraph::CANONICAL || mode == DeBruijnGraph::PRIMARY) {
-                ref_kmers.insert("CATTC");
-                ref_kmers.insert("GCATT");
+            for (const auto &test_type : test_types) {
+                DifferentialAssemblyConfig config {
+                    .test_type = test_type,
+                    .outfbase = "",
+                };
+
+                if (test_type != "poisson_likelihoodratio") {
+                    auto [masked_dbg_in, masked_dbg_out] = mask_nodes_by_label_dual<sdsl::int_vector<>>(
+                        graph_ptr,
+                        columns,
+                        column_values_all,
+                        groups,
+                        config, num_threads, num_threads,
+                        false
+                    );
+
+                    masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                        auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                        obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                        obs_kmers.insert(kmer);
+                    });
+
+                    EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
+                    EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
+                } else {
+                    try {
+                        mask_nodes_by_label_dual<sdsl::int_vector<>>(
+                            graph_ptr,
+                            columns,
+                            column_values_all,
+                            groups,
+                            config, num_threads, num_threads,
+                            false
+                        );
+
+                        FAIL() << k << "\t" << test_type;
+                    } catch (std::domain_error&) {
+                        continue;
+                    } catch (std::exception &e) {
+                        FAIL() << k << "\t" << test_type << "\t" << e.what();
+                    }
+                }
             }
-
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 0.50, other_frac, ref_kmers, true, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
-
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 0.75, other_frac, ref_kmers, true, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
-
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 1.0, other_frac, ref_kmers, true, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
         }
     }
 }
 
-TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskUnitigsByLabelCanonical) {
-    for (DeBruijnGraph::Mode mode : { DeBruijnGraph::CANONICAL,
-                                      DeBruijnGraph::PRIMARY }) {
-        std::string mode_str = mode == DeBruijnGraph::CANONICAL ? "CANONICAL" : "PRIMARY";
-        for (double other_frac : std::vector<double>{ 1.0, 0.0 }) {
-            std::unordered_set<std::string> ref_kmers;
-
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 0.0, other_frac, ref_kmers, false, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
-
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 0.25, other_frac, ref_kmers, false, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
-
-
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 0.49, other_frac, ref_kmers, false, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
-
-            ref_kmers.insert("GAATG");
-            ref_kmers.insert("AATGC");
-            ref_kmers.insert("CATTC");
-            ref_kmers.insert("GCATT");
-
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 0.50, other_frac, ref_kmers, false, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
-
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 0.75, other_frac, ref_kmers, false, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
-
-            EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-                                                   typename TypeParam::second_type>(
-                1.0, 1.0, other_frac, ref_kmers, false, mode
-            )), ref_kmers) << other_frac << " " << mode_str;
-        }
-    }
-}
-#endif
+// TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelOverlap) {
+//     typedef typename TypeParam::first_type Graph;
+//     typedef typename TypeParam::second_type Annotation;
+//     for (size_t num_threads : { 1, 4 }) {
+//         size_t k = 5;
+//         const tsl::hopscotch_set<std::string> ingroup { "A", "B", "C" };
+//         const tsl::hopscotch_set<std::string> outgroup { "A", "B", "C" };
+
+//         const std::vector<std::string> sequences {
+//             "TTTTTAAAAATTTTTTTTTT",
+//             "CCCCCAAAAACCCCCCCCCC",
+//             "GGGGGAAAAAGGGGGGGGGG"
+//         };
+//         const std::vector<std::string> labels { "A", "B", "C" };
+
+//         auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels);
+
+//         std::unordered_set<std::string> obs_kmers;
+//         const std::unordered_set<std::string> ref_kmers {
+//             "TTTTT", "TTTTA", "TTTAA", "TTAAA", "TAAAA", "AAAAT", "AAATT", "AATTT", "ATTTT",
+//             "CCCCC", "CCCCA", "CCCAA", "CCAAA", "CAAAA", "AAAAC", "AAACC", "AACCC", "ACCCC",
+//             "GGGGG", "GGGGA", "GGGAA", "GGAAA", "GAAAA", "AAAAG", "AAAGG", "AAGGG", "AGGGG",
+//         };
+//         DifferentialAssemblyConfig config {
+//             .label_mask_in_unitig_fraction = 0.0,
+//             .label_mask_in_kmer_fraction = 0.0,
+//             .label_mask_out_unitig_fraction = 1.0,
+//             .label_mask_out_kmer_fraction = 0.99,
+//             .label_mask_other_unitig_fraction = 1.0,
+//             .outfbase = "",
+//         };
+
+//         auto masked_dbg = mask_nodes_by_label(*anno_graph,
+//                                               ingroup, outgroup,
+//                                               {}, {},
+//                                               config, num_threads);
+
+//         masked_dbg->call_kmers([&](auto, const std::string &kmer) {
+//             obs_kmers.insert(kmer);
+//         });
+
+//         EXPECT_EQ(ref_kmers, obs_kmers);
+//     }
+// }
+
+// template <class Graph, class Annotation = ColumnCompressed<>>
+// void test_mask_unitigs(double inlabel_fraction,
+//                        double outlabel_fraction,
+//                        double other_label_fraction,
+//                        const std::unordered_set<std::string> &ref_kmers) {
+//     for (size_t num_threads : {1, 4}) {
+//         const tsl::hopscotch_set<std::string> ingroup { "B", "C" };
+//         const tsl::hopscotch_set<std::string> outgroup { "A" };
+//         size_t k = 3;
+
+//         {
+//             /*  B                  AB  AB
+//                CGA                 GCC-CCT
+//                   \ BC  BC  BC ABC/
+//                    GAA-AAT-ATG-TGC
+//                  C/               \  C   C
+//                GGA                 GCA-CAC
+//             */
+//             const std::vector<std::string> sequences {
+//                     "TGCCT",
+//                 "CGAATGCCT",
+//                 "GGAATGCAC",
+//                 "TTTTTTTTTTTTTT"
+//             };
+//             const std::vector<std::string> labels { "A", "B", "C", "D" };
+
+//             auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels);
+
+//             std::unordered_set<std::string> obs_kmers;
+
+//             DifferentialAssemblyConfig config {
+//                 .label_mask_in_unitig_fraction = inlabel_fraction,
+//                 .label_mask_in_kmer_fraction = 1.0,
+//                 .label_mask_out_unitig_fraction = outlabel_fraction,
+//                 .label_mask_out_kmer_fraction = 0.0,
+//                 .label_mask_other_unitig_fraction = other_label_fraction,
+//                 .outfbase = "",
+//             };
+
+//             auto masked_dbg = mask_nodes_by_label(*anno_graph,
+//                                                   ingroup, outgroup,
+//                                                   {}, {},
+//                                                   config, num_threads);
+
+//             masked_dbg->call_kmers([&](auto, const std::string &kmer) { obs_kmers.insert(kmer); });
+
+//             EXPECT_EQ(ref_kmers, obs_kmers)
+//                 << k << " "
+//                 << inlabel_fraction << " "
+//                 << outlabel_fraction << " "
+//                 << other_label_fraction;
+//         }
+//     }
+// }
+
+// TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskUnitigsByLabel) {
+//     for (double other_frac : std::vector<double>{ 0.0, 1.0 }) {
+//         std::unordered_set<std::string> ref_kmers;
+//         test_mask_unitigs<typename TypeParam::first_type,
+//                           typename TypeParam::second_type>(1.0, 0.0, other_frac, ref_kmers);
+
+//         test_mask_unitigs<typename TypeParam::first_type,
+//                           typename TypeParam::second_type>(1.0, 0.24, other_frac, ref_kmers);
+
+//         ref_kmers.insert("GAA");
+//         ref_kmers.insert("AAT");
+//         ref_kmers.insert("ATG");
+//         ref_kmers.insert("TGC");
+
+//         test_mask_unitigs<typename TypeParam::first_type,
+//                           typename TypeParam::second_type>(1.0, 0.25, other_frac, ref_kmers);
+
+//         test_mask_unitigs<typename TypeParam::first_type,
+//                           typename TypeParam::second_type>(1.0, 0.50, other_frac, ref_kmers);
+
+//         test_mask_unitigs<typename TypeParam::first_type,
+//                           typename TypeParam::second_type>(1.0, 0.75, other_frac, ref_kmers);
+
+//         test_mask_unitigs<typename TypeParam::first_type,
+//                           typename TypeParam::second_type>(1.0, 1.0, other_frac, ref_kmers);
+//     }
+// }
+
+// #if ! _PROTEIN_GRAPH
+// template <class Graph, class Annotation = ColumnCompressed<>>
+// std::unordered_set<std::string>
+// test_mask_unitigs_canonical(double inlabel_fraction,
+//                             double outlabel_fraction,
+//                             double other_label_fraction,
+//                             const std::unordered_set<std::string> &ref_kmers,
+//                             bool add_complement,
+//                             DeBruijnGraph::Mode mode) {
+//     for (size_t num_threads : { 1, 4 }) {
+//         const tsl::hopscotch_set<std::string> ingroup { "B", "C" };
+//         const tsl::hopscotch_set<std::string> outgroup { "A" };
+//         size_t k = 5;
+
+//         {
+//             /*
+//                 B                AB    AB
+//                CGAAT             ATGCC-TGCCT
+//                     \ BC   ABC  /
+//                      GAATG-AATGC
+//                  C  /           \  C     C
+//                GGAAT             ATGCA-TGCAC
+//                                    C  /  C                B
+//                                  GTGCA-TGCAT             ATTCG
+//                                             \ABC    BC  /
+//                                              GCATT-CATTC
+//                                  AB    AB   /           \  C
+//                                  AGGCA-GGCAT             ATTCC
+//             */
+//             const std::vector<std::string> sequences {
+//                   "AATGCCT",     // "AGGCATT"
+//                 "CGAATGCCT",     // "AGGCATTCG"
+//                 "GGAATGCAC",     // "GTGCATTCC"
+//                 "TTTTTTTTTTTTTT" // "AAAAAAAAAAAAAA"
+//             };
+//             const std::vector<std::string> labels { "A", "B", "C", "D" };
+
+//             // if add_complement, then the differential
+//             auto anno_graph = build_anno_graph<Graph, Annotation>(
+//                 k, sequences, labels, mode
+//             );
+
+//             std::unordered_set<std::string> obs_kmers;
+
+//             DifferentialAssemblyConfig config {
+//                 .label_mask_in_unitig_fraction = inlabel_fraction,
+//                 .label_mask_in_kmer_fraction = 1.0,
+//                 .label_mask_out_unitig_fraction = outlabel_fraction,
+//                 .label_mask_out_kmer_fraction = 0.0,
+//                 .label_mask_other_unitig_fraction = other_label_fraction,
+//                 .add_complement = add_complement,
+//                 .outfbase = "",
+//             };
+
+//             auto masked_dbg = mask_nodes_by_label(*anno_graph,
+//                                                   ingroup, outgroup,
+//                                                   {}, {},
+//                                                   config, num_threads);
+
+//             masked_dbg->call_kmers([&](auto, const std::string &kmer) { obs_kmers.insert(kmer); });
+
+//             if (ref_kmers != obs_kmers)
+//                 return obs_kmers;
+//         }
+//     }
+
+//     return ref_kmers;
+// }
+
+// TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskUnitigsByLabelAddCanonical) {
+//     for (DeBruijnGraph::Mode mode : { DeBruijnGraph::BASIC,
+//                                       DeBruijnGraph::CANONICAL,
+//                                       DeBruijnGraph::PRIMARY }) {
+//         std::string mode_str = mode == DeBruijnGraph::BASIC
+//             ? "BASIC"
+//             : (mode == DeBruijnGraph::CANONICAL ? "CANONICAL" : "PRIMARY");
+
+//         for (double other_frac : std::vector<double>{ 1.0, 0.0 }) {
+//             std::unordered_set<std::string> ref_kmers;
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 0.0, other_frac, ref_kmers, true, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 0.25, other_frac, ref_kmers, true, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 0.49, other_frac, ref_kmers, true, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+
+//             ref_kmers.insert("GAATG");
+//             ref_kmers.insert("AATGC");
+
+//             if (mode == DeBruijnGraph::CANONICAL || mode == DeBruijnGraph::PRIMARY) {
+//                 ref_kmers.insert("CATTC");
+//                 ref_kmers.insert("GCATT");
+//             }
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 0.50, other_frac, ref_kmers, true, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 0.75, other_frac, ref_kmers, true, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 1.0, other_frac, ref_kmers, true, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+//         }
+//     }
+// }
+
+// TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskUnitigsByLabelCanonical) {
+//     for (DeBruijnGraph::Mode mode : { DeBruijnGraph::CANONICAL,
+//                                       DeBruijnGraph::PRIMARY }) {
+//         std::string mode_str = mode == DeBruijnGraph::CANONICAL ? "CANONICAL" : "PRIMARY";
+//         for (double other_frac : std::vector<double>{ 1.0, 0.0 }) {
+//             std::unordered_set<std::string> ref_kmers;
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 0.0, other_frac, ref_kmers, false, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 0.25, other_frac, ref_kmers, false, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 0.49, other_frac, ref_kmers, false, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+
+//             ref_kmers.insert("GAATG");
+//             ref_kmers.insert("AATGC");
+//             ref_kmers.insert("CATTC");
+//             ref_kmers.insert("GCATT");
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 0.50, other_frac, ref_kmers, false, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 0.75, other_frac, ref_kmers, false, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+
+//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+//                                                    typename TypeParam::second_type>(
+//                 1.0, 1.0, other_frac, ref_kmers, false, mode
+//             )), ref_kmers) << other_frac << " " << mode_str;
+//         }
+//     }
+// }
+// #endif
 
 } // namespace

From 518554f5fdee920f11c32fbe27acaf9715c12bef Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 5 Jun 2024 14:59:00 +0200
Subject: [PATCH 168/410] count scaling

---
 .../src/graph/annotated_graph_algorithm.cpp   | 60 +++++++++++++++----
 1 file changed, 47 insertions(+), 13 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index dd342c0e1f..cbc03d4c5a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -358,16 +358,38 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double p = static_cast<double>(in_kmers) / total_kmers;
 
             auto bdist = boost::math::binomial(n, p);
-            double p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
 
-            // we want |2s - n| >= d, so
-            // 2s - n >= d || n - 2s >= d
-            // s >= (d + n) / 2 || (n - d) / 2 >= s
-            uint64_t d = abs(in_sum - out_sum);
-            int64_t lower = (n - d) / 2;
-            int64_t upper = ceil(static_cast<double>(n + d) / 2);
-            double pval = boost::math::cdf(bdist, lower)
-                            + boost::math::cdf(boost::math::complement(bdist, upper - 1));
+            double d = abs(static_cast<double>(in_sum) / in_kmers - static_cast<double>(out_sum) / out_kmers);
+
+            double d0 = static_cast<double>(n) / out_kmers;
+            double dn = static_cast<double>(n) / in_kmers;
+
+            double p_min = 0;
+            if (d0 > dn) {
+                p_min = boost::math::pdf(bdist, 0);
+            } else if (d0 < dn) {
+                p_min = boost::math::pdf(bdist, n);
+            } else {
+                p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
+            }
+
+            // we want |s / in - t / out| >= d, so
+            // s / in - t / out >= d || t / out - s / in >= d
+            // P(s <= t/out*in - d*in) + P(s >= d*in + t/out*in)
+            // P(s <= lower) + P(s >= upper)
+            int64_t lower = static_cast<double>(out_sum) / out_kmers * in_kmers - d * in_kmers;
+            int64_t upper = ceil(d * in_kmers + static_cast<double>(out_sum) / out_kmers * in_kmers);
+
+            double pval = 0;
+            if (upper <= 0 || lower >= n) {
+                pval = 1.0;
+            } else {
+                if (lower >= 0)
+                    pval += boost::math::cdf(bdist, lower);
+
+                if (upper <= n)
+                    pval += boost::math::cdf(boost::math::complement(bdist, upper - 1 + (upper == lower)));
+            }
 
             if (pval > 1.0) {
                 common::logger->error("{} > 1.0\t{}\t{},{}", pval, p_min, in_sum, out_sum);
@@ -627,11 +649,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 p_min = get_pmf(0) + get_pmf(n);
             }
 
-            double pval = 0;
             double d = abs(static_cast<double>(in_sum) / in_kmers_adj - static_cast<double>(out_sum) / out_kmers_adj);
-            for (int64_t s = 0; s <= n; ++s) {
-                double cur_d = abs(static_cast<double>(s) / in_kmers_adj - static_cast<double>(n - s) / out_kmers_adj);
-                if (cur_d >= d)
+
+            // we want |s / in - t / out| >= d, so
+            // s / in - t / out >= d || t / out - s / in >= d
+            // P(s <= t/out*in - d*in) + P(s >= d*in + t/out*in)
+            // P(s <= lower) + P(s >= upper)
+            int64_t lower = static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj - d * in_kmers_adj;
+            int64_t upper = ceil(d * in_kmers_adj + static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj);
+
+            double pval = 0;
+            if (upper <= 0 || lower >= n) {
+                pval = 1.0;
+            } else {
+                for (int64_t s = 0; s <= lower; ++s)
+                    pval += get_pmf(s);
+
+                for (int64_t s = upper + (upper == lower); s <= n; ++s)
                     pval += get_pmf(s);
             }
 

From 92c19726b7fbcd095199235a5b61f7c14eeb8075 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 5 Jun 2024 17:13:17 +0200
Subject: [PATCH 169/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 88 ++++++++++++++-----
 1 file changed, 66 insertions(+), 22 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index cbc03d4c5a..46f1935b61 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -358,28 +358,47 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double p = static_cast<double>(in_kmers) / total_kmers;
 
             auto bdist = boost::math::binomial(n, p);
-
             double d = abs(static_cast<double>(in_sum) / in_kmers - static_cast<double>(out_sum) / out_kmers);
 
-            double d0 = static_cast<double>(n) / out_kmers;
-            double dn = static_cast<double>(n) / in_kmers;
+            // we want all s s.t. | s/in - (n-s)/out | >= d, so
+            // s/in - (n-s)/out >= d || (n-s)/out - s/in >= d
+            // s/in - n/out + s/out >= d || n/out - s/out - s/in >= d
+            // s/in + s/out >= d + n/out || n/out - d >= s/in + s/out
+            // s >= (d + n/out) / (1/in + 1/out) || s <= (n/out - d) / (1/in + 1/out)
+            double factor = 1.0 / in_kmers + 1.0 / out_kmers;
+            double lower_d = (static_cast<double>(n) / out_kmers - d) / factor;
+            double upper_d = (d + static_cast<double>(n) / out_kmers) / factor;
+
+            // correct for floating-point errors
+            if (abs(lower_d - round(lower_d)) < 1e-10)
+                lower_d = round(lower_d);
+
+            if (abs(upper_d - round(upper_d)) < 1e-10)
+                upper_d = round(upper_d);
+
+            int64_t lower = floor(lower_d);
+            int64_t upper = ceil(upper_d);
+
+            assert(upper >= lower);
+            assert(d == 0 || upper > lower);
+            assert(in_sum <= lower || in_sum >= upper);
+
+            double d_0 = static_cast<double>(n) / out_kmers;
+            double d_n = static_cast<double>(n) / in_kmers;
+            assert(d_0 >= d || d_n >= d);
+
+            assert(d_0 < d || 0 <= lower || 0 >= upper);
+            assert(d_n < d || n <= lower || n >= upper);
 
             double p_min = 0;
-            if (d0 > dn) {
+            if (d_0 > d_n) {
                 p_min = boost::math::pdf(bdist, 0);
-            } else if (d0 < dn) {
+            } else if (d_0 < d_n) {
                 p_min = boost::math::pdf(bdist, n);
             } else {
                 p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
             }
 
-            // we want |s / in - t / out| >= d, so
-            // s / in - t / out >= d || t / out - s / in >= d
-            // P(s <= t/out*in - d*in) + P(s >= d*in + t/out*in)
-            // P(s <= lower) + P(s >= upper)
-            int64_t lower = static_cast<double>(out_sum) / out_kmers * in_kmers - d * in_kmers;
-            int64_t upper = ceil(d * in_kmers + static_cast<double>(out_sum) / out_kmers * in_kmers);
-
             double pval = 0;
             if (upper <= 0 || lower >= n) {
                 pval = 1.0;
@@ -638,8 +657,38 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
             };
 
+            double d = abs(static_cast<double>(in_sum) / in_kmers_adj - static_cast<double>(out_sum) / out_kmers_adj);
+
+            // we want all s s.t. | s/in - (n-s)/out | >= d, so
+            // s/in - (n-s)/out >= d || (n-s)/out - s/in >= d
+            // s/in - n/out + s/out >= d || n/out - s/out - s/in >= d
+            // s/in + s/out >= d + n/out || n/out - d >= s/in + s/out
+            // s >= (d + n/out) / (1/in + 1/out) || s <= (n/out - d) / (1/in + 1/out)
+            double factor = 1.0 / in_kmers_adj + 1.0 / out_kmers_adj;
+            double lower_d = (static_cast<double>(n) / out_kmers_adj - d) / factor;
+            double upper_d = (d + static_cast<double>(n) / out_kmers_adj) / factor;
+
+            // correct for floating-point errors
+            if (abs(lower_d - round(lower_d)) < 1e-10)
+                lower_d = round(lower_d);
+
+            if (abs(upper_d - round(upper_d)) < 1e-10)
+                upper_d = round(upper_d);
+
+            int64_t lower = floor(lower_d);
+            int64_t upper = ceil(upper_d);
+
+            assert(upper >= lower);
+            assert(d == 0 || upper > lower);
+            assert(in_sum <= lower || in_sum >= upper);
+
             double d_0 = static_cast<double>(n) / out_kmers_adj;
             double d_n = static_cast<double>(n) / in_kmers_adj;
+            assert(d_0 >= d || d_n >= d);
+
+            assert(d_0 < d || 0 <= lower || 0 >= upper);
+            assert(d_n < d || n <= lower || n >= upper);
+
             double p_min = 0;
             if (d_0 > d_n) {
                 p_min = get_pmf(0);
@@ -649,15 +698,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 p_min = get_pmf(0) + get_pmf(n);
             }
 
-            double d = abs(static_cast<double>(in_sum) / in_kmers_adj - static_cast<double>(out_sum) / out_kmers_adj);
-
-            // we want |s / in - t / out| >= d, so
-            // s / in - t / out >= d || t / out - s / in >= d
-            // P(s <= t/out*in - d*in) + P(s >= d*in + t/out*in)
-            // P(s <= lower) + P(s >= upper)
-            int64_t lower = static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj - d * in_kmers_adj;
-            int64_t upper = ceil(d * in_kmers_adj + static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj);
-
             double pval = 0;
             if (upper <= 0 || lower >= n) {
                 pval = 1.0;
@@ -669,8 +709,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     pval += get_pmf(s);
             }
 
+            if (p_min > pval) {
+                common::logger->error("{}\t{},{}", n, lower, upper);
+            }
+
             if (pval >= 1.1) {
-                common::logger->error("{} >= 1.1\t{}\t{},{}", pval, p_min, in_sum, out_sum);
+                common::logger->error("{} >= 1.1\t{}\t{},{}\t{},{}", pval, p_min, in_sum, out_sum, lower, upper);
                 throw std::runtime_error("pval fail");
             }
 

From be0da98c31753cdee9dcf9697841b66f2c566ca2 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 5 Jun 2024 17:40:02 +0200
Subject: [PATCH 170/410] fixes

---
 metagraph/src/common/vectors/transpose.hpp       |  6 ++++--
 .../src/graph/annotated_graph_algorithm.cpp      | 16 ++++++++--------
 .../test_annotated_graph_algorithm.cpp           |  4 ++--
 3 files changed, 14 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/common/vectors/transpose.hpp b/metagraph/src/common/vectors/transpose.hpp
index e98712034e..48e3de67b7 100644
--- a/metagraph/src/common/vectors/transpose.hpp
+++ b/metagraph/src/common/vectors/transpose.hpp
@@ -99,8 +99,10 @@ void call_rows(const std::vector<BitmapPtr> &columns,
                     rows[i - begin].emplace_back(j, 1);
                 });
             } else {
-                const auto &col = *column_values[j];
-                if constexpr(std::is_same_v<ValuesPtr, std::unique_ptr<sdsl::int_vector_buffer<>>>) {
+                using Container = std::remove_const_t<typename ValuesPtr::element_type>;
+                const Container &col = *column_values[j];
+
+                if constexpr(std::is_same_v<Container, sdsl::int_vector_buffer<>>) {
                     sdsl::int_vector_buffer<> cur_col(col.filename(), std::ios::in, col.buffersize());
                     columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
                         rows[i - begin].emplace_back(j, cur_col[columns[j]->rank1(i) - 1]);
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 46f1935b61..9ffaafcf72 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -251,7 +251,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         if (count >= config.min_recurrence
                                 && count_in >= config.min_in_recurrence
                                 && count_out >= config.min_out_recurrence) {
-                            keep_row = true;;
+                            keep_row = true;
                         }
 
                         if (count_in > config.max_in_recurrence || count_out > config.max_out_recurrence) {
@@ -338,7 +338,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     using RowStats = std::tuple<double, double, double, double>;
     std::function<RowStats(const PairContainer&)> compute_pval;
 
-    if (config.test_type == "binomial") {
+    if (config.test_type == "poisson_exact") {
         compute_pval = [&](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0, 1.1);
@@ -422,7 +422,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         };
     } else if (config.test_type == "poisson_likelihoodratio" || config.test_type == "cmh") {
         if (in_kmers == 0 || out_kmers == 0) {
-            common::logger->error("Test invalid, try binomial instead");
+            common::logger->error("Test invalid, try poisson_exact instead");
             throw std::domain_error("Test fail");
         }
         compute_pval = [&,dist=boost::math::chi_squared(1)](const auto &row) {
@@ -457,18 +457,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 assert(n2 == out_kmers);
 
                 if (m1 == 0 || m2 == 0) {
-                    common::logger->error("Test invalid, try binomial instead");
+                    common::logger->error("Test invalid, try poisson_exact instead");
                     throw std::domain_error("Test fail");
                 }
 
                 chi_stat = pow(a - n1 * m1 / t, 2.0) * t * t * (t - 1) / (n1 * n2 * m1 * m2);
                 if (chi_stat < 0) {
-                    common::logger->error("Test statistic {} < 0, too few data points. Use binomial instead", chi_stat);
+                    common::logger->error("Test statistic {} < 0, too few data points. Use poisson_exact instead", chi_stat);
                     throw std::domain_error("Test failed");
                 }
             } else {
                 if (in_sum == 0 || out_sum == 0) {
-                    common::logger->error("Empty row: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'binomial' instead");
+                    common::logger->error("Empty row: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
                     throw std::domain_error("Test failed");
                 }
 
@@ -480,7 +480,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double mean_denom_out = theta * out_kmers;
 
                 if (mean_denom_in == 0 || mean_denom_out == 0) {
-                    common::logger->error("Empty null hypothesis: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'binomial' instead");
+                    common::logger->error("Empty null hypothesis: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
                     throw std::domain_error("Test failed");
                 }
 
@@ -507,7 +507,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                    static_cast<double>(out_sum) / out_kmers * in_kmers,
                                    1.1);
         };
-    } else if (config.test_type == "dmn") {
+    } else if (config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting negative binomial distributions");
         auto get_rp = [&](double mu, double var, const auto &hist) {
             double r = boost::math::tools::newton_raphson_iterate([&](double r) {
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index f15ff1748e..8e38aa338a 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -174,8 +174,8 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
             column_values_all.resize(groups.size());
 
             std::vector<std::string> test_types = {
-                "binomial",
-                "dmn",
+                "poisson_exact",
+                "nbinom_exact",
                 "poisson_likelihoodratio",
             };
 

From 4ebe05c3cfd2a5461b447729b79dbefc9494912d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 6 Jun 2024 00:01:52 +0200
Subject: [PATCH 171/410] fisher exact

---
 .../src/graph/annotated_graph_algorithm.cpp   | 54 +++++++++++++++++--
 .../test_annotated_graph_algorithm.cpp        |  1 +
 2 files changed, 51 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9ffaafcf72..b7885bd3a7 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -420,9 +420,55 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                    static_cast<double>(out_sum) / out_kmers * in_kmers,
                                    p_min);
         };
-    } else if (config.test_type == "poisson_likelihoodratio" || config.test_type == "cmh") {
+    } else if (config.test_type == "fisher") {
+        compute_pval = [&](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+
+            for (const auto &[j, c] : row) {
+                if (groups[j]) {
+                    out_sum += c;
+                } else {
+                    in_sum += c;
+                }
+            }
+
+            int64_t t = total_kmers;
+            int64_t n1 = in_kmers;
+            int64_t n2 = out_kmers;
+            int64_t m1 = in_sum + out_sum;
+            int64_t m2 = t - m1;
+
+            double lbase = lgamma(n1 + 1) + lgamma(n2 + 1) + lgamma(m1 + 1) + lgamma(m2 + 1) - lgamma(t + 1);
+
+            auto get_pval = [&](int64_t a) {
+                int64_t b = n1 - a;
+                int64_t c = m1 - a;
+                int64_t d = m2 - b;
+                assert(d == n2 - c);
+
+                return b < 0 || c < 0 || d < 0 ? 1.0 : exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
+            };
+
+            double pval = get_pval(in_sum);
+            double pmin = std::min({ get_pval(0), get_pval(n1), get_pval(m1) });
+
+            return std::make_tuple(pval,
+                                   static_cast<double>(in_sum),
+                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
+                                   pmin);
+        };
+    } else if (config.test_type == "poisson_likelihoodratio"
+            || config.test_type == "cmh") {
         if (in_kmers == 0 || out_kmers == 0) {
-            common::logger->error("Test invalid, try poisson_exact instead");
+            if (config.test_type == "poisson_likelihoodratio") {
+                common::logger->error("Test invalid, try poisson_exact instead");
+            } else {
+                common::logger->error("Test invalid, try fisher instead");
+            }
             throw std::domain_error("Test fail");
         }
         compute_pval = [&,dist=boost::math::chi_squared(1)](const auto &row) {
@@ -457,13 +503,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 assert(n2 == out_kmers);
 
                 if (m1 == 0 || m2 == 0) {
-                    common::logger->error("Test invalid, try poisson_exact instead");
+                    common::logger->error("Test invalid, try fisher instead");
                     throw std::domain_error("Test fail");
                 }
 
                 chi_stat = pow(a - n1 * m1 / t, 2.0) * t * t * (t - 1) / (n1 * n2 * m1 * m2);
                 if (chi_stat < 0) {
-                    common::logger->error("Test statistic {} < 0, too few data points. Use poisson_exact instead", chi_stat);
+                    common::logger->error("Test statistic {} < 0, too few data points. Use fisher instead", chi_stat);
                     throw std::domain_error("Test failed");
                 }
             } else {
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index 8e38aa338a..0c04ec00c8 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -177,6 +177,7 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                 "poisson_exact",
                 "nbinom_exact",
                 "poisson_likelihoodratio",
+                "fisher"
             };
 
             for (const auto &test_type : test_types) {

From 0fc53da03e06c40daffa5b243f8fd0336a2880b8 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 6 Jun 2024 01:02:47 +0200
Subject: [PATCH 172/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 171 +++++++++++-------
 .../test_annotated_graph_algorithm.cpp        |  22 ++-
 2 files changed, 123 insertions(+), 70 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b7885bd3a7..e64b6eb488 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -271,6 +271,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     double nelem = ignored.size() - sdsl::util::cnt_one_bits(ignored);
 
     common::logger->trace("Computing aggregate statistics");
+    std::mutex agg_mu;
     double in_kmers = 0;
     double out_kmers = 0;
     std::vector<double> medians(groups.size());
@@ -311,16 +312,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             medians[j] = boost::math::statistics::median(vals);
 
         sums[j] = sum;
-        if (groups[j]) {
-            out_kmers += sum;
-        } else {
-            in_kmers += sum;
-        }
         sums_of_squares[j] = sum_of_squares;
         n_kmers[j] = n;
 
         common::logger->trace("{}: kept: {} / {}\tsum: {}\tmedian: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
                               j, n, i, sum, medians[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
+
+        std::lock_guard<std::mutex> lock(agg_mu);
+        if (groups[j]) {
+            out_kmers += sum;
+        } else {
+            in_kmers += sum;
+        }
     }
 
     double total_kmers = in_kmers + out_kmers;
@@ -450,28 +453,30 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 int64_t d = m2 - b;
                 assert(d == n2 - c);
 
-                return b < 0 || c < 0 || d < 0 ? 1.0 : exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
+                return b < 0 || c < 0 || d < 0
+                    ? 1.0
+                    : exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
             };
 
             double pval = get_pval(in_sum);
-            double pmin = std::min({ get_pval(0), get_pval(n1), get_pval(m1) });
+            double pmin = std::min(get_pval(0), get_pval(std::min(n1, m1)));
 
             return std::make_tuple(pval,
                                    static_cast<double>(in_sum),
                                    static_cast<double>(out_sum) / out_kmers * in_kmers,
                                    pmin);
         };
-    } else if (config.test_type == "poisson_likelihoodratio"
-            || config.test_type == "cmh") {
-        if (in_kmers == 0 || out_kmers == 0) {
-            if (config.test_type == "poisson_likelihoodratio") {
-                common::logger->error("Test invalid, try poisson_exact instead");
-            } else {
-                common::logger->error("Test invalid, try fisher instead");
-            }
+    } else if (config.test_type == "cmh") {
+        if (in_kmers == 0 || out_kmers == 0 || total_kmers == 1) {
+            common::logger->error("Test invalid, try fisher instead");
             throw std::domain_error("Test fail");
         }
-        compute_pval = [&,dist=boost::math::chi_squared(1)](const auto &row) {
+
+        int64_t t = total_kmers;
+        int64_t n1 = in_kmers;
+        int64_t n2 = out_kmers;
+
+        compute_pval = [&,t,n1,n2,dist=boost::math::chi_squared(1)](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
@@ -486,72 +491,114 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            double chi_stat = 0;
-            if (config.test_type == "cmh") {
-                double a = in_sum;
-                double b = in_kmers - in_sum;
-                double c = out_sum;
-                double d = out_kmers - out_sum;
-                double n1 = a + b;
-                double n2 = c + d;
-                double m1 = a + c;
-                double m2 = b + d;
-                double t = n1 + n2;
-
-                assert(t == total_kmers);
-                assert(n1 == in_kmers);
-                assert(n2 == out_kmers);
-
-                if (m1 == 0 || m2 == 0) {
-                    common::logger->error("Test invalid, try fisher instead");
-                    throw std::domain_error("Test fail");
-                }
+            int64_t m1 = in_sum + out_sum;
+            int64_t m2 = t - m1;
+            assert(m2 >= 0);
 
-                chi_stat = pow(a - n1 * m1 / t, 2.0) * t * t * (t - 1) / (n1 * n2 * m1 * m2);
-                if (chi_stat < 0) {
-                    common::logger->error("Test statistic {} < 0, too few data points. Use fisher instead", chi_stat);
-                    throw std::domain_error("Test failed");
-                }
-            } else {
-                if (in_sum == 0 || out_sum == 0) {
-                    common::logger->error("Empty row: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
-                    throw std::domain_error("Test failed");
-                }
+            if (m2 == 0) {
+                common::logger->error("This row has all counts. Too few data points. Use fisher instead");
+                throw std::domain_error("Test failed");
+            }
 
-                double log_likelihood_in = -in_sum + log(static_cast<double>(in_sum)) * in_sum;
-                double log_likelihood_out = -out_sum + log(static_cast<double>(out_sum)) * out_sum;
+            double lbase = log(n1) + log(n2) + log(m1) + log(m2) - log(t) * 2 - log(t - 1);
+            double factor = static_cast<double>(n1 * m1) / t;
 
-                double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
-                double mean_denom_in = theta * in_kmers;
-                double mean_denom_out = theta * out_kmers;
+            auto get_chi_stat = [&](int64_t a) {
+                int64_t b = n1 - a;
+                int64_t c = m1 - a;
+                int64_t d = m2 - b;
+                assert(d == n2 - c);
 
-                if (mean_denom_in == 0 || mean_denom_out == 0) {
-                    common::logger->error("Empty null hypothesis: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
-                    throw std::domain_error("Test failed");
-                }
+                if (b < 0 || c < 0 || d < 0)
+                    return 0.0;
+
+                double a_shift = static_cast<double>(a) - factor;
+
+                if (a_shift > 0)
+                    return exp(log(a_shift) * 2.0 - lbase);
+
+                return a_shift * a_shift / exp(lbase);
+            };
+
+            double chi_stat = get_chi_stat(in_sum);
+            if (chi_stat <= 0) {
+                common::logger->error("Test statistic {} <= 0, too few data points. Use fisher instead.\t{}\t{},{},{},{}\t{}\t{}", chi_stat, in_sum, n1,n2,m1,m2, t, factor);
+                throw std::domain_error("Test failed");
+            }
 
-                double log_likelihood_null = -mean_denom_in + in_sum * log(mean_denom_in);
-                log_likelihood_null += -mean_denom_out + out_sum * log(mean_denom_out);
+            double max_chi_stat = std::max(get_chi_stat(0), get_chi_stat(std::min(n1, m1)));
+            if (max_chi_stat <= 0) {
+                common::logger->error("Best test statistic {} <= 0, too few data points. Use fisher instead.\t{},{},{},{}\t{}\t{}", max_chi_stat, n1,n2,m1,m2, t, factor);
+                throw std::domain_error("Test failed");
+            }
 
-                chi_stat = (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
+            double pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
+            double pmin = boost::math::cdf(boost::math::complement(dist, max_chi_stat));
+
+            return std::make_tuple(pval,
+                                   static_cast<double>(in_sum),
+                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
+                                   pmin);
+        };
+    } else if (config.test_type == "poisson_likelihoodratio") {
+        if (in_kmers == 0 || out_kmers == 0) {
+            common::logger->error("Test invalid, try poisson_exact instead");
+            throw std::domain_error("Test fail");
+        }
+        compute_pval = [&,dist=boost::math::chi_squared(1)](const auto &row) {
+            if (row.empty())
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
 
-                if (chi_stat < 0) {
-                    common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
-                    throw std::runtime_error("Test failed");
+            for (const auto &[j, c] : row) {
+                if (groups[j]) {
+                    out_sum += c;
+                } else {
+                    in_sum += c;
                 }
             }
 
+            if (in_sum == 0 || out_sum == 0) {
+                common::logger->error("Empty row: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
+                throw std::domain_error("Test failed");
+            }
+
+            double log_likelihood_in = -in_sum + log(static_cast<double>(in_sum)) * in_sum;
+            double log_likelihood_out = -out_sum + log(static_cast<double>(out_sum)) * out_sum;
+
+            double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
+            double mean_denom_in = theta * in_kmers;
+            double mean_denom_out = theta * out_kmers;
+
+            if (mean_denom_in == 0 || mean_denom_out == 0) {
+                common::logger->error("Empty null hypothesis: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
+                throw std::domain_error("Test failed");
+            }
+
+            double log_likelihood_null = -mean_denom_in + in_sum * log(mean_denom_in);
+            log_likelihood_null += -mean_denom_out + out_sum * log(mean_denom_out);
+
+            double chi_stat = (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
+
+            if (chi_stat < 0) {
+                common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
+                throw std::runtime_error("Test failed");
+            }
+
             if (chi_stat == 0)
                 return std::make_tuple(1.0, static_cast<double>(in_sum), static_cast<double>(out_sum) / out_kmers * in_kmers, 1.1);
 
             assert(chi_stat > 0);
 
             double pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
+            double pmin = 1.1;
 
             return std::make_tuple(pval,
                                    static_cast<double>(in_sum),
                                    static_cast<double>(out_sum) / out_kmers * in_kmers,
-                                   1.1);
+                                   pmin);
         };
     } else if (config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting negative binomial distributions");
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index 0c04ec00c8..de7bac35a6 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -177,7 +177,8 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                 "poisson_exact",
                 "nbinom_exact",
                 "poisson_likelihoodratio",
-                "fisher"
+                "fisher",
+                "cmh"
             };
 
             for (const auto &test_type : test_types) {
@@ -196,11 +197,16 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                         false
                     );
 
-                    masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
-                        auto cur_labels = anno_graph->get_labels(kmer, 0.0);
-                        obs_labels.insert(cur_labels.begin(), cur_labels.end());
-                        obs_kmers.insert(kmer);
-                    });
+                    try {
+                        masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                            auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                            obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                            obs_kmers.insert(kmer);
+                        });
+                    } catch (std::domain_error &e) {
+                        EXPECT_TRUE(false) << k << "\t" << test_type << "\t" << e.what();
+                        continue;
+                    }
 
                     EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
                     EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
@@ -215,11 +221,11 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                             false
                         );
 
-                        FAIL() << k << "\t" << test_type;
+                        EXPECT_TRUE(false) << k << "\t" << test_type;
                     } catch (std::domain_error&) {
                         continue;
                     } catch (std::exception &e) {
-                        FAIL() << k << "\t" << test_type << "\t" << e.what();
+                        EXPECT_TRUE(false) << k << "\t" << test_type << "\t" << e.what();
                     }
                 }
             }

From 65e4b0045b45f93e966c50e13b49d45ba8223ca9 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 6 Jun 2024 01:16:46 +0200
Subject: [PATCH 173/410] cleanup

---
 .../test_annotated_graph_algorithm.cpp        | 37 +++++++------------
 1 file changed, 13 insertions(+), 24 deletions(-)

diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index de7bac35a6..b216f6916e 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -187,7 +187,7 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                     .outfbase = "",
                 };
 
-                if (test_type != "poisson_likelihoodratio") {
+                try {
                     auto [masked_dbg_in, masked_dbg_out] = mask_nodes_by_label_dual<sdsl::int_vector<>>(
                         graph_ptr,
                         columns,
@@ -197,36 +197,25 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                         false
                     );
 
-                    try {
+                    if (test_type != "poisson_likelihoodratio") {
                         masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
                             auto cur_labels = anno_graph->get_labels(kmer, 0.0);
                             obs_labels.insert(cur_labels.begin(), cur_labels.end());
                             obs_kmers.insert(kmer);
                         });
-                    } catch (std::domain_error &e) {
-                        EXPECT_TRUE(false) << k << "\t" << test_type << "\t" << e.what();
-                        continue;
-                    }
-
-                    EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
-                    EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
-                } else {
-                    try {
-                        mask_nodes_by_label_dual<sdsl::int_vector<>>(
-                            graph_ptr,
-                            columns,
-                            column_values_all,
-                            groups,
-                            config, num_threads, num_threads,
-                            false
-                        );
-
+                        EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
+                        EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
+                    } else {
                         EXPECT_TRUE(false) << k << "\t" << test_type;
-                    } catch (std::domain_error&) {
-                        continue;
-                    } catch (std::exception &e) {
-                        EXPECT_TRUE(false) << k << "\t" << test_type << "\t" << e.what();
                     }
+                } catch (std::domain_error &e) {
+                    if (test_type == "poisson_likelihoodratio")
+                        continue;
+
+                    EXPECT_TRUE(false) << k << "\t" << test_type << "\t" << e.what();
+                    continue;
+                } catch (std::exception &e) {
+                    EXPECT_TRUE(false) << k << "\t" << test_type << "\t" << e.what();
                 }
             }
         }

From 03e7057587aac056a42d180e763df675f8b4d8b7 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 6 Jun 2024 02:27:42 +0200
Subject: [PATCH 174/410] brunner-munzel

---
 .../src/graph/annotated_graph_algorithm.cpp   | 158 ++++++++++++------
 .../test_annotated_graph_algorithm.cpp        |   4 +-
 2 files changed, 109 insertions(+), 53 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index e64b6eb488..99f2868928 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -816,11 +816,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                    static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj,
                                    std::min(p_min, 1.0));
         };
-    } else if (config.test_type == "nonparametric" || config.test_type == "nonparametric_u") {
-        compute_pval = [&](const auto &row) {
+    } else if (config.test_type == "brunnermunzel" || config.test_type == "mannwhitney_u") {
+        size_t nx = num_labels_in;
+        size_t ny = num_labels_out;
+
+        compute_pval = [&,nx,ny](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
+            double p_min = 1.1;
             auto norm = boost::math::normal_distribution();
             auto rankdata = [&](const std::vector<double> &c) {
                 auto c_p = c;
@@ -855,8 +859,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             out_counts.resize(num_labels_out);
 
             double p = 0.0;
-            size_t nx = in_counts.size();
-            size_t ny = out_counts.size();
 
             std::vector<double> c;
             c.reserve(in_counts.size() + out_counts.size());
@@ -866,66 +868,118 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
             double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
 
-            double p_min = 1.1;
-
-            if (config.test_type == "nonparametric") {
+            if (config.test_type == "bunnermunzel") {
                 if (nx > 1 && ny > 1) {
-                    auto [rankx, countsx] = rankdata(in_counts);
-                    auto [ranky, countsy] = rankdata(out_counts);
-                    double rankx_mean = boost::math::statistics::mean(rankx.begin(), rankx.end());
-                    double ranky_mean = boost::math::statistics::mean(ranky.begin(), ranky.end());
-
-                    double Sx = 0;
-                    for (size_t i = 0; i < nx; ++i) {
-                        Sx += std::pow(rankc[i] - rankx[i] - rankcx_mean + rankx_mean, 2.0);
-                    }
-                    Sx /= nx - 1;
-                    double Sy = 0;
-                    for (size_t i = 0; i < ny; ++i) {
-                        Sy += std::pow(rankc[nx + i] - ranky[i] - rankcy_mean + ranky_mean, 2.0);
-                    }
-                    Sy /= ny - 1;
+                    auto get_p = [&]() {
+                        double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
+                        double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
+                        auto [rankx, countsx] = rankdata(in_counts);
+                        auto [ranky, countsy] = rankdata(out_counts);
+                        double rankx_mean = boost::math::statistics::mean(rankx.begin(), rankx.end());
+                        double ranky_mean = boost::math::statistics::mean(ranky.begin(), ranky.end());
+
+                        double Sx = 0;
+                        for (size_t i = 0; i < nx; ++i) {
+                            Sx += std::pow(rankc[i] - rankx[i] - rankcx_mean + rankx_mean, 2.0);
+                        }
+                        Sx /= nx - 1;
+                        double Sy = 0;
+                        for (size_t i = 0; i < ny; ++i) {
+                            Sy += std::pow(rankc[nx + i] - ranky[i] - rankcy_mean + ranky_mean, 2.0);
+                        }
+                        Sy /= ny - 1;
 
-                    double wbfn = (rankcy_mean - rankcx_mean) * nx * ny;
-                    wbfn /= std::sqrt(Sx * nx + Sy * ny) * (nx + ny);
+                        double wbfn = (rankcy_mean - rankcx_mean) * nx * ny;
+                        wbfn /= std::sqrt(Sx * nx + Sy * ny) * (nx + ny);
 
-                    double df_numer = std::pow(Sx * nx + Sy * ny, 2.0);
-                    double df_denom = std::pow(Sx * nx, 2.0) / (nx - 1) + std::pow(Sy * ny, 2.0) / (ny - 1);
+                        double df_numer = std::pow(Sx * nx + Sy * ny, 2.0);
+                        double df_denom = std::pow(Sx * nx, 2.0) / (nx - 1) + std::pow(Sy * ny, 2.0) / (ny - 1);
 
-                    if (df_denom != 0) {
-                        wbfn = abs(wbfn);
-                        double df = df_numer / df_denom;
-                        boost::math::students_t dist(df);
-                        p = 2 * boost::math::cdf(boost::math::complement(dist, wbfn));
-                    }
+                        if (df_denom != 0) {
+                            wbfn = abs(wbfn);
+                            double df = df_numer / df_denom;
+                            boost::math::students_t dist(df);
+                            return 2 * boost::math::cdf(boost::math::complement(dist, wbfn));
+                        }
+
+                        return 0.0;
+                    };
+
+                    p = get_p();
+
+                    std::sort(rankc.begin(), rankc.end());
+                    std::copy(rankc.begin(), rankc.begin() + nx, in_counts.begin());
+                    std::copy(rankc.begin() + nx, rankc.end(), out_counts.begin());
+                    p_min = get_p();
+
+                    std::sort(rankc.begin(), rankc.end(), std::greater<double>());
+                    std::copy(rankc.begin(), rankc.begin() + nx, in_counts.begin());
+                    std::copy(rankc.begin() + nx, rankc.end(), out_counts.begin());
+                    p_min = std::min(p_min, get_p());
+                } else {
+                    p_min = 0.0;
                 }
             } else {
-                p_min = 2.0 / exp(lgamma(groups.size() + 1) - lgamma(num_labels_in + 1) - lgamma(num_labels_out + 1));
-                if (!nx || !ny) {
-                    p = 1.0;
-                } else {
-                    // https://datatab.net/tutorial/mann-whitney-u-test
+                // https://datatab.net/tutorial/mann-whitney-u-test
+                double u1_f = nx * ny + static_cast<double>(nx * (nx + 1))/2;
+                double u2_f = nx * ny + static_cast<double>(ny * (ny + 1))/2;
+                auto get_u = [&]() {
                     double rankcx_sum = std::accumulate(rankc.begin(), rankc.begin() + nx, double(0.0));
                     double rankcy_sum = std::accumulate(rankc.begin() + nx, rankc.end(), double(0.0));
-                    double u1 = nx * ny + static_cast<double>(nx * (nx + 1))/2 - rankcx_sum;
-                    double u2 = nx * ny + static_cast<double>(ny * (ny + 1))/2 - rankcy_sum;
-                    double u = std::min(u1, u2);
+                    double u1 = u1_f - rankcx_sum;
+                    double u2 = u2_f - rankcy_sum;
+                    return std::min(u1, u2);
+                };
+
+                double u = get_u();
+
+                if (nx + ny < 20) {
+                    std::sort(rankc.begin(), rankc.end());
+                    size_t num_better = 0;
+                    size_t num_max = 0;
+                    double max_u = -std::numeric_limits<double>::infinity();
+                    while (std::next_permutation(rankc.begin(), rankc.end())) {
+                        double cur_u = get_u();
+                        num_better += (cur_u >= u);
+                        if (cur_u > max_u) {
+                            max_u = cur_u;
+                            num_max = 1;
+                        } else if (cur_u == max_u) {
+                            ++num_max;
+                        }
+                    }
 
-                    double n = nx + ny;
+                    p = exp(log(static_cast<double>(num_better)) - lgamma(nx + ny + 1)) * 2.0;
+                    p_min = exp(log(static_cast<double>(num_max)) - lgamma(nx + ny + 1)) * 2.0;
+                } else {
                     // normal approximation
-                    double mu = static_cast<double>(nx * ny) / 2;
-                    double corr = 0;
-                    for (const auto &[val,rks] : countsc) {
-                        const auto &[rk, c] = rks;
-                        if (c > 1)
-                            corr += c * c * c - c;
-                    }
-                    double sd = sqrt(static_cast<double>(nx * ny) / n / (n - 1)) * sqrt((n * n * n - n)/12 - corr / 12);
+                    if (!nx || !ny) {
+                        p = 1.0;
+                        p_min = 1.0;
+                    } else {
+                        double n = nx + ny;
+                        double mu = static_cast<double>(nx * ny) / 2;
+                        double corr = 0;
+                        for (const auto &[val,rks] : countsc) {
+                            const auto &[rk, c] = rks;
+                            if (c > 1)
+                                corr += c * c * c - c;
+                        }
+                        double sd = sqrt(static_cast<double>(nx * ny) / n / (n - 1)) * sqrt((n * n * n - n)/12 - corr / 12);
+
+                        auto get_p = [&](double u) {
+                            double z = abs((u - mu) / sd);
 
-                    double z = abs((u - mu) / sd);
+                            // hack just in case sd == 0
+                            return u != mu ? boost::math::cdf(boost::math::complement(norm, z)) * 2 : 1.0;
+                        };
 
-                    // hack just in case sd == 0
-                    p = u != mu ? boost::math::cdf(boost::math::complement(norm, z)) * 2 : 1.0;
+                        p = get_p(u);
+                        std::sort(rankc.begin(), rankc.end());
+                        p_min = get_p(get_u());
+                        std::sort(rankc.begin(), rankc.end(), std::greater<double>());
+                        p_min = std::min(p_min, get_p(get_u()));
+                    }
                 }
             }
 
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index b216f6916e..48927fc7b8 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -178,7 +178,9 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                 "nbinom_exact",
                 "poisson_likelihoodratio",
                 "fisher",
-                "cmh"
+                "cmh",
+                "mannwhitney_u",
+                "brunnermunzel"
             };
 
             for (const auto &test_type : test_types) {

From 3b1bd1950f4779a7058d368e1b6637e59cf42af4 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 6 Jun 2024 12:01:48 +0200
Subject: [PATCH 175/410] proper min p-val calculation for
 poisson_likelihoodratio

---
 .../src/graph/annotated_graph_algorithm.cpp   | 73 +++++++++----------
 1 file changed, 36 insertions(+), 37 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 99f2868928..2cc3b25ff0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -280,6 +280,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<uint64_t> sums(groups.size());
     std::vector<uint64_t> sums_of_squares(groups.size());
 
+    bool compute_medians = (config.test_type == "mannwhitney_u" || config.test_type == "brunnermunzel");
+
     #pragma omp parallel for num_threads(num_parallel_files)
     for (size_t j = 0; j < groups.size(); ++j) {
         const auto &column = *columns_all[j];
@@ -291,22 +293,27 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         uint64_t sum_of_squares = 0;
         size_t i = 0;
         size_t n = 0;
+        uint64_t max_obs_val = 0;
 
         std::vector<uint64_t> vals;
-        vals.reserve(num_vals);
+        if (compute_medians)
+            vals.reserve(num_vals);
+
         column.call_ones([&](uint64_t row_i) {
             if (!ignored[row_i]) {
                 if (uint64_t c = adjust_count(column_values_all.size() ? (*column_values_all[j])[i] : 1, j, row_i)) {
-                    vals.emplace_back(c);
+                    max_obs_val = std::max(max_obs_val, c);
                     sum += c;
                     sum_of_squares += c * c;
                     ++n;
+                    if (compute_medians)
+                        vals.emplace_back(c);
                 }
             }
             ++i;
         });
 
-        max_obs_vals[j] = *std::max_element(vals.begin(), vals.end());
+        max_obs_vals[j] = max_obs_val;
 
         if (vals.size())
             medians[j] = boost::math::statistics::median(vals);
@@ -315,8 +322,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         sums_of_squares[j] = sum_of_squares;
         n_kmers[j] = n;
 
-        common::logger->trace("{}: kept: {} / {}\tsum: {}\tmedian: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
-                              j, n, i, sum, medians[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
+        common::logger->trace("{}: kept: {} / {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
+                              j, n, i, sum, max_obs_vals[j], min_counts[j], check_cutoff[j]);
 
         std::lock_guard<std::mutex> lock(agg_mu);
         if (groups[j]) {
@@ -565,35 +572,41 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 throw std::domain_error("Test failed");
             }
 
-            double log_likelihood_in = -in_sum + log(static_cast<double>(in_sum)) * in_sum;
-            double log_likelihood_out = -out_sum + log(static_cast<double>(out_sum)) * out_sum;
+            int64_t total_sum = in_sum + out_sum;
 
-            double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
-            double mean_denom_in = theta * in_kmers;
-            double mean_denom_out = theta * out_kmers;
+            auto get_chi_stat = [&](int64_t in_sum) {
+                int64_t out_sum = total_sum - in_sum;
+                double log_likelihood_in = -in_sum + log(static_cast<double>(in_sum)) * in_sum;
+                double log_likelihood_out = -out_sum + log(static_cast<double>(out_sum)) * out_sum;
 
-            if (mean_denom_in == 0 || mean_denom_out == 0) {
-                common::logger->error("Empty null hypothesis: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
-                throw std::domain_error("Test failed");
-            }
+                double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
+                double mean_denom_in = theta * in_kmers;
+                double mean_denom_out = theta * out_kmers;
 
-            double log_likelihood_null = -mean_denom_in + in_sum * log(mean_denom_in);
-            log_likelihood_null += -mean_denom_out + out_sum * log(mean_denom_out);
+                if (mean_denom_in == 0 || mean_denom_out == 0) {
+                    common::logger->error("Empty null hypothesis: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
+                    throw std::domain_error("Test failed");
+                }
 
-            double chi_stat = (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
+                double log_likelihood_null = -mean_denom_in + in_sum * log(mean_denom_in);
+                log_likelihood_null += -mean_denom_out + out_sum * log(mean_denom_out);
+
+                return (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
+            };
+
+            double chi_stat = get_chi_stat(in_sum);
 
             if (chi_stat < 0) {
                 common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
                 throw std::runtime_error("Test failed");
             }
 
-            if (chi_stat == 0)
-                return std::make_tuple(1.0, static_cast<double>(in_sum), static_cast<double>(out_sum) / out_kmers * in_kmers, 1.1);
+            double pval = chi_stat != 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
 
-            assert(chi_stat > 0);
+            double max_chi_stat = std::max(get_chi_stat(1), get_chi_stat(std::min(total_sum - 1, static_cast<int64_t>(in_kmers))));
+            assert(max_chi_stat >= chi_stat);
 
-            double pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
-            double pmin = 1.1;
+            double pmin = max_chi_stat != 0 ? boost::math::cdf(boost::math::complement(dist, max_chi_stat)) : 1.0;
 
             return std::make_tuple(pval,
                                    static_cast<double>(in_sum),
@@ -1053,20 +1066,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         bool out_kmer = (in_stat < out_stat) || (in_stat != in_stat && out_stat == out_stat);
         uint64_t k = std::numeric_limits<uint64_t>::max();
 
-        if (pval_min > 1.0) {
-            PairContainer best;
-            for (const auto &[j, c] : row) {
-                if ((in_kmer && !groups[j]) || (out_kmer && groups[j])) {
-                    best.emplace_back(j, max_obs_vals[j]);
-                } else {
-                    best.emplace_back(j, 1);
-                }
-            }
-
-            auto [pm, in_sum_m, out_sum_m, pm_m] = compute_pval(best);
-            pval_min = pm;
-        }
-
         if (pval_min - pval > 1e-10) {
             common::logger->error("Min p-val estimate too high: min {} > cur {}\ttest: {}", pval_min, pval, config.test_type);
             throw std::runtime_error("Test failed");
@@ -1085,7 +1084,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        if (in_stat != out_stat && pval < 0.05)
+        if (in_kmer != out_kmer && pval < 0.05)
             set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
 
         if (config.test_type != "notest") {

From e0c844dccce4f5e7e940c4001f401b0c659e1416 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 6 Jun 2024 17:35:12 +0200
Subject: [PATCH 176/410] stream pvalues vector

---
 metagraph/src/cli/assemble.cpp                |  44 +++---
 metagraph/src/common/vectors/transpose.hpp    |   8 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 145 +++++++++++++-----
 .../src/graph/annotated_graph_algorithm.hpp   |  10 +-
 .../test_annotated_graph_algorithm.cpp        |  51 +++---
 5 files changed, 159 insertions(+), 99 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 01fd4b2d4b..53f4e55cc9 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -213,23 +213,33 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             auto filenames = (config->infbase_annotators.size() > 1) ? config->infbase_annotators : config->fnames;
 
-            auto [ingraph, outgraph] = mask_nodes_by_label_dual(graph_ptr,
+            auto [ingraph, outgraph, pvals, tmp_file] = graph::mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(graph_ptr,
                                         filenames,
                                         foreground_labels,
                                         background_labels,
                                         diff_config, num_threads,
+                                        config->tmp_dir,
                                         config->parallel_nodes);
 
+            {
+                std::ofstream fout_all(config->outfbase + ".all.pvals", ios::out | ios::app);
+                for (uint64_t pval : pvals) {
+                    static_assert(sizeof(double) == sizeof(pval));
+                    double pval_d;
+                    memcpy(&pval_d, &pval, sizeof(pval));
+                    fout_all << pval_d << "\n";
+                }
+                pvals.close();
+            }
+
             sdsl::bit_vector mask;
             callback(*ingraph, exp_name + "in.");
-            if (auto masked_graph = std::dynamic_pointer_cast<MaskedDeBruijnGraph>(ingraph)) {
-                std::ofstream fout_all(config->outfbase + ".all.pvals", ios::out | ios::app);
-                for (double pval : masked_graph->likelihood_ratios) {
-                    fout_all << pval << "\n";
+            if (diff_config.assemble_shared) {
+                if (auto masked_graph = std::dynamic_pointer_cast<MaskedDeBruijnGraph>(ingraph)) {
+                    std::unique_ptr<bitmap_vector> inmask(static_cast<bitmap_vector*>(masked_graph->release_mask()));
+                    mask = const_cast<sdsl::bit_vector&&>(inmask->data());
+                    inmask.reset();
                 }
-                std::unique_ptr<bitmap_vector> inmask(static_cast<bitmap_vector*>(masked_graph->release_mask()));
-                mask = const_cast<sdsl::bit_vector&&>(inmask->data());
-                inmask.reset();
             }
             ingraph.reset();
 
@@ -289,27 +299,17 @@ int assemble(Config *config) {
         std::filesystem::remove(config->outfbase + ".all.pvals");
 
         auto graph_callback = [&](const graph::DeBruijnGraph &graph, const std::string &header) {
-            const auto *masked_graph = dynamic_cast<const graph::MaskedDeBruijnGraph*>(&graph);
             seq_io::FastaWriter writer(config->outfbase,
                                        header,
                                        config->enumerate_out_sequences,
                                        num_threads > 1, /* async write */
                                        "a" /* append mode */);
-            std::ofstream fout(config->outfbase + ".pvals", ios::out | ios::app);
 
             if (config->unitigs || config->min_tip_size > 1) {
                 graph.call_unitigs(
                     [&](const std::string &unitig, auto&& path) {
-                        std::vector<double> pvals;
-                        pvals.reserve(path.size());
-                        for (auto node : path) {
-                            pvals.emplace_back(masked_graph->likelihood_ratios[node]);
-                        }
                         std::lock_guard<std::mutex> lock(write_mutex);
                         writer.write(unitig);
-                        for (double p : pvals) {
-                            fout << p << "\n";
-                        }
                     },
                     num_threads, config->min_tip_size,
                     config->kmers_in_single_form
@@ -317,16 +317,8 @@ int assemble(Config *config) {
             } else {
                 graph.call_sequences(
                     [&](const std::string &seq, auto&& path) {
-                        std::vector<double> pvals;
-                        pvals.reserve(path.size());
-                        for (auto node : path) {
-                            pvals.emplace_back(masked_graph->likelihood_ratios[node]);
-                        }
                         std::lock_guard<std::mutex> lock(write_mutex);
                         writer.write(seq);
-                        for (double p : pvals) {
-                            fout << p << "\n";
-                        }
                     },
                     num_threads, config->kmers_in_single_form
                 );
diff --git a/metagraph/src/common/vectors/transpose.hpp b/metagraph/src/common/vectors/transpose.hpp
index 48e3de67b7..94a6dfdcab 100644
--- a/metagraph/src/common/vectors/transpose.hpp
+++ b/metagraph/src/common/vectors/transpose.hpp
@@ -65,14 +65,14 @@ void call_rows(const std::vector<BitmapPtr> &columns,
 template <class BitmapPtr, class ValuesPtr, typename PairVector = std::vector<std::pair<uint64_t, uint8_t>>, bool ordered = true>
 void call_rows(const std::vector<BitmapPtr> &columns,
                const std::vector<ValuesPtr> &column_values,
-               const std::function<void(uint64_t, const PairVector &)> &callback,
+               const std::function<void(uint64_t, const PairVector &, size_t)> &callback,
                uint64_t num_rows = 0,
                bool show_progress = mtg::common::get_verbose()) {
     if (!columns.size()) {
         ProgressBar progress_bar(num_rows, "Constructed rows", std::cerr, !show_progress);
         PairVector empty;
         for (uint64_t i = 0; i < num_rows; ++i) {
-            callback(i, empty);
+            callback(i, empty, 0);
         }
         return;
     }
@@ -119,14 +119,14 @@ void call_rows(const std::vector<BitmapPtr> &columns,
             #pragma omp ordered
             {
                 for (uint64_t j = 0; j < rows.size(); ++j) {
-                    callback(j + begin, rows[j]);
+                    callback(j + begin, rows[j], begin);
                     ++progress_bar;
                     rows[j].resize(0);
                 }
             }
         } else {
             for (uint64_t j = 0; j < rows.size(); ++j) {
-                callback(j + begin, rows[j]);
+                callback(j + begin, rows[j], begin);
                 ++progress_bar;
                 rows[j].resize(0);
             }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 2cc3b25ff0..113bb9807c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -91,13 +91,34 @@ inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
     return n > 0;
 }
 
-std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
+template<class To, class From>
+std::enable_if_t<
+    sizeof(To) == sizeof(From) &&
+    std::is_trivially_copyable_v<From> &&
+    std::is_trivially_copyable_v<To>,
+    To>
+// constexpr support needs compiler magic
+bit_cast(const From& src) noexcept
+{
+    static_assert(std::is_trivially_constructible_v<To>,
+        "This implementation additionally requires "
+        "destination type to be trivially constructible");
+
+    static_assert(sizeof(To) == sizeof(From));
+    To dst;
+    std::memcpy(&dst, &src, sizeof(To));
+    return dst;
+}
+
+template <class PValStorage>
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const std::vector<std::string> &files,
                          const tsl::hopscotch_set<Label> &labels_in,
                          const tsl::hopscotch_set<Label> &labels_out,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads,
+                         std::filesystem::path tmp_dir,
                          size_t num_parallel_files) {
     using ValuesContainer = sdsl::int_vector_buffer<>;
 
@@ -119,20 +140,42 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         num_parallel_files
     );
 
-    return mask_nodes_by_label_dual<ValuesContainer>(
+    return mask_nodes_by_label_dual<ValuesContainer, PValStorage>(
         graph_ptr, columns_all, column_values_all, groups,
-        config, num_threads, num_parallel_files, true
+        config, num_threads, tmp_dir, num_parallel_files, true
     );
 }
 
-template <class ValuesContainer>
-std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
+template
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
+                         const std::vector<std::string> &,
+                         const tsl::hopscotch_set<Label> &,
+                         const tsl::hopscotch_set<Label> &,
+                         const DifferentialAssemblyConfig &,
+                         size_t,
+                         std::filesystem::path,
+                         size_t);
+template
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
+                         const std::vector<std::string> &,
+                         const tsl::hopscotch_set<Label> &,
+                         const tsl::hopscotch_set<Label> &,
+                         const DifferentialAssemblyConfig &,
+                         size_t,
+                         std::filesystem::path,
+                         size_t);
+
+template <class ValuesContainer, class PValStorage>
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          std::vector<std::unique_ptr<const bit_vector>> &columns_all,
                          std::vector<std::unique_ptr<const ValuesContainer>> &column_values_all,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads,
+                         std::filesystem::path tmp_dir,
                          size_t num_parallel_files,
                          bool deallocate) {
     num_parallel_files = std::min(num_parallel_files, num_threads);
@@ -230,7 +273,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         utils::call_rows<std::unique_ptr<const bit_vector>,
                          std::unique_ptr<const ValuesContainer>,
                          PairContainer, false>(columns_all, column_values_all,
-                                               [&](uint64_t row_i, const auto &row) {
+                                               [&](uint64_t row_i, const auto &row, size_t) {
             if (row.empty()) {
                 set_bit(ignored.data(), row_i, parallel, MO_RELAXED);
             } else if (filter_rows) {
@@ -342,7 +385,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     sdsl::bit_vector indicator_in(graph_ptr->max_index() + 1, false);
     sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
 
-    std::vector<double> pvals(graph_ptr->max_index() + 1, 1.1);
+    auto nullpval = bit_cast<uint64_t>(double(1.1));
+
+    std::unique_ptr<utils::TempFile> tmp_file;
+    PValStorage pvals;
+    if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+        tmp_file = std::make_unique<utils::TempFile>(tmp_dir);
+        pvals = sdsl::int_vector_buffer<64>(tmp_file->name(), std::ios::out);
+    }
+
+    for (size_t i = 0; i <= graph_ptr->max_index(); ++i) {
+        pvals.push_back(nullpval);
+    }
 
     VectorMap<size_t, std::vector<uint64_t>> m;
     using RowStats = std::tuple<double, double, double, double>;
@@ -567,29 +621,25 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            if (in_sum == 0 || out_sum == 0) {
-                common::logger->error("Empty row: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
-                throw std::domain_error("Test failed");
-            }
+            if (in_sum == 0 || out_sum == 0)
+                common::logger->warn("Empty row: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
 
             int64_t total_sum = in_sum + out_sum;
 
             auto get_chi_stat = [&](int64_t in_sum) {
                 int64_t out_sum = total_sum - in_sum;
-                double log_likelihood_in = -in_sum + log(static_cast<double>(in_sum)) * in_sum;
-                double log_likelihood_out = -out_sum + log(static_cast<double>(out_sum)) * out_sum;
+                double log_likelihood_in = in_sum > 0 ? -in_sum + log(static_cast<double>(in_sum)) * in_sum : 0.0;
+                double log_likelihood_out = out_sum > 0 ? -out_sum + log(static_cast<double>(out_sum)) * out_sum : 0.0;
 
                 double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
                 double mean_denom_in = theta * in_kmers;
                 double mean_denom_out = theta * out_kmers;
 
-                if (mean_denom_in == 0 || mean_denom_out == 0) {
-                    common::logger->error("Empty null hypothesis: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
-                    throw std::domain_error("Test failed");
-                }
+                if (mean_denom_in == 0 || mean_denom_out == 0)
+                    common::logger->warn("Empty null hypothesis: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
 
-                double log_likelihood_null = -mean_denom_in + in_sum * log(mean_denom_in);
-                log_likelihood_null += -mean_denom_out + out_sum * log(mean_denom_out);
+                double log_likelihood_null = mean_denom_in > 0 ? -mean_denom_in + in_sum * log(mean_denom_in) : 0.0;
+                log_likelihood_null += mean_denom_out > 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
 
                 return (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
             };
@@ -603,7 +653,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double pval = chi_stat != 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
 
-            double max_chi_stat = std::max(get_chi_stat(1), get_chi_stat(std::min(total_sum - 1, static_cast<int64_t>(in_kmers))));
+            double max_chi_stat = std::max(get_chi_stat(0),
+                                           get_chi_stat(std::min(total_sum, static_cast<int64_t>(in_kmers))));
             assert(max_chi_stat >= chi_stat);
 
             double pmin = max_chi_stat != 0 ? boost::math::cdf(boost::math::complement(dist, max_chi_stat)) : 1.0;
@@ -1030,8 +1081,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     utils::call_rows<std::unique_ptr<const bit_vector>,
                      std::unique_ptr<const ValuesContainer>,
-                     PairContainer, false>(columns_all, column_values_all,
-                                           [&](uint64_t row_i, const auto &raw_row) {
+                     PairContainer, true>(columns_all, column_values_all,
+                                          [&](uint64_t row_i, const auto &raw_row, size_t batch_id) {
         if (ex)
             return;
 
@@ -1057,8 +1108,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return;
         }
 
-        pvals[node] = pval;
-
         if (pval >= 1.1)
             return;
 
@@ -1090,6 +1139,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (config.test_type != "notest") {
             std::lock_guard<std::mutex> lock(pval_mu);
             m[k].emplace_back(node);
+            pvals[node] = bit_cast<uint64_t>(pval);
         }
     });
 
@@ -1139,7 +1189,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             VectorMap<double, double> pval_map;
-            for (double p : pvals) {
+            for (uint64_t p_u : pvals) {
+                auto p = bit_cast<double>(p_u);
                 if (p <= 1.0)
                     ++pval_map[p];
             }
@@ -1157,7 +1208,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (k == 0) {
                 common::logger->trace("Falling back to Benjamini-Yekutieli");
                 for (size_t i = 0; i < pvals.size(); ++i) {
-                    if (pvals[i] < 0.05 && pvals[i] * pval_map[pvals[i]] >= 0.05) {
+                    double p = bit_cast<double, uint64_t>(pvals[i]);
+                    if (p < 0.05 && p * pval_map[p] >= 0.05) {
                         indicator_in[i] = false;
                         indicator_out[i] = false;
                     }
@@ -1167,7 +1219,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 for (auto jt = m_data.begin(); jt != m_data.end(); ++jt) {
                     const auto &[cur_k, bucket] = *jt;
                     for (uint64_t i : bucket) {
-                        if (jt < begin || pvals[i] * k >= 0.05) {
+                        if (jt < begin || bit_cast<double, uint64_t>(pvals[i]) * k >= 0.05) {
                             indicator_in[i] = false;
                             indicator_out[i] = false;
                         }
@@ -1181,35 +1233,58 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
     );
-    masked_graph_in->likelihood_ratios = pvals;
 
     auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
         graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_out)), true,
         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
     );
-    masked_graph_out->likelihood_ratios = std::move(pvals);
 
-    return std::make_pair(masked_graph_in, masked_graph_out);
+    return std::make_tuple(masked_graph_in, masked_graph_out, std::move(pvals), std::move(tmp_file));
 }
 
 template
-std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
-mask_nodes_by_label_dual<sdsl::int_vector_buffer<>>(std::shared_ptr<const DeBruijnGraph>,
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<sdsl::int_vector_buffer<>, std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
+                         std::vector<std::unique_ptr<const bit_vector>> &,
+                         std::vector<std::unique_ptr<const sdsl::int_vector_buffer<>>> &,
+                         const std::vector<bool> &,
+                         const DifferentialAssemblyConfig &,
+                         size_t,
+                         std::filesystem::path,
+                         size_t,
+                         bool);
+template
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<sdsl::int_vector<>, std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
+                         std::vector<std::unique_ptr<const bit_vector>> &,
+                         std::vector<std::unique_ptr<const sdsl::int_vector<>>> &,
+                         const std::vector<bool> &,
+                         const DifferentialAssemblyConfig &,
+                         size_t,
+                         std::filesystem::path,
+                         size_t,
+                         bool);
+
+template
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<sdsl::int_vector_buffer<>, sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
                          std::vector<std::unique_ptr<const bit_vector>> &,
                          std::vector<std::unique_ptr<const sdsl::int_vector_buffer<>>> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
+                         std::filesystem::path,
                          size_t,
                          bool);
 template
-std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
-mask_nodes_by_label_dual<sdsl::int_vector<>>(std::shared_ptr<const DeBruijnGraph>,
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<sdsl::int_vector<>, sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
                          std::vector<std::unique_ptr<const bit_vector>> &,
                          std::vector<std::unique_ptr<const sdsl::int_vector<>>> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
+                         std::filesystem::path,
                          size_t,
                          bool);
 
@@ -1416,7 +1491,7 @@ std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const D
     utils::call_rows<std::unique_ptr<bit_vector>,
                      std::unique_ptr<sdsl::int_vector_buffer<>>,
                      std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
-                                                                [&](uint64_t row_id, const auto &row) {
+                                                                [&](uint64_t row_id, const auto &row, size_t) {
                         // auto kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
                         if (row.size() == 0) { // if the kmer is not present in any sample
                             mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 70943746a9..b76e4c7e34 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -5,6 +5,7 @@
 
 #include "graph/annotated_dbg.hpp"
 #include "common/vectors/bit_vector.hpp"
+#include "common/utils/file_utils.hpp"
 
 
 namespace mtg {
@@ -85,23 +86,26 @@ mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     size_t num_threads = 1,
                     size_t num_parallel_files = std::numeric_limits<size_t>::max());
 
-std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
+template <class PValStorage>
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const std::vector<std::string> &files,
                          const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
                          const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads = 1,
+                         std::filesystem::path = "",
                          size_t num_parallel_files = std::numeric_limits<size_t>::max());
 
-template <class ValuesContainer>
-std::pair<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>>
+template <class ValuesContainer, class PValStorage>
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          std::vector<std::unique_ptr<const bit_vector>> &columns_all,
                          std::vector<std::unique_ptr<const ValuesContainer>> &column_values_all,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads = 1,
+                         std::filesystem::path = "",
                          size_t num_parallel_files = std::numeric_limits<size_t>::max(),
                          bool deallocate = false);
 
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index 48927fc7b8..dfa845528e 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -20,6 +20,9 @@ using namespace mtg::graph;
 using namespace mtg::annot;
 using namespace mtg::test;
 
+const std::string test_data_dir = "../tests/data";
+const std::string test_dump_basename = test_data_dir + "/dump_test";
+
 
 template <typename GraphAnnotationPair>
 class MaskedDeBruijnGraphAlgorithm : public ::testing::Test {};
@@ -79,7 +82,7 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
             std::vector<bool> groups = { true, false, false };
             columns.resize(groups.size());
 
-            auto [masked_dbg_in, masked_dbg_out] = mask_nodes_by_label_dual<sdsl::int_vector<>>(
+            auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<sdsl::int_vector<>, std::vector<uint64_t>>(
                 graph_ptr,
                 columns,
                 column_values_all,
@@ -189,36 +192,22 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                     .outfbase = "",
                 };
 
-                try {
-                    auto [masked_dbg_in, masked_dbg_out] = mask_nodes_by_label_dual<sdsl::int_vector<>>(
-                        graph_ptr,
-                        columns,
-                        column_values_all,
-                        groups,
-                        config, num_threads, num_threads,
-                        false
-                    );
-
-                    if (test_type != "poisson_likelihoodratio") {
-                        masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
-                            auto cur_labels = anno_graph->get_labels(kmer, 0.0);
-                            obs_labels.insert(cur_labels.begin(), cur_labels.end());
-                            obs_kmers.insert(kmer);
-                        });
-                        EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
-                        EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
-                    } else {
-                        EXPECT_TRUE(false) << k << "\t" << test_type;
-                    }
-                } catch (std::domain_error &e) {
-                    if (test_type == "poisson_likelihoodratio")
-                        continue;
-
-                    EXPECT_TRUE(false) << k << "\t" << test_type << "\t" << e.what();
-                    continue;
-                } catch (std::exception &e) {
-                    EXPECT_TRUE(false) << k << "\t" << test_type << "\t" << e.what();
-                }
+                auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<sdsl::int_vector<>, std::vector<uint64_t>>(
+                    graph_ptr,
+                    columns,
+                    column_values_all,
+                    groups,
+                    config, num_threads, test_dump_basename, num_threads,
+                    false
+                );
+
+                masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                    auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                    obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                    obs_kmers.insert(kmer);
+                });
+                EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
+                EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
             }
         }
     }

From 941ad64ee4e968f413e508336d1aa05d0042927d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 7 Jun 2024 11:45:36 +0200
Subject: [PATCH 177/410] update p estimate for nbinom_exact

---
 .../src/graph/annotated_graph_algorithm.cpp    | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 113bb9807c..d2aeef4409 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -680,8 +680,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_pair(r, r / (r + mu));
         };
 
-        double target_p_num = 0.0;
-        double target_p_denom = 0.0;
         std::vector<std::pair<double, double>> nb_params(groups.size());
         std::vector<VectorMap<uint64_t, size_t>> hists(groups.size());
 
@@ -712,12 +710,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double mu2 = mu * mu;
             double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
             nb_params[j] = get_rp(mu, var, hist);
-            double p = nb_params[j].second;
+        }
 
-            target_p_num += p * p;
-            target_p_denom += p;
+        double c_a = 0;
+        double c_b = 0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            double p = nb_params[j].second;
+            if (groups[j]) {
+                c_b += p * sums[j];
+            } else {
+                c_a += p * sums[j];
+            }
         }
-        double target_p = target_p_num / target_p_denom;
+
+        double target_p = 2.0 / total_kmers * (c_a * c_a + c_b * c_b) / (c_a + c_b);
 
         std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
         double r_in = 0;

From 41fe6bb3c2573df3194f331341fc25aba7a0911b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 7 Jun 2024 16:28:04 +0200
Subject: [PATCH 178/410] skip tests, don't filter out counts

---
 .../src/graph/annotated_graph_algorithm.cpp   | 84 ++++++++-----------
 1 file changed, 37 insertions(+), 47 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index d2aeef4409..a50c8d0b8c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -266,47 +266,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Marking discarded k-mers");
     if (groups.size()) {
-        bool filter_rows = config.clean || config.min_count || config.min_recurrence
-                            || config.min_in_recurrence || config.min_out_recurrence
-                            || config.max_in_recurrence || config.max_out_recurrence;
         std::atomic_thread_fence(std::memory_order_release);
         utils::call_rows<std::unique_ptr<const bit_vector>,
                          std::unique_ptr<const ValuesContainer>,
                          PairContainer, false>(columns_all, column_values_all,
                                                [&](uint64_t row_i, const auto &row, size_t) {
-            if (row.empty()) {
-                set_bit(ignored.data(), row_i, parallel, MO_RELAXED);
-            } else if (filter_rows) {
-                size_t count = 0;
-                size_t count_in = 0;
-                size_t count_out = 0;
-                bool keep_row = false;
-                for (const auto &[j, raw_c] : row) {
-                    if (adjust_count(raw_c, j, row_i)) {
-                        ++count;
-
-                        if (groups[j]) {
-                            ++count_out;
-                        } else {
-                            ++count_in;
-                        }
-
-                        if (count >= config.min_recurrence
-                                && count_in >= config.min_in_recurrence
-                                && count_out >= config.min_out_recurrence) {
-                            keep_row = true;
-                        }
-
-                        if (count_in > config.max_in_recurrence || count_out > config.max_out_recurrence) {
-                            keep_row = false;
-                            break;
-                        }
-                    }
-                }
-
-                if (!keep_row)
-                    set_bit(ignored.data(), row_i, parallel, MO_RELAXED);
+            for (const auto &[j, raw_c] : row) {
+                if (adjust_count(raw_c, j, row_i))
+                    return;
             }
+
+            set_bit(ignored.data(), row_i, parallel, MO_RELAXED);
         });
         std::atomic_thread_fence(std::memory_order_acquire);
     }
@@ -666,22 +636,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         };
     } else if (config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting negative binomial distributions");
-        auto get_rp = [&](double mu, double var, const auto &hist) {
+        auto get_rp = [&](double mu, double var, const auto &hist, double f = 1.0) {
             double r = boost::math::tools::newton_raphson_iterate([&](double r) {
-                double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
-                double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
+                double dl = nelem * (log(r) - log(r + mu * f) - boost::math::digamma(r));
+                double ddl = nelem * (1.0 / r - 1.0 / (r + mu * f) - boost::math::trigamma(r));
                 for (const auto &[k, c] : hist) {
-                    dl += boost::math::digamma(k + r) * c;
-                    ddl += boost::math::trigamma(k + r) * c;
+                    dl += boost::math::digamma(k * f + r) * c;
+                    ddl += boost::math::trigamma(k * f + r) * c;
                 }
                 return std::make_pair(dl, ddl);
-            }, mu * mu / (var - mu), std::numeric_limits<double>::min(), mu * nelem, 30);
+            }, f * f * mu * mu / (var - mu * f), std::numeric_limits<double>::min(), f * mu * nelem, 30);
 
-            return std::make_pair(r, r / (r + mu));
+            return std::make_pair(r, r / (r + mu * f));
         };
 
         std::vector<std::pair<double, double>> nb_params(groups.size());
         std::vector<VectorMap<uint64_t, size_t>> hists(groups.size());
+        std::vector<double> scales(groups.size());
 
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
@@ -710,6 +681,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double mu2 = mu * mu;
             double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
             nb_params[j] = get_rp(mu, var, hist);
+
+            scales[j] = total_kmers / 2.0 / sums[j];
+            if (groups[j]) {
+                scales[j] /= num_labels_out;
+            } else {
+                scales[j] /= num_labels_in;
+            }
         }
 
         double c_a = 0;
@@ -1094,10 +1072,25 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         PairContainer row;
         if (!ignored[row_i]) {
+            size_t count_in = 0;
+            size_t count_out = 0;
             for (const auto &[j, raw_c] : raw_row) {
-                if (uint64_t c = adjust_count(raw_c, j, row_i))
+                if (uint64_t c = adjust_count(raw_c, j, row_i)) {
                     row.emplace_back(j, c);
+                    if (groups[j]) {
+                        ++count_out;
+                    } else {
+                        ++count_in;
+                    }
+                }
             }
+
+            if (count_in + count_out < config.min_count
+                    || count_in < config.min_in_recurrence
+                    || count_out < config.min_out_recurrence
+                    || count_in > config.max_in_recurrence
+                    || count_out > config.max_out_recurrence)
+                row.clear();
         }
 
         node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
@@ -1171,10 +1164,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
         size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
                                     [](size_t sum, const auto &a) { return sum + a.second.size(); });
-        if (acc != nelem) {
-            common::logger->error("acc != nelem: {} != {}", acc, nelem);
-            throw std::runtime_error("internal failure");
-        }
+        common::logger->trace("Performed {}/{} tests", acc, nelem);
         size_t total_tests = acc;
         common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
 

From 7d443950c8f0260e69e7926bdb6356f72f122eed Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 7 Jun 2024 18:47:49 +0200
Subject: [PATCH 179/410] fix corner cases

---
 .../src/graph/annotated_graph_algorithm.cpp   | 30 +++++++++----------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a50c8d0b8c..785f0ff078 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -258,8 +258,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     boost::math::poisson(repeats_mu), CDF_CUTOFF
                 ));
 
-                if (repeats_cutoff > outlier_cutoff)
-                    check_cutoff[j] = repeats_cutoff;
+                // if (repeats_cutoff > outlier_cutoff)
+                //     check_cutoff[j] = repeats_cutoff;
             }
         }
     }
@@ -652,7 +652,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         std::vector<std::pair<double, double>> nb_params(groups.size());
         std::vector<VectorMap<uint64_t, size_t>> hists(groups.size());
-        std::vector<double> scales(groups.size());
 
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
@@ -681,13 +680,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double mu2 = mu * mu;
             double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
             nb_params[j] = get_rp(mu, var, hist);
-
-            scales[j] = total_kmers / 2.0 / sums[j];
-            if (groups[j]) {
-                scales[j] /= num_labels_out;
-            } else {
-                scales[j] /= num_labels_in;
-            }
         }
 
         double c_a = 0;
@@ -720,6 +712,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             boost::math::negative_binomial nb(r, p);
             boost::math::negative_binomial nb_out(r_map, target_p);
+            double scale = exp(log(p) - log(target_p));
 
             // ensure that 0 maps to 0
             count_maps[j][0] = std::make_pair(0, 0);
@@ -727,12 +720,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             for (const auto &[k, c] : hists[j]) {
                 if (!count_maps[j].count(k)) {
                     double cdf = boost::math::cdf(nb, k);
-                    if (cdf == 1.0 || cdf == 0.0) {
-                        double ccdf = boost::math::cdf(boost::math::complement(nb, k));
-                        count_maps[j][k] = std::make_pair(round(boost::math::quantile(boost::math::complement(nb_out, ccdf))), c);
+
+                    uint64_t new_k = 0;
+                    if (cdf < 1.0) {
+                        new_k = boost::math::quantile(nb_out, cdf);
                     } else {
-                        count_maps[j][k] = std::make_pair(round(boost::math::quantile(nb_out, cdf)), c);
+                        double ccdf = boost::math::cdf(boost::math::complement(nb, k));
+                        if (ccdf > 0.0) {
+                            new_k = boost::math::quantile(boost::math::complement(nb_out, ccdf));
+                        } else {
+                            new_k = ceil(scale * k);
+                        }
                     }
+                    count_maps[j][k] = std::make_pair(std::max(new_k, uint64_t(1)), c);
                 } else {
                     count_maps[j][k].second += c;
                 }
@@ -851,7 +851,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             if (p_min > pval) {
-                common::logger->error("{}\t{},{}", n, lower, upper);
+                common::logger->error("pmin fail\t{} > {}\t{}\t{},{}", p_min, pval, n, lower, upper);
             }
 
             if (pval >= 1.1) {

From 0ce82e39b3bef7e2c823b2137e358d9d9ab76c1d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 7 Jun 2024 18:57:41 +0200
Subject: [PATCH 180/410] only compute pval map when falling back

---
 .../src/graph/annotated_graph_algorithm.cpp   | 33 +++++++++----------
 1 file changed, 16 insertions(+), 17 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 785f0ff078..bea9ee1123 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1184,25 +1184,24 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 acc -= bucket.size();
             }
 
-            VectorMap<double, double> pval_map;
-            for (uint64_t p_u : pvals) {
-                auto p = bit_cast<double>(p_u);
-                if (p <= 1.0)
-                    ++pval_map[p];
-            }
-            auto p_data = const_cast<std::vector<std::pair<double, double>>&&>(pval_map.values_container());
-            std::sort(p_data.begin(), p_data.end(), utils::GreaterFirst());
-
-            double cm = boost::math::digamma(total_tests) + 0.5772156649015329; // std::numbers::e_gamma_v<double>
-            pval_map = decltype(pval_map)();
-            size_t cur_rank = 0;
-            for (const auto &[p, c] : p_data) {
-                cur_rank += c;
-                pval_map[p] = cm * cur_rank;
-            }
-
             if (k == 0) {
                 common::logger->trace("Falling back to Benjamini-Yekutieli");
+                VectorMap<double, double> pval_map;
+                for (uint64_t p_u : pvals) {
+                    auto p = bit_cast<double>(p_u);
+                    if (p <= 1.0)
+                        ++pval_map[p];
+                }
+                auto p_data = const_cast<std::vector<std::pair<double, double>>&&>(pval_map.values_container());
+                std::sort(p_data.begin(), p_data.end(), utils::GreaterFirst());
+
+                double cm = boost::math::digamma(total_tests) + 0.5772156649015329; // std::numbers::e_gamma_v<double>
+                pval_map = decltype(pval_map)();
+                size_t cur_rank = 0;
+                for (const auto &[p, c] : p_data) {
+                    cur_rank += c;
+                    pval_map[p] = cm * cur_rank;
+                }
                 for (size_t i = 0; i < pvals.size(); ++i) {
                     double p = bit_cast<double, uint64_t>(pvals[i]);
                     if (p < 0.05 && p * pval_map[p] >= 0.05) {

From 4345dce886b6d8c8503abd6252ab05cbf5cb3753 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 7 Jun 2024 19:03:09 +0200
Subject: [PATCH 181/410] don't filter out outliers

---
 .../src/graph/annotated_graph_algorithm.cpp   | 91 ++-----------------
 1 file changed, 10 insertions(+), 81 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index bea9ee1123..a7b95f7066 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -40,56 +40,6 @@ constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
 double CDF_CUTOFF = 0.95;
 uint64_t N_BUCKETS_FOR_ESTIMATION = 3;
 
-template <typename ForwardIterator>
-inline auto quartiles(ForwardIterator first, ForwardIterator last)
-{
-    auto m = std::distance(first,last);
-    BOOST_MATH_ASSERT_MSG(m >= 3, "At least 3 samples are required to compute the interquartile range.");
-    auto k = m/4;
-    auto j = m - (4*k);
-    // m = 4k+j.
-    // If j = 0 or j = 1, then there are an even number of samples below the median, and an even number above the median.
-    //    Then we must average adjacent elements to get the quartiles.
-    // If j = 2 or j = 3, there are an odd number of samples above and below the median, these elements may be directly extracted to get the quartiles.
-
-    double Q1;
-    double Q3;
-    if (j==2 || j==3)
-    {
-        auto q1 = first + k;
-        auto q3 = first + 3*k + j - 1;
-        std::nth_element(first, q1, last);
-        Q1 = *q1;
-        std::nth_element(q1, q3, last);
-        Q3 = *q3;
-    } else {
-        // j == 0 or j==1:
-        auto q1 = first + k - 1;
-        auto q3 = first + 3*k - 1 + j;
-        std::nth_element(first, q1, last);
-        double a = *q1;
-        std::nth_element(q1, q1 + 1, last);
-        double b = *(q1 + 1);
-        Q1 = (a+b)/2;
-        std::nth_element(q1, q3, last);
-        a = *q3;
-        std::nth_element(q3, q3 + 1, last);
-        b = *(q3 + 1);
-        Q3 = (a+b)/2;
-    }
-
-    // return std::make_tuple(Q1, boost::math::statistics::median(first, last), Q3);
-    return std::make_pair(Q1, Q3);
-}
-
-inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
-    int n;
-    std::unique_ptr<uint64_t, decltype(std::free)*> r {
-        sdust(0, (const uint8_t*)s.data(), s.size(), T, W, &n),
-        std::free
-    };
-    return n > 0;
-}
 
 template<class To, class From>
 std::enable_if_t<
@@ -230,37 +180,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             min_counts[j] = boost::math::quantile(
                 boost::math::poisson(mean_est), exp(-mean_est) - CDF_CUTOFF * expm1(-mean_est)
             );
-
-            // quartile calculation modifies the underlying vector, so we have to make a copy
-            std::vector<uint64_t> vals;
-            vals.reserve(column_values.size());
-            for (size_t i = 0; i < column_values.size(); ++i) {
-                if (column_values[i] >= min_counts[j])
-                    vals.emplace_back(column_values[i]);
-            }
-
-            // set cutoff for upper end of distribution
-            auto [q1, q3] = quartiles(vals.begin(), vals.end());
-            double outlier_cutoff = q3 + 1.5 * (q3 - q1);
-            double repeats_mu = 0;
-            double repeats_cutoff = 0;
-            size_t num_large = 0;
-            for (uint64_t c : vals) {
-                if (c >= outlier_cutoff) {
-                    repeats_mu += c;
-                    ++num_large;
-                }
-            }
-            if (num_large) {
-                repeats_mu /= num_large;
-
-                repeats_cutoff = boost::math::quantile(boost::math::complement(
-                    boost::math::poisson(repeats_mu), CDF_CUTOFF
-                ));
-
-                // if (repeats_cutoff > outlier_cutoff)
-                //     check_cutoff[j] = repeats_cutoff;
-            }
         }
     }
 
@@ -1283,6 +1202,16 @@ mask_nodes_by_label_dual<sdsl::int_vector<>, sdsl::int_vector_buffer<64>>(std::s
                          size_t,
                          bool);
 
+inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
+    int n;
+    std::unique_ptr<uint64_t, decltype(std::free)*> r {
+        sdust(0, (const uint8_t*)s.data(), s.size(), T, W, &n),
+        std::free
+    };
+    return n > 0;
+}
+
+
 typedef std::function<size_t()> LabelCountCallback;
 
 uint64_t atomic_fetch(const sdsl::int_vector<> &vector,

From 7888d39a78f581c797deabe99700c4ca69e915d3 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 9 Jun 2024 18:55:37 +0200
Subject: [PATCH 182/410] find target_p via MLE

---
 .../src/graph/annotated_graph_algorithm.cpp   | 40 +++++++++++++++++--
 1 file changed, 37 insertions(+), 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a7b95f7066..5c3b829899 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -571,6 +571,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         std::vector<std::pair<double, double>> nb_params(groups.size());
         std::vector<VectorMap<uint64_t, size_t>> hists(groups.size());
+        std::vector<double> target_scale(groups.size());
 
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
@@ -599,6 +600,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double mu2 = mu * mu;
             double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
             nb_params[j] = get_rp(mu, var, hist);
+
+            target_scale[j] = total_kmers / 2.0 / sums[j];
+            if (groups[j]) {
+                target_scale[j] /= num_labels_out;
+            } else {
+                target_scale[j] /= num_labels_in;
+            }
         }
 
         double c_a = 0;
@@ -612,7 +620,34 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        double target_p = 2.0 / total_kmers * (c_a * c_a + c_b * c_b) / (c_a + c_b);
+        std::vector<double> r_maps(groups.size(), 1.0);
+        double target_p = boost::math::tools::newton_raphson_iterate([&](double p) {
+            double dl = 0;
+            double ddl = 0;
+            for (size_t j = 0; j < groups.size(); ++j) {
+                double f = target_scale[j];
+                double &r = r_maps[j];
+                r = boost::math::tools::newton_raphson_iterate([&](double r) {
+                    double dl = nelem * (log(p) - boost::math::digamma(r));
+                    double ddl = -nelem * boost::math::trigamma(r);
+                    for (const auto &[k, c] : hists[j]) {
+                        dl += boost::math::digamma(k * f + r) * c;
+                        ddl += boost::math::trigamma(k * f + r) * c;
+                    }
+                    return std::make_pair(dl, ddl);
+                }, r, std::numeric_limits<double>::min(), f * sums[j], 30);
+                double factor = nelem * r / p;
+                dl += factor;
+                ddl -= factor / p;
+                for (const auto &[k, c] : hists[j]) {
+                    double factor = k * f * c / (1 - p);
+                    dl -= factor;
+                    ddl -= factor / (1 - p);
+                }
+            }
+
+            return std::make_pair(dl, ddl);
+        }, 2.0 / total_kmers * (c_a * c_a + c_b * c_b) / (c_a + c_b), std::numeric_limits<double>::min(), 1.0, 30);
 
         std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
         double r_in = 0;
@@ -623,8 +658,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto &[r, p] = nb_params[j];
 
-            // approx out nb params
-            double r_map = exp(log(r) + log1p(-p) - log1p(-target_p));
+            double r_map = r_maps[j];
 
             common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
                                   j, r, p, r_map, target_p);

From 60050663af178e36131915b8781b03a5333185d3 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 10 Jun 2024 12:44:36 +0200
Subject: [PATCH 183/410] fixes

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5c3b829899..ef1b1d21a1 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1038,7 +1038,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            if (count_in + count_out < config.min_count
+            if (count_in + count_out < config.min_recurrence
                     || count_in < config.min_in_recurrence
                     || count_out < config.min_out_recurrence
                     || count_in > config.max_in_recurrence

From 837b1d8dcd24dca45096fc1e41fd94f07e328daf Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 10 Jun 2024 22:39:03 +0200
Subject: [PATCH 184/410] test

---
 metagraph/src/cli/assemble.cpp                |  31 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 500 ++++++++++++++----
 2 files changed, 429 insertions(+), 102 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 53f4e55cc9..d844f0e847 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -54,7 +54,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.family_wise_error_rate = experiment.get("family_wise_error_rate", 0.05).asDouble();
     diff_config.test_by_unitig = experiment.get("test_by_unitig", false).asBool();
     diff_config.evaluate_assembly = experiment.get("evaluate_assembly", false).asBool();
-    diff_config.test_type = experiment.get("test_type", "brunner_munzel").asString();
+    diff_config.test_type = experiment.get("test_type", "nbinom_exact").asString();
     diff_config.filter = experiment.get("filter", true).asBool();
     diff_config.clean = experiment.get("clean", false).asBool();
     diff_config.min_count = experiment.get("min_count", 0).asUInt64();
@@ -213,15 +213,40 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             auto filenames = (config->infbase_annotators.size() > 1) ? config->infbase_annotators : config->fnames;
 
-            auto [ingraph, outgraph, pvals, tmp_file] = graph::mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(graph_ptr,
+            std::shared_ptr<DeBruijnGraph> ingraph;
+            std::shared_ptr<DeBruijnGraph> outgraph;
+
+            if (diff_config.test_by_unitig) {
+                auto [cur_ingraph, cur_outgraph, pvals, tmp_file] =
+                    graph::mask_nodes_by_label_dual<std::vector<uint64_t>>(graph_ptr,
+                                        filenames,
+                                        foreground_labels,
+                                        background_labels,
+                                        diff_config, num_threads,
+                                        config->tmp_dir,
+                                        config->parallel_nodes);
+                std::swap(ingraph, cur_ingraph);
+                std::swap(outgraph, cur_outgraph);
+
+                std::ofstream fout_all(config->outfbase + ".all.pvals", ios::out | ios::app);
+                for (uint64_t pval : pvals) {
+                    static_assert(sizeof(double) == sizeof(pval));
+                    double pval_d;
+                    memcpy(&pval_d, &pval, sizeof(pval));
+                    fout_all << pval_d << "\n";
+                }
+            } else {
+                auto [cur_ingraph, cur_outgraph, pvals, tmp_file] =
+                    graph::mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(graph_ptr,
                                         filenames,
                                         foreground_labels,
                                         background_labels,
                                         diff_config, num_threads,
                                         config->tmp_dir,
                                         config->parallel_nodes);
+                std::swap(ingraph, cur_ingraph);
+                std::swap(outgraph, cur_outgraph);
 
-            {
                 std::ofstream fout_all(config->outfbase + ".all.pvals", ios::out | ios::app);
                 for (uint64_t pval : pvals) {
                     static_assert(sizeof(double) == sizeof(pval));
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ef1b1d21a1..dd45a07cb0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -70,30 +70,48 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          size_t num_threads,
                          std::filesystem::path tmp_dir,
                          size_t num_parallel_files) {
-    using ValuesContainer = sdsl::int_vector_buffer<>;
-
     common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
     common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
 
     size_t total_labels = labels_in.size() + labels_out.size();
     std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
-    std::vector<std::unique_ptr<const ValuesContainer>> column_values_all(total_labels);
     std::vector<bool> groups(total_labels);
 
-    annot::ColumnCompressed<>::load_columns_and_values(
-        files,
-        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
-            groups[offset] = labels_out.count(label);
-            columns_all[offset].reset(column.release());
-            column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
-        },
-        num_parallel_files
-    );
+    if (config.test_by_unitig) {
+        using ValuesContainer = sdsl::int_vector<>;
+        std::vector<std::unique_ptr<const ValuesContainer>> column_values_all(total_labels);
+        annot::ColumnCompressed<>::load_columns_and_values(
+            files,
+            [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
+                groups[offset] = labels_out.count(label);
+                columns_all[offset].reset(column.release());
+                column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
+            },
+            num_parallel_files
+        );
 
-    return mask_nodes_by_label_dual<ValuesContainer, PValStorage>(
-        graph_ptr, columns_all, column_values_all, groups,
-        config, num_threads, tmp_dir, num_parallel_files, true
-    );
+        return mask_nodes_by_label_dual<ValuesContainer, PValStorage>(
+            graph_ptr, columns_all, column_values_all, groups,
+            config, num_threads, tmp_dir, num_parallel_files, true
+        );
+    } else {
+        using ValuesContainer = sdsl::int_vector_buffer<>;
+        std::vector<std::unique_ptr<const ValuesContainer>> column_values_all(total_labels);
+        annot::ColumnCompressed<>::load_columns_and_values(
+            files,
+            [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
+                groups[offset] = labels_out.count(label);
+                columns_all[offset].reset(column.release());
+                column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
+            },
+            num_parallel_files
+        );
+
+        return mask_nodes_by_label_dual<ValuesContainer, PValStorage>(
+            graph_ptr, columns_all, column_values_all, groups,
+            config, num_threads, tmp_dir, num_parallel_files, true
+        );
+    }
 }
 
 template
@@ -142,7 +160,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         max_width = std::max(max_width, col_vals->width());
     }
 
-    sdsl::bit_vector ignored(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), false);
+    sdsl::bit_vector kept(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), true);
 
     std::vector<uint64_t> min_counts(groups.size(), config.min_count);
     std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
@@ -195,12 +213,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return;
             }
 
-            set_bit(ignored.data(), row_i, parallel, MO_RELAXED);
+            unset_bit(kept.data(), row_i, parallel, MO_RELAXED);
         });
         std::atomic_thread_fence(std::memory_order_acquire);
     }
 
-    double nelem = ignored.size() - sdsl::util::cnt_one_bits(ignored);
+    double nelem = sdsl::util::cnt_one_bits(kept);
 
     common::logger->trace("Computing aggregate statistics");
     std::mutex agg_mu;
@@ -232,8 +250,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             vals.reserve(num_vals);
 
         column.call_ones([&](uint64_t row_i) {
-            if (!ignored[row_i]) {
-                if (uint64_t c = adjust_count(column_values_all.size() ? (*column_values_all[j])[i] : 1, j, row_i)) {
+            if (kept[row_i]) {
+                if (uint64_t c = column_values_all.size() ? (*column_values_all[j])[i] : 1) {
+                // if (uint64_t c = adjust_count(column_values_all.size() ? (*column_values_all[j])[i] : 1, j, row_i)) {
                     max_obs_val = std::max(max_obs_val, c);
                     sum += c;
                     sum_of_squares += c * c;
@@ -287,9 +306,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         pvals.push_back(nullpval);
     }
 
-    VectorMap<size_t, std::vector<uint64_t>> m;
+    VectorMap<size_t, std::pair<size_t, std::vector<uint64_t>>> m;
     using RowStats = std::tuple<double, double, double, double>;
     std::function<RowStats(const PairContainer&)> compute_pval;
+    std::function<RowStats(const std::vector<PairContainer>&)> compute_pval_unitig;
 
     if (config.test_type == "poisson_exact") {
         compute_pval = [&](const auto &row) {
@@ -373,6 +393,89 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                    static_cast<double>(out_sum) / out_kmers * in_kmers,
                                    p_min);
         };
+
+        compute_pval_unitig = [&](const auto &rows) {
+            if (std::all_of(rows.begin(), rows.end(), [](const auto &row) { return row.empty(); }))
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+            for (const auto &row : rows) {
+                for (const auto &[j, c] : row) {
+                    if (groups[j]) {
+                        out_sum += c;
+                    } else {
+                        in_sum += c;
+                    }
+                }
+            }
+
+            int64_t n = in_sum + out_sum;
+            double p = static_cast<double>(in_kmers) / total_kmers;
+
+            auto bdist = boost::math::binomial(n, p);
+            double d = abs(static_cast<double>(in_sum) / in_kmers - static_cast<double>(out_sum) / out_kmers);
+
+            // we want all s s.t. | s/in - (n-s)/out | >= d, so
+            // s/in - (n-s)/out >= d || (n-s)/out - s/in >= d
+            // s/in - n/out + s/out >= d || n/out - s/out - s/in >= d
+            // s/in + s/out >= d + n/out || n/out - d >= s/in + s/out
+            // s >= (d + n/out) / (1/in + 1/out) || s <= (n/out - d) / (1/in + 1/out)
+            double factor = 1.0 / in_kmers + 1.0 / out_kmers;
+            double lower_d = (static_cast<double>(n) / out_kmers - d) / factor;
+            double upper_d = (d + static_cast<double>(n) / out_kmers) / factor;
+
+            // correct for floating-point errors
+            if (abs(lower_d - round(lower_d)) < 1e-10)
+                lower_d = round(lower_d);
+
+            if (abs(upper_d - round(upper_d)) < 1e-10)
+                upper_d = round(upper_d);
+
+            int64_t lower = floor(lower_d);
+            int64_t upper = ceil(upper_d);
+
+            assert(upper >= lower);
+            assert(d == 0 || upper > lower);
+            assert(in_sum <= lower || in_sum >= upper);
+
+            double d_0 = static_cast<double>(n) / out_kmers;
+            double d_n = static_cast<double>(n) / in_kmers;
+            assert(d_0 >= d || d_n >= d);
+
+            assert(d_0 < d || 0 <= lower || 0 >= upper);
+            assert(d_n < d || n <= lower || n >= upper);
+
+            double p_min = 0;
+            if (d_0 > d_n) {
+                p_min = boost::math::pdf(bdist, 0);
+            } else if (d_0 < d_n) {
+                p_min = boost::math::pdf(bdist, n);
+            } else {
+                p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
+            }
+
+            double pval = 0;
+            if (upper <= 0 || lower >= n) {
+                pval = 1.0;
+            } else {
+                if (lower >= 0)
+                    pval += boost::math::cdf(bdist, lower);
+
+                if (upper <= n)
+                    pval += boost::math::cdf(boost::math::complement(bdist, upper - 1 + (upper == lower)));
+            }
+
+            if (pval > 1.0) {
+                common::logger->error("{} > 1.0\t{}\t{},{}", pval, p_min, in_sum, out_sum);
+                throw std::runtime_error("pval fail");
+            }
+
+            return std::make_tuple(pval,
+                                   static_cast<double>(in_sum),
+                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
+                                   p_min);
+        };
     } else if (config.test_type == "fisher") {
         compute_pval = [&](const auto &row) {
             if (row.empty())
@@ -555,7 +658,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         };
     } else if (config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting negative binomial distributions");
-        auto get_rp = [&](double mu, double var, const auto &hist, double f = 1.0) {
+        auto get_rp = [&](size_t j, double mu, double var, const auto &hist, double f = 1.0) {
+            double var_orig = var;
+            var = std::max(var, mu + 0.1);
+            double r_guess = f * f * mu * mu / (var - mu * f);
+            common::logger->trace("{}: initial guess:\tmu: {}\tvar: {}\tr: {}\tp: {}\t",
+                                  j, mu, var_orig, r_guess, mu / var);
             double r = boost::math::tools::newton_raphson_iterate([&](double r) {
                 double dl = nelem * (log(r) - log(r + mu * f) - boost::math::digamma(r));
                 double ddl = nelem * (1.0 / r - 1.0 / (r + mu * f) - boost::math::trigamma(r));
@@ -564,7 +672,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     ddl += boost::math::trigamma(k * f + r) * c;
                 }
                 return std::make_pair(dl, ddl);
-            }, f * f * mu * mu / (var - mu * f), std::numeric_limits<double>::min(), f * mu * nelem, 30);
+            }, r_guess, std::numeric_limits<double>::min(), f * mu * nelem, 30);
 
             return std::make_pair(r, r / (r + mu * f));
         };
@@ -581,15 +689,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (column_values_all.size()) {
                 const auto &column_values = *column_values_all[j];
                 column.call_ones([&](uint64_t row_i) {
-                    if (!ignored[row_i])
-                        ++hist[adjust_count(column_values[i], j, row_i)];
+                    if (kept[row_i])
+                        ++hist[column_values[i]];
+                        // ++hist[adjust_count(column_values[i], j, row_i)];
 
                     ++i;
                 });
             } else {
                 column.call_ones([&](uint64_t row_i) {
-                    if (!ignored[row_i])
-                        ++hist[adjust_count(1, j, row_i)];
+                    if (kept[row_i])
+                        ++hist[1];
+                        // ++hist[adjust_count(1, j, row_i)];
 
                     ++i;
                 });
@@ -599,7 +709,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double mu = s / nelem;
             double mu2 = mu * mu;
             double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
-            nb_params[j] = get_rp(mu, var, hist);
+            nb_params[j] = get_rp(j, mu, var, hist);
 
             target_scale[j] = total_kmers / 2.0 / sums[j];
             if (groups[j]) {
@@ -817,6 +927,104 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                    static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj,
                                    std::min(p_min, 1.0));
         };
+
+        compute_pval_unitig = [&,r_in_row=r_in,r_out_row=r_out,count_maps,in_kmers_adj_row=in_kmers_adj,out_kmers_adj_row=out_kmers_adj](const auto &rows) {
+            if (std::all_of(rows.begin(), rows.end(), [](const auto &row) { return row.empty(); }))
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+            for (const auto &row : rows) {
+                for (const auto &[j, c] : row) {
+                    if (groups[j]) {
+                        out_sum += count_maps[j].find(c)->second.first;
+                    } else {
+                        in_sum += count_maps[j].find(c)->second.first;
+                    }
+                }
+            }
+
+            double r_in = r_in_row * rows.size();
+            double r_out = r_out_row * rows.size();
+            double in_kmers_adj = in_kmers_adj_row * rows.size();
+            double out_kmers_adj = out_kmers_adj_row * rows.size();
+
+            int64_t n = in_sum + out_sum;
+
+            double lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+            auto get_pmf = [&](int64_t s) {
+                int64_t t = n - s;
+                double rs = r_in + s;
+                double rt = r_out + t;
+                return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
+            };
+
+            double d = abs(static_cast<double>(in_sum) / in_kmers_adj - static_cast<double>(out_sum) / out_kmers_adj);
+
+            // we want all s s.t. | s/in - (n-s)/out | >= d, so
+            // s/in - (n-s)/out >= d || (n-s)/out - s/in >= d
+            // s/in - n/out + s/out >= d || n/out - s/out - s/in >= d
+            // s/in + s/out >= d + n/out || n/out - d >= s/in + s/out
+            // s >= (d + n/out) / (1/in + 1/out) || s <= (n/out - d) / (1/in + 1/out)
+            double factor = 1.0 / in_kmers_adj + 1.0 / out_kmers_adj;
+            double lower_d = (static_cast<double>(n) / out_kmers_adj - d) / factor;
+            double upper_d = (d + static_cast<double>(n) / out_kmers_adj) / factor;
+
+            // correct for floating-point errors
+            if (abs(lower_d - round(lower_d)) < 1e-10)
+                lower_d = round(lower_d);
+
+            if (abs(upper_d - round(upper_d)) < 1e-10)
+                upper_d = round(upper_d);
+
+            int64_t lower = floor(lower_d);
+            int64_t upper = ceil(upper_d);
+
+            assert(upper >= lower);
+            assert(d == 0 || upper > lower);
+            assert(in_sum <= lower || in_sum >= upper);
+
+            double d_0 = static_cast<double>(n) / out_kmers_adj;
+            double d_n = static_cast<double>(n) / in_kmers_adj;
+            assert(d_0 >= d || d_n >= d);
+
+            assert(d_0 < d || 0 <= lower || 0 >= upper);
+            assert(d_n < d || n <= lower || n >= upper);
+
+            double p_min = 0;
+            if (d_0 > d_n) {
+                p_min = get_pmf(0);
+            } else if (d_0 < d_n) {
+                p_min = get_pmf(n);
+            } else {
+                p_min = get_pmf(0) + get_pmf(n);
+            }
+
+            double pval = 0;
+            if (upper <= 0 || lower >= n) {
+                pval = 1.0;
+            } else {
+                for (int64_t s = 0; s <= lower; ++s)
+                    pval += get_pmf(s);
+
+                for (int64_t s = upper + (upper == lower); s <= n; ++s)
+                    pval += get_pmf(s);
+            }
+
+            if (p_min > pval) {
+                common::logger->error("pmin fail\t{} > {}\t{}\t{},{}", p_min, pval, n, lower, upper);
+            }
+
+            if (pval >= 1.1) {
+                common::logger->error("{} >= 1.1\t{}\t{},{}\t{},{}", pval, p_min, in_sum, out_sum, lower, upper);
+                throw std::runtime_error("pval fail");
+            }
+
+            return std::make_tuple(std::min(pval, 1.0),
+                                   static_cast<double>(in_sum),
+                                   static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj,
+                                   std::min(p_min, 1.0));
+        };
     } else if (config.test_type == "brunnermunzel" || config.test_type == "mannwhitney_u") {
         size_t nx = num_labels_in;
         size_t ny = num_labels_out;
@@ -1016,84 +1224,175 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::mutex pval_mu;
     std::atomic_thread_fence(std::memory_order_release);
 
-    utils::call_rows<std::unique_ptr<const bit_vector>,
-                     std::unique_ptr<const ValuesContainer>,
-                     PairContainer, true>(columns_all, column_values_all,
-                                          [&](uint64_t row_i, const auto &raw_row, size_t batch_id) {
-        if (ex)
-            return;
-
-        PairContainer row;
-        if (!ignored[row_i]) {
-            size_t count_in = 0;
-            size_t count_out = 0;
-            for (const auto &[j, raw_c] : raw_row) {
-                if (uint64_t c = adjust_count(raw_c, j, row_i)) {
-                    row.emplace_back(j, c);
-                    if (groups[j]) {
-                        ++count_out;
-                    } else {
-                        ++count_in;
+    if (config.test_by_unitig) {
+        MaskedDeBruijnGraph clean_masked_graph(
+            graph_ptr,
+            [&](node_index node) {
+                return node != DeBruijnGraph::npos
+                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
+            },
+            true,
+            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+        );
+
+        clean_masked_graph.call_unitigs([&](const std::string&, const auto &path) {
+            if (ex)
+                return;
+
+            std::vector<PairContainer> rows;
+            rows.reserve(path.size());
+            for (node_index node : path) {
+                auto &row = rows.emplace_back();
+                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    const auto &col = *columns_all[j];
+                    const auto &col_vals = *column_values_all[j];
+                    if (uint64_t r = col.conditional_rank1(row_i)) {
+                        row.emplace_back(j, col_vals[r - 1]);
+                        // uint64_t raw_c = col_vals[r - 1];
+                        // if (uint64_t c = adjust_count(raw_c, j, row_i))
+                            // row.emplace_back(j, c);
                     }
                 }
             }
 
-            if (count_in + count_out < config.min_recurrence
-                    || count_in < config.min_in_recurrence
-                    || count_out < config.min_out_recurrence
-                    || count_in > config.max_in_recurrence
-                    || count_out > config.max_out_recurrence)
-                row.clear();
-        }
+            double pval;
+            double in_stat;
+            double out_stat;
+            double pval_min;
+            try {
+                std::tie(pval, in_stat, out_stat, pval_min) = compute_pval_unitig(rows);
+            } catch (...) {
+                std::lock_guard<std::mutex> lock(pval_mu);
+                ex = std::current_exception();
+                return;
+            }
 
-        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-
-        double pval;
-        double in_stat;
-        double out_stat;
-        double pval_min;
-        try {
-            std::tie(pval, in_stat, out_stat, pval_min) = compute_pval(row);
-        } catch (...) {
-            std::lock_guard<std::mutex> lock(pval_mu);
-            ex = std::current_exception();
-            return;
-        }
+            if (pval >= 1.1)
+                return;
 
-        if (pval >= 1.1)
-            return;
+            bool in_kmer = (in_stat > out_stat) || (out_stat != out_stat && in_stat == in_stat);
+            bool out_kmer = (in_stat < out_stat) || (in_stat != in_stat && out_stat == out_stat);
+            uint64_t k = std::numeric_limits<uint64_t>::max();
 
-        bool in_kmer = (in_stat > out_stat) || (out_stat != out_stat && in_stat == in_stat);
-        bool out_kmer = (in_stat < out_stat) || (in_stat != in_stat && out_stat == out_stat);
-        uint64_t k = std::numeric_limits<uint64_t>::max();
+            if (pval_min - pval > 1e-10) {
+                common::logger->error("Min p-val estimate too high: min {} > cur {}\ttest: {}", pval_min, pval, config.test_type);
+                throw std::runtime_error("Test failed");
+            }
 
-        if (pval_min - pval > 1e-10) {
-            common::logger->error("Min p-val estimate too high: min {} > cur {}\ttest: {}", pval_min, pval, config.test_type);
-            throw std::runtime_error("Test failed");
-        }
+            if (pval_min >= 0.05) {
+                k = 0;
+            } else if (pval_min > 0) {
+                double lkd = log2(0.05) - log2(pval_min);
+                if (lkd <= 64)
+                    k = pow(2.0, lkd);
 
-        if (pval_min >= 0.05) {
-            k = 0;
-        } else if (pval_min > 0) {
-            double lkd = log2(0.05) - log2(pval_min);
-            if (lkd <= 64)
-                k = pow(2.0, lkd);
+                if (k == 0) {
+                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
+                    throw std::runtime_error("Min failed");
+                }
+            }
 
-            if (k == 0) {
-                common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
-                throw std::runtime_error("Min failed");
+            if (in_kmer != out_kmer && pval < 0.05) {
+                for (size_t i = 0; i < path.size(); ++i) {
+                    set_bit((in_kmer ? indicator_in : indicator_out).data(), path[i], parallel, MO_RELAXED);
+                }
             }
-        }
 
-        if (in_kmer != out_kmer && pval < 0.05)
-            set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
+            if (config.test_type != "notest") {
+                std::lock_guard<std::mutex> lock(pval_mu);
+                auto &bucket = m[k];
+                // ++bucket.first;
+                bucket.first += path.size();
+                for (size_t i = 0; i < path.size(); ++i) {
+                    bucket.second.emplace_back(path[i]);
+                    pvals[path[i]] = bit_cast<uint64_t>(pval);
+                }
+            }
+        }, num_threads);
+    } else {
+        utils::call_rows<std::unique_ptr<const bit_vector>,
+                         std::unique_ptr<const ValuesContainer>,
+                         PairContainer, true>(columns_all, column_values_all,
+                                              [&](uint64_t row_i, const auto &raw_row, size_t batch_id) {
+            if (ex)
+                return;
 
-        if (config.test_type != "notest") {
-            std::lock_guard<std::mutex> lock(pval_mu);
-            m[k].emplace_back(node);
-            pvals[node] = bit_cast<uint64_t>(pval);
-        }
-    });
+            PairContainer row;
+            if (kept[row_i]) {
+                size_t count_in = 0;
+                size_t count_out = 0;
+                for (const auto &[j, raw_c] : raw_row) {
+                    if (uint64_t c = raw_c) {
+                    // if (uint64_t c = adjust_count(raw_c, j, row_i)) {
+                        row.emplace_back(j, c);
+                        if (groups[j]) {
+                            ++count_out;
+                        } else {
+                            ++count_in;
+                        }
+                    }
+                }
+
+                if (count_in + count_out < config.min_recurrence
+                        || count_in < config.min_in_recurrence
+                        || count_out < config.min_out_recurrence
+                        || count_in > config.max_in_recurrence
+                        || count_out > config.max_out_recurrence)
+                    row.clear();
+            }
+
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+
+            double pval;
+            double in_stat;
+            double out_stat;
+            double pval_min;
+            try {
+                std::tie(pval, in_stat, out_stat, pval_min) = compute_pval(row);
+            } catch (...) {
+                std::lock_guard<std::mutex> lock(pval_mu);
+                ex = std::current_exception();
+                return;
+            }
+
+            if (pval >= 1.1)
+                return;
+
+            bool in_kmer = (in_stat > out_stat) || (out_stat != out_stat && in_stat == in_stat);
+            bool out_kmer = (in_stat < out_stat) || (in_stat != in_stat && out_stat == out_stat);
+            uint64_t k = std::numeric_limits<uint64_t>::max();
+
+            if (pval_min - pval > 1e-10) {
+                common::logger->error("Min p-val estimate too high: min {} > cur {}\ttest: {}", pval_min, pval, config.test_type);
+                throw std::runtime_error("Test failed");
+            }
+
+            if (pval_min >= 0.05) {
+                k = 0;
+            } else if (pval_min > 0) {
+                double lkd = log2(0.05) - log2(pval_min);
+                if (lkd <= 64)
+                    k = pow(2.0, lkd);
+
+                if (k == 0) {
+                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
+                    throw std::runtime_error("Min failed");
+                }
+            }
+
+            if (in_kmer != out_kmer && pval < 0.05)
+                set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
+
+            if (config.test_type != "notest") {
+                std::lock_guard<std::mutex> lock(pval_mu);
+                auto &bucket = m[k];
+                ++bucket.first;
+                bucket.second.emplace_back(node);
+                pvals[node] = bit_cast<uint64_t>(pval);
+            }
+        });
+    }
 
     std::atomic_thread_fence(std::memory_order_acquire);
 
@@ -1112,11 +1411,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     if (config.test_type != "notest") {
         uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
-        auto m_data = const_cast<std::vector<std::pair<size_t, std::vector<uint64_t>>>&&>(m.values_container());
+        auto m_data = const_cast<std::vector<std::pair<size_t, std::pair<size_t, std::vector<uint64_t>>>>&&>(m.values_container());
 
         std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
         size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
-                                    [](size_t sum, const auto &a) { return sum + a.second.size(); });
+                                     [](size_t sum, const auto &a) { return sum + a.second.first; });
         common::logger->trace("Performed {}/{} tests", acc, nelem);
         size_t total_tests = acc;
         common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
@@ -1126,7 +1425,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             size_t k = 0;
             size_t last_k = 0;
             for ( ; begin != m_data.end(); ++begin) {
-                const auto &[cur_k, bucket] = *begin;
+                const auto &[cur_k, s_bucket] = *begin;
+                const auto &[s, bucket] = s_bucket;
                 common::logger->trace("k: {}\tm(k): {}", cur_k, acc);
                 if (acc <= cur_k) {
                     k = std::max(acc, last_k + 1);
@@ -1134,10 +1434,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 last_k = cur_k;
-                acc -= bucket.size();
+                acc -= s;
             }
 
             if (k == 0) {
+                // TODO: fix for unitig
                 common::logger->trace("Falling back to Benjamini-Yekutieli");
                 VectorMap<double, double> pval_map;
                 for (uint64_t p_u : pvals) {
@@ -1165,7 +1466,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             } else {
                 common::logger->trace("k: {}\talpha_corr: {}", k, 0.05 / k);
                 for (auto jt = m_data.begin(); jt != m_data.end(); ++jt) {
-                    const auto &[cur_k, bucket] = *jt;
+                    const auto &[cur_k, s_bucket] = *jt;
+                    const auto &[s, bucket] = s_bucket;
                     for (uint64_t i : bucket) {
                         if (jt < begin || bit_cast<double, uint64_t>(pvals[i]) * k >= 0.05) {
                             indicator_in[i] = false;

From cf35ca1dd8eebf09243456d26575c34901efb1eb Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 01:30:44 +0200
Subject: [PATCH 185/410] many fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 364 +++---------------
 1 file changed, 60 insertions(+), 304 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index dd45a07cb0..926419722e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -328,142 +328,30 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             int64_t n = in_sum + out_sum;
-            double p = static_cast<double>(in_kmers) / total_kmers;
-
-            auto bdist = boost::math::binomial(n, p);
-            double d = abs(static_cast<double>(in_sum) / in_kmers - static_cast<double>(out_sum) / out_kmers);
-
-            // we want all s s.t. | s/in - (n-s)/out | >= d, so
-            // s/in - (n-s)/out >= d || (n-s)/out - s/in >= d
-            // s/in - n/out + s/out >= d || n/out - s/out - s/in >= d
-            // s/in + s/out >= d + n/out || n/out - d >= s/in + s/out
-            // s >= (d + n/out) / (1/in + 1/out) || s <= (n/out - d) / (1/in + 1/out)
-            double factor = 1.0 / in_kmers + 1.0 / out_kmers;
-            double lower_d = (static_cast<double>(n) / out_kmers - d) / factor;
-            double upper_d = (d + static_cast<double>(n) / out_kmers) / factor;
-
-            // correct for floating-point errors
-            if (abs(lower_d - round(lower_d)) < 1e-10)
-                lower_d = round(lower_d);
-
-            if (abs(upper_d - round(upper_d)) < 1e-10)
-                upper_d = round(upper_d);
-
-            int64_t lower = floor(lower_d);
-            int64_t upper = ceil(upper_d);
-
-            assert(upper >= lower);
-            assert(d == 0 || upper > lower);
-            assert(in_sum <= lower || in_sum >= upper);
-
-            double d_0 = static_cast<double>(n) / out_kmers;
-            double d_n = static_cast<double>(n) / in_kmers;
-            assert(d_0 >= d || d_n >= d);
-
-            assert(d_0 < d || 0 <= lower || 0 >= upper);
-            assert(d_n < d || n <= lower || n >= upper);
-
-            double p_min = 0;
-            if (d_0 > d_n) {
-                p_min = boost::math::pdf(bdist, 0);
-            } else if (d_0 < d_n) {
-                p_min = boost::math::pdf(bdist, n);
-            } else {
-                p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
-            }
-
-            double pval = 0;
-            if (upper <= 0 || lower >= n) {
-                pval = 1.0;
-            } else {
-                if (lower >= 0)
-                    pval += boost::math::cdf(bdist, lower);
-
-                if (upper <= n)
-                    pval += boost::math::cdf(boost::math::complement(bdist, upper - 1 + (upper == lower)));
-            }
-
-            if (pval > 1.0) {
-                common::logger->error("{} > 1.0\t{}\t{},{}", pval, p_min, in_sum, out_sum);
-                throw std::runtime_error("pval fail");
-            }
-
-            return std::make_tuple(pval,
-                                   static_cast<double>(in_sum),
-                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
-                                   p_min);
-        };
-
-        compute_pval_unitig = [&](const auto &rows) {
-            if (std::all_of(rows.begin(), rows.end(), [](const auto &row) { return row.empty(); }))
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
-
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-            for (const auto &row : rows) {
-                for (const auto &[j, c] : row) {
-                    if (groups[j]) {
-                        out_sum += c;
-                    } else {
-                        in_sum += c;
-                    }
-                }
-            }
+            double pval = 1.0;
+            double p_min = 1.0;
 
-            int64_t n = in_sum + out_sum;
             double p = static_cast<double>(in_kmers) / total_kmers;
-
             auto bdist = boost::math::binomial(n, p);
-            double d = abs(static_cast<double>(in_sum) / in_kmers - static_cast<double>(out_sum) / out_kmers);
-
-            // we want all s s.t. | s/in - (n-s)/out | >= d, so
-            // s/in - (n-s)/out >= d || (n-s)/out - s/in >= d
-            // s/in - n/out + s/out >= d || n/out - s/out - s/in >= d
-            // s/in + s/out >= d + n/out || n/out - d >= s/in + s/out
-            // s >= (d + n/out) / (1/in + 1/out) || s <= (n/out - d) / (1/in + 1/out)
-            double factor = 1.0 / in_kmers + 1.0 / out_kmers;
-            double lower_d = (static_cast<double>(n) / out_kmers - d) / factor;
-            double upper_d = (d + static_cast<double>(n) / out_kmers) / factor;
-
-            // correct for floating-point errors
-            if (abs(lower_d - round(lower_d)) < 1e-10)
-                lower_d = round(lower_d);
-
-            if (abs(upper_d - round(upper_d)) < 1e-10)
-                upper_d = round(upper_d);
-
-            int64_t lower = floor(lower_d);
-            int64_t upper = ceil(upper_d);
-
-            assert(upper >= lower);
-            assert(d == 0 || upper > lower);
-            assert(in_sum <= lower || in_sum >= upper);
-
-            double d_0 = static_cast<double>(n) / out_kmers;
-            double d_n = static_cast<double>(n) / in_kmers;
-            assert(d_0 >= d || d_n >= d);
-
-            assert(d_0 < d || 0 <= lower || 0 >= upper);
-            assert(d_n < d || n <= lower || n >= upper);
-
-            double p_min = 0;
-            if (d_0 > d_n) {
-                p_min = boost::math::pdf(bdist, 0);
-            } else if (d_0 < d_n) {
-                p_min = boost::math::pdf(bdist, n);
-            } else {
-                p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
-            }
 
-            double pval = 0;
-            if (upper <= 0 || lower >= n) {
-                pval = 1.0;
+            if (num_labels_in == num_labels_out) {
+                p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
+                if (in_sum != out_sum) {
+                    int64_t d = std::min(in_sum, out_sum);
+                    pval = boost::math::cdf(bdist, d) + boost::math::cdf(boost::math::complement(bdist, n - d - 1));
+                }
             } else {
-                if (lower >= 0)
-                    pval += boost::math::cdf(bdist, lower);
-
-                if (upper <= n)
-                    pval += boost::math::cdf(boost::math::complement(bdist, upper - 1 + (upper == lower)));
+                p_min = std::min(boost::math::pdf(bdist, 0), boost::math::pdf(bdist, n));
+                double d = abs(static_cast<double>(in_sum) / num_labels_in - static_cast<double>(out_sum) / num_labels_out);
+                if (d > 0) {
+                    // we want s s.t. | s/num_labels_in - (n-s)/num_labels_out | >= d
+                    // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
+                    // s/num_labels_in + s/num_labels_out >= d + n/num_labels_out || n/num_labels_out - d >= s/num_labels_in + s/num_labels_out
+                    double factor = double(1.0) / num_labels_in + double(1.0) / num_labels_out;
+                    int64_t lower = (static_cast<double>(n) / num_labels_out - d) / factor;
+                    int64_t upper = (d + static_cast<double>(n) / num_labels_out) / factor;
+                    pval = boost::math::cdf(bdist, lower) + boost::math::cdf(boost::math::complement(bdist, upper - 1));
+                }
             }
 
             if (pval > 1.0) {
@@ -679,7 +567,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         std::vector<std::pair<double, double>> nb_params(groups.size());
         std::vector<VectorMap<uint64_t, size_t>> hists(groups.size());
-        std::vector<double> target_scale(groups.size());
+
+        // geometric mean
+        double target_sum = 0.0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            target_sum += log(static_cast<double>(sums[j]));
+        }
+        target_sum = exp(target_sum / groups.size());
+
+        double min_p = 1.0;
 
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
@@ -710,24 +606,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double mu2 = mu * mu;
             double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
             nb_params[j] = get_rp(j, mu, var, hist);
-
-            target_scale[j] = total_kmers / 2.0 / sums[j];
-            if (groups[j]) {
-                target_scale[j] /= num_labels_out;
-            } else {
-                target_scale[j] /= num_labels_in;
-            }
-        }
-
-        double c_a = 0;
-        double c_b = 0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            double p = nb_params[j].second;
-            if (groups[j]) {
-                c_b += p * sums[j];
-            } else {
-                c_a += p * sums[j];
-            }
+            min_p = std::min(min_p, nb_params[j].second);
         }
 
         std::vector<double> r_maps(groups.size(), 1.0);
@@ -735,7 +614,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double dl = 0;
             double ddl = 0;
             for (size_t j = 0; j < groups.size(); ++j) {
-                double f = target_scale[j];
+                double f = target_sum / sums[j];
                 double &r = r_maps[j];
                 r = boost::math::tools::newton_raphson_iterate([&](double r) {
                     double dl = nelem * (log(p) - boost::math::digamma(r));
@@ -757,7 +636,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             return std::make_pair(dl, ddl);
-        }, 2.0 / total_kmers * (c_a * c_a + c_b * c_b) / (c_a + c_b), std::numeric_limits<double>::min(), 1.0, 30);
+        }, min_p, std::numeric_limits<double>::min(), 1.0, 30);
 
         std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
         double r_in = 0;
@@ -852,6 +731,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             int64_t n = in_sum + out_sum;
+            double p_min = 1.0;
+            double pval = 1.0;
 
             double lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
             auto get_pmf = [&](int64_t s) {
@@ -861,164 +742,40 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
             };
 
-            double d = abs(static_cast<double>(in_sum) / in_kmers_adj - static_cast<double>(out_sum) / out_kmers_adj);
-
-            // we want all s s.t. | s/in - (n-s)/out | >= d, so
-            // s/in - (n-s)/out >= d || (n-s)/out - s/in >= d
-            // s/in - n/out + s/out >= d || n/out - s/out - s/in >= d
-            // s/in + s/out >= d + n/out || n/out - d >= s/in + s/out
-            // s >= (d + n/out) / (1/in + 1/out) || s <= (n/out - d) / (1/in + 1/out)
-            double factor = 1.0 / in_kmers_adj + 1.0 / out_kmers_adj;
-            double lower_d = (static_cast<double>(n) / out_kmers_adj - d) / factor;
-            double upper_d = (d + static_cast<double>(n) / out_kmers_adj) / factor;
-
-            // correct for floating-point errors
-            if (abs(lower_d - round(lower_d)) < 1e-10)
-                lower_d = round(lower_d);
-
-            if (abs(upper_d - round(upper_d)) < 1e-10)
-                upper_d = round(upper_d);
-
-            int64_t lower = floor(lower_d);
-            int64_t upper = ceil(upper_d);
-
-            assert(upper >= lower);
-            assert(d == 0 || upper > lower);
-            assert(in_sum <= lower || in_sum >= upper);
-
-            double d_0 = static_cast<double>(n) / out_kmers_adj;
-            double d_n = static_cast<double>(n) / in_kmers_adj;
-            assert(d_0 >= d || d_n >= d);
-
-            assert(d_0 < d || 0 <= lower || 0 >= upper);
-            assert(d_n < d || n <= lower || n >= upper);
-
-            double p_min = 0;
-            if (d_0 > d_n) {
-                p_min = get_pmf(0);
-            } else if (d_0 < d_n) {
-                p_min = get_pmf(n);
-            } else {
+            if (num_labels_in == num_labels_out) {
                 p_min = get_pmf(0) + get_pmf(n);
-            }
-
-            double pval = 0;
-            if (upper <= 0 || lower >= n) {
-                pval = 1.0;
-            } else {
-                for (int64_t s = 0; s <= lower; ++s)
-                    pval += get_pmf(s);
-
-                for (int64_t s = upper + (upper == lower); s <= n; ++s)
-                    pval += get_pmf(s);
-            }
-
-            if (p_min > pval) {
-                common::logger->error("pmin fail\t{} > {}\t{}\t{},{}", p_min, pval, n, lower, upper);
-            }
-
-            if (pval >= 1.1) {
-                common::logger->error("{} >= 1.1\t{}\t{},{}\t{},{}", pval, p_min, in_sum, out_sum, lower, upper);
-                throw std::runtime_error("pval fail");
-            }
-
-            return std::make_tuple(std::min(pval, 1.0),
-                                   static_cast<double>(in_sum),
-                                   static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj,
-                                   std::min(p_min, 1.0));
-        };
-
-        compute_pval_unitig = [&,r_in_row=r_in,r_out_row=r_out,count_maps,in_kmers_adj_row=in_kmers_adj,out_kmers_adj_row=out_kmers_adj](const auto &rows) {
-            if (std::all_of(rows.begin(), rows.end(), [](const auto &row) { return row.empty(); }))
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
-
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-            for (const auto &row : rows) {
-                for (const auto &[j, c] : row) {
-                    if (groups[j]) {
-                        out_sum += count_maps[j].find(c)->second.first;
-                    } else {
-                        in_sum += count_maps[j].find(c)->second.first;
+                if (in_sum != out_sum) {
+                    lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+                    int64_t d = std::min(in_sum, out_sum);
+                    pval = p_min;
+                    for (int64_t s = 1; s <= d; ++s) {
+                        pval += get_pmf(s) + get_pmf(n - s);
                     }
                 }
-            }
-
-            double r_in = r_in_row * rows.size();
-            double r_out = r_out_row * rows.size();
-            double in_kmers_adj = in_kmers_adj_row * rows.size();
-            double out_kmers_adj = out_kmers_adj_row * rows.size();
-
-            int64_t n = in_sum + out_sum;
-
-            double lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
-            auto get_pmf = [&](int64_t s) {
-                int64_t t = n - s;
-                double rs = r_in + s;
-                double rt = r_out + t;
-                return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
-            };
-
-            double d = abs(static_cast<double>(in_sum) / in_kmers_adj - static_cast<double>(out_sum) / out_kmers_adj);
-
-            // we want all s s.t. | s/in - (n-s)/out | >= d, so
-            // s/in - (n-s)/out >= d || (n-s)/out - s/in >= d
-            // s/in - n/out + s/out >= d || n/out - s/out - s/in >= d
-            // s/in + s/out >= d + n/out || n/out - d >= s/in + s/out
-            // s >= (d + n/out) / (1/in + 1/out) || s <= (n/out - d) / (1/in + 1/out)
-            double factor = 1.0 / in_kmers_adj + 1.0 / out_kmers_adj;
-            double lower_d = (static_cast<double>(n) / out_kmers_adj - d) / factor;
-            double upper_d = (d + static_cast<double>(n) / out_kmers_adj) / factor;
-
-            // correct for floating-point errors
-            if (abs(lower_d - round(lower_d)) < 1e-10)
-                lower_d = round(lower_d);
-
-            if (abs(upper_d - round(upper_d)) < 1e-10)
-                upper_d = round(upper_d);
-
-            int64_t lower = floor(lower_d);
-            int64_t upper = ceil(upper_d);
-
-            assert(upper >= lower);
-            assert(d == 0 || upper > lower);
-            assert(in_sum <= lower || in_sum >= upper);
-
-            double d_0 = static_cast<double>(n) / out_kmers_adj;
-            double d_n = static_cast<double>(n) / in_kmers_adj;
-            assert(d_0 >= d || d_n >= d);
-
-            assert(d_0 < d || 0 <= lower || 0 >= upper);
-            assert(d_n < d || n <= lower || n >= upper);
-
-            double p_min = 0;
-            if (d_0 > d_n) {
-                p_min = get_pmf(0);
-            } else if (d_0 < d_n) {
-                p_min = get_pmf(n);
             } else {
-                p_min = get_pmf(0) + get_pmf(n);
-            }
-
-            double pval = 0;
-            if (upper <= 0 || lower >= n) {
-                pval = 1.0;
-            } else {
-                for (int64_t s = 0; s <= lower; ++s)
-                    pval += get_pmf(s);
-
-                for (int64_t s = upper + (upper == lower); s <= n; ++s)
-                    pval += get_pmf(s);
-            }
+                p_min = std::min(get_pmf(0), get_pmf(n));
+                double d = abs(static_cast<double>(in_sum) / num_labels_in - static_cast<double>(out_sum) / num_labels_out);
+                if (d > 0) {
+                    // we want s s.t. | s/num_labels_in - (n-s)/num_labels_out | >= d
+                    // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
+                    // s/num_labels_in + s/num_labels_out >= d + n/num_labels_out || n/num_labels_out - d >= s/num_labels_in + s/num_labels_out
+                    double factor = double(1.0) / num_labels_in + double(1.0) / num_labels_out;
+                    int64_t lower = (static_cast<double>(n) / num_labels_out - d) / factor;
+                    int64_t upper = (d + static_cast<double>(n) / num_labels_out) / factor;
+
+                    pval = 0;
+                    for (int64_t s = 0; s <= lower; ++s) {
+                        pval += get_pmf(s);
+                    }
 
-            if (p_min > pval) {
-                common::logger->error("pmin fail\t{} > {}\t{}\t{},{}", p_min, pval, n, lower, upper);
+                    for (int64_t s = upper; s <= n; ++s) {
+                        pval += get_pmf(s);
+                    }
+                }
             }
 
-            if (pval >= 1.1) {
-                common::logger->error("{} >= 1.1\t{}\t{},{}\t{},{}", pval, p_min, in_sum, out_sum, lower, upper);
+            if (pval >= 1.1)
                 throw std::runtime_error("pval fail");
-            }
 
             return std::make_tuple(std::min(pval, 1.0),
                                    static_cast<double>(in_sum),
@@ -1302,8 +1059,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (config.test_type != "notest") {
                 std::lock_guard<std::mutex> lock(pval_mu);
                 auto &bucket = m[k];
-                // ++bucket.first;
-                bucket.first += path.size();
+                ++bucket.first;
                 for (size_t i = 0; i < path.size(); ++i) {
                     bucket.second.emplace_back(path[i]);
                     pvals[path[i]] = bit_cast<uint64_t>(pval);

From e18c615feb345304304e5d40374f04376c2fab82 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 10:23:27 +0200
Subject: [PATCH 186/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 245 +++++++++++-------
 1 file changed, 157 insertions(+), 88 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 926419722e..c78608371e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -202,6 +202,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
 
     common::logger->trace("Marking discarded k-mers");
+    std::mutex agg_mu;
+    std::vector<VectorMap<uint64_t, size_t>> hists(groups.size());
+
     if (groups.size()) {
         std::atomic_thread_fence(std::memory_order_release);
         utils::call_rows<std::unique_ptr<const bit_vector>,
@@ -209,8 +212,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          PairContainer, false>(columns_all, column_values_all,
                                                [&](uint64_t row_i, const auto &row, size_t) {
             for (const auto &[j, raw_c] : row) {
-                if (adjust_count(raw_c, j, row_i))
+                if (adjust_count(raw_c, j, row_i)) {
+                    std::lock_guard<std::mutex> lock(agg_mu);
+                    for (const auto &[j, raw_c] : row) {
+                        ++hists[j][raw_c];
+                    }
                     return;
+                }
             }
 
             unset_bit(kept.data(), row_i, parallel, MO_RELAXED);
@@ -221,7 +229,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     double nelem = sdsl::util::cnt_one_bits(kept);
 
     common::logger->trace("Computing aggregate statistics");
-    std::mutex agg_mu;
     double in_kmers = 0;
     double out_kmers = 0;
     std::vector<double> medians(groups.size());
@@ -230,58 +237,22 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<uint64_t> sums(groups.size());
     std::vector<uint64_t> sums_of_squares(groups.size());
 
-    bool compute_medians = (config.test_type == "mannwhitney_u" || config.test_type == "brunnermunzel");
-
-    #pragma omp parallel for num_threads(num_parallel_files)
     for (size_t j = 0; j < groups.size(); ++j) {
-        const auto &column = *columns_all[j];
-        size_t num_vals = column_values_all.size()
-            ? column_values_all[j]->size()
-            : column.num_set_bits();
-
-        uint64_t sum = 0;
-        uint64_t sum_of_squares = 0;
-        size_t i = 0;
-        size_t n = 0;
-        uint64_t max_obs_val = 0;
-
-        std::vector<uint64_t> vals;
-        if (compute_medians)
-            vals.reserve(num_vals);
-
-        column.call_ones([&](uint64_t row_i) {
-            if (kept[row_i]) {
-                if (uint64_t c = column_values_all.size() ? (*column_values_all[j])[i] : 1) {
-                // if (uint64_t c = adjust_count(column_values_all.size() ? (*column_values_all[j])[i] : 1, j, row_i)) {
-                    max_obs_val = std::max(max_obs_val, c);
-                    sum += c;
-                    sum_of_squares += c * c;
-                    ++n;
-                    if (compute_medians)
-                        vals.emplace_back(c);
-                }
-            }
-            ++i;
-        });
-
-        max_obs_vals[j] = max_obs_val;
-
-        if (vals.size())
-            medians[j] = boost::math::statistics::median(vals);
-
-        sums[j] = sum;
-        sums_of_squares[j] = sum_of_squares;
-        n_kmers[j] = n;
-
-        common::logger->trace("{}: kept: {} / {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
-                              j, n, i, sum, max_obs_vals[j], min_counts[j], check_cutoff[j]);
+        for (const auto &[k, c] : hists[j]) {
+            sums[j] += k * c;
+            sums_of_squares[j] += k * k * c;
+            n_kmers[j] += c;
+            max_obs_vals[j] = std::max(max_obs_vals[j], k);
+        }
 
-        std::lock_guard<std::mutex> lock(agg_mu);
         if (groups[j]) {
-            out_kmers += sum;
+            out_kmers += sums[j];
         } else {
-            in_kmers += sum;
+            in_kmers += sums[j];
         }
+
+        common::logger->trace("{}: sum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
+                              j, sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
     }
 
     double total_kmers = in_kmers + out_kmers;
@@ -289,12 +260,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Number of kept unique k-mers: {}\tNumber of kept k-mers: {}",
                           nelem, total_kmers);
 
-    common::logger->trace("Running differential tests");
-    sdsl::bit_vector indicator_in(graph_ptr->max_index() + 1, false);
-    sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
-
+    common::logger->trace("Allocating p-value storage");
     auto nullpval = bit_cast<uint64_t>(double(1.1));
-
     std::unique_ptr<utils::TempFile> tmp_file;
     PValStorage pvals;
     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
@@ -306,11 +273,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         pvals.push_back(nullpval);
     }
 
+    common::logger->trace("Allocating k-mer bitmasks");
+    sdsl::bit_vector indicator_in(graph_ptr->max_index() + 1, false);
+    sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
+
     VectorMap<size_t, std::pair<size_t, std::vector<uint64_t>>> m;
     using RowStats = std::tuple<double, double, double, double>;
     std::function<RowStats(const PairContainer&)> compute_pval;
     std::function<RowStats(const std::vector<PairContainer>&)> compute_pval_unitig;
 
+    common::logger->trace("Test: {}", config.test_type);
     if (config.test_type == "poisson_exact") {
         compute_pval = [&](const auto &row) {
             if (row.empty())
@@ -348,9 +320,29 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
                     // s/num_labels_in + s/num_labels_out >= d + n/num_labels_out || n/num_labels_out - d >= s/num_labels_in + s/num_labels_out
                     double factor = double(1.0) / num_labels_in + double(1.0) / num_labels_out;
-                    int64_t lower = (static_cast<double>(n) / num_labels_out - d) / factor;
-                    int64_t upper = (d + static_cast<double>(n) / num_labels_out) / factor;
-                    pval = boost::math::cdf(bdist, lower) + boost::math::cdf(boost::math::complement(bdist, upper - 1));
+
+                    double lower_d = (static_cast<double>(n) / num_labels_out - d) / factor;
+                    double upper_d = (d + static_cast<double>(n) / num_labels_out) / factor;
+
+                    // fix floating point errors
+                    if (abs(lower_d - round(lower_d)) < 1e-10)
+                        lower_d = round(lower_d);
+
+                    if (abs(upper_d - round(upper_d)) < 1e-10)
+                        upper_d = round(upper_d);
+
+                    int64_t lower = floor(lower_d);
+                    int64_t upper = ceil(upper_d);
+
+                    if (lower < n && upper > 0 && lower != upper) {
+                        pval = 0.0;
+
+                        if (lower >= 0)
+                            pval += boost::math::cdf(bdist, lower);
+
+                        if (upper <= n)
+                            pval += boost::math::cdf(boost::math::complement(bdist, upper - 1));
+                    }
                 }
             }
 
@@ -565,9 +557,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_pair(r, r / (r + mu * f));
         };
 
-        std::vector<std::pair<double, double>> nb_params(groups.size());
-        std::vector<VectorMap<uint64_t, size_t>> hists(groups.size());
-
         // geometric mean
         double target_sum = 0.0;
         for (size_t j = 0; j < groups.size(); ++j) {
@@ -577,30 +566,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         double min_p = 1.0;
 
+        std::vector<std::pair<double, double>> nb_params(groups.size());
+
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
             auto &hist = hists[j];
-            size_t i = 0;
-            const auto &column = *columns_all[j];
-            if (column_values_all.size()) {
-                const auto &column_values = *column_values_all[j];
-                column.call_ones([&](uint64_t row_i) {
-                    if (kept[row_i])
-                        ++hist[column_values[i]];
-                        // ++hist[adjust_count(column_values[i], j, row_i)];
-
-                    ++i;
-                });
-            } else {
-                column.call_ones([&](uint64_t row_i) {
-                    if (kept[row_i])
-                        ++hist[1];
-                        // ++hist[adjust_count(1, j, row_i)];
-
-                    ++i;
-                });
-            }
-
             double s = static_cast<double>(sums[j]);
             double mu = s / nelem;
             double mu2 = mu * mu;
@@ -760,16 +730,114 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
                     // s/num_labels_in + s/num_labels_out >= d + n/num_labels_out || n/num_labels_out - d >= s/num_labels_in + s/num_labels_out
                     double factor = double(1.0) / num_labels_in + double(1.0) / num_labels_out;
-                    int64_t lower = (static_cast<double>(n) / num_labels_out - d) / factor;
-                    int64_t upper = (d + static_cast<double>(n) / num_labels_out) / factor;
 
-                    pval = 0;
-                    for (int64_t s = 0; s <= lower; ++s) {
-                        pval += get_pmf(s);
+                    double lower_d = (static_cast<double>(n) / num_labels_out - d) / factor;
+                    double upper_d = (d + static_cast<double>(n) / num_labels_out) / factor;
+
+                    // fix floating point errors
+                    if (abs(lower_d - round(lower_d)) < 1e-10)
+                        lower_d = round(lower_d);
+
+                    if (abs(upper_d - round(upper_d)) < 1e-10)
+                        upper_d = round(upper_d);
+
+                    int64_t lower = floor(lower_d);
+                    int64_t upper = ceil(upper_d);
+
+                    if (lower < n && upper > 0 && lower != upper) {
+                        pval = 0;
+                        for (int64_t s = 0; s <= lower; ++s) {
+                            pval += get_pmf(s);
+                        }
+
+                        for (int64_t s = upper; s <= n; ++s) {
+                            pval += get_pmf(s);
+                        }
                     }
+                }
+            }
+
+            if (pval >= 1.1)
+                throw std::runtime_error("pval fail");
 
-                    for (int64_t s = upper; s <= n; ++s) {
-                        pval += get_pmf(s);
+            return std::make_tuple(std::min(pval, 1.0),
+                                   static_cast<double>(in_sum),
+                                   static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj,
+                                   std::min(p_min, 1.0));
+        };
+
+        compute_pval_unitig = [&,r_in_row=r_in,r_out_row=r_out,count_maps,in_kmers_adj,out_kmers_adj](const auto &rows) {
+            if (std::all_of(rows.begin(), rows.end(), [](const auto &row) { return row.empty(); }))
+                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+            for (const auto &row : rows) {
+                for (const auto &[j, c] : row) {
+                    if (groups[j]) {
+                        out_sum += count_maps[j].find(c)->second.first;
+                    } else {
+                        in_sum += count_maps[j].find(c)->second.first;
+                    }
+                }
+            }
+
+            double r_in = r_in_row * (1 - target_p) / (rows.size() - target_p) * rows.size();
+            double r_out = r_out_row * (1 - target_p) / (rows.size() - target_p) * rows.size();
+
+            int64_t n = in_sum + out_sum;
+            double p_min = 1.0;
+            double pval = 1.0;
+
+            double lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+            auto get_pmf = [&](int64_t s) {
+                int64_t t = n - s;
+                double rs = r_in + s;
+                double rt = r_out + t;
+                return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
+            };
+
+            if (num_labels_in == num_labels_out) {
+                p_min = get_pmf(0) + get_pmf(n);
+                if (in_sum != out_sum) {
+                    lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+                    int64_t d = std::min(in_sum, out_sum);
+                    pval = p_min;
+                    for (int64_t s = 1; s <= d; ++s) {
+                        pval += get_pmf(s) + get_pmf(n - s);
+                    }
+                }
+            } else {
+                p_min = std::min(get_pmf(0), get_pmf(n));
+                double d = abs(static_cast<double>(in_sum) / num_labels_in - static_cast<double>(out_sum) / num_labels_out);
+                if (d > 0) {
+                    // we want s s.t. | s/num_labels_in - (n-s)/num_labels_out | >= d
+                    // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
+                    // s/num_labels_in + s/num_labels_out >= d + n/num_labels_out || n/num_labels_out - d >= s/num_labels_in + s/num_labels_out
+                    double factor = double(1.0) / num_labels_in + double(1.0) / num_labels_out;
+
+                    double lower_d = (static_cast<double>(n) / num_labels_out - d) / factor;
+                    double upper_d = (d + static_cast<double>(n) / num_labels_out) / factor;
+
+                    // fix floating point errors
+                    if (abs(lower_d - round(lower_d)) < 1e-10)
+                        lower_d = round(lower_d);
+
+                    if (abs(upper_d - round(upper_d)) < 1e-10)
+                        upper_d = round(upper_d);
+
+                    int64_t lower = floor(lower_d);
+                    int64_t upper = ceil(upper_d);
+
+                    if (lower < n && upper > 0 && lower != upper) {
+                        pval = 0;
+                        for (int64_t s = 0; s <= lower; ++s) {
+                            pval += get_pmf(s);
+                        }
+
+                        for (int64_t s = upper; s <= n; ++s) {
+                            pval += get_pmf(s);
+                        }
                     }
                 }
             }
@@ -976,6 +1044,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         throw std::runtime_error("Test not implemented");
     }
 
+    common::logger->trace("Running differential tests");
     std::exception_ptr ex = nullptr;
 
     std::mutex pval_mu;

From 1a932a6bb4263bd4bd935c9552388b6268590c4a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 10:26:44 +0200
Subject: [PATCH 187/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 251 +-----------------
 .../test_annotated_graph_algorithm.cpp        |   3 -
 2 files changed, 2 insertions(+), 252 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c78608371e..7589444ed8 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -212,7 +212,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          PairContainer, false>(columns_all, column_values_all,
                                                [&](uint64_t row_i, const auto &row, size_t) {
             for (const auto &[j, raw_c] : row) {
-                if (adjust_count(raw_c, j, row_i)) {
+                if (raw_c >= min_counts[j] && raw_c <= check_cutoff[j]) {
                     std::lock_guard<std::mutex> lock(agg_mu);
                     for (const auto &[j, raw_c] : row) {
                         ++hists[j][raw_c];
@@ -468,69 +468,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
             double pmin = boost::math::cdf(boost::math::complement(dist, max_chi_stat));
 
-            return std::make_tuple(pval,
-                                   static_cast<double>(in_sum),
-                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
-                                   pmin);
-        };
-    } else if (config.test_type == "poisson_likelihoodratio") {
-        if (in_kmers == 0 || out_kmers == 0) {
-            common::logger->error("Test invalid, try poisson_exact instead");
-            throw std::domain_error("Test fail");
-        }
-        compute_pval = [&,dist=boost::math::chi_squared(1)](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
-
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-
-            for (const auto &[j, c] : row) {
-                if (groups[j]) {
-                    out_sum += c;
-                } else {
-                    in_sum += c;
-                }
-            }
-
-            if (in_sum == 0 || out_sum == 0)
-                common::logger->warn("Empty row: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
-
-            int64_t total_sum = in_sum + out_sum;
-
-            auto get_chi_stat = [&](int64_t in_sum) {
-                int64_t out_sum = total_sum - in_sum;
-                double log_likelihood_in = in_sum > 0 ? -in_sum + log(static_cast<double>(in_sum)) * in_sum : 0.0;
-                double log_likelihood_out = out_sum > 0 ? -out_sum + log(static_cast<double>(out_sum)) * out_sum : 0.0;
-
-                double theta = static_cast<double>(in_sum + out_sum) / total_kmers;
-                double mean_denom_in = theta * in_kmers;
-                double mean_denom_out = theta * out_kmers;
-
-                if (mean_denom_in == 0 || mean_denom_out == 0)
-                    common::logger->warn("Empty null hypothesis: Poisson likelihood ratio test not valid at boundary conditions (i.e., counts equal to 0), please set test type to 'poisson_exact' instead");
-
-                double log_likelihood_null = mean_denom_in > 0 ? -mean_denom_in + in_sum * log(mean_denom_in) : 0.0;
-                log_likelihood_null += mean_denom_out > 0 ? -mean_denom_out + out_sum * log(mean_denom_out) : 0.0;
-
-                return (log_likelihood_in + log_likelihood_out - log_likelihood_null) * 2;
-            };
-
-            double chi_stat = get_chi_stat(in_sum);
-
-            if (chi_stat < 0) {
-                common::logger->error("Detected likelihood ratio {} < 0", chi_stat);
-                throw std::runtime_error("Test failed");
-            }
-
-            double pval = chi_stat != 0 ? boost::math::cdf(boost::math::complement(dist, chi_stat)) : 1.0;
-
-            double max_chi_stat = std::max(get_chi_stat(0),
-                                           get_chi_stat(std::min(total_sum, static_cast<int64_t>(in_kmers))));
-            assert(max_chi_stat >= chi_stat);
-
-            double pmin = max_chi_stat != 0 ? boost::math::cdf(boost::math::complement(dist, max_chi_stat)) : 1.0;
-
             return std::make_tuple(pval,
                                    static_cast<double>(in_sum),
                                    static_cast<double>(out_sum) / out_kmers * in_kmers,
@@ -659,20 +596,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
-
         common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
         double in_kmers_adj = 0;
         double out_kmers_adj = 0;
-        double in_kmers_adj_exp = 0;
-        double out_kmers_adj_exp = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
-            double exp_size = sums[j] * nb_params[j].second / target_p;
-            if (groups[j]) {
-                out_kmers_adj_exp += exp_size;
-            } else {
-                in_kmers_adj_exp += exp_size;
-            }
             for (const auto &[k, m] : count_maps[j]) {
                 const auto &[v, c] = m;
                 if (groups[j]) {
@@ -683,7 +611,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        common::logger->trace("Expected Scaled Totals: in: {}\tout: {}", in_kmers_adj_exp, out_kmers_adj_exp);
         common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers_adj, out_kmers_adj);
 
         compute_pval = [&,r_in,r_out,count_maps,in_kmers_adj,out_kmers_adj](const auto &row) {
@@ -850,175 +777,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                    static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj,
                                    std::min(p_min, 1.0));
         };
-    } else if (config.test_type == "brunnermunzel" || config.test_type == "mannwhitney_u") {
-        size_t nx = num_labels_in;
-        size_t ny = num_labels_out;
-
-        compute_pval = [&,nx,ny](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
-
-            double p_min = 1.1;
-            auto norm = boost::math::normal_distribution();
-            auto rankdata = [&](const std::vector<double> &c) {
-                auto c_p = c;
-                std::sort(c_p.begin(), c_p.end());
-                tsl::hopscotch_map<double, std::pair<size_t, size_t>> counts;
-                for (size_t i = 0; i < c_p.size(); ++i) {
-                    auto &[rank_sum, nranks] = counts[c_p[i]];
-                    rank_sum += i + 1;
-                    ++nranks;
-                }
-
-                std::vector<double> ranks;
-                ranks.reserve(c.size());
-                for (double v : c) {
-                    ranks.emplace_back(static_cast<double>(counts[v].first)/counts[v].second);
-                }
-
-                return std::make_pair(std::move(ranks), std::move(counts));
-            };
-
-            std::vector<double> in_counts;
-            in_counts.reserve(num_labels_in);
-            std::vector<double> out_counts;
-            out_counts.reserve(num_labels_out);
-
-            for (const auto &[j, c] : row) {
-                auto &bucket = groups[j] ? out_counts : in_counts;
-                bucket.emplace_back(static_cast<double>(c) / medians[j]);
-            }
-
-            in_counts.resize(num_labels_in);
-            out_counts.resize(num_labels_out);
-
-            double p = 0.0;
-
-            std::vector<double> c;
-            c.reserve(in_counts.size() + out_counts.size());
-            std::copy(in_counts.begin(), in_counts.end(), std::back_inserter(c));
-            std::copy(out_counts.begin(), out_counts.end(), std::back_inserter(c));
-            auto [rankc, countsc] = rankdata(c);
-            double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
-            double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
-
-            if (config.test_type == "bunnermunzel") {
-                if (nx > 1 && ny > 1) {
-                    auto get_p = [&]() {
-                        double rankcx_mean = boost::math::statistics::mean(rankc.begin(), rankc.begin() + nx);
-                        double rankcy_mean = boost::math::statistics::mean(rankc.begin() + nx, rankc.end());
-                        auto [rankx, countsx] = rankdata(in_counts);
-                        auto [ranky, countsy] = rankdata(out_counts);
-                        double rankx_mean = boost::math::statistics::mean(rankx.begin(), rankx.end());
-                        double ranky_mean = boost::math::statistics::mean(ranky.begin(), ranky.end());
-
-                        double Sx = 0;
-                        for (size_t i = 0; i < nx; ++i) {
-                            Sx += std::pow(rankc[i] - rankx[i] - rankcx_mean + rankx_mean, 2.0);
-                        }
-                        Sx /= nx - 1;
-                        double Sy = 0;
-                        for (size_t i = 0; i < ny; ++i) {
-                            Sy += std::pow(rankc[nx + i] - ranky[i] - rankcy_mean + ranky_mean, 2.0);
-                        }
-                        Sy /= ny - 1;
-
-                        double wbfn = (rankcy_mean - rankcx_mean) * nx * ny;
-                        wbfn /= std::sqrt(Sx * nx + Sy * ny) * (nx + ny);
-
-                        double df_numer = std::pow(Sx * nx + Sy * ny, 2.0);
-                        double df_denom = std::pow(Sx * nx, 2.0) / (nx - 1) + std::pow(Sy * ny, 2.0) / (ny - 1);
-
-                        if (df_denom != 0) {
-                            wbfn = abs(wbfn);
-                            double df = df_numer / df_denom;
-                            boost::math::students_t dist(df);
-                            return 2 * boost::math::cdf(boost::math::complement(dist, wbfn));
-                        }
-
-                        return 0.0;
-                    };
-
-                    p = get_p();
-
-                    std::sort(rankc.begin(), rankc.end());
-                    std::copy(rankc.begin(), rankc.begin() + nx, in_counts.begin());
-                    std::copy(rankc.begin() + nx, rankc.end(), out_counts.begin());
-                    p_min = get_p();
-
-                    std::sort(rankc.begin(), rankc.end(), std::greater<double>());
-                    std::copy(rankc.begin(), rankc.begin() + nx, in_counts.begin());
-                    std::copy(rankc.begin() + nx, rankc.end(), out_counts.begin());
-                    p_min = std::min(p_min, get_p());
-                } else {
-                    p_min = 0.0;
-                }
-            } else {
-                // https://datatab.net/tutorial/mann-whitney-u-test
-                double u1_f = nx * ny + static_cast<double>(nx * (nx + 1))/2;
-                double u2_f = nx * ny + static_cast<double>(ny * (ny + 1))/2;
-                auto get_u = [&]() {
-                    double rankcx_sum = std::accumulate(rankc.begin(), rankc.begin() + nx, double(0.0));
-                    double rankcy_sum = std::accumulate(rankc.begin() + nx, rankc.end(), double(0.0));
-                    double u1 = u1_f - rankcx_sum;
-                    double u2 = u2_f - rankcy_sum;
-                    return std::min(u1, u2);
-                };
-
-                double u = get_u();
-
-                if (nx + ny < 20) {
-                    std::sort(rankc.begin(), rankc.end());
-                    size_t num_better = 0;
-                    size_t num_max = 0;
-                    double max_u = -std::numeric_limits<double>::infinity();
-                    while (std::next_permutation(rankc.begin(), rankc.end())) {
-                        double cur_u = get_u();
-                        num_better += (cur_u >= u);
-                        if (cur_u > max_u) {
-                            max_u = cur_u;
-                            num_max = 1;
-                        } else if (cur_u == max_u) {
-                            ++num_max;
-                        }
-                    }
-
-                    p = exp(log(static_cast<double>(num_better)) - lgamma(nx + ny + 1)) * 2.0;
-                    p_min = exp(log(static_cast<double>(num_max)) - lgamma(nx + ny + 1)) * 2.0;
-                } else {
-                    // normal approximation
-                    if (!nx || !ny) {
-                        p = 1.0;
-                        p_min = 1.0;
-                    } else {
-                        double n = nx + ny;
-                        double mu = static_cast<double>(nx * ny) / 2;
-                        double corr = 0;
-                        for (const auto &[val,rks] : countsc) {
-                            const auto &[rk, c] = rks;
-                            if (c > 1)
-                                corr += c * c * c - c;
-                        }
-                        double sd = sqrt(static_cast<double>(nx * ny) / n / (n - 1)) * sqrt((n * n * n - n)/12 - corr / 12);
-
-                        auto get_p = [&](double u) {
-                            double z = abs((u - mu) / sd);
-
-                            // hack just in case sd == 0
-                            return u != mu ? boost::math::cdf(boost::math::complement(norm, z)) * 2 : 1.0;
-                        };
-
-                        p = get_p(u);
-                        std::sort(rankc.begin(), rankc.end());
-                        p_min = get_p(get_u());
-                        std::sort(rankc.begin(), rankc.end(), std::greater<double>());
-                        p_min = std::min(p_min, get_p(get_u()));
-                    }
-                }
-            }
-
-            return std::make_tuple(p, rankcx_mean, rankcy_mean, p_min);
-        };
     } else if (config.test_type == "notest") {
         compute_pval = [&](const auto &row) {
             if (row.empty())
@@ -1073,12 +831,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 for (size_t j = 0; j < groups.size(); ++j) {
                     const auto &col = *columns_all[j];
                     const auto &col_vals = *column_values_all[j];
-                    if (uint64_t r = col.conditional_rank1(row_i)) {
+                    if (uint64_t r = col.conditional_rank1(row_i))
                         row.emplace_back(j, col_vals[r - 1]);
-                        // uint64_t raw_c = col_vals[r - 1];
-                        // if (uint64_t c = adjust_count(raw_c, j, row_i))
-                            // row.emplace_back(j, c);
-                    }
                 }
             }
 
@@ -1149,7 +903,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 size_t count_out = 0;
                 for (const auto &[j, raw_c] : raw_row) {
                     if (uint64_t c = raw_c) {
-                    // if (uint64_t c = adjust_count(raw_c, j, row_i)) {
                         row.emplace_back(j, c);
                         if (groups[j]) {
                             ++count_out;
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index dfa845528e..aa20c6262a 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -179,11 +179,8 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
             std::vector<std::string> test_types = {
                 "poisson_exact",
                 "nbinom_exact",
-                "poisson_likelihoodratio",
                 "fisher",
                 "cmh",
-                "mannwhitney_u",
-                "brunnermunzel"
             };
 
             for (const auto &test_type : test_types) {

From a57f3c00e4a55ee9ba38fa64512007e5c0632726 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 11:09:19 +0200
Subject: [PATCH 188/410] more cleanup

---
 metagraph/src/cli/assemble.cpp                |  118 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 1379 -----------------
 .../src/graph/annotated_graph_algorithm.hpp   |   63 +-
 .../representation/base/sequence_graph.cpp    |    4 -
 .../src/graph/representation/masked_graph.hpp |    2 -
 5 files changed, 8 insertions(+), 1558 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index d844f0e847..85e65474df 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -44,18 +44,10 @@ void check_labels(const tsl::hopscotch_set<std::string> &label_set,
 
 DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     DifferentialAssemblyConfig diff_config;
-    diff_config.label_mask_in_kmer_fraction = experiment.get("in_min_fraction", 1.0).asDouble();
-    diff_config.label_mask_in_unitig_fraction = experiment.get("unitig_in_min_fraction", 0.0).asDouble();
-    diff_config.label_mask_out_kmer_fraction = experiment.get("out_max_fraction", 0.0).asDouble();
-    diff_config.label_mask_out_unitig_fraction = experiment.get("unitig_out_max_fraction", 1.0).asDouble();
-    diff_config.label_mask_other_unitig_fraction = experiment.get("unitig_other_max_fraction", 1.0).asDouble();
-    diff_config.add_complement = experiment.get("forward_and_reverse", false).asBool();
     diff_config.count_kmers = experiment.get("count_kmers", false).asBool();
     diff_config.family_wise_error_rate = experiment.get("family_wise_error_rate", 0.05).asDouble();
     diff_config.test_by_unitig = experiment.get("test_by_unitig", false).asBool();
-    diff_config.evaluate_assembly = experiment.get("evaluate_assembly", false).asBool();
     diff_config.test_type = experiment.get("test_type", "nbinom_exact").asString();
-    diff_config.filter = experiment.get("filter", true).asBool();
     diff_config.clean = experiment.get("clean", false).asBool();
     diff_config.min_count = experiment.get("min_count", 0).asUInt64();
     diff_config.min_recurrence = experiment.get("min_recurrence", 1).asUInt64();
@@ -63,106 +55,13 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
     diff_config.min_out_recurrence = experiment.get("min_out_recurrence", 0).asUInt64();
     diff_config.max_in_recurrence = experiment.get("max_in_recurrence", std::numeric_limits<uint64_t>::max()).asUInt64();
     diff_config.max_out_recurrence = experiment.get("max_out_recurrence", std::numeric_limits<uint64_t>::max()).asUInt64();
-    diff_config.num_tests = experiment.get("num_tests", 0).asInt();
     diff_config.assemble_shared = experiment.get("assemble_shared", false).asBool();
-
-
-    logger->trace("Per-kmer mask in fraction:\t\t{}", diff_config.label_mask_in_kmer_fraction);
-    logger->trace("Per-unitig mask in fraction:\t\t{}", diff_config.label_mask_in_unitig_fraction);
-    logger->trace("Per-kmer mask out fraction:\t\t{}", diff_config.label_mask_out_kmer_fraction);
-    logger->trace("Per-unitig mask out fraction:\t\t{}", diff_config.label_mask_out_unitig_fraction);
-    logger->trace("Per-unitig other label fraction:\t{}", diff_config.label_mask_other_unitig_fraction);
-    logger->trace("Include reverse complements:\t\t{}", diff_config.add_complement);
-    logger->trace("Include k-mer counts if preseadd_complementnt in the graph:\t\t{}", diff_config.count_kmers); // if node weights (k-mer count) are present in the graph and should be included in the differential assembly.
-    logger->trace("Family wise error rate for the Bonferroni test:\t{}", diff_config.family_wise_error_rate);
-    logger->trace("count_kmers:\t{}", diff_config.count_kmers);
-    logger->trace("Test type:\t{}", diff_config.test_type);
-    logger->trace("Filter:\t{}", diff_config.filter);
-    logger->trace("Min count:\t{}", diff_config.min_count);
-
-
-    // TODO get the folder with the annotation columns from the .json or command, because it is redundant to have to give all of them as an input.
-            //  How to? For all the in_labels and out_labels, collect the folder/<filename>.column.annodbg file and push them in the list of config->infbase_annotators.
-//    config->enumerate_out_sequences;
-//    config->infbase_annotators = ;
-    // but you would still need to match them?
     return diff_config;
 }
 
 typedef std::function<void(const graph::DeBruijnGraph&,
                            const std::string& /* header */)> CallMaskedGraphHeader;
 
-void call_masked_graphs(const AnnotatedDBG &anno_graph,
-                        Config *config,
-                        const CallMaskedGraphHeader &callback) {
-    if (!std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()).get())
-        throw std::runtime_error("Masking only supported for DeBruijnGraph");
-
-    assert(!config->assembly_config_file.empty());
-
-    std::ifstream fin(config->assembly_config_file);
-    if (!fin.good())
-        throw std::iostream::failure("Failed to read assembly config JSON from " + config->assembly_config_file);
-
-    size_t num_threads = std::max(1u, get_num_threads());
-
-    Json::Value diff_json;
-    fin >> diff_json;
-
-    for (const Json::Value &group : diff_json["groups"]) {
-        tsl::hopscotch_set<std::string> shared_foreground_labels;
-        tsl::hopscotch_set<std::string> shared_background_labels;
-
-        if (group["shared_labels"]) {
-            for (const Json::Value &in_label : group["shared_labels"]["in"]) {
-                shared_foreground_labels.emplace(in_label.asString());
-            }
-
-            for (const Json::Value &out_label : group["shared_labels"]["out"]) {
-                shared_background_labels.emplace(out_label.asString());
-            }
-
-            check_labels(shared_foreground_labels, anno_graph);
-            check_labels(shared_background_labels, anno_graph);
-        }
-
-        if (!group["experiments"])
-            throw std::runtime_error("Missing experiments in group");
-
-        for (const Json::Value &experiment : group["experiments"]) {
-            tsl::hopscotch_set<std::string> foreground_labels;
-            tsl::hopscotch_set<std::string> background_labels;
-
-            DifferentialAssemblyConfig diff_config = diff_assembly_config(experiment);
-            diff_config.outfbase = config->outfbase;
-
-            std::string exp_name = experiment["name"].asString()
-                                    + (config->enumerate_out_sequences ? "." : "");
-
-            for (const Json::Value &in_label : experiment["in"]) {
-                foreground_labels.emplace(in_label.asString());
-            }
-
-            for (const Json::Value &out_label : experiment["out"]) {
-                background_labels.emplace(out_label.asString());
-            }
-
-            check_labels(foreground_labels, anno_graph);
-            check_labels(background_labels, anno_graph);
-
-            logger->trace("Running assembly: {}", exp_name);
-
-            callback(*mask_nodes_by_label(anno_graph,
-                                          foreground_labels,
-                                          background_labels,
-                                          shared_foreground_labels,
-                                          shared_background_labels,
-                                          diff_config, num_threads,
-                                          config->parallel_nodes), exp_name);
-        }
-    }
-}
-
 void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         Config *config,
                         const CallMaskedGraphHeader &callback) {
@@ -184,10 +83,6 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     fin >> diff_json;
 
     for (const Json::Value &group : diff_json["groups"]) {
-        if (group["shared_labels"]) {
-            throw std::runtime_error("Shared labels not supported with multiple annotation files");
-        }
-
         if (!group["experiments"])
             throw std::runtime_error("Missing experiments in group");
 
@@ -279,7 +174,6 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         graph_ptr, std::make_unique<bitmap_vector>(std::move(mask)), true,
                         graph_ptr->get_mode() == DeBruijnGraph::PRIMARY ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
                     );
-                    masked_graph_null->likelihood_ratios = std::move(masked_graph->likelihood_ratios);
                     callback(*masked_graph_null, exp_name + "shared.");
                 }
             }
@@ -311,7 +205,7 @@ int assemble(Config *config) {
 
     logger->trace("Graph loaded in {} sec", timer.elapsed());
 
-    if (config->infbase_annotators.size() || files.size() > 1) {
+    if (config->infbase_annotators.size() > 1 || files.size() > 1) {
         logger->trace("Generating masked graphs...");
         std::mutex write_mutex;
         size_t num_threads = std::max(1u, get_num_threads());
@@ -350,14 +244,8 @@ int assemble(Config *config) {
             }
         };
 
-        if (files.size() > 1 or config->infbase_annotators.size() > 1) { // Checks the infbase_annotators in the case that annotation columns are provided in the commandline using '-a' or '--annotator'
-            config->fnames.erase(config->fnames.begin(), config->fnames.begin() + 1);
-            call_masked_graphs(graph, config, graph_callback); // Does NOT support kmer-counts assembly
-        } else {
-            config->infbase = files.at(0);
-            auto anno_graph = initialize_annotated_dbg(graph, *config);
-            call_masked_graphs(*anno_graph, config, graph_callback); // Does support kmer-counts assembly
-        }
+        config->fnames.erase(config->fnames.begin(), config->fnames.begin() + 1);
+        call_masked_graphs(graph, config, graph_callback);
 
         return 0;
     }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 7589444ed8..65cdacca40 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1116,1385 +1116,6 @@ mask_nodes_by_label_dual<sdsl::int_vector<>, sdsl::int_vector_buffer<64>>(std::s
                          size_t,
                          bool);
 
-inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
-    int n;
-    std::unique_ptr<uint64_t, decltype(std::free)*> r {
-        sdust(0, (const uint8_t*)s.data(), s.size(), T, W, &n),
-        std::free
-    };
-    return n > 0;
-}
-
-
-typedef std::function<size_t()> LabelCountCallback;
-
-uint64_t atomic_fetch(const sdsl::int_vector<> &vector,
-                      uint64_t i,
-                      std::mutex &backup_mutex,
-                      int mo) {
-    const uint8_t width = vector.width();
-    if (width == 64)
-        return __atomic_load_n(vector.data() + i, mo);
-
-    if (width == 32)
-        return __atomic_load_n((uint32_t*)vector.data() + i, mo);
-
-    if (width == 16)
-        return __atomic_load_n((uint16_t*)vector.data() + i, mo);
-
-    if (width == 8)
-        return __atomic_load_n((uint8_t*)vector.data() + i, mo);
-
-    std::lock_guard<std::mutex> lock(backup_mutex);
-    return vector[i];
-}
-
-uint64_t atomic_fetch_and_add(sdsl::int_vector<> &vector,
-                              uint64_t i,
-                              uint64_t val,
-                              std::mutex &backup_mutex,
-                              int mo) {
-    const uint8_t width = vector.width();
-    if (width == 64)
-        return __atomic_fetch_add(vector.data() + i, val, mo);
-
-    if (width == 32)
-        return __atomic_fetch_add((uint32_t*)vector.data() + i, val, mo);
-
-    if (width == 16)
-        return __atomic_fetch_add((uint16_t*)vector.data() + i, val, mo);
-
-    if (width == 8)
-        return __atomic_fetch_add((uint8_t*)vector.data() + i, val, mo);
-
-    std::lock_guard<std::mutex> lock(backup_mutex);
-    uint64_t old_val = vector[i];
-    vector[i] += val;
-    return old_val;
-}
-
-
-/**
- * Return an int_vector<>, bit_vector of lengths anno_graph.get_graph().max_index() * 2
- * and anno_graph.get_graph().max_index(), respectively, and a bit_vector of length
- * equal to the total number of labels.
- * For an index i, the int_vector at indices 2*i and 2*i + 1 represent the
- * number of labels in labels_in and labels_out which the k-mer of index i is
- * annotated with, respectively. The width of the int_vector<> is computed to be
- * wide enough to contain counts up to num_labels.
- * The returned bit_vector is a k-mer mask indicating those k-mers annotated
- * with at least one in-label. If add_out_labels_to_mask is true, then it also indicates
- * which those k-mers with at least one out-label.
- * The second bit vector indicates which of the labels correspond to an "other"
- * label (i.e., not in- or out-).
- */
-
-
-using ValueCallback = std::function<void(uint64_t /* row index */, uint64_t /* row value */)>;
-using ValueGenerator = std::function<void(const ValueCallback&)>;
-using ColumnCallback = std::function<void(uint64_t /* offset */, const Label& /* annotation column */, const ValueGenerator&)>;
-using ColumnGenerator = std::function<void(const ColumnCallback&)>;
-
-template <typename T>
-std::string outstring(std::vector<T> out_array);
-template <typename T>
-std::string outstring(std::vector<T> out_array){
-    std::string out = "";
-    for (auto &item: out_array) { std::string x = std::to_string(item); out = out + x + ", "; }
-    return out;
-}
-
-void kmer_distribution_table(const ColumnGenerator &generate_columns,
-                             const tsl::hopscotch_set<Label> &labels_in,
-                             const tsl::hopscotch_set<Label> &labels_out,
-                             size_t max_index,
-                             MaskedDeBruijnGraph &masked_graph, // std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                             const DifferentialAssemblyConfig &config,
-                             size_t num_threads);  // For kmer distribution.
-
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>>
-construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
-                                  const std::vector<std::string> &files,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  const DifferentialAssemblyConfig &config,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  size_t num_threads,
-                                  bool add_out_labels_to_mask);
-
-// Regions of a graph mask which should be kept (i.e., masked in)
-typedef std::vector<std::pair<size_t, size_t>> Intervals;
-
-// Returns a vector of kept regions given a unitig and its corresponding path
-typedef std::function<Intervals(const std::string&, const std::vector<node_index>&)> GetKeptIntervals;
-
-// Compare the first element of two pairs (used for sorting)
-bool sortByPValue(const std::pair<int, double>& a, const std::pair<int, double>& b) {
-    return a.first < b.first; // Sort in ascending order of p-values
-}
-
-// Function for calculating
-// the median
-template <class Container>
-double findMedian(const Container &a,
-                    int threshold) {
-    logger->trace("non zero kmers: {}", a.size());
-    std::vector<double> a_filtered;
-    for (size_t i = 0; i < a.size(); i++){
-        int value = a[i];
-        if (value > threshold){
-            a_filtered.push_back(value);
-        }
-    }
-    // auto it = std::copy_if(a.begin(), a.end(), std::back_inserter(a_filtered), [&](int i) {
-    //     return (i > threshold);
-    // });
-    logger->trace("kmers above threshold: {}", a_filtered.size());
-    int n = a_filtered.size();
-    // If size of the arr[] is even
-    if (n % 2 == 0) {
-
-        // Applying nth_element
-        // on n/2th index
-        std::nth_element(a_filtered.begin(),
-                    a_filtered.begin() + n / 2,
-                    a_filtered.end());
-
-        // Applying nth_element
-        // on (n-1)/2 th index
-        std::nth_element(a_filtered.begin(),
-                    a_filtered.begin() + (n - 1) / 2,
-                    a_filtered.end());
-
-        // Find the average of value at
-        // index N/2 and (N-1)/2
-        return (double)(a_filtered[(n - 1) / 2]
-                        + a_filtered[n / 2])
-               / 2.0;
-    }
-
-    // If size of the arr[] is odd
-    else {
-
-        // Applying nth_element
-        // on n/2
-        std::nth_element(a_filtered.begin(),
-                    a_filtered.begin() + n / 2,
-                    a_filtered.end());
-
-        // Value at index (N/2)th
-        // is the median
-        return (double)a_filtered[n / 2];
-    }
-}
-
-bool filtering_row(std::vector<double> in_counts_non_zero,
-                    std::vector<double> out_counts_non_zero,
-                    int total_samples,
-                    std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                    int64_t row_id);
-
-std::shared_ptr<MaskedDeBruijnGraph> brunner_munzel_test(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                                                        std::shared_ptr<MaskedDeBruijnGraph> masked_graph,
-                                                        const std::vector<std::string> &files,
-                                                        const tsl::hopscotch_set<Label> &labels_in,
-                                                        const tsl::hopscotch_set<Label> &labels_out,
-                                                        const DifferentialAssemblyConfig &config,
-                                                        size_t total_unitigs,
-                                                        std::vector<double> medians) {
-
-    const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
-    sdsl::bit_vector mask = in_mask;
-    // brunner munzel test
-    // call kmer matrix per row
-    std::vector<std::unique_ptr<bit_vector>> columns_all;
-    std::vector<std::unique_ptr<sdsl::int_vector_buffer<>>> column_values_all;
-    std::vector<bool> column_label;
-    annot::ColumnCompressed<>::load_columns_and_values(files,
-        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector_buffer<> && column_values) {
-            columns_all.push_back(std::move(column));
-            column_values_all.push_back(std::make_unique<sdsl::int_vector_buffer<>>(std::move(column_values)));
-            if (labels_in.find(label) != labels_in.end()) // if the label is in the in labels set column_label to 1, else 0
-                column_label.push_back(1);
-            else if (labels_out.find(label) != labels_out.end())
-                column_label.push_back(0);
-            else throw std::runtime_error("Label not found in labels_in or labels_out");
-        }
-    );
-    int64_t num_tests = 0;
-    int64_t kept_nodes = 0;
-    // calculate the p-value for the brunner munzel test
-    auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, 0, 0, 0);
-    utils::call_rows<std::unique_ptr<bit_vector>,
-                     std::unique_ptr<sdsl::int_vector_buffer<>>,
-                     std::vector<std::pair<uint64_t, uint8_t>>>(columns_all, column_values_all,
-                                                                [&](uint64_t row_id, const auto &row, size_t) {
-                        // auto kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
-                        if (row.size() == 0) { // if the kmer is not present in any sample
-                            mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
-                            return;
-                        }
-                        else{
-                            // populate the vector for the brunner munzel test with the kmer counts for each sample
-                            std::vector<double> in_counts;
-                            std::vector<double> out_counts;
-                            for (size_t i = 0; i < row.size(); i++){
-                                double median = medians[row[i].first];
-                                if (median == 0) median = 1; // if the median is 0, set it to 1 (to avoid division by 0)
-                                if (column_label[row[i].first])
-                                    in_counts.push_back(row[i].second/median);
-                                else
-                                    out_counts.push_back(row[i].second/median);
-                            }
-                            // add 0 to in_counts and out_counts if the kmer is not present in the sample
-                            if (filtering_row(in_counts, out_counts, files.size(), graph_ptr, row_id)) {
-                                num_tests++;
-                                if (in_counts.size() < labels_in.size())
-                                    in_counts.resize(labels_in.size(), 0);
-                                if (out_counts.size() < labels_out.size())
-                                    out_counts.resize(labels_out.size(), 0);
-                                // run test and adjust masked graph accordingly
-                                auto [keep, pvalue] = statistical_model.brunner_munzel_test(in_counts, out_counts);
-                                if (keep == false)
-                                    mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
-                                else{
-                                    kept_nodes++;
-                                }
-                            }
-                            else{
-                                mask[AnnotatedDBG::anno_to_graph_index(row_id)] = false;
-                            }
-
-                        }
-    });
-    logger->trace("number of tests with 0 var: {}", statistical_model.var_0);
-    logger->trace("number of tests: {}", num_tests);
-    masked_graph->set_mask(new bitmap_vector(std::move(mask)));
-    logger->trace("number of kept nodes: {}", kept_nodes);
-    return masked_graph;
-}
-// returns true if the kmer passes the filtering criteria
-bool filtering_row(std::vector<double> in_counts_non_zero,
-                    std::vector<double> out_counts_non_zero,
-                    int total_samples,
-                    std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                    int64_t row_id){
-    // minimum present in 20% of samples
-    bool is_present_in_20_percent = (in_counts_non_zero.size() + out_counts_non_zero.size()) > 0.2 * double(total_samples);
-    if (is_present_in_20_percent == false)
-        return false;
-    // present more in in than out
-    bool is_present_more_in_foreground = in_counts_non_zero.size() > out_counts_non_zero.size();
-    // low complexity filter
-    if (is_present_more_in_foreground == false)
-        return false;
-    std::string kmer_string = graph_ptr->get_node_sequence(AnnotatedDBG::anno_to_graph_index(row_id));
-    bool high_complexity =  !is_low_complexity(kmer_string);
-    return (high_complexity);
-}
-std::vector<double> filtering_column(const std::vector<std::string> &files,
-               const tsl::hopscotch_set<Label> &labels_in,
-               const tsl::hopscotch_set<Label> &labels_out,
-               const DifferentialAssemblyConfig &config,
-               const ColumnCallback &column_callback
-               ) {
-    // median normalisation
-    // threshold of 2
-    double threshold = config.min_count; // TODO get from config
-    assert(files.size() > 0);
-    // generate a vector of the total number of kmers per sample for each group (in and out)
-    std::vector<double> medians;
-    logger->trace("filtering_column");
-    annot::ColumnCompressed<>::load_columns_and_values(files,
-        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector_buffer<> && column_values) { // goes through all files in a group
-            uint64_t max_col_width = std::pow(2, 16); // max value for int16_t
-            column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0); // at least one kmer in sample
-                assert(column->num_set_bits() > 0); // at least one kmer present in sample
-                assert(column->num_set_bits() == column_values.size()); // same number of kmers in column as in column_values
-                call_ones(*column, [&](uint64_t i) {
-                    uint64_t column_value = column_values[column->rank1(i)-1];
-                    if (column_value > threshold){
-                        column_value = std::min(column_value, max_col_width); // normalize value
-                        value_callback(i, column_value);
-                    }
-                });
-                double median = findMedian(std::move(column_values), threshold);
-                medians.push_back(median);
-                logger->trace("median: {}", median);
-            });
-        }
-    );
-    return medians;
-}
-
-// Assemble unitigs from the masked graph, then use get_kept_intervals to generate
-// regions which should be masked in. Update the graph mask accordingly.
-void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
-                                   const GetKeptIntervals &get_kept_intervals,
-                                   size_t num_threads);
-
-// Given an initial mask and counts, generate a masked graph. If add_complement
-// is true, then add the reverse complements of all nodes to the graph as well.
-std::shared_ptr<MaskedDeBruijnGraph>
-make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                          sdsl::int_vector<> &counts,
-                          sdsl::bit_vector&& mask,
-                          bool add_complement,
-                          size_t num_threads);
-
-// Helper function for calls in interface
-std::shared_ptr<MaskedDeBruijnGraph>
-mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                    const AnnotatedDBG *anno_graph,
-                    sdsl::int_vector<>&& counts,
-                    sdsl::bit_vector&& mask,
-                    sdsl::bit_vector&& other_mask,
-                    size_t num_labels,
-                    const std::vector<std::string> &files,
-                    const tsl::hopscotch_set<Label> &labels_in,
-                    const tsl::hopscotch_set<Label> &labels_out,
-                    const tsl::hopscotch_set<Label> &labels_in_round2,
-                    const tsl::hopscotch_set<Label> &labels_out_round2,
-                    std::tuple<size_t, size_t> total_kmers,
-                    const DifferentialAssemblyConfig &config,
-                    size_t num_threads,
-                    std::vector<double> medians);
-
-std::shared_ptr<MaskedDeBruijnGraph> // This version of mask_nodes_by_label is for when the columns are already loaded.
-mask_nodes_by_label(const AnnotatedDBG &anno_graph,
-                    const tsl::hopscotch_set<Label> &labels_in,
-                    const tsl::hopscotch_set<Label> &labels_out,
-                    const tsl::hopscotch_set<Label> &labels_in_round2,
-                    const tsl::hopscotch_set<Label> &labels_out_round2,
-                    const DifferentialAssemblyConfig &config,
-                    size_t num_threads,
-                    size_t num_parallel_files) {
-    num_parallel_files = std::min(num_threads, num_parallel_files);
-
-    auto graph_ptr = std::static_pointer_cast<const DeBruijnGraph>(
-        anno_graph.get_graph_ptr()
-    );
-
-    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
-    bool unitig_mode = check_other || labels_in_round2.size() || labels_out_round2.size()
-            || config.label_mask_in_unitig_fraction != 0.0
-            || config.label_mask_out_unitig_fraction != 1.0
-            || config.label_mask_other_unitig_fraction != 1.0;
-
-    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
-        && (config.add_complement || unitig_mode);
-    logger->trace("add_complement: {}", add_complement);
-
-    logger->trace("Generating initial mask");
-
-    auto count_vector = construct_diff_label_count_vector(
-        [&](const ColumnCallback &column_callback) {
-            if (config.count_kmers) {
-                throw std::runtime_error("AnnotatedDBG with counts not supported yet");
-            } else {
-                size_t offset = 0;
-                for (const auto &label : labels_in) {
-                    column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                        anno_graph.call_annotated_nodes(label, [&](node_index i) {
-                            i = AnnotatedDBG::graph_to_anno_index(i);
-                            value_callback(i, 1);
-                        });
-                    });
-                    ++offset;
-                }
-                for (const auto &label : labels_out) {
-                    column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                        anno_graph.call_annotated_nodes(label, [&](node_index i) {
-                            i = AnnotatedDBG::graph_to_anno_index(i);
-                            value_callback(i, 1);
-                        });
-                    });
-                    ++offset;
-                }
-            };
-        },
-        { }, labels_in, labels_out, config, graph_ptr->max_index(),
-        std::max(labels_in.size(), labels_out.size()), num_parallel_files,
-        add_complement
-    );
-
-    auto &[counts, init_mask, other_labels, total_kmers] = count_vector; //ERROR  only 3 names provided for structured binding, std::vector<long unsigned int, std::allocator<long unsigned int> > >’ decomposes into 4 elements
-    sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
-    return mask_nodes_by_label(graph_ptr, &anno_graph,
-                               std::move(counts), std::move(init_mask),
-                               std::move(other_mask),
-                               anno_graph.get_annotator().num_labels(),
-                               {},
-                               labels_in, labels_out,
-                               labels_in_round2, labels_out_round2, total_kmers,
-                               config, num_threads, {});
-}
-
-std::shared_ptr<DeBruijnGraph>
-mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr, // Myrthe: this version i don't need to change at first
-                    const std::vector<std::string> &files,
-                    const tsl::hopscotch_set<Label> &labels_in,
-                    const tsl::hopscotch_set<Label> &labels_out,
-                    const DifferentialAssemblyConfig &config,
-                    size_t num_threads,
-                    size_t num_parallel_files) {
-    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
-    bool unitig_mode = check_other
-            || config.label_mask_in_unitig_fraction != 0.0
-            || config.label_mask_out_unitig_fraction != 1.0
-            || config.label_mask_other_unitig_fraction != 1.0;
-
-    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
-        && (config.add_complement || unitig_mode);
-    logger->trace("add_complement: {}", add_complement);
-    logger->trace("Generating initial mask");
-    bool filter = config.filter;
-    std::vector<double> medians;
-    auto count_vector = construct_diff_label_count_vector( // called for each group (in and out)
-            [&](const ColumnCallback &column_callback) {
-            if (config.count_kmers) {
-                if (filter){
-                    medians = filtering_column(files, labels_in, labels_out, config, column_callback);
-                }
-                else{
-                    assert(files.size() > 0);
-                    // generate a vector of the total number of kmers per sample for each group (in and out)
-                    annot::ColumnCompressed<>::load_columns_and_values(files,
-                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector_buffer<> && column_values) { // goes through all files in a group
-                            int max_col_width = std::pow(2, 16); // max value for int16_t
-                            column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                                assert(std::accumulate(column_values.begin(), column_values.end(), 0) > 0); // at least one kmer in sample
-                                assert(column->num_set_bits() > 0); // at least one kmer present in sample
-                                assert(column->num_set_bits() == column_values.size()); // same number of kmers in column as in column_values
-                                call_ones(*column, [&](uint64_t i) {
-                                    uint64_t column_value = column_values[column->rank1(i)-1];
-                                    column_value = std::min((int) column_value, max_col_width); // normalize value
-                                    if (column_value > config.min_count)
-                                        value_callback(i, column_value);
-                                });
-                            });
-                        }
-                    );
-                }
-            } else {
-                if (!config.clean) {
-                    annot::ColumnCompressed<>::merge_load(files,
-                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column) {
-                            column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                                call_ones(*column, [&](uint64_t i) {
-                                    value_callback(i, 1);
-                                });
-                            });
-                        }
-                    );
-                } else {
-                    annot::ColumnCompressed<>::load_columns_and_values(files,
-                        [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, sdsl::int_vector<>&& column_values) {
-                            column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                                call_ones(*column, [&](uint64_t i) {
-                                    auto val = column_values[column->rank1(i) - 1];
-                                    if (val > config.min_count)
-                                        value_callback(i, 1);
-                                });
-                            });
-                            // size_t min_count = config.min_count;
-
-                            // auto mask = std::make_unique<bitmap_lazy>([&](node_index node) {
-                            //     if (!node)
-                            //         return false;
-
-                            //     return (*column)[AnnotatedDBG::graph_to_anno_index(node)];
-                            // }, column->size() + 1);
-                            // bool is_primary = (graph_ptr->get_mode() == DeBruijnGraph::PRIMARY);
-                            // std::shared_ptr<DeBruijnGraph> masked
-                            //     = std::make_shared<MaskedDeBruijnGraph>(graph_ptr, std::move(mask), true,
-                            //                            is_primary
-                            //                             ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
-
-                            // std::shared_ptr<CanonicalDBG> wrapped;
-                            // if (is_primary) {
-                            //     wrapped = std::make_shared<CanonicalDBG>(masked);
-                            //     masked = wrapped;
-                            // }
-                            // column_callback(offset, label, [&](const ValueCallback &value_callback) {
-                            //     assert(column->num_set_bits() == column_values.size());
-                            //     // call_ones(*column, [&](uint64_t i) {
-                            //     //     auto val = column_values[column->rank1(i) - 1];
-                            //     //     if (val >= min_count && val < max_count)
-                            //     //         value_callback(i, 1);
-                            //     // });
-                            //     masked->call_unitigs([&](const std::string&, const auto &path) {
-                            //         std::vector<uint64_t> counts;
-                            //         std::vector<uint64_t> rows;
-                            //         counts.reserve(path.size());
-                            //         rows.reserve(path.size());
-                            //         for (node_index node : path) {
-                            //             auto row = AnnotatedDBG::graph_to_anno_index(
-                            //                 is_primary ? wrapped->get_base_node(node) : node
-                            //             );
-                            //             rows.emplace_back(row);
-                            //             counts.emplace_back(column_values[column->rank1(row) - 1]);
-                            //         }
-                            //         std::sort(counts.begin(), counts.end());
-                            //         double median = counts[counts.size() / 2];
-
-                            //         if (counts.size() % 2 == 1)
-                            //             median = (median + counts[counts.size() / 2 - 1])/2;
-
-                            //         if (median >= min_count && median < max_count) {
-                            //             for (auto row : rows) {
-                            //                 value_callback(row, 1);
-                            //             }
-                            //         }
-                            //     });
-                            // });
-                        }
-                    );
-                }
-            }
-        },
-        files, labels_in, labels_out, config, graph_ptr->max_index(),
-        std::max(labels_in.size(), labels_out.size()), num_parallel_files,
-        config.test_by_unitig
-    );
-
-    auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
-    // total_kmers: tuple of in_total_kmers and out_total_kmers (number of kmers in foreground and background)
-    // init_mask: bit_vector of length graph_ptr->max_index() indicating which nodes are in the foreground (size = number of unique kmers total)
-    // counts: int_vector of length graph_ptr->max_index() * 2 indicating the number of in and out labels for each node (size = number of unique kmers total * 2)
-    // other_labels: bit_vector of length num_labels indicating which labels are not in or out labels
-/*
-    if (config.evaluate_assembly){     // Myrthe temporary: evaluate what k-mers overlap
-
-        logger->trace("Evaluate confusion table");
-        assert(counts.size() > 1);
-        assert( std::accumulate(counts.begin(), counts.end(), 0) > 0);
-        int true_positive = 0; int true_negative = 0; int false_positive = 0; int false_negative =0;
-        for (size_t i = 0; i < counts.size() ; i+=2){
-            auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
-            uint64_t true_count = counts[i];
-            uint64_t found_count = counts[i + 1];
-            if (true_count == found_count){
-                true_positive += true_count; // if 0 nothing will be added.
-            }else if (true_count > found_count){
-                false_negative += (true_count - found_count);
-                true_positive += found_count; // add to true_positive
-            }else{false_positive += (found_count - true_count);
-                true_positive += true_count;} // TODO :Why are there no false positives?
-        };
-        // true_negatives: total number of possible k-mers? Total number of k-mers in the original foreground samples from which the result had been calculated ?
-        auto precision = true_positive / ( true_positive - true_negative); // accuracy/precision = Ratio of true positives to total predicted positives.
-        auto recall = true_positive / (true_positive + false_negative);  //recall / sensitivity:  ratio of true positives to total (actual) positives in the data.
-        //auto specificity = true_negative / (true_negative + false_positive); // Specificity is the Ratio of true negatives to total negatives in the data.
-        std::cout<< "precision" << precision << std::endl << std::flush;
-        std::cout<< "recall" << recall << std::endl << std::flush;
-        std::cout << std::accumulate(counts.begin(), counts.end(), 0) << std::flush;
-        assert(true_positive + false_negative + false_positive == std::accumulate(counts.begin(), counts.end(), 0));
-    } // in mask probably only has sequences
-*/
-
-    sdsl::bit_vector other_mask(init_mask.size() * check_other, false);
-    if (check_other && sdsl::util::cnt_one_bits(other_labels)) { // currently not used
-        bool parallel = num_parallel_files > 1;
-        size_t j = 0;
-        std::atomic_thread_fence(std::memory_order_release);
-        for (size_t i = 0; i < files.size(); ++i) {
-            size_t num_labels_per_file = annot::ColumnCompressed<>::read_label_encoder(files[i]).size();
-            if (count_ones(other_labels, j, j + num_labels_per_file)) {
-                annot::ColumnCompressed<>::merge_load({ files[i] },
-                    [&](size_t offset, const auto &, auto&& column) {
-                        auto &[counts, init_mask, other_labels, total_kmers] = count_vector;
-                        if (other_labels[j + offset]) {
-                            call_ones(init_mask, [&](size_t i) { // check how many set bits are also in other labels.
-                                if ((*column)[AnnotatedDBG::graph_to_anno_index(i)])
-                                    set_bit(other_mask.data(), i, parallel, MO_RELAXED);
-                            });
-                        }
-                    },
-                    num_parallel_files
-                );
-            }
-
-            j += num_labels_per_file;
-        }
-        std::atomic_thread_fence(std::memory_order_acquire);
-    }
-
-/*
-    if (config.family_wise_error_rate == 0){  // Temporary code to make k-mer distribution
-        logger->trace("K-mer distribution");
-
-        auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
-                                                      add_complement, num_threads);
-        kmer_distribution_table(
-                [&](const ColumnCallback &column_callback) {
-                    annot::ColumnCompressed<>::load_columns_and_values(files,
-                       [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> && column, auto&& column_values) {
-                           column_callback(offset, label, [&](const ValueCallback &value_callback) {
-
-                               if (config.test_by_unitig == false) {
-                                   call_ones(*column, [&](uint64_t i) {
-                                       value_callback(i, column_values[column->rank1(i)- 1]);
-                                   });
-                               }
-                               else {
-                                   for(size_t i = 0; i < column->size(); i++){
-                                       double value = (*column)[i];
-                                       if (value) value = column_values[column->rank1(i)- 1];
-                                       value_callback(i, value);
-                                   }
-                               }
-                           });
-                       }
-                    );
-                },
-                labels_in, labels_out, graph_ptr->max_index(), *masked_graph, config, num_threads
-        );
-        std::exit(EXIT_SUCCESS);
-    }
-*/
-    auto masked_graph = mask_nodes_by_label(graph_ptr, nullptr,
-                               std::move(counts), std::move(init_mask),
-                               std::move(other_mask),
-                               files.size(),
-                               files,
-                               labels_in, labels_out, {}, {},
-                               total_kmers,
-                               config, num_threads, medians);
-
-    if (masked_graph->get_mode() != DeBruijnGraph::PRIMARY)
-        return masked_graph;
-
-    return std::make_shared<CanonicalDBG>(masked_graph);
-}
-
-// does differential testing
-std::shared_ptr<MaskedDeBruijnGraph> // TODO add all columns and columns label as param
-mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                    const AnnotatedDBG *anno_graph,
-                    sdsl::int_vector<>&& counts,
-                    sdsl::bit_vector&& init_mask,
-                    sdsl::bit_vector&& other_mask,
-                    size_t num_labels,
-                    const std::vector<std::string> &files,
-                    const tsl::hopscotch_set<Label> &labels_in,
-                    const tsl::hopscotch_set<Label> &labels_out,
-                    const tsl::hopscotch_set<Label> &labels_in_round2,
-                    const tsl::hopscotch_set<Label> &labels_out_round2,
-                    std::tuple<size_t, size_t> total_kmers,
-                    const DifferentialAssemblyConfig &config,
-                    size_t num_threads,
-                    std::vector<double> medians) {
-    // in and out counts are stored interleaved in the counts vector
-    logger->trace("aaaaaaaaaaaaaaaaaaaaaA counts {}", counts.size());
-    assert(counts.size() == init_mask.size() * 2);
-
-    bool parallel = num_threads > 1;
-    bool check_other = config.label_mask_other_unitig_fraction != 1.0;
-    bool unitig_mode = check_other || labels_in_round2.size() || labels_out_round2.size()
-            || config.label_mask_in_unitig_fraction != 0.0
-            || config.label_mask_out_unitig_fraction != 1.0
-            || config.label_mask_other_unitig_fraction != 1.0;
-
-    size_t num_in_labels = labels_in.size() + labels_in_round2.size(); // number of in labels before filtering
-    size_t num_out_labels = labels_out.size() + labels_out_round2.size(); // number of out labels before filtering
-
-    bool add_complement = graph_ptr->get_mode() == DeBruijnGraph::CANONICAL
-            && (config.add_complement || unitig_mode);
-    logger->trace("add_complement: {}", add_complement);
-    auto masked_graph = make_initial_masked_graph(graph_ptr, counts, std::move(init_mask),
-                                                  add_complement, num_threads);
-
-    auto mask_or = [&](sdsl::bit_vector &a, const sdsl::bit_vector &b,
-                       const std::vector<node_index> &id_map) {
-        call_ones(b, [&](size_t i) {
-            if (id_map[i])
-                set_bit(a.data(), id_map[i], parallel, MO_RELAXED);
-        });
-    };
-
-    // check all other labels and round 2 labels
-    if (anno_graph && (check_other || labels_in_round2.size() || labels_out_round2.size())) { // currently not used? only in binary?
-
-        sdsl::bit_vector union_mask
-                = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
-        std::mutex vector_backup_mutex;
-        std::atomic_thread_fence(std::memory_order_release);
-
-        auto count_merge
-                = [&](sdsl::bit_vector
-                              &a, // Myrthe ANSWER: what does count_merge do? Is a hack, I do not have to touch this now. Count_add would be better name. If you have a dense column, and include it in foreground or background. Wate time, Instead first create mask.  You only access dense columns, for instance a reference.
-                      const sdsl::bit_vector &b, const std::vector<node_index> &id_map,
-                      size_t offset = 0) {
-                      call_ones(b, [&](size_t i) {
-                          if (id_map[i]) {
-                              set_bit(a.data(), id_map[i], parallel, MO_RELAXED);
-                              atomic_fetch_and_add(counts, id_map[i] * 2 + offset, 1,
-                                                   vector_backup_mutex, MO_RELAXED);
-                          }
-                      });
-                  };
-        logger->trace("Checking shared and other labels");
-        masked_graph->call_sequences(
-                [&](const std::string &contig, const std::vector<node_index> &path) {
-                    for (const auto &[label, sig] :
-                         anno_graph->get_top_label_signatures(contig, num_labels)) {
-                        bool found_in = labels_in.count(label);
-                        bool found_out = labels_out.count(label);
-                        bool found_in_round2 = labels_in_round2.count(label);
-                        bool found_out_round2 = labels_out_round2.count(label);
-                        if (!found_in && !found_out && !found_in_round2
-                            && !found_out_round2 && check_other) {
-                            mask_or(other_mask, sig, path);
-                        }
-
-                        if (found_in_round2)
-                            count_merge(union_mask, sig, path);
-
-                        if (found_out_round2)
-                            count_merge(union_mask, sig, path, 1);
-                    }
-                },
-                num_threads);
-
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-        masked_graph->set_mask(new bitmap_vector(std::move(union_mask)));
-    }
-
-
-    // Filter unitigs from masked graph based on filtration criteria
-    logger->trace("Filtering out background");
-    if (config.count_kmers) {
-        auto &[in_total_kmers, out_total_kmers] = total_kmers;
-        logger->trace("I'm here");
-        if (config.test_by_unitig == false) { // Statistical testing part when k-mer counts are included.
-            std::atomic<uint64_t> total_unitigs(0);
-            if (config.test_type == "brunner_munzel"){
-                logger->trace("Brunner Munzel test");
-                masked_graph->likelihood_ratios.resize(counts.size()/2+1);
-                masked_graph->call_unitigs([&](const std::string &, const std::vector<node_index> &) {
-                    ++total_unitigs;
-                    // total_unitigs.fetch_add(1, MO_RELAXED);
-                }, get_num_threads());
-                masked_graph = brunner_munzel_test(graph_ptr, masked_graph,files,labels_in,labels_out,config,total_unitigs, medians);
-                masked_graph->likelihood_ratios.resize(counts.size()/2+1);
-            }
-            else if (config.test_type == "likelihoodratio"){
-                const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
-                total_unitigs = static_cast<const bitmap_vector &>(masked_graph->get_mask()).num_set_bits();
-                logger->trace("Likelihood ratio test");
-                logger->trace("ooooo total unitigs {}", total_unitigs); // the total number of hypotheses tested: number of unitigs, to accounts for dependence
-                auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs,
-                                                        std::min((int) std::distance(counts.begin(), std::max_element(counts.begin(), counts.end())), (int) 100000),
-                                                        in_total_kmers, out_total_kmers);
-                logger->trace("test_by_unitig == false");
-                sdsl::bit_vector mask = in_mask;
-                size_t total_nodes = masked_graph->num_nodes();
-                size_t kept_nodes = 0;
-                //std::vector<int> nodes;
-                std::vector<std::pair<double,int>> likelihood_ratios_nodes;
-                call_ones(in_mask, [&](node_index node) { // only run if counts_case > counts_control
-                    uint64_t in_sum = counts[node * 2];
-                    uint64_t out_sum = counts[node * 2 + 1];
-                    // filter kmers in samples less noise
-                    // if (in_sum + out_sum >= int(total_nodes/20)) { // check for minimum occurance of k-mer
-                        double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
-                        if (in_sum > out_sum_normalized) { // check that kmer occurs more in foreground than in background
-                            // check for high complexity
-                            // auto kmer_string = graph_ptr->get_node_sequence(node);
-                            // auto complexity_low = is_low_complexity(kmer_string);
-                            // logger->trace("kmer_string: {}", kmer_string);
-                            // logger->trace("complexity_low: {}", complexity_low);
-                            auto [keep, likelihood_ratio] = statistical_model.likelihood_ratio_test(in_sum, out_sum);
-                            // likelihood_ratios_nodes.push_back(std::make_pair(likelihood_ratio, node));
-                            if (keep){
-                                mask[node] = true;
-                                kept_nodes++;
-                            }
-                            else
-                                mask[node] = false;
-                        }
-                        else
-                            mask[node] = false;
-                    // }
-                    // else
-                    //     mask[node] = false;
-                });
-                // // get index for benjamini-yekutieli correction
-                // std::sort(likelihood_ratios_nodes.begin(), likelihood_ratios_nodes.end(), sortByPValue);
-                // // double alpha = statistical_model.get_t_test_alpha(statistical_model.get_df_approx(), 0.05) // 0.05 is the alpha value, TODO: add params.
-                // int max_i = statistical_model.benjamini_yekutieli(likelihood_ratios_nodes, statistical_model.lrt_threshold());
-                // // reject all hypotheses with index <= max_i
-                // for (int i = 0; i <= max_i; i++){
-                //     mask[likelihood_ratios_nodes[i].second] = false;
-                // }
-                // logger->trace("max_i {}", max_i);
-                // logger->trace("likelihood_ratios__nodes_elem {}, {}, {}", likelihood_ratios_nodes[0].first, likelihood_ratios_nodes[1].first, likelihood_ratios_nodes[2].first);
-                // logger->trace("likelihood_ratios_nodes_size {}", likelihood_ratios_nodes.size());
-                masked_graph->set_mask(new bitmap_vector(std::move(mask)));
-                masked_graph->likelihood_ratios.resize(counts.size()/2+1);
-                logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
-            }
-
-        } else {
-            logger->trace("test_by_unitig == true");
-            masked_graph->likelihood_ratios.resize(counts.size()/2+1);
-            std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0);
-            std::atomic<uint64_t> total_unitigs(0);
-            masked_graph->call_unitigs([&](const std::string &, const std::vector<node_index> &) {
-                total_unitigs.fetch_add(1, MO_RELAXED);
-            });
-            auto statistical_model = DifferentialTest(config.family_wise_error_rate, total_unitigs, // total number of hypotheses equals the number of unitigs
-                                               std::min((int) std::distance(counts.begin(),
-                                               std::max_element(counts.begin(), counts.end())), (int) 100000), // Myrthe: the factorial should increase for the unitig mode.
-                                               in_total_kmers, out_total_kmers); // Myrthe: I don't know what to do with the last two arguments.
-            update_masked_graph_by_unitig(*masked_graph, // intitial mask
-                [&](const std::string &, const std::vector<node_index> &path) -> Intervals { // return a set of intervals to keep in the graph
-                    int in_kmer_count_unitig = 0;
-                    int out_kmer_count_unitig = 0;
-                    for (size_t i = 0; i < path.size(); ++i) {
-                        in_kmer_count_unitig += counts[path[i] * 2];
-                        out_kmer_count_unitig += counts[path[i] * 2 + 1];
-                    }
-                    auto [significance, likelihood_ratio] = statistical_model.likelihood_ratio_test(in_kmer_count_unitig, out_kmer_count_unitig);
-
-                    if (significance){
-                        for (size_t i = 0; i < path.size(); ++i) { // if significant add the likelihood ratio or the effect (in_kmer_count_unitig - out_kmer_count_unitig) to the likelihood_ratios vector. This is a temporary solution
-                            masked_graph->likelihood_ratios[path[i]] = in_kmer_count_unitig - out_kmer_count_unitig; //likelihood_ratio;
-                        }
-                        return { std::make_pair(0, path.size()) };
-                    }
-                    else
-                        return {};
-                },
-            num_threads);
-        }
-            //std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0.01); // Myrthe: For testing. temporary
-        return masked_graph;
-    }
-
-
-    size_t min_label_in_count = std::ceil(config.label_mask_in_kmer_fraction
-                                    * num_in_labels);
-    size_t max_label_out_count = std::floor(config.label_mask_out_kmer_fraction
-                                    * num_out_labels);
-
-    if (config.test_by_unitig) {
-        sdsl::bit_vector mask(masked_graph->get_mask().size(), false);
-
-        std::shared_ptr<DeBruijnGraph> test_graph = masked_graph;
-        std::shared_ptr<CanonicalDBG> wrapped;
-        if (masked_graph->get_mode() == DeBruijnGraph::PRIMARY) {
-            wrapped = std::make_shared<CanonicalDBG>(masked_graph);
-            test_graph = wrapped;
-        }
-
-        test_graph->call_unitigs([&](const std::string &, const auto &path) {
-            uint64_t total_in_count = 0;
-            uint64_t total_out_count = 0;
-            auto begin = path.end();
-            auto end = path.begin();
-            for (auto it = path.begin(); it != path.end(); ++it) {
-                node_index node = wrapped ? wrapped->get_base_node(*it) : *it;
-                auto in_count = counts[node * 2];
-                total_in_count += in_count;
-                total_out_count += counts[node * 2 + 1];
-                if (in_count) {
-                    begin = std::min(it, begin);
-                    end = std::max(it, end);
-                }
-            }
-
-            size_t path_size = end - begin;
-            if (total_in_count >= min_label_in_count*path_size && total_out_count <= max_label_out_count*path_size) {
-                std::for_each(begin, end, [&](node_index node) {
-                    mask[wrapped ? wrapped->get_base_node(node) : node] = true;
-                });
-            }
-        });
-
-        /*
-        size_t kept_nodes = 0;
-
-        call_ones(masked_graph->get_mask(), [&](node_index node) {
-            uint64_t in_count = counts[node * 2];
-            uint64_t out_count = counts[node * 2 + 1];
-
-            if (in_count >= min_label_in_count && out_count <= max_label_out_count) {
-                ++kept_nodes;
-            } else {
-                mask[node] = false;
-            }
-        });
-
-        size_t num_filled = 0;
-        masked_graph->call_unitigs([&](const std::string &, const auto &path) {
-            auto in_mask = [&](node_index node) { return mask[node]; };
-            auto begin = std::find_if(path.begin(), path.end(), in_mask);
-            if (begin == path.end())
-                return;
-
-            auto end = std::find_if(path.rbegin(), path.rend(), in_mask).base();
-
-            size_t num_in_mask = std::count_if(begin, end, in_mask);
-
-            if (static_cast<double>(num_in_mask) / path.size() >= 0.8) {
-                std::for_each(path.begin(), path.end(), [&](node_index node) {
-                    if (!mask[node]) {
-                        mask[node] = true;
-                        ++kept_nodes;
-                        ++num_filled;
-                    }
-                });
-                return;
-            }
-
-            if (static_cast<double>(num_in_mask) / (end - begin) >= 0.8) {
-                std::for_each(begin, end, [&](node_index node) {
-                    if (!mask[node]) {
-                        mask[node] = true;
-                        ++kept_nodes;
-                        ++num_filled;
-                    }
-                });
-            }
-
-            for (size_t i = 1; i + 1 < path.size(); ++i) {
-                node_index node = path[i];
-                if (!mask[node]) {
-                    if (mask[path[i - 1]] && mask[path[i + 1]]) {
-                        mask[node] = true;
-                        ++kept_nodes;
-                        ++num_filled;
-                    }
-                }
-            }
-        });
-        */
-
-        masked_graph->set_mask(new bitmap_vector(std::move(mask)));
-
-        // logger->trace("Kept {} out of {} nodes, {} filled", kept_nodes, masked_graph->get_graph().num_nodes(), num_filled);
-        masked_graph->likelihood_ratios.resize(counts.size()/2+1);
-        std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0);
-
-        return masked_graph;
-    }
-
-    if (config.label_mask_in_unitig_fraction == 0.0
-            && config.label_mask_out_unitig_fraction == 1.0
-            && config.label_mask_other_unitig_fraction == 1.0) {
-        logger->trace("Filtering by node");
-        size_t total_nodes = masked_graph->num_nodes();
-        const auto &in_mask = static_cast<const bitmap_vector &>(masked_graph->get_mask()).data();
-        sdsl::bit_vector mask = in_mask;
-
-        // TODO: make this part multithreaded
-        size_t kept_nodes = 0;
-        call_ones(in_mask, [&](node_index node) {
-            uint64_t in_count = counts[node * 2];
-            uint64_t out_count = counts[node * 2 + 1];
-
-            if (in_count >= min_label_in_count && out_count <= max_label_out_count) {
-                ++kept_nodes;
-            } else {
-                mask[node] = false;
-            }
-        });
-
-        masked_graph->set_mask(new bitmap_vector(std::move(mask)));
-
-        logger->trace("Kept {} out of {} nodes", kept_nodes, total_nodes);
-        masked_graph->likelihood_ratios.resize(counts.size()/2+1);
-        std::fill(masked_graph->likelihood_ratios.begin(), masked_graph->likelihood_ratios.end(), 0);
-
-        return masked_graph;
-    }
-
-    logger->trace("Filtering by unitig");
-
-    update_masked_graph_by_unitig(*masked_graph,
-        [&](const std::string &, const std::vector<node_index> &path) -> Intervals {
-            // return a set of intervals to keep in the graph
-            size_t in_kmer_count = 0;
-
-            size_t begin = path.size();
-            size_t end = 0;
-            for (size_t i = 0; i < path.size(); ++i) {
-                if (counts[path[i] * 2] >= min_label_in_count) {
-                    if (begin == path.size())
-                        begin = i;
-
-                    end = std::max(end, i + 1);
-
-                    ++in_kmer_count;
-                }
-            }
-
-            if (begin >= end)
-                return {};
-
-            size_t size = end - begin;
-            size_t label_in_cutoff = std::ceil(config.label_mask_in_unitig_fraction * size);
-            if (in_kmer_count < label_in_cutoff)
-                return {};
-
-            size_t out_kmer_count = 0;
-            size_t other_kmer_count = 0;
-            size_t label_out_cutoff = std::floor(config.label_mask_out_unitig_fraction * size);
-            size_t other_cutoff = std::floor(config.label_mask_other_unitig_fraction * size);
-
-
-            for (size_t i = begin; i < end; ++i) {
-                if (counts[path[i] * 2 + 1] > max_label_out_count
-                        && ++out_kmer_count > label_out_cutoff) {
-                    return {};
-                }
-
-                if (check_other && other_mask[path[i]]
-                        && ++other_kmer_count > other_cutoff) {
-                    return {};
-                }
-            }
-
-            return { std::make_pair(begin, end) };
-        },
-        num_threads
-    );
-
-    return masked_graph;
-}
-
-
-/**
- * Helpers
- */
-
-std::shared_ptr<MaskedDeBruijnGraph>
-make_initial_masked_graph(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                          sdsl::int_vector<> &counts, // Myrthe later: int_vector_buffer and make it a template class. Otherwise the function stays the same because initial mask should only check if a k-mer is present just once in either set.
-                          sdsl::bit_vector&& mask,
-                          bool add_complement,
-                          size_t num_threads) {
-    // counts is a double-length vector storing the in-label and out-label
-    // counts interleaved
-    assert(counts.size() == mask.size() * 2);
-
-    auto masked_graph = std::make_shared<MaskedDeBruijnGraph>(
-        graph_ptr,
-        add_complement
-            ? std::make_unique<bitmap_vector>(mask)
-            : std::make_unique<bitmap_vector>(std::move(mask)),
-            true,
-            graph_ptr->get_mode() == DeBruijnGraph::PRIMARY ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    );
-
-    logger->trace("Constructed masked graph with {} nodes", masked_graph->num_nodes());
-
-    if (add_complement) {
-        logger->trace("Adding reverse complements");
-        std::mutex vector_backup_mutex;
-        std::atomic_thread_fence(std::memory_order_release);
-        masked_graph->call_sequences([&](const std::string &seq, const std::vector<node_index> &path) {
-            std::string rc_seq = seq;
-            std::vector<DeBruijnGraph::node_index> rc_path = path;
-            reverse_complement_seq_path(*graph_ptr, rc_seq, rc_path);
-
-            auto it = rc_path.rbegin();
-            for (size_t i = 0; i < path.size(); ++i, ++it) {
-                if (*it) {
-                    uint64_t in_count = atomic_fetch(counts, path[i] * 2, vector_backup_mutex, MO_RELAXED);
-                    uint64_t out_count = atomic_fetch(counts, path[i] * 2 + 1, vector_backup_mutex, MO_RELAXED);
-                    atomic_fetch_and_add(counts, *it * 2, in_count, vector_backup_mutex, MO_RELAXED);
-                    atomic_fetch_and_add(counts, *it * 2 + 1, out_count, vector_backup_mutex, MO_RELAXED);
-
-                    set_bit(mask.data(), *it, in_count, MO_RELAXED);
-                }
-            }
-        }, num_threads, true);
-
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-        masked_graph->set_mask(new bitmap_vector(std::move(mask)));
-
-        logger->trace("Updated masked graph has {} nodes", masked_graph->num_nodes());
-    }
-
-    return masked_graph;
-}
-
-
-std::tuple<sdsl::int_vector<>, sdsl::bit_vector, sdsl::bit_vector, std::tuple<size_t, size_t>>
-construct_diff_label_count_vector(const ColumnGenerator &generate_columns,
-                                  const std::vector<std::string> &files,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  const DifferentialAssemblyConfig &config,
-                                  size_t max_index,
-                                  size_t num_labels,
-                                  size_t num_threads,
-                                  bool add_out_labels_to_mask) {
-    bool parallel = (num_threads > 1);
-    logger->trace("Allocating mask vector");
-
-    size_t width;
-
-    if (config.count_kmers){ // calculate the sum of column widths to derive an upper bound for the required counts vector width.
-        assert(labels_in.size() > 0);
-        size_t sum_widths_in = 0; size_t sum_widths_out = 0;
-        for (size_t i = 0; i < files.size(); ++i) { // loopes over files
-            const auto &values_fname = utils::remove_suffix(files[i],
-                 annot::ColumnCompressed<>::kExtension) + annot::ColumnCompressed<>::kCountExtension;
-            sdsl::int_vector_size_type col_size; uint8_t col_width;
-            std::ifstream in_stream(values_fname);
-            sdsl::int_vector<>::read_header(col_size, col_width, in_stream);
-            auto label_encoder = annot::ColumnCompressed<>::read_label_encoder(files[i]);
-            for (size_t c = 0; c < label_encoder.size(); ++c) { // loopes over kmers
-                auto label = label_encoder.decode(c);
-                if (labels_in.count(label)) sum_widths_in += std::pow(2, col_width);
-                if (labels_out.count(label)) sum_widths_out += std::pow(2, col_width);
-            }
-        }
-        width = std::min((size_t) 32, (size_t) std::ceil(std::log(std::max(sum_widths_in, sum_widths_out)))); // TODO, check if this is really correct. this should be 8.5 -> 9 rather then 7.
-    } else{
-        width = (sdsl::bits::hi(num_labels) + 1) * (1 + add_out_labels_to_mask);
-    }
-    assert(width);
-
-    sdsl::bit_vector indicator(max_index + 1, false);
-
-    logger->trace("Allocating count vector");
-    // the in and out counts are stored interleaved
-    sdsl::int_vector<> counts = aligned_int_vector(indicator.size() * 2, 0, width, 16); //Myrthe later: int_vector_buffer. In that case the function also to be a template too, or a template based argument, if vector_type== buffered_int_vector, then it should be a buffered (// if constexpr (std::is_same_v<vector_type, Bitmap>) { } //sdsl::int_vector_buffer<> counts = aligned_int_vector(
-
-    logger->trace("done");
-
-    sdsl::bit_vector other_labels(num_labels, false);
-    std::mutex vector_backup_mutex;
-
-    logger->trace("Populating count vector");
-    std::atomic_thread_fence(std::memory_order_release); //Myrthe ANSWER: outer loop is over the columns, inner loop over the kmers (i indicating the rows)
-            // Myrthe later: For the buffered_int_vector we would have to use operators [ ] [ ], but it might be problematic if it runs out of memory and push_back would need to be used instead.
-            // Myrthe later: The second problem with buffered_int_vector might be that it is expensive, because we do random access on it. Instead a solution might be to use a piority queue of L columns, storing the smallest element as a triple (column , index, value), such that always k-mers in row x are processed before row x + 1.
-
-    size_t in_total_kmers = 0; size_t out_total_kmers = 0;
-
-    generate_columns([&](uint64_t offset, const Label &label, const ValueGenerator &value_generator) {
-        uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
-        if (labels_out.count(label))
-            col_indicator |= 2;
-
-        if (!col_indicator)
-            set_bit(other_labels.data(), offset, parallel, MO_RELAXED);
-
-        ValueCallback add_in = [&](Column i, uint64_t value) {
-            i = AnnotatedDBG::anno_to_graph_index(i); // indices in the annotation matrix have an offset of 1 compared to those in the graph. Since the mask should be compatible with the graph, we have to convert the index.
-            assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED); // set the corresponding bit to true in the mask, with the 'indicator' representing the mask.
-            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED); // TODO Myrthe later: make sure that values do not overflow in neighbouring cells.
-            in_total_kmers += value;
-        };
-
-        ValueCallback add_out = [&](Column i, uint64_t value) {
-            i = AnnotatedDBG::anno_to_graph_index(i);
-            assert(i != DeBruijnGraph::npos);
-            if (add_out_labels_to_mask || config.test_type == "likelihoodratio")
-                set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
-            out_total_kmers += value;
-        };
-
-        ValueCallback add_both = [&](Column i, uint64_t value) { // theoretically a label should not be contained in both the in- and out-labels, but this is just in case.
-            i = AnnotatedDBG::anno_to_graph_index(i);
-            assert(i != DeBruijnGraph::npos);
-            set_bit(indicator.data(), i, parallel, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2, value, vector_backup_mutex, MO_RELAXED);
-            atomic_fetch_and_add(counts, i * 2 + 1, value, vector_backup_mutex, MO_RELAXED);
-            out_total_kmers += value;
-            in_total_kmers += value;
-        };
-
-        switch (col_indicator) {
-            case 1: { value_generator(add_in); } break;
-            case 2: { value_generator(add_out); } break;
-            case 3: { value_generator(add_both); } break;
-            default: {}
-        }
-    });
-
-    size_t num_tested_kmers = 0;
-    for(size_t i = 0; i < counts.size(); i+=2){
-        if (counts[i] > counts[i+1])
-        num_tested_kmers += 1;
-    };
-    logger->trace("num_tested_kmers: {}", num_tested_kmers);
-
-    std::atomic_thread_fence(std::memory_order_acquire);
-    logger->trace("done");
-    assert( std::accumulate(counts.begin(), counts.end(), 0) > 0); // assert that the sum of the count vector is greater than 0
-
-    return std::make_tuple(std::move(counts), std::move(indicator), std::move(other_labels),
-                           std::make_tuple(std::move(in_total_kmers), std::move(out_total_kmers)));
-}
-
-
-
-void kmer_distribution_table(const ColumnGenerator &generate_columns,
-                                  const tsl::hopscotch_set<Label> &labels_in,
-                                  const tsl::hopscotch_set<Label> &labels_out,
-                                  size_t max_index,
-                                  MaskedDeBruijnGraph &masked_graph, // std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                             const DifferentialAssemblyConfig &config,
-                             size_t num_threads) {
-
-    logger->trace("Allocating count vector");
-    std::vector<std::vector<int>> count_matrix(max_index*2 + 2); // outer array 'max_index + 1'*2, with arrays of initially zero lenght or of max size in labels/out_labels.
-
-    logger->trace("Populating count matrix");
-    generate_columns([&](uint64_t offset, const Label &label, const ValueGenerator &value_generator) {
-        std::ignore = offset;// to overcome the error that 'offset' is set but not used.
-        uint8_t col_indicator = static_cast<bool>(labels_in.count(label));
-        std::cout << "col_indicator: " << std::to_string(col_indicator) << std::endl;
-        if (labels_out.count(label))
-            col_indicator |= 2;
-
-        ValueCallback add_in = [&](Column i, uint64_t value) {
-            std::cout << "i: " << std::to_string(i) << std::endl;
-            std::cout << "value: " << std::to_string(value) << std::endl;
-            count_matrix[i * 2].push_back(value);
-        };
-
-        ValueCallback add_out = [&](Column i, uint64_t value) {
-            count_matrix[i * 2 + 1].push_back(value);
-        };
-
-        ValueCallback add_both = [&](Column i, uint64_t value) { // theoretically a label should not be contained in both the in- and out-labels, but this is just in case.
-            count_matrix[i * 2].push_back(value);
-            count_matrix[i * 2 + 1].push_back(value);
-
-        };
-
-        switch (col_indicator) {
-            case 1: { value_generator(add_in); } break;
-            case 2: { value_generator(add_out); } break;
-            case 3: { value_generator(add_both); } break;
-            default: {}
-        }
-
-        // Calculate the product of the poisson probabilities and compare with negative binom
-    });
-
-    if (config.test_by_unitig){         // unitig mode.
-        std::vector<std::vector<int>> unitig_count_matrix;
-        update_masked_graph_by_unitig(masked_graph, // intitial mask
-            [&](const std::string &, const std::vector<node_index> &path)
-                    -> Intervals {
-                // return a set of intervals to keep in the graph
-                std::vector<int> in_kmer_unitig(labels_in.size(), 0);
-                std::vector<int> out_kmer_unitig(labels_out.size(), 0);
-                for (size_t i = 0; i < path.size(); ++i) { // first strategy is summing counts per unitig. // The update_mask.. function takes a path. Count vector per path. QUESTION: where is the count vector created, such that unitig k-mers can be accessed sequentially?
-                    assert(count_matrix.size() >= path[i] * 2);
-                    if (count_matrix[path[i] * 2].size() != in_kmer_unitig.size() or count_matrix[path[i] * 2 + 1].size() != out_kmer_unitig.size()){
-                        std::cout << "sizes" << std::endl;
-                        std::cout << std::to_string(count_matrix[path[i] * 2].size()) << std::endl << std::flush;
-                        std::cout << std::to_string(in_kmer_unitig.size()) << std::endl << std::flush;
-                        std::cout << std::to_string(count_matrix[path[i] * 2 + 1].size()) << std::endl << std::flush;
-                        std::cout << std::to_string(out_kmer_unitig.size()) << std::endl << std::flush;
-                        std::cout << std::to_string(path[i] * 2) << std::endl << std::flush; // TODO: do I have to convert the index? It was not done in the original code
-                    }else{
-                        assert(count_matrix[path[i] * 2].size() == in_kmer_unitig.size());
-                        assert(count_matrix[path[i] * 2 + 1].size() == out_kmer_unitig.size());
-                        std::transform(in_kmer_unitig.begin(), in_kmer_unitig.end(), count_matrix[path[i] * 2].begin(), in_kmer_unitig.begin(), std::plus<int>()); // sum the values of count_matrix[x] to in_kmer_count.
-                        std::transform(out_kmer_unitig.begin(), out_kmer_unitig.end(), count_matrix[path[i] * 2 + 1].begin(), out_kmer_unitig.begin(), std::plus<int>());
-                    }
-                }
-                unitig_count_matrix.push_back(in_kmer_unitig);
-                unitig_count_matrix.push_back(out_kmer_unitig); // add to matrix
-                return { };
-                },
-            num_threads);
-        count_matrix = unitig_count_matrix;
-    }
-
-    int matrix_sum = 0;
-    for (size_t i = 0; i < count_matrix.size(); i+=2){
-        matrix_sum += std::accumulate(count_matrix[i].begin(), count_matrix[i].end(), 0);
-    }
-    assert(matrix_sum > 0);
-
-    logger->trace("Writing k-mer count matrix to table");
-
-    std::ofstream out_stream("kmer_dist_table.py");
-    out_stream << "count_matrix_in = [" ;
-    for (size_t i = 0; i < std::min(count_matrix.size(), (size_t) 50000000); i+=2){
-        if (count_matrix[i+1].size() > 3 or count_matrix[i].size() > 3){ // labels_out.size()/3
-                std::vector<int> zeros_vector(labels_in.size()-count_matrix[i].size(), 0);
-                count_matrix[i].insert(count_matrix[i].end(), zeros_vector.begin(), zeros_vector.end());
-                out_stream << "[" + outstring(count_matrix[i]) << "]," <<std::endl;
-            }
-    }
-    out_stream << "]" << std::endl;
-    out_stream << "count_matrix_out = [" ;
-    for (size_t i = 1; i < std::min(count_matrix.size(), (size_t) 50000000); i+=2){
-        if (count_matrix[i].size() > 3 or count_matrix[i-1].size() > 3){ // labels_out.size()/3
-            std::vector<int> zeros_vector(labels_out.size()-count_matrix[i].size(), 0);
-            count_matrix[i].insert(count_matrix[i].end(), zeros_vector.begin(), zeros_vector.end());
-            out_stream << "[" + outstring(count_matrix[i]) << "]," <<std::endl;        }
-    }
-    out_stream << "]"<< std::endl;
-    out_stream.close();
-}
-
-void update_masked_graph_by_unitig(MaskedDeBruijnGraph &masked_graph,
-                                   const GetKeptIntervals &get_kept_intervals,
-                                   size_t num_threads) { // traverses unitigs and takes a callback that does the filtering.
-    std::atomic<uint64_t> kept_unitigs(0);
-    std::atomic<uint64_t> total_unitigs(0);
-    std::atomic<uint64_t> num_kept_nodes(0);
-    bool parallel = num_threads > 1;
-
-    sdsl::bit_vector mask = static_cast<const bitmap_vector&>(masked_graph.get_mask()).data();
-
-    std::atomic_thread_fence(std::memory_order_release);
-
-    masked_graph.call_unitigs([&](const std::string &unitig,
-                                  const std::vector<node_index> &path) {
-        total_unitigs.fetch_add(1, MO_RELAXED);
-
-        size_t last = 0;
-        for (const auto &pair : get_kept_intervals(unitig, path)) {
-            const auto &[begin, end] = pair;
-            kept_unitigs.fetch_add(1, MO_RELAXED);
-            num_kept_nodes.fetch_add(end - begin, MO_RELAXED);
-            for ( ; last < begin; ++last) {
-                unset_bit(mask.data(), path[last], parallel, MO_RELAXED);
-            }
-            last = end;
-        }
-
-        for ( ; last < path.size(); ++last) {
-            unset_bit(mask.data(), path[last], parallel, MO_RELAXED);
-        }
-
-    }, num_threads);
-    std::atomic_thread_fence(std::memory_order_acquire);
-
-    masked_graph.set_mask(new bitmap_vector(std::move(mask)));
-
-    logger->trace("Kept {} out of {} unitigs with average length {} and a total of {} nodes",
-                  kept_unitigs, total_unitigs,
-                  static_cast<double>(num_kept_nodes + kept_unitigs * (masked_graph.get_k() - 1))
-                      / kept_unitigs,
-                  num_kept_nodes);
-}
-
-//%TODO A post-filtering step is done to filter out short contigs, percentage/cutoff , that is set to by default.  Short unitigs are removed afterwards
-//        % TODO For each contig the confidence score is output
-
-
 } // namespace graph
 } // namespace mtg
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index b76e4c7e34..d578fd2d0d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -15,33 +15,14 @@ namespace graph {
 class MaskedDeBruijnGraph;
 
 /**
- * A container for differential assembly parameters. These are:
- * label_mask_in_kmer_fraction: minimum fraction of in-labels which should be
- *                              present for a node to be kept
- * label_mask_in_unitig_fraction: minimum fraction of nodes within a unitig which
- *                                must satisfy the in-label criteria
- * label_mask_out_kmer_fraction: maximum fraction of out-labels which can be
- *                               present in a kept node
- * label_mask_out_unitig_fraction: maximum fraction of nodes within a unitig which
- *                                 can satisfy the out-label criteria
- * label_mask_other_unitig_fraction: maximum fraction of nodes within a unitig which
- *                                   can contain labels not in the in- and out-groups
- * add_complement: also consider the reverse complements of nodes
+ * A container for differential assembly parameters.
  */
 struct DifferentialAssemblyConfig {
-    double label_mask_in_unitig_fraction = 1.0;
-    double label_mask_in_kmer_fraction = 1.0;
-    double label_mask_out_unitig_fraction = 0.0;
-    double label_mask_out_kmer_fraction = 0.0;
-    double label_mask_other_unitig_fraction = 1.0;
-    bool add_complement = false;
-    bool count_kmers = false; // Myrthe
+    bool count_kmers = false;
     bool clean = false;
-    double family_wise_error_rate = 0.05; //Myrthe: differential assembly statistical tests, or the family wise rate for bonferonni or a p-value
-    bool test_by_unitig = false; // Myrthe
-    std::string test_type = "brunner_munzel"; // default is "brunner_munzel", alternatives are: "likelihoodratio", "binary"
-    bool evaluate_assembly = false; // Myrthe temporary : evaluate an assembly
-    bool filter = true;
+    double family_wise_error_rate = 0.05;
+    bool test_by_unitig = false;
+    std::string test_type = "nbinom_exact";
     bool assemble_shared = false;
     uint64_t min_count = 1;
     uint64_t min_recurrence = 1;
@@ -49,43 +30,9 @@ struct DifferentialAssemblyConfig {
     uint64_t min_out_recurrence = 0;
     uint64_t max_in_recurrence = std::numeric_limits<uint64_t>::max();
     uint64_t max_out_recurrence = std::numeric_limits<uint64_t>::max();
-    uint64_t num_tests = 0;
     std::string outfbase;
 };
 
-/**
- * Given an AnnotatedDBG and sets of foreground (in) and background (out) labels,
- * return a MaskedDeBruijnGraph with the nodes of anno_graph masked according to
- * the parameters specified by config. In round 1, a masked graph is constructed
- * containing the nodes corresponding to labels_in and labels_out, with a vector
- * counting how many of labels_in and labels_out correspond to each node,
- * respectively.
- * In round 2, if any of the sequences of the initial masked graph have the
- * labels in labels_in_round2 or labels_out_round2, or if they have other labels,
- * the count
- * vector is updated accordingly.
- * In round 3, the config rules are used to discard (i.e., mask out) nodes from
- * the masked graph. The resulting graph is returned.
- */
-std::shared_ptr<MaskedDeBruijnGraph>
-mask_nodes_by_label(const AnnotatedDBG &anno_graph,
-                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
-                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
-                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in_round2,
-                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out_round2,
-                    const DifferentialAssemblyConfig &config,
-                    size_t num_threads = 1,
-                    size_t num_parallel_files = std::numeric_limits<size_t>::max());
-
-std::shared_ptr<DeBruijnGraph>
-mask_nodes_by_label(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                    const std::vector<std::string> &files,
-                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
-                    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
-                    const DifferentialAssemblyConfig &config,
-                    size_t num_threads = 1,
-                    size_t num_parallel_files = std::numeric_limits<size_t>::max());
-
 template <class PValStorage>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
diff --git a/metagraph/src/graph/representation/base/sequence_graph.cpp b/metagraph/src/graph/representation/base/sequence_graph.cpp
index 26006cbd4d..c11e48846e 100644
--- a/metagraph/src/graph/representation/base/sequence_graph.cpp
+++ b/metagraph/src/graph/representation/base/sequence_graph.cpp
@@ -129,9 +129,6 @@ void call_sequences_from(const DeBruijnGraph &graph,
     std::string sequence;
 
     std::vector<std::pair<node_index, char>> targets;
-    const mtg::graph::MaskedDeBruijnGraph *downcasted_graph = // Create a downcasted graph object, such that the likelihood ratio vector can be accessed.
-            // dynamic_cast<const mtg::graph::MaskedDeBruijnGraph*>(&graph);
-            nullptr;
 
     // keep traversing until we have worked off all branches from the queue
     while (queue.size()) {
@@ -210,7 +207,6 @@ void call_sequences_from(const DeBruijnGraph &graph,
                         queue.push_back(next); // The queue grows a bit faster compared to the original case.
                         // TODO: make sure that the selected node is not added to the queue --> problems if run on  multiple threads.
                     }
-                    std::ignore = downcasted_graph;
                     // if (!call_unitigs // In the unitig mode (i.e. if call_unitigs is true), simply the unitigs must be returned, rather than longer contigs
                     //     && downcasted_graph // TODO feature is temporary
                     //     && downcasted_graph->likelihood_ratios[next] > next_likelihood){ // If the likelihood of this unitig is better than the current likelihood
diff --git a/metagraph/src/graph/representation/masked_graph.hpp b/metagraph/src/graph/representation/masked_graph.hpp
index 0918731a92..2bf8af1338 100644
--- a/metagraph/src/graph/representation/masked_graph.hpp
+++ b/metagraph/src/graph/representation/masked_graph.hpp
@@ -130,8 +130,6 @@ class MaskedDeBruijnGraph : public DBGWrapper<DeBruijnGraph> {
         });
     }
 
-    std::vector<double> likelihood_ratios{}; // likelihood ratios
-
   private:
     std::unique_ptr<bitmap> kmers_in_graph_;
     bool only_valid_nodes_in_mask_;

From d94e342455129e838ffb4070cc6cc2402812366c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 11:14:14 +0200
Subject: [PATCH 189/410] more cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   |   4 -
 metagraph/src/graph/differential_tests.hpp    |  58 ----
 .../src/graph/differential_tests_myrthe.cpp   | 169 -----------
 .../src/graph/differential_tests_uemmi.cpp    | 275 ------------------
 metagraph/src/graph/look_up_table_t_dist.cpp  |  17 --
 metagraph/src/graph/look_up_table_t_dist.hpp  |  20 --
 .../src/graph/lookup_table_chisqrd_cdf.cpp    |   9 -
 .../src/graph/lookup_table_chisqrd_cdf.hpp    |  20 --
 metagraph/src/graph/timer.hpp                 |  23 --
 9 files changed, 595 deletions(-)
 delete mode 100644 metagraph/src/graph/differential_tests.hpp
 delete mode 100644 metagraph/src/graph/differential_tests_myrthe.cpp
 delete mode 100644 metagraph/src/graph/differential_tests_uemmi.cpp
 delete mode 100644 metagraph/src/graph/look_up_table_t_dist.cpp
 delete mode 100644 metagraph/src/graph/look_up_table_t_dist.hpp
 delete mode 100644 metagraph/src/graph/lookup_table_chisqrd_cdf.cpp
 delete mode 100644 metagraph/src/graph/lookup_table_chisqrd_cdf.hpp
 delete mode 100644 metagraph/src/graph/timer.hpp

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 65cdacca40..ad125904de 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -9,21 +9,17 @@
 #include <boost/math/distributions/poisson.hpp>
 #include <boost/math/distributions/binomial.hpp>
 #include <boost/math/distributions/negative_binomial.hpp>
-#include <boost/math/statistics/univariate_statistics.hpp>
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
-#include <boost/math/special_functions/beta.hpp>
 
 #include "common/logger.hpp"
 #include "common/vectors/bitmap.hpp"
 #include "common/vector_map.hpp"
 #include "common/vectors/transpose.hpp"
 #include "graph/representation/masked_graph.hpp"
-#include "graph/representation/canonical_dbg.hpp"
 #include "graph/graph_cleaning.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
-#include "differential_tests.hpp"
 
 namespace mtg {
 namespace graph {
diff --git a/metagraph/src/graph/differential_tests.hpp b/metagraph/src/graph/differential_tests.hpp
deleted file mode 100644
index 69160a656f..0000000000
--- a/metagraph/src/graph/differential_tests.hpp
+++ /dev/null
@@ -1,58 +0,0 @@
-#include <cmath>
-#include <cstdint>
-#include <vector>
-#include <tuple>
-#include <numeric>
-#include "look_up_table_t_dist.hpp"
-
-namespace mtg {
-
-class DifferentialTest {
-  private:
-    double family_wise_error_rate;
-    size_t total_hypotheses;
-    size_t in_total_kmers;
-    size_t out_total_kmers;
-    size_t in_unique_kmers;
-    size_t out_unique_kmers;
-    TDistributionTable t_table;
-    double alpha_precalc = -1;
-    int df_precalc = -1;
-    const double pi = std::atan(1) * 4;
-    double gamma = std::sqrt(pi);
-    double likelihood_ratio_threshold = DifferentialTest::lrt_threshold();
-
-  public:
-    int64_t var_0 = 0;
-    DifferentialTest(double family_wise_error_rate,
-                     size_t total_hypotheses,
-                     size_t preload_table_size,
-                     size_t in_total_kmers,
-                     size_t out_total_kmers);
-
-    double poisson_prob(int k, double lambda);
-    std::tuple<bool, double> brunner_munzel_test(std::vector<double> in_counts, std::vector<double> out_counts);
-
-    int benjamini_yekutieli(std::vector<std::pair<double, int>> likelihood_ratios, double lrt_threshold);
-
-    bool bonferroni_correction(double &pvalue);
-
-    double lrt_threshold();
-
-    double get_t_test_alpha(int df, double alpha);
-
-    std::tuple<std::vector<double>, std::vector<double>> get_overall_midrank(std::vector<double> in_counts, std::vector<double> out_counts, int N);
-
-    double get_var_midranks(std::vector<double> mid_ranks_within, std::vector<double> mid_ranks_overall, int m);
-
-    std::vector<double> get_midranks(std::vector<double> in_counts, int size_in_counts);
-
-    std::tuple<bool, double> likelihood_ratio_test(double in_sum, double out_sum);
-
-    int get_df_approx(std::vector<double> in_counts, std::vector<double> out_counts);
-
-    int get_df_conservative(std::vector<double> in_counts, std::vector<double> out_counts);
-
-    double get_var(std::vector<double> counts, int n);
-};
-} // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/differential_tests_myrthe.cpp b/metagraph/src/graph/differential_tests_myrthe.cpp
deleted file mode 100644
index e207aa70ac..0000000000
--- a/metagraph/src/graph/differential_tests_myrthe.cpp
+++ /dev/null
@@ -1,169 +0,0 @@
-// #include <math.h>
-// #include <string>
-// #include <numbers>
-// #include <iostream>
-// #include <cassert>
-
-// #include "differential_tests.hpp"
-// #include "lookup_table_chisqrd_cdf.cpp"
-
-// // https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/log_factorial_table.hpp
-// // cpp https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/log_factorial_table.cpp
-// namespace mtg{
-// //LogFactorialTable::LogFactorialTable(size_t size)
-// //        : m_size(size)
-// //{
-// //    m_table.reserve(m_size);
-// //    for (size_t i=0; i<size; i++)
-// //        m_table.push_back(log_factorial(i));
-// //}
-// //
-// //double LogFactorialTable::log_factorial(size_t k) // TODO this can be calculated more efficiently.
-// //{
-// //    double res = 0;
-// //    while (k > 1)
-// //    {
-// //        res += log(k);
-// //        k--;
-// //    }
-// //    return res;
-// //}
-// //
-// //double LogFactorialTable::approximate_log_factorial(size_t k) {
-// //    return k * log(k) - k * log2e_v;
-// //}
-
-
-// //double DifferentialTest::chi_sqrd_1_cdf(double x)// DOF = 1 // https://www.codeproject.com/Articles/432194/How-to-Calculate-the-Chi-Squared-P-Value
-// //{
-// //    assert(x > 0); // assert that it is positive.
-// //    double igf = lower_incomplete_gamma_function(x/0.5);
-// //    if(std::isnan(igf) or std::isinf(igf) or igf <= 1e-8) return 1e-14;
-// //    return 1.0 - igf / gamma;
-// //}
-// //
-// //double DifferentialTest::lower_incomplete_gamma_function(double x) // lower incomplete_gamma_function lower incomplete. with s = 1/2
-// //{ // https://en.wikipedia.org/wiki/Incomplete_gamma_function#Lower_incomplete_gamma_function
-// //    double Sc = 2 * std::sqrt(x) * exp(-x);
-// //    double s = 0.5; double sum = 1.0; double nominator = 1.0; double denominator = 1.0;
-// //    for(int I = 0; I < 200; I++)
-// //    {
-// //        s++;
-// //        nominator *= x;
-// //        denominator *= s;
-// //        sum += (nominator / denominator);
-// //    }
-// //    return sum * Sc;
-// //}
-
-// //double DifferentialTest::poisson_pmf(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
-// //{
-// //    if (lambda <= 0) return 0;
-// //    if (k < 0) k = 0;
-// //    return (-lambda + (k * std::log(lambda)) - lf_table[k]); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
-// //}
-// //
-// //double DifferentialTest::binomial_pmf(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
-// //{
-// //    if (lambda <= 0) return 0;
-// //    if (k < 0) k = 0;
-// //    return (-lambda + (k * std::log(lambda)) - lf_table[k]); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
-// //}
-// //
-
-
-// DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t ,
-//                                    size_t in_total_kmers, size_t out_total_kmers) : //preload_table_size(preload_table_size), // similar to the width of the counts vector, this should be the maximum joint coverage over the in_labels resp. out_labels
-//       family_wise_error_rate(family_wise_error_rate),
-//       total_hypotheses(total_hypotheses),
-//       out_total_kmers(out_total_kmers),
-//       in_total_kmers(in_total_kmers)
-// {}
-
-// double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
-// {
-//     if (lambda <= 0) return 0;
-//     if (k < 0) k = 0;
-//     return (-lambda + (k * std::log(lambda))); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
-// }
-
-
-
-// bool DifferentialTest::bonferroni_correction(double& pvalue)
-// {
-//     return pvalue < (family_wise_error_rate / total_hypotheses); //  store "family_wise_error_rate / total_hypotheses" as a class variable, corrected_pvalue_threshold (2.4e-8)
-// }
-
-
-// double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test
-//     double corrected_pvalue = family_wise_error_rate/total_hypotheses; // Take into account the Bonferonni multiple testing correction
-//     if (family_wise_error_rate ==1)// Myrthe: ugly temporair method to put multiple testing off, and simply use a pvalue of 1 instead.
-//         return (double) 3840 / 1000 / 2;//corrected_pvalue = 0.05;
-//     Chi2PLookup lookup;
-//     int low = 0;
-//     int high = lookup.divisor * lookup.cutoff[0] - 1;
-//     while (low <= high) {
-//         int mid = (low + high) >> 1; // the mean of low and high
-//         double current_pvalue = lookup.getPValue(mid / lookup.divisor, 1);
-//         if (current_pvalue > corrected_pvalue)
-//         {low = mid + 1;}
-//         else
-//         {high = mid - 1;}
-//     } // this binary search is not perfect yet: for 0.05 mid = 4000, wheras it should be more like 3840
-//     return (double)  low / lookup.divisor / 2.0; // Note that 2*LRT is Chi-squared distributed.
-// }
-
-
-// // adapted code from kmdiff.
-// // previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
-// std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
-// {   
-//     std::cout << "TEST";
-//     double mean = (out_sum + in_sum) / static_cast<double>(out_total_kmers + in_total_kmers);
-
-//     double alt_hypothesis = poisson_prob(out_sum, out_sum) +
-//             poisson_prob(in_sum, in_sum); // K2 equals N2Theta2 because Theta2 = K2/N2 (?). Shouldn't Theta2 be = K2/|background labels| ?
-
-//     double null_hypothesis = poisson_prob(out_sum, mean * out_total_kmers) +
-//             poisson_prob(in_sum, mean * in_total_kmers);
-
-//     double likelihood_ratio = alt_hypothesis - null_hypothesis;
-//     std::cout << "threshold " << likelihood_ratio << "ratio " << likelihood_ratio << "\n";
-
-//     double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
-//     bool sign = false;
-//     if (out_sum_normalized < in_sum) sign = true;
-//     if (out_sum_normalized >= in_sum // This checks if the rate for the foreground group is higher compared to the background.
-//         and likelihood_ratio > likelihood_ratio_threshold)
-//         // std::cout << sign;
-//     if (out_sum_normalized < in_sum // This checks if the rate for the foreground group is higher compared to the background.
-//         and likelihood_ratio > likelihood_ratio_threshold)
-//         return std::make_tuple(true, likelihood_ratio);
-
-// //
-// //
-// //    if (likelihood_ratio < 0) likelihood_ratio = 0;
-// //    Chi2PLookup Chi2PLookupTable; // generated with https://github.com/MoseleyBioinformaticsLab/chi2plookup
-// //    double pvalue = Chi2PLookupTable.getPValue(2 * likelihood_ratio, 1);
-// //
-// //
-
-// //    bool efficient_test = (likelihood_ratio > likelihood_ratio_threshold);
-// //    bool normal_test = (sign and bonferroni_correction(pvalue));
-// //    std::ignore = efficient_test;
-// //    std::ignore = normal_test;
-// //
-// //    if ((likelihood_ratio > likelihood_ratio_threshold) != (sign and bonferroni_correction(pvalue))){
-// //        std::cout  << "efficient test: " + std::to_string(likelihood_ratio > likelihood_ratio_threshold) << std::endl;  // TODO test this instead of the code below.
-// //        std::cout << "normal test: " + std::to_string(sign and bonferroni_correction(pvalue)) << std::endl; // The efficient test always returns false it seems.
-// //    }
-// //    assert((likelihood_ratio > likelihood_ratio_threshold) == (sign and bonferroni_correction(pvalue)));
-// //
-// //    if (sign)
-// //        if (bonferroni_correction(pvalue))
-// //            return std::make_tuple(true, likelihood_ratio);
-
-//     return std::make_tuple(false, likelihood_ratio);
-// }
-
-// } // namespace mtg
diff --git a/metagraph/src/graph/differential_tests_uemmi.cpp b/metagraph/src/graph/differential_tests_uemmi.cpp
deleted file mode 100644
index 12b15a093d..0000000000
--- a/metagraph/src/graph/differential_tests_uemmi.cpp
+++ /dev/null
@@ -1,275 +0,0 @@
-#include <math.h>
-#include <string>
-// #include <numbers>
-#include <iostream>
-#include <cassert>
-#include <algorithm>
-#include "common/logger.hpp"
-
-#include "differential_tests.hpp"
-#include "lookup_table_chisqrd_cdf.hpp"
-
-
-// https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/include/kmdiff/log_factorial_table.hpp
-// cpp https://github.com/tlemane/kmdiff/blob/6a56ce6f20abbf63928a19ebbfecb1968efd4cd3/src/log_factorial_table.cpp
-namespace mtg{
-
-DifferentialTest::DifferentialTest(double family_wise_error_rate, size_t total_hypotheses, size_t ,
-                                   size_t in_total_kmers, size_t out_total_kmers):
-    family_wise_error_rate(family_wise_error_rate),
-    total_hypotheses(total_hypotheses),
-    in_total_kmers(in_total_kmers),
-    out_total_kmers(out_total_kmers)
-    {}
-
-// loglikelihood of poisson dist.
-double DifferentialTest::poisson_prob(int k, double lambda) // https://en.wikipedia.org/wiki/Poisson_distribution and https://elifesciences.org/articles/32920#appendix-1
-{
-    if (lambda <= 0) return 0;
-    if (k < 0) k = 0;
-    return (-lambda + (k * std::log(lambda))); // One does not need to subtract the factorial of k because it divides away in the likelihood ratio.
-}
-
-double DifferentialTest::get_t_test_alpha(int df, double alpha=0.05){
-    // alpha = alpha/2; // convert to one sided test
-    alpha = alpha/total_hypotheses; // bonferroni correction
-    common::logger->trace("alpha: {}\tdf:{}", alpha, df);
-    double critical_value = t_table.getCriticalValue(alpha, df);
-    common::logger->trace("Critical value: {}", critical_value);
-    return critical_value;
-}
-
-std::vector<double> DifferentialTest::get_midranks(std::vector<double> in_counts, int size_in_counts){
-    std::vector<double> mid_ranks;
-    std::sort(in_counts.begin(), in_counts.end());
-    double curr_value = in_counts[0];
-    int tied = 1;
-    for (int i = 1; i<size_in_counts; i++){
-        if (in_counts[i] == curr_value){
-            tied++;
-            if (i == size_in_counts-1){
-                i += 1;
-                for (int j = i-tied; j<i; j++){
-                    mid_ranks.push_back((i-tied) + (tied+1)/2.0);
-                }
-            }
-        }
-        else{
-            if (i == size_in_counts-1){
-                for (int j = i-tied; j<i; j++){
-                    mid_ranks.push_back((i-tied) + (tied+1)/2.0);
-                }
-                mid_ranks.push_back(i+1);
-            }
-            else{
-                for (int j = i-tied; j<i; j++){
-                    mid_ranks.push_back((i-tied) + (tied+1)/2.0);
-                }
-                curr_value = in_counts[i];
-                tied = 1;
-            }
-
-        }
-    }
-    return mid_ranks;
-}
-
-std::tuple<std::vector<double>, std::vector<double>> DifferentialTest::get_overall_midrank(std::vector<double> in_counts, std::vector<double> out_counts, int N){
-    std::vector<std::tuple<double, int>> all_counts;
-    for (int i = 0; i < (int) in_counts.size(); i++){
-        all_counts.push_back(std::make_tuple(in_counts[i], 1));
-    }
-    for (int i = 0; i < (int) out_counts.size(); i++){
-        all_counts.push_back(std::make_tuple(out_counts[i], 0));
-    }
-    std::vector<std::tuple<double, int>> midranks_all;
-    double curr_value = std::get<0>(all_counts[0]);
-    int tied = 1;
-    for (int i = 1; i<N; i++){
-        if (std::get<0>(all_counts[i]) == curr_value){
-            tied++;
-            if (i == N-1){
-                i += 1;
-                for (int j = i-tied; j<i; j++){
-                    midranks_all.push_back(std::make_tuple((i-tied) + (tied+1)/2.0, std::get<1>(all_counts[midranks_all.size()])));
-                }
-            }
-        }
-        else{
-            if (i == N-1){
-                for (int j = i-tied; j<i; j++){
-                    midranks_all.push_back(std::make_tuple((i-tied) + (tied+1)/2.0, std::get<1>(all_counts[midranks_all.size()])));
-                }
-                midranks_all.push_back(std::make_tuple(i+1, std::get<1>(all_counts[midranks_all.size()])));
-            }
-            else{
-                for (int j = i-tied; j<i; j++){
-                    midranks_all.push_back(std::make_tuple((i-tied) + (tied+1)/2.0, std::get<1>(all_counts[midranks_all.size()])));
-                }
-                curr_value = std::get<0>(all_counts[i]);
-                tied = 1;
-            }
-
-        }
-    }
-    std::vector<double> in_ranks;
-    std::vector<double> out_ranks;
-    for (int i = 0; i<N; i++){
-        if (std::get<1>(midranks_all[i]) == 1){
-            in_ranks.push_back(std::get<0>(midranks_all[i]));
-        }
-        else{
-            out_ranks.push_back(std::get<0>(midranks_all[i]));
-        }
-    }
-    return std::make_tuple(in_ranks, out_ranks);
-}
-
-int DifferentialTest::get_df_approx(std::vector<double> in_counts, std::vector<double> out_counts){
-    int m = in_counts.size();
-    int n = out_counts.size();
-    double var_in = get_var(in_counts, m);
-    double var_out = get_var(out_counts, n);
-    if (var_in == 0 && var_out == 0){
-        return std::min(m, n) - 1;
-    }
-    double df_approx = (var_in*var_in/m + var_out*var_out/n)*(var_in*var_in/m + var_out*var_out/n) / (((var_in*var_in/m)*(var_in*var_in/m)/(m - 1)) + ((var_out*var_out/n)*(var_out*var_out/n)/(n - 1)));
-    return int(std::floor(df_approx));
-}
-
-int DifferentialTest::get_df_conservative(std::vector<double> in_counts, std::vector<double> out_counts){
-    int m = in_counts.size();
-    int n = out_counts.size();
-    return std::max(1,std::min(m, n) - 1);
-}
-
-double DifferentialTest::get_var(std::vector<double> counts, int n){
-    double mean = std::accumulate(counts.begin(), counts.end(), 0.0) / n;
-    double mean_of_sqaures = std::inner_product(counts.begin(), counts.end(), counts.begin(), 0)/n;
-    double var = mean_of_sqaures - mean*mean;
-    return var;
-}
-
-double DifferentialTest::get_var_midranks(std::vector<double> mid_ranks_within, std::vector<double> mid_ranks_overall, int m){
-    double mean_mid_rank = std::accumulate(mid_ranks_overall.begin(), mid_ranks_overall.end(), 0.0) / mid_ranks_overall.size();
-    double var = 0;
-    for (int i = 0; i < m; i++){
-        var = var + (mid_ranks_overall[i] - mid_ranks_within[i] - mean_mid_rank + (m+1)/2) * (mid_ranks_overall[i] - mid_ranks_within[i] - mean_mid_rank + (m+1)/2);
-    }
-    return var/(m-1);
-}
-
-
-std::tuple<bool, double> DifferentialTest::brunner_munzel_test(std::vector<double> in_counts, std::vector<double> out_counts){
-    int m = in_counts.size();
-    int n = out_counts.size();
-    int N = m + n;
-    // get overall midranks
-    auto [mid_ranks_in, mid_ranks_out] = get_overall_midrank(in_counts, out_counts, N);
-    // get means of midranks
-    double mean_mid_rank_in = std::accumulate(mid_ranks_in.begin(), mid_ranks_in.end(), 0.0) / mid_ranks_in.size();
-    double mean_mid_rank_out = std::accumulate(mid_ranks_out.begin(), mid_ranks_out.end(), 0.0) / mid_ranks_out.size();
-    // get midranks within groups
-    std::vector<double> in_ranks = get_midranks(in_counts, m);
-    std::vector<double> out_ranks = get_midranks(out_counts, n);
-    // get variances
-    double var_in = get_var_midranks(in_ranks, mid_ranks_in, m);
-    double var_out = get_var_midranks(out_ranks, mid_ranks_out, n);
-    if (var_in == 0 && var_out == 0){
-        var_0++;
-        var_in = 1;
-        var_out = 1;
-    }
-    // get test statistic
-    double b = (mean_mid_rank_out - mean_mid_rank_in) / (N * std::sqrt(var_in/(m*n*n) + var_out/(m*m*n)));
-    // get degrees of freedom
-    if (df_precalc == -1){
-        common::logger->trace("df_precalc not set, calculating df_precalc");
-        df_precalc = get_df_conservative(in_counts, out_counts);
-    }
-    // get t statistic alpha value
-    if (alpha_precalc == -1){
-        common::logger->trace("alpha_precalc not set, calculating alpha_precalc");
-        alpha_precalc = get_t_test_alpha(df_precalc, 0.05);
-    }
-    if( std::abs(b) > alpha_precalc){
-        return std::tuple(true, b);
-    }
-    else{
-        return std::tuple(false, b);
-    }
-}
-
-
-std::tuple<std::vector<int>, std::vector<double>> sorted_pvalues(){
-    std::vector<int> nodes_sorted;
-    std::vector<double> pvalues_sorted;
-
-    return std::make_tuple(nodes_sorted, pvalues_sorted);
-}
-
-// not in use at the moment
-bool DifferentialTest::bonferroni_correction(double& pvalue)
-{
-    return pvalue < (family_wise_error_rate / total_hypotheses);
-}
-
-// return likelihood ratio thereshold corresponding to corrected alpha value
-double DifferentialTest::lrt_threshold(){ // Binary search to find the threshold on the liklihood ratio test
-    double corrected_pvalue = family_wise_error_rate/total_hypotheses; // Take into account the Bonferonni multiple testing correction
-    std::cout << "total_hypotheses " << total_hypotheses << "\n";
-    if (family_wise_error_rate ==1) // TODO: change how to handle turning off mutliple testing correction. currtenly used like a flag, has no statistical meaning
-        return (double) 3840 / 1000 / 2; //corrected_pvalue = 0.05;
-    Chi2PLookup lookup;
-    int low = 0;
-    int high = lookup.divisor * lookup.cutoff[0] - 1;
-    while (low <= high) {
-        int mid = (low + high) >> 1; // the mean of low and high
-        double current_pvalue = lookup.getPValue(mid / lookup.divisor, 1);
-        if (current_pvalue > corrected_pvalue)
-        {low = mid + 1;}
-        else
-        {high = mid - 1;}
-    } // get index where pvalue is closest to corrected pvalue, from that index get the corresponding likelihood ratio threshold
-    return (double)  low / lookup.divisor / 2.0; // Note that 2*LRT is Chi-squared distributed.
-}
-
-int DifferentialTest::benjamini_yekutieli(std::vector<std::pair<double, int>> likelihood_ratios, double lrt_threshold){
-    int m = likelihood_ratios.size();
-    double gamma = 0.5772156649; // euler-mascheroni constant
-    double c = std::log(m)  + gamma + 1/(2*m); // taylor expansion for sum(1/i) from 1 to m
-    int max_i = 0;
-    for (int i = 0; i <= m-1; i++){
-        double pvalue = likelihood_ratios[i].first;
-        double qvalue = lrt_threshold * (i+1) / (m * c);
-        if (pvalue <= qvalue){
-            max_i = i;
-        }
-    }
-    return max_i;
-}
-
-
-// adapted code from kmdiff.
-// previous is equivalent to mean_control,  mean_case, latter is equivalent to m_sum_controls, m_sum_cases
-std::tuple<bool, double> DifferentialTest::likelihood_ratio_test(double in_sum, double out_sum)
-{
-    // add pseudocounts
-    in_sum += 1;
-    out_sum += 1;
-    double mean = (out_sum + in_sum)/ static_cast<double>(out_total_kmers + in_total_kmers);
-    double alt_hypothesis = poisson_prob(out_sum, out_sum) +
-            poisson_prob(in_sum, in_sum); // K2 equals N2Theta2 because Theta2 = K2/N2 (?). Shouldn't Theta2 be = K2/|background labels| ?
-
-    double null_hypothesis = poisson_prob(out_sum, mean * out_total_kmers) +
-            poisson_prob(in_sum, mean * in_total_kmers);
-
-    double likelihood_ratio = alt_hypothesis - null_hypothesis;
-    // double out_sum_normalized = (double) out_sum * in_total_kmers / out_total_kmers;
-    if (likelihood_ratio > likelihood_ratio_threshold){
-        return std::make_tuple(true, likelihood_ratio);
-    }
-    return std::make_tuple(false, likelihood_ratio);
-}
-
-} // namespace mtg
diff --git a/metagraph/src/graph/look_up_table_t_dist.cpp b/metagraph/src/graph/look_up_table_t_dist.cpp
deleted file mode 100644
index 825152cf0d..0000000000
--- a/metagraph/src/graph/look_up_table_t_dist.cpp
+++ /dev/null
@@ -1,17 +0,0 @@
-#include "look_up_table_t_dist.hpp"
-
-namespace mtg{
-
-TDistributionTable::TDistributionTable(){};
-    // populate t table
-
-double TDistributionTable::getCriticalValue(double alpha, int df){
-    // Create Student's t-distribution object
-    boost::math::students_t dist(df);
-
-    // Get critical value for two-tailed test
-    double critical_value = boost::math::quantile(dist, 1 - alpha);
-    return critical_value;
-}   
-
-}; // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/look_up_table_t_dist.hpp b/metagraph/src/graph/look_up_table_t_dist.hpp
deleted file mode 100644
index 4ed3edf424..0000000000
--- a/metagraph/src/graph/look_up_table_t_dist.hpp
+++ /dev/null
@@ -1,20 +0,0 @@
-#ifndef __TLOOKUP_HPP__
-#define __TLOOKUP_HPP__
-
-#include <iostream>
-#include <cmath>
-#include <vector>
-#include <boost/math/distributions/students_t.hpp>
-
-namespace mtg{
-class TDistributionTable {
-private:
-
-public:
-    TDistributionTable();
-
-    double getCriticalValue(double alpha, int df);
-};
-}
-
-#endif // __TLOOKUP_HPP__
\ No newline at end of file
diff --git a/metagraph/src/graph/lookup_table_chisqrd_cdf.cpp b/metagraph/src/graph/lookup_table_chisqrd_cdf.cpp
deleted file mode 100644
index 9b06e21934..0000000000
--- a/metagraph/src/graph/lookup_table_chisqrd_cdf.cpp
+++ /dev/null
@@ -1,9 +0,0 @@
-#include "lookup_table_chisqrd_cdf.hpp"
-namespace mtg{
-static double pValues_1[] = {1.0, 0.9747728793699604, 0.9643294082703201, 0.9563199039155036, 0.9495709711511051, 0.9436280222029834, 0.9382578759648362, 0.9333219882913968, 0.9287300745648026, 0.9244194121866707, 0.920344325445942, 0.9164703270093635, 0.9127706190965621, 0.9092238872176422, 0.9058128467277439, 0.9025232501776794, 0.8993431885613663, 0.8962625866214807, 0.8932728301422118, 0.8903664853411608, 0.887537083981715, 0.884778956317105, 0.8820870994622765, 0.8794570724210615, 0.8768849114533788, 0.8743670611628918, 0.8719003178766185, 0.8694817827381617, 0.8671088225520472, 0.8647790368687397, 0.8624902301359216, 0.8602403879942262, 0.8580276569875211, 0.8558503271050759, 0.8537068166869359, 0.8515956593128358, 0.8495154923650348, 0.847465047011011, 0.8454431393963031, 0.8434486628734481, 0.841480581121794, 0.839537922036432, 0.8376197722836831, 0.83572527243636, 0.833853612615066, 0.8320040285726364, 0.8301757981678541, 0.8283682381821483, 0.826580701439357, 0.824812574194006, 0.8230632737581214, 0.8213322463404729, 0.819618965075448, 0.8179229282216066, 0.8162436575123876, 0.8145806966435467, 0.8129336098837189, 0.8113019807960693, 0.8096854110603592, 0.8080835193859561, 0.8064959405073401, 0.8049223242545844, 0.8033623346920775, 0.8018156493194588, 0.8002819583293617, 0.7987609639171032, 0.7972523796379415, 0.7957559298079551, 0.7942713489449782, 0.7927983812463655, 0.7913367801006603, 0.7898863076305189, 0.788446734264471, 0.7870178383353312, 0.7855994057032555, 0.7841912294016191, 0.7827931093040512, 0.7814048518110996, 0.7800262695551269, 0.778657181122161, 0.7772974107895214, 0.775946788278143, 0.7746051485185987, 0.7732723314299123, 0.7719481817103101, 0.7706325486391387, 0.769325285889223, 0.7680262513490048, 0.766735306953841, 0.7654523185258961, 0.7641771556220947, 0.7629096913896449, 0.7616498024286804, 0.7603973686615905, 0.7591522732086494, 0.7579144022695748, 0.7566836450106742, 0.7554598934572603, 0.7542430423910372, 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6.946190974632671e-07, 6.942587442715364e-07, 6.938985785964746e-07, 6.935386002160371e-07, 6.931788090192015e-07, 6.928192051169901e-07, 6.924597881763361e-07, 6.921005581972395e-07, 6.917415150686779e-07, 6.91382658679629e-07, 6.910239890300929e-07, 6.906655058980249e-07, 6.90307209283425e-07, 6.899490989642487e-07, 6.895911749404959e-07, 6.892334371011444e-07, 6.888758853351717e-07, 6.885185195315557e-07, 6.881613395792741e-07, 6.878043454783267e-07, 6.874475371176914e-07, 6.870909142753234e-07, 6.867344769512229e-07, 6.863782250343675e-07, 6.860221584137349e-07, 6.856662769783028e-07, 6.85310580617049e-07, 6.849550693299733e-07, 6.845997428950312e-07, 6.842446014232451e-07, 6.838896445815479e-07, 6.835348723699397e-07, 6.831802846773982e-07, 6.828258813929011e-07, 6.824716625164484e-07, 6.821176278259955e-07, 6.817637773215424e-07, 6.814101107810444e-07, 6.810566283155239e-07, 6.807033297029363e-07, 6.80350214721237e-07, 6.799972835924706e-07, 6.796445358725478e-07, 6.79291971672491e-07, 6.789395908812779e-07, 6.785873933878861e-07, 6.782353790812934e-07, 6.778835478504774e-07, 6.77531899584416e-07, 6.771804341720866e-07, 6.768291517245117e-07, 6.764780519086244e-07, 6.761271346134023e-07, 6.757763999498678e-07, 6.754258476959762e-07, 6.750754777407053e-07, 6.74725290084055e-07, 6.743752845039808e-07, 6.740254610004826e-07, 6.736758193515158e-07, 6.733263596681027e-07, 6.729770817281988e-07, 6.726279854207817e-07, 6.722790707458515e-07, 6.719303374813634e-07, 6.715817856273176e-07, 6.712334150726917e-07, 6.708852255954412e-07, 6.705372173065882e-07, 6.701893900951106e-07, 6.698417437389637e-07, 6.694942781271251e-07, 6.69146993259595e-07, 6.68799889025351e-07, 6.684529653133708e-07, 6.681062221236544e-07, 6.677596592341573e-07, 6.67413276533857e-07, 6.670670740227536e-07, 6.667210515898248e-07, 6.663752091240482e-07, 6.660295465144017e-07, 6.656840636498629e-07, 6.653387605304317e-07, 6.649936369340637e-07, 6.646486928607587e-07, 6.643039281994945e-07, 6.639593428392487e-07, 6.636149367800215e-07, 6.632707096887458e-07, 6.629266617874663e-07, 6.62582792743116e-07, 6.622391025556951e-07, 6.618955911141811e-07, 6.615522583075517e-07, 6.612091040247847e-07, 6.608661282658801e-07, 6.605233309198155e-07, 6.601807118755687e-07, 6.59838270911095e-07, 6.594960081374168e-07, 6.591539233324895e-07, 6.588120163852906e-07, 6.584702874068427e-07, 6.581287360640786e-07, 6.577873623569985e-07, 6.574461661745801e-07, 6.57105147405801e-07, 6.567643060506612e-07, 6.564236419981384e-07, 6.560831550261881e-07, 6.557428451348102e-07, 6.554027123240047e-07, 6.550627563717271e-07, 6.547229771669549e-07, 6.543833747096883e-07, 6.540439488889049e-07, 6.537046995935825e-07, 6.533656267126986e-07, 6.53026730135231e-07, 6.526880098611798e-07, 6.523494656685003e-07, 6.520110976682147e-07, 6.516729055272563e-07, 6.513348893566473e-07, 6.509970489343431e-07, 6.506593841493213e-07, 6.503218950015821e-07, 6.499845814911254e-07, 6.496474432848842e-07, 6.493104803828587e-07, 6.489736927850487e-07, 6.48637080380432e-07, 6.48300642946964e-07, 6.479643804846447e-07, 6.476282928824517e-07, 6.472923801403851e-07, 6.469566420364004e-07, 6.466210785704973e-07, 6.462856896316538e-07, 6.459504751088474e-07, 6.456154348910559e-07, 6.452805688672569e-07, 6.449458770374505e-07, 6.446113591795921e-07, 6.442770154047039e-07, 6.439428454907414e-07, 6.436088493266823e-07, 6.432750268015042e-07, 6.429413779152071e-07, 6.426079025567688e-07, 6.422746006151669e-07, 6.419414720904015e-07, 6.416085166494057e-07, 6.412757345142239e-07, 6.409431253517894e-07, 6.406106891621022e-07, 6.402784258341399e-07, 6.399463353679025e-07, 6.396144175413454e-07, 6.392826723544687e-07, 6.389510995852277e-07, 6.386196993446447e-07, 6.382884714106751e-07, 6.37957415783319e-07, 6.376265322405317e-07, 6.372958207823132e-07, 6.369652812976412e-07, 6.366349137865157e-07, 6.363047180268921e-07, 6.359746939077482e-07, 6.356448415401061e-07, 6.353151605908991e-07, 6.349856511711494e-07, 6.3465631294779e-07, 6.343271461428657e-07, 6.339981504233094e-07, 6.336693259001436e-07, 6.333406722403012e-07, 6.330121895548046e-07, 6.326838777326316e-07, 6.323557365517374e-07, 6.320277660121221e-07, 6.316999661137856e-07, 6.313723366346835e-07, 6.310448774637933e-07, 6.307175886011152e-07, 6.303904700466489e-07, 6.300635214673278e-07, 6.29736742974174e-07, 6.29410134345143e-07, 6.290836955802348e-07, 6.28757426568427e-07, 6.284313271986974e-07, 6.28105397471046e-07, 6.277796371634281e-07, 6.274540462758438e-07, 6.271286246972707e-07, 6.268033723166866e-07, 6.264782891340914e-07, 6.261533749274406e-07, 6.258286296967341e-07, 6.255040533309497e-07, 6.251796458300873e-07, 6.248554069721024e-07, 6.245313366459726e-07, 6.242074348516979e-07, 6.238837015892784e-07, 6.235601365256471e-07, 6.232367397718264e-07, 6.229135112167938e-07, 6.225904507495272e-07, 6.222675582590043e-07, 6.219448336342026e-07, 6.216222767641e-07, 6.212998877597187e-07, 6.209776662879918e-07, 6.206556124599416e-07, 6.203337259425012e-07, 6.200120069577153e-07, 6.196904551725169e-07, 6.193690706979282e-07, 6.190478532008825e-07, 6.18726802792402e-07, 6.184059193614644e-07, 6.180852026860251e-07, 6.177646528771064e-07, 6.174442697126636e-07, 6.171240531926969e-07, 6.168040030951616e-07, 6.164841194200577e-07, 6.161644021673851e-07, 6.15844851004077e-07, 6.155254661521781e-07, 6.152062472786213e-07, 6.14887194494429e-07, 6.145683074665342e-07, 6.142495863059594e-07, 6.139310309016821e-07, 6.136126411426801e-07, 6.132944169179311e-07, 6.12976358227435e-07, 6.126584648491473e-07, 6.123407366720457e-07, 6.120231738071524e-07, 6.117057761434452e-07, 6.113885433478572e-07, 6.110714756424329e-07, 6.107545726941055e-07, 6.104378346138972e-07, 6.101212611797635e-07, 6.09804852280682e-07, 6.094886080276751e-07, 6.091725281986982e-07, 6.088566125717065e-07, 6.085408613687449e-07, 6.082252742567462e-07, 6.079098512357106e-07, 6.075945923056381e-07, 6.072794972444839e-07, 6.069645660522482e-07, 6.066497985068864e-07, 6.063351947194207e-07, 6.060207544678065e-07, 6.057064777520438e-07, 6.05392364350088e-07, 6.050784143729615e-07, 6.047646275986196e-07, 6.0445100391604e-07, 6.04137543436245e-07, 6.038242458261678e-07, 6.035111111968305e-07, 6.031981393261887e-07, 6.028853302142423e-07, 6.025726836389467e-07, 6.022601997113242e-07, 6.019478783203525e-07, 6.01635719243987e-07, 6.013237224822277e-07, 6.010118879240522e-07, 6.007002154584384e-07, 6.003887050853862e-07, 6.000773566938733e-07, 5.997661701728774e-07, 5.994551454113761e-07, 5.991442824093696e-07, 5.988335810558354e-07, 5.985230412397513e-07, 5.98212662850095e-07, 5.979024458868665e-07, 5.975923901280211e-07, 5.972824955735589e-07, 5.969727622234799e-07, 5.966631898557395e-07, 5.963537784703377e-07, 5.960445279562521e-07, 5.957354382024604e-07, 5.954265092089628e-07, 5.951177408647368e-07, 5.948091329477379e-07, 5.945006855689883e-07, 5.941923985064435e-07, 5.938842717601034e-07, 5.935763051079235e-07, 5.93268498660926e-07, 5.929608523080887e-07, 5.926533658273669e-07, 5.923460392187607e-07, 5.920388723712477e-07, 5.91731865284828e-07, 5.914250177374569e-07, 5.911183298401568e-07, 5.908118012598607e-07, 5.905054322186132e-07, 5.901992222723251e-07, 5.898931716430411e-07, 5.895872801087165e-07, 5.892815475583291e-07, 5.88975974102901e-07, 5.886705594093655e-07, 5.883653034777225e-07, 5.880602063079721e-07, 5.877552676780695e-07, 5.874504876990372e-07, 5.871458661488305e-07, 5.868414029164271e-07, 5.86537098001827e-07, 5.862329512940079e-07, 5.859289626819475e-07, 5.856251321656458e-07, 5.853214596340806e-07, 5.850179449762294e-07, 5.847145879700477e-07, 5.844113888375801e-07, 5.841083472457598e-07, 5.838054633056089e-07, 5.835027367950829e-07, 5.832001676031595e-07, 5.828977557298387e-07, 5.825955010640982e-07, 5.82293403605938e-07, 5.819914632443357e-07, 5.816896797572468e-07, 5.813880531446713e-07, 5.810865834066092e-07, 5.807852704320382e-07, 5.804841139989136e-07, 5.801831142182579e-07, 5.798822708680262e-07, 5.795815839482188e-07, 5.792810533478132e-07, 5.789806790668095e-07, 5.78680460883163e-07, 5.783803986858516e-07, 5.780804925858973e-07, 5.777807423612558e-07, 5.774811480119268e-07, 5.77181709315866e-07, 5.768824263840955e-07, 5.765832989945707e-07, 5.762843271472917e-07, 5.759855107312362e-07, 5.756868496353817e-07, 5.753883438597285e-07, 5.750899931822318e-07, 5.747917977139139e-07, 5.744937572327302e-07, 5.741958716276585e-07, 5.738981408986987e-07, 5.736005649348286e-07, 5.733031437360481e-07};
-
-const double * Chi2PLookup::pValues[] = {pValues_1};
-const int Chi2PLookup::divisor = 1000;
-const int Chi2PLookup::cutoff[] = {25};
-
-} // namespace mtg
\ No newline at end of file
diff --git a/metagraph/src/graph/lookup_table_chisqrd_cdf.hpp b/metagraph/src/graph/lookup_table_chisqrd_cdf.hpp
deleted file mode 100644
index 94295694da..0000000000
--- a/metagraph/src/graph/lookup_table_chisqrd_cdf.hpp
+++ /dev/null
@@ -1,20 +0,0 @@
-#ifndef CHI2PLOOKUP_H
-#define CHI2PLOOKUP_H
-
-namespace mtg{
-// https://www.codeproject.com/Articles/432194/How-to-Calculate-the-Chi-Squared-P-Value
-// table containing pvalues for chi squared distribution with degrees of freedom 1
-struct Chi2PLookup
-{
-    static const double * pValues[];
-    static const int cutoff[];
-    static const int divisor;
-
-    inline double getPValue(double statistic, int df) {
-        return((statistic >= cutoff[df-1]) ? 0.0 : pValues[df-1][int(divisor * statistic)]);
-    }
-
-};
-} // namespace mtg
-#endif // CHI2PLOOKUP_H
-
diff --git a/metagraph/src/graph/timer.hpp b/metagraph/src/graph/timer.hpp
deleted file mode 100644
index 733cba6052..0000000000
--- a/metagraph/src/graph/timer.hpp
+++ /dev/null
@@ -1,23 +0,0 @@
-// timer 
-#include <chrono> // for std::chrono functions
-
-class Timer
-{
-private:
-	// Type aliases to make accessing nested type easier
-	using Clock = std::chrono::steady_clock;
-	using Second = std::chrono::duration<double, std::ratio<1> >;
-
-	std::chrono::time_point<Clock> m_beg { Clock::now() };
-
-public:
-	void reset()
-	{
-		m_beg = Clock::now();
-	}
-
-	double elapsed() const
-	{
-		return std::chrono::duration_cast<Second>(Clock::now() - m_beg).count();
-	}
-};
\ No newline at end of file

From 1d6293902dd849d8651060e7f119530ebd53d71a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 11:22:16 +0200
Subject: [PATCH 190/410] cleanup

---
 metagraph/benchmarks/kmer_distributions.py | 63 ----------------------
 metagraph/src/cli/align.cpp                | 43 +--------------
 2 files changed, 1 insertion(+), 105 deletions(-)
 delete mode 100644 metagraph/benchmarks/kmer_distributions.py

diff --git a/metagraph/benchmarks/kmer_distributions.py b/metagraph/benchmarks/kmer_distributions.py
deleted file mode 100644
index dad47927d2..0000000000
--- a/metagraph/benchmarks/kmer_distributions.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import matplotlib.pyplot as plt
-import importlib
-import scipy
-# plot histograms
-
-# statistical tests whether they are poisson?
-# import the python file from build/
-def evaluate_cpp_probability_distribution(dir):
-    i = importlib.import_module(dir + "_distribution")
-    i.results_exhaustive
-
-def statistical_test(kmer_counts_row):
-    result = scipy.stats.kstest(kmer_counts_row, "poisson")
-    return result.pvalue, result.sign
-
-def plot_distribution(kmer_counts_row, title):
-    n, bins, patches = plt.hist(x=kmer_counts_row, bins='auto', color='#0504aa',
-                                alpha=0.7, rwidth=0.85)
-    plt.grid(axis='y', alpha=0.75)
-    plt.xlabel("K-mer's occurance")
-    plt.ylabel('Frequency')
-    plt.title(title)
-    pvalue, sign = statistical_test(kmer_counts_row)
-    plt.suptitle("Kolmogorov-Smirnov: p-value "+ pvalue + ", sign: " + sign)
-    plt.savefig(dir.replace(".", "\\") + "_dist.png")
-    plt.show()
-
-print("start")
-folder = "C:/Users/myrth/Desktop/coding/metagraph/metagraph/build/"
-
-i = importlib.import_module(folder + "kmer_dist_table.py")
-counts_matrix = i.counts_matrix_in
-counts_matrix = i.counts_matrix_out
-
-random_rows = [2, 18, 20, 300, 240]# only sample even numbers
-for i in random_rows:
-    plot_distribution(counts_matrix_in[i], "in labels") # for in_labels
-    plot_distribution(counts_matrix_out[i], "out labels") # for in_labels
-    plot_distribution(counts_matrix_in[i] + counts_matrix_out[i], "union of in and out labels") # for in_labels
-
-
-
-# read in the .fai file, and select the hepatitis.  'C:/Users/myrth/Desktop/coding/diff_assembly_counts/split/virus.shuf1.100.fa.fai'
-#     f = open("C:/Users/myrth/Desktop/coding/metagraph/metagraph/build/virus_data/virus.shuf1.100.fa.fai","r")
-#     lines = f.readlines()
-#     labels_in = []
-#     labels_out = []
-#
-#     for line in lines:
-#         line = line.replace(",", "")
-#         line_list = line.split("|")
-#         #line_list = line_list.split("\t")
-#         filename = line_list[3]
-#         tag = line_list[4]
-#         if tag[:8] == "Hepatitis"[:8]:
-#             labels_in.append(filename + ".fa")
-#         else:
-#             labels_out.append(filename + ".fa")
-#     print("\", \"".join(labels_in))
-#     print("\", \"".join(labels_out))
-#
-
-# Once you have established this, the next question is whether the distribution is homogeneous or not. This means whether all parts of the data are handled by the same poisson distribution or is there a variation in this based on some aspect like time or space. Once you have convinced of these aspects, try the following three tests
\ No newline at end of file
diff --git a/metagraph/src/cli/align.cpp b/metagraph/src/cli/align.cpp
index 0237e7e803..a4d91a1bad 100644
--- a/metagraph/src/cli/align.cpp
+++ b/metagraph/src/cli/align.cpp
@@ -15,7 +15,6 @@
 #include "config/config.hpp"
 #include "load/load_graph.hpp"
 #include "load/load_annotated_graph.hpp"
-#include "graph/representation/hash/dbg_hash_fast.hpp"
 
 namespace mtg {
 namespace cli {
@@ -86,42 +85,7 @@ void map_sequences_in_file(const std::string &file,
 
     Timer data_reading_timer;
 
-    sdsl::bit_vector visited;
-    size_t num_testable_kmers = graph.max_index();
-    const auto *graph_ptr = &graph;
-    const auto *canonical = dynamic_cast<const CanonicalDBG*>(&graph);
-
-    if (canonical)
-        graph_ptr = &canonical->get_graph();
-
-    if (config.count_testable_kmers) {
-        if (const auto *succ = dynamic_cast<const DBGSuccinct*>(graph_ptr)) {
-            visited = succ->get_boss().mark_all_dummy_edges(get_num_threads());
-            num_testable_kmers = visited.size() - sdsl::util::cnt_one_bits(visited);
-        } else {
-            visited = sdsl::bit_vector(graph_ptr->max_index(), false);
-            num_testable_kmers = visited.size();
-        }
-    }
-
-    size_t total_kmers = num_testable_kmers;
-
     seq_io::read_fasta_file_critical(file, [&](kseq_t *read_stream) {
-        if (config.count_testable_kmers) {
-            auto nodes = canonical
-                ? map_to_nodes(graph, read_stream->seq.s)
-                : map_to_nodes_sequentially(graph, read_stream->seq.s);
-
-            for (size_t i = 0; i + 1 < nodes.size(); ++i) {
-                if (!visited[nodes[i + 1]])
-                    --num_testable_kmers;
-
-                visited[nodes[i + 1]] = true;
-            }
-
-            return;
-        }
-
         logger->trace("Sequence: {}", read_stream->seq.s);
 
         if (config.query_presence
@@ -210,11 +174,6 @@ void map_sequences_in_file(const std::string &file,
 
     }, config.forward_and_reverse);
 
-    if (config.count_testable_kmers) {
-        logger->info("Num k-mers:\t{}\tNum testable k-mers:\t{}",
-                     total_kmers, num_testable_kmers);
-    }
-
     logger->trace("File {} processed in {} sec, current mem usage: {} MB, total time {} sec",
                   file, data_reading_timer.elapsed(), get_curr_RSS() / 1e6, timer.elapsed());
 }
@@ -355,7 +314,7 @@ int align_to_graph(Config *config) {
     ThreadPool thread_pool(get_num_threads());
     std::mutex print_mutex;
 
-    if (config->map_sequences || config->count_testable_kmers) {
+    if (config->map_sequences) {
         if (graph->get_mode() == DeBruijnGraph::PRIMARY) {
             graph = std::make_shared<CanonicalDBG>(graph);
             logger->trace("Primary graph wrapped into canonical");

From c76dbb87651af3236972a7587748c8c6ab878449 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 11:25:35 +0200
Subject: [PATCH 191/410] more cleanup

---
 .../annotate_column_compressed.cpp            |   5 +-
 metagraph/src/cli/stats.cpp                   |  59 -----
 .../src/common/vectors/vector_algorithm.hpp   | 218 +-----------------
 .../representation/base/sequence_graph.cpp    |  62 ++---
 4 files changed, 23 insertions(+), 321 deletions(-)

diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
index 9ccfe137ba..e6beb69433 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.cpp
@@ -488,7 +488,7 @@ bool ColumnCompressed<Label>::load(const std::string &filename) {
 }
 
 template <typename Label>
-bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenames) {  // TODO Myrthe: QUESTION how does this partial streaming exactly work?
+bool ColumnCompressed<Label>::merge_load(const std::vector<std::string> &filenames) {
     // release the columns stored
     cached_columns_.Clear();
     bitmatrix_.clear();
@@ -775,9 +775,6 @@ ::load_columns_and_values(const std::vector<std::string> &filenames,
                 if (column_values.size() != column->num_set_bits())
                     throw std::ifstream::failure("inconsistent size of the value vector");
 
-//                 for (auto &item: column_values) { std::string out_string = std::to_string(item); out = out + x + ", "; }
-//                    std::cout << out_string << ' ' << std::endl; }// Myrthe: temporary for debugging
-
                 callback(offsets[i] + c,
                          label_encoder_load.decode(c),
                          std::move(column), std::move(column_values));
diff --git a/metagraph/src/cli/stats.cpp b/metagraph/src/cli/stats.cpp
index e848dd716d..f4a2cee9c6 100644
--- a/metagraph/src/cli/stats.cpp
+++ b/metagraph/src/cli/stats.cpp
@@ -1,7 +1,6 @@
 #include "stats.hpp"
 
 #include <ips4o/ips4o.hpp>
-#include <progress_bar.hpp>
 
 #include "common/algorithms.hpp"
 #include "common/logger.hpp"
@@ -11,8 +10,6 @@
 #include "graph/representation/succinct/dbg_succinct.hpp"
 #include "graph/representation/succinct/boss.hpp"
 #include "graph/graph_extensions/node_weights.hpp"
-#include "graph/annotated_dbg.hpp"
-#include "graph/representation/canonical_dbg.hpp"
 #include "annotation/representation/row_compressed/annotate_row_compressed.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 #include "annotation/representation/annotation_matrix/static_annotators_def.hpp"
@@ -388,62 +385,6 @@ int print_stats(Config *config) {
 
     const auto &files = config->fnames;
 
-    if (config->count_testable_kmers) {
-        auto graph = load_critical_dbg(files[0]);
-
-        uint64_t num_testable_kmers = graph->max_index();
-        if (auto dbg_succ = dynamic_cast<graph::DBGSuccinct*>(graph.get())) {
-            const auto &boss_graph = dbg_succ->get_boss();
-            uint64_t num_source_dummy_edges
-                = boss_graph.mark_source_dummy_edges(NULL, get_num_threads());
-            uint64_t num_sink_dummy_edges = boss_graph.mark_sink_dummy_edges(NULL);
-            num_testable_kmers -= num_source_dummy_edges + num_sink_dummy_edges;
-        }
-
-        uint64_t total_kmers = num_testable_kmers;
-
-        std::shared_ptr<graph::CanonicalDBG> canonical;
-        if (graph->get_mode() == graph::DeBruijnGraph::PRIMARY) {
-            canonical = std::make_shared<graph::CanonicalDBG>(graph);
-            graph = canonical;
-            logger->trace("Primary graph wrapped into canonical");
-        }
-
-        using ValuesContainer = sdsl::int_vector<>;
-        sdsl::bit_vector visited(graph->max_index(), false);
-        annot::ColumnCompressed<>::load_columns_and_values(config->infbase_annotators,
-            [&](uint64_t, const auto&, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
-                uint64_t rk = 0;
-                ProgressBar progress(column_values.size(),
-                                    "Testing k-mers",
-                                    std::cerr, !common::get_verbose());
-                column->call_ones([&](uint64_t row_i) {
-                    ++progress;
-                    uint64_t cur_count = column_values[rk++];
-                    auto node = graph::AnnotatedDBG::anno_to_graph_index(row_i);
-                    graph->adjacent_outgoing_nodes(node, [&](auto next) {
-                        if (canonical)
-                            next = canonical->get_base_node(next);
-
-                        uint64_t row_j = graph::AnnotatedDBG::graph_to_anno_index(next);
-                        if (uint64_t rk_next = column->conditional_rank1(row_j)) {
-                            uint64_t next_count = column_values[rk_next];
-                            if (cur_count == next_count && !visited[next])
-                                --num_testable_kmers;
-
-                            visited[next] = true;
-                        }
-                    });
-                });
-            }
-        );
-
-        common::logger->info("Total k-mers:\t{}\tNum testable k-mers:\t{}",
-                             total_kmers, num_testable_kmers);
-
-        return 0;
-    }
-
     for (const auto &file : files) {
         if (utils::ends_with(file, ".annodbg")) {
             print_annotation_stats(file, *config);
diff --git a/metagraph/src/common/vectors/vector_algorithm.hpp b/metagraph/src/common/vectors/vector_algorithm.hpp
index 677531a353..5dcc5fd183 100644
--- a/metagraph/src/common/vectors/vector_algorithm.hpp
+++ b/metagraph/src/common/vectors/vector_algorithm.hpp
@@ -219,10 +219,10 @@ class select_support_scan_offset : public select_support_scan<t_b, t_pat_len> {
     using typename select_support_scan<t_b, t_pat_len>::size_type;
 
     explicit select_support_scan_offset(const bit_vector *v = nullptr)
-        : select_support_scan<t_b, t_pat_len>::select_support_scan(v) {}
+          : select_support_scan<t_b, t_pat_len>::select_support_scan(v) {}
 
     select_support_scan_offset(const select_support_scan<t_b,t_pat_len> &ss)
-        : select_support_scan<t_b, t_pat_len>::select_support_scan(ss.m_v) {}
+          : select_support_scan<t_b, t_pat_len>::select_support_scan(ss.m_v) {}
 
     inline size_type select_offset(size_type i, size_type offset = 0) const {
         using trait = select_support_trait<t_b, t_pat_len>;
@@ -233,7 +233,7 @@ class select_support_scan_offset : public select_support_scan<t_b, t_pat_len> {
         size_type args = trait::args_in_the_first_word(*data, word_off, carry);
         if (args >= i) {
             return (word_pos << 6)
-                    + trait::ith_arg_pos_in_the_first_word(*data, i, word_off, carry);
+                + trait::ith_arg_pos_in_the_first_word(*data, i, word_off, carry);
         }
         word_pos++;
         size_type sum_args = args;
@@ -248,7 +248,7 @@ class select_support_scan_offset : public select_support_scan<t_b, t_pat_len> {
             word_pos++;
         }
         return (word_pos << 6)
-                + trait::ith_arg_pos_in_the_word(*data, i - sum_args, old_carry);
+            + trait::ith_arg_pos_in_the_word(*data, i - sum_args, old_carry);
     }
 };
 
@@ -293,8 +293,8 @@ inline bool fetch_and_unset_bit(uint64_t *v, uint64_t i, bool atomic, int mo) {
 
 inline bool fetch_bit(const uint64_t *v, uint64_t i, bool atomic, int mo) {
     return atomic
-            ? ((__atomic_load_n(&v[i >> 6], mo) >> (i & 0x3F)) & 1)
-            : ((v[i >> 6] >> (i & 0x3F)) & 1);
+        ? ((__atomic_load_n(&v[i >> 6], mo) >> (i & 0x3F)) & 1)
+        : ((v[i >> 6] >> (i & 0x3F)) & 1);
 }
 
 inline void set_bit(uint64_t *v, uint64_t i, bool atomic, int mo) {
@@ -313,210 +313,6 @@ inline void unset_bit(uint64_t *v, uint64_t i, bool atomic, int mo) {
     }
 }
 
-inline uint64_t atomic_fetch(const sdsl::int_vector<> &vector,
-                             uint64_t i,
-                             std::mutex &backup_mutex,
-                             int mo) {
-    const uint8_t width = vector.width();
-    const size_t bit_pos = i * width;
-    const uint64_t mask = (1llu << width) - 1;
-    if (width + 7 > 64) {
-        // there is no way to reliably fetch without a mutex
-        std::lock_guard<std::mutex> lock(backup_mutex);
-        return vector[i];
-    } else if ((bit_pos & 0x3F) + width <= 64) {
-        // element fits in an aligned word
-        const uint64_t *word = &vector.data()[bit_pos >> 6];
-        return (__atomic_load_n(word, mo) >> (bit_pos & 0x3F)) & mask;
-#if defined(MODE_TI) && defined(__CX16__)
-    } else if ((bit_pos & 0x7F) + width <= 128) {
-        // element fits in an aligned double word
-        const __uint128_t *word = &reinterpret_cast<const __uint128_t*>(vector.data())[bit_pos >> 7];
-        return (__atomic_load_n(word, mo) >> (bit_pos & 0x7F)) & mask;
-#endif
-    } else {
-        uint8_t shift = bit_pos & 0x7;
-        if (shift + width <= 8) {
-            const uint8_t *word = &reinterpret_cast<const uint8_t*>(vector.data())[bit_pos >> 3];
-            // read from a byte
-            return (__atomic_load_n(word, mo) >> shift) & mask;
-        } else if (shift + width <= 16) {
-            const uint16_t *word = &reinterpret_cast<const uint16_t*>(vector.data())[bit_pos >> 4];
-            shift = bit_pos & 0xF;
-            // unaligned read from two bytes
-            return (__atomic_load_n((const uint16_t*)word, mo) >> shift) & mask;
-        } else if (shift + width <= 32) {
-            const uint32_t *word = &reinterpret_cast<const uint32_t*>(vector.data())[bit_pos >> 5];
-            shift = bit_pos & 0x1F;
-            // unaligned read from four bytes
-            return (__atomic_load_n((const uint32_t*)word, mo) >> shift) & mask;
-        } else if (shift + width <= 64) {
-            const uint64_t *word = &vector.data()[bit_pos >> 6];
-            shift = bit_pos & 0x3F;
-            // unaligned read from eight bytes
-            return (__atomic_load_n((const uint64_t*)word, mo) >> shift) & mask;
-        } else {
-            assert(false);
-        }
-    }
-
-    return 0;
-}
-
-inline uint64_t atomic_fetch_and_add(sdsl::int_vector<> &vector,
-                                     uint64_t i,
-                                     uint64_t val,
-                                     std::mutex &backup_mutex,
-                                     int mo) {
-    const uint8_t width = vector.width();
-    const size_t bit_pos = i * width;
-    const uint64_t mask = (1llu << width) - 1;
-    if (width + 7 > 64) {
-        // there is no way to reliably modify without a mutex
-        std::lock_guard<std::mutex> lock(backup_mutex);
-        const uint64_t old_val = vector[i];
-        vector[i] = old_val + val;
-        return old_val;
-    } else if ((bit_pos & 0x3F) + width <= 64) {
-        // element fits in an aligned word
-        uint64_t *word = &vector.data()[bit_pos >> 6];
-        const uint8_t shift = bit_pos & 0x3F;
-        return (__atomic_fetch_add(word, val << shift, mo) >> shift) & mask;
-#if defined(MODE_TI) && defined(__CX16__)
-    } else if ((bit_pos & 0x7F) + width <= 128) {
-        // element fits in an aligned double word
-        __uint128_t *word = &reinterpret_cast<__uint128_t*>(vector.data())[bit_pos >> 7];
-        const uint8_t shift = bit_pos & 0x7F;
-        // TODO: GCC only generates cmpxchg16b instruction with older __sync functions
-        return (__sync_fetch_and_add(word, __uint128_t(val) << shift) >> shift) & mask;
-#endif
-    } else {
-        uint8_t shift = bit_pos & 0x7;
-        if (shift + width <= 8) {
-            // read from a byte
-            uint8_t *word = &reinterpret_cast<uint8_t*>(vector.data())[bit_pos >> 3];
-            return (__atomic_fetch_add(word, val << shift, mo) >> shift) & mask;
-        } else if (shift + width <= 16) {
-            // unaligned read from two bytes
-            uint16_t *word = &reinterpret_cast<uint16_t*>(vector.data())[bit_pos >> 4];
-            shift = bit_pos & 0xF;
-            return (__atomic_fetch_add(word, val << shift, mo) >> shift) & mask;
-        } else if (shift + width <= 32) {
-            // unaligned read from four bytes
-            uint32_t *word = &reinterpret_cast<uint32_t*>(vector.data())[bit_pos >> 5];
-            shift = bit_pos & 0x1F;
-            return (__atomic_fetch_add(word, val << shift, mo) >> shift) & mask;
-        } else if (shift + width <= 64) {
-            // unaligned read from eight bytes
-            uint64_t *word = &vector.data()[bit_pos >> 6];
-            shift = bit_pos & 0x3F;
-            return (__atomic_fetch_add(word, val << shift, mo) >> shift) & mask;
-        } else {
-            assert(false);
-        }
-    }
-
-    return 0;
-}
-
-inline uint64_t atomic_exchange(sdsl::int_vector<> &vector,
-                                uint64_t i,
-                                uint64_t val,
-                                std::mutex &backup_mutex,
-                                int mo) {
-    const uint8_t width = vector.width();
-    const size_t bit_pos = i * width;
-    const uint64_t mask = (1llu << width) - 1;
-    if (width + 7 > 64) {
-        // there is no way to reliably modify without a mutex
-        std::lock_guard<std::mutex> lock(backup_mutex);
-        const uint64_t old_val = vector[i];
-        vector[i] = val;
-        return old_val;
-    } else if ((bit_pos & 0x3F) + width <= 64) {
-        // element fits in an aligned word
-        uint64_t *word = &vector.data()[bit_pos >> 6];
-        const uint8_t shift = bit_pos & 0x3F;
-        uint64_t desired;
-        uint64_t exp = *word;
-        const uint64_t inv_mask = ~(mask << shift);
-        val <<= shift;
-        do {
-            desired = val | (inv_mask & exp);
-        } while (!__atomic_compare_exchange(word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
-        return (exp >> shift) & mask;
-#if defined(MODE_TI) && defined(__CX16__)
-    } else if ((bit_pos & 0x7F) + width <= 128) {
-        // element fits in an aligned double word
-        __uint128_t *word = &reinterpret_cast<__uint128_t*>(vector.data())[bit_pos >> 7];
-        const uint8_t shift = bit_pos & 0x7F;
-        __uint128_t desired;
-        __uint128_t exp;
-        __uint128_t inv_mask = ~(__uint128_t(mask) << shift);
-        __uint128_t big_val = __uint128_t(val) << shift;
-        // TODO: GCC only generates cmpxchg16b instruction with older __sync functions
-        do {
-            exp = *word;
-            desired = big_val | (inv_mask & exp);
-        } while (!__sync_bool_compare_and_swap(word, exp, desired));
-        return (exp >> shift) & mask;
-#endif
-    } else {
-        uint8_t shift = bit_pos & 0x7;
-        if (shift + width <= 8) {
-            uint8_t *word = &reinterpret_cast<uint8_t*>(vector.data())[bit_pos >> 3];
-            // read from a byte
-            uint8_t desired;
-            uint8_t exp = *word;
-            const uint8_t inv_mask = ~(mask << shift);
-            val <<= shift;
-            do {
-                desired = val | (inv_mask & exp);
-            } while (!__atomic_compare_exchange(word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
-            return (exp >> shift) & mask;
-        } else if (shift + width <= 16) {
-            uint16_t *word = &reinterpret_cast<uint16_t*>(vector.data())[bit_pos >> 4];
-            shift = bit_pos & 0xF;
-            // unaligned read from two bytes
-            uint16_t desired;
-            uint16_t exp = *word;
-            const uint16_t inv_mask = ~(mask << shift);
-            val <<= shift;
-            do {
-                desired = val | (inv_mask & exp);
-            } while (!__atomic_compare_exchange(word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
-            return (exp >> shift) & mask;
-        } else if (shift + width <= 32) {
-            // unaligned read from four bytes
-            uint32_t *word = &reinterpret_cast<uint32_t*>(vector.data())[bit_pos >> 5];
-            shift = bit_pos & 0x1F;
-            uint32_t desired;
-            uint32_t exp = *word;
-            const uint32_t inv_mask = ~(mask << shift);
-            val <<= shift;
-            do {
-                desired = val | (inv_mask & exp);
-            } while (!__atomic_compare_exchange(word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
-            return (exp >> shift) & mask;
-        } else if (shift + width <= 64) {
-            // unaligned read from eight bytes
-            uint64_t *word = &vector.data()[bit_pos >> 6];
-            shift = bit_pos & 0x3F;
-            uint64_t desired;
-            uint64_t exp = *word;
-            const uint64_t inv_mask = ~(mask << shift);
-            val <<= shift;
-            do {
-                desired = val | (inv_mask & exp);
-            } while (!__atomic_compare_exchange(word, &exp, &desired, true, mo, __ATOMIC_RELAXED));
-            return (exp >> shift) & mask;
-        } else {
-            assert(false);
-        }
-    }
-
-    return 0;
-}
 
 template <class Bitmap, class Callback>
 void call_ones(const Bitmap &vector, uint64_t begin, uint64_t end, Callback callback) {
@@ -711,7 +507,7 @@ void call_nonzeros(const sdsl::int_vector<> &vector,
                 it = std::max(it, w * 64 / vector.width());
 
                 auto it_end = std::min(end,
-                                       ((w + 1) * 64 + vector.width() - 1) / vector.width());
+                    ((w + 1) * 64 + vector.width() - 1) / vector.width());
 
                 for (; it < it_end; ++it) {
                     if (vector[it])
diff --git a/metagraph/src/graph/representation/base/sequence_graph.cpp b/metagraph/src/graph/representation/base/sequence_graph.cpp
index c11e48846e..bc19dede84 100644
--- a/metagraph/src/graph/representation/base/sequence_graph.cpp
+++ b/metagraph/src/graph/representation/base/sequence_graph.cpp
@@ -9,7 +9,6 @@
 #include "common/threads/threading.hpp"
 #include "common/vectors/vector_algorithm.hpp"
 #include "graph/representation/canonical_dbg.hpp"
-#include "graph/representation/masked_graph.hpp"
 
 
 namespace mtg {
@@ -115,7 +114,7 @@ void call_sequences_from(const DeBruijnGraph &graph,
                          bool call_unitigs,
                          uint64_t min_tip_size,
                          bool kmers_in_single_form) {
-    assert(start >= 1 && start <= graph.max_index()); // TODO Myrthe: pick as starting node one with the highest signficance or effect .
+    assert(start >= 1 && start <= graph.max_index());
     assert((min_tip_size <= 1 || call_unitigs)
                 && "tip pruning works only for unitig extraction");
     assert(visited);
@@ -154,7 +153,7 @@ void call_sequences_from(const DeBruijnGraph &graph,
             ++progress_bar;
 
             targets.clear();
-            graph.call_outgoing_kmers(node, // finds the subsequent nodes and adds them to the target vector
+            graph.call_outgoing_kmers(node,
                 [&](node_index next, char c) { targets.emplace_back(next, c); }
             );
 
@@ -183,48 +182,25 @@ void call_sequences_from(const DeBruijnGraph &graph,
             }
 
             node_index next_node = DeBruijnGraph::npos;
-            char next_c = '\0';
             //  _____.___
             //      \.___
-
-
-                        // Original way
-                        node_index next_node_original = next_node;
-                        for (const auto &[next, c] : targets) {
-                            if (next_node_original == DeBruijnGraph::npos
-                                && !call_unitigs // Myrthe: if not in unitig mode, (which just returns the unitigs)
-                                && !(*visited)[next]) { // Myrthe: return call-back here.  input a callback as input to call_sequences_from , with default return false. also adapt in sequence_graph.hpp
-                                (*discovered)[next] = true; // Myrthe: if the node was not visited yet.
-                                next_node_original = next; // then  `next_node == DeBruijnGraph::npos` becomes false.
-                            }
-                        }
-
-            // double next_likelihood = 0; // start with a likelihood of 0, s.t. only unitigs are added with a better Likelihood Ratio.
-            for (const auto &[next, c] : targets) { // If there are multiple descendents.
-                if (!(*visited)[next]){ // the node was not visited yet.
-                    if (!(*discovered)[next]) { // The node was not encountered before.
-                        (*discovered)[next] = true;
-                        queue.push_back(next); // The queue grows a bit faster compared to the original case.
-                        // TODO: make sure that the selected node is not added to the queue --> problems if run on  multiple threads.
-                    }
-                    // if (!call_unitigs // In the unitig mode (i.e. if call_unitigs is true), simply the unitigs must be returned, rather than longer contigs
-                    //     && downcasted_graph // TODO feature is temporary
-                    //     && downcasted_graph->likelihood_ratios[next] > next_likelihood){ // If the likelihood of this unitig is better than the current likelihood
-                    //     next_likelihood = downcasted_graph->likelihood_ratios[next];
-                    //     next_node = next;
-                    //     next_c = c;
-                    // }
+            for (const auto &[next, c] : targets) {
+                if (next_node == DeBruijnGraph::npos
+                        && !call_unitigs
+                        && !(*visited)[next]) {
+                    (*discovered)[next] = true;
+                    next_node = next;
+                    sequence.push_back(c);
+                    path.push_back(next);
+                } else if (!(*discovered)[next]) {
+                    (*discovered)[next] = true;
+                    queue.push_back(next);
                 }
             }
 
-            // if (next_node != next_node_original)
-            //     std::cout << "different node selected compared to original method " <<std::flush;
             if (next_node == DeBruijnGraph::npos)
                 break;
-            else {
-                sequence.push_back(next_c);
-                path.push_back(next_node);
-            }
+
             node = next_node;
         }
 
@@ -345,10 +321,6 @@ void call_sequences(const DeBruijnGraph &graph,
                              "Traverse graph",
                              std::cerr, !common::get_verbose());
 
-//    // Myrthe: TODO define callback
-//    const std::function<bool(node_index, node_index)> &pick_edge
-//            = [](node_index, node_index){ return false; };
-
     auto call_paths_from = [&](node_index node) {
         call_sequences_from(graph,
                             node,
@@ -358,9 +330,7 @@ void call_sequences(const DeBruijnGraph &graph,
                             progress_bar,
                             call_unitigs,
                             min_tip_size,
-                            kmers_in_single_form
-                            //pick_edge
-                            );
+                            kmers_in_single_form);
     };
 
     if (call_unitigs) {
@@ -506,8 +476,6 @@ std::ostream& operator<<(std::ostream &out, const DeBruijnGraph &graph) {
     return out;
 }
 
-//DeBruijnGraph::DeBruijnGraph(const std::vector<double> &lrs) : lrs(lrs) {} //Myrthe
-
 // returns the edge rank, starting from zero
 size_t incoming_edge_rank(const SequenceGraph &graph,
                           SequenceGraph::node_index source,

From 85f3774dc47f6391e4f646fb1212f6611b8a9fac Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 11:28:24 +0200
Subject: [PATCH 192/410] more cleanup

---
 metagraph/src/cli/config/config.cpp           |  2 --
 metagraph/src/cli/config/config.hpp           |  1 -
 metagraph/src/graph/annotated_dbg.cpp         |  2 +-
 .../src/graph/representation/masked_graph.cpp | 22 +++++++++----------
 .../graph/representation/succinct/boss.cpp    |  6 -----
 5 files changed, 12 insertions(+), 21 deletions(-)

diff --git a/metagraph/src/cli/config/config.cpp b/metagraph/src/cli/config/config.cpp
index 2e85afa449..5dd62b1512 100644
--- a/metagraph/src/cli/config/config.cpp
+++ b/metagraph/src/cli/config/config.cpp
@@ -339,8 +339,6 @@ Config::Config(int argc, char *argv[]) {
             output_compacted = true;
         } else if (!strcmp(argv[i], "--clean-ztp")) {
             clean_ztp = true;
-        } else if (!strcmp(argv[i], "--count-testable-kmers")) {
-            count_testable_kmers = true;
         } else if (!strcmp(argv[i], "--json")) {
             output_json = true;
         } else if (!strcmp(argv[i], "--unitigs")) {
diff --git a/metagraph/src/cli/config/config.hpp b/metagraph/src/cli/config/config.hpp
index 9f7f98fe0b..e66c66d4cc 100644
--- a/metagraph/src/cli/config/config.hpp
+++ b/metagraph/src/cli/config/config.hpp
@@ -62,7 +62,6 @@ class Config {
     bool coordinates = false;
     bool advanced = false;
     bool clean_ztp = false;
-    bool count_testable_kmers = false;
 
     unsigned int k = 3;
 
diff --git a/metagraph/src/graph/annotated_dbg.cpp b/metagraph/src/graph/annotated_dbg.cpp
index 6054e7b16e..430365365a 100644
--- a/metagraph/src/graph/annotated_dbg.cpp
+++ b/metagraph/src/graph/annotated_dbg.cpp
@@ -697,7 +697,7 @@ bool AnnotatedSequenceGraph::label_exists(const Label &label) const {
     return annotator_->get_label_encoder().label_exists(label);
 }
 
-void AnnotatedSequenceGraph //TODO Myrthe: Create such a function that supports count annotation?
+void AnnotatedSequenceGraph
 ::call_annotated_nodes(const Label &label,
                        std::function<void(node_index)> callback) const {
     assert(check_compatibility());
diff --git a/metagraph/src/graph/representation/masked_graph.cpp b/metagraph/src/graph/representation/masked_graph.cpp
index 3494247608..fd8a109ba9 100644
--- a/metagraph/src/graph/representation/masked_graph.cpp
+++ b/metagraph/src/graph/representation/masked_graph.cpp
@@ -107,21 +107,21 @@ bit_vector_stat get_boss_mask(const DBGSuccinct &dbg_succ,
 void MaskedDeBruijnGraph::call_sequences(const CallPath &callback,
                                          size_t num_threads,
                                          bool kmers_in_single_form) const {
-   if (auto *dbg_succ = dynamic_cast<const DBGSuccinct*>(graph_.get())) {
-           bit_vector_stat mask = get_boss_mask(*dbg_succ, *kmers_in_graph_,
-                                            only_valid_nodes_in_mask_);
+    if (auto *dbg_succ = dynamic_cast<const DBGSuccinct*>(graph_.get())) {
+        bit_vector_stat mask = get_boss_mask(*dbg_succ, *kmers_in_graph_,
+                                             only_valid_nodes_in_mask_);
 
-       dbg_succ->get_boss().call_sequences([&](std::string&& sequence, auto&& path) {
-           for (auto &node : path) {
-               node = dbg_succ->boss_to_kmer_index(node);
-           }
-           callback(sequence, path);
+        dbg_succ->get_boss().call_sequences([&](std::string&& sequence, auto&& path) {
+            for (auto &node : path) {
+                node = dbg_succ->boss_to_kmer_index(node);
+            }
+            callback(sequence, path);
 
-       }, num_threads, kmers_in_single_form, &mask);
+        }, num_threads, kmers_in_single_form, &mask);
 
-   } else {
+    } else {
         DeBruijnGraph::call_sequences(callback, num_threads, kmers_in_single_form);
-    } //Myrthe temporaryly mute
+    }
 }
 
 void MaskedDeBruijnGraph::call_unitigs(const CallPath &callback,
diff --git a/metagraph/src/graph/representation/succinct/boss.cpp b/metagraph/src/graph/representation/succinct/boss.cpp
index fb5d11c78f..5888399197 100644
--- a/metagraph/src/graph/representation/succinct/boss.cpp
+++ b/metagraph/src/graph/representation/succinct/boss.cpp
@@ -2109,10 +2109,7 @@ void BOSS::call_paths(Call<std::vector<edge_index> &&, std::vector<TAlphabet> &&
     ThreadPool thread_pool(num_threads ? num_threads : 1, TASK_POOL_SIZE);
     bool async = true;
 
-    size_t num_starts = 0;
-
     auto enqueue_start = [&](ThreadPool &thread_pool, edge_index start) {
-        ++num_starts;
         thread_pool.enqueue([&,start]() {
             ::mtg::graph::boss::call_paths(
                     *this, EdgeQueue(start), callback,
@@ -2220,7 +2217,6 @@ void BOSS::call_paths(Call<std::vector<edge_index> &&, std::vector<TAlphabet> &&
         // Ensures that call_path is called only once for each cycle
         auto rep = std::min_element(path.begin(), path.end());
         if (!fetch_bit(visited.data(), *rep, async)) {
-            __atomic_add_fetch(&num_starts, 1, __ATOMIC_SEQ_CST);
             EdgeQueue queue;
             queue.emplace_back(*rep,
                                sequence.begin() + (rep - path.begin()),
@@ -2266,8 +2262,6 @@ void BOSS::call_paths(Call<std::vector<edge_index> &&, std::vector<TAlphabet> &&
 #ifndef NDEBUG
     assert_no_leftovers(*this, visited);
 #endif
-
-    common::logger->trace("Starts:\t{}", num_starts);
 }
 
 void call_paths(const BOSS &boss,

From 72d292643d0888c310f6f1ee7dce4771821cb3bb Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 11:39:42 +0200
Subject: [PATCH 193/410] minor

---
 .../annotate_column_compressed.hpp               | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
index 5a25522025..c720be9566 100644
--- a/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
+++ b/metagraph/src/annotation/representation/column_compressed/annotate_column_compressed.hpp
@@ -101,18 +101,18 @@ class ColumnCompressed : public MultiLabelAnnotation<Label> {
                                         size_t num_threads = 1);
 
     using ColumnsValuesStreamCallback = std::function<void(uint64_t offset,
-                                                     const Label &,
-                                                     std::unique_ptr<bit_vector>&&,
-                                                     sdsl::int_vector_buffer<>&&)>;
+                                                           const Label &,
+                                                           std::unique_ptr<bit_vector>&&,
+                                                           sdsl::int_vector_buffer<>&&)>;
     static void load_columns_and_values(const std::vector<std::string> &filenames,
                                         const ColumnsValuesStreamCallback &callback,
-                                        size_t num_threads = 1);                                    
+                                        size_t num_threads = 1);
 
     using ColumnsDelimsValuesCallback = std::function<void(uint64_t offset,
-                                                      const Label &,
-                                                      std::unique_ptr<bit_vector>&&,
-                                                      bit_vector_smart&&,
-                                                      sdsl::int_vector<>&&)>;
+                                                           const Label &,
+                                                           std::unique_ptr<bit_vector>&&,
+                                                           bit_vector_smart&&,
+                                                           sdsl::int_vector<>&&)>;
     static void load_columns_delims_and_values(const std::vector<std::string> &filenames,
                                                const ColumnsDelimsValuesCallback &callback,
                                                size_t num_threads = 1);

From 54141be6e2b5cf7d3ea5c171266858618fd72bb2 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 11:40:54 +0200
Subject: [PATCH 194/410] unused variable

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ad125904de..facb25fa0c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -161,13 +161,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<uint64_t> min_counts(groups.size(), config.min_count);
     std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
 
-    auto adjust_count = [&](uint64_t raw_count, size_t j, uint64_t row_i) -> uint64_t {
-        std::ignore = row_i;
-        return raw_count >= min_counts[j] && raw_count <= check_cutoff[j]
-            ? raw_count
-            : 0;
-    };
-
     if (config.clean && column_values_all.empty())
         common::logger->warn("Can't clean when no counts provided, skipping cleaning");
 

From 700adb1f9d87b6a5dab23d41b050d8b60de4d878 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 11:47:12 +0200
Subject: [PATCH 195/410] cleanup

---
 .../src/annotation/binary_matrix/base/binary_matrix.cpp     | 6 +-----
 .../src/annotation/binary_matrix/base/binary_matrix.hpp     | 4 ++--
 .../annotation/binary_matrix/column_sparse/column_major.cpp | 2 +-
 .../annotation/binary_matrix/column_sparse/column_major.hpp | 2 +-
 4 files changed, 5 insertions(+), 9 deletions(-)

diff --git a/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp b/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp
index 2c62692628..10e2a065c2 100644
--- a/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp
+++ b/metagraph/src/annotation/binary_matrix/base/binary_matrix.cpp
@@ -1,7 +1,6 @@
 #include "binary_matrix.hpp"
 
 #include <ips4o.hpp>
-#include <progress_bar.hpp>
 
 #include "common/vectors/bitmap.hpp"
 #include "common/serialization.hpp"
@@ -58,14 +57,11 @@ BinaryMatrix::get_rows_dict(std::vector<Row> *rows, size_t num_threads) const {
 }
 
 void BinaryMatrix::call_columns(const std::vector<Column> &column_ids,
-                                const std::function<void(size_t, const bitmap_generator&)> &callback,
+                                const std::function<void(size_t, const bitmap&)> &callback,
                                 size_t num_threads) const {
-    ProgressBar progress_bar(column_ids.size(), "Parsing columns",
-                             std::cerr, !common::get_verbose());
     #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
     for (size_t k = 0; k < column_ids.size(); ++k) {
         callback(k, bitmap_generator(get_column(column_ids[k]), num_rows()));
-        ++progress_bar;
     }
 }
 
diff --git a/metagraph/src/annotation/binary_matrix/base/binary_matrix.hpp b/metagraph/src/annotation/binary_matrix/base/binary_matrix.hpp
index 5d02d997ae..b23c2c7323 100644
--- a/metagraph/src/annotation/binary_matrix/base/binary_matrix.hpp
+++ b/metagraph/src/annotation/binary_matrix/base/binary_matrix.hpp
@@ -9,7 +9,7 @@
 #include "common/vector.hpp"
 
 
-class bitmap_generator;
+class bitmap;
 
 namespace mtg {
 namespace annot {
@@ -40,7 +40,7 @@ class BinaryMatrix {
     // For each column id in columns, run callback on its respective index in columns
     // and a bitmap represnting the column
     virtual void call_columns(const std::vector<Column> &columns,
-                              const std::function<void(size_t, const bitmap_generator&)> &callback,
+                              const std::function<void(size_t, const bitmap&)> &callback,
                               size_t num_threads = 1) const;
 
     virtual bool load(std::istream &in) = 0;
diff --git a/metagraph/src/annotation/binary_matrix/column_sparse/column_major.cpp b/metagraph/src/annotation/binary_matrix/column_sparse/column_major.cpp
index 421ae61e58..5dfcb9e582 100644
--- a/metagraph/src/annotation/binary_matrix/column_sparse/column_major.cpp
+++ b/metagraph/src/annotation/binary_matrix/column_sparse/column_major.cpp
@@ -77,7 +77,7 @@ ColumnMajor::get_column_ranks(const std::vector<Row> &row_ids) const {
 }
 
 void ColumnMajor::call_columns(const std::vector<Column> &columns,
-                               const std::function<void(size_t, const bitmap_generator&)> &callback,
+                               const std::function<void(size_t, const bitmap&)> &callback,
                                size_t num_threads) const {
     #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
     for (size_t j = 0; j < columns.size(); ++j) {
diff --git a/metagraph/src/annotation/binary_matrix/column_sparse/column_major.hpp b/metagraph/src/annotation/binary_matrix/column_sparse/column_major.hpp
index 213d3ffb2c..776f62e4af 100644
--- a/metagraph/src/annotation/binary_matrix/column_sparse/column_major.hpp
+++ b/metagraph/src/annotation/binary_matrix/column_sparse/column_major.hpp
@@ -29,7 +29,7 @@ class ColumnMajor : public BinaryMatrix, public GetEntrySupport {
     std::vector<Row> get_column(Column column) const override;
 
     void call_columns(const std::vector<Column> &columns,
-                      const std::function<void(size_t, const bitmap_generator&)> &callback,
+                      const std::function<void(size_t, const bitmap&)> &callback,
                       size_t num_threads = 1) const override;
 
     bool load(std::istream &in) override;

From 6f0e8c23959883d6100ed5944ffd48185edee730 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 11:52:13 +0200
Subject: [PATCH 196/410] cleanup

---
 metagraph/src/common/vectors/bitmap.cpp |  7 -------
 metagraph/src/common/vectors/bitmap.hpp | 11 ++---------
 2 files changed, 2 insertions(+), 16 deletions(-)

diff --git a/metagraph/src/common/vectors/bitmap.cpp b/metagraph/src/common/vectors/bitmap.cpp
index 6d31c5f7c8..f4888735e4 100644
--- a/metagraph/src/common/vectors/bitmap.cpp
+++ b/metagraph/src/common/vectors/bitmap.cpp
@@ -356,13 +356,6 @@ uint64_t bitmap_lazy::num_set_bits() const {
     return num_set_bits_;
 }
 
-std::optional<uint64_t> bitmap_lazy::try_get_num_set_bits() const {
-    if (num_set_bits_ == static_cast<size_t>(-1))
-        return std::nullopt;
-
-    return num_set_bits_;
-}
-
 void bitmap_lazy::add_to(sdsl::bit_vector *other) const {
     assert(other);
     assert(other->size() == size());
diff --git a/metagraph/src/common/vectors/bitmap.hpp b/metagraph/src/common/vectors/bitmap.hpp
index 9f8d68438a..b5b855a5eb 100644
--- a/metagraph/src/common/vectors/bitmap.hpp
+++ b/metagraph/src/common/vectors/bitmap.hpp
@@ -176,7 +176,6 @@ class bitmap_lazy : public bitmap {
 
     uint64_t size() const { return size_; }
     uint64_t num_set_bits() const;
-    std::optional<uint64_t> try_get_num_set_bits() const;
 
     void add_to(sdsl::bit_vector *other) const;
     void call_ones_in_range(uint64_t begin, uint64_t end,
@@ -198,18 +197,12 @@ class bitmap_generator : public bitmap {
                            std::for_each(s.begin(), s.end(), callback);
                        }) {}
 
-    bitmap_generator(const bitmap &bits) noexcept
-          : size_(bits.size()), num_set_bits_(bits.num_set_bits()),
-            generator_([&bits](const VoidCall<uint64_t> &callback) {
-                           bits.call_ones(callback);
-                       }) {}
-
     bitmap_generator(std::function<void(const VoidCall<uint64_t>&)>&& generator,
                      size_t size,
                      size_t num_set_bits = -1) noexcept;
 
-    bool operator[](uint64_t) const { throw std::runtime_error("operator[] not implemented for bitmap_generator"); }
-    uint64_t get_int(uint64_t, uint32_t) const { throw std::runtime_error("get_int not implemented for bitmap_generator"); }
+    bool operator[](uint64_t) const { throw std::runtime_error("Not implemented"); }
+    uint64_t get_int(uint64_t, uint32_t) const { throw std::runtime_error("Not implemented"); }
 
     uint64_t size() const { return size_; }
     uint64_t num_set_bits() const { return num_set_bits_; }

From c401264d4ecbf97783be9ee4ca2ca2404b945471 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 12:03:06 +0200
Subject: [PATCH 197/410] remove CMH test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 74 -------------------
 1 file changed, 74 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index facb25fa0c..ec1362946c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -383,80 +383,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double pval = get_pval(in_sum);
             double pmin = std::min(get_pval(0), get_pval(std::min(n1, m1)));
 
-            return std::make_tuple(pval,
-                                   static_cast<double>(in_sum),
-                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
-                                   pmin);
-        };
-    } else if (config.test_type == "cmh") {
-        if (in_kmers == 0 || out_kmers == 0 || total_kmers == 1) {
-            common::logger->error("Test invalid, try fisher instead");
-            throw std::domain_error("Test fail");
-        }
-
-        int64_t t = total_kmers;
-        int64_t n1 = in_kmers;
-        int64_t n2 = out_kmers;
-
-        compute_pval = [&,t,n1,n2,dist=boost::math::chi_squared(1)](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
-
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-
-            for (const auto &[j, c] : row) {
-                if (groups[j]) {
-                    out_sum += c;
-                } else {
-                    in_sum += c;
-                }
-            }
-
-            int64_t m1 = in_sum + out_sum;
-            int64_t m2 = t - m1;
-            assert(m2 >= 0);
-
-            if (m2 == 0) {
-                common::logger->error("This row has all counts. Too few data points. Use fisher instead");
-                throw std::domain_error("Test failed");
-            }
-
-            double lbase = log(n1) + log(n2) + log(m1) + log(m2) - log(t) * 2 - log(t - 1);
-            double factor = static_cast<double>(n1 * m1) / t;
-
-            auto get_chi_stat = [&](int64_t a) {
-                int64_t b = n1 - a;
-                int64_t c = m1 - a;
-                int64_t d = m2 - b;
-                assert(d == n2 - c);
-
-                if (b < 0 || c < 0 || d < 0)
-                    return 0.0;
-
-                double a_shift = static_cast<double>(a) - factor;
-
-                if (a_shift > 0)
-                    return exp(log(a_shift) * 2.0 - lbase);
-
-                return a_shift * a_shift / exp(lbase);
-            };
-
-            double chi_stat = get_chi_stat(in_sum);
-            if (chi_stat <= 0) {
-                common::logger->error("Test statistic {} <= 0, too few data points. Use fisher instead.\t{}\t{},{},{},{}\t{}\t{}", chi_stat, in_sum, n1,n2,m1,m2, t, factor);
-                throw std::domain_error("Test failed");
-            }
-
-            double max_chi_stat = std::max(get_chi_stat(0), get_chi_stat(std::min(n1, m1)));
-            if (max_chi_stat <= 0) {
-                common::logger->error("Best test statistic {} <= 0, too few data points. Use fisher instead.\t{},{},{},{}\t{}\t{}", max_chi_stat, n1,n2,m1,m2, t, factor);
-                throw std::domain_error("Test failed");
-            }
-
-            double pval = boost::math::cdf(boost::math::complement(dist, chi_stat));
-            double pmin = boost::math::cdf(boost::math::complement(dist, max_chi_stat));
-
             return std::make_tuple(pval,
                                    static_cast<double>(in_sum),
                                    static_cast<double>(out_sum) / out_kmers * in_kmers,

From f122ab34a55a0e611cfee12d4d9ba731cc00603b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 12:33:50 +0200
Subject: [PATCH 198/410] fix

---
 metagraph/tests/annotation/test_annotated_graph_algorithm.cpp | 1 -
 1 file changed, 1 deletion(-)

diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index aa20c6262a..0896930f6b 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -180,7 +180,6 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                 "poisson_exact",
                 "nbinom_exact",
                 "fisher",
-                "cmh",
             };
 
             for (const auto &test_type : test_types) {

From 1296adad489b94333689740c40ba1c4386d91b39 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 13:17:39 +0200
Subject: [PATCH 199/410] fallbacks

---
 .../src/graph/annotated_graph_algorithm.cpp   | 171 ++++++++++++------
 1 file changed, 118 insertions(+), 53 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ec1362946c..83678ea092 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -12,6 +12,7 @@
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
+#include <boost/math/special_functions/polygamma.hpp>
 
 #include "common/logger.hpp"
 #include "common/vectors/bitmap.hpp"
@@ -215,7 +216,47 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::atomic_thread_fence(std::memory_order_acquire);
     }
 
-    double nelem = sdsl::util::cnt_one_bits(kept);
+    std::shared_ptr<MaskedDeBruijnGraph> clean_masked_graph;
+    std::vector<VectorMap<uint64_t, size_t>> unitig_hists(groups.size());
+    size_t num_unitigs = 0;
+    std::vector<uint64_t> sums_of_squares_unitigs(groups.size());
+    if (config.test_by_unitig) {
+        clean_masked_graph = std::make_shared<MaskedDeBruijnGraph>(
+            graph_ptr,
+            [&](node_index node) {
+                return node != DeBruijnGraph::npos
+                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
+            },
+            true,
+            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+        );
+
+        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+            std::vector<uint64_t> unitig_sums(groups.size());
+            for (node_index node : path) {
+                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    const auto &col = *columns_all[j];
+                    const auto &col_vals = *column_values_all[j];
+                    if (uint64_t r = col.conditional_rank1(row_i)) {
+                        uint64_t c = col_vals[r - 1];
+                        unitig_sums[j] += c;
+                        std::lock_guard<std::mutex> lock(agg_mu);
+                        ++unitig_hists[j][c];
+                    }
+                }
+            }
+
+            for (size_t j = 0; j < groups.size(); ++j) {
+                sums_of_squares_unitigs[j] += unitig_sums[j] * unitig_sums[j];
+            }
+
+            std::lock_guard<std::mutex> lock(agg_mu);
+            ++num_unitigs;
+        }, num_threads);
+    }
+
+    double nelem = !config.test_by_unitig ? sdsl::util::cnt_one_bits(kept) : num_unitigs;
 
     common::logger->trace("Computing aggregate statistics");
     double in_kmers = 0;
@@ -389,24 +430,31 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                    pmin);
         };
     } else if (config.test_type == "nbinom_exact") {
-        common::logger->trace("Fitting negative binomial distributions");
-        auto get_rp = [&](size_t j, double mu, double var, const auto &hist, double f = 1.0) {
+        common::logger->trace("Fitting per-sample negative binomial distributions");
+        auto get_rp = [&](size_t j, double mu, double var, const auto &hist) {
             double var_orig = var;
             var = std::max(var, mu + 0.1);
-            double r_guess = f * f * mu * mu / (var - mu * f);
+            double r_guess = mu * mu / (var - mu);
             common::logger->trace("{}: initial guess:\tmu: {}\tvar: {}\tr: {}\tp: {}\t",
                                   j, mu, var_orig, r_guess, mu / var);
-            double r = boost::math::tools::newton_raphson_iterate([&](double r) {
-                double dl = nelem * (log(r) - log(r + mu * f) - boost::math::digamma(r));
-                double ddl = nelem * (1.0 / r - 1.0 / (r + mu * f) - boost::math::trigamma(r));
-                for (const auto &[k, c] : hist) {
-                    dl += boost::math::digamma(k * f + r) * c;
-                    ddl += boost::math::trigamma(k * f + r) * c;
-                }
-                return std::make_pair(dl, ddl);
-            }, r_guess, std::numeric_limits<double>::min(), f * mu * nelem, 30);
+            double r = r_guess;
+            try {
+                r = boost::math::tools::newton_raphson_iterate([&](double r) {
+                    double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
+                    double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
+                    for (const auto &[k, c] : hist) {
+                        dl += boost::math::digamma(k + r) * c;
+                        ddl += boost::math::trigamma(k + r) * c;
+                    }
+                    return std::make_pair(dl, ddl);
+                }, r_guess, std::numeric_limits<double>::min(), mu * nelem, 30);
+            } catch (std::exception &e) {
+                common::logger->warn("Caught exception for sample {}: Falling back to initial guess", j);
+                common::logger->warn("{}", e.what());
+                r = r_guess;
+            }
 
-            return std::make_pair(r, r / (r + mu * f));
+            return std::make_pair(r, r / (r + mu));
         };
 
         // geometric mean
@@ -422,49 +470,79 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
-            auto &hist = hists[j];
+            const auto &hist = config.test_by_unitig ? unitig_hists[j] : hists[j];
             double s = static_cast<double>(sums[j]);
             double mu = s / nelem;
             double mu2 = mu * mu;
-            double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
+            double var = static_cast<double>(config.test_by_unitig ? sums_of_squares_unitigs[j] : sums_of_squares[j]) / nelem - mu2;
             nb_params[j] = get_rp(j, mu, var, hist);
             min_p = std::min(min_p, nb_params[j].second);
         }
 
+        common::logger->trace("Fitting common p");
         std::vector<double> r_maps(groups.size(), 1.0);
-        double target_p = boost::math::tools::newton_raphson_iterate([&](double p) {
-            double dl = 0;
-            double ddl = 0;
+        double target_p = min_p;
+        try {
+            target_p = boost::math::tools::newton_raphson_iterate([&](double p) {
+                double dl = 0;
+                double ddl = 0;
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    double f = target_sum / sums[j];
+                    double &r = r_maps[j];
+                    r = boost::math::tools::newton_raphson_iterate([&](double r) {
+                        double dl = nelem * (log(p) - boost::math::digamma(r));
+                        double ddl = -nelem * boost::math::trigamma(r);
+                        for (const auto &[k, c] : hists[j]) {
+                            dl += boost::math::digamma(k * f + r) * c;
+                            ddl += boost::math::trigamma(k * f + r) * c;
+                        }
+                        return std::make_pair(dl, ddl);
+                    }, r, std::numeric_limits<double>::min(), f * sums[j], 30);
+                    double factor = nelem * r / p;
+                    dl += factor;
+                    ddl -= factor / p;
+                    const auto &hist = config.test_by_unitig ? unitig_hists[j] : hists[j];
+                    for (const auto &[k, c] : hist) {
+                        double factor = k * f * c / (1 - p);
+                        dl -= factor;
+                        ddl -= factor / (1 - p);
+                    }
+                }
+
+                return std::make_pair(dl, ddl);
+            }, min_p, std::numeric_limits<double>::min(), 1.0, 30);
+        } catch (std::exception &e) {
+            common::logger->warn("Caught exception while fitting p: Falling back to initial guess");
+            common::logger->warn("{}", e.what());
+            target_p = min_p;
             for (size_t j = 0; j < groups.size(); ++j) {
                 double f = target_sum / sums[j];
                 double &r = r_maps[j];
-                r = boost::math::tools::newton_raphson_iterate([&](double r) {
-                    double dl = nelem * (log(p) - boost::math::digamma(r));
-                    double ddl = -nelem * boost::math::trigamma(r);
-                    for (const auto &[k, c] : hists[j]) {
-                        dl += boost::math::digamma(k * f + r) * c;
-                        ddl += boost::math::trigamma(k * f + r) * c;
-                    }
-                    return std::make_pair(dl, ddl);
-                }, r, std::numeric_limits<double>::min(), f * sums[j], 30);
-                double factor = nelem * r / p;
-                dl += factor;
-                ddl -= factor / p;
-                for (const auto &[k, c] : hists[j]) {
-                    double factor = k * f * c / (1 - p);
-                    dl -= factor;
-                    ddl -= factor / (1 - p);
+                try {
+                    r = boost::math::tools::newton_raphson_iterate([&](double r) {
+                        double dl = nelem * (log(target_p) - boost::math::digamma(r));
+                        double ddl = -nelem * boost::math::trigamma(r);
+                        for (const auto &[k, c] : hists[j]) {
+                            dl += boost::math::digamma(k * f + r) * c;
+                            ddl += boost::math::trigamma(k * f + r) * c;
+                        }
+                        return std::make_pair(dl, ddl);
+                    }, r, std::numeric_limits<double>::min(), f * sums[j], 30);
+                } catch (std::exception &e) {
+                    common::logger->warn("Caught exception while fitting r for {}: Falling back to initial guess", j);
+                    common::logger->warn("{}", e.what());
+                    double mu = static_cast<double>(sums[j]) * f / nelem;
+                    r = target_p * mu / (1 - target_p);
                 }
             }
-
-            return std::make_pair(dl, ddl);
-        }, min_p, std::numeric_limits<double>::min(), 1.0, 30);
+        }
 
         std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
         double r_in = 0;
         double r_out = 0;
         std::mutex r_mu;
 
+        common::logger->trace("Computing quantile maps");
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto &[r, p] = nb_params[j];
@@ -608,7 +686,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                    std::min(p_min, 1.0));
         };
 
-        compute_pval_unitig = [&,r_in_row=r_in,r_out_row=r_out,count_maps,in_kmers_adj,out_kmers_adj](const auto &rows) {
+        compute_pval_unitig = [&,r_in,r_out,count_maps,in_kmers_adj,out_kmers_adj](const auto &rows) {
             if (std::all_of(rows.begin(), rows.end(), [](const auto &row) { return row.empty(); }))
                 return std::make_tuple(1.1, 0.0, 0.0, 1.1);
 
@@ -624,9 +702,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            double r_in = r_in_row * (1 - target_p) / (rows.size() - target_p) * rows.size();
-            double r_out = r_out_row * (1 - target_p) / (rows.size() - target_p) * rows.size();
-
             int64_t n = in_sum + out_sum;
             double p_min = 1.0;
             double pval = 1.0;
@@ -724,17 +799,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::atomic_thread_fence(std::memory_order_release);
 
     if (config.test_by_unitig) {
-        MaskedDeBruijnGraph clean_masked_graph(
-            graph_ptr,
-            [&](node_index node) {
-                return node != DeBruijnGraph::npos
-                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
-            },
-            true,
-            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-        );
-
-        clean_masked_graph.call_unitigs([&](const std::string&, const auto &path) {
+        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
             if (ex)
                 return;
 

From 443c05956678c9a00277c3042ff9ba8de42b4c00 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 16:36:46 +0200
Subject: [PATCH 200/410] simplification

---
 .../src/graph/annotated_graph_algorithm.cpp   | 99 +++++--------------
 1 file changed, 25 insertions(+), 74 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 83678ea092..270c9bd1a5 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -12,7 +12,6 @@
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
-#include <boost/math/special_functions/polygamma.hpp>
 
 #include "common/logger.hpp"
 #include "common/vectors/bitmap.hpp"
@@ -238,12 +237,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 for (size_t j = 0; j < groups.size(); ++j) {
                     const auto &col = *columns_all[j];
                     const auto &col_vals = *column_values_all[j];
-                    if (uint64_t r = col.conditional_rank1(row_i)) {
-                        uint64_t c = col_vals[r - 1];
-                        unitig_sums[j] += c;
-                        std::lock_guard<std::mutex> lock(agg_mu);
-                        ++unitig_hists[j][c];
-                    }
+                    if (uint64_t r = col.conditional_rank1(row_i))
+                        unitig_sums[j] += col_vals[r - 1];
                 }
             }
 
@@ -253,6 +248,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             std::lock_guard<std::mutex> lock(agg_mu);
             ++num_unitigs;
+            for (size_t j = 0; j < groups.size(); ++j) {
+                ++unitig_hists[j][unitig_sums[j]];
+            }
         }, num_threads);
     }
 
@@ -435,8 +433,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double var_orig = var;
             var = std::max(var, mu + 0.1);
             double r_guess = mu * mu / (var - mu);
-            common::logger->trace("{}: initial guess:\tmu: {}\tvar: {}\tr: {}\tp: {}\t",
-                                  j, mu, var_orig, r_guess, mu / var);
+            double p_guess = mu / var;
             double r = r_guess;
             try {
                 r = boost::math::tools::newton_raphson_iterate([&](double r) {
@@ -454,17 +451,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 r = r_guess;
             }
 
-            return std::make_pair(r, r / (r + mu));
-        };
-
-        // geometric mean
-        double target_sum = 0.0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            target_sum += log(static_cast<double>(sums[j]));
-        }
-        target_sum = exp(target_sum / groups.size());
+            double p = r / (r + mu);
+            common::logger->trace("{}: mu: {}\tvar: {}\tmoment est:\tr: {}\tp: {}\tmle: r: {}\tp: {}",
+                                  j, mu, var_orig, r_guess, p_guess, r, p);
 
-        double min_p = 1.0;
+            return std::make_pair(r, p);
+        };
 
         std::vector<std::pair<double, double>> nb_params(groups.size());
 
@@ -476,65 +468,24 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double mu2 = mu * mu;
             double var = static_cast<double>(config.test_by_unitig ? sums_of_squares_unitigs[j] : sums_of_squares[j]) / nelem - mu2;
             nb_params[j] = get_rp(j, mu, var, hist);
-            min_p = std::min(min_p, nb_params[j].second);
         }
 
+        // geometric mean
+        double target_sum = 0.0;
+        double p_guess = 0.0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            target_sum += log(static_cast<double>(sums[j]));
+            p_guess += log(nb_params[j].second);
+        }
+        target_sum = exp(target_sum / groups.size());
+        p_guess = exp(p_guess / groups.size());
+
         common::logger->trace("Fitting common p");
         std::vector<double> r_maps(groups.size(), 1.0);
-        double target_p = min_p;
-        try {
-            target_p = boost::math::tools::newton_raphson_iterate([&](double p) {
-                double dl = 0;
-                double ddl = 0;
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    double f = target_sum / sums[j];
-                    double &r = r_maps[j];
-                    r = boost::math::tools::newton_raphson_iterate([&](double r) {
-                        double dl = nelem * (log(p) - boost::math::digamma(r));
-                        double ddl = -nelem * boost::math::trigamma(r);
-                        for (const auto &[k, c] : hists[j]) {
-                            dl += boost::math::digamma(k * f + r) * c;
-                            ddl += boost::math::trigamma(k * f + r) * c;
-                        }
-                        return std::make_pair(dl, ddl);
-                    }, r, std::numeric_limits<double>::min(), f * sums[j], 30);
-                    double factor = nelem * r / p;
-                    dl += factor;
-                    ddl -= factor / p;
-                    const auto &hist = config.test_by_unitig ? unitig_hists[j] : hists[j];
-                    for (const auto &[k, c] : hist) {
-                        double factor = k * f * c / (1 - p);
-                        dl -= factor;
-                        ddl -= factor / (1 - p);
-                    }
-                }
-
-                return std::make_pair(dl, ddl);
-            }, min_p, std::numeric_limits<double>::min(), 1.0, 30);
-        } catch (std::exception &e) {
-            common::logger->warn("Caught exception while fitting p: Falling back to initial guess");
-            common::logger->warn("{}", e.what());
-            target_p = min_p;
-            for (size_t j = 0; j < groups.size(); ++j) {
-                double f = target_sum / sums[j];
-                double &r = r_maps[j];
-                try {
-                    r = boost::math::tools::newton_raphson_iterate([&](double r) {
-                        double dl = nelem * (log(target_p) - boost::math::digamma(r));
-                        double ddl = -nelem * boost::math::trigamma(r);
-                        for (const auto &[k, c] : hists[j]) {
-                            dl += boost::math::digamma(k * f + r) * c;
-                            ddl += boost::math::trigamma(k * f + r) * c;
-                        }
-                        return std::make_pair(dl, ddl);
-                    }, r, std::numeric_limits<double>::min(), f * sums[j], 30);
-                } catch (std::exception &e) {
-                    common::logger->warn("Caught exception while fitting r for {}: Falling back to initial guess", j);
-                    common::logger->warn("{}", e.what());
-                    double mu = static_cast<double>(sums[j]) * f / nelem;
-                    r = target_p * mu / (1 - target_p);
-                }
-            }
+        double target_p = p_guess;
+        double r_map_base = target_p / nelem / (1 - target_p);
+        for (size_t j = 0; j < groups.size(); ++j) {
+            r_maps[j] = r_map_base * sums[j];
         }
 
         std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());

From ed9a5d1feee66304b61957628a23fe9e0dda7d7f Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 16:42:17 +0200
Subject: [PATCH 201/410] cleanup

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 270c9bd1a5..b78a13d072 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -471,13 +471,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         // geometric mean
-        double target_sum = 0.0;
         double p_guess = 0.0;
         for (size_t j = 0; j < groups.size(); ++j) {
-            target_sum += log(static_cast<double>(sums[j]));
             p_guess += log(nb_params[j].second);
         }
-        target_sum = exp(target_sum / groups.size());
         p_guess = exp(p_guess / groups.size());
 
         common::logger->trace("Fitting common p");

From 96a75bd8c7e6e103fb0ec6d4d3ae9172bd858ea8 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 17:13:34 +0200
Subject: [PATCH 202/410] fix

---
 .../src/graph/annotated_graph_algorithm.cpp   | 36 ++++++++++++-------
 1 file changed, 23 insertions(+), 13 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b78a13d072..6bcfd05bf3 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -429,9 +429,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         };
     } else if (config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
-        auto get_rp = [&](size_t j, double mu, double var, const auto &hist) {
+        auto get_rp = [&](size_t j, double mu, double var, const auto &hist, double f = 1.0) {
             double var_orig = var;
             var = std::max(var, mu + 0.1);
+            mu *= f;
+            var *= f * f;
+
             double r_guess = mu * mu / (var - mu);
             double p_guess = mu / var;
             double r = r_guess;
@@ -440,8 +443,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
                     double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
                     for (const auto &[k, c] : hist) {
-                        dl += boost::math::digamma(k + r) * c;
-                        ddl += boost::math::trigamma(k + r) * c;
+                        dl += boost::math::digamma(f * k + r) * c;
+                        ddl += boost::math::trigamma(f * k + r) * c;
                     }
                     return std::make_pair(dl, ddl);
                 }, r_guess, std::numeric_limits<double>::min(), mu * nelem, 30);
@@ -452,13 +455,22 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             double p = r / (r + mu);
-            common::logger->trace("{}: mu: {}\tvar: {}\tmoment est:\tr: {}\tp: {}\tmle: r: {}\tp: {}",
-                                  j, mu, var_orig, r_guess, p_guess, r, p);
+            common::logger->trace("{}: scale: {}\tmu: {}\tvar: {}\tmoment est:\tr: {}\tp: {}\tmle: r: {}\tp: {}",
+                                  j, f, mu, var_orig, r_guess, p_guess, r, p);
 
             return std::make_pair(r, p);
         };
 
+        common::logger->trace("Scaling distributions");
+        // geometric mean
+        double target_sum = 0.0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            target_sum += log(static_cast<double>(sums[j]));
+        }
+        target_sum = exp(target_sum / groups.size());
+
         std::vector<std::pair<double, double>> nb_params(groups.size());
+        std::vector<std::pair<double, double>> scaled_nb_params(groups.size());
 
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
@@ -468,22 +480,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double mu2 = mu * mu;
             double var = static_cast<double>(config.test_by_unitig ? sums_of_squares_unitigs[j] : sums_of_squares[j]) / nelem - mu2;
             nb_params[j] = get_rp(j, mu, var, hist);
+            scaled_nb_params[j] = get_rp(j, mu, var, hist, target_sum / s);
         }
 
-        // geometric mean
+        common::logger->trace("Finding common p");
         double p_guess = 0.0;
         for (size_t j = 0; j < groups.size(); ++j) {
-            p_guess += log(nb_params[j].second);
+            p_guess += log(scaled_nb_params[j].second);
         }
         p_guess = exp(p_guess / groups.size());
 
-        common::logger->trace("Fitting common p");
-        std::vector<double> r_maps(groups.size(), 1.0);
         double target_p = p_guess;
-        double r_map_base = target_p / nelem / (1 - target_p);
-        for (size_t j = 0; j < groups.size(); ++j) {
-            r_maps[j] = r_map_base * sums[j];
-        }
+        double r_map_base = target_p / nelem / (1 - target_p) * target_sum;
+        std::vector<double> r_maps(groups.size(), r_map_base);
+
 
         std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
         double r_in = 0;

From aba7f157d0017a50d2505266a07e7690efd54ffb Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 17:26:18 +0200
Subject: [PATCH 203/410] cleanup

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6bcfd05bf3..87b3d75cf0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -491,9 +491,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         p_guess = exp(p_guess / groups.size());
 
         double target_p = p_guess;
-        double r_map_base = target_p / nelem / (1 - target_p) * target_sum;
-        std::vector<double> r_maps(groups.size(), r_map_base);
-
+        double r_map = target_p / nelem / (1 - target_p) * target_sum;
 
         std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
         double r_in = 0;
@@ -505,8 +503,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto &[r, p] = nb_params[j];
 
-            double r_map = r_maps[j];
-
             common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
                                   j, r, p, r_map, target_p);
 

From a467a8031df791b24f9982d12694f10a429e39ed Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Jun 2024 18:08:20 +0200
Subject: [PATCH 204/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 48 ++++++++-----------
 1 file changed, 19 insertions(+), 29 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 87b3d75cf0..cfcb69fab7 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -305,7 +305,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     sdsl::bit_vector indicator_in(graph_ptr->max_index() + 1, false);
     sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
 
-    VectorMap<size_t, std::pair<size_t, std::vector<uint64_t>>> m;
+    VectorMap<size_t, size_t> m;
     using RowStats = std::tuple<double, double, double, double>;
     std::function<RowStats(const PairContainer&)> compute_pval;
     std::function<RowStats(const std::vector<PairContainer>&)> compute_pval_unitig;
@@ -794,10 +794,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 throw std::runtime_error("Test failed");
             }
 
-            if (pval_min >= 0.05) {
+            if (pval_min >= config.family_wise_error_rate) {
                 k = 0;
             } else if (pval_min > 0) {
-                double lkd = log2(0.05) - log2(pval_min);
+                double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
                 if (lkd <= 64)
                     k = pow(2.0, lkd);
 
@@ -807,7 +807,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            if (in_kmer != out_kmer && pval < 0.05) {
+            if (in_kmer != out_kmer && pval < config.family_wise_error_rate) {
                 for (size_t i = 0; i < path.size(); ++i) {
                     set_bit((in_kmer ? indicator_in : indicator_out).data(), path[i], parallel, MO_RELAXED);
                 }
@@ -815,10 +815,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             if (config.test_type != "notest") {
                 std::lock_guard<std::mutex> lock(pval_mu);
-                auto &bucket = m[k];
-                ++bucket.first;
+                ++m[k];
                 for (size_t i = 0; i < path.size(); ++i) {
-                    bucket.second.emplace_back(path[i]);
                     pvals[path[i]] = bit_cast<uint64_t>(pval);
                 }
             }
@@ -880,10 +878,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 throw std::runtime_error("Test failed");
             }
 
-            if (pval_min >= 0.05) {
+            if (pval_min >= config.family_wise_error_rate) {
                 k = 0;
             } else if (pval_min > 0) {
-                double lkd = log2(0.05) - log2(pval_min);
+                double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
                 if (lkd <= 64)
                     k = pow(2.0, lkd);
 
@@ -893,14 +891,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            if (in_kmer != out_kmer && pval < 0.05)
+            if (in_kmer != out_kmer && pval < config.family_wise_error_rate)
                 set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
 
             if (config.test_type != "notest") {
                 std::lock_guard<std::mutex> lock(pval_mu);
-                auto &bucket = m[k];
-                ++bucket.first;
-                bucket.second.emplace_back(node);
+                ++m[k];
                 pvals[node] = bit_cast<uint64_t>(pval);
             }
         });
@@ -923,22 +919,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     if (config.test_type != "notest") {
         uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
-        auto m_data = const_cast<std::vector<std::pair<size_t, std::pair<size_t, std::vector<uint64_t>>>>&&>(m.values_container());
+        auto m_data = const_cast<std::vector<std::pair<size_t, size_t>>&&>(m.values_container());
 
         std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
         size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
-                                     [](size_t sum, const auto &a) { return sum + a.second.first; });
+                                     [](size_t sum, const auto &a) { return sum + a.second; });
         common::logger->trace("Performed {}/{} tests", acc, nelem);
         size_t total_tests = acc;
         common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
 
         if (total_sig) {
-            auto begin = m_data.begin();
             size_t k = 0;
             size_t last_k = 0;
-            for ( ; begin != m_data.end(); ++begin) {
-                const auto &[cur_k, s_bucket] = *begin;
-                const auto &[s, bucket] = s_bucket;
+            for (const auto &[cur_k, s] : m_data) {
                 common::logger->trace("k: {}\tm(k): {}", cur_k, acc);
                 if (acc <= cur_k) {
                     k = std::max(acc, last_k + 1);
@@ -970,21 +963,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
                 for (size_t i = 0; i < pvals.size(); ++i) {
                     double p = bit_cast<double, uint64_t>(pvals[i]);
-                    if (p < 0.05 && p * pval_map[p] >= 0.05) {
+                    if (p < config.family_wise_error_rate && p * pval_map[p] >= config.family_wise_error_rate) {
                         indicator_in[i] = false;
                         indicator_out[i] = false;
                     }
                 }
             } else {
-                common::logger->trace("k: {}\talpha_corr: {}", k, 0.05 / k);
-                for (auto jt = m_data.begin(); jt != m_data.end(); ++jt) {
-                    const auto &[cur_k, s_bucket] = *jt;
-                    const auto &[s, bucket] = s_bucket;
-                    for (uint64_t i : bucket) {
-                        if (jt < begin || bit_cast<double, uint64_t>(pvals[i]) * k >= 0.05) {
-                            indicator_in[i] = false;
-                            indicator_out[i] = false;
-                        }
+                common::logger->trace("k: {}\talpha_corr: {}", k, config.family_wise_error_rate / k);
+                for (size_t i = 0; i < indicator_in.size(); ++i) {
+                    double p = bit_cast<double, uint64_t>(pvals[i]);
+                    if (p < config.family_wise_error_rate && p * k >= config.family_wise_error_rate) {
+                        indicator_in[i] = false;
+                        indicator_out[i] = false;
                     }
                 }
             }

From 39520f2472c35fa0287445ee42eadd85a534b517 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 13 Jun 2024 10:30:20 +0200
Subject: [PATCH 205/410] mark unitig starts

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index cfcb69fab7..f6e5390d62 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -215,6 +215,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::atomic_thread_fence(std::memory_order_acquire);
     }
 
+    sdsl::bit_vector unitig_start;
+    if (config.test_by_unitig)
+        unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, true);
+
     std::shared_ptr<MaskedDeBruijnGraph> clean_masked_graph;
     std::vector<VectorMap<uint64_t, size_t>> unitig_hists(groups.size());
     size_t num_unitigs = 0;
@@ -231,6 +235,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         );
 
         clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+            unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
             std::vector<uint64_t> unitig_sums(groups.size());
             for (node_index node : path) {
                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
@@ -981,6 +986,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
+    call_ones(unitig_start, [&](node_index node) { pvals[node] = nullpval; });
+
     auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
         graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC

From 6833ea6733603628f38df3ab0e0bcf04e08c1658 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 13 Jun 2024 10:31:54 +0200
Subject: [PATCH 206/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 86 +++++++++++--------
 1 file changed, 52 insertions(+), 34 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index f6e5390d62..b976e2cfb2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -313,7 +313,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     VectorMap<size_t, size_t> m;
     using RowStats = std::tuple<double, double, double, double>;
     std::function<RowStats(const PairContainer&)> compute_pval;
-    std::function<RowStats(const std::vector<PairContainer>&)> compute_pval_unitig;
 
     common::logger->trace("Test: {}", config.test_type);
     if (config.test_type == "poisson_exact") {
@@ -434,11 +433,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         };
     } else if (config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
-        auto get_rp = [&](size_t j, double mu, double var, const auto &hist, double f = 1.0) {
+        auto get_rp = [&](size_t j, double mu, double var, const auto &hist) {
             double var_orig = var;
             var = std::max(var, mu + 0.1);
-            mu *= f;
-            var *= f * f;
 
             double r_guess = mu * mu / (var - mu);
             double p_guess = mu / var;
@@ -448,8 +445,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
                     double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
                     for (const auto &[k, c] : hist) {
-                        dl += boost::math::digamma(f * k + r) * c;
-                        ddl += boost::math::trigamma(f * k + r) * c;
+                        dl += boost::math::digamma(k + r) * c;
+                        ddl += boost::math::trigamma(k + r) * c;
                     }
                     return std::make_pair(dl, ddl);
                 }, r_guess, std::numeric_limits<double>::min(), mu * nelem, 30);
@@ -460,8 +457,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             double p = r / (r + mu);
-            common::logger->trace("{}: scale: {}\tmu: {}\tvar: {}\tmoment est:\tr: {}\tp: {}\tmle: r: {}\tp: {}",
-                                  j, f, mu, var_orig, r_guess, p_guess, r, p);
+            common::logger->trace("{}: mu: {}\tvar: {}\tmoment est:\tr: {}\tp: {}\tmle: r: {}\tp: {}",
+                                  j, mu, var_orig, r_guess, p_guess, r, p);
 
             return std::make_pair(r, p);
         };
@@ -475,7 +472,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         target_sum = exp(target_sum / groups.size());
 
         std::vector<std::pair<double, double>> nb_params(groups.size());
-        std::vector<std::pair<double, double>> scaled_nb_params(groups.size());
 
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
@@ -483,25 +479,52 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double s = static_cast<double>(sums[j]);
             double mu = s / nelem;
             double mu2 = mu * mu;
-            double var = static_cast<double>(config.test_by_unitig ? sums_of_squares_unitigs[j] : sums_of_squares[j]) / nelem - mu2;
+            double var = static_cast<double>(config.test_by_unitig
+                                                ? sums_of_squares_unitigs[j]
+                                                : sums_of_squares[j]) / nelem - mu2;
             nb_params[j] = get_rp(j, mu, var, hist);
-            scaled_nb_params[j] = get_rp(j, mu, var, hist, target_sum / s);
         }
 
-        common::logger->trace("Finding common p");
-        double p_guess = 0.0;
+        common::logger->trace("Finding common p and r.");
+        double real_sum = 0.0;
+        double real_sum_sq = 0.0;
         for (size_t j = 0; j < groups.size(); ++j) {
-            p_guess += log(scaled_nb_params[j].second);
+            double f = target_sum / sums[j];
+            const auto &hist = config.test_by_unitig ? unitig_hists[j] : hists[j];
+            for (const auto &[k, c] : hist) {
+                double val = f * k;
+                real_sum += val * c;
+                real_sum_sq += val * val * c;
+            }
         }
-        p_guess = exp(p_guess / groups.size());
 
-        double target_p = p_guess;
-        double r_map = target_p / nelem / (1 - target_p) * target_sum;
+        // nelem * r * groups.size() / p - target_sum * groups.size() / (1 - p) = 0
+        // nelem * r / p = target_sum / (1 - p)
+        // r / target_mu = p / (1 - p)
+        // r / target_mu = p + p * r / target_mu
+        // p = r / target_mu / (r / target_mu + 1)
+        // p = r / (r + target_mu)
+        double target_mu = real_sum / nelem / groups.size();
+        double target_mu2 = target_mu * target_mu;
+        double target_var = real_sum_sq / nelem / groups.size() - target_mu2;
+        double r_guess = target_mu2 / (target_var - target_mu);
+
+        double r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
+            double dl = nelem * groups.size() * (log(r) - log(r + target_mu) - boost::math::digamma(r));
+            double ddl = nelem * groups.size() * (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r));
+            for (size_t j = 0; j < groups.size(); ++j) {
+                double f = target_sum / sums[j];
+                const auto &hist = config.test_by_unitig ? unitig_hists[j] : hists[j];
+                for (const auto &[k, c] : hist) {
+                    dl += boost::math::digamma(f * k + r) * c;
+                    ddl += boost::math::trigamma(f * k + r) * c;
+                }
+            }
+            return std::make_pair(dl, ddl);
+        }, r_guess, std::numeric_limits<double>::min(), real_sum, 30);
+        double target_p = r_map / (r_map + target_mu);
 
         std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
-        double r_in = 0;
-        double r_out = 0;
-        std::mutex r_mu;
 
         common::logger->trace("Computing quantile maps");
         #pragma omp parallel for num_threads(num_parallel_files)
@@ -513,12 +536,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             boost::math::negative_binomial nb(r, p);
             boost::math::negative_binomial nb_out(r_map, target_p);
-            double scale = exp(log(p) - log(target_p));
+            double scale = target_sum / sums[j];
 
             // ensure that 0 maps to 0
             count_maps[j][0] = std::make_pair(0, 0);
 
-            for (const auto &[k, c] : hists[j]) {
+            const auto &hist = config.test_by_unitig ? unitig_hists[j] : hists[j];
+            for (const auto &[k, c] : hist) {
                 if (!count_maps[j].count(k)) {
                     double cdf = boost::math::cdf(nb, k);
 
@@ -538,15 +562,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     count_maps[j][k].second += c;
                 }
             }
-
-            std::lock_guard<std::mutex> lock(r_mu);
-            if (groups[j]) {
-                r_out += r_map;
-            } else {
-                r_in += r_map;
-            }
         }
 
+        double r_in = r_map * num_labels_in;
+        double r_out = r_map * num_labels_out;
+
         common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
         common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
@@ -762,16 +782,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (ex)
                 return;
 
-            std::vector<PairContainer> rows;
-            rows.reserve(path.size());
+            PairContainer merged_row;
             for (node_index node : path) {
-                auto &row = rows.emplace_back();
                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
                 for (size_t j = 0; j < groups.size(); ++j) {
                     const auto &col = *columns_all[j];
                     const auto &col_vals = *column_values_all[j];
                     if (uint64_t r = col.conditional_rank1(row_i))
-                        row.emplace_back(j, col_vals[r - 1]);
+                        merged_row.emplace_back(j, col_vals[r - 1]);
                 }
             }
 
@@ -780,7 +798,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double out_stat;
             double pval_min;
             try {
-                std::tie(pval, in_stat, out_stat, pval_min) = compute_pval_unitig(rows);
+                std::tie(pval, in_stat, out_stat, pval_min) = compute_pval(merged_row);
             } catch (...) {
                 std::lock_guard<std::mutex> lock(pval_mu);
                 ex = std::current_exception();

From c4a6379f9021634404dca37ac5d06319afe7d57c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 13 Jun 2024 11:38:06 +0200
Subject: [PATCH 207/410] bug fix

---
 .../src/graph/annotated_graph_algorithm.cpp   | 100 ++++--------------
 1 file changed, 18 insertions(+), 82 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b976e2cfb2..c927ee064e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -345,8 +345,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     pval = boost::math::cdf(bdist, d) + boost::math::cdf(boost::math::complement(bdist, n - d - 1));
                 }
             } else {
-                p_min = std::min(boost::math::pdf(bdist, 0), boost::math::pdf(bdist, n));
                 double d = abs(static_cast<double>(in_sum) / num_labels_in - static_cast<double>(out_sum) / num_labels_out);
+                double d0 = static_cast<double>(n) / num_labels_out;
+                double dn = static_cast<double>(n) / num_labels_in;
+                if (d0 > dn) {
+                    p_min = boost::math::pdf(bdist, 0);
+                } else if (d0 < dn) {
+                    p_min = boost::math::pdf(bdist, n);
+                } else {
+                    p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
+                }
+
                 if (d > 0) {
                     // we want s s.t. | s/num_labels_in - (n-s)/num_labels_out | >= d
                     // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
@@ -622,90 +631,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 }
             } else {
-                p_min = std::min(get_pmf(0), get_pmf(n));
                 double d = abs(static_cast<double>(in_sum) / num_labels_in - static_cast<double>(out_sum) / num_labels_out);
-                if (d > 0) {
-                    // we want s s.t. | s/num_labels_in - (n-s)/num_labels_out | >= d
-                    // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
-                    // s/num_labels_in + s/num_labels_out >= d + n/num_labels_out || n/num_labels_out - d >= s/num_labels_in + s/num_labels_out
-                    double factor = double(1.0) / num_labels_in + double(1.0) / num_labels_out;
-
-                    double lower_d = (static_cast<double>(n) / num_labels_out - d) / factor;
-                    double upper_d = (d + static_cast<double>(n) / num_labels_out) / factor;
-
-                    // fix floating point errors
-                    if (abs(lower_d - round(lower_d)) < 1e-10)
-                        lower_d = round(lower_d);
-
-                    if (abs(upper_d - round(upper_d)) < 1e-10)
-                        upper_d = round(upper_d);
-
-                    int64_t lower = floor(lower_d);
-                    int64_t upper = ceil(upper_d);
-
-                    if (lower < n && upper > 0 && lower != upper) {
-                        pval = 0;
-                        for (int64_t s = 0; s <= lower; ++s) {
-                            pval += get_pmf(s);
-                        }
-
-                        for (int64_t s = upper; s <= n; ++s) {
-                            pval += get_pmf(s);
-                        }
-                    }
-                }
-            }
-
-            if (pval >= 1.1)
-                throw std::runtime_error("pval fail");
-
-            return std::make_tuple(std::min(pval, 1.0),
-                                   static_cast<double>(in_sum),
-                                   static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj,
-                                   std::min(p_min, 1.0));
-        };
-
-        compute_pval_unitig = [&,r_in,r_out,count_maps,in_kmers_adj,out_kmers_adj](const auto &rows) {
-            if (std::all_of(rows.begin(), rows.end(), [](const auto &row) { return row.empty(); }))
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
-
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-            for (const auto &row : rows) {
-                for (const auto &[j, c] : row) {
-                    if (groups[j]) {
-                        out_sum += count_maps[j].find(c)->second.first;
-                    } else {
-                        in_sum += count_maps[j].find(c)->second.first;
-                    }
+                double d0 = static_cast<double>(n) / num_labels_out;
+                double dn = static_cast<double>(n) / num_labels_in;
+                if (d0 > dn) {
+                    p_min = get_pmf(0);
+                } else if (d0 < dn) {
+                    p_min = get_pmf(n);
+                } else {
+                    p_min = get_pmf(0) + get_pmf(n);
                 }
-            }
-
-            int64_t n = in_sum + out_sum;
-            double p_min = 1.0;
-            double pval = 1.0;
-
-            double lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
-            auto get_pmf = [&](int64_t s) {
-                int64_t t = n - s;
-                double rs = r_in + s;
-                double rt = r_out + t;
-                return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
-            };
 
-            if (num_labels_in == num_labels_out) {
-                p_min = get_pmf(0) + get_pmf(n);
-                if (in_sum != out_sum) {
-                    lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
-                    int64_t d = std::min(in_sum, out_sum);
-                    pval = p_min;
-                    for (int64_t s = 1; s <= d; ++s) {
-                        pval += get_pmf(s) + get_pmf(n - s);
-                    }
-                }
-            } else {
-                p_min = std::min(get_pmf(0), get_pmf(n));
-                double d = abs(static_cast<double>(in_sum) / num_labels_in - static_cast<double>(out_sum) / num_labels_out);
                 if (d > 0) {
                     // we want s s.t. | s/num_labels_in - (n-s)/num_labels_out | >= d
                     // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d

From 5f695663baa9c0c21e0d9661dee4eab6e012a90f Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 13 Jun 2024 22:18:16 +0200
Subject: [PATCH 208/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 115 ++++++++++++------
 1 file changed, 75 insertions(+), 40 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c927ee064e..a4bd3f4569 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -220,9 +220,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, true);
 
     std::shared_ptr<MaskedDeBruijnGraph> clean_masked_graph;
-    std::vector<VectorMap<uint64_t, size_t>> unitig_hists(groups.size());
-    size_t num_unitigs = 0;
-    std::vector<uint64_t> sums_of_squares_unitigs(groups.size());
+    std::atomic<size_t> num_unitigs(0);
     if (config.test_by_unitig) {
         clean_masked_graph = std::make_shared<MaskedDeBruijnGraph>(
             graph_ptr,
@@ -234,32 +232,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
         );
 
-        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-            unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
-            std::vector<uint64_t> unitig_sums(groups.size());
-            for (node_index node : path) {
-                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    const auto &col = *columns_all[j];
-                    const auto &col_vals = *column_values_all[j];
-                    if (uint64_t r = col.conditional_rank1(row_i))
-                        unitig_sums[j] += col_vals[r - 1];
-                }
-            }
-
-            for (size_t j = 0; j < groups.size(); ++j) {
-                sums_of_squares_unitigs[j] += unitig_sums[j] * unitig_sums[j];
-            }
-
-            std::lock_guard<std::mutex> lock(agg_mu);
-            ++num_unitigs;
-            for (size_t j = 0; j < groups.size(); ++j) {
-                ++unitig_hists[j][unitig_sums[j]];
-            }
-        }, num_threads);
+        if (config.test_type != "nbinom_exact") {
+            std::atomic_thread_fence(std::memory_order_release);
+            clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+                unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
+                num_unitigs.fetch_add(1, MO_RELAXED);
+            }, num_threads);
+            std::atomic_thread_fence(std::memory_order_acquire);
+        }
     }
 
-    double nelem = !config.test_by_unitig ? sdsl::util::cnt_one_bits(kept) : num_unitigs;
+    double nelem = sdsl::util::cnt_one_bits(kept);
 
     common::logger->trace("Computing aggregate statistics");
     double in_kmers = 0;
@@ -484,13 +467,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
-            const auto &hist = config.test_by_unitig ? unitig_hists[j] : hists[j];
+            const auto &hist = hists[j];
             double s = static_cast<double>(sums[j]);
             double mu = s / nelem;
             double mu2 = mu * mu;
-            double var = static_cast<double>(config.test_by_unitig
-                                                ? sums_of_squares_unitigs[j]
-                                                : sums_of_squares[j]) / nelem - mu2;
+            double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
             nb_params[j] = get_rp(j, mu, var, hist);
         }
 
@@ -499,7 +480,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         double real_sum_sq = 0.0;
         for (size_t j = 0; j < groups.size(); ++j) {
             double f = target_sum / sums[j];
-            const auto &hist = config.test_by_unitig ? unitig_hists[j] : hists[j];
+            const auto &hist = hists[j];
             for (const auto &[k, c] : hist) {
                 double val = f * k;
                 real_sum += val * c;
@@ -523,7 +504,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double ddl = nelem * groups.size() * (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r));
             for (size_t j = 0; j < groups.size(); ++j) {
                 double f = target_sum / sums[j];
-                const auto &hist = config.test_by_unitig ? unitig_hists[j] : hists[j];
+                const auto &hist = hists[j];
                 for (const auto &[k, c] : hist) {
                     dl += boost::math::digamma(f * k + r) * c;
                     ddl += boost::math::trigamma(f * k + r) * c;
@@ -550,7 +531,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             // ensure that 0 maps to 0
             count_maps[j][0] = std::make_pair(0, 0);
 
-            const auto &hist = config.test_by_unitig ? unitig_hists[j] : hists[j];
+            const auto &hist = hists[j];
             for (const auto &[k, c] : hist) {
                 if (!count_maps[j].count(k)) {
                     double cdf = boost::math::cdf(nb, k);
@@ -573,12 +554,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        double r_in = r_map * num_labels_in;
-        double r_out = r_map * num_labels_out;
-
-        common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
         common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
-
         double in_kmers_adj = 0;
         double out_kmers_adj = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
@@ -591,9 +567,68 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
         }
-
         common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers_adj, out_kmers_adj);
 
+        if (config.test_by_unitig) {
+            std::vector<VectorMap<uint64_t, size_t>> unitig_hists(groups.size());
+            std::atomic_thread_fence(std::memory_order_release);
+            clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+                unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
+                num_unitigs.fetch_add(1, MO_RELAXED);
+                std::vector<uint64_t> unitig_sums(groups.size(), 0);
+                for (node_index node : path) {
+                    uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                    for (size_t j = 0; j < groups.size(); ++j) {
+                        const auto &col = *columns_all[j];
+                        const auto &col_vals = *column_values_all[j];
+                        if (uint64_t r = col.conditional_rank1(row_i))
+                            unitig_sums[j] += count_maps[j].find(col_vals[r - 1])->second.first;
+                    }
+                }
+
+                std::lock_guard<std::mutex> lock(agg_mu);
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    ++unitig_hists[j][unitig_sums[j]];
+                }
+            }, num_threads);
+            std::atomic_thread_fence(std::memory_order_acquire);
+
+            nelem = num_unitigs.load();
+
+            double target_mu = 0;
+            double target_var = 0;
+            for (const auto &hist : unitig_hists) {
+                for (const auto &[k, c] : hist) {
+                    target_mu += k * c;
+                    target_var += k * k * c;
+                }
+            }
+
+            target_mu /= nelem * groups.size();
+            target_mu2 = target_mu * target_mu;
+            target_var = target_var / nelem / groups.size() - target_mu2;
+            r_guess = target_mu2 / (target_var - target_mu);
+            common::logger->trace("Unitig fit: mu: {}\tvar: {}\tr_guess: {}\tp_guess: {}",
+                                  target_mu, target_var, r_guess, target_mu / target_var);
+
+            r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
+                double dl = nelem * groups.size() * (log(r) - log(r + target_mu) - boost::math::digamma(r));
+                double ddl = nelem * groups.size() * (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r));
+                for (const auto &hist : unitig_hists) {
+                    for (const auto &[k, c] : hist) {
+                        dl += boost::math::digamma(k + r) * c;
+                        ddl += boost::math::trigamma(k + r) * c;
+                    }
+                }
+                return std::make_pair(dl, ddl);
+            }, r_guess, std::numeric_limits<double>::min(), real_sum, 30);
+            target_p = r_map / (r_map + target_mu);
+        }
+
+        double r_in = r_map * num_labels_in;
+        double r_out = r_map * num_labels_out;
+        common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
+
         compute_pval = [&,r_in,r_out,count_maps,in_kmers_adj,out_kmers_adj](const auto &row) {
             if (row.empty())
                 return std::make_tuple(1.1, 0.0, 0.0, 1.1);

From a5c95577d6f511d0b9e5becde1819ca7f22d6d9d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 13 Jun 2024 22:49:37 +0200
Subject: [PATCH 209/410] get rid of BY multiple testing correction

---
 .../src/graph/annotated_graph_algorithm.cpp   | 29 ++-----------------
 1 file changed, 3 insertions(+), 26 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a4bd3f4569..848197be75 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -919,7 +919,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
                                      [](size_t sum, const auto &a) { return sum + a.second; });
         common::logger->trace("Performed {}/{} tests", acc, nelem);
-        size_t total_tests = acc;
         common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
 
         if (total_sig) {
@@ -937,31 +936,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             if (k == 0) {
-                // TODO: fix for unitig
-                common::logger->trace("Falling back to Benjamini-Yekutieli");
-                VectorMap<double, double> pval_map;
-                for (uint64_t p_u : pvals) {
-                    auto p = bit_cast<double>(p_u);
-                    if (p <= 1.0)
-                        ++pval_map[p];
-                }
-                auto p_data = const_cast<std::vector<std::pair<double, double>>&&>(pval_map.values_container());
-                std::sort(p_data.begin(), p_data.end(), utils::GreaterFirst());
-
-                double cm = boost::math::digamma(total_tests) + 0.5772156649015329; // std::numbers::e_gamma_v<double>
-                pval_map = decltype(pval_map)();
-                size_t cur_rank = 0;
-                for (const auto &[p, c] : p_data) {
-                    cur_rank += c;
-                    pval_map[p] = cm * cur_rank;
-                }
-                for (size_t i = 0; i < pvals.size(); ++i) {
-                    double p = bit_cast<double, uint64_t>(pvals[i]);
-                    if (p < config.family_wise_error_rate && p * pval_map[p] >= config.family_wise_error_rate) {
-                        indicator_in[i] = false;
-                        indicator_out[i] = false;
-                    }
-                }
+                common::logger->trace("No significant k-mers found");
+                sdsl::util::set_to_value(indicator_in, false);
+                sdsl::util::set_to_value(indicator_out, false);
             } else {
                 common::logger->trace("k: {}\talpha_corr: {}", k, config.family_wise_error_rate / k);
                 for (size_t i = 0; i < indicator_in.size(); ++i) {

From 1d21670368f7576d9feb9df9df93b36a0ccaa5c3 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 13 Jun 2024 23:57:44 +0200
Subject: [PATCH 210/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 325 +++++++++---------
 1 file changed, 159 insertions(+), 166 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 848197be75..a8a504b7dc 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -245,8 +245,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     double nelem = sdsl::util::cnt_one_bits(kept);
 
     common::logger->trace("Computing aggregate statistics");
-    double in_kmers = 0;
-    double out_kmers = 0;
+    int64_t in_kmers = 0;
+    int64_t out_kmers = 0;
     std::vector<double> medians(groups.size());
     std::vector<uint64_t> max_obs_vals(groups.size());
     std::vector<size_t> n_kmers(groups.size());
@@ -271,7 +271,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                               j, sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
     }
 
-    double total_kmers = in_kmers + out_kmers;
+    int64_t total_kmers = in_kmers + out_kmers;
 
     common::logger->trace("Number of kept unique k-mers: {}\tNumber of kept k-mers: {}",
                           nelem, total_kmers);
@@ -294,134 +294,124 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
 
     VectorMap<size_t, size_t> m;
-    using RowStats = std::tuple<double, double, double, double>;
-    std::function<RowStats(const PairContainer&)> compute_pval;
+    std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
+    std::function<double(int64_t)> compute_min_pval;
+
+    std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps;
 
     common::logger->trace("Test: {}", config.test_type);
     if (config.test_type == "poisson_exact") {
-        compute_pval = [&](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
-
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
+        compute_min_pval = [&](int64_t n) {
+            if (n == 0)
+                return 1.1;
 
-            for (const auto &[j, c] : row) {
-                if (groups[j]) {
-                    out_sum += c;
-                } else {
-                    in_sum += c;
+            double p = static_cast<double>(in_kmers) / total_kmers;
+            auto bdist = boost::math::binomial(n, p);
+            if (num_labels_in != num_labels_out) {
+                double d0 = static_cast<double>(n) / num_labels_out;
+                double dn = static_cast<double>(n) / num_labels_in;
+                if (d0 > dn) {
+                    return boost::math::pdf(bdist, 0);
+                } else if (d0 < dn) {
+                    return boost::math::pdf(bdist, n);
                 }
             }
 
-            int64_t n = in_sum + out_sum;
-            double pval = 1.0;
-            double p_min = 1.0;
+            return boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
+        };
 
+        compute_pval = [&](int64_t in_sum, int64_t out_sum, const auto &row) {
+            if (row.empty())
+                return 1.1;
+
+            int64_t n = in_sum + out_sum;
             double p = static_cast<double>(in_kmers) / total_kmers;
             auto bdist = boost::math::binomial(n, p);
 
             if (num_labels_in == num_labels_out) {
-                p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
                 if (in_sum != out_sum) {
                     int64_t d = std::min(in_sum, out_sum);
-                    pval = boost::math::cdf(bdist, d) + boost::math::cdf(boost::math::complement(bdist, n - d - 1));
-                }
-            } else {
-                double d = abs(static_cast<double>(in_sum) / num_labels_in - static_cast<double>(out_sum) / num_labels_out);
-                double d0 = static_cast<double>(n) / num_labels_out;
-                double dn = static_cast<double>(n) / num_labels_in;
-                if (d0 > dn) {
-                    p_min = boost::math::pdf(bdist, 0);
-                } else if (d0 < dn) {
-                    p_min = boost::math::pdf(bdist, n);
-                } else {
-                    p_min = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
+                    return boost::math::cdf(bdist, d) + boost::math::cdf(boost::math::complement(bdist, n - d - 1));
                 }
 
-                if (d > 0) {
-                    // we want s s.t. | s/num_labels_in - (n-s)/num_labels_out | >= d
-                    // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
-                    // s/num_labels_in + s/num_labels_out >= d + n/num_labels_out || n/num_labels_out - d >= s/num_labels_in + s/num_labels_out
-                    double factor = double(1.0) / num_labels_in + double(1.0) / num_labels_out;
+                return 1.0;
+            }
 
-                    double lower_d = (static_cast<double>(n) / num_labels_out - d) / factor;
-                    double upper_d = (d + static_cast<double>(n) / num_labels_out) / factor;
+            double d = abs(static_cast<double>(in_sum) / num_labels_in - static_cast<double>(out_sum) / num_labels_out);
+            if (d > 0) {
+                // we want s s.t. | s/num_labels_in - (n-s)/num_labels_out | >= d
+                // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
+                // s/num_labels_in + s/num_labels_out >= d + n/num_labels_out || n/num_labels_out - d >= s/num_labels_in + s/num_labels_out
+                double factor = double(1.0) / num_labels_in + double(1.0) / num_labels_out;
 
-                    // fix floating point errors
-                    if (abs(lower_d - round(lower_d)) < 1e-10)
-                        lower_d = round(lower_d);
+                double lower_d = (static_cast<double>(n) / num_labels_out - d) / factor;
+                double upper_d = (d + static_cast<double>(n) / num_labels_out) / factor;
 
-                    if (abs(upper_d - round(upper_d)) < 1e-10)
-                        upper_d = round(upper_d);
+                // fix floating point errors
+                if (abs(lower_d - round(lower_d)) < 1e-10)
+                    lower_d = round(lower_d);
 
-                    int64_t lower = floor(lower_d);
-                    int64_t upper = ceil(upper_d);
+                if (abs(upper_d - round(upper_d)) < 1e-10)
+                    upper_d = round(upper_d);
 
-                    if (lower < n && upper > 0 && lower != upper) {
-                        pval = 0.0;
+                int64_t lower = floor(lower_d);
+                int64_t upper = ceil(upper_d);
 
-                        if (lower >= 0)
-                            pval += boost::math::cdf(bdist, lower);
+                if (lower < n && upper > 0 && lower != upper) {
+                    double pval = 0.0;
 
-                        if (upper <= n)
-                            pval += boost::math::cdf(boost::math::complement(bdist, upper - 1));
-                    }
-                }
-            }
+                    if (lower >= 0)
+                        pval += boost::math::cdf(bdist, lower);
 
-            if (pval > 1.0) {
-                common::logger->error("{} > 1.0\t{}\t{},{}", pval, p_min, in_sum, out_sum);
-                throw std::runtime_error("pval fail");
+                    if (upper <= n)
+                        pval += boost::math::cdf(boost::math::complement(bdist, upper - 1));
+
+                    return pval;
+                }
             }
 
-            return std::make_tuple(pval,
-                                   static_cast<double>(in_sum),
-                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
-                                   p_min);
+            return 1.0;
         };
     } else if (config.test_type == "fisher") {
-        compute_pval = [&](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+        double lbase_shared = lgamma(in_kmers + 1) + lgamma(out_kmers + 1) - lgamma(total_kmers + 1);
 
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
+        compute_min_pval = [&,lbase_shared](int64_t m1) {
+            if (m1 == 0)
+                return 1.1;
 
-            for (const auto &[j, c] : row) {
-                if (groups[j]) {
-                    out_sum += c;
-                } else {
-                    in_sum += c;
-                }
-            }
-
-            int64_t t = total_kmers;
-            int64_t n1 = in_kmers;
-            int64_t n2 = out_kmers;
-            int64_t m1 = in_sum + out_sum;
-            int64_t m2 = t - m1;
-
-            double lbase = lgamma(n1 + 1) + lgamma(n2 + 1) + lgamma(m1 + 1) + lgamma(m2 + 1) - lgamma(t + 1);
+            int64_t m2 = total_kmers - m1;
 
             auto get_pval = [&](int64_t a) {
-                int64_t b = n1 - a;
+                double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
+
+                int64_t b = in_kmers - a;
                 int64_t c = m1 - a;
                 int64_t d = m2 - b;
-                assert(d == n2 - c);
+                assert(d == out_kmers - c);
 
-                return b < 0 || c < 0 || d < 0
+                return out_kmers < c
                     ? 1.0
                     : exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
             };
 
-            double pval = get_pval(in_sum);
-            double pmin = std::min(get_pval(0), get_pval(std::min(n1, m1)));
+            return std::min(get_pval(0), get_pval(std::min(in_kmers, m1)));
+        };
+
+        compute_pval = [&,lbase_shared](int64_t in_sum, int64_t out_sum, const auto &row) {
+            if (row.empty())
+                return 1.1;
+
+            int64_t m1 = in_sum + out_sum;
+            int64_t m2 = total_kmers - m1;
+
+            double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
 
-            return std::make_tuple(pval,
-                                   static_cast<double>(in_sum),
-                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
-                                   pmin);
+            int64_t a = in_sum;
+            int64_t b = in_kmers - in_sum;
+            int64_t c = out_sum;
+            int64_t d = out_kmers - out_sum;
+
+            return exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
         };
     } else if (config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
@@ -514,7 +504,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }, r_guess, std::numeric_limits<double>::min(), real_sum, 30);
         double target_p = r_map / (r_map + target_mu);
 
-        std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps(groups.size());
+        count_maps.resize(groups.size());
 
         common::logger->trace("Computing quantile maps");
         #pragma omp parallel for num_threads(num_parallel_files)
@@ -555,19 +545,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
-        double in_kmers_adj = 0;
-        double out_kmers_adj = 0;
+        in_kmers = 0;
+        out_kmers = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
             for (const auto &[k, m] : count_maps[j]) {
                 const auto &[v, c] = m;
                 if (groups[j]) {
-                    out_kmers_adj += v * c;
+                    out_kmers += v * c;
                 } else {
-                    in_kmers_adj += v * c;
+                    in_kmers += v * c;
                 }
             }
         }
-        common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers_adj, out_kmers_adj);
+        common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
         if (config.test_by_unitig) {
             std::vector<VectorMap<uint64_t, size_t>> unitig_hists(groups.size());
@@ -629,25 +619,39 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         double r_out = r_map * num_labels_out;
         common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
 
-        compute_pval = [&,r_in,r_out,count_maps,in_kmers_adj,out_kmers_adj](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
+        double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+        compute_min_pval = [&,lscaling_base,r_in,r_out](int64_t n) {
+            if (n == 0)
+                return 1.1;
 
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-            for (const auto &[j, c] : row) {
-                if (groups[j]) {
-                    out_sum += count_maps[j].find(c)->second.first;
-                } else {
-                    in_sum += count_maps[j].find(c)->second.first;
+            double lscaling = lgamma(n + 1) + lscaling_base;
+            auto get_pmf = [&](int64_t s) {
+                int64_t t = n - s;
+                double rs = r_in + s;
+                double rt = r_out + t;
+                return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
+            };
+
+            if (num_labels_in != num_labels_out) {
+                double d0 = static_cast<double>(n) / num_labels_out;
+                double dn = static_cast<double>(n) / num_labels_in;
+                if (d0 > dn) {
+                    return get_pmf(0);
+                } else if (d0 < dn) {
+                    return get_pmf(n);
                 }
             }
 
+            return std::min(1.0, get_pmf(0) + get_pmf(n));
+        };
+
+        compute_pval = [&,lscaling_base,r_in,r_out](int64_t in_sum, int64_t out_sum, const auto &row) {
+            if (row.empty())
+                return 1.1;
+
             int64_t n = in_sum + out_sum;
-            double p_min = 1.0;
-            double pval = 1.0;
 
-            double lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+            double lscaling = lgamma(n + 1) + lscaling_base;
             auto get_pmf = [&](int64_t s) {
                 int64_t t = n - s;
                 double rs = r_in + s;
@@ -655,28 +659,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
             };
 
+            double pval = 1.0;
             if (num_labels_in == num_labels_out) {
-                p_min = get_pmf(0) + get_pmf(n);
                 if (in_sum != out_sum) {
                     lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
                     int64_t d = std::min(in_sum, out_sum);
-                    pval = p_min;
-                    for (int64_t s = 1; s <= d; ++s) {
+                    pval = 0.0;
+                    for (int64_t s = 0; s <= d; ++s) {
                         pval += get_pmf(s) + get_pmf(n - s);
                     }
                 }
             } else {
                 double d = abs(static_cast<double>(in_sum) / num_labels_in - static_cast<double>(out_sum) / num_labels_out);
-                double d0 = static_cast<double>(n) / num_labels_out;
-                double dn = static_cast<double>(n) / num_labels_in;
-                if (d0 > dn) {
-                    p_min = get_pmf(0);
-                } else if (d0 < dn) {
-                    p_min = get_pmf(n);
-                } else {
-                    p_min = get_pmf(0) + get_pmf(n);
-                }
-
                 if (d > 0) {
                     // we want s s.t. | s/num_labels_in - (n-s)/num_labels_out | >= d
                     // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
@@ -709,35 +703,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            if (pval >= 1.1)
-                throw std::runtime_error("pval fail");
-
-            return std::make_tuple(std::min(pval, 1.0),
-                                   static_cast<double>(in_sum),
-                                   static_cast<double>(out_sum) / out_kmers_adj * in_kmers_adj,
-                                   std::min(p_min, 1.0));
+            return std::min(pval, 1.0);
         };
     } else if (config.test_type == "notest") {
-        compute_pval = [&](const auto &row) {
-            if (row.empty())
-                return std::make_tuple(1.1, 0.0, 0.0, 1.1);
-
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-
-            for (const auto &[j, c] : row) {
-                if (groups[j]) {
-                    out_sum += c;
-                } else {
-                    in_sum += c;
-                }
-            }
-
-            return std::make_tuple(0.0,
-                                   static_cast<double>(in_sum),
-                                   static_cast<double>(out_sum) / out_kmers * in_kmers,
-                                   0.0);
-        };
+        compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
+        compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };
     } else {
         throw std::runtime_error("Test not implemented");
     }
@@ -753,23 +723,34 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (ex)
                 return;
 
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
             PairContainer merged_row;
             for (node_index node : path) {
                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
                 for (size_t j = 0; j < groups.size(); ++j) {
                     const auto &col = *columns_all[j];
                     const auto &col_vals = *column_values_all[j];
-                    if (uint64_t r = col.conditional_rank1(row_i))
-                        merged_row.emplace_back(j, col_vals[r - 1]);
+                    if (uint64_t r = col.conditional_rank1(row_i)) {
+                        uint64_t c = col_vals[r - 1];
+                        if (count_maps.size())
+                            c = count_maps[j].find(c)->second.first;
+
+                        merged_row.emplace_back(j, c);
+                        if (groups[j]) {
+                            out_sum += c;
+                        } else {
+                            in_sum += c;
+                        }
+                    }
                 }
             }
 
             double pval;
-            double in_stat;
-            double out_stat;
             double pval_min;
             try {
-                std::tie(pval, in_stat, out_stat, pval_min) = compute_pval(merged_row);
+                pval_min = compute_min_pval(in_sum + out_sum);
+                pval = compute_pval(in_sum, out_sum, merged_row);
             } catch (...) {
                 std::lock_guard<std::mutex> lock(pval_mu);
                 ex = std::current_exception();
@@ -779,6 +760,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (pval >= 1.1)
                 return;
 
+            double in_stat = in_sum;
+            double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
             bool in_kmer = (in_stat > out_stat) || (out_stat != out_stat && in_stat == in_stat);
             bool out_kmer = (in_stat < out_stat) || (in_stat != in_stat && out_stat == out_stat);
             uint64_t k = std::numeric_limits<uint64_t>::max();
@@ -823,18 +806,24 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (ex)
                 return;
 
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
             PairContainer row;
             if (kept[row_i]) {
                 size_t count_in = 0;
                 size_t count_out = 0;
                 for (const auto &[j, raw_c] : raw_row) {
-                    if (uint64_t c = raw_c) {
-                        row.emplace_back(j, c);
-                        if (groups[j]) {
-                            ++count_out;
-                        } else {
-                            ++count_in;
-                        }
+                    uint64_t c = raw_c;
+                    if (count_maps.size())
+                        c = count_maps[j].find(c)->second.first;
+
+                    row.emplace_back(j, c);
+                    if (groups[j]) {
+                        out_sum += c;
+                        ++count_out;
+                    } else {
+                        in_sum += c;
+                        ++count_in;
                     }
                 }
 
@@ -842,18 +831,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         || count_in < config.min_in_recurrence
                         || count_out < config.min_out_recurrence
                         || count_in > config.max_in_recurrence
-                        || count_out > config.max_out_recurrence)
+                        || count_out > config.max_out_recurrence) {
                     row.clear();
+                    in_sum = 0;
+                    out_sum = 0;
+                }
             }
 
             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
 
             double pval;
-            double in_stat;
-            double out_stat;
             double pval_min;
             try {
-                std::tie(pval, in_stat, out_stat, pval_min) = compute_pval(row);
+                pval_min = compute_min_pval(in_sum + out_sum);
+                pval = compute_pval(in_sum, out_sum, row);
             } catch (...) {
                 std::lock_guard<std::mutex> lock(pval_mu);
                 ex = std::current_exception();
@@ -863,6 +854,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (pval >= 1.1)
                 return;
 
+            double in_stat = in_sum;
+            double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
             bool in_kmer = (in_stat > out_stat) || (out_stat != out_stat && in_stat == in_stat);
             bool out_kmer = (in_stat < out_stat) || (in_stat != in_stat && out_stat == out_stat);
             uint64_t k = std::numeric_limits<uint64_t>::max();

From 716760db19c3bfa8aab455222f512206a407ab70 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 14 Jun 2024 17:48:05 +0200
Subject: [PATCH 211/410] fix

---
 .../src/graph/annotated_graph_algorithm.cpp   | 127 +++++++-----------
 1 file changed, 48 insertions(+), 79 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a8a504b7dc..6775cffd85 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -215,6 +215,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::atomic_thread_fence(std::memory_order_acquire);
     }
 
+    double nelem = sdsl::util::cnt_one_bits(kept);
+
     sdsl::bit_vector unitig_start;
     if (config.test_by_unitig)
         unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, true);
@@ -232,17 +234,35 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
         );
 
-        if (config.test_type != "nbinom_exact") {
-            std::atomic_thread_fence(std::memory_order_release);
-            clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-                unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
-                num_unitigs.fetch_add(1, MO_RELAXED);
-            }, num_threads);
-            std::atomic_thread_fence(std::memory_order_acquire);
+        for (size_t j = 0; j < groups.size(); ++j) {
+            hists[j].clear();
         }
-    }
 
-    double nelem = sdsl::util::cnt_one_bits(kept);
+        std::atomic_thread_fence(std::memory_order_release);
+        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+            unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
+            num_unitigs.fetch_add(1, MO_RELAXED);
+
+            std::vector<uint64_t> unitig_sums(groups.size(), 0);
+            for (node_index node : path) {
+                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    const auto &col = *columns_all[j];
+                    const auto &col_vals = *column_values_all[j];
+                    if (uint64_t r = col.conditional_rank1(row_i))
+                        unitig_sums[j] += col_vals[r - 1];
+                }
+            }
+
+            std::lock_guard<std::mutex> lock(agg_mu);
+            for (size_t j = 0; j < groups.size(); ++j) {
+                ++hists[j][unitig_sums[j]];
+            }
+        }, num_threads);
+        std::atomic_thread_fence(std::memory_order_acquire);
+
+        nelem = num_unitigs.load();
+    }
 
     common::logger->trace("Computing aggregate statistics");
     int64_t in_kmers = 0;
@@ -559,62 +579,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
         common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
-        if (config.test_by_unitig) {
-            std::vector<VectorMap<uint64_t, size_t>> unitig_hists(groups.size());
-            std::atomic_thread_fence(std::memory_order_release);
-            clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-                unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
-                num_unitigs.fetch_add(1, MO_RELAXED);
-                std::vector<uint64_t> unitig_sums(groups.size(), 0);
-                for (node_index node : path) {
-                    uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-                    for (size_t j = 0; j < groups.size(); ++j) {
-                        const auto &col = *columns_all[j];
-                        const auto &col_vals = *column_values_all[j];
-                        if (uint64_t r = col.conditional_rank1(row_i))
-                            unitig_sums[j] += count_maps[j].find(col_vals[r - 1])->second.first;
-                    }
-                }
-
-                std::lock_guard<std::mutex> lock(agg_mu);
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    ++unitig_hists[j][unitig_sums[j]];
-                }
-            }, num_threads);
-            std::atomic_thread_fence(std::memory_order_acquire);
-
-            nelem = num_unitigs.load();
-
-            double target_mu = 0;
-            double target_var = 0;
-            for (const auto &hist : unitig_hists) {
-                for (const auto &[k, c] : hist) {
-                    target_mu += k * c;
-                    target_var += k * k * c;
-                }
-            }
-
-            target_mu /= nelem * groups.size();
-            target_mu2 = target_mu * target_mu;
-            target_var = target_var / nelem / groups.size() - target_mu2;
-            r_guess = target_mu2 / (target_var - target_mu);
-            common::logger->trace("Unitig fit: mu: {}\tvar: {}\tr_guess: {}\tp_guess: {}",
-                                  target_mu, target_var, r_guess, target_mu / target_var);
-
-            r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
-                double dl = nelem * groups.size() * (log(r) - log(r + target_mu) - boost::math::digamma(r));
-                double ddl = nelem * groups.size() * (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r));
-                for (const auto &hist : unitig_hists) {
-                    for (const auto &[k, c] : hist) {
-                        dl += boost::math::digamma(k + r) * c;
-                        ddl += boost::math::trigamma(k + r) * c;
-                    }
-                }
-                return std::make_pair(dl, ddl);
-            }, r_guess, std::numeric_limits<double>::min(), real_sum, 30);
-            target_p = r_map / (r_map + target_mu);
-        }
-
         double r_in = r_map * num_labels_in;
         double r_out = r_map * num_labels_out;
         common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
@@ -723,25 +687,30 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (ex)
                 return;
 
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-            PairContainer merged_row;
+            std::vector<int64_t> row_counts(groups.size());
             for (node_index node : path) {
                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
                 for (size_t j = 0; j < groups.size(); ++j) {
                     const auto &col = *columns_all[j];
                     const auto &col_vals = *column_values_all[j];
-                    if (uint64_t r = col.conditional_rank1(row_i)) {
-                        uint64_t c = col_vals[r - 1];
-                        if (count_maps.size())
-                            c = count_maps[j].find(c)->second.first;
-
-                        merged_row.emplace_back(j, c);
-                        if (groups[j]) {
-                            out_sum += c;
-                        } else {
-                            in_sum += c;
-                        }
+                    if (uint64_t r = col.conditional_rank1(row_i))
+                        row_counts[j] += col_vals[r - 1];
+                }
+            }
+
+            PairContainer merged_row;
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+            for (size_t j = 0; j < groups.size(); ++j) {
+                if (row_counts[j] > 0) {
+                    if (count_maps.size())
+                        row_counts[j] = count_maps[j].find(row_counts[j])->second.first;
+
+                    merged_row.emplace_back(j, row_counts[j]);
+                    if (groups[j]) {
+                        out_sum += row_counts[j];
+                    } else {
+                        in_sum += row_counts[j];
                     }
                 }
             }

From faddd79080ee273e74c28edde9045b5d45e5b4ec Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 17 Jun 2024 14:08:37 +0200
Subject: [PATCH 212/410] pick extreme based on deviance

---
 metagraph/src/cli/config/config.cpp           |   4 +-
 metagraph/src/cli/transform_annotation.cpp    | 102 ++++++++++--
 .../src/graph/annotated_graph_algorithm.cpp   | 149 +++++++++++-------
 3 files changed, 189 insertions(+), 66 deletions(-)

diff --git a/metagraph/src/cli/config/config.cpp b/metagraph/src/cli/config/config.cpp
index 5dd62b1512..41c6f53fcf 100644
--- a/metagraph/src/cli/config/config.cpp
+++ b/metagraph/src/cli/config/config.cpp
@@ -613,8 +613,8 @@ Config::Config(int argc, char *argv[]) {
         if (to_row_diff && !infbase.size()) {
             std::cerr << "Path to graph must be passed with '-i <GRAPH>'" << std::endl;
             print_usage_and_exit = true;
-        } else if (!to_row_diff && infbase.size()) {
-            std::cerr << "Graph is only required for transform to row_diff types" << std::endl;
+        } else if (!to_row_diff && !count_kmers && !unitigs && !dump_text_anno && infbase.size()) {
+            std::cerr << "Graph is only required for transform to row_diff types and dumping per-unitig counts" << std::endl;
             print_usage_and_exit = true;
         }
     }
diff --git a/metagraph/src/cli/transform_annotation.cpp b/metagraph/src/cli/transform_annotation.cpp
index d7549de9b3..2dfb4bd420 100644
--- a/metagraph/src/cli/transform_annotation.cpp
+++ b/metagraph/src/cli/transform_annotation.cpp
@@ -13,6 +13,9 @@
 #include "annotation/annotation_converters.hpp"
 #include "config/config.hpp"
 #include "load/load_annotation.hpp"
+#include "load/load_graph.hpp"
+#include "graph/annotated_dbg.hpp"
+#include "graph/representation/masked_graph.hpp"
 
 
 namespace mtg {
@@ -326,16 +329,97 @@ int transform_annotation(Config *config) {
 
         if (input_anno_type == Config::ColumnCompressed) {
             if (config->count_kmers) {
-                ColumnCompressed<>::load_columns_and_values(files,
-                    [&](uint64_t offset, const auto &label, std::unique_ptr<bit_vector> &&column, sdsl::int_vector_buffer<>&& column_values) {
-                        std::ofstream outstream(utils::remove_suffix(config->outfbase, ColumnCompressed<>::kExtension)
-                                    + fmt::format(".{}.tsv", offset));
-                        uint64_t rk = 0;
-                        column->call_ones([&](uint64_t pos) {
-                            outstream << pos << "\t" << column_values[rk++] << "\n";
-                        });
+                if (config->unitigs) {
+                    std::vector<std::unique_ptr<bit_vector>> columns_all;
+                    std::vector<sdsl::int_vector<>> column_values_all;
+                    ColumnCompressed<>::load_columns_and_values(files,
+                        [&](uint64_t offset, const auto &label, std::unique_ptr<bit_vector> &&column, sdsl::int_vector<>&& column_values) {
+                            if (offset >= columns_all.size()) {
+                                columns_all.resize(offset + 1);
+                                column_values_all.resize(offset + 1);
+                            }
+
+                            std::swap(columns_all[offset], column);
+                            std::swap(column_values_all[offset], column_values);
+                        }
+                    );
+
+                    auto graph = load_critical_dbg(config->infbase);
+
+                    std::mutex out_mu;
+                    std::ofstream outstream(utils::remove_suffix(config->outfbase, ColumnCompressed<>::kExtension)
+                                        + ".unitigs.tsv");
+                    if (config->min_count <= 1) {
+                        graph->call_unitigs([&](const std::string&, const auto &path) {
+                            std::vector<uint64_t> unitig_sums(columns_all.size());
+                            for (graph::DeBruijnGraph::node_index node : path) {
+                                auto row_i = graph::AnnotatedDBG::graph_to_anno_index(node);
+                                for (size_t j = 0; j < columns_all.size(); ++j) {
+                                    if (uint64_t r = columns_all[j]->conditional_rank1(row_i))
+                                        unitig_sums[j] += column_values_all[j][r - 1];
+                                }
+                            }
+
+                            std::lock_guard<std::mutex> lock(out_mu);
+                            outstream << fmt::format("{}\t{}\t{}\n", path[0], path.size(), fmt::join(unitig_sums, "\t"));
+                        }, get_num_threads());
+                    } else {
+                        bool parallel = get_num_threads() > 1;
+                        sdsl::bit_vector mask(graph->max_index() + 1, true);
+                        mask[0] = false;
+                        std::atomic_thread_fence(std::memory_order_release);
+                        graph->call_unitigs([&](const std::string&, const auto &path) {
+                            std::vector<uint64_t> unitig_sums(columns_all.size());
+                            for (graph::DeBruijnGraph::node_index node : path) {
+                                auto row_i = graph::AnnotatedDBG::graph_to_anno_index(node);
+                                for (size_t j = 0; j < columns_all.size(); ++j) {
+                                    if (uint64_t r = columns_all[j]->conditional_rank1(row_i))
+                                        unitig_sums[j] += column_values_all[j][r - 1];
+                                }
+                            }
+
+                            if (std::all_of(unitig_sums.begin(), unitig_sums.end(), [&](uint64_t c) { return c < config->min_count; })) {
+                                for (graph::DeBruijnGraph::node_index node : path) {
+                                    unset_bit(mask.data(), node, parallel, std::memory_order_relaxed);
+                                }
+                            }
+                        }, get_num_threads());
+                        std::atomic_thread_fence(std::memory_order_acquire);
+
+                        graph::MaskedDeBruijnGraph clean_graph(
+                            graph,
+                            std::make_unique<bitmap_vector>(std::move(mask)),
+                            true,
+                            graph->get_mode() == graph::DeBruijnGraph::PRIMARY
+                                ? graph::DeBruijnGraph::PRIMARY : graph::DeBruijnGraph::BASIC
+                        );
+
+                        clean_graph.call_unitigs([&](const std::string&, const auto &path) {
+                            std::vector<uint64_t> unitig_sums(columns_all.size());
+                            for (graph::DeBruijnGraph::node_index node : path) {
+                                auto row_i = graph::AnnotatedDBG::graph_to_anno_index(node);
+                                for (size_t j = 0; j < columns_all.size(); ++j) {
+                                    if (uint64_t r = columns_all[j]->conditional_rank1(row_i))
+                                        unitig_sums[j] += column_values_all[j][r - 1];
+                                }
+                            }
+
+                            std::lock_guard<std::mutex> lock(out_mu);
+                            outstream << fmt::format("{}\t{}\t{}\n", path[0], path.size(), fmt::join(unitig_sums, "\t"));
+                        }, get_num_threads());
                     }
-                );
+                } else {
+                    ColumnCompressed<>::load_columns_and_values(files,
+                        [&](uint64_t offset, const auto &label, std::unique_ptr<bit_vector> &&column, sdsl::int_vector_buffer<>&& column_values) {
+                            std::ofstream outstream(utils::remove_suffix(config->outfbase, ColumnCompressed<>::kExtension)
+                                        + fmt::format(".{}.tsv", offset));
+                            uint64_t rk = 0;
+                            column->call_ones([&](uint64_t pos) {
+                                outstream << pos << "\t" << column_values[rk++] << "\n";
+                            });
+                        }
+                    );
+                }
             } else if (!dynamic_cast<ColumnCompressed<>&>(*annotation).merge_load(files)) {
                 logger->error("Cannot load annotations");
                 exit(1);
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6775cffd85..c743740a62 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -238,22 +238,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             hists[j].clear();
         }
 
+        std::atomic<uint64_t> discarded_counts(0);
         std::atomic_thread_fence(std::memory_order_release);
         clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-            unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
-            num_unitigs.fetch_add(1, MO_RELAXED);
-
             std::vector<uint64_t> unitig_sums(groups.size(), 0);
+            std::vector<bool> all_below_cutoff(groups.size(), true);
             for (node_index node : path) {
                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
                 for (size_t j = 0; j < groups.size(); ++j) {
                     const auto &col = *columns_all[j];
                     const auto &col_vals = *column_values_all[j];
-                    if (uint64_t r = col.conditional_rank1(row_i))
-                        unitig_sums[j] += col_vals[r - 1];
+                    if (uint64_t r = col.conditional_rank1(row_i)) {
+                        uint64_t c = col_vals[r - 1];
+                        unitig_sums[j] += c;
+                        if (c >= min_counts[j])
+                            all_below_cutoff[j] = false;
+                    }
                 }
             }
 
+            unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
+            num_unitigs.fetch_add(1, MO_RELAXED);
+
             std::lock_guard<std::mutex> lock(agg_mu);
             for (size_t j = 0; j < groups.size(); ++j) {
                 ++hists[j][unitig_sums[j]];
@@ -261,6 +267,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }, num_threads);
         std::atomic_thread_fence(std::memory_order_acquire);
 
+        common::logger->trace("Discarded {} counts", discarded_counts.load());
+
         nelem = num_unitigs.load();
     }
 
@@ -584,7 +592,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
 
         double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
-        compute_min_pval = [&,lscaling_base,r_in,r_out](int64_t n) {
+
+        compute_min_pval = [&,lscaling_base,r_in,r_out,target_p](int64_t n) {
             if (n == 0)
                 return 1.1;
 
@@ -596,20 +605,48 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
             };
 
-            if (num_labels_in != num_labels_out) {
-                double d0 = static_cast<double>(n) / num_labels_out;
-                double dn = static_cast<double>(n) / num_labels_in;
-                if (d0 > dn) {
-                    return get_pmf(0);
-                } else if (d0 < dn) {
-                    return get_pmf(n);
+            auto get_deviance = [&](double y, double mu, double invphi) {
+                y += 1e-8;
+                mu += 1e-8;
+                return 2.0 * (y * log( y/mu ) + (y + invphi) * log( (mu + invphi)/(y + invphi) ) );
+            };
+
+            double mu1 = r_in * (1.0 - target_p) / target_p;
+            double mu2 = r_out * (1.0 - target_p) / target_p;
+
+            std::vector<double> devs;
+            devs.reserve(n + 1);
+            for (int64_t s = 0; s <= n; ++s) {
+                devs.emplace_back(get_deviance(s, mu1, r_in) + get_deviance(n - s, mu2, r_out));
+            }
+            double max_d = *std::max_element(devs.begin(), devs.end());
+            double pval = 0.0;
+            int64_t s = 0;
+            for ( ; s <= n; ++s) {
+                if (devs[s] == max_d) {
+                    pval += get_pmf(s);
+                } else {
+                    break;
                 }
             }
 
-            return std::min(1.0, get_pmf(0) + get_pmf(n));
+            for (int64_t sp = n; sp >= s; --sp) {
+                if (devs[sp] == max_d) {
+                    pval += get_pmf(sp);
+                } else {
+                    break;
+                }
+            }
+
+            if (pval == 0.0) {
+                common::logger->error("Devs: {}", fmt::join(devs, ","));
+                throw std::runtime_error("Fail");
+            }
+
+            return std::min(1.0, pval);
         };
 
-        compute_pval = [&,lscaling_base,r_in,r_out](int64_t in_sum, int64_t out_sum, const auto &row) {
+        compute_pval = [&,lscaling_base,r_in,r_out,target_p](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
                 return 1.1;
 
@@ -623,51 +660,39 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
             };
 
-            double pval = 1.0;
-            if (num_labels_in == num_labels_out) {
-                if (in_sum != out_sum) {
-                    lscaling = lgamma(n + 1) + lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
-                    int64_t d = std::min(in_sum, out_sum);
-                    pval = 0.0;
-                    for (int64_t s = 0; s <= d; ++s) {
-                        pval += get_pmf(s) + get_pmf(n - s);
-                    }
+            auto get_deviance = [&](double y, double mu, double invphi) {
+                y += 1e-8;
+                mu += 1e-8;
+                return 2.0 * (y * log( y/mu ) + (y + invphi) * log( (mu + invphi)/(y + invphi) ) );
+            };
+
+            double mu1 = r_in * (1.0 - target_p) / target_p;
+            double mu2 = r_out * (1.0 - target_p) / target_p;
+
+            std::vector<double> devs;
+            devs.reserve(n + 1);
+            for (int64_t s = 0; s <= n; ++s) {
+                devs.emplace_back(get_deviance(s, mu1, r_in) + get_deviance(n - s, mu2, r_out));
+            }
+            double pval = 0.0;
+            int64_t s = 0;
+            for ( ; s <= n; ++s) {
+                if (devs[s] >= devs[in_sum]) {
+                    pval += get_pmf(s);
+                } else {
+                    break;
                 }
-            } else {
-                double d = abs(static_cast<double>(in_sum) / num_labels_in - static_cast<double>(out_sum) / num_labels_out);
-                if (d > 0) {
-                    // we want s s.t. | s/num_labels_in - (n-s)/num_labels_out | >= d
-                    // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
-                    // s/num_labels_in + s/num_labels_out >= d + n/num_labels_out || n/num_labels_out - d >= s/num_labels_in + s/num_labels_out
-                    double factor = double(1.0) / num_labels_in + double(1.0) / num_labels_out;
-
-                    double lower_d = (static_cast<double>(n) / num_labels_out - d) / factor;
-                    double upper_d = (d + static_cast<double>(n) / num_labels_out) / factor;
-
-                    // fix floating point errors
-                    if (abs(lower_d - round(lower_d)) < 1e-10)
-                        lower_d = round(lower_d);
-
-                    if (abs(upper_d - round(upper_d)) < 1e-10)
-                        upper_d = round(upper_d);
-
-                    int64_t lower = floor(lower_d);
-                    int64_t upper = ceil(upper_d);
-
-                    if (lower < n && upper > 0 && lower != upper) {
-                        pval = 0;
-                        for (int64_t s = 0; s <= lower; ++s) {
-                            pval += get_pmf(s);
-                        }
+            }
 
-                        for (int64_t s = upper; s <= n; ++s) {
-                            pval += get_pmf(s);
-                        }
-                    }
+            for (int64_t sp = n; sp >= s; --sp) {
+                if (devs[sp] >= devs[in_sum]) {
+                    pval += get_pmf(sp);
+                } else {
+                    break;
                 }
             }
 
-            return std::min(pval, 1.0);
+            return std::min(1.0, pval);
         };
     } else if (config.test_type == "notest") {
         compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
@@ -701,6 +726,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             PairContainer merged_row;
             int64_t in_sum = 0;
             int64_t out_sum = 0;
+            size_t count_in = 0;
+            size_t count_out = 0;
             for (size_t j = 0; j < groups.size(); ++j) {
                 if (row_counts[j] > 0) {
                     if (count_maps.size())
@@ -709,12 +736,24 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     merged_row.emplace_back(j, row_counts[j]);
                     if (groups[j]) {
                         out_sum += row_counts[j];
+                        ++count_out;
                     } else {
                         in_sum += row_counts[j];
+                        ++count_in;
                     }
                 }
             }
 
+            if (count_in + count_out < config.min_recurrence
+                    || count_in < config.min_in_recurrence
+                    || count_out < config.min_out_recurrence
+                    || count_in > config.max_in_recurrence
+                    || count_out > config.max_out_recurrence) {
+                merged_row.clear();
+                in_sum = 0;
+                out_sum = 0;
+            }
+
             double pval;
             double pval_min;
             try {

From 34563cde12525810feeba34be9db08a0729dc3a9 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 18 Jun 2024 14:11:53 +0200
Subject: [PATCH 213/410] fix fits

---
 .../src/graph/annotated_graph_algorithm.cpp   | 296 ++++++++++++------
 1 file changed, 192 insertions(+), 104 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c743740a62..f02f134863 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -34,6 +34,7 @@ using PairContainer = std::vector<std::pair<uint64_t, uint64_t>>;
 constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
 
 double CDF_CUTOFF = 0.95;
+double HIST_CUTOFF = 0.99;
 uint64_t N_BUCKETS_FOR_ESTIMATION = 3;
 
 
@@ -192,7 +193,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Marking discarded k-mers");
     std::mutex agg_mu;
-    std::vector<VectorMap<uint64_t, size_t>> hists(groups.size());
+    std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
 
     if (groups.size()) {
         std::atomic_thread_fence(std::memory_order_release);
@@ -202,9 +203,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                                [&](uint64_t row_i, const auto &row, size_t) {
             for (const auto &[j, raw_c] : row) {
                 if (raw_c >= min_counts[j] && raw_c <= check_cutoff[j]) {
-                    std::lock_guard<std::mutex> lock(agg_mu);
-                    for (const auto &[j, raw_c] : row) {
-                        ++hists[j][raw_c];
+                    if (!config.test_by_unitig) {
+                        std::lock_guard<std::mutex> lock(agg_mu);
+                        sdsl::bit_vector marker(groups.size(), false);
+                        for (const auto &[j, raw_c] : row) {
+                            ++hists_map[j][raw_c];
+                            marker[j] = true;
+                        }
+
+                        for (size_t j = 0; j < marker.size(); ++j) {
+                            if (!marker[j])
+                                ++hists_map[j][0];
+                        }
                     }
                     return;
                 }
@@ -215,7 +225,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::atomic_thread_fence(std::memory_order_acquire);
     }
 
-    double nelem = sdsl::util::cnt_one_bits(kept);
+    size_t nelem = sdsl::util::cnt_one_bits(kept);
 
     sdsl::bit_vector unitig_start;
     if (config.test_by_unitig)
@@ -234,11 +244,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
         );
 
-        for (size_t j = 0; j < groups.size(); ++j) {
-            hists[j].clear();
-        }
-
-        std::atomic<uint64_t> discarded_counts(0);
         std::atomic_thread_fence(std::memory_order_release);
         clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
             std::vector<uint64_t> unitig_sums(groups.size(), 0);
@@ -262,16 +267,32 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             std::lock_guard<std::mutex> lock(agg_mu);
             for (size_t j = 0; j < groups.size(); ++j) {
-                ++hists[j][unitig_sums[j]];
+                ++hists_map[j][unitig_sums[j]];
             }
         }, num_threads);
         std::atomic_thread_fence(std::memory_order_acquire);
 
-        common::logger->trace("Discarded {} counts", discarded_counts.load());
-
         nelem = num_unitigs.load();
     }
 
+    std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
+    for (size_t j = 0; j < hists.size(); ++j) {
+        hists[j] = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(hists_map[j].values_container());
+        std::sort(hists[j].begin(), hists[j].end(), utils::LessFirst());
+    }
+
+    for (size_t j = 0; j < groups.size(); ++j) {
+        size_t total_c = 0;
+        for (const auto &[k, c] : hists[j]) {
+            total_c += c;
+        }
+
+        if (total_c != nelem) {
+            common::logger->error("FAIL {}: {} != {}", j, total_c, nelem);
+            throw std::runtime_error("GGG");
+        }
+    }
+
     common::logger->trace("Computing aggregate statistics");
     int64_t in_kmers = 0;
     int64_t out_kmers = 0;
@@ -285,7 +306,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         for (const auto &[k, c] : hists[j]) {
             sums[j] += k * c;
             sums_of_squares[j] += k * k * c;
-            n_kmers[j] += c;
+            if (k != 0)
+                n_kmers[j] += c;
+
             max_obs_vals[j] = std::max(max_obs_vals[j], k);
         }
 
@@ -335,17 +358,41 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double p = static_cast<double>(in_kmers) / total_kmers;
             auto bdist = boost::math::binomial(n, p);
-            if (num_labels_in != num_labels_out) {
-                double d0 = static_cast<double>(n) / num_labels_out;
-                double dn = static_cast<double>(n) / num_labels_in;
-                if (d0 > dn) {
-                    return boost::math::pdf(bdist, 0);
-                } else if (d0 < dn) {
-                    return boost::math::pdf(bdist, n);
-                }
+            auto get_deviance = [&](double y, double mu) {
+                y += 1e-8;
+                mu += 1e-8;
+                return 2 * (y * log(y/mu) - y + mu);
+            };
+
+            double mu1 = static_cast<double>(in_kmers) / nelem;
+            double mu2 = static_cast<double>(out_kmers) / nelem;
+
+            std::vector<double> devs;
+            devs.reserve(n + 1);
+            for (int64_t s = 0; s <= n; ++s) {
+                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+            }
+            double max_d = *std::max_element(devs.begin(), devs.end());
+            double pval = 0.0;
+
+            int64_t s = 0;
+            for ( ; s <= n; ++s) {
+                if (devs[s] != max_d)
+                    break;
+            }
+            if (s > 0)
+                pval += boost::math::cdf(bdist, s - 1);
+
+            int64_t sp = n;
+            for ( ; sp >= s; --sp) {
+                if (devs[sp] != max_d)
+                    break;
             }
 
-            return boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
+            if (sp < n)
+                pval += boost::math::cdf(boost::math::complement(bdist, sp));
+
+            return pval;
         };
 
         compute_pval = [&](int64_t in_sum, int64_t out_sum, const auto &row) {
@@ -355,50 +402,40 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             int64_t n = in_sum + out_sum;
             double p = static_cast<double>(in_kmers) / total_kmers;
             auto bdist = boost::math::binomial(n, p);
+            auto get_deviance = [&](double y, double mu) {
+                y += 1e-8;
+                mu += 1e-8;
+                return 2 * (y * log(y/mu) - y + mu);
+            };
 
-            if (num_labels_in == num_labels_out) {
-                if (in_sum != out_sum) {
-                    int64_t d = std::min(in_sum, out_sum);
-                    return boost::math::cdf(bdist, d) + boost::math::cdf(boost::math::complement(bdist, n - d - 1));
-                }
+            double mu1 = static_cast<double>(in_kmers) / nelem;
+            double mu2 = static_cast<double>(out_kmers) / nelem;
 
-                return 1.0;
+            std::vector<double> devs;
+            devs.reserve(n + 1);
+            for (int64_t s = 0; s <= n; ++s) {
+                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
             }
+            double pval = 0.0;
 
-            double d = abs(static_cast<double>(in_sum) / num_labels_in - static_cast<double>(out_sum) / num_labels_out);
-            if (d > 0) {
-                // we want s s.t. | s/num_labels_in - (n-s)/num_labels_out | >= d
-                // s/num_labels_in - (n-s)/num_labels_out >= d || (n-s)/num_labels_out - s/num_labels_in >= d
-                // s/num_labels_in + s/num_labels_out >= d + n/num_labels_out || n/num_labels_out - d >= s/num_labels_in + s/num_labels_out
-                double factor = double(1.0) / num_labels_in + double(1.0) / num_labels_out;
-
-                double lower_d = (static_cast<double>(n) / num_labels_out - d) / factor;
-                double upper_d = (d + static_cast<double>(n) / num_labels_out) / factor;
-
-                // fix floating point errors
-                if (abs(lower_d - round(lower_d)) < 1e-10)
-                    lower_d = round(lower_d);
-
-                if (abs(upper_d - round(upper_d)) < 1e-10)
-                    upper_d = round(upper_d);
-
-                int64_t lower = floor(lower_d);
-                int64_t upper = ceil(upper_d);
-
-                if (lower < n && upper > 0 && lower != upper) {
-                    double pval = 0.0;
-
-                    if (lower >= 0)
-                        pval += boost::math::cdf(bdist, lower);
-
-                    if (upper <= n)
-                        pval += boost::math::cdf(boost::math::complement(bdist, upper - 1));
+            int64_t s = 0;
+            for ( ; s <= n; ++s) {
+                if (devs[s] < devs[in_sum])
+                    break;
+            }
+            if (s > 0)
+                pval += boost::math::cdf(bdist, s - 1);
 
-                    return pval;
-                }
+            int64_t sp = n;
+            for ( ; sp >= s; --sp) {
+                if (devs[sp] < devs[in_sum])
+                    break;
             }
 
-            return 1.0;
+            if (sp < n)
+                pval += boost::math::cdf(boost::math::complement(bdist, sp));
+
+            return pval;
         };
     } else if (config.test_type == "fisher") {
         double lbase_shared = lgamma(in_kmers + 1) + lgamma(out_kmers + 1) - lgamma(total_kmers + 1);
@@ -443,23 +480,39 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         };
     } else if (config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
-        auto get_rp = [&](size_t j, double mu, double var, const auto &hist) {
-            double var_orig = var;
-            var = std::max(var, mu + 0.1);
+        auto get_rp = [&](size_t j, auto begin, auto end) {
+            double mu = 0;
+            double var = 0;
+            size_t total = 0;
+            std::for_each(begin, end, [&](const auto &a) {
+                const auto &[k, c] = a;
+                mu += k * c;
+                var += k * k * c;
+                total += c;
+            });
+            mu /= total;
+            double mu2 = mu * mu;
+            var = (var - mu2 * total) / (total - 1);
+
+            if (mu >= var) {
+                common::logger->warn("Fit {} failed, falling back to Poisson: mu: {} >= var: {}", j, mu, var);
+                return std::make_pair(0.0, 1.0);
+            }
 
             double r_guess = mu * mu / (var - mu);
             double p_guess = mu / var;
             double r = r_guess;
             try {
                 r = boost::math::tools::newton_raphson_iterate([&](double r) {
-                    double dl = nelem * (log(r) - log(r + mu) - boost::math::digamma(r));
-                    double ddl = nelem * (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r));
-                    for (const auto &[k, c] : hist) {
+                    double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
+                    double ddl = (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r)) * total;
+                    std::for_each(begin, end, [&](const auto &a) {
+                        const auto &[k, c] = a;
                         dl += boost::math::digamma(k + r) * c;
                         ddl += boost::math::trigamma(k + r) * c;
-                    }
+                    });
                     return std::make_pair(dl, ddl);
-                }, r_guess, std::numeric_limits<double>::min(), mu * nelem, 30);
+                }, r_guess, std::numeric_limits<double>::min(), mu * total, 30);
             } catch (std::exception &e) {
                 common::logger->warn("Caught exception for sample {}: Falling back to initial guess", j);
                 common::logger->warn("{}", e.what());
@@ -468,42 +521,58 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double p = r / (r + mu);
             common::logger->trace("{}: mu: {}\tvar: {}\tmoment est:\tr: {}\tp: {}\tmle: r: {}\tp: {}",
-                                  j, mu, var_orig, r_guess, p_guess, r, p);
+                                  j, mu, var, r_guess, p_guess, r, p);
 
             return std::make_pair(r, p);
         };
 
-        common::logger->trace("Scaling distributions");
-        // geometric mean
-        double target_sum = 0.0;
+        std::vector<std::pair<double, double>> nb_params(groups.size());
+        #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
-            target_sum += log(static_cast<double>(sums[j]));
+            const auto &hist = hists[j];
+            size_t total = 0;
+            size_t total_cutoff = n_kmers[j] * HIST_CUTOFF;
+            auto it = hist.begin();
+            for ( ; it != hists[j].end() && total < total_cutoff; ++it) {
+                if (it->first != 0)
+                    total += it->second;
+            }
+            nb_params[j] = get_rp(j, hist.begin(), it);
         }
-        target_sum = exp(target_sum / groups.size());
 
-        std::vector<std::pair<double, double>> nb_params(groups.size());
+        common::logger->trace("Scaling distributions");
 
-        #pragma omp parallel for num_threads(num_parallel_files)
+        double target_sum = 0.0;
         for (size_t j = 0; j < groups.size(); ++j) {
-            const auto &hist = hists[j];
-            double s = static_cast<double>(sums[j]);
-            double mu = s / nelem;
-            double mu2 = mu * mu;
-            double var = static_cast<double>(sums_of_squares[j]) / nelem - mu2;
-            nb_params[j] = get_rp(j, mu, var, hist);
+            target_sum += log(static_cast<double>(sums[j]));
         }
+        target_sum = exp(target_sum / groups.size());
 
         common::logger->trace("Finding common p and r.");
         double real_sum = 0.0;
         double real_sum_sq = 0.0;
+        size_t matrix_size = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
+            size_t total = 0;
             double f = target_sum / sums[j];
+            common::logger->trace("{}: scale factor: {}", j, f);
+            size_t total_cutoff = n_kmers[j] * HIST_CUTOFF;
             const auto &hist = hists[j];
             for (const auto &[k, c] : hist) {
+                if (k == 0) {
+                    matrix_size += c;
+                    continue;
+                }
+
+                if (total >= total_cutoff)
+                    break;
+
                 double val = f * k;
                 real_sum += val * c;
                 real_sum_sq += val * val * c;
+                total += c;
             }
+            matrix_size += total;
         }
 
         // nelem * r * groups.size() / p - target_sum * groups.size() / (1 - p) = 0
@@ -512,20 +581,32 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         // r / target_mu = p + p * r / target_mu
         // p = r / target_mu / (r / target_mu + 1)
         // p = r / (r + target_mu)
-        double target_mu = real_sum / nelem / groups.size();
+        double target_mu = real_sum / matrix_size;
         double target_mu2 = target_mu * target_mu;
-        double target_var = real_sum_sq / nelem / groups.size() - target_mu2;
+        double target_var = (real_sum_sq - target_mu2 * matrix_size) / (matrix_size - 1);
+        if (target_mu >= target_var) {
+            common::logger->error("Target fit failed: mu: {} >= var: {}\t{},{},{}", target_mu, target_var, real_sum, real_sum_sq, target_mu2);
+            throw std::domain_error("Fit failed");
+        }
+
         double r_guess = target_mu2 / (target_var - target_mu);
 
         double r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
-            double dl = nelem * groups.size() * (log(r) - log(r + target_mu) - boost::math::digamma(r));
-            double ddl = nelem * groups.size() * (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r));
+            double dl = (log(r) - log(r + target_mu) - boost::math::digamma(r)) * matrix_size;
+            double ddl = (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r)) * matrix_size;
             for (size_t j = 0; j < groups.size(); ++j) {
                 double f = target_sum / sums[j];
                 const auto &hist = hists[j];
+                size_t total = 0;
+                size_t total_cutoff = n_kmers[j] * HIST_CUTOFF;
                 for (const auto &[k, c] : hist) {
+                    if (total >= total_cutoff)
+                        break;
+
                     dl += boost::math::digamma(f * k + r) * c;
                     ddl += boost::math::trigamma(f * k + r) * c;
+                    if (k != 0)
+                        total += c;
                 }
             }
             return std::make_pair(dl, ddl);
@@ -542,33 +623,40 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
                                   j, r, p, r_map, target_p);
 
-            boost::math::negative_binomial nb(r, p);
             boost::math::negative_binomial nb_out(r_map, target_p);
             double scale = target_sum / sums[j];
 
-            // ensure that 0 maps to 0
-            count_maps[j][0] = std::make_pair(0, 0);
+            auto compute_map = [&](const auto &nb) {
+                // ensure that 0 maps to 0
+                count_maps[j][0] = std::make_pair(0, 0);
 
-            const auto &hist = hists[j];
-            for (const auto &[k, c] : hist) {
-                if (!count_maps[j].count(k)) {
-                    double cdf = boost::math::cdf(nb, k);
+                const auto &hist = hists[j];
+                for (const auto &[k, c] : hist) {
+                    if (!count_maps[j].count(k)) {
+                        double cdf = boost::math::cdf(nb, k);
 
-                    uint64_t new_k = 0;
-                    if (cdf < 1.0) {
-                        new_k = boost::math::quantile(nb_out, cdf);
-                    } else {
-                        double ccdf = boost::math::cdf(boost::math::complement(nb, k));
-                        if (ccdf > 0.0) {
-                            new_k = boost::math::quantile(boost::math::complement(nb_out, ccdf));
+                        uint64_t new_k = 0;
+                        if (cdf < 1.0) {
+                            new_k = boost::math::quantile(nb_out, cdf);
                         } else {
-                            new_k = ceil(scale * k);
+                            double ccdf = boost::math::cdf(boost::math::complement(nb, k));
+                            if (ccdf > 0.0) {
+                                new_k = boost::math::quantile(boost::math::complement(nb_out, ccdf));
+                            } else {
+                                new_k = ceil(scale * k);
+                            }
                         }
+                        count_maps[j][k] = std::make_pair(std::max(new_k, uint64_t(1)), c);
+                    } else {
+                        count_maps[j][k].second += c;
                     }
-                    count_maps[j][k] = std::make_pair(std::max(new_k, uint64_t(1)), c);
-                } else {
-                    count_maps[j][k].second += c;
                 }
+            };
+
+            if (p < 1.0) {
+                compute_map(boost::math::negative_binomial(r, p));
+            } else {
+                compute_map(boost::math::poisson(static_cast<double>(sums[j]) * scale / nelem));
             }
         }
 

From a3ab60c9d95c4c7a342fbdc74541a6a1f716997c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 18 Jun 2024 15:06:26 +0200
Subject: [PATCH 214/410] minor

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index f02f134863..308eb804b8 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -710,9 +710,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double max_d = *std::max_element(devs.begin(), devs.end());
             double pval = 0.0;
             int64_t s = 0;
+            bool found = false;
             for ( ; s <= n; ++s) {
                 if (devs[s] == max_d) {
                     pval += get_pmf(s);
+                    found = true;
                 } else {
                     break;
                 }
@@ -721,12 +723,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             for (int64_t sp = n; sp >= s; --sp) {
                 if (devs[sp] == max_d) {
                     pval += get_pmf(sp);
+                    found = true;
                 } else {
                     break;
                 }
             }
 
-            if (pval == 0.0) {
+            if (!found) {
                 common::logger->error("Devs: {}", fmt::join(devs, ","));
                 throw std::runtime_error("Fail");
             }

From e5fea40b13481303056f07e4527b596a24a18d41 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 18 Jun 2024 19:10:52 +0200
Subject: [PATCH 215/410] extra checks

---
 .../src/graph/annotated_graph_algorithm.cpp   | 114 +++++++++++-------
 1 file changed, 68 insertions(+), 46 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 308eb804b8..c4f1a40b4f 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -34,7 +34,7 @@ using PairContainer = std::vector<std::pair<uint64_t, uint64_t>>;
 constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
 
 double CDF_CUTOFF = 0.95;
-double HIST_CUTOFF = 0.99;
+double HIST_CUTOFF = 1.00;
 uint64_t N_BUCKETS_FOR_ESTIMATION = 3;
 
 
@@ -278,8 +278,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
     for (size_t j = 0; j < hists.size(); ++j) {
         hists[j] = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(hists_map[j].values_container());
+        hists_map[j].clear();
         std::sort(hists[j].begin(), hists[j].end(), utils::LessFirst());
     }
+    hists_map.clear();
 
     for (size_t j = 0; j < groups.size(); ++j) {
         size_t total_c = 0;
@@ -520,24 +522,31 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             double p = r / (r + mu);
-            common::logger->trace("{}: mu: {}\tvar: {}\tmoment est:\tr: {}\tp: {}\tmle: r: {}\tp: {}",
-                                  j, mu, var, r_guess, p_guess, r, p);
+            common::logger->trace("{}: size: {}\tmax_val: {}\tmu: {}\tvar: {}\tmoment est:\tr: {}\tp: {}\tmle: r: {}\tp: {}",
+                                  j, sums[j], (end - 1)->first, mu, var, r_guess, p_guess, r, p);
 
             return std::make_pair(r, p);
         };
 
         std::vector<std::pair<double, double>> nb_params(groups.size());
+        std::vector<std::vector<std::pair<uint64_t, size_t>>::const_iterator> hist_its(groups.size());
+        size_t matrix_size = 0;
+
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto &hist = hists[j];
             size_t total = 0;
             size_t total_cutoff = n_kmers[j] * HIST_CUTOFF;
             auto it = hist.begin();
-            for ( ; it != hists[j].end() && total < total_cutoff; ++it) {
+            for ( ; it != hist.end() && total < total_cutoff; ++it) {
                 if (it->first != 0)
                     total += it->second;
             }
+            hist_its[j] = it;
             nb_params[j] = get_rp(j, hist.begin(), it);
+
+            #pragma omp critical
+            matrix_size += total + (hist.size() && hist[0].first == 0 ? hist[0].second : 0);
         }
 
         common::logger->trace("Scaling distributions");
@@ -551,67 +560,76 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Finding common p and r.");
         double real_sum = 0.0;
         double real_sum_sq = 0.0;
-        size_t matrix_size = 0;
+        std::vector<std::pair<double, size_t>> joint_hists_sorted;
         for (size_t j = 0; j < groups.size(); ++j) {
-            size_t total = 0;
             double f = target_sum / sums[j];
             common::logger->trace("{}: scale factor: {}", j, f);
-            size_t total_cutoff = n_kmers[j] * HIST_CUTOFF;
-            const auto &hist = hists[j];
-            for (const auto &[k, c] : hist) {
-                if (k == 0) {
-                    matrix_size += c;
-                    continue;
+            std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
+                const auto &[k, c] = a;
+                if (k != 0) {
+                    double val = f * k;
+                    real_sum += val * c;
+                    real_sum_sq += val * val * c;
+                    joint_hists_sorted.emplace_back(val, j);
                 }
-
-                if (total >= total_cutoff)
-                    break;
-
-                double val = f * k;
-                real_sum += val * c;
-                real_sum_sq += val * val * c;
-                total += c;
-            }
-            matrix_size += total;
+            });
         }
 
-        // nelem * r * groups.size() / p - target_sum * groups.size() / (1 - p) = 0
-        // nelem * r / p = target_sum / (1 - p)
-        // r / target_mu = p / (1 - p)
-        // r / target_mu = p + p * r / target_mu
-        // p = r / target_mu / (r / target_mu + 1)
-        // p = r / (r + target_mu)
-        double target_mu = real_sum / matrix_size;
-        double target_mu2 = target_mu * target_mu;
-        double target_var = (real_sum_sq - target_mu2 * matrix_size) / (matrix_size - 1);
-        if (target_mu >= target_var) {
-            common::logger->error("Target fit failed: mu: {} >= var: {}\t{},{},{}", target_mu, target_var, real_sum, real_sum_sq, target_mu2);
-            throw std::domain_error("Fit failed");
-        }
+        std::sort(joint_hists_sorted.begin(), joint_hists_sorted.end(), utils::LessFirst());
+
+        size_t min_group_size = std::min(num_labels_in, num_labels_out);
+        double r_min = double(1.0) / min_group_size;
+        double r_guess = 0.0;
+        double target_mu = 0.0;
+        double target_var = 0.0;
+
+        do {
+            // nelem * r * groups.size() / p - target_sum * groups.size() / (1 - p) = 0
+            // nelem * r / p = target_sum / (1 - p)
+            // r / target_mu = p / (1 - p)
+            // r / target_mu = p + p * r / target_mu
+            // p = r / target_mu / (r / target_mu + 1)
+            // p = r / (r + target_mu)
+            target_mu = real_sum / matrix_size;
+            double target_mu2 = target_mu * target_mu;
+            target_var = (real_sum_sq - target_mu2 * matrix_size) / (matrix_size - 1);
+
+            r_guess = target_mu2 / (target_var - target_mu);
+            if (matrix_size <= 1 || target_var == target_mu || r_guess <= r_min) {
+                auto [max_count, picked_hist] = joint_hists_sorted.back();
+                joint_hists_sorted.pop_back();
+                --hist_its[picked_hist];
+                uint64_t abundance = hist_its[picked_hist]->second;
+                real_sum -= max_count * abundance;
+                real_sum_sq -= max_count * max_count * abundance;
+                matrix_size -= abundance;
+            }
+        } while (joint_hists_sorted.size() && (target_var == target_mu || r_guess <= r_min));
 
-        double r_guess = target_mu2 / (target_var - target_mu);
+        if (joint_hists_sorted.empty())
+            throw std::domain_error("Too few samples, could not fit. Use poisson_exact instead");
 
         double r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
             double dl = (log(r) - log(r + target_mu) - boost::math::digamma(r)) * matrix_size;
             double ddl = (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r)) * matrix_size;
             for (size_t j = 0; j < groups.size(); ++j) {
                 double f = target_sum / sums[j];
-                const auto &hist = hists[j];
-                size_t total = 0;
-                size_t total_cutoff = n_kmers[j] * HIST_CUTOFF;
-                for (const auto &[k, c] : hist) {
-                    if (total >= total_cutoff)
-                        break;
-
+                std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
+                    const auto &[k, c] = a;
                     dl += boost::math::digamma(f * k + r) * c;
                     ddl += boost::math::trigamma(f * k + r) * c;
-                    if (k != 0)
-                        total += c;
-                }
+                });
             }
             return std::make_pair(dl, ddl);
         }, r_guess, std::numeric_limits<double>::min(), real_sum, 30);
         double target_p = r_map / (r_map + target_mu);
+        double fit_mu = r_map * (1.0 - target_p) / target_p;
+        double fit_var = fit_mu / target_p;
+        common::logger->trace("Common params: r: {}\tp: {}\tmu: {}\tvar: {}",
+                              r_map, target_p, fit_mu, fit_var);
+
+        if (fit_var / fit_mu - 1.0 < 1e-5)
+            common::logger->warn("Fit parameters are close to a Poisson distribution");
 
         count_maps.resize(groups.size());
 
@@ -943,6 +961,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double pval_min;
             try {
                 pval_min = compute_min_pval(in_sum + out_sum);
+
+                if (pval_min >= 1.1)
+                    return;
+
                 pval = compute_pval(in_sum, out_sum, row);
             } catch (...) {
                 std::lock_guard<std::mutex> lock(pval_mu);

From 629c8e16c67f9ee977f5e106d6f47464eb1d9232 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 19 Jun 2024 01:55:33 +0200
Subject: [PATCH 216/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 230 +++++++++---------
 1 file changed, 115 insertions(+), 115 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c4f1a40b4f..65f7390416 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -560,7 +560,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Finding common p and r.");
         double real_sum = 0.0;
         double real_sum_sq = 0.0;
-        std::vector<std::pair<double, size_t>> joint_hists_sorted;
         for (size_t j = 0; j < groups.size(); ++j) {
             double f = target_sum / sums[j];
             common::logger->trace("{}: scale factor: {}", j, f);
@@ -570,44 +569,50 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     double val = f * k;
                     real_sum += val * c;
                     real_sum_sq += val * val * c;
-                    joint_hists_sorted.emplace_back(val, j);
                 }
             });
         }
 
-        std::sort(joint_hists_sorted.begin(), joint_hists_sorted.end(), utils::LessFirst());
+        auto get_deviance = [&](double y, double mu, double invphi) {
+            y += 1e-8;
+            mu += 1e-8;
+            return 2.0 * (y * log( y/mu ) + (y + invphi) * log( (mu + invphi)/(y + invphi) ) );
+        };
 
-        size_t min_group_size = std::min(num_labels_in, num_labels_out);
-        double r_min = double(1.0) / min_group_size;
         double r_guess = 0.0;
         double target_mu = 0.0;
         double target_var = 0.0;
 
-        do {
-            // nelem * r * groups.size() / p - target_sum * groups.size() / (1 - p) = 0
-            // nelem * r / p = target_sum / (1 - p)
-            // r / target_mu = p / (1 - p)
-            // r / target_mu = p + p * r / target_mu
-            // p = r / target_mu / (r / target_mu + 1)
-            // p = r / (r + target_mu)
-            target_mu = real_sum / matrix_size;
-            double target_mu2 = target_mu * target_mu;
-            target_var = (real_sum_sq - target_mu2 * matrix_size) / (matrix_size - 1);
+        auto map_value = [&](uint64_t k, double scale, const auto &old_dist, const auto &new_dist) {
+            double cdf = boost::math::cdf(old_dist, k);
 
-            r_guess = target_mu2 / (target_var - target_mu);
-            if (matrix_size <= 1 || target_var == target_mu || r_guess <= r_min) {
-                auto [max_count, picked_hist] = joint_hists_sorted.back();
-                joint_hists_sorted.pop_back();
-                --hist_its[picked_hist];
-                uint64_t abundance = hist_its[picked_hist]->second;
-                real_sum -= max_count * abundance;
-                real_sum_sq -= max_count * max_count * abundance;
-                matrix_size -= abundance;
-            }
-        } while (joint_hists_sorted.size() && (target_var == target_mu || r_guess <= r_min));
+            if (cdf < 1.0)
+                return boost::math::quantile(new_dist, cdf);
 
-        if (joint_hists_sorted.empty())
-            throw std::domain_error("Too few samples, could not fit. Use poisson_exact instead");
+            double ccdf = boost::math::cdf(boost::math::complement(old_dist, k));
+
+            if (ccdf > 0.0)
+                return boost::math::quantile(boost::math::complement(new_dist, ccdf));
+
+            return ceil(scale * k);
+        };
+
+        // nelem * r * groups.size() / p - target_sum * groups.size() / (1 - p) = 0
+        // nelem * r / p = target_sum / (1 - p)
+        // r / target_mu = p / (1 - p)
+        // r / target_mu = p + p * r / target_mu
+        // p = r / target_mu / (r / target_mu + 1)
+        // p = r / (r + target_mu)
+        target_mu = real_sum / matrix_size;
+        double target_mu2 = target_mu * target_mu;
+        target_var = (real_sum_sq - target_mu2 * matrix_size) / (matrix_size - 1);
+
+        if (target_var > target_mu) {
+            r_guess = target_mu2 / (target_var - target_mu);
+        } else {
+            common::logger->error("Data is underdispersed: {} >= {}. Use poisson_exact instead", target_mu, target_var);
+            throw std::domain_error("Fit failed");
+        }
 
         double r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
             double dl = (log(r) - log(r + target_mu) - boost::math::digamma(r)) * matrix_size;
@@ -625,81 +630,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         double target_p = r_map / (r_map + target_mu);
         double fit_mu = r_map * (1.0 - target_p) / target_p;
         double fit_var = fit_mu / target_p;
-        common::logger->trace("Common params: r: {}\tp: {}\tmu: {}\tvar: {}",
-                              r_map, target_p, fit_mu, fit_var);
-
-        if (fit_var / fit_mu - 1.0 < 1e-5)
-            common::logger->warn("Fit parameters are close to a Poisson distribution");
-
-        count_maps.resize(groups.size());
-
-        common::logger->trace("Computing quantile maps");
-        #pragma omp parallel for num_threads(num_parallel_files)
-        for (size_t j = 0; j < groups.size(); ++j) {
-            const auto &[r, p] = nb_params[j];
-
-            common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
-                                  j, r, p, r_map, target_p);
-
-            boost::math::negative_binomial nb_out(r_map, target_p);
-            double scale = target_sum / sums[j];
-
-            auto compute_map = [&](const auto &nb) {
-                // ensure that 0 maps to 0
-                count_maps[j][0] = std::make_pair(0, 0);
-
-                const auto &hist = hists[j];
-                for (const auto &[k, c] : hist) {
-                    if (!count_maps[j].count(k)) {
-                        double cdf = boost::math::cdf(nb, k);
-
-                        uint64_t new_k = 0;
-                        if (cdf < 1.0) {
-                            new_k = boost::math::quantile(nb_out, cdf);
-                        } else {
-                            double ccdf = boost::math::cdf(boost::math::complement(nb, k));
-                            if (ccdf > 0.0) {
-                                new_k = boost::math::quantile(boost::math::complement(nb_out, ccdf));
-                            } else {
-                                new_k = ceil(scale * k);
-                            }
-                        }
-                        count_maps[j][k] = std::make_pair(std::max(new_k, uint64_t(1)), c);
-                    } else {
-                        count_maps[j][k].second += c;
-                    }
-                }
-            };
-
-            if (p < 1.0) {
-                compute_map(boost::math::negative_binomial(r, p));
-            } else {
-                compute_map(boost::math::poisson(static_cast<double>(sums[j]) * scale / nelem));
-            }
-        }
-
-        common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
-        in_kmers = 0;
-        out_kmers = 0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            for (const auto &[k, m] : count_maps[j]) {
-                const auto &[v, c] = m;
-                if (groups[j]) {
-                    out_kmers += v * c;
-                } else {
-                    in_kmers += v * c;
-                }
-            }
-        }
-        common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
+        common::logger->trace("Common params: r_guess: {}\tr: {}\tp: {}\tmu: {}\tvar: {}",
+                              r_guess, r_map, target_p, fit_mu, fit_var);
 
         double r_in = r_map * num_labels_in;
         double r_out = r_map * num_labels_out;
         common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
 
         double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+        double mu1 = r_in * (1.0 - target_p) / target_p;
+        double mu2 = r_out * (1.0 - target_p) / target_p;
 
-        compute_min_pval = [&,lscaling_base,r_in,r_out,target_p](int64_t n) {
+        if (fit_var / fit_mu - 1.0 < 1e-5)
+            common::logger->warn("Fit parameters are close to a Poisson distribution");
+
+        compute_min_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t n) {
             if (n == 0)
                 return 1.1;
 
@@ -711,15 +656,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
             };
 
-            auto get_deviance = [&](double y, double mu, double invphi) {
-                y += 1e-8;
-                mu += 1e-8;
-                return 2.0 * (y * log( y/mu ) + (y + invphi) * log( (mu + invphi)/(y + invphi) ) );
-            };
-
-            double mu1 = r_in * (1.0 - target_p) / target_p;
-            double mu2 = r_out * (1.0 - target_p) / target_p;
-
             std::vector<double> devs;
             devs.reserve(n + 1);
             for (int64_t s = 0; s <= n; ++s) {
@@ -755,7 +691,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::min(1.0, pval);
         };
 
-        compute_pval = [&,lscaling_base,r_in,r_out,target_p](int64_t in_sum, int64_t out_sum, const auto &row) {
+        compute_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
                 return 1.1;
 
@@ -769,15 +705,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
             };
 
-            auto get_deviance = [&](double y, double mu, double invphi) {
-                y += 1e-8;
-                mu += 1e-8;
-                return 2.0 * (y * log( y/mu ) + (y + invphi) * log( (mu + invphi)/(y + invphi) ) );
-            };
-
-            double mu1 = r_in * (1.0 - target_p) / target_p;
-            double mu2 = r_out * (1.0 - target_p) / target_p;
-
             std::vector<double> devs;
             devs.reserve(n + 1);
             for (int64_t s = 0; s <= n; ++s) {
@@ -803,6 +730,79 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return std::min(1.0, pval);
         };
+
+        count_maps.resize(groups.size());
+
+        common::logger->trace("Computing quantile maps");
+        #pragma omp parallel for num_threads(num_parallel_files)
+        for (size_t j = 0; j < groups.size(); ++j) {
+            const auto &[r, p] = nb_params[j];
+
+            common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
+                                  j, r, p, r_map, target_p);
+
+            boost::math::negative_binomial nb_out(r_map, target_p);
+            double scale = target_sum / sums[j];
+
+            auto compute_map = [&](const auto &nb) {
+                // ensure that 0 maps to 0
+                count_maps[j][0] = std::make_pair(0, 0);
+
+                const auto &hist = hists[j];
+                for (const auto &[k, c] : hist) {
+                    if (!count_maps[j].count(k)) {
+                        uint64_t new_k = map_value(k, scale, nb, nb_out);
+                        count_maps[j][k] = std::make_pair(std::max(new_k, uint64_t(1)), c);
+                    } else {
+                        count_maps[j][k].second += c;
+                    }
+                }
+            };
+
+            if (p < 1.0) {
+                compute_map(boost::math::negative_binomial(r, p));
+            } else {
+                compute_map(boost::math::poisson(static_cast<double>(sums[j]) * scale / nelem));
+            }
+        }
+
+        common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
+        in_kmers = 0;
+        out_kmers = 0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            for (const auto &[k, m] : count_maps[j]) {
+                const auto &[v, c] = m;
+                if (groups[j]) {
+                    out_kmers += v * c;
+                } else {
+                    in_kmers += v * c;
+                }
+            }
+        }
+        total_kmers = in_kmers + out_kmers;
+        common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
+
+        PairContainer best_row;
+        int64_t in_sum = 0;
+        int64_t out_sum = 0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            if (hists[j].size()) {
+                uint64_t k = count_maps[j].find(hists[j].back().first)->second.first;
+                best_row.emplace_back(j, k);
+                if (groups[j]) {
+                    out_sum += k;
+                } else {
+                    in_sum += k;
+                }
+            }
+        }
+
+        double pval = compute_pval(in_sum, out_sum, best_row);
+        common::logger->trace("Best achievable p-value: {}", pval);
+        if (pval >= config.family_wise_error_rate) {
+            common::logger->error("Best achievable p-value is too big. Use poisson_exact instead");
+            throw std::domain_error("Too few samples");
+        }
     } else if (config.test_type == "notest") {
         compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
         compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };

From 959994fb13aaf68941e867834ba18d1a2500c33d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 19 Jun 2024 02:01:31 +0200
Subject: [PATCH 217/410] fix

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 65f7390416..ef51d36b2e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -782,23 +782,26 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         total_kmers = in_kmers + out_kmers;
         common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
-        PairContainer best_row;
+        PairContainer best_in_row;
+        PairContainer best_out_row;
         int64_t in_sum = 0;
         int64_t out_sum = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
             if (hists[j].size()) {
                 uint64_t k = count_maps[j].find(hists[j].back().first)->second.first;
-                best_row.emplace_back(j, k);
                 if (groups[j]) {
+                    best_out_row.emplace_back(j, k);
                     out_sum += k;
                 } else {
+                    best_in_row.emplace_back(j, k);
                     in_sum += k;
                 }
             }
         }
 
-        double pval = compute_pval(in_sum, out_sum, best_row);
-        common::logger->trace("Best achievable p-value: {}", pval);
+        double pval = std::min(compute_pval(in_sum, 0, best_in_row),
+                               compute_pval(0, out_sum, best_out_row));
+        common::logger->trace("Best achievable p-value: {}\tin_sum: {}\tout_sum: {}", pval, in_sum, out_sum);
         if (pval >= config.family_wise_error_rate) {
             common::logger->error("Best achievable p-value is too big. Use poisson_exact instead");
             throw std::domain_error("Too few samples");

From 26bb42329a91751c6c6df97291cd847b275a3949 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 20 Jun 2024 16:36:38 +0200
Subject: [PATCH 218/410] fix check filtering

---
 .../src/graph/annotated_graph_algorithm.cpp   | 46 +++++++++----------
 1 file changed, 22 insertions(+), 24 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ef51d36b2e..b4767f6d17 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -840,6 +840,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             int64_t out_sum = 0;
             size_t count_in = 0;
             size_t count_out = 0;
+            bool in_kmer = false;
+            bool out_kmer = false;
             for (size_t j = 0; j < groups.size(); ++j) {
                 if (row_counts[j] > 0) {
                     if (count_maps.size())
@@ -856,20 +858,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            if (count_in + count_out < config.min_recurrence
-                    || count_in < config.min_in_recurrence
-                    || count_out < config.min_out_recurrence
-                    || count_in > config.max_in_recurrence
-                    || count_out > config.max_out_recurrence) {
-                merged_row.clear();
-                in_sum = 0;
-                out_sum = 0;
+            if (count_in + count_out >= config.min_recurrence) {
+                double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+                in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+                out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
             }
 
+            if (!in_kmer && !out_kmer)
+                merged_row.clear();
+
             double pval;
             double pval_min;
             try {
                 pval_min = compute_min_pval(in_sum + out_sum);
+
+                if (pval_min >= 1.1)
+                    return;
+
                 pval = compute_pval(in_sum, out_sum, merged_row);
             } catch (...) {
                 std::lock_guard<std::mutex> lock(pval_mu);
@@ -880,10 +885,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (pval >= 1.1)
                 return;
 
-            double in_stat = in_sum;
-            double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-            bool in_kmer = (in_stat > out_stat) || (out_stat != out_stat && in_stat == in_stat);
-            bool out_kmer = (in_stat < out_stat) || (in_stat != in_stat && out_stat == out_stat);
             uint64_t k = std::numeric_limits<uint64_t>::max();
 
             if (pval_min - pval > 1e-10) {
@@ -928,6 +929,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             int64_t in_sum = 0;
             int64_t out_sum = 0;
+            bool in_kmer = false;
+            bool out_kmer = false;
             PairContainer row;
             if (kept[row_i]) {
                 size_t count_in = 0;
@@ -947,17 +950,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 }
 
-                if (count_in + count_out < config.min_recurrence
-                        || count_in < config.min_in_recurrence
-                        || count_out < config.min_out_recurrence
-                        || count_in > config.max_in_recurrence
-                        || count_out > config.max_out_recurrence) {
-                    row.clear();
-                    in_sum = 0;
-                    out_sum = 0;
+                if (count_in + count_out >= config.min_recurrence) {
+                    double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+                    in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+                    out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
                 }
             }
 
+            if (!in_kmer && !out_kmer)
+                row.clear();
+
             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
 
             double pval;
@@ -978,10 +980,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (pval >= 1.1)
                 return;
 
-            double in_stat = in_sum;
-            double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-            bool in_kmer = (in_stat > out_stat) || (out_stat != out_stat && in_stat == in_stat);
-            bool out_kmer = (in_stat < out_stat) || (in_stat != in_stat && out_stat == out_stat);
             uint64_t k = std::numeric_limits<uint64_t>::max();
 
             if (pval_min - pval > 1e-10) {

From 6bd53f715866ebe3ea7219216a8302b92678beda Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 21 Jun 2024 14:44:56 +0200
Subject: [PATCH 219/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 75 +++++++++++--------
 .../src/graph/annotated_graph_algorithm.hpp   |  1 +
 .../test_annotated_graph_algorithm.cpp        | 16 +++-
 3 files changed, 57 insertions(+), 35 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b4767f6d17..0465faa03f 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -2,6 +2,7 @@
 
 #include <typeinfo>
 #include <mutex>
+#include <variant>
 
 #include <sdust.h>
 
@@ -30,7 +31,6 @@ typedef AnnotatedDBG::node_index node_index;
 typedef AnnotatedDBG::Annotator Annotator;
 typedef AnnotatedDBG::Annotator::Label Label;
 using Column = annot::matrix::BinaryMatrix::Column;
-using PairContainer = std::vector<std::pair<uint64_t, uint64_t>>;
 constexpr std::memory_order MO_RELAXED = std::memory_order_relaxed;
 
 double CDF_CUTOFF = 0.95;
@@ -71,32 +71,31 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
 
     size_t total_labels = labels_in.size() + labels_out.size();
-    std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
-    std::vector<bool> groups(total_labels);
 
-    if (config.test_by_unitig) {
-        using ValuesContainer = sdsl::int_vector<>;
-        std::vector<std::unique_ptr<const ValuesContainer>> column_values_all(total_labels);
-        annot::ColumnCompressed<>::load_columns_and_values(
-            files,
-            [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
-                groups[offset] = labels_out.count(label);
-                columns_all[offset].reset(column.release());
-                column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
-            },
-            num_parallel_files
-        );
+    using ValuesContainerMem = sdsl::int_vector<>;
+    using ColumnValuesMem = std::vector<std::unique_ptr<const ValuesContainerMem>>;
 
-        return mask_nodes_by_label_dual<ValuesContainer, PValStorage>(
-            graph_ptr, columns_all, column_values_all, groups,
-            config, num_threads, tmp_dir, num_parallel_files, true
-        );
+    using ValuesContainerDisk = sdsl::int_vector_buffer<>;
+    using ColumnValuesDisk = std::vector<std::unique_ptr<const ValuesContainerDisk>>;
+
+    std::variant<ColumnValuesMem, ColumnValuesDisk> column_values_all;
+
+    if (config.test_by_unitig) {
+        column_values_all = ColumnValuesMem(total_labels);
     } else {
-        using ValuesContainer = sdsl::int_vector_buffer<>;
-        std::vector<std::unique_ptr<const ValuesContainer>> column_values_all(total_labels);
+        column_values_all = ColumnValuesDisk(total_labels);
+    }
+
+    return std::visit([&](auto&& column_values_all) {
+        using ColumnValues = typename std::decay<decltype(column_values_all)>::type;
+        using ValuesContainerPtr = typename ColumnValues::value_type;
+        using ValuesContainer = typename std::decay<typename ValuesContainerPtr::element_type>::type;
+        using value_type = typename ValuesContainer::value_type;
+        std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
+        std::vector<bool> groups(total_labels);
         annot::ColumnCompressed<>::load_columns_and_values(
             files,
-            [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector> &&column, ValuesContainer&& column_values) {
+            [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector>&& column, ValuesContainer&& column_values) {
                 groups[offset] = labels_out.count(label);
                 columns_all[offset].reset(column.release());
                 column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
@@ -105,10 +104,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         );
 
         return mask_nodes_by_label_dual<ValuesContainer, PValStorage>(
-            graph_ptr, columns_all, column_values_all, groups,
+            graph_ptr, columns_all, column_values_all,
+            [&](uint64_t row_i, uint64_t col_j) -> value_type {
+                const auto &col = *columns_all[col_j];
+                const auto &col_vals = *column_values_all[col_j];
+                if (uint64_t r = col.conditional_rank1(row_i))
+                    return col_vals[r - 1];
+
+                return 0;
+            },
+            groups,
             config, num_threads, tmp_dir, num_parallel_files, true
         );
-    }
+    }, column_values_all);
 }
 
 template
@@ -137,6 +145,7 @@ std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValS
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          std::vector<std::unique_ptr<const bit_vector>> &columns_all,
                          std::vector<std::unique_ptr<const ValuesContainer>> &column_values_all,
+                         const std::function<typename ValuesContainer::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &get_value,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads,
@@ -195,6 +204,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::mutex agg_mu;
     std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
 
+    using value_type = typename ValuesContainer::value_type;
+    using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
+
     if (groups.size()) {
         std::atomic_thread_fence(std::memory_order_release);
         utils::call_rows<std::unique_ptr<const bit_vector>,
@@ -251,10 +263,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             for (node_index node : path) {
                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
                 for (size_t j = 0; j < groups.size(); ++j) {
-                    const auto &col = *columns_all[j];
-                    const auto &col_vals = *column_values_all[j];
-                    if (uint64_t r = col.conditional_rank1(row_i)) {
-                        uint64_t c = col_vals[r - 1];
+                    if (uint64_t c = get_value(row_i, j)) {
                         unitig_sums[j] += c;
                         if (c >= min_counts[j])
                             all_below_cutoff[j] = false;
@@ -828,10 +837,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             for (node_index node : path) {
                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
                 for (size_t j = 0; j < groups.size(); ++j) {
-                    const auto &col = *columns_all[j];
-                    const auto &col_vals = *column_values_all[j];
-                    if (uint64_t r = col.conditional_rank1(row_i))
-                        row_counts[j] += col_vals[r - 1];
+                    if (uint64_t c = get_value(row_i, j))
+                        row_counts[j] += c;
                 }
             }
 
@@ -1087,6 +1094,7 @@ std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::
 mask_nodes_by_label_dual<sdsl::int_vector_buffer<>, std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
                          std::vector<std::unique_ptr<const bit_vector>> &,
                          std::vector<std::unique_ptr<const sdsl::int_vector_buffer<>>> &,
+                         const std::function<typename sdsl::int_vector_buffer<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
@@ -1098,6 +1106,7 @@ std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::
 mask_nodes_by_label_dual<sdsl::int_vector<>, std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
                          std::vector<std::unique_ptr<const bit_vector>> &,
                          std::vector<std::unique_ptr<const sdsl::int_vector<>>> &,
+                         const std::function<typename sdsl::int_vector<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
@@ -1110,6 +1119,7 @@ std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl:
 mask_nodes_by_label_dual<sdsl::int_vector_buffer<>, sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
                          std::vector<std::unique_ptr<const bit_vector>> &,
                          std::vector<std::unique_ptr<const sdsl::int_vector_buffer<>>> &,
+                         const std::function<typename sdsl::int_vector_buffer<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
@@ -1121,6 +1131,7 @@ std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl:
 mask_nodes_by_label_dual<sdsl::int_vector<>, sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
                          std::vector<std::unique_ptr<const bit_vector>> &,
                          std::vector<std::unique_ptr<const sdsl::int_vector<>>> &,
+                         const std::function<typename sdsl::int_vector<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index d578fd2d0d..44ae9e0e46 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -49,6 +49,7 @@ std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValS
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          std::vector<std::unique_ptr<const bit_vector>> &columns_all,
                          std::vector<std::unique_ptr<const ValuesContainer>> &column_values_all,
+                         const std::function<typename ValuesContainer::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &get_value,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads = 1,
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index 0896930f6b..dfbf9a47c9 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -60,8 +60,6 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
                                return std::make_unique<const bit_vector_stat>(a->to_vector());
                            });
 
-            std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values_all;
-
             std::unordered_set<std::string> obs_labels, obs_kmers;
             const std::unordered_set<std::string> ref_kmers {
                 std::string(k - 1, 'A') + "C"
@@ -82,10 +80,14 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
             std::vector<bool> groups = { true, false, false };
             columns.resize(groups.size());
 
+            std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
             auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<sdsl::int_vector<>, std::vector<uint64_t>>(
                 graph_ptr,
                 columns,
-                column_values_all,
+                column_values,
+                [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
+                    return (*columns[col_j])[row_i];
+                },
                 groups,
                 config, num_threads
             );
@@ -192,6 +194,14 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                     graph_ptr,
                     columns,
                     column_values_all,
+                    [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
+                        const auto &col = *columns[col_j];
+                        const auto &col_vals = *column_values_all[col_j];
+                        if (uint64_t r = col.conditional_rank1(row_i))
+                            return col_vals[r - 1];
+
+                        return 0;
+                    },
                     groups,
                     config, num_threads, test_dump_basename, num_threads,
                     false

From f265a0ffcd718bb1a00928c5dfcbad1664e5a387 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 21 Jun 2024 16:44:56 +0200
Subject: [PATCH 220/410] cleanup2

---
 .../src/graph/annotated_graph_algorithm.cpp   | 104 +++++++++++-------
 .../src/graph/annotated_graph_algorithm.hpp   |   3 +-
 2 files changed, 68 insertions(+), 39 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0465faa03f..420a012c3b 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -103,6 +103,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             num_parallel_files
         );
 
+        uint8_t max_width = 0;
+        for (const auto &col_vals : column_values_all) {
+            max_width = std::max(max_width, col_vals->width());
+        }
+
         return mask_nodes_by_label_dual<ValuesContainer, PValStorage>(
             graph_ptr, columns_all, column_values_all,
             [&](uint64_t row_i, uint64_t col_j) -> value_type {
@@ -114,7 +119,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return 0;
             },
             groups,
-            config, num_threads, tmp_dir, num_parallel_files, true
+            config, num_threads, tmp_dir, num_parallel_files, true, max_width
         );
     }, column_values_all);
 }
@@ -151,7 +156,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          size_t num_threads,
                          std::filesystem::path tmp_dir,
                          size_t num_parallel_files,
-                         bool deallocate) {
+                         bool deallocate,
+                         uint8_t max_width) {
     num_parallel_files = std::min(num_parallel_files, num_threads);
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
     bool parallel = num_parallel_files > 1;
@@ -161,32 +167,54 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
 
-    uint8_t max_width = 0;
-    for (const auto &col_vals : column_values_all) {
-        max_width = std::max(max_width, col_vals->width());
-    }
-
     sdsl::bit_vector kept(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), true);
 
     std::vector<uint64_t> min_counts(groups.size(), config.min_count);
     std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
 
-    if (config.clean && column_values_all.empty())
-        common::logger->warn("Can't clean when no counts provided, skipping cleaning");
+    std::mutex agg_mu;
+    std::vector<std::mutex> column_mus(groups.size());
+    std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
+    using value_type = typename ValuesContainer::value_type;
+    using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
+
+    common::logger->trace("Calculating count histograms");
+    utils::call_rows<std::unique_ptr<const bit_vector>,
+                     std::unique_ptr<const ValuesContainer>,
+                     PairContainer, false>(columns_all, column_values_all,
+                                           [&](uint64_t row_i, const auto &row, size_t) {
+        if (row.empty())
+            return;
+
+        sdsl::bit_vector marker(groups.size(), false);
+        for (const auto &[j, raw_c] : row) {
+            marker[j] = true;
+            std::lock_guard<std::mutex> lock(column_mus[j]);
+            ++hists_map[j][raw_c];
+        }
+
+        for (size_t j = 0; j < marker.size(); ++j) {
+            if (!marker[j]) {
+                std::lock_guard<std::mutex> lock(column_mus[j]);
+                ++hists_map[j][0];
+            }
+        }
+    });
 
     if (config.clean && column_values_all.size()) {
         common::logger->trace("Cleaning count columns");
 
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
-            const auto &column_values = *column_values_all[j];
-
             // set cutoff for lower end of distribution
             auto [mean_est, nzeros_est] = estimate_ztp_mean(
                 [&](const auto &callback) {
-                    for (size_t i = 0; i < column_values.size(); ++i) {
-                        if (column_values[i] <= N_BUCKETS_FOR_ESTIMATION)
-                            callback(column_values[i]);
+                    for (const auto &[k, c] : hists_map[j]) {
+                        if (k > 0 && k <= N_BUCKETS_FOR_ESTIMATION) {
+                            for (size_t i = 0; i < c; ++i) {
+                                callback(k);
+                            }
+                        }
                     }
                 },
                 max_width,
@@ -201,11 +229,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
 
     common::logger->trace("Marking discarded k-mers");
-    std::mutex agg_mu;
-    std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
-
-    using value_type = typename ValuesContainer::value_type;
-    using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
 
     if (groups.size()) {
         std::atomic_thread_fence(std::memory_order_release);
@@ -213,26 +236,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          std::unique_ptr<const ValuesContainer>,
                          PairContainer, false>(columns_all, column_values_all,
                                                [&](uint64_t row_i, const auto &row, size_t) {
+            std::vector<uint64_t> counts(groups.size(), 0);
             for (const auto &[j, raw_c] : row) {
-                if (raw_c >= min_counts[j] && raw_c <= check_cutoff[j]) {
-                    if (!config.test_by_unitig) {
-                        std::lock_guard<std::mutex> lock(agg_mu);
-                        sdsl::bit_vector marker(groups.size(), false);
-                        for (const auto &[j, raw_c] : row) {
-                            ++hists_map[j][raw_c];
-                            marker[j] = true;
-                        }
-
-                        for (size_t j = 0; j < marker.size(); ++j) {
-                            if (!marker[j])
-                                ++hists_map[j][0];
-                        }
-                    }
+                if (raw_c >= min_counts[j] && raw_c <= check_cutoff[j])
                     return;
-                }
+
+                counts[j] = raw_c;
             }
 
             unset_bit(kept.data(), row_i, parallel, MO_RELAXED);
+            if (row.size()) {
+                for (size_t j = 0; j < counts.size(); ++j) {
+                    size_t &count = hists_map[j].find(counts[j]).value();
+                    size_t old_val = __atomic_fetch_sub(&count, 1, MO_RELAXED);
+                    std::ignore = old_val;
+                    assert(old_val);
+                }
+            }
         });
         std::atomic_thread_fence(std::memory_order_acquire);
     }
@@ -256,6 +276,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
         );
 
+        for (size_t j = 0; j < groups.size(); ++j) {
+            hists_map[j].clear();
+        }
+
         std::atomic_thread_fence(std::memory_order_release);
         clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
             std::vector<uint64_t> unitig_sums(groups.size(), 0);
@@ -1100,7 +1124,8 @@ mask_nodes_by_label_dual<sdsl::int_vector_buffer<>, std::vector<uint64_t>>(std::
                          size_t,
                          std::filesystem::path,
                          size_t,
-                         bool);
+                         bool,
+                         uint8_t);
 template
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual<sdsl::int_vector<>, std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
@@ -1112,7 +1137,8 @@ mask_nodes_by_label_dual<sdsl::int_vector<>, std::vector<uint64_t>>(std::shared_
                          size_t,
                          std::filesystem::path,
                          size_t,
-                         bool);
+                         bool,
+                         uint8_t);
 
 template
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
@@ -1125,7 +1151,8 @@ mask_nodes_by_label_dual<sdsl::int_vector_buffer<>, sdsl::int_vector_buffer<64>>
                          size_t,
                          std::filesystem::path,
                          size_t,
-                         bool);
+                         bool,
+                         uint8_t);
 template
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual<sdsl::int_vector<>, sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
@@ -1137,7 +1164,8 @@ mask_nodes_by_label_dual<sdsl::int_vector<>, sdsl::int_vector_buffer<64>>(std::s
                          size_t,
                          std::filesystem::path,
                          size_t,
-                         bool);
+                         bool,
+                         uint8_t);
 
 } // namespace graph
 } // namespace mtg
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 44ae9e0e46..ce426088bd 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -55,7 +55,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          size_t num_threads = 1,
                          std::filesystem::path = "",
                          size_t num_parallel_files = std::numeric_limits<size_t>::max(),
-                         bool deallocate = false);
+                         bool deallocate = false,
+                         uint8_t max_width = 64);
 
 } // namespace graph
 } // namespace mtg

From 3433fe0b0509619e4e16060412bc5c0ed30f4cb9 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 21 Jun 2024 17:17:42 +0200
Subject: [PATCH 221/410] refactor

---
 .../src/graph/annotated_graph_algorithm.cpp   | 100 ++++++------------
 .../src/graph/annotated_graph_algorithm.hpp   |   9 +-
 .../test_annotated_graph_algorithm.cpp        |  50 ++++++---
 3 files changed, 72 insertions(+), 87 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 420a012c3b..6fd826cf69 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -91,6 +91,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         using ValuesContainerPtr = typename ColumnValues::value_type;
         using ValuesContainer = typename std::decay<typename ValuesContainerPtr::element_type>::type;
         using value_type = typename ValuesContainer::value_type;
+        using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
+
         std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
         std::vector<bool> groups(total_labels);
         annot::ColumnCompressed<>::load_columns_and_values(
@@ -108,8 +110,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             max_width = std::max(max_width, col_vals->width());
         }
 
-        return mask_nodes_by_label_dual<ValuesContainer, PValStorage>(
-            graph_ptr, columns_all, column_values_all,
+        return mask_nodes_by_label_dual<value_type, PValStorage>(
+            graph_ptr,
+            [&](const auto &callback) {
+                utils::call_rows<std::unique_ptr<const bit_vector>,
+                                 std::unique_ptr<const ValuesContainer>,
+                                 PairContainer, true>(columns_all, column_values_all,
+                                                      [&](uint64_t row_i, const auto &row, size_t) {
+                    callback(row_i, row);
+                });
+            },
             [&](uint64_t row_i, uint64_t col_j) -> value_type {
                 const auto &col = *columns_all[col_j];
                 const auto &col_vals = *column_values_all[col_j];
@@ -119,7 +129,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return 0;
             },
             groups,
-            config, num_threads, tmp_dir, num_parallel_files, true, max_width
+            config, num_threads, tmp_dir, num_parallel_files,
+            [&]() {
+                columns_all.clear();
+                column_values_all.clear();
+            },
+            max_width
         );
     }, column_values_all);
 }
@@ -145,18 +160,17 @@ mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBr
                          std::filesystem::path,
                          size_t);
 
-template <class ValuesContainer, class PValStorage>
+template <typename value_type, class PValStorage>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         std::vector<std::unique_ptr<const bit_vector>> &columns_all,
-                         std::vector<std::unique_ptr<const ValuesContainer>> &column_values_all,
-                         const std::function<typename ValuesContainer::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &get_value,
+                         const std::function<void(const std::function<void(uint64_t, const std::vector<std::pair<uint64_t, value_type>>)>&)> &generate_rows,
+                         const std::function<value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &get_value,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads,
                          std::filesystem::path tmp_dir,
                          size_t num_parallel_files,
-                         bool deallocate,
+                         const std::function<void()> &deallocate,
                          uint8_t max_width) {
     num_parallel_files = std::min(num_parallel_files, num_threads);
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
@@ -175,14 +189,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::mutex agg_mu;
     std::vector<std::mutex> column_mus(groups.size());
     std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
-    using value_type = typename ValuesContainer::value_type;
     using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
 
     common::logger->trace("Calculating count histograms");
-    utils::call_rows<std::unique_ptr<const bit_vector>,
-                     std::unique_ptr<const ValuesContainer>,
-                     PairContainer, false>(columns_all, column_values_all,
-                                           [&](uint64_t row_i, const auto &row, size_t) {
+    generate_rows([&](uint64_t row_i, const auto &row) {
         if (row.empty())
             return;
 
@@ -201,7 +211,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     });
 
-    if (config.clean && column_values_all.size()) {
+    if (config.clean) {
         common::logger->trace("Cleaning count columns");
 
         #pragma omp parallel for num_threads(num_parallel_files)
@@ -232,10 +242,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     if (groups.size()) {
         std::atomic_thread_fence(std::memory_order_release);
-        utils::call_rows<std::unique_ptr<const bit_vector>,
-                         std::unique_ptr<const ValuesContainer>,
-                         PairContainer, false>(columns_all, column_values_all,
-                                               [&](uint64_t row_i, const auto &row, size_t) {
+        generate_rows([&](uint64_t row_i, const auto &row) {
             std::vector<uint64_t> counts(groups.size(), 0);
             for (const auto &[j, raw_c] : row) {
                 if (raw_c >= min_counts[j] && raw_c <= check_cutoff[j])
@@ -951,10 +958,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }, num_threads);
     } else {
-        utils::call_rows<std::unique_ptr<const bit_vector>,
-                         std::unique_ptr<const ValuesContainer>,
-                         PairContainer, true>(columns_all, column_values_all,
-                                              [&](uint64_t row_i, const auto &raw_row, size_t batch_id) {
+        generate_rows([&](uint64_t row_i, const auto &raw_row) {
             if (ex)
                 return;
 
@@ -1044,19 +1048,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     std::atomic_thread_fence(std::memory_order_acquire);
 
-    if (deallocate) {
-        for (auto &col : columns_all) {
-            col.reset();
-        }
-
-        for (auto &col_vals : column_values_all) {
-            col_vals.reset();
-        }
-    }
-
     if (ex)
         std::rethrow_exception(ex);
 
+    deallocate();
+
     if (config.test_type != "notest") {
         uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
         auto m_data = const_cast<std::vector<std::pair<size_t, size_t>>&&>(m.values_container());
@@ -1115,56 +1111,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
 template
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<sdsl::int_vector_buffer<>, std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
-                         std::vector<std::unique_ptr<const bit_vector>> &,
-                         std::vector<std::unique_ptr<const sdsl::int_vector_buffer<>>> &,
-                         const std::function<typename sdsl::int_vector_buffer<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
-                         const std::vector<bool> &,
-                         const DifferentialAssemblyConfig &,
-                         size_t,
-                         std::filesystem::path,
-                         size_t,
-                         bool,
-                         uint8_t);
-template
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<sdsl::int_vector<>, std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
-                         std::vector<std::unique_ptr<const bit_vector>> &,
-                         std::vector<std::unique_ptr<const sdsl::int_vector<>>> &,
+mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
+                         const std::function<void(const std::function<void(uint64_t, const std::vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>)>&)> &generate_rows,
                          const std::function<typename sdsl::int_vector<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
                          std::filesystem::path,
                          size_t,
-                         bool,
+                         const std::function<void()> &,
                          uint8_t);
 
 template
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<sdsl::int_vector_buffer<>, sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
-                         std::vector<std::unique_ptr<const bit_vector>> &,
-                         std::vector<std::unique_ptr<const sdsl::int_vector_buffer<>>> &,
-                         const std::function<typename sdsl::int_vector_buffer<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
-                         const std::vector<bool> &,
-                         const DifferentialAssemblyConfig &,
-                         size_t,
-                         std::filesystem::path,
-                         size_t,
-                         bool,
-                         uint8_t);
-template
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<sdsl::int_vector<>, sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
-                         std::vector<std::unique_ptr<const bit_vector>> &,
-                         std::vector<std::unique_ptr<const sdsl::int_vector<>>> &,
+mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
+                         const std::function<void(const std::function<void(uint64_t, const std::vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>)>&)> &generate_rows,
                          const std::function<typename sdsl::int_vector<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
                          std::filesystem::path,
                          size_t,
-                         bool,
+                         const std::function<void()> &,
                          uint8_t);
 
 } // namespace graph
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index ce426088bd..7895125f2c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -44,18 +44,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          std::filesystem::path = "",
                          size_t num_parallel_files = std::numeric_limits<size_t>::max());
 
-template <class ValuesContainer, class PValStorage>
+template <typename value_type, class PValStorage>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         std::vector<std::unique_ptr<const bit_vector>> &columns_all,
-                         std::vector<std::unique_ptr<const ValuesContainer>> &column_values_all,
-                         const std::function<typename ValuesContainer::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &get_value,
+                         const std::function<void(const std::function<void(uint64_t, const std::vector<std::pair<uint64_t, value_type>>)>&)> &generate_rows,
+                         const std::function<value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &get_value,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads = 1,
                          std::filesystem::path = "",
                          size_t num_parallel_files = std::numeric_limits<size_t>::max(),
-                         bool deallocate = false,
+                         const std::function<void()> &deallocate = []() {},
                          uint8_t max_width = 64);
 
 } // namespace graph
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index dfbf9a47c9..fa45da24ec 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -11,6 +11,7 @@
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 #include "annotation/int_matrix/rank_extended/tuple_csc_matrix.hpp"
 #include "common/vectors/bit_vector_sdsl.hpp"
+#include "common/vectors/transpose.hpp"
 
 
 namespace {
@@ -80,16 +81,27 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
             std::vector<bool> groups = { true, false, false };
             columns.resize(groups.size());
 
+            using PairContainer = std::vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
             std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
-            auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<sdsl::int_vector<>, std::vector<uint64_t>>(
+            auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
                 graph_ptr,
-                columns,
-                column_values,
+                [&](const auto &callback) {
+                    utils::call_rows<std::unique_ptr<const bit_vector>,
+                                     std::unique_ptr<const sdsl::int_vector<>>,
+                                     PairContainer, true>(columns, column_values,
+                                                          [&](uint64_t row_i, const auto &row, size_t) {
+                        callback(row_i, row);
+                    });
+                },
                 [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
                     return (*columns[col_j])[row_i];
                 },
                 groups,
-                config, num_threads
+                config, num_threads, test_dump_basename, num_threads,
+                [&]() {
+                    columns.clear();
+                    column_values.clear();
+                }
             );
 
             EXPECT_EQ(0u, masked_dbg_out->num_nodes());
@@ -144,12 +156,12 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
 
             ASSERT_LT(0u, columns.size());
 
-            std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values_all;
+            std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
             std::vector<sdsl::int_vector<>::iterator> pos;
-            column_values_all.reserve(columns.size());
+            column_values.reserve(columns.size());
             for (const auto &bin_col : columns) {
-                column_values_all.emplace_back(std::make_unique<const sdsl::int_vector<>>(bin_col->num_set_bits()));
-                pos.emplace_back(const_cast<sdsl::int_vector<>&>(*column_values_all.back()).begin());
+                column_values.emplace_back(std::make_unique<const sdsl::int_vector<>>(bin_col->num_set_bits()));
+                pos.emplace_back(const_cast<sdsl::int_vector<>&>(*column_values.back()).begin());
             }
 
             std::vector<uint64_t> row_indices(columns[0]->size());
@@ -175,8 +187,8 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
             std::vector<bool> groups { true, false, false };
             columns.resize(groups.size());
 
-            ASSERT_GE(column_values_all.size(), groups.size());
-            column_values_all.resize(groups.size());
+            ASSERT_GE(column_values.size(), groups.size());
+            column_values.resize(groups.size());
 
             std::vector<std::string> test_types = {
                 "poisson_exact",
@@ -190,21 +202,27 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                     .outfbase = "",
                 };
 
-                auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<sdsl::int_vector<>, std::vector<uint64_t>>(
+                using PairContainer = std::vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
+                auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
                     graph_ptr,
-                    columns,
-                    column_values_all,
+                    [&](const auto &callback) {
+                        utils::call_rows<std::unique_ptr<const bit_vector>,
+                                         std::unique_ptr<const sdsl::int_vector<>>,
+                                         PairContainer, true>(columns, column_values,
+                                                              [&](uint64_t row_i, const auto &row, size_t) {
+                            callback(row_i, row);
+                        });
+                    },
                     [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
                         const auto &col = *columns[col_j];
-                        const auto &col_vals = *column_values_all[col_j];
+                        const auto &col_vals = *column_values[col_j];
                         if (uint64_t r = col.conditional_rank1(row_i))
                             return col_vals[r - 1];
 
                         return 0;
                     },
                     groups,
-                    config, num_threads, test_dump_basename, num_threads,
-                    false
+                    config, num_threads, test_dump_basename, num_threads
                 );
 
                 masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {

From 73a082651ce699714d221ceb9baaaf9a465133bb Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 21 Jun 2024 18:29:10 +0200
Subject: [PATCH 222/410] add code for general annotators

---
 .../src/graph/annotated_graph_algorithm.cpp   | 111 +++++++-
 .../src/graph/annotated_graph_algorithm.hpp   |  11 +-
 .../test_annotated_graph_algorithm.cpp        | 266 ++++++++++--------
 3 files changed, 267 insertions(+), 121 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6fd826cf69..fddf6d1adb 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -21,6 +21,7 @@
 #include "graph/representation/masked_graph.hpp"
 #include "graph/graph_cleaning.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
+#include "annotation/int_matrix/base/int_matrix.hpp"
 
 namespace mtg {
 namespace graph {
@@ -72,11 +73,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     size_t total_labels = labels_in.size() + labels_out.size();
 
-    using ValuesContainerMem = sdsl::int_vector<>;
-    using ColumnValuesMem = std::vector<std::unique_ptr<const ValuesContainerMem>>;
-
-    using ValuesContainerDisk = sdsl::int_vector_buffer<>;
-    using ColumnValuesDisk = std::vector<std::unique_ptr<const ValuesContainerDisk>>;
+    using ColumnValuesMem = std::vector<std::unique_ptr<const sdsl::int_vector<>>>;
+    using ColumnValuesDisk = std::vector<std::unique_ptr<const sdsl::int_vector_buffer<>>>;
 
     std::variant<ColumnValuesMem, ColumnValuesDisk> column_values_all;
 
@@ -91,7 +89,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         using ValuesContainerPtr = typename ColumnValues::value_type;
         using ValuesContainer = typename std::decay<typename ValuesContainerPtr::element_type>::type;
         using value_type = typename ValuesContainer::value_type;
-        using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
+        using PairContainer = Vector<std::pair<uint64_t, value_type>>;
 
         std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
         std::vector<bool> groups(total_labels);
@@ -160,10 +158,105 @@ mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBr
                          std::filesystem::path,
                          size_t);
 
+template <class PValStorage>
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
+                         const DifferentialAssemblyConfig &config,
+                         size_t num_threads,
+                         std::filesystem::path tmp_dir) {
+    common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
+    common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
+
+    size_t total_labels = labels_in.size() + labels_out.size();
+    const auto &annotation = anno_graph.get_annotator();
+
+    std::vector<bool> groups;
+    groups.reserve(total_labels);
+    for (const auto &label : annotation.get_label_encoder().get_labels()) {
+        groups.emplace_back(labels_out.count(label));
+    }
+
+    const auto &matrix = annotation.get_matrix();
+    using Row = annot::matrix::BinaryMatrix::Row;
+
+    if (const auto *int_matrix = dynamic_cast<const annot::matrix::IntMatrix*>(&matrix)) {
+        using value_type = annot::matrix::IntMatrix::Value;
+        return mask_nodes_by_label_dual<value_type, PValStorage>(
+            std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
+            [&](const auto &callback) {
+                std::vector<Row> row_ids { 0 };
+                for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
+                    row_ids[0] = row_i;
+                    callback(row_i, int_matrix->get_row_values(row_ids)[0]);
+                }
+            },
+            [&](uint64_t row_i, uint64_t col_j) -> value_type {
+                std::vector<Row> row_ids { Row(row_i) };
+                for (const auto &[j, c] : int_matrix->get_row_values(row_ids)[0]) {
+                    if (j == col_j)
+                        return c;
+                }
+
+                return 0;
+            },
+            groups,
+            config, num_threads, tmp_dir, num_threads
+        );
+    } else {
+        return mask_nodes_by_label_dual<uint64_t, PValStorage>(
+            std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
+            [&](const auto &callback) {
+                std::vector<Row> row_ids { 0 };
+                for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
+                    row_ids[0] = row_i;
+                    auto set_bits = matrix.get_rows(row_ids);
+                    Vector<std::pair<uint64_t, uint64_t>> container;
+                    container.reserve(set_bits[0].size());
+                    for (auto j : set_bits[0]) {
+                        container.emplace_back(j, 1);
+                    }
+
+                    callback(row_i, container);
+                }
+            },
+            [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
+                std::vector<Row> row_ids { Row(row_i) };
+                for (auto j : matrix.get_rows(row_ids)[0]) {
+                    if (j == col_j)
+                        return 1;
+                }
+
+                return 0;
+            },
+            groups,
+            config, num_threads, tmp_dir, num_threads
+        );
+    }
+}
+
+template
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<std::vector<uint64_t>>(const AnnotatedDBG &,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
+                         const DifferentialAssemblyConfig &,
+                         size_t,
+                         std::filesystem::path);
+template
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(const AnnotatedDBG &,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
+                         const DifferentialAssemblyConfig &,
+                         size_t,
+                         std::filesystem::path);
+
 template <typename value_type, class PValStorage>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         const std::function<void(const std::function<void(uint64_t, const std::vector<std::pair<uint64_t, value_type>>)>&)> &generate_rows,
+                         const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, value_type>>)>&)> &generate_rows,
                          const std::function<value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &get_value,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
@@ -1112,7 +1205,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 template
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
-                         const std::function<void(const std::function<void(uint64_t, const std::vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>)>&)> &generate_rows,
+                         const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>)>&)> &generate_rows,
                          const std::function<typename sdsl::int_vector<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
@@ -1125,7 +1218,7 @@ mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<ui
 template
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
-                         const std::function<void(const std::function<void(uint64_t, const std::vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>)>&)> &generate_rows,
+                         const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>)>&)> &generate_rows,
                          const std::function<typename sdsl::int_vector<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 7895125f2c..64761f2068 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -44,10 +44,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          std::filesystem::path = "",
                          size_t num_parallel_files = std::numeric_limits<size_t>::max());
 
+template <class PValStorage>
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
+                         const DifferentialAssemblyConfig &config,
+                         size_t num_threads = 1,
+                         std::filesystem::path = "");
+
 template <typename value_type, class PValStorage>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         const std::function<void(const std::function<void(uint64_t, const std::vector<std::pair<uint64_t, value_type>>)>&)> &generate_rows,
+                         const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, value_type>>)>&)> &generate_rows,
                          const std::function<value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &get_value,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index fa45da24ec..c3ba9e1fc3 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -30,7 +30,8 @@ class MaskedDeBruijnGraphAlgorithm : public ::testing::Test {};
 // test with DBGBitmap and DBGHashFast to have k-mers in different order
 typedef ::testing::Types<std::pair<DBGBitmap, ColumnCompressed<>>,
                          std::pair<DBGHashFast, ColumnCompressed<>>,
-                         std::pair<DBGSuccinct, ColumnCompressed<>>
+                         std::pair<DBGSuccinct, ColumnCompressed<>>,
+                         std::pair<DBGSuccinct, RowDiffColumnAnnotator>
                         > GraphAnnoTypes;
 TYPED_TEST_SUITE(MaskedDeBruijnGraphAlgorithm, GraphAnnoTypes);
 
@@ -53,13 +54,6 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
             auto graph_ptr = std::static_pointer_cast<const Graph>(anno_graph->get_graph_ptr());
 
             const auto &annotation = static_cast<const Annotation&>(anno_graph->get_annotator());
-            const auto &in_columns = annotation.get_matrix().data();
-            std::vector<std::unique_ptr<const bit_vector>> columns;
-            columns.reserve(in_columns.size());
-            std::transform(in_columns.begin(), in_columns.end(), std::back_inserter(columns),
-                           [&](const auto &a) {
-                               return std::make_unique<const bit_vector_stat>(a->to_vector());
-                           });
 
             std::unordered_set<std::string> obs_labels, obs_kmers;
             const std::unordered_set<std::string> ref_kmers {
@@ -79,38 +73,62 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
             // ingroup: B, C
             // outgroup: A
             std::vector<bool> groups = { true, false, false };
-            columns.resize(groups.size());
-
-            using PairContainer = std::vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
-            std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
-            auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
-                graph_ptr,
-                [&](const auto &callback) {
-                    utils::call_rows<std::unique_ptr<const bit_vector>,
-                                     std::unique_ptr<const sdsl::int_vector<>>,
-                                     PairContainer, true>(columns, column_values,
-                                                          [&](uint64_t row_i, const auto &row, size_t) {
-                        callback(row_i, row);
-                    });
-                },
-                [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
-                    return (*columns[col_j])[row_i];
-                },
-                groups,
-                config, num_threads, test_dump_basename, num_threads,
-                [&]() {
-                    columns.clear();
-                    column_values.clear();
-                }
-            );
+            tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_in { "B", "C" };
+            tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_out { "A" };
+
+            if constexpr(std::is_same_v<Annotation, ColumnCompressed<>>) {
+                const auto &in_columns = annotation.get_matrix().data();
+                std::vector<std::unique_ptr<const bit_vector>> columns;
+                columns.reserve(in_columns.size());
+                std::transform(in_columns.begin(), in_columns.end(), std::back_inserter(columns),
+                            [&](const auto &a) {
+                                return std::make_unique<const bit_vector_stat>(a->to_vector());
+                            });
+
+
+                columns.resize(groups.size());
+
+                using PairContainer = Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
+                std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
+                auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
+                    graph_ptr,
+                    [&](const auto &callback) {
+                        utils::call_rows<std::unique_ptr<const bit_vector>,
+                                        std::unique_ptr<const sdsl::int_vector<>>,
+                                        PairContainer, true>(columns, column_values,
+                                                            [&](uint64_t row_i, const auto &row, size_t) {
+                            callback(row_i, row);
+                        });
+                    },
+                    [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
+                        return (*columns[col_j])[row_i];
+                    },
+                    groups,
+                    config, num_threads, test_dump_basename, num_threads,
+                    [&]() {
+                        columns.clear();
+                        column_values.clear();
+                    }
+                );
 
-            EXPECT_EQ(0u, masked_dbg_out->num_nodes());
+                masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                    auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                    obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                    obs_kmers.insert(kmer);
+                });
+            } else {
+                auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
+                    *anno_graph,
+                    labels_in, labels_out,
+                    config, num_threads, test_dump_basename
+                );
 
-            masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
-                auto cur_labels = anno_graph->get_labels(kmer, 0.0);
-                obs_labels.insert(cur_labels.begin(), cur_labels.end());
-                obs_kmers.insert(kmer);
-            });
+                masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                    auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                    obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                    obs_kmers.insert(kmer);
+                });
+            }
 
             EXPECT_EQ(ref_labels, obs_labels) << k;
             EXPECT_EQ(ref_kmers, obs_kmers) << k;
@@ -121,6 +139,7 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
 TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
     typedef typename TypeParam::first_type Graph;
     typedef typename TypeParam::second_type Annotation;
+
     for (size_t num_threads : { 1, 4 }) {
         size_t max_k = std::min(max_test_k<Graph>(), (size_t)15);
         for (size_t k = 3; k < max_k; ++k) {
@@ -138,42 +157,6 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
             };
 
             std::vector<std::string> labels { "A", "B", "C", "D", "E" };
-
-            auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels, DeBruijnGraph::BASIC, true);
-            auto graph_ptr = std::static_pointer_cast<const Graph>(anno_graph->get_graph_ptr());
-
-            using BinMatType = matrix::ColumnMajor;
-            using CoordMatType = matrix::TupleCSCMatrix<BinMatType>;
-            const auto &annotation = static_cast<const ColumnCoordAnnotator&>(anno_graph->get_annotator());
-            const auto &count_matrix = static_cast<const CoordMatType&>(annotation.get_matrix());
-            const auto &in_binary_columns = static_cast<const BinMatType&>(count_matrix.get_binary_matrix()).data();
-            std::vector<std::unique_ptr<const bit_vector>> columns;
-            columns.reserve(in_binary_columns.size());
-            std::transform(in_binary_columns.begin(), in_binary_columns.end(), std::back_inserter(columns),
-                           [&](const auto &a) {
-                               return std::make_unique<const bit_vector_stat>(a->to_vector());
-                           });
-
-            ASSERT_LT(0u, columns.size());
-
-            std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
-            std::vector<sdsl::int_vector<>::iterator> pos;
-            column_values.reserve(columns.size());
-            for (const auto &bin_col : columns) {
-                column_values.emplace_back(std::make_unique<const sdsl::int_vector<>>(bin_col->num_set_bits()));
-                pos.emplace_back(const_cast<sdsl::int_vector<>&>(*column_values.back()).begin());
-            }
-
-            std::vector<uint64_t> row_indices(columns[0]->size());
-            std::iota(row_indices.begin(), row_indices.end(), 0);
-            auto row_values = count_matrix.get_row_values(row_indices);
-            for (size_t i = 0; i < row_indices.size(); ++i) {
-                for (const auto &[j, c] : row_values[i]) {
-                    *pos[j] = c;
-                    ++pos[j];
-                }
-            }
-
             std::unordered_set<std::string> obs_labels, obs_kmers;
             const std::unordered_set<std::string> ref_kmers {
                 std::string(k - 1, 'A') + "C"
@@ -185,10 +168,8 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
             // ingroup: B, C
             // outgroup: A
             std::vector<bool> groups { true, false, false };
-            columns.resize(groups.size());
-
-            ASSERT_GE(column_values.size(), groups.size());
-            column_values.resize(groups.size());
+            tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_in { "B", "C" };
+            tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_out { "A" };
 
             std::vector<std::string> test_types = {
                 "poisson_exact",
@@ -196,42 +177,105 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                 "fisher",
             };
 
-            for (const auto &test_type : test_types) {
-                DifferentialAssemblyConfig config {
-                    .test_type = test_type,
-                    .outfbase = "",
-                };
+            auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels, DeBruijnGraph::BASIC, true);
+            auto graph_ptr = std::static_pointer_cast<const Graph>(anno_graph->get_graph_ptr());
 
-                using PairContainer = std::vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
-                auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
-                    graph_ptr,
-                    [&](const auto &callback) {
-                        utils::call_rows<std::unique_ptr<const bit_vector>,
-                                         std::unique_ptr<const sdsl::int_vector<>>,
-                                         PairContainer, true>(columns, column_values,
-                                                              [&](uint64_t row_i, const auto &row, size_t) {
-                            callback(row_i, row);
-                        });
-                    },
-                    [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
-                        const auto &col = *columns[col_j];
-                        const auto &col_vals = *column_values[col_j];
-                        if (uint64_t r = col.conditional_rank1(row_i))
-                            return col_vals[r - 1];
+            if constexpr(std::is_same_v<Annotation, ColumnCompressed<>>) {
+                using BinMatType = matrix::ColumnMajor;
+                using CoordMatType = matrix::TupleCSCMatrix<BinMatType>;
+                const auto &annotation = static_cast<const ColumnCoordAnnotator&>(anno_graph->get_annotator());
+                const auto &count_matrix = static_cast<const CoordMatType&>(annotation.get_matrix());
+                const auto &in_binary_columns = static_cast<const BinMatType&>(count_matrix.get_binary_matrix()).data();
+                std::vector<std::unique_ptr<const bit_vector>> columns;
+                columns.reserve(in_binary_columns.size());
+                std::transform(in_binary_columns.begin(), in_binary_columns.end(), std::back_inserter(columns),
+                            [&](const auto &a) {
+                                return std::make_unique<const bit_vector_stat>(a->to_vector());
+                            });
+
+                ASSERT_LT(0u, columns.size());
+
+                std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
+                std::vector<sdsl::int_vector<>::iterator> pos;
+                column_values.reserve(columns.size());
+                for (const auto &bin_col : columns) {
+                    column_values.emplace_back(std::make_unique<const sdsl::int_vector<>>(bin_col->num_set_bits()));
+                    pos.emplace_back(const_cast<sdsl::int_vector<>&>(*column_values.back()).begin());
+                }
 
-                        return 0;
-                    },
-                    groups,
-                    config, num_threads, test_dump_basename, num_threads
-                );
+                std::vector<uint64_t> row_indices(columns[0]->size());
+                std::iota(row_indices.begin(), row_indices.end(), 0);
+                auto row_values = count_matrix.get_row_values(row_indices);
+                for (size_t i = 0; i < row_indices.size(); ++i) {
+                    for (const auto &[j, c] : row_values[i]) {
+                        *pos[j] = c;
+                        ++pos[j];
+                    }
+                }
 
-                masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
-                    auto cur_labels = anno_graph->get_labels(kmer, 0.0);
-                    obs_labels.insert(cur_labels.begin(), cur_labels.end());
-                    obs_kmers.insert(kmer);
-                });
-                EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
-                EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
+                columns.resize(groups.size());
+
+                ASSERT_GE(column_values.size(), groups.size());
+                column_values.resize(groups.size());
+
+                for (const auto &test_type : test_types) {
+                    DifferentialAssemblyConfig config {
+                        .test_type = test_type,
+                        .outfbase = "",
+                    };
+
+                    using PairContainer = Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
+                    auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
+                        graph_ptr,
+                        [&](const auto &callback) {
+                            utils::call_rows<std::unique_ptr<const bit_vector>,
+                                            std::unique_ptr<const sdsl::int_vector<>>,
+                                            PairContainer, true>(columns, column_values,
+                                                                [&](uint64_t row_i, const auto &row, size_t) {
+                                callback(row_i, row);
+                            });
+                        },
+                        [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
+                            const auto &col = *columns[col_j];
+                            const auto &col_vals = *column_values[col_j];
+                            if (uint64_t r = col.conditional_rank1(row_i))
+                                return col_vals[r - 1];
+
+                            return 0;
+                        },
+                        groups,
+                        config, num_threads, test_dump_basename, num_threads
+                    );
+
+                    masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                        auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                        obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                        obs_kmers.insert(kmer);
+                    });
+                    EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
+                    EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
+                }
+            } else {
+                for (const auto &test_type : test_types) {
+                    DifferentialAssemblyConfig config {
+                        .test_type = test_type,
+                        .outfbase = "",
+                    };
+
+                    auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
+                        *anno_graph,
+                        labels_in, labels_out,
+                        config, num_threads, test_dump_basename
+                    );
+
+                    masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                        auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                        obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                        obs_kmers.insert(kmer);
+                    });
+                    EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
+                    EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
+                }
             }
         }
     }

From 35828b1e1c973cfb81b4108f59cd8b1716453bf4 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Jun 2024 01:28:02 +0200
Subject: [PATCH 223/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 207 +++++++++++++-----
 1 file changed, 147 insertions(+), 60 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fddf6d1adb..a759d596f1 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -364,7 +364,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, true);
 
     std::shared_ptr<MaskedDeBruijnGraph> clean_masked_graph;
-    std::atomic<size_t> num_unitigs(0);
     if (config.test_by_unitig) {
         clean_masked_graph = std::make_shared<MaskedDeBruijnGraph>(
             graph_ptr,
@@ -376,36 +375,36 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
         );
 
-        for (size_t j = 0; j < groups.size(); ++j) {
-            hists_map[j].clear();
-        }
-
-        std::atomic_thread_fence(std::memory_order_release);
-        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-            std::vector<uint64_t> unitig_sums(groups.size(), 0);
-            std::vector<bool> all_below_cutoff(groups.size(), true);
-            for (node_index node : path) {
-                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    if (uint64_t c = get_value(row_i, j)) {
-                        unitig_sums[j] += c;
-                        if (c >= min_counts[j])
-                            all_below_cutoff[j] = false;
-                    }
-                }
-            }
-
-            unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
-            num_unitigs.fetch_add(1, MO_RELAXED);
-
-            std::lock_guard<std::mutex> lock(agg_mu);
-            for (size_t j = 0; j < groups.size(); ++j) {
-                ++hists_map[j][unitig_sums[j]];
-            }
-        }, num_threads);
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-        nelem = num_unitigs.load();
+        // for (size_t j = 0; j < groups.size(); ++j) {
+        //     hists_map[j].clear();
+        // }
+
+        // std::atomic_thread_fence(std::memory_order_release);
+        // clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+        //     std::vector<uint64_t> unitig_sums(groups.size(), 0);
+        //     std::vector<bool> all_below_cutoff(groups.size(), true);
+        //     for (node_index node : path) {
+        //         uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+        //         for (size_t j = 0; j < groups.size(); ++j) {
+        //             if (uint64_t c = get_value(row_i, j)) {
+        //                 unitig_sums[j] += c;
+        //                 if (c >= min_counts[j])
+        //                     all_below_cutoff[j] = false;
+        //             }
+        //         }
+        //     }
+
+        //     unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
+        //     num_unitigs.fetch_add(1, MO_RELAXED);
+
+        //     std::lock_guard<std::mutex> lock(agg_mu);
+        //     for (size_t j = 0; j < groups.size(); ++j) {
+        //         ++hists_map[j][unitig_sums[j]];
+        //     }
+        // }, num_threads);
+        // std::atomic_thread_fence(std::memory_order_acquire);
+
+        // nelem = num_unitigs.load();
     }
 
     std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
@@ -480,14 +479,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
 
     VectorMap<size_t, size_t> m;
-    std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
-    std::function<double(int64_t)> compute_min_pval;
+    std::function<double(int64_t, int64_t, const PairContainer&, size_t)> compute_pval;
+    std::function<double(int64_t, size_t)> compute_min_pval;
 
     std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps;
+    VectorMap<size_t, std::pair<double, double>> path_params;
 
     common::logger->trace("Test: {}", config.test_type);
     if (config.test_type == "poisson_exact") {
-        compute_min_pval = [&](int64_t n) {
+        compute_min_pval = [&](int64_t n, size_t) {
             if (n == 0)
                 return 1.1;
 
@@ -530,7 +530,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return pval;
         };
 
-        compute_pval = [&](int64_t in_sum, int64_t out_sum, const auto &row) {
+        compute_pval = [&](int64_t in_sum, int64_t out_sum, const auto &row, size_t) {
             if (row.empty())
                 return 1.1;
 
@@ -575,7 +575,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     } else if (config.test_type == "fisher") {
         double lbase_shared = lgamma(in_kmers + 1) + lgamma(out_kmers + 1) - lgamma(total_kmers + 1);
 
-        compute_min_pval = [&,lbase_shared](int64_t m1) {
+        compute_min_pval = [&,lbase_shared](int64_t m1, size_t) {
             if (m1 == 0)
                 return 1.1;
 
@@ -597,7 +597,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::min(get_pval(0), get_pval(std::min(in_kmers, m1)));
         };
 
-        compute_pval = [&,lbase_shared](int64_t in_sum, int64_t out_sum, const auto &row) {
+        compute_pval = [&,lbase_shared](int64_t in_sum, int64_t out_sum, const auto &row, size_t) {
             if (row.empty())
                 return 1.1;
 
@@ -747,20 +747,63 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             throw std::domain_error("Fit failed");
         }
 
-        double r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
-            double dl = (log(r) - log(r + target_mu) - boost::math::digamma(r)) * matrix_size;
-            double ddl = (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r)) * matrix_size;
-            for (size_t j = 0; j < groups.size(); ++j) {
-                double f = target_sum / sums[j];
-                std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
-                    const auto &[k, c] = a;
-                    dl += boost::math::digamma(f * k + r) * c;
-                    ddl += boost::math::trigamma(f * k + r) * c;
-                });
+        auto get_r_map_target_p = [&,target_mu,matrix_size,hist_its,target_sum](int64_t c = 1) {
+            double r_map = 0;
+            double local_target_mu = 0;
+            if (c == 1) {
+                local_target_mu = target_mu * c;
+                double local_r_guess = target_mu2 * c / (target_var * c - target_mu);
+                r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
+                    double dl = (log(r) - log(r + local_target_mu) - boost::math::digamma(r)) * matrix_size;
+                    double ddl = (1.0 / r - 1.0 / (r + local_target_mu) - boost::math::trigamma(r)) * matrix_size;
+                    for (size_t j = 0; j < groups.size(); ++j) {
+                        double f = target_sum / sums[j] * c;
+                        std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
+                            const auto &[k, c] = a;
+                            dl += boost::math::digamma(f * k + r) * c;
+                            ddl += boost::math::trigamma(f * k + r) * c;
+                        });
+                    }
+                    return std::make_pair(dl, ddl);
+                }, local_r_guess, std::numeric_limits<double>::min(), real_sum * c, 30);
+            } else {
+                double local_target_var = 0.0;
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
+                        auto [k, cnt] = a;
+                        k *= c;
+                        if (count_maps.size())
+                            cnt = count_maps[j].find(cnt)->second.first;
+
+                        local_target_mu += k * cnt;
+                        local_target_var += k * k * cnt;
+                    });
+                }
+                local_target_mu /= matrix_size;
+                double local_target_mu2 = local_target_mu * local_target_mu;
+                local_target_var = (local_target_var - local_target_mu2 * matrix_size) / (matrix_size - 1);
+                double local_r_guess = local_target_mu2 / (local_target_var - local_target_mu);
+                r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
+                    double dl = (log(r) - log(r + local_target_mu) - boost::math::digamma(r)) * matrix_size;
+                    double ddl = (1.0 / r - 1.0 / (r + local_target_mu) - boost::math::trigamma(r)) * matrix_size;
+                    for (size_t j = 0; j < groups.size(); ++j) {
+                        std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
+                            auto [k, cnt] = a;
+                            k *= c;
+                            if (count_maps.size())
+                                cnt = count_maps[j].find(cnt)->second.first;
+                            dl += boost::math::digamma(k + r) * cnt;
+                            ddl += boost::math::trigamma(k + r) * cnt;
+                        });
+                    }
+                    return std::make_pair(dl, ddl);
+                }, local_r_guess, std::numeric_limits<double>::min(), real_sum * c, 30);
             }
-            return std::make_pair(dl, ddl);
-        }, r_guess, std::numeric_limits<double>::min(), real_sum, 30);
-        double target_p = r_map / (r_map + target_mu);
+            double target_p = r_map / (r_map + local_target_mu);
+            return std::make_pair(r_map, target_p);
+        };
+
+        auto [r_map, target_p] = get_r_map_target_p();
         double fit_mu = r_map * (1.0 - target_p) / target_p;
         double fit_var = fit_mu / target_p;
         common::logger->trace("Common params: r_guess: {}\tr: {}\tp: {}\tmu: {}\tvar: {}",
@@ -777,10 +820,30 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (fit_var / fit_mu - 1.0 < 1e-5)
             common::logger->warn("Fit parameters are close to a Poisson distribution");
 
-        compute_min_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t n) {
+        compute_min_pval = [&,lscaling_base_row=lscaling_base,r_in_row=r_in,r_out_row=r_out,target_p_row=target_p,get_deviance,mu1_row=mu1,mu2_row=mu2,get_r_map_target_p](int64_t n, size_t path_size) {
             if (n == 0)
                 return 1.1;
 
+            double r_in = r_in_row;
+            double r_out = r_out_row;
+            double target_p = target_p_row;
+            double lscaling_base = lscaling_base_row;
+            double mu1 = mu1_row;
+            double mu2 = mu2_row;
+            if (path_size > 1) {
+                std::lock_guard<std::mutex> lock(agg_mu);
+                auto find = path_params.find(path_size);
+                if (find == path_params.end())
+                    find = path_params.emplace(path_size, get_r_map_target_p(path_size)).first;
+
+                r_in = find->second.first * num_labels_in;
+                r_out = find->second.first * num_labels_out;
+                target_p = find->second.second;
+                lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+                mu1 = r_in * (1.0 - target_p) / target_p;
+                mu2 = r_out * (1.0 - target_p) / target_p;
+            }
+
             double lscaling = lgamma(n + 1) + lscaling_base;
             auto get_pmf = [&](int64_t s) {
                 int64_t t = n - s;
@@ -824,10 +887,30 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::min(1.0, pval);
         };
 
-        compute_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t in_sum, int64_t out_sum, const auto &row) {
+        compute_pval = [&,lscaling_base_row=lscaling_base,r_in_row=r_in,r_out_row=r_out,target_p_row=target_p,get_deviance,mu1_row=mu1,mu2_row=mu2,get_r_map_target_p](int64_t in_sum, int64_t out_sum, const auto &row, size_t path_size) {
             if (row.empty())
                 return 1.1;
 
+            double r_in = r_in_row;
+            double r_out = r_out_row;
+            double target_p = target_p_row;
+            double lscaling_base = lscaling_base_row;
+            double mu1 = mu1_row;
+            double mu2 = mu2_row;
+            if (path_size > 1) {
+                std::lock_guard<std::mutex> lock(agg_mu);
+                auto find = path_params.find(path_size);
+                if (find == path_params.end())
+                    find = path_params.emplace(path_size, get_r_map_target_p(path_size)).first;
+
+                r_in = find->second.first * num_labels_in;
+                r_out = find->second.first * num_labels_out;
+                target_p = find->second.second;
+                lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+                mu1 = r_in * (1.0 - target_p) / target_p;
+                mu2 = r_out * (1.0 - target_p) / target_p;
+            }
+
             int64_t n = in_sum + out_sum;
 
             double lscaling = lgamma(n + 1) + lscaling_base;
@@ -932,16 +1015,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        double pval = std::min(compute_pval(in_sum, 0, best_in_row),
-                               compute_pval(0, out_sum, best_out_row));
+        double pval = std::min(compute_pval(in_sum, 0, best_in_row, 1),
+                               compute_pval(0, out_sum, best_out_row, 1));
         common::logger->trace("Best achievable p-value: {}\tin_sum: {}\tout_sum: {}", pval, in_sum, out_sum);
         if (pval >= config.family_wise_error_rate) {
             common::logger->error("Best achievable p-value is too big. Use poisson_exact instead");
             throw std::domain_error("Too few samples");
         }
     } else if (config.test_type == "notest") {
-        compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
-        compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };
+        compute_min_pval = [&](int64_t n, size_t) { return n > 0 ? 0.0 : 1.1; };
+        compute_pval = [&](int64_t, int64_t, const auto &row, size_t) { return row.size() ? 0.0 : 1.1; };
     } else {
         throw std::runtime_error("Test not implemented");
     }
@@ -952,11 +1035,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::mutex pval_mu;
     std::atomic_thread_fence(std::memory_order_release);
 
+    std::atomic<size_t> num_unitigs(0);
     if (config.test_by_unitig) {
         clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
             if (ex)
                 return;
 
+            ++num_unitigs;
+            unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
+
             std::vector<int64_t> row_counts(groups.size());
             for (node_index node : path) {
                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
@@ -1001,12 +1088,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double pval;
             double pval_min;
             try {
-                pval_min = compute_min_pval(in_sum + out_sum);
+                pval_min = compute_min_pval(in_sum + out_sum, path.size());
 
                 if (pval_min >= 1.1)
                     return;
 
-                pval = compute_pval(in_sum, out_sum, merged_row);
+                pval = compute_pval(in_sum, out_sum, merged_row, path.size());
             } catch (...) {
                 std::lock_guard<std::mutex> lock(pval_mu);
                 ex = std::current_exception();
@@ -1093,12 +1180,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double pval;
             double pval_min;
             try {
-                pval_min = compute_min_pval(in_sum + out_sum);
+                pval_min = compute_min_pval(in_sum + out_sum, 1);
 
                 if (pval_min >= 1.1)
                     return;
 
-                pval = compute_pval(in_sum, out_sum, row);
+                pval = compute_pval(in_sum, out_sum, row, 1);
             } catch (...) {
                 std::lock_guard<std::mutex> lock(pval_mu);
                 ex = std::current_exception();

From 54ea4d0b72f2aa8e4ee03ea1665134b3472118ec Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Jun 2024 16:26:00 +0200
Subject: [PATCH 224/410] Revert "test"

This reverts commit 35828b1e1c973cfb81b4108f59cd8b1716453bf4.
---
 .../src/graph/annotated_graph_algorithm.cpp   | 207 +++++-------------
 1 file changed, 60 insertions(+), 147 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a759d596f1..fddf6d1adb 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -364,6 +364,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, true);
 
     std::shared_ptr<MaskedDeBruijnGraph> clean_masked_graph;
+    std::atomic<size_t> num_unitigs(0);
     if (config.test_by_unitig) {
         clean_masked_graph = std::make_shared<MaskedDeBruijnGraph>(
             graph_ptr,
@@ -375,36 +376,36 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
         );
 
-        // for (size_t j = 0; j < groups.size(); ++j) {
-        //     hists_map[j].clear();
-        // }
-
-        // std::atomic_thread_fence(std::memory_order_release);
-        // clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-        //     std::vector<uint64_t> unitig_sums(groups.size(), 0);
-        //     std::vector<bool> all_below_cutoff(groups.size(), true);
-        //     for (node_index node : path) {
-        //         uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-        //         for (size_t j = 0; j < groups.size(); ++j) {
-        //             if (uint64_t c = get_value(row_i, j)) {
-        //                 unitig_sums[j] += c;
-        //                 if (c >= min_counts[j])
-        //                     all_below_cutoff[j] = false;
-        //             }
-        //         }
-        //     }
-
-        //     unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
-        //     num_unitigs.fetch_add(1, MO_RELAXED);
-
-        //     std::lock_guard<std::mutex> lock(agg_mu);
-        //     for (size_t j = 0; j < groups.size(); ++j) {
-        //         ++hists_map[j][unitig_sums[j]];
-        //     }
-        // }, num_threads);
-        // std::atomic_thread_fence(std::memory_order_acquire);
-
-        // nelem = num_unitigs.load();
+        for (size_t j = 0; j < groups.size(); ++j) {
+            hists_map[j].clear();
+        }
+
+        std::atomic_thread_fence(std::memory_order_release);
+        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+            std::vector<uint64_t> unitig_sums(groups.size(), 0);
+            std::vector<bool> all_below_cutoff(groups.size(), true);
+            for (node_index node : path) {
+                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    if (uint64_t c = get_value(row_i, j)) {
+                        unitig_sums[j] += c;
+                        if (c >= min_counts[j])
+                            all_below_cutoff[j] = false;
+                    }
+                }
+            }
+
+            unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
+            num_unitigs.fetch_add(1, MO_RELAXED);
+
+            std::lock_guard<std::mutex> lock(agg_mu);
+            for (size_t j = 0; j < groups.size(); ++j) {
+                ++hists_map[j][unitig_sums[j]];
+            }
+        }, num_threads);
+        std::atomic_thread_fence(std::memory_order_acquire);
+
+        nelem = num_unitigs.load();
     }
 
     std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
@@ -479,15 +480,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
 
     VectorMap<size_t, size_t> m;
-    std::function<double(int64_t, int64_t, const PairContainer&, size_t)> compute_pval;
-    std::function<double(int64_t, size_t)> compute_min_pval;
+    std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
+    std::function<double(int64_t)> compute_min_pval;
 
     std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps;
-    VectorMap<size_t, std::pair<double, double>> path_params;
 
     common::logger->trace("Test: {}", config.test_type);
     if (config.test_type == "poisson_exact") {
-        compute_min_pval = [&](int64_t n, size_t) {
+        compute_min_pval = [&](int64_t n) {
             if (n == 0)
                 return 1.1;
 
@@ -530,7 +530,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return pval;
         };
 
-        compute_pval = [&](int64_t in_sum, int64_t out_sum, const auto &row, size_t) {
+        compute_pval = [&](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
                 return 1.1;
 
@@ -575,7 +575,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     } else if (config.test_type == "fisher") {
         double lbase_shared = lgamma(in_kmers + 1) + lgamma(out_kmers + 1) - lgamma(total_kmers + 1);
 
-        compute_min_pval = [&,lbase_shared](int64_t m1, size_t) {
+        compute_min_pval = [&,lbase_shared](int64_t m1) {
             if (m1 == 0)
                 return 1.1;
 
@@ -597,7 +597,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::min(get_pval(0), get_pval(std::min(in_kmers, m1)));
         };
 
-        compute_pval = [&,lbase_shared](int64_t in_sum, int64_t out_sum, const auto &row, size_t) {
+        compute_pval = [&,lbase_shared](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
                 return 1.1;
 
@@ -747,63 +747,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             throw std::domain_error("Fit failed");
         }
 
-        auto get_r_map_target_p = [&,target_mu,matrix_size,hist_its,target_sum](int64_t c = 1) {
-            double r_map = 0;
-            double local_target_mu = 0;
-            if (c == 1) {
-                local_target_mu = target_mu * c;
-                double local_r_guess = target_mu2 * c / (target_var * c - target_mu);
-                r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
-                    double dl = (log(r) - log(r + local_target_mu) - boost::math::digamma(r)) * matrix_size;
-                    double ddl = (1.0 / r - 1.0 / (r + local_target_mu) - boost::math::trigamma(r)) * matrix_size;
-                    for (size_t j = 0; j < groups.size(); ++j) {
-                        double f = target_sum / sums[j] * c;
-                        std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
-                            const auto &[k, c] = a;
-                            dl += boost::math::digamma(f * k + r) * c;
-                            ddl += boost::math::trigamma(f * k + r) * c;
-                        });
-                    }
-                    return std::make_pair(dl, ddl);
-                }, local_r_guess, std::numeric_limits<double>::min(), real_sum * c, 30);
-            } else {
-                double local_target_var = 0.0;
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
-                        auto [k, cnt] = a;
-                        k *= c;
-                        if (count_maps.size())
-                            cnt = count_maps[j].find(cnt)->second.first;
-
-                        local_target_mu += k * cnt;
-                        local_target_var += k * k * cnt;
-                    });
-                }
-                local_target_mu /= matrix_size;
-                double local_target_mu2 = local_target_mu * local_target_mu;
-                local_target_var = (local_target_var - local_target_mu2 * matrix_size) / (matrix_size - 1);
-                double local_r_guess = local_target_mu2 / (local_target_var - local_target_mu);
-                r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
-                    double dl = (log(r) - log(r + local_target_mu) - boost::math::digamma(r)) * matrix_size;
-                    double ddl = (1.0 / r - 1.0 / (r + local_target_mu) - boost::math::trigamma(r)) * matrix_size;
-                    for (size_t j = 0; j < groups.size(); ++j) {
-                        std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
-                            auto [k, cnt] = a;
-                            k *= c;
-                            if (count_maps.size())
-                                cnt = count_maps[j].find(cnt)->second.first;
-                            dl += boost::math::digamma(k + r) * cnt;
-                            ddl += boost::math::trigamma(k + r) * cnt;
-                        });
-                    }
-                    return std::make_pair(dl, ddl);
-                }, local_r_guess, std::numeric_limits<double>::min(), real_sum * c, 30);
+        double r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
+            double dl = (log(r) - log(r + target_mu) - boost::math::digamma(r)) * matrix_size;
+            double ddl = (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r)) * matrix_size;
+            for (size_t j = 0; j < groups.size(); ++j) {
+                double f = target_sum / sums[j];
+                std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
+                    const auto &[k, c] = a;
+                    dl += boost::math::digamma(f * k + r) * c;
+                    ddl += boost::math::trigamma(f * k + r) * c;
+                });
             }
-            double target_p = r_map / (r_map + local_target_mu);
-            return std::make_pair(r_map, target_p);
-        };
-
-        auto [r_map, target_p] = get_r_map_target_p();
+            return std::make_pair(dl, ddl);
+        }, r_guess, std::numeric_limits<double>::min(), real_sum, 30);
+        double target_p = r_map / (r_map + target_mu);
         double fit_mu = r_map * (1.0 - target_p) / target_p;
         double fit_var = fit_mu / target_p;
         common::logger->trace("Common params: r_guess: {}\tr: {}\tp: {}\tmu: {}\tvar: {}",
@@ -820,30 +777,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (fit_var / fit_mu - 1.0 < 1e-5)
             common::logger->warn("Fit parameters are close to a Poisson distribution");
 
-        compute_min_pval = [&,lscaling_base_row=lscaling_base,r_in_row=r_in,r_out_row=r_out,target_p_row=target_p,get_deviance,mu1_row=mu1,mu2_row=mu2,get_r_map_target_p](int64_t n, size_t path_size) {
+        compute_min_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t n) {
             if (n == 0)
                 return 1.1;
 
-            double r_in = r_in_row;
-            double r_out = r_out_row;
-            double target_p = target_p_row;
-            double lscaling_base = lscaling_base_row;
-            double mu1 = mu1_row;
-            double mu2 = mu2_row;
-            if (path_size > 1) {
-                std::lock_guard<std::mutex> lock(agg_mu);
-                auto find = path_params.find(path_size);
-                if (find == path_params.end())
-                    find = path_params.emplace(path_size, get_r_map_target_p(path_size)).first;
-
-                r_in = find->second.first * num_labels_in;
-                r_out = find->second.first * num_labels_out;
-                target_p = find->second.second;
-                lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
-                mu1 = r_in * (1.0 - target_p) / target_p;
-                mu2 = r_out * (1.0 - target_p) / target_p;
-            }
-
             double lscaling = lgamma(n + 1) + lscaling_base;
             auto get_pmf = [&](int64_t s) {
                 int64_t t = n - s;
@@ -887,30 +824,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::min(1.0, pval);
         };
 
-        compute_pval = [&,lscaling_base_row=lscaling_base,r_in_row=r_in,r_out_row=r_out,target_p_row=target_p,get_deviance,mu1_row=mu1,mu2_row=mu2,get_r_map_target_p](int64_t in_sum, int64_t out_sum, const auto &row, size_t path_size) {
+        compute_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
                 return 1.1;
 
-            double r_in = r_in_row;
-            double r_out = r_out_row;
-            double target_p = target_p_row;
-            double lscaling_base = lscaling_base_row;
-            double mu1 = mu1_row;
-            double mu2 = mu2_row;
-            if (path_size > 1) {
-                std::lock_guard<std::mutex> lock(agg_mu);
-                auto find = path_params.find(path_size);
-                if (find == path_params.end())
-                    find = path_params.emplace(path_size, get_r_map_target_p(path_size)).first;
-
-                r_in = find->second.first * num_labels_in;
-                r_out = find->second.first * num_labels_out;
-                target_p = find->second.second;
-                lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
-                mu1 = r_in * (1.0 - target_p) / target_p;
-                mu2 = r_out * (1.0 - target_p) / target_p;
-            }
-
             int64_t n = in_sum + out_sum;
 
             double lscaling = lgamma(n + 1) + lscaling_base;
@@ -1015,16 +932,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        double pval = std::min(compute_pval(in_sum, 0, best_in_row, 1),
-                               compute_pval(0, out_sum, best_out_row, 1));
+        double pval = std::min(compute_pval(in_sum, 0, best_in_row),
+                               compute_pval(0, out_sum, best_out_row));
         common::logger->trace("Best achievable p-value: {}\tin_sum: {}\tout_sum: {}", pval, in_sum, out_sum);
         if (pval >= config.family_wise_error_rate) {
             common::logger->error("Best achievable p-value is too big. Use poisson_exact instead");
             throw std::domain_error("Too few samples");
         }
     } else if (config.test_type == "notest") {
-        compute_min_pval = [&](int64_t n, size_t) { return n > 0 ? 0.0 : 1.1; };
-        compute_pval = [&](int64_t, int64_t, const auto &row, size_t) { return row.size() ? 0.0 : 1.1; };
+        compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
+        compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };
     } else {
         throw std::runtime_error("Test not implemented");
     }
@@ -1035,15 +952,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::mutex pval_mu;
     std::atomic_thread_fence(std::memory_order_release);
 
-    std::atomic<size_t> num_unitigs(0);
     if (config.test_by_unitig) {
         clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
             if (ex)
                 return;
 
-            ++num_unitigs;
-            unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
-
             std::vector<int64_t> row_counts(groups.size());
             for (node_index node : path) {
                 uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
@@ -1088,12 +1001,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double pval;
             double pval_min;
             try {
-                pval_min = compute_min_pval(in_sum + out_sum, path.size());
+                pval_min = compute_min_pval(in_sum + out_sum);
 
                 if (pval_min >= 1.1)
                     return;
 
-                pval = compute_pval(in_sum, out_sum, merged_row, path.size());
+                pval = compute_pval(in_sum, out_sum, merged_row);
             } catch (...) {
                 std::lock_guard<std::mutex> lock(pval_mu);
                 ex = std::current_exception();
@@ -1180,12 +1093,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double pval;
             double pval_min;
             try {
-                pval_min = compute_min_pval(in_sum + out_sum, 1);
+                pval_min = compute_min_pval(in_sum + out_sum);
 
                 if (pval_min >= 1.1)
                     return;
 
-                pval = compute_pval(in_sum, out_sum, row, 1);
+                pval = compute_pval(in_sum, out_sum, row);
             } catch (...) {
                 std::lock_guard<std::mutex> lock(pval_mu);
                 ex = std::current_exception();

From 50664e4fcc2b0a2f29f8d6603243ccff6dfbe5ee Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Jun 2024 20:09:58 +0200
Subject: [PATCH 225/410] simplier unitig-based test. code cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 381 ++++++++----------
 .../src/graph/annotated_graph_algorithm.hpp   |   1 -
 .../test_annotated_graph_algorithm.cpp        |  11 -
 3 files changed, 158 insertions(+), 235 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fddf6d1adb..fd6793837c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -13,6 +13,7 @@
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
+#include <boost/math/distributions/cauchy.hpp>
 
 #include "common/logger.hpp"
 #include "common/vectors/bitmap.hpp"
@@ -118,14 +119,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     callback(row_i, row);
                 });
             },
-            [&](uint64_t row_i, uint64_t col_j) -> value_type {
-                const auto &col = *columns_all[col_j];
-                const auto &col_vals = *column_values_all[col_j];
-                if (uint64_t r = col.conditional_rank1(row_i))
-                    return col_vals[r - 1];
-
-                return 0;
-            },
             groups,
             config, num_threads, tmp_dir, num_parallel_files,
             [&]() {
@@ -192,15 +185,6 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
                     callback(row_i, int_matrix->get_row_values(row_ids)[0]);
                 }
             },
-            [&](uint64_t row_i, uint64_t col_j) -> value_type {
-                std::vector<Row> row_ids { Row(row_i) };
-                for (const auto &[j, c] : int_matrix->get_row_values(row_ids)[0]) {
-                    if (j == col_j)
-                        return c;
-                }
-
-                return 0;
-            },
             groups,
             config, num_threads, tmp_dir, num_threads
         );
@@ -221,15 +205,6 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
                     callback(row_i, container);
                 }
             },
-            [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
-                std::vector<Row> row_ids { Row(row_i) };
-                for (auto j : matrix.get_rows(row_ids)[0]) {
-                    if (j == col_j)
-                        return 1;
-                }
-
-                return 0;
-            },
             groups,
             config, num_threads, tmp_dir, num_threads
         );
@@ -257,7 +232,6 @@ template <typename value_type, class PValStorage>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, value_type>>)>&)> &generate_rows,
-                         const std::function<value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &get_value,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads,
@@ -359,55 +333,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     size_t nelem = sdsl::util::cnt_one_bits(kept);
 
-    sdsl::bit_vector unitig_start;
-    if (config.test_by_unitig)
-        unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, true);
-
-    std::shared_ptr<MaskedDeBruijnGraph> clean_masked_graph;
-    std::atomic<size_t> num_unitigs(0);
-    if (config.test_by_unitig) {
-        clean_masked_graph = std::make_shared<MaskedDeBruijnGraph>(
-            graph_ptr,
-            [&](node_index node) {
-                return node != DeBruijnGraph::npos
-                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
-            },
-            true,
-            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-        );
-
-        for (size_t j = 0; j < groups.size(); ++j) {
-            hists_map[j].clear();
-        }
-
-        std::atomic_thread_fence(std::memory_order_release);
-        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-            std::vector<uint64_t> unitig_sums(groups.size(), 0);
-            std::vector<bool> all_below_cutoff(groups.size(), true);
-            for (node_index node : path) {
-                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    if (uint64_t c = get_value(row_i, j)) {
-                        unitig_sums[j] += c;
-                        if (c >= min_counts[j])
-                            all_below_cutoff[j] = false;
-                    }
-                }
-            }
-
-            unset_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
-            num_unitigs.fetch_add(1, MO_RELAXED);
-
-            std::lock_guard<std::mutex> lock(agg_mu);
-            for (size_t j = 0; j < groups.size(); ++j) {
-                ++hists_map[j][unitig_sums[j]];
-            }
-        }, num_threads);
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-        nelem = num_unitigs.load();
-    }
-
     std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
     for (size_t j = 0; j < hists.size(); ++j) {
         hists[j] = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(hists_map[j].values_container());
@@ -465,16 +390,38 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Allocating p-value storage");
     auto nullpval = bit_cast<uint64_t>(double(1.1));
     std::unique_ptr<utils::TempFile> tmp_file;
+    std::unique_ptr<utils::TempFile> tmp_file_min;
     PValStorage pvals;
     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
         tmp_file = std::make_unique<utils::TempFile>(tmp_dir);
         pvals = sdsl::int_vector_buffer<64>(tmp_file->name(), std::ios::out);
     }
 
+    std::unique_ptr<PValStorage> pvals_min;
+
     for (size_t i = 0; i <= graph_ptr->max_index(); ++i) {
         pvals.push_back(nullpval);
     }
 
+    std::unique_ptr<PValStorage> eff_size;
+    std::unique_ptr<utils::TempFile> tmp_file_eff;
+    if (config.test_by_unitig) {
+        pvals_min = std::make_unique<PValStorage>();
+        eff_size = std::make_unique<PValStorage>();
+        if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+            tmp_file_min = std::make_unique<utils::TempFile>(tmp_dir);
+            pvals_min = std::make_unique<sdsl::int_vector_buffer<64>>(tmp_file_min->name(), std::ios::out);
+
+            tmp_file_eff = std::make_unique<utils::TempFile>(tmp_dir);
+            eff_size = std::make_unique<sdsl::int_vector_buffer<64>>(tmp_file_eff->name(), std::ios::out);
+        }
+
+        for (size_t i = 0; i <= graph_ptr->max_index(); ++i) {
+            pvals_min->push_back(nullpval);
+            eff_size->push_back(0);
+        }
+    }
+
     common::logger->trace("Allocating k-mer bitmasks");
     sdsl::bit_vector indicator_in(graph_ptr->max_index() + 1, false);
     sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
@@ -952,199 +899,191 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::mutex pval_mu;
     std::atomic_thread_fence(std::memory_order_release);
 
-    if (config.test_by_unitig) {
-        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-            if (ex)
-                return;
-
-            std::vector<int64_t> row_counts(groups.size());
-            for (node_index node : path) {
-                uint64_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    if (uint64_t c = get_value(row_i, j))
-                        row_counts[j] += c;
-                }
-            }
+    generate_rows([&](uint64_t row_i, const auto &raw_row) {
+        if (ex)
+            return;
 
-            PairContainer merged_row;
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
+        int64_t in_sum = 0;
+        int64_t out_sum = 0;
+        int64_t out_stat_int = 0;
+        bool in_kmer = false;
+        bool out_kmer = false;
+        PairContainer row;
+        if (kept[row_i]) {
             size_t count_in = 0;
             size_t count_out = 0;
-            bool in_kmer = false;
-            bool out_kmer = false;
-            for (size_t j = 0; j < groups.size(); ++j) {
-                if (row_counts[j] > 0) {
-                    if (count_maps.size())
-                        row_counts[j] = count_maps[j].find(row_counts[j])->second.first;
-
-                    merged_row.emplace_back(j, row_counts[j]);
-                    if (groups[j]) {
-                        out_sum += row_counts[j];
-                        ++count_out;
-                    } else {
-                        in_sum += row_counts[j];
-                        ++count_in;
-                    }
+            for (const auto &[j, raw_c] : raw_row) {
+                uint64_t c = raw_c;
+                if (count_maps.size())
+                    c = count_maps[j].find(c)->second.first;
+
+                row.emplace_back(j, c);
+                if (groups[j]) {
+                    out_sum += c;
+                    ++count_out;
+                } else {
+                    in_sum += c;
+                    ++count_in;
                 }
             }
 
             if (count_in + count_out >= config.min_recurrence) {
                 double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+                if (out_stat != 0)
+                    out_stat_int = out_stat > 0 ? ceil(out_stat) : floor(out_stat);
+
                 in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
                 out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
             }
+        }
 
-            if (!in_kmer && !out_kmer)
-                merged_row.clear();
+        if (!in_kmer && !out_kmer)
+            row.clear();
 
-            double pval;
-            double pval_min;
-            try {
-                pval_min = compute_min_pval(in_sum + out_sum);
+        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
 
-                if (pval_min >= 1.1)
-                    return;
+        double pval;
+        double pval_min;
+        try {
+            pval_min = compute_min_pval(in_sum + out_sum);
 
-                pval = compute_pval(in_sum, out_sum, merged_row);
-            } catch (...) {
-                std::lock_guard<std::mutex> lock(pval_mu);
-                ex = std::current_exception();
+            if (pval_min >= 1.1)
                 return;
-            }
 
-            if (pval >= 1.1)
-                return;
+            pval = compute_pval(in_sum, out_sum, row);
+        } catch (...) {
+            std::lock_guard<std::mutex> lock(pval_mu);
+            ex = std::current_exception();
+            return;
+        }
 
-            uint64_t k = std::numeric_limits<uint64_t>::max();
+        if (pval >= 1.1)
+            return;
 
-            if (pval_min - pval > 1e-10) {
-                common::logger->error("Min p-val estimate too high: min {} > cur {}\ttest: {}", pval_min, pval, config.test_type);
-                throw std::runtime_error("Test failed");
-            }
+        uint64_t k = std::numeric_limits<uint64_t>::max();
 
-            if (pval_min >= config.family_wise_error_rate) {
-                k = 0;
-            } else if (pval_min > 0) {
-                double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
-                if (lkd <= 64)
-                    k = pow(2.0, lkd);
+        if (pval_min - pval > 1e-10) {
+            common::logger->error("Min p-val estimate too high: min {} > cur {}\ttest: {}", pval_min, pval, config.test_type);
+            throw std::runtime_error("Test failed");
+        }
 
-                if (k == 0) {
-                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
-                    throw std::runtime_error("Min failed");
-                }
-            }
+        if (config.test_by_unitig) {
+            assert(pvals_min);
+            (*pvals_min)[node] = bit_cast<uint64_t>(pval_min);
+        }
 
-            if (in_kmer != out_kmer && pval < config.family_wise_error_rate) {
-                for (size_t i = 0; i < path.size(); ++i) {
-                    set_bit((in_kmer ? indicator_in : indicator_out).data(), path[i], parallel, MO_RELAXED);
-                }
+        if (pval_min >= config.family_wise_error_rate) {
+            k = 0;
+        } else if (pval_min > 0) {
+            double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
+            if (lkd <= 64)
+                k = pow(2.0, lkd);
+
+            if (k == 0) {
+                common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
+                throw std::runtime_error("Min failed");
             }
+        }
 
-            if (config.test_type != "notest") {
+        if (in_kmer != out_kmer && (pval < config.family_wise_error_rate || config.test_by_unitig)) {
+            set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
+            if (config.test_by_unitig) {
                 std::lock_guard<std::mutex> lock(pval_mu);
-                ++m[k];
-                for (size_t i = 0; i < path.size(); ++i) {
-                    pvals[path[i]] = bit_cast<uint64_t>(pval);
-                }
+                (*eff_size)[node] = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
             }
-        }, num_threads);
-    } else {
-        generate_rows([&](uint64_t row_i, const auto &raw_row) {
-            if (ex)
-                return;
+        }
 
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-            bool in_kmer = false;
-            bool out_kmer = false;
-            PairContainer row;
-            if (kept[row_i]) {
-                size_t count_in = 0;
-                size_t count_out = 0;
-                for (const auto &[j, raw_c] : raw_row) {
-                    uint64_t c = raw_c;
-                    if (count_maps.size())
-                        c = count_maps[j].find(c)->second.first;
-
-                    row.emplace_back(j, c);
-                    if (groups[j]) {
-                        out_sum += c;
-                        ++count_out;
-                    } else {
-                        in_sum += c;
-                        ++count_in;
-                    }
-                }
+        if (config.test_type != "notest") {
+            std::lock_guard<std::mutex> lock(pval_mu);
+            ++m[k];
+            pvals[node] = bit_cast<uint64_t>(pval);
+        }
+    });
 
-                if (count_in + count_out >= config.min_recurrence) {
-                    double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-                    in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-                    out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
-                }
-            }
+    std::atomic_thread_fence(std::memory_order_acquire);
 
-            if (!in_kmer && !out_kmer)
-                row.clear();
+    if (ex)
+        std::rethrow_exception(ex);
 
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+    deallocate();
 
-            double pval;
-            double pval_min;
-            try {
-                pval_min = compute_min_pval(in_sum + out_sum);
+    if (config.test_by_unitig) {
+        common::logger->trace("Combining p-values within unitigs");
+        m.clear();
+        MaskedDeBruijnGraph clean_masked_graph(
+            graph_ptr,
+            [&](node_index node) {
+                return node != DeBruijnGraph::npos
+                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
+            },
+            true,
+            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+        );
 
-                if (pval_min >= 1.1)
-                    return;
+        auto dist = boost::math::cauchy();
 
-                pval = compute_pval(in_sum, out_sum, row);
-            } catch (...) {
+        std::atomic_thread_fence(std::memory_order_release);
+        clean_masked_graph.call_unitigs([&](const std::string&, const auto &path) {
+            double stat = 0;
+            double stat_min = 0;
+            int64_t comb_eff_size = 0;
+            {
                 std::lock_guard<std::mutex> lock(pval_mu);
-                ex = std::current_exception();
-                return;
+                for (node_index node : path) {
+                    comb_eff_size += bit_cast<int64_t, uint64_t>((*eff_size)[node]);
+                    double pval = bit_cast<double, uint64_t>(pvals[node]);
+                    double pval_min = bit_cast<double, uint64_t>((*pvals_min)[node]);
+                    stat += tan((0.5 - pval) * M_PI);
+                    stat_min += tan((0.5 - pval_min) * M_PI);
+                }
+            }
+            stat /= path.size();
+            stat_min /= path.size();
+            double comb_pval = boost::math::cdf(boost::math::complement(dist, stat));
+            for (node_index node : path) {
+                if (comb_pval >= config.family_wise_error_rate || comb_eff_size == 0) {
+                    unset_bit(indicator_in.data(), node, parallel, MO_RELAXED);
+                    unset_bit(indicator_out.data(), node, parallel, MO_RELAXED);
+                } else if (comb_eff_size > 0) {
+                    set_bit(indicator_in.data(), node, parallel, MO_RELAXED);
+                    unset_bit(indicator_out.data(), node, parallel, MO_RELAXED);
+                } else {
+                    unset_bit(indicator_in.data(), node, parallel, MO_RELAXED);
+                    set_bit(indicator_out.data(), node, parallel, MO_RELAXED);
+                }
             }
 
-            if (pval >= 1.1)
-                return;
+            double comb_pval_min = boost::math::cdf(boost::math::complement(dist, stat_min));
 
             uint64_t k = std::numeric_limits<uint64_t>::max();
-
-            if (pval_min - pval > 1e-10) {
-                common::logger->error("Min p-val estimate too high: min {} > cur {}\ttest: {}", pval_min, pval, config.test_type);
-                throw std::runtime_error("Test failed");
-            }
-
-            if (pval_min >= config.family_wise_error_rate) {
+            if (comb_pval_min >= config.family_wise_error_rate) {
                 k = 0;
-            } else if (pval_min > 0) {
-                double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
+            } else if (comb_pval_min > 0) {
+                double lkd = log2(config.family_wise_error_rate) - log2(comb_pval_min);
                 if (lkd <= 64)
                     k = pow(2.0, lkd);
 
                 if (k == 0) {
-                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
+                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, comb_pval_min, comb_pval);
                     throw std::runtime_error("Min failed");
                 }
             }
 
-            if (in_kmer != out_kmer && pval < config.family_wise_error_rate)
-                set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
-
-            if (config.test_type != "notest") {
-                std::lock_guard<std::mutex> lock(pval_mu);
-                ++m[k];
-                pvals[node] = bit_cast<uint64_t>(pval);
-            }
-        });
-    }
+            uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
 
-    std::atomic_thread_fence(std::memory_order_acquire);
+            std::lock_guard<std::mutex> lock(pval_mu);
+            ++m[k];
 
-    if (ex)
-        std::rethrow_exception(ex);
+            pvals[path[0]] = comb_pval_enc;
+            for (size_t i = 1; i < path.size(); ++i) {
+                pvals[path[i]] = nullpval;
+            }
+        }, num_threads);
+        std::atomic_thread_fence(std::memory_order_acquire);
 
-    deallocate();
+        eff_size.reset();
+        pvals_min.reset();
+    }
 
     if (config.test_type != "notest") {
         uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
@@ -1187,8 +1126,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
-    call_ones(unitig_start, [&](node_index node) { pvals[node] = nullpval; });
-
     auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
         graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
@@ -1206,7 +1143,6 @@ template
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
                          const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>)>&)> &generate_rows,
-                         const std::function<typename sdsl::int_vector<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
@@ -1219,7 +1155,6 @@ template
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
                          const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>)>&)> &generate_rows,
-                         const std::function<typename sdsl::int_vector<>::value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &,
                          const std::vector<bool> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 64761f2068..181ea99a2e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -57,7 +57,6 @@ template <typename value_type, class PValStorage>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, value_type>>)>&)> &generate_rows,
-                         const std::function<value_type(uint64_t /* row_i */, uint64_t /* col_j */)> &get_value,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads = 1,
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index c3ba9e1fc3..5f6428c994 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -100,9 +100,6 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
                             callback(row_i, row);
                         });
                     },
-                    [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
-                        return (*columns[col_j])[row_i];
-                    },
                     groups,
                     config, num_threads, test_dump_basename, num_threads,
                     [&]() {
@@ -235,14 +232,6 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                                 callback(row_i, row);
                             });
                         },
-                        [&](uint64_t row_i, uint64_t col_j) -> uint64_t {
-                            const auto &col = *columns[col_j];
-                            const auto &col_vals = *column_values[col_j];
-                            if (uint64_t r = col.conditional_rank1(row_i))
-                                return col_vals[r - 1];
-
-                            return 0;
-                        },
                         groups,
                         config, num_threads, test_dump_basename, num_threads
                     );

From 20c3d513db866b0b7a1517729e26323ac6711a5e Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Jun 2024 20:24:25 +0200
Subject: [PATCH 226/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 75 ++++++++++---------
 1 file changed, 38 insertions(+), 37 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fd6793837c..57cd0d6288 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -406,6 +406,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::unique_ptr<PValStorage> eff_size;
     std::unique_ptr<utils::TempFile> tmp_file_eff;
     if (config.test_by_unitig) {
+        common::logger->trace("Allocating p-value storage");
         pvals_min = std::make_unique<PValStorage>();
         eff_size = std::make_unique<PValStorage>();
         if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
@@ -422,9 +423,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
-    common::logger->trace("Allocating k-mer bitmasks");
-    sdsl::bit_vector indicator_in(graph_ptr->max_index() + 1, false);
-    sdsl::bit_vector indicator_out(graph_ptr->max_index() + 1, false);
+    sdsl::bit_vector indicator_in;
+    sdsl::bit_vector indicator_out;
+
+    if (!config.test_by_unitig) {
+        common::logger->trace("Allocating k-mer bitmasks");
+        indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+        indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+    }
 
     VectorMap<size_t, size_t> m;
     std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
@@ -960,8 +966,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (pval >= 1.1)
             return;
 
-        uint64_t k = std::numeric_limits<uint64_t>::max();
-
         if (pval_min - pval > 1e-10) {
             common::logger->error("Min p-val estimate too high: min {} > cur {}\ttest: {}", pval_min, pval, config.test_type);
             throw std::runtime_error("Test failed");
@@ -969,35 +973,35 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         if (config.test_by_unitig) {
             assert(pvals_min);
+            std::lock_guard<std::mutex> lock(pval_mu);
             (*pvals_min)[node] = bit_cast<uint64_t>(pval_min);
-        }
+            pvals[node] = bit_cast<uint64_t>(pval);
+            (*eff_size)[node] = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
+        } else {
+            uint64_t k = std::numeric_limits<uint64_t>::max();
+            if (pval_min >= config.family_wise_error_rate) {
+                k = 0;
+            } else if (pval_min > 0) {
+                double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
+                if (lkd <= 64)
+                    k = pow(2.0, lkd);
 
-        if (pval_min >= config.family_wise_error_rate) {
-            k = 0;
-        } else if (pval_min > 0) {
-            double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
-            if (lkd <= 64)
-                k = pow(2.0, lkd);
+                if (k == 0) {
+                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
+                    throw std::runtime_error("Min failed");
+                }
+            }
 
-            if (k == 0) {
-                common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
-                throw std::runtime_error("Min failed");
+            if (in_kmer != out_kmer && (pval < config.family_wise_error_rate || config.test_by_unitig)) {
+                set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
             }
-        }
 
-        if (in_kmer != out_kmer && (pval < config.family_wise_error_rate || config.test_by_unitig)) {
-            set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
-            if (config.test_by_unitig) {
+            if (config.test_type != "notest") {
                 std::lock_guard<std::mutex> lock(pval_mu);
-                (*eff_size)[node] = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
+                ++m[k];
+                pvals[node] = bit_cast<uint64_t>(pval);
             }
         }
-
-        if (config.test_type != "notest") {
-            std::lock_guard<std::mutex> lock(pval_mu);
-            ++m[k];
-            pvals[node] = bit_cast<uint64_t>(pval);
-        }
     });
 
     std::atomic_thread_fence(std::memory_order_acquire);
@@ -1008,8 +1012,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     deallocate();
 
     if (config.test_by_unitig) {
+        common::logger->trace("Allocating k-mer bitmasks");
+        indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+        indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+
         common::logger->trace("Combining p-values within unitigs");
-        m.clear();
         MaskedDeBruijnGraph clean_masked_graph(
             graph_ptr,
             [&](node_index node) {
@@ -1040,16 +1047,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             stat /= path.size();
             stat_min /= path.size();
             double comb_pval = boost::math::cdf(boost::math::complement(dist, stat));
-            for (node_index node : path) {
-                if (comb_pval >= config.family_wise_error_rate || comb_eff_size == 0) {
-                    unset_bit(indicator_in.data(), node, parallel, MO_RELAXED);
-                    unset_bit(indicator_out.data(), node, parallel, MO_RELAXED);
-                } else if (comb_eff_size > 0) {
-                    set_bit(indicator_in.data(), node, parallel, MO_RELAXED);
-                    unset_bit(indicator_out.data(), node, parallel, MO_RELAXED);
-                } else {
-                    unset_bit(indicator_in.data(), node, parallel, MO_RELAXED);
-                    set_bit(indicator_out.data(), node, parallel, MO_RELAXED);
+            if (comb_pval < config.family_wise_error_rate && comb_eff_size != 0) {
+                auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
+                for (node_index node : path) {
+                    set_bit(data, node, parallel, MO_RELAXED);
                 }
             }
 

From ea87fb8b84520f10f5f75a402c6129f525e10240 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 26 Jun 2024 00:12:44 +0200
Subject: [PATCH 227/410] more parallel

---
 .../src/graph/annotated_graph_algorithm.cpp   | 20 ++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 57cd0d6288..a8affdf90c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -264,15 +264,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return;
 
         sdsl::bit_vector marker(groups.size(), false);
+        std::lock_guard<std::mutex> lock(agg_mu);
         for (const auto &[j, raw_c] : row) {
             marker[j] = true;
-            std::lock_guard<std::mutex> lock(column_mus[j]);
+            // std::lock_guard<std::mutex> lock(column_mus[j]);
             ++hists_map[j][raw_c];
         }
 
         for (size_t j = 0; j < marker.size(); ++j) {
             if (!marker[j]) {
-                std::lock_guard<std::mutex> lock(column_mus[j]);
+                // std::lock_guard<std::mutex> lock(column_mus[j]);
                 ++hists_map[j][0];
             }
         }
@@ -1116,17 +1117,26 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 sdsl::util::set_to_value(indicator_out, false);
             } else {
                 common::logger->trace("k: {}\talpha_corr: {}", k, config.family_wise_error_rate / k);
+
+                std::atomic_thread_fence(std::memory_order_release);
+                #pragma omp parallel for ordered num_threads(num_threads)
                 for (size_t i = 0; i < indicator_in.size(); ++i) {
-                    double p = bit_cast<double, uint64_t>(pvals[i]);
+                    double p = 1.0;
+
+                    #pragma omp ordered
+                    p = bit_cast<double, uint64_t>(pvals[i]);
+
                     if (p < config.family_wise_error_rate && p * k >= config.family_wise_error_rate) {
-                        indicator_in[i] = false;
-                        indicator_out[i] = false;
+                        unset_bit(indicator_in.data(), i, parallel, MO_RELAXED);
+                        unset_bit(indicator_out.data(), i, parallel, MO_RELAXED);
                     }
                 }
+                std::atomic_thread_fence(std::memory_order_acquire);
             }
         }
     }
 
+    common::logger->trace("Done! Assembling contigs.");
     auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
         graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC

From f615a61eea70c54b42ede002333562b5c75f2fdd Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 26 Jun 2024 02:19:02 +0200
Subject: [PATCH 228/410] parallel

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a8affdf90c..1b3bcc6f56 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1121,14 +1121,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 std::atomic_thread_fence(std::memory_order_release);
                 #pragma omp parallel for ordered num_threads(num_threads)
                 for (size_t i = 0; i < indicator_in.size(); ++i) {
-                    double p = 1.0;
+                    if (fetch_bit(indicator_in.data(), i, parallel, MO_RELAXED)
+                            || fetch_bit(indicator_out.data(), i, parallel, MO_RELAXED)) {
+                        double p = 1.0;
 
-                    #pragma omp ordered
-                    p = bit_cast<double, uint64_t>(pvals[i]);
+                        #pragma omp ordered
+                        p = bit_cast<double, uint64_t>(pvals[i]);
 
-                    if (p < config.family_wise_error_rate && p * k >= config.family_wise_error_rate) {
-                        unset_bit(indicator_in.data(), i, parallel, MO_RELAXED);
-                        unset_bit(indicator_out.data(), i, parallel, MO_RELAXED);
+                        if (p < config.family_wise_error_rate && p * k >= config.family_wise_error_rate) {
+                            unset_bit(indicator_in.data(), i, parallel, MO_RELAXED);
+                            unset_bit(indicator_out.data(), i, parallel, MO_RELAXED);
+                        }
                     }
                 }
                 std::atomic_thread_fence(std::memory_order_acquire);

From d87acdffdfa091e304ff54f3e505d3b3a6935d64 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 2 Jul 2024 12:16:58 +0200
Subject: [PATCH 229/410] int row flat

---
 .../annotation_matrix/annotation_matrix.cpp   |  1 +
 .../static_annotators_def.hpp                 |  5 ++
 metagraph/src/cli/assemble.cpp                | 79 ++++++++++---------
 metagraph/src/cli/config/config.cpp           |  6 +-
 metagraph/src/cli/config/config.hpp           |  1 +
 metagraph/src/cli/load/load_annotation.cpp    |  7 ++
 metagraph/src/cli/transform_annotation.cpp    | 53 +++++++++++++
 metagraph/src/graph/annotated_dbg.cpp         | 14 +++-
 8 files changed, 125 insertions(+), 41 deletions(-)

diff --git a/metagraph/src/annotation/representation/annotation_matrix/annotation_matrix.cpp b/metagraph/src/annotation/representation/annotation_matrix/annotation_matrix.cpp
index bd4b15afcc..7d09680aeb 100644
--- a/metagraph/src/annotation/representation/annotation_matrix/annotation_matrix.cpp
+++ b/metagraph/src/annotation/representation/annotation_matrix/annotation_matrix.cpp
@@ -190,6 +190,7 @@ template class StaticBinRelAnnotator<CSCMatrix<BRWT, CountsVector>, std::string>
 template class StaticBinRelAnnotator<IntRowDiff<CSCMatrix<BRWT, CountsVector>>, std::string>;
 
 template class StaticBinRelAnnotator<CSRMatrix, std::string>;
+template class StaticBinRelAnnotator<CSRMatrixFlat, std::string>;
 
 template class StaticBinRelAnnotator<TupleCSCMatrix<ColumnMajor>, std::string>;
 template class StaticBinRelAnnotator<TupleCSCMatrix<BRWT>, std::string>;
diff --git a/metagraph/src/annotation/representation/annotation_matrix/static_annotators_def.hpp b/metagraph/src/annotation/representation/annotation_matrix/static_annotators_def.hpp
index 151054550d..a42b86cb39 100644
--- a/metagraph/src/annotation/representation/annotation_matrix/static_annotators_def.hpp
+++ b/metagraph/src/annotation/representation/annotation_matrix/static_annotators_def.hpp
@@ -17,6 +17,7 @@
 #include "annotation/int_matrix/row_diff/int_row_diff.hpp"
 #include "annotation/int_matrix/row_diff/tuple_row_diff.hpp"
 #include "annotation/int_matrix/csr_matrix/csr_matrix.hpp"
+#include "annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp"
 #include "annotation/int_matrix/row_disk/int_row_disk.hpp"
 #include "annotation/int_matrix/row_disk/coord_row_disk.hpp"
 #include "annotation/int_matrix/rank_extended/tuple_csc_matrix.hpp"
@@ -57,6 +58,8 @@ typedef StaticBinRelAnnotator<matrix::IntRowDiff<matrix::CSCMatrix<matrix::BRWT,
 
 typedef StaticBinRelAnnotator<matrix::CSRMatrix, std::string> IntRowAnnotator;
 
+typedef StaticBinRelAnnotator<matrix::CSRMatrixFlat, std::string> IntRowFlatAnnotator;
+
 typedef StaticBinRelAnnotator<matrix::IntRowDiff<matrix::IntRowDisk>, std::string> IntRowDiffDiskAnnotator;
 
 typedef StaticBinRelAnnotator<matrix::TupleRowDiff<matrix::CoordRowDisk>, std::string> RowDiffDiskCoordAnnotator;
@@ -97,6 +100,8 @@ inline const std::string RowDiffDiskAnnotator::kExtension = ".row_diff_disk.anno
 template <>
 inline const std::string IntMultiBRWTAnnotator::kExtension = ".int_brwt.annodbg";
 template <>
+inline const std::string IntRowFlatAnnotator::kExtension = ".int_row_flat.annodbg";
+template <>
 inline const std::string IntRowDiffBRWTAnnotator::kExtension = ".row_diff_int_brwt.annodbg";
 template <>
 inline const std::string IntRowAnnotator::kExtension = ".int_csr.annodbg";
diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 85e65474df..76a6efd104 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -1,5 +1,7 @@
 #include "assemble.hpp"
 
+#include <variant>
+
 #include <json/json.h>
 #include <spdlog/fmt/fmt.h>
 #include <tsl/hopscotch_set.h>
@@ -62,7 +64,7 @@ DifferentialAssemblyConfig diff_assembly_config(const Json::Value &experiment) {
 typedef std::function<void(const graph::DeBruijnGraph&,
                            const std::string& /* header */)> CallMaskedGraphHeader;
 
-void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+void call_masked_graphs(std::shared_ptr<DeBruijnGraph> graph_ptr,
                         Config *config,
                         const CallMaskedGraphHeader &callback) {
     assert(!config->assembly_config_file.empty());
@@ -71,9 +73,11 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     if (!fin.good())
         throw std::iostream::failure("Failed to read assembly config JSON from " + config->assembly_config_file);
 
-    for (const auto &name : config->fnames) {
-        if (parse_annotation_type(name) != Config::ColumnCompressed) {
-            throw std::runtime_error("Multiple annotation files must be ColumnCompressed");
+    if (config->infbase_annotators.size() > 1) {
+        for (const auto &name : config->infbase_annotators) {
+            if (parse_annotation_type(name) != Config::ColumnCompressed) {
+                throw std::runtime_error("Multiple annotation files must be ColumnCompressed");
+            }
         }
     }
 
@@ -106,41 +110,36 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             logger->trace("Running assembly: {}", exp_name);
 
-            auto filenames = (config->infbase_annotators.size() > 1) ? config->infbase_annotators : config->fnames;
-
             std::shared_ptr<DeBruijnGraph> ingraph;
             std::shared_ptr<DeBruijnGraph> outgraph;
 
+            std::variant<std::vector<uint64_t>, sdsl::int_vector_buffer<64>> pvals;
             if (diff_config.test_by_unitig) {
-                auto [cur_ingraph, cur_outgraph, pvals, tmp_file] =
-                    graph::mask_nodes_by_label_dual<std::vector<uint64_t>>(graph_ptr,
-                                        filenames,
-                                        foreground_labels,
-                                        background_labels,
-                                        diff_config, num_threads,
-                                        config->tmp_dir,
-                                        config->parallel_nodes);
-                std::swap(ingraph, cur_ingraph);
-                std::swap(outgraph, cur_outgraph);
+                pvals = sdsl::int_vector_buffer<64>();
+            } else {
+                pvals = std::vector<uint64_t>();
+            }
 
-                std::ofstream fout_all(config->outfbase + ".all.pvals", ios::out | ios::app);
-                for (uint64_t pval : pvals) {
-                    static_assert(sizeof(double) == sizeof(pval));
-                    double pval_d;
-                    memcpy(&pval_d, &pval, sizeof(pval));
-                    fout_all << pval_d << "\n";
+            std::tie(ingraph, outgraph) = std::visit([&](auto &pvals) {
+                using T = std::decay_t<decltype(pvals)>;
+                std::unique_ptr<utils::TempFile> tmp_file;
+                if (config->infbase_annotators.size() > 1
+                        || parse_annotation_type(config->infbase_annotators[0]) == Config::ColumnCompressed) {
+                    std::tie(ingraph, outgraph, pvals, tmp_file) = graph::mask_nodes_by_label_dual<T>(graph_ptr,
+                                            config->infbase_annotators,
+                                            foreground_labels,
+                                            background_labels,
+                                            diff_config, num_threads,
+                                            config->tmp_dir,
+                                            config->parallel_nodes);
+                } else {
+                    auto anno_graph = initialize_annotated_dbg(graph_ptr, *config);
+                    std::tie(ingraph, outgraph, pvals, tmp_file) = graph::mask_nodes_by_label_dual<T>(*anno_graph,
+                                            foreground_labels,
+                                            background_labels,
+                                            diff_config, num_threads,
+                                            config->tmp_dir);
                 }
-            } else {
-                auto [cur_ingraph, cur_outgraph, pvals, tmp_file] =
-                    graph::mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(graph_ptr,
-                                        filenames,
-                                        foreground_labels,
-                                        background_labels,
-                                        diff_config, num_threads,
-                                        config->tmp_dir,
-                                        config->parallel_nodes);
-                std::swap(ingraph, cur_ingraph);
-                std::swap(outgraph, cur_outgraph);
 
                 std::ofstream fout_all(config->outfbase + ".all.pvals", ios::out | ios::app);
                 for (uint64_t pval : pvals) {
@@ -149,8 +148,15 @@ void call_masked_graphs(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     memcpy(&pval_d, &pval, sizeof(pval));
                     fout_all << pval_d << "\n";
                 }
-                pvals.close();
-            }
+
+                if constexpr(std::is_same_v<T, sdsl::int_vector_buffer<64>>) {
+                    pvals.close();
+                } else {
+                    pvals = T();
+                }
+
+                return std::make_pair(std::move(ingraph), std::move(outgraph));
+            }, pvals);
 
             sdsl::bit_vector mask;
             callback(*ingraph, exp_name + "in.");
@@ -205,7 +211,7 @@ int assemble(Config *config) {
 
     logger->trace("Graph loaded in {} sec", timer.elapsed());
 
-    if (config->infbase_annotators.size() > 1 || files.size() > 1) {
+    if (config->infbase_annotators.size()) {
         logger->trace("Generating masked graphs...");
         std::mutex write_mutex;
         size_t num_threads = std::max(1u, get_num_threads());
@@ -244,7 +250,6 @@ int assemble(Config *config) {
             }
         };
 
-        config->fnames.erase(config->fnames.begin(), config->fnames.begin() + 1);
         call_masked_graphs(graph, config, graph_callback);
 
         return 0;
diff --git a/metagraph/src/cli/config/config.cpp b/metagraph/src/cli/config/config.cpp
index 41c6f53fcf..bc14c70dd9 100644
--- a/metagraph/src/cli/config/config.cpp
+++ b/metagraph/src/cli/config/config.cpp
@@ -735,6 +735,8 @@ std::string Config::annotype_to_string(AnnotationType state) {
             return "row_diff_disk_coord";
         case IntBRWT:
             return "int_brwt";
+        case IntRowFlat:
+            return "int_row_flat";
         case IntRowDiffBRWT:
             return "row_diff_int_brwt";
         case ColumnCoord:
@@ -782,6 +784,8 @@ Config::AnnotationType Config::string_to_annotype(const std::string &string) {
         return AnnotationType::RowDiffDiskCoord;
     } else if (string == "int_brwt") {
         return AnnotationType::IntBRWT;
+    } else if (string == "int_row_flat") {
+        return AnnotationType::IntRowFlat;
     } else if (string == "row_diff_int_brwt") {
         return AnnotationType::IntRowDiffBRWT;
     } else if (string == "column_coord") {
@@ -890,7 +894,7 @@ QueryMode Config::string_to_querymode(const std::string &string) {
 
 
 void Config::print_usage(const std::string &prog_name, IdentityType identity) {
-    const char annotation_list[] = "\t\t( column, brwt, rb_brwt, int_brwt,\n"
+    const char annotation_list[] = "\t\t( column, brwt, rb_brwt, int_brwt, int_row_flat,\n"
                                    "\t\t  column_coord, brwt_coord, row_diff_coord, row_diff_brwt_coord,\n"
                                    "\t\t  row_diff, row_diff_brwt, row_diff_flat, row_diff_sparse, row_diff_int_brwt,\n"
                                    "\t\t  row_diff_disk, row_diff_int_disk, row_diff_disk_coord,\n"
diff --git a/metagraph/src/cli/config/config.hpp b/metagraph/src/cli/config/config.hpp
index e66c66d4cc..5a127fc9d8 100644
--- a/metagraph/src/cli/config/config.hpp
+++ b/metagraph/src/cli/config/config.hpp
@@ -208,6 +208,7 @@ class Config {
         RBFish,
         RbBRWT,
         IntBRWT,
+        IntRowFlat,
         IntRowDiffBRWT,
         IntRowDiffDisk,
         ColumnCoord,
diff --git a/metagraph/src/cli/load/load_annotation.cpp b/metagraph/src/cli/load/load_annotation.cpp
index 4a682db6d7..02a8379fa3 100644
--- a/metagraph/src/cli/load/load_annotation.cpp
+++ b/metagraph/src/cli/load/load_annotation.cpp
@@ -78,6 +78,9 @@ Config::AnnotationType parse_annotation_type(const std::string &filename) {
     } else if (utils::ends_with(filename, annot::IntMultiBRWTAnnotator::kExtension)) {
         return Config::AnnotationType::IntBRWT;
 
+    } else if (utils::ends_with(filename, annot::IntRowFlatAnnotator::kExtension)) {
+        return Config::AnnotationType::IntRowFlat;
+
     } else if (utils::ends_with(filename, annot::IntRowDiffBRWTAnnotator::kExtension)) {
         return Config::AnnotationType::IntRowDiffBRWT;
 
@@ -168,6 +171,10 @@ initialize_annotation(Config::AnnotationType anno_type,
             annotation.reset(new annot::IntMultiBRWTAnnotator());
             break;
         }
+        case Config::IntRowFlat: {
+            annotation.reset(new annot::IntRowFlatAnnotator());
+            break;
+        }
         case Config::IntRowDiffBRWT: {
             annotation.reset(new annot::IntRowDiffBRWTAnnotator());
             break;
diff --git a/metagraph/src/cli/transform_annotation.cpp b/metagraph/src/cli/transform_annotation.cpp
index 2dfb4bd420..29b8de2d2d 100644
--- a/metagraph/src/cli/transform_annotation.cpp
+++ b/metagraph/src/cli/transform_annotation.cpp
@@ -16,6 +16,7 @@
 #include "load/load_graph.hpp"
 #include "graph/annotated_dbg.hpp"
 #include "graph/representation/masked_graph.hpp"
+#include "common/vectors/transpose.hpp"
 
 
 namespace mtg {
@@ -281,6 +282,51 @@ convert_to_IntMultiBRWT(const std::vector<std::string> &files,
                 brwt_annotator->get_label_encoder());
 }
 
+IntRowFlatAnnotator
+convert_to_IntRowFlat(const std::vector<std::string> &files,
+                      const Config &config,
+                      const Timer &timer) {
+    size_t total_labels = get_num_columns(files, Config::ColumnCompressed);
+    std::vector<std::string> labels(total_labels);
+    std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
+    using ValuesContainer = sdsl::int_vector_buffer<>;
+    std::vector<std::unique_ptr<const ValuesContainer>> column_values_all(total_labels);
+    using PairContainer = Vector<std::pair<uint64_t, uint64_t>>;
+    uint64_t num_rows = 0;
+    uint64_t num_nonzeros = 0;
+    uint8_t max_width = 0;
+    ColumnCompressed<>::load_columns_and_values(
+        files,
+        [&](uint64_t offset, const std::string &label, std::unique_ptr<bit_vector>&& column, ValuesContainer&& column_values) {
+            max_width = std::max(max_width, column_values.width());
+            labels[offset] = label;
+            num_rows = column->size();
+            num_nonzeros += column_values.size();
+            columns_all[offset].reset(column.release());
+            column_values_all[offset] = std::make_unique<const ValuesContainer>(std::move(column_values));
+        },
+        get_num_threads()
+    );
+
+    common::logger->trace("Num rows: {}\tNum columns: {}\tNum nonzeros: {}",
+                          num_rows, total_labels, num_nonzeros);
+
+    LabelEncoder label_encoder;
+    for (std::string &label : labels) {
+        label_encoder.insert_and_encode(std::move(label));
+    }
+    auto mat = std::make_unique<matrix::CSRMatrixFlat>([&](const auto &row_callback) {
+        utils::call_rows<std::unique_ptr<const bit_vector>,
+                         std::unique_ptr<const ValuesContainer>,
+                         PairContainer, true>(columns_all, column_values_all,
+                                              [&](uint64_t, const auto &row, size_t) {
+            row_callback(row);
+        }, num_rows);
+    }, total_labels, num_rows, num_nonzeros, max_width);
+
+    return IntRowFlatAnnotator(std::move(mat), std::move(label_encoder));
+}
+
 int transform_annotation(Config *config) {
     assert(config);
 
@@ -875,6 +921,13 @@ int transform_annotation(Config *config) {
                 logger->trace("Serialized to {}", config->outfbase);
                 break;
             }
+            case Config::IntRowFlat: {
+                auto annotator = convert_to_IntRowFlat(files, *config, timer);
+                logger->trace("Annotation converted in {} sec", timer.elapsed());
+                annotator.serialize(config->outfbase);
+                logger->trace("Serialized to {}", config->outfbase);
+                break;
+            }
             case Config::BinRelWT: {
                 convert<BinRelWTAnnotator>(std::move(*annotator), config->outfbase);
                 break;
diff --git a/metagraph/src/graph/annotated_dbg.cpp b/metagraph/src/graph/annotated_dbg.cpp
index 430365365a..07c14b5f61 100644
--- a/metagraph/src/graph/annotated_dbg.cpp
+++ b/metagraph/src/graph/annotated_dbg.cpp
@@ -710,10 +710,18 @@ ::call_annotated_nodes(const Label &label,
 
 bool AnnotatedSequenceGraph::check_compatibility() const {
     // TODO: what if CanonicalDBG is not the highest level? find a better way to do this
-    if (const auto *canonical = dynamic_cast<const CanonicalDBG *>(graph_.get()))
-        return canonical->get_graph().max_index() == annotator_->num_objects();
+    uint64_t max_index;
+    if (const auto *canonical = dynamic_cast<const CanonicalDBG *>(graph_.get())) {
+        max_index = canonical->get_graph().max_index();
+    } else {
+        max_index = graph_->max_index();
+    }
+
+    uint64_t num_obs = annotator_->num_objects();
+    if (max_index != num_obs)
+        common::logger->error("Graph: {}\tAnnotator: {}", max_index, num_obs);
 
-    return graph_->max_index() == annotator_->num_objects();
+    return max_index == num_obs;
 }
 
 

From 4b915ae4d1ac999f78bd427083977b111f127776 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 17 Jul 2024 02:52:42 +0200
Subject: [PATCH 230/410] fixes

---
 metagraph/src/cli/transform_annotation.cpp    | 28 +++++++++++++------
 .../src/graph/annotated_graph_algorithm.cpp   | 26 +++++++++--------
 metagraph/src/graph/graph_cleaning.cpp        |  3 ++
 3 files changed, 38 insertions(+), 19 deletions(-)

diff --git a/metagraph/src/cli/transform_annotation.cpp b/metagraph/src/cli/transform_annotation.cpp
index 29b8de2d2d..0bf4e2bb5f 100644
--- a/metagraph/src/cli/transform_annotation.cpp
+++ b/metagraph/src/cli/transform_annotation.cpp
@@ -287,34 +287,43 @@ convert_to_IntRowFlat(const std::vector<std::string> &files,
                       const Config &config,
                       const Timer &timer) {
     size_t total_labels = get_num_columns(files, Config::ColumnCompressed);
+    if (!total_labels)
+        return {};
+
     std::vector<std::string> labels(total_labels);
     std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
     using ValuesContainer = sdsl::int_vector_buffer<>;
     std::vector<std::unique_ptr<const ValuesContainer>> column_values_all(total_labels);
     using PairContainer = Vector<std::pair<uint64_t, uint64_t>>;
-    uint64_t num_rows = 0;
-    uint64_t num_nonzeros = 0;
-    uint8_t max_width = 0;
     ColumnCompressed<>::load_columns_and_values(
         files,
         [&](uint64_t offset, const std::string &label, std::unique_ptr<bit_vector>&& column, ValuesContainer&& column_values) {
-            max_width = std::max(max_width, column_values.width());
             labels[offset] = label;
-            num_rows = column->size();
-            num_nonzeros += column_values.size();
             columns_all[offset].reset(column.release());
             column_values_all[offset] = std::make_unique<const ValuesContainer>(std::move(column_values));
         },
         get_num_threads()
     );
 
-    common::logger->trace("Num rows: {}\tNum columns: {}\tNum nonzeros: {}",
-                          num_rows, total_labels, num_nonzeros);
+    uint64_t num_rows = columns_all[0]->size();
+    uint64_t num_nonzeros = 0;
+    uint8_t max_width = 0;
+
+    for (size_t j = 0; j < columns_all.size(); ++j) {
+        assert(num_rows == columns_all[j]->size());
+        num_nonzeros += column_values_all[j]->size();
+        max_width = std::max(max_width, column_values_all[j]->width());
+    }
 
     LabelEncoder label_encoder;
     for (std::string &label : labels) {
         label_encoder.insert_and_encode(std::move(label));
     }
+
+    assert(label_encoder.size() == total_labels);
+
+    common::logger->trace("Num rows: {}\tNum columns: {}\tNum nonzeros: {}",
+                          num_rows, total_labels, num_nonzeros);
     auto mat = std::make_unique<matrix::CSRMatrixFlat>([&](const auto &row_callback) {
         utils::call_rows<std::unique_ptr<const bit_vector>,
                          std::unique_ptr<const ValuesContainer>,
@@ -323,6 +332,9 @@ convert_to_IntRowFlat(const std::vector<std::string> &files,
             row_callback(row);
         }, num_rows);
     }, total_labels, num_rows, num_nonzeros, max_width);
+    assert(mat->num_columns() == total_labels);
+    assert(mat->num_rows() == num_rows);
+    assert(mat->num_relations() == num_nonzeros);
 
     return IntRowFlatAnnotator(std::move(mat), std::move(label_encoder));
 }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 1b3bcc6f56..30b3e216cd 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -179,10 +179,15 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         return mask_nodes_by_label_dual<value_type, PValStorage>(
             std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
             [&](const auto &callback) {
-                std::vector<Row> row_ids { 0 };
+                ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
+                #pragma omp parallel for num_threads(num_threads) ordered
                 for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
-                    row_ids[0] = row_i;
-                    callback(row_i, int_matrix->get_row_values(row_ids)[0]);
+                    ++progress_bar;
+                    std::vector<Row> row_ids { row_i };
+                    auto row_vals = int_matrix->get_row_values(row_ids)[0];
+
+                    #pragma omp ordered
+                    callback(row_i, row_vals);
                 }
             },
             groups,
@@ -192,9 +197,11 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         return mask_nodes_by_label_dual<uint64_t, PValStorage>(
             std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
             [&](const auto &callback) {
-                std::vector<Row> row_ids { 0 };
+                ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
+                #pragma omp parallel for num_threads(num_threads) ordered
                 for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
-                    row_ids[0] = row_i;
+                    ++progress_bar;
+                    std::vector<Row> row_ids(1, row_i);
                     auto set_bits = matrix.get_rows(row_ids);
                     Vector<std::pair<uint64_t, uint64_t>> container;
                     container.reserve(set_bits[0].size());
@@ -202,6 +209,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
                         container.emplace_back(j, 1);
                     }
 
+                    #pragma omp ordered
                     callback(row_i, container);
                 }
             },
@@ -259,23 +267,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
 
     common::logger->trace("Calculating count histograms");
-    generate_rows([&](uint64_t row_i, const auto &row) {
+    generate_rows([&](uint64_t, const auto &row) {
         if (row.empty())
             return;
 
         sdsl::bit_vector marker(groups.size(), false);
-        std::lock_guard<std::mutex> lock(agg_mu);
         for (const auto &[j, raw_c] : row) {
             marker[j] = true;
-            // std::lock_guard<std::mutex> lock(column_mus[j]);
             ++hists_map[j][raw_c];
         }
 
         for (size_t j = 0; j < marker.size(); ++j) {
-            if (!marker[j]) {
-                // std::lock_guard<std::mutex> lock(column_mus[j]);
+            if (!marker[j])
                 ++hists_map[j][0];
-            }
         }
     });
 
diff --git a/metagraph/src/graph/graph_cleaning.cpp b/metagraph/src/graph/graph_cleaning.cpp
index 9d2081b42e..9d2dab7ea2 100644
--- a/metagraph/src/graph/graph_cleaning.cpp
+++ b/metagraph/src/graph/graph_cleaning.cpp
@@ -119,6 +119,9 @@ std::pair<double, double> estimate_ztp_mean(const std::function<void(const std::
         hist[1] = num_singleton_kmers;
     }
 
+    if (hist.empty())
+        return {};
+
     uint64_t max_size = 1llu << width;
     if (hist.size() == max_size)
         hist.pop_back();

From e96a7c3a8a3b43ac954d81f188c7cf42f8d6bc13 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 17 Jul 2024 05:37:58 +0200
Subject: [PATCH 231/410] fixes

---
 .../annotation/int_matrix/base/int_matrix.cpp  | 18 ++++++++++++++++++
 .../annotation/int_matrix/base/int_matrix.hpp  |  2 ++
 .../src/graph/annotated_graph_algorithm.cpp    | 14 ++------------
 3 files changed, 22 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
index cebb89de9e..3fadd59040 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
@@ -1,5 +1,10 @@
 #include "int_matrix.hpp"
 
+#include <progress_bar.hpp>
+
+#include "common/logger.hpp"
+#include "common/threads/threading.hpp"
+
 
 namespace mtg {
 namespace annot {
@@ -8,6 +13,19 @@ namespace matrix {
 using Row = BinaryMatrix::Row;
 using Column = BinaryMatrix::Column;
 
+void IntMatrix::call_row_values(const std::function<void(uint64_t, const RowValues&)> &callback) const {
+    size_t n = get_binary_matrix().num_rows();
+    ProgressBar progress_bar(n, "Streaming rows", std::cerr, !common::get_verbose());
+    #pragma omp parallel for num_threads(get_num_threads()) ordered
+    for (size_t row_i = 0; row_i < n; ++row_i) {
+        ++progress_bar;
+        std::vector<Row> row_ids { row_i };
+        auto row_vals = get_row_values(row_ids)[0];
+
+        #pragma omp ordered
+        callback(row_i, row_vals);
+    }
+}
 
 IntMatrix::RowValues
 IntMatrix::sum_row_values(const std::vector<std::pair<Row, size_t>> &index_counts,
diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
index 58de8dcb1b..8283ac3ec8 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
@@ -20,6 +20,8 @@ class IntMatrix {
     // |row| is in [0, num_rows), |column| is in [0, num_columns)
     virtual std::vector<RowValues> get_row_values(const std::vector<BinaryMatrix::Row> &rows) const = 0;
 
+    virtual void call_row_values(const std::function<void(uint64_t, const RowValues&)> &callback) const;
+
     // sum up values for each column with at least |min_count| non-zero values
     virtual RowValues
     sum_row_values(const std::vector<std::pair<BinaryMatrix::Row, size_t>> &index_counts,
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 30b3e216cd..02c4bc8a68 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -178,18 +178,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         using value_type = annot::matrix::IntMatrix::Value;
         return mask_nodes_by_label_dual<value_type, PValStorage>(
             std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
-            [&](const auto &callback) {
-                ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
-                #pragma omp parallel for num_threads(num_threads) ordered
-                for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
-                    ++progress_bar;
-                    std::vector<Row> row_ids { row_i };
-                    auto row_vals = int_matrix->get_row_values(row_ids)[0];
-
-                    #pragma omp ordered
-                    callback(row_i, row_vals);
-                }
-            },
+            [&](const auto &callback) { int_matrix->call_row_values(callback); },
             groups,
             config, num_threads, tmp_dir, num_threads
         );
@@ -272,6 +261,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return;
 
         sdsl::bit_vector marker(groups.size(), false);
+        std::lock_guard<std::mutex> lock(agg_mu);
         for (const auto &[j, raw_c] : row) {
             marker[j] = true;
             ++hists_map[j][raw_c];

From 4fcf1f60e0024a6cee6eac9e2aa56aac365a8611 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 17 Jul 2024 06:28:44 +0200
Subject: [PATCH 232/410] streaming

---
 .../annotation_matrix/annotation_matrix.cpp              | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/metagraph/src/annotation/representation/annotation_matrix/annotation_matrix.cpp b/metagraph/src/annotation/representation/annotation_matrix/annotation_matrix.cpp
index 7d09680aeb..9548c4f920 100644
--- a/metagraph/src/annotation/representation/annotation_matrix/annotation_matrix.cpp
+++ b/metagraph/src/annotation/representation/annotation_matrix/annotation_matrix.cpp
@@ -42,7 +42,14 @@ bool StaticBinRelAnnotator<BinaryMatrixType, Label>::load(const std::string &fil
 
     try {
         assert(matrix_.get());
-        return label_encoder_.load(*in) && matrix_->load(*in);
+        if (!label_encoder_.load(*in))
+            return false;
+
+        if constexpr(std::is_same_v<T, IntRowFlatAnnotator>) {
+            return matrix_->load(fname, in->tellg());
+        }
+
+        return matrix_->load(*in);
     } catch (...) {
         return false;
     }

From 7677bb79689d1768c2210d07f3b1d257c8c14c0a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 17 Jul 2024 06:45:36 +0200
Subject: [PATCH 233/410] make dumping pvals optional

---
 metagraph/src/cli/assemble.cpp      | 15 +++++++++------
 metagraph/src/cli/config/config.cpp |  2 ++
 metagraph/src/cli/config/config.hpp |  1 +
 3 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 76a6efd104..466c9a3f2b 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -141,12 +141,15 @@ void call_masked_graphs(std::shared_ptr<DeBruijnGraph> graph_ptr,
                                             config->tmp_dir);
                 }
 
-                std::ofstream fout_all(config->outfbase + ".all.pvals", ios::out | ios::app);
-                for (uint64_t pval : pvals) {
-                    static_assert(sizeof(double) == sizeof(pval));
-                    double pval_d;
-                    memcpy(&pval_d, &pval, sizeof(pval));
-                    fout_all << pval_d << "\n";
+                if (config->output_pvals) {
+                    common::logger->trace("Writing p-values");
+                    std::ofstream fout_all(config->outfbase + ".all.pvals", ios::out | ios::app);
+                    for (uint64_t pval : pvals) {
+                        static_assert(sizeof(double) == sizeof(pval));
+                        double pval_d;
+                        memcpy(&pval_d, &pval, sizeof(pval));
+                        fout_all << pval_d << "\n";
+                    }
                 }
 
                 if constexpr(std::is_same_v<T, sdsl::int_vector_buffer<64>>) {
diff --git a/metagraph/src/cli/config/config.cpp b/metagraph/src/cli/config/config.cpp
index bc14c70dd9..80879e7c7d 100644
--- a/metagraph/src/cli/config/config.cpp
+++ b/metagraph/src/cli/config/config.cpp
@@ -337,6 +337,8 @@ Config::Config(int argc, char *argv[]) {
             to_gfa = true;
         } else if (!strcmp(argv[i], "--compacted")) {
             output_compacted = true;
+        } else if (!strcmp(argv[i], "--dump-pvals")) {
+            output_pvals = true;
         } else if (!strcmp(argv[i], "--clean-ztp")) {
             clean_ztp = true;
         } else if (!strcmp(argv[i], "--json")) {
diff --git a/metagraph/src/cli/config/config.hpp b/metagraph/src/cli/config/config.hpp
index 5a127fc9d8..4645e6003f 100644
--- a/metagraph/src/cli/config/config.hpp
+++ b/metagraph/src/cli/config/config.hpp
@@ -35,6 +35,7 @@ class Config {
     bool enumerate_out_sequences = false;
     bool to_gfa = false;
     bool output_compacted = false;
+    bool output_pvals = false;
     bool unitigs = false;
     bool kmers_in_single_form = false;
     bool initialize_bloom = false;

From d54697709662963b25a173f75458c1c7ee1f48c0 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 17 Jul 2024 19:19:09 +0200
Subject: [PATCH 234/410] cleanup

---
 .../annotation/int_matrix/base/int_matrix.cpp |  11 +-
 .../annotation/int_matrix/base/int_matrix.hpp |   3 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 380 +++++++++---------
 .../src/graph/annotated_graph_algorithm.hpp   |  12 -
 .../test_annotated_graph_algorithm.cpp        | 106 ++---
 5 files changed, 249 insertions(+), 263 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
index 3fadd59040..e0b6ba7e8d 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
@@ -13,7 +13,8 @@ namespace matrix {
 using Row = BinaryMatrix::Row;
 using Column = BinaryMatrix::Column;
 
-void IntMatrix::call_row_values(const std::function<void(uint64_t, const RowValues&)> &callback) const {
+void IntMatrix::call_row_values(const std::function<void(uint64_t, const RowValues&)> &callback,
+                                bool ordered) const {
     size_t n = get_binary_matrix().num_rows();
     ProgressBar progress_bar(n, "Streaming rows", std::cerr, !common::get_verbose());
     #pragma omp parallel for num_threads(get_num_threads()) ordered
@@ -22,8 +23,12 @@ void IntMatrix::call_row_values(const std::function<void(uint64_t, const RowValu
         std::vector<Row> row_ids { row_i };
         auto row_vals = get_row_values(row_ids)[0];
 
-        #pragma omp ordered
-        callback(row_i, row_vals);
+        if (ordered) {
+            #pragma omp ordered
+            callback(row_i, row_vals);
+        } else {
+            callback(row_i, row_vals);
+        }
     }
 }
 
diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
index 8283ac3ec8..3fb498a4ef 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
@@ -20,7 +20,8 @@ class IntMatrix {
     // |row| is in [0, num_rows), |column| is in [0, num_columns)
     virtual std::vector<RowValues> get_row_values(const std::vector<BinaryMatrix::Row> &rows) const = 0;
 
-    virtual void call_row_values(const std::function<void(uint64_t, const RowValues&)> &callback) const;
+    virtual void call_row_values(const std::function<void(uint64_t, const RowValues&)> &callback,
+                                 bool ordered = true) const;
 
     // sum up values for each column with at least |min_count| non-zero values
     virtual RowValues
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 02c4bc8a68..506b2fc853 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -59,183 +59,18 @@ bit_cast(const From& src) noexcept
     return dst;
 }
 
-template <class PValStorage>
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         const std::vector<std::string> &files,
-                         const tsl::hopscotch_set<Label> &labels_in,
-                         const tsl::hopscotch_set<Label> &labels_out,
-                         const DifferentialAssemblyConfig &config,
-                         size_t num_threads,
-                         std::filesystem::path tmp_dir,
-                         size_t num_parallel_files) {
-    common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
-    common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
-
-    size_t total_labels = labels_in.size() + labels_out.size();
-
-    using ColumnValuesMem = std::vector<std::unique_ptr<const sdsl::int_vector<>>>;
-    using ColumnValuesDisk = std::vector<std::unique_ptr<const sdsl::int_vector_buffer<>>>;
-
-    std::variant<ColumnValuesMem, ColumnValuesDisk> column_values_all;
-
-    if (config.test_by_unitig) {
-        column_values_all = ColumnValuesMem(total_labels);
-    } else {
-        column_values_all = ColumnValuesDisk(total_labels);
-    }
-
-    return std::visit([&](auto&& column_values_all) {
-        using ColumnValues = typename std::decay<decltype(column_values_all)>::type;
-        using ValuesContainerPtr = typename ColumnValues::value_type;
-        using ValuesContainer = typename std::decay<typename ValuesContainerPtr::element_type>::type;
-        using value_type = typename ValuesContainer::value_type;
-        using PairContainer = Vector<std::pair<uint64_t, value_type>>;
-
-        std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
-        std::vector<bool> groups(total_labels);
-        annot::ColumnCompressed<>::load_columns_and_values(
-            files,
-            [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector>&& column, ValuesContainer&& column_values) {
-                groups[offset] = labels_out.count(label);
-                columns_all[offset].reset(column.release());
-                column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
-            },
-            num_parallel_files
-        );
-
-        uint8_t max_width = 0;
-        for (const auto &col_vals : column_values_all) {
-            max_width = std::max(max_width, col_vals->width());
-        }
-
-        return mask_nodes_by_label_dual<value_type, PValStorage>(
-            graph_ptr,
-            [&](const auto &callback) {
-                utils::call_rows<std::unique_ptr<const bit_vector>,
-                                 std::unique_ptr<const ValuesContainer>,
-                                 PairContainer, true>(columns_all, column_values_all,
-                                                      [&](uint64_t row_i, const auto &row, size_t) {
-                    callback(row_i, row);
-                });
-            },
-            groups,
-            config, num_threads, tmp_dir, num_parallel_files,
-            [&]() {
-                columns_all.clear();
-                column_values_all.clear();
-            },
-            max_width
-        );
-    }, column_values_all);
-}
-
-template
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
-                         const std::vector<std::string> &,
-                         const tsl::hopscotch_set<Label> &,
-                         const tsl::hopscotch_set<Label> &,
-                         const DifferentialAssemblyConfig &,
-                         size_t,
-                         std::filesystem::path,
-                         size_t);
-template
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
-                         const std::vector<std::string> &,
-                         const tsl::hopscotch_set<Label> &,
-                         const tsl::hopscotch_set<Label> &,
-                         const DifferentialAssemblyConfig &,
-                         size_t,
-                         std::filesystem::path,
-                         size_t);
-
-template <class PValStorage>
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
-                         const DifferentialAssemblyConfig &config,
-                         size_t num_threads,
-                         std::filesystem::path tmp_dir) {
-    common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
-    common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
-
-    size_t total_labels = labels_in.size() + labels_out.size();
-    const auto &annotation = anno_graph.get_annotator();
-
-    std::vector<bool> groups;
-    groups.reserve(total_labels);
-    for (const auto &label : annotation.get_label_encoder().get_labels()) {
-        groups.emplace_back(labels_out.count(label));
-    }
-
-    const auto &matrix = annotation.get_matrix();
-    using Row = annot::matrix::BinaryMatrix::Row;
-
-    if (const auto *int_matrix = dynamic_cast<const annot::matrix::IntMatrix*>(&matrix)) {
-        using value_type = annot::matrix::IntMatrix::Value;
-        return mask_nodes_by_label_dual<value_type, PValStorage>(
-            std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
-            [&](const auto &callback) { int_matrix->call_row_values(callback); },
-            groups,
-            config, num_threads, tmp_dir, num_threads
-        );
-    } else {
-        return mask_nodes_by_label_dual<uint64_t, PValStorage>(
-            std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
-            [&](const auto &callback) {
-                ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
-                #pragma omp parallel for num_threads(num_threads) ordered
-                for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
-                    ++progress_bar;
-                    std::vector<Row> row_ids(1, row_i);
-                    auto set_bits = matrix.get_rows(row_ids);
-                    Vector<std::pair<uint64_t, uint64_t>> container;
-                    container.reserve(set_bits[0].size());
-                    for (auto j : set_bits[0]) {
-                        container.emplace_back(j, 1);
-                    }
-
-                    #pragma omp ordered
-                    callback(row_i, container);
-                }
-            },
-            groups,
-            config, num_threads, tmp_dir, num_threads
-        );
-    }
-}
-
-template
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<std::vector<uint64_t>>(const AnnotatedDBG &,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
-                         const DifferentialAssemblyConfig &,
-                         size_t,
-                         std::filesystem::path);
-template
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(const AnnotatedDBG &,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
-                         const DifferentialAssemblyConfig &,
-                         size_t,
-                         std::filesystem::path);
-
 template <typename value_type, class PValStorage>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, value_type>>)>&)> &generate_rows,
+                         const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, value_type>>)>&,
+                                                  bool)> &generate_rows,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
-                         size_t num_threads,
-                         std::filesystem::path tmp_dir,
-                         size_t num_parallel_files,
-                         const std::function<void()> &deallocate,
-                         uint8_t max_width) {
+                         size_t num_threads = 1,
+                         std::filesystem::path tmp_dir = "",
+                         size_t num_parallel_files = std::numeric_limits<size_t>::max(),
+                         const std::function<void()> &deallocate = []() {},
+                         uint8_t max_width = 64) {
     num_parallel_files = std::min(num_parallel_files, num_threads);
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
     bool parallel = num_parallel_files > 1;
@@ -260,18 +95,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (row.empty())
             return;
 
-        sdsl::bit_vector marker(groups.size(), false);
-        std::lock_guard<std::mutex> lock(agg_mu);
+        Vector<uint64_t> counts(groups.size(), 0);
         for (const auto &[j, raw_c] : row) {
-            marker[j] = true;
-            ++hists_map[j][raw_c];
+            counts[j] = raw_c;
         }
 
-        for (size_t j = 0; j < marker.size(); ++j) {
-            if (!marker[j])
-                ++hists_map[j][0];
+        #pragma omp critical
+        {
+        for (size_t j = 0; j < counts.size(); ++j) {
+            ++hists_map[j][counts[j]];
+        }
         }
-    });
+    }, false);
 
     if (config.clean) {
         common::logger->trace("Cleaning count columns");
@@ -322,7 +157,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     assert(old_val);
                 }
             }
-        });
+        }, false);
         std::atomic_thread_fence(std::memory_order_acquire);
     }
 
@@ -997,7 +832,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 pvals[node] = bit_cast<uint64_t>(pval);
             }
         }
-    });
+    }, true);
 
     std::atomic_thread_fence(std::memory_order_acquire);
 
@@ -1147,29 +982,186 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     return std::make_tuple(masked_graph_in, masked_graph_out, std::move(pvals), std::move(tmp_file));
 }
 
+template <class PValStorage>
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                         const std::vector<std::string> &files,
+                         const tsl::hopscotch_set<Label> &labels_in,
+                         const tsl::hopscotch_set<Label> &labels_out,
+                         const DifferentialAssemblyConfig &config,
+                         size_t num_threads,
+                         std::filesystem::path tmp_dir,
+                         size_t num_parallel_files) {
+    common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
+    common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
+
+    size_t total_labels = labels_in.size() + labels_out.size();
+
+    using ColumnValuesMem = std::vector<std::unique_ptr<const sdsl::int_vector<>>>;
+    using ColumnValuesDisk = std::vector<std::unique_ptr<const sdsl::int_vector_buffer<>>>;
+
+    std::variant<ColumnValuesMem, ColumnValuesDisk> column_values_all;
+
+    if (config.test_by_unitig) {
+        column_values_all = ColumnValuesMem(total_labels);
+    } else {
+        column_values_all = ColumnValuesDisk(total_labels);
+    }
+
+    return std::visit([&](auto&& column_values_all) {
+        using ColumnValues = typename std::decay<decltype(column_values_all)>::type;
+        using ValuesContainerPtr = typename ColumnValues::value_type;
+        using ValuesContainer = typename std::decay<typename ValuesContainerPtr::element_type>::type;
+        using value_type = typename ValuesContainer::value_type;
+        using PairContainer = Vector<std::pair<uint64_t, value_type>>;
+
+        std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
+        std::vector<bool> groups(total_labels);
+        annot::ColumnCompressed<>::load_columns_and_values(
+            files,
+            [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector>&& column, ValuesContainer&& column_values) {
+                groups[offset] = labels_out.count(label);
+                columns_all[offset].reset(column.release());
+                column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
+            },
+            num_parallel_files
+        );
+
+        uint8_t max_width = 0;
+        for (const auto &col_vals : column_values_all) {
+            max_width = std::max(max_width, col_vals->width());
+        }
+
+        return mask_nodes_by_label_dual<value_type, PValStorage>(
+            graph_ptr,
+            [&](const auto &callback, bool ordered) {
+                if (ordered) {
+                    utils::call_rows<std::unique_ptr<const bit_vector>,
+                                 std::unique_ptr<const ValuesContainer>,
+                                 PairContainer, true>(columns_all, column_values_all,
+                                                      [&](uint64_t row_i, const auto &row, size_t) {
+                        callback(row_i, row);
+                    });
+                } else {
+                    utils::call_rows<std::unique_ptr<const bit_vector>,
+                                     std::unique_ptr<const ValuesContainer>,
+                                     PairContainer, false>(columns_all, column_values_all,
+                                                           [&](uint64_t row_i, const auto &row, size_t) {
+                        callback(row_i, row);
+                    });
+                }
+            },
+            groups,
+            config, num_threads, tmp_dir, num_parallel_files,
+            [&]() {
+                columns_all.clear();
+                column_values_all.clear();
+            },
+            max_width
+        );
+    }, column_values_all);
+}
+
 template
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
-                         const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>)>&)> &generate_rows,
-                         const std::vector<bool> &,
+mask_nodes_by_label_dual<std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
+                         const std::vector<std::string> &,
+                         const tsl::hopscotch_set<Label> &,
+                         const tsl::hopscotch_set<Label> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
                          std::filesystem::path,
-                         size_t,
-                         const std::function<void()> &,
-                         uint8_t);
-
+                         size_t);
 template
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
-                         const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>)>&)> &generate_rows,
-                         const std::vector<bool> &,
+mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
+                         const std::vector<std::string> &,
+                         const tsl::hopscotch_set<Label> &,
+                         const tsl::hopscotch_set<Label> &,
                          const DifferentialAssemblyConfig &,
                          size_t,
                          std::filesystem::path,
+                         size_t);
+
+template <class PValStorage>
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
+                         const DifferentialAssemblyConfig &config,
+                         size_t num_threads,
+                         std::filesystem::path tmp_dir) {
+    common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
+    common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
+
+    size_t total_labels = labels_in.size() + labels_out.size();
+    const auto &annotation = anno_graph.get_annotator();
+
+    std::vector<bool> groups;
+    groups.reserve(total_labels);
+    for (const auto &label : annotation.get_label_encoder().get_labels()) {
+        groups.emplace_back(labels_out.count(label));
+    }
+
+    const auto &matrix = annotation.get_matrix();
+    using Row = annot::matrix::BinaryMatrix::Row;
+
+    if (const auto *int_matrix = dynamic_cast<const annot::matrix::IntMatrix*>(&matrix)) {
+        using value_type = annot::matrix::IntMatrix::Value;
+        return mask_nodes_by_label_dual<value_type, PValStorage>(
+            std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
+            [&](const auto &callback, bool ordered) {
+                int_matrix->call_row_values(callback, ordered);
+            },
+            groups,
+            config, num_threads, tmp_dir, num_threads
+        );
+    } else {
+        return mask_nodes_by_label_dual<uint64_t, PValStorage>(
+            std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
+            [&](const auto &callback, bool ordered) {
+                ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
+                #pragma omp parallel for num_threads(num_threads) ordered
+                for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
+                    ++progress_bar;
+                    std::vector<Row> row_ids(1, row_i);
+                    auto set_bits = matrix.get_rows(row_ids);
+                    Vector<std::pair<uint64_t, uint64_t>> container;
+                    container.reserve(set_bits[0].size());
+                    for (auto j : set_bits[0]) {
+                        container.emplace_back(j, 1);
+                    }
+
+                    if (ordered) {
+                        #pragma omp ordered
+                        callback(row_i, container);
+                    } else {
+                        callback(row_i, container);
+                    }
+                }
+            },
+            groups,
+            config, num_threads, tmp_dir, num_threads
+        );
+    }
+}
+
+template
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<std::vector<uint64_t>>(const AnnotatedDBG &,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
+                         const DifferentialAssemblyConfig &,
                          size_t,
-                         const std::function<void()> &,
-                         uint8_t);
+                         std::filesystem::path);
+template
+std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(const AnnotatedDBG &,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
+                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
+                         const DifferentialAssemblyConfig &,
+                         size_t,
+                         std::filesystem::path);
 
 } // namespace graph
 } // namespace mtg
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 181ea99a2e..33f173bf14 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -53,18 +53,6 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
                          size_t num_threads = 1,
                          std::filesystem::path = "");
 
-template <typename value_type, class PValStorage>
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, value_type>>)>&)> &generate_rows,
-                         const std::vector<bool> &groups,
-                         const DifferentialAssemblyConfig &config,
-                         size_t num_threads = 1,
-                         std::filesystem::path = "",
-                         size_t num_parallel_files = std::numeric_limits<size_t>::max(),
-                         const std::function<void()> &deallocate = []() {},
-                         uint8_t max_width = 64);
-
 } // namespace graph
 } // namespace mtg
 
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index 5f6428c994..a1d50369a3 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -81,38 +81,38 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
                 std::vector<std::unique_ptr<const bit_vector>> columns;
                 columns.reserve(in_columns.size());
                 std::transform(in_columns.begin(), in_columns.end(), std::back_inserter(columns),
-                            [&](const auto &a) {
-                                return std::make_unique<const bit_vector_stat>(a->to_vector());
-                            });
+                               [&](const auto &a) {
+                                  return std::make_unique<const bit_vector_stat>(a->to_vector());
+                               });
 
 
                 columns.resize(groups.size());
 
-                using PairContainer = Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
-                std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
-                auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
-                    graph_ptr,
-                    [&](const auto &callback) {
-                        utils::call_rows<std::unique_ptr<const bit_vector>,
-                                        std::unique_ptr<const sdsl::int_vector<>>,
-                                        PairContainer, true>(columns, column_values,
-                                                            [&](uint64_t row_i, const auto &row, size_t) {
-                            callback(row_i, row);
-                        });
-                    },
-                    groups,
-                    config, num_threads, test_dump_basename, num_threads,
-                    [&]() {
-                        columns.clear();
-                        column_values.clear();
-                    }
-                );
-
-                masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
-                    auto cur_labels = anno_graph->get_labels(kmer, 0.0);
-                    obs_labels.insert(cur_labels.begin(), cur_labels.end());
-                    obs_kmers.insert(kmer);
-                });
+                // using PairContainer = Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
+                // std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
+                // auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
+                //     graph_ptr,
+                //     [&](const auto &callback, bool) {
+                //         utils::call_rows<std::unique_ptr<const bit_vector>,
+                //                         std::unique_ptr<const sdsl::int_vector<>>,
+                //                         PairContainer, true>(columns, column_values,
+                //                                             [&](uint64_t row_i, const auto &row, size_t) {
+                //             callback(row_i, row);
+                //         });
+                //     },
+                //     groups,
+                //     config, num_threads, test_dump_basename, num_threads,
+                //     [&]() {
+                //         columns.clear();
+                //         column_values.clear();
+                //     }
+                // );
+
+                // masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                //     auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                //     obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                //     obs_kmers.insert(kmer);
+                // });
             } else {
                 auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
                     *anno_graph,
@@ -186,9 +186,9 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                 std::vector<std::unique_ptr<const bit_vector>> columns;
                 columns.reserve(in_binary_columns.size());
                 std::transform(in_binary_columns.begin(), in_binary_columns.end(), std::back_inserter(columns),
-                            [&](const auto &a) {
-                                return std::make_unique<const bit_vector_stat>(a->to_vector());
-                            });
+                               [&](const auto &a) {
+                                  return std::make_unique<const bit_vector_stat>(a->to_vector());
+                               });
 
                 ASSERT_LT(0u, columns.size());
 
@@ -221,28 +221,28 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
                         .outfbase = "",
                     };
 
-                    using PairContainer = Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
-                    auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
-                        graph_ptr,
-                        [&](const auto &callback) {
-                            utils::call_rows<std::unique_ptr<const bit_vector>,
-                                            std::unique_ptr<const sdsl::int_vector<>>,
-                                            PairContainer, true>(columns, column_values,
-                                                                [&](uint64_t row_i, const auto &row, size_t) {
-                                callback(row_i, row);
-                            });
-                        },
-                        groups,
-                        config, num_threads, test_dump_basename, num_threads
-                    );
-
-                    masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
-                        auto cur_labels = anno_graph->get_labels(kmer, 0.0);
-                        obs_labels.insert(cur_labels.begin(), cur_labels.end());
-                        obs_kmers.insert(kmer);
-                    });
-                    EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
-                    EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
+                    // using PairContainer = Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
+                    // auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
+                    //     graph_ptr,
+                    //     [&](const auto &callback, bool) {
+                    //         utils::call_rows<std::unique_ptr<const bit_vector>,
+                    //                         std::unique_ptr<const sdsl::int_vector<>>,
+                    //                         PairContainer, true>(columns, column_values,
+                    //                                             [&](uint64_t row_i, const auto &row, size_t) {
+                    //             callback(row_i, row);
+                    //         });
+                    //     },
+                    //     groups,
+                    //     config, num_threads, test_dump_basename, num_threads
+                    // );
+
+                    // masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                    //     auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                    //     obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                    //     obs_kmers.insert(kmer);
+                    // });
+                    // EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
+                    // EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
                 }
             } else {
                 for (const auto &test_type : test_types) {

From 51341c1039050ee7616b764bebb937ea543c68da Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 17 Jul 2024 20:07:16 +0200
Subject: [PATCH 235/410] refactor

---
 .../annotation/int_matrix/base/int_matrix.cpp | 23 ++++++
 .../annotation/int_matrix/base/int_matrix.hpp |  3 +
 .../src/graph/annotated_graph_algorithm.cpp   | 75 +++++++++++++------
 3 files changed, 78 insertions(+), 23 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
index e0b6ba7e8d..13c3aee457 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
@@ -32,6 +32,29 @@ void IntMatrix::call_row_values(const std::function<void(uint64_t, const RowValu
     }
 }
 
+std::vector<VectorMap<uint64_t, size_t>> IntMatrix::get_histograms() const {
+    common::logger->trace("Calculating count histograms");
+    std::vector<VectorMap<uint64_t, size_t>> hists_map(get_binary_matrix().num_columns());
+    call_row_values([&](auto, const auto &row) {
+        if (row.empty())
+            return;
+
+        Vector<uint64_t> counts(hists_map.size());
+        for (const auto &[j, raw_c] : row) {
+            counts[j] = raw_c;
+        }
+
+        #pragma omp critical
+        {
+            for (size_t j = 0; j < counts.size(); ++j) {
+                ++hists_map[j][counts[j]];
+            }
+        }
+    }, false);
+
+    return hists_map;
+}
+
 IntMatrix::RowValues
 IntMatrix::sum_row_values(const std::vector<std::pair<Row, size_t>> &index_counts,
                           size_t min_count) const {
diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
index 3fb498a4ef..6b054e1b90 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
@@ -4,6 +4,7 @@
 #include <vector>
 
 #include "annotation/binary_matrix/base/binary_matrix.hpp"
+#include "common/vector_map.hpp"
 
 
 namespace mtg {
@@ -23,6 +24,8 @@ class IntMatrix {
     virtual void call_row_values(const std::function<void(uint64_t, const RowValues&)> &callback,
                                  bool ordered = true) const;
 
+    virtual std::vector<VectorMap<uint64_t, size_t>> get_histograms() const;
+
     // sum up values for each column with at least |min_count| non-zero values
     virtual RowValues
     sum_row_values(const std::vector<std::pair<BinaryMatrix::Row, size_t>> &index_counts,
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 506b2fc853..d224358011 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -59,11 +59,11 @@ bit_cast(const From& src) noexcept
     return dst;
 }
 
-template <typename value_type, class PValStorage>
+template <typename value_type, class PValStorage, typename Generator>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         const std::function<void(const std::function<void(uint64_t, const Vector<std::pair<uint64_t, value_type>>)>&,
-                                                  bool)> &generate_rows,
+                         std::vector<VectorMap<uint64_t, size_t>>&& hists_map,
+                         const Generator &generate_rows,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads = 1,
@@ -86,28 +86,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
 
     std::mutex agg_mu;
-    std::vector<std::mutex> column_mus(groups.size());
-    std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
     using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
 
-    common::logger->trace("Calculating count histograms");
-    generate_rows([&](uint64_t, const auto &row) {
-        if (row.empty())
-            return;
-
-        Vector<uint64_t> counts(groups.size(), 0);
-        for (const auto &[j, raw_c] : row) {
-            counts[j] = raw_c;
-        }
-
-        #pragma omp critical
-        {
-        for (size_t j = 0; j < counts.size(); ++j) {
-            ++hists_map[j][counts[j]];
-        }
-        }
-    }, false);
-
     if (config.clean) {
         common::logger->trace("Cleaning count columns");
 
@@ -1032,8 +1012,31 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             max_width = std::max(max_width, col_vals->width());
         }
 
+        common::logger->trace("Calculating count histograms");
+        std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
+        utils::call_rows<std::unique_ptr<const bit_vector>,
+                         std::unique_ptr<const ValuesContainer>,
+                         PairContainer, false>(columns_all, column_values_all,
+                                               [&](uint64_t row_i, const auto &row, size_t) {
+            if (row.empty())
+                return;
+
+            Vector<uint64_t> counts(groups.size());
+            for (const auto &[j, raw_c] : row) {
+                counts[j] = raw_c;
+            }
+
+            #pragma omp critical
+            {
+                for (size_t j = 0; j < counts.size(); ++j) {
+                    ++hists_map[j][counts[j]];
+                }
+            }
+        });
+
         return mask_nodes_by_label_dual<value_type, PValStorage>(
             graph_ptr,
+            std::move(hists_map),
             [&](const auto &callback, bool ordered) {
                 if (ordered) {
                     utils::call_rows<std::unique_ptr<const bit_vector>,
@@ -1110,6 +1113,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         using value_type = annot::matrix::IntMatrix::Value;
         return mask_nodes_by_label_dual<value_type, PValStorage>(
             std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
+            int_matrix->get_histograms(),
             [&](const auto &callback, bool ordered) {
                 int_matrix->call_row_values(callback, ordered);
             },
@@ -1117,8 +1121,33 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
             config, num_threads, tmp_dir, num_threads
         );
     } else {
+        common::logger->trace("Calculating count histograms");
+        std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
+        for (size_t j = 0; j < groups.size(); ++j) {
+            hists_map[j][0] = 0;
+            hists_map[j][1] = 0;
+        }
+        ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
+        #pragma omp parallel for num_threads(num_threads)
+        for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
+            ++progress_bar;
+            std::vector<Row> row_ids(1, row_i);
+            auto set_bits = matrix.get_rows(row_ids);
+            std::vector<bool> container(groups.size());
+            for (auto j : set_bits[0]) {
+                container[j] = true;
+            }
+
+            #pragma omp critical
+            {
+            for (size_t j = 0; j < container.size(); ++j) {
+                ++hists_map[j][container[j]];
+            }
+            }
+        }
         return mask_nodes_by_label_dual<uint64_t, PValStorage>(
             std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
+            std::move(hists_map),
             [&](const auto &callback, bool ordered) {
                 ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
                 #pragma omp parallel for num_threads(num_threads) ordered

From 46859ed1d6587b1ea40a3cfee3184e7cd0899494 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 18 Jul 2024 16:26:11 +0200
Subject: [PATCH 236/410] test

---
 .../annotation/int_matrix/base/int_matrix.cpp | 12 ++++++------
 .../annotation/int_matrix/base/int_matrix.hpp |  4 ++--
 .../src/graph/annotated_graph_algorithm.cpp   | 19 ++++++++++---------
 3 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
index 13c3aee457..d28d9dd797 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
@@ -13,7 +13,7 @@ namespace matrix {
 using Row = BinaryMatrix::Row;
 using Column = BinaryMatrix::Column;
 
-void IntMatrix::call_row_values(const std::function<void(uint64_t, const RowValues&)> &callback,
+void IntMatrix::call_row_values(const std::function<void(uint64_t, RowValues&&)> &callback,
                                 bool ordered) const {
     size_t n = get_binary_matrix().num_rows();
     ProgressBar progress_bar(n, "Streaming rows", std::cerr, !common::get_verbose());
@@ -21,22 +21,22 @@ void IntMatrix::call_row_values(const std::function<void(uint64_t, const RowValu
     for (size_t row_i = 0; row_i < n; ++row_i) {
         ++progress_bar;
         std::vector<Row> row_ids { row_i };
-        auto row_vals = get_row_values(row_ids)[0];
+        auto row_vals = get_row_values(row_ids);
 
         if (ordered) {
             #pragma omp ordered
-            callback(row_i, row_vals);
+            callback(row_i, std::move(row_vals[0]));
         } else {
-            callback(row_i, row_vals);
+            callback(row_i, std::move(row_vals[0]));
         }
     }
 }
 
-std::vector<VectorMap<uint64_t, size_t>> IntMatrix::get_histograms() const {
+std::vector<VectorMap<uint64_t, size_t>> IntMatrix::get_histograms(bool ignore_empty) const {
     common::logger->trace("Calculating count histograms");
     std::vector<VectorMap<uint64_t, size_t>> hists_map(get_binary_matrix().num_columns());
     call_row_values([&](auto, const auto &row) {
-        if (row.empty())
+        if (ignore_empty && row.empty())
             return;
 
         Vector<uint64_t> counts(hists_map.size());
diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
index 6b054e1b90..3838933ed0 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
@@ -21,10 +21,10 @@ class IntMatrix {
     // |row| is in [0, num_rows), |column| is in [0, num_columns)
     virtual std::vector<RowValues> get_row_values(const std::vector<BinaryMatrix::Row> &rows) const = 0;
 
-    virtual void call_row_values(const std::function<void(uint64_t, const RowValues&)> &callback,
+    virtual void call_row_values(const std::function<void(uint64_t, RowValues&&)> &callback,
                                  bool ordered = true) const;
 
-    virtual std::vector<VectorMap<uint64_t, size_t>> get_histograms() const;
+    virtual std::vector<VectorMap<uint64_t, size_t>> get_histograms(bool ignore_empty = false) const;
 
     // sum up values for each column with at least |min_count| non-zero values
     virtual RowValues
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index d224358011..1101268edf 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -120,16 +120,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     if (groups.size()) {
         std::atomic_thread_fence(std::memory_order_release);
         generate_rows([&](uint64_t row_i, const auto &row) {
-            std::vector<uint64_t> counts(groups.size(), 0);
-            for (const auto &[j, raw_c] : row) {
-                if (raw_c >= min_counts[j] && raw_c <= check_cutoff[j])
-                    return;
+            if (row.size()) {
+                std::vector<uint64_t> counts(groups.size(), 0);
+                for (const auto &[j, raw_c] : row) {
+                    if (raw_c >= min_counts[j] && raw_c <= check_cutoff[j])
+                        return;
 
-                counts[j] = raw_c;
-            }
+                    counts[j] = raw_c;
+                }
 
-            unset_bit(kept.data(), row_i, parallel, MO_RELAXED);
-            if (row.size()) {
                 for (size_t j = 0; j < counts.size(); ++j) {
                     size_t &count = hists_map[j].find(counts[j]).value();
                     size_t old_val = __atomic_fetch_sub(&count, 1, MO_RELAXED);
@@ -137,6 +136,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     assert(old_val);
                 }
             }
+
+            unset_bit(kept.data(), row_i, parallel, MO_RELAXED);
         }, false);
         std::atomic_thread_fence(std::memory_order_acquire);
     }
@@ -1113,7 +1114,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         using value_type = annot::matrix::IntMatrix::Value;
         return mask_nodes_by_label_dual<value_type, PValStorage>(
             std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
-            int_matrix->get_histograms(),
+            int_matrix->get_histograms(true),
             [&](const auto &callback, bool ordered) {
                 int_matrix->call_row_values(callback, ordered);
             },

From ca3bb57a47c3d43b369ec8b90b6af02302fead15 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 19 Jul 2024 22:41:02 +0200
Subject: [PATCH 237/410] scaling

---
 .../annotation/int_matrix/base/int_matrix.cpp | 27 ++++++--
 .../annotation/int_matrix/base/int_matrix.hpp |  5 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 61 ++++++++++---------
 3 files changed, 60 insertions(+), 33 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
index d28d9dd797..057edecfca 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
@@ -4,6 +4,7 @@
 
 #include "common/logger.hpp"
 #include "common/threads/threading.hpp"
+#include "common/vectors/vector_algorithm.hpp"
 
 
 namespace mtg {
@@ -32,12 +33,29 @@ void IntMatrix::call_row_values(const std::function<void(uint64_t, RowValues&&)>
     }
 }
 
-std::vector<VectorMap<uint64_t, size_t>> IntMatrix::get_histograms(bool ignore_empty) const {
+std::vector<VectorMap<uint64_t, size_t>> IntMatrix::get_histograms(const std::vector<size_t> &min_counts,
+                                                                   sdsl::bit_vector *unmark_discarded) const {
     common::logger->trace("Calculating count histograms");
+    bool parallel = get_num_threads() > 0;
     std::vector<VectorMap<uint64_t, size_t>> hists_map(get_binary_matrix().num_columns());
-    call_row_values([&](auto, const auto &row) {
-        if (ignore_empty && row.empty())
-            return;
+    std::atomic_thread_fence(std::memory_order_release);
+    call_row_values([&](uint64_t row_i, const auto &row) {
+        if (min_counts.size()) {
+            bool keep_row = false;
+            for (const auto &[j, c] : row) {
+                if (c >= min_counts[j]) {
+                    keep_row = true;
+                    break;
+                }
+            }
+
+            if (!keep_row) {
+                if (unmark_discarded)
+                    unset_bit(unmark_discarded->data(), row_i, parallel, std::memory_order_relaxed);
+
+                return;
+            }
+        }
 
         Vector<uint64_t> counts(hists_map.size());
         for (const auto &[j, raw_c] : row) {
@@ -51,6 +69,7 @@ std::vector<VectorMap<uint64_t, size_t>> IntMatrix::get_histograms(bool ignore_e
             }
         }
     }, false);
+    std::atomic_thread_fence(std::memory_order_acquire);
 
     return hists_map;
 }
diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
index 3838933ed0..7a2a399495 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
@@ -3,6 +3,8 @@
 
 #include <vector>
 
+#include <sdsl/int_vector.hpp>
+
 #include "annotation/binary_matrix/base/binary_matrix.hpp"
 #include "common/vector_map.hpp"
 
@@ -24,7 +26,8 @@ class IntMatrix {
     virtual void call_row_values(const std::function<void(uint64_t, RowValues&&)> &callback,
                                  bool ordered = true) const;
 
-    virtual std::vector<VectorMap<uint64_t, size_t>> get_histograms(bool ignore_empty = false) const;
+    virtual std::vector<VectorMap<uint64_t, size_t>> get_histograms(const std::vector<size_t> &min_counts = {},
+                                                                    sdsl::bit_vector *unmark_discarded = nullptr) const;
 
     // sum up values for each column with at least |min_count| non-zero values
     virtual RowValues
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 1101268edf..caac54628b 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -59,10 +59,10 @@ bit_cast(const From& src) noexcept
     return dst;
 }
 
-template <typename value_type, class PValStorage, typename Generator>
+template <typename value_type, class PValStorage, typename HistGetter, typename Generator>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         std::vector<VectorMap<uint64_t, size_t>>&& hists_map,
+                         const HistGetter &get_hist_map,
                          const Generator &generate_rows,
                          const std::vector<bool> &groups,
                          const DifferentialAssemblyConfig &config,
@@ -80,9 +80,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
 
+    auto hists_map = get_hist_map(std::vector<size_t>(groups.size(), 1), nullptr);
+
     sdsl::bit_vector kept(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), true);
 
-    std::vector<uint64_t> min_counts(groups.size(), config.min_count);
+    std::vector<size_t> min_counts(groups.size(), config.min_count);
     std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
 
     std::mutex agg_mu;
@@ -118,28 +120,29 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Marking discarded k-mers");
 
     if (groups.size()) {
-        std::atomic_thread_fence(std::memory_order_release);
-        generate_rows([&](uint64_t row_i, const auto &row) {
-            if (row.size()) {
-                std::vector<uint64_t> counts(groups.size(), 0);
-                for (const auto &[j, raw_c] : row) {
-                    if (raw_c >= min_counts[j] && raw_c <= check_cutoff[j])
-                        return;
-
-                    counts[j] = raw_c;
-                }
-
-                for (size_t j = 0; j < counts.size(); ++j) {
-                    size_t &count = hists_map[j].find(counts[j]).value();
-                    size_t old_val = __atomic_fetch_sub(&count, 1, MO_RELAXED);
-                    std::ignore = old_val;
-                    assert(old_val);
-                }
-            }
-
-            unset_bit(kept.data(), row_i, parallel, MO_RELAXED);
-        }, false);
-        std::atomic_thread_fence(std::memory_order_acquire);
+        hists_map = get_hist_map(min_counts, &kept);
+        // std::atomic_thread_fence(std::memory_order_release);
+        // generate_rows([&](uint64_t row_i, const auto &row) {
+        //     if (row.size()) {
+        //         std::vector<uint64_t> counts(groups.size(), 0);
+        //         for (const auto &[j, raw_c] : row) {
+        //             if (raw_c >= min_counts[j] && raw_c <= check_cutoff[j])
+        //                 return;
+
+        //             counts[j] = raw_c;
+        //         }
+
+        //         for (size_t j = 0; j < counts.size(); ++j) {
+        //             size_t &count = hists_map[j].find(counts[j]).value();
+        //             size_t old_val = __atomic_fetch_sub(&count, 1, MO_RELAXED);
+        //             std::ignore = old_val;
+        //             assert(old_val);
+        //         }
+        //     }
+
+        //     unset_bit(kept.data(), row_i, parallel, MO_RELAXED);
+        // }, false);
+        // std::atomic_thread_fence(std::memory_order_acquire);
     }
 
     size_t nelem = sdsl::util::cnt_one_bits(kept);
@@ -1037,7 +1040,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         return mask_nodes_by_label_dual<value_type, PValStorage>(
             graph_ptr,
-            std::move(hists_map),
+            [&](const std::vector<size_t> &, sdsl::bit_vector *) -> std::vector<VectorMap<uint64_t, size_t>> { throw std::runtime_error("not imf"); return {}; },
             [&](const auto &callback, bool ordered) {
                 if (ordered) {
                     utils::call_rows<std::unique_ptr<const bit_vector>,
@@ -1114,7 +1117,9 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         using value_type = annot::matrix::IntMatrix::Value;
         return mask_nodes_by_label_dual<value_type, PValStorage>(
             std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
-            int_matrix->get_histograms(true),
+            [&](const std::vector<size_t> &min_counts, sdsl::bit_vector *unmark_discarded) {
+                return int_matrix->get_histograms(min_counts, unmark_discarded);
+            },
             [&](const auto &callback, bool ordered) {
                 int_matrix->call_row_values(callback, ordered);
             },
@@ -1148,7 +1153,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         }
         return mask_nodes_by_label_dual<uint64_t, PValStorage>(
             std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
-            std::move(hists_map),
+            [&](const std::vector<size_t> &, sdsl::bit_vector *) -> std::vector<VectorMap<uint64_t, size_t>> { throw std::runtime_error("not imf"); return {}; },
             [&](const auto &callback, bool ordered) {
                 ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
                 #pragma omp parallel for num_threads(num_threads) ordered

From fe200e3bbe59192708a467e7063cb2d69e326d6b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 20 Jul 2024 17:11:15 +0200
Subject: [PATCH 238/410] much more parallel

---
 .../src/graph/annotated_graph_algorithm.cpp   | 27 ++-----------------
 1 file changed, 2 insertions(+), 25 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index caac54628b..ce78c56275 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -119,31 +119,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Marking discarded k-mers");
 
-    if (groups.size()) {
+    if (groups.size())
         hists_map = get_hist_map(min_counts, &kept);
-        // std::atomic_thread_fence(std::memory_order_release);
-        // generate_rows([&](uint64_t row_i, const auto &row) {
-        //     if (row.size()) {
-        //         std::vector<uint64_t> counts(groups.size(), 0);
-        //         for (const auto &[j, raw_c] : row) {
-        //             if (raw_c >= min_counts[j] && raw_c <= check_cutoff[j])
-        //                 return;
-
-        //             counts[j] = raw_c;
-        //         }
-
-        //         for (size_t j = 0; j < counts.size(); ++j) {
-        //             size_t &count = hists_map[j].find(counts[j]).value();
-        //             size_t old_val = __atomic_fetch_sub(&count, 1, MO_RELAXED);
-        //             std::ignore = old_val;
-        //             assert(old_val);
-        //         }
-        //     }
-
-        //     unset_bit(kept.data(), row_i, parallel, MO_RELAXED);
-        // }, false);
-        // std::atomic_thread_fence(std::memory_order_acquire);
-    }
 
     size_t nelem = sdsl::util::cnt_one_bits(kept);
 
@@ -816,7 +793,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 pvals[node] = bit_cast<uint64_t>(pval);
             }
         }
-    }, true);
+    }, false);
 
     std::atomic_thread_fence(std::memory_order_acquire);
 

From 5ea1a439ffcdacdf9e3de845ab8065f9873aee6c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 20 Jul 2024 17:36:28 +0200
Subject: [PATCH 239/410] t

---
 .../src/graph/annotated_graph_algorithm.cpp   | 167 +++++++++++-------
 1 file changed, 99 insertions(+), 68 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ce78c56275..bd53e75313 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -993,48 +993,69 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             max_width = std::max(max_width, col_vals->width());
         }
 
-        common::logger->trace("Calculating count histograms");
-        std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
-        utils::call_rows<std::unique_ptr<const bit_vector>,
-                         std::unique_ptr<const ValuesContainer>,
-                         PairContainer, false>(columns_all, column_values_all,
-                                               [&](uint64_t row_i, const auto &row, size_t) {
-            if (row.empty())
-                return;
-
-            Vector<uint64_t> counts(groups.size());
-            for (const auto &[j, raw_c] : row) {
-                counts[j] = raw_c;
+        auto generate_rows = [&](const auto &callback, bool ordered) {
+            if (ordered) {
+                utils::call_rows<std::unique_ptr<const bit_vector>,
+                                 std::unique_ptr<const ValuesContainer>,
+                                 PairContainer, true>(columns_all, column_values_all,
+                                                      [&](uint64_t row_i, const auto &row, size_t) {
+                    callback(row_i, row);
+                });
+            } else {
+                utils::call_rows<std::unique_ptr<const bit_vector>,
+                                 std::unique_ptr<const ValuesContainer>,
+                                 PairContainer, false>(columns_all, column_values_all,
+                                                       [&](uint64_t row_i, const auto &row, size_t) {
+                    callback(row_i, row);
+                });
             }
+        };
 
-            #pragma omp critical
-            {
-                for (size_t j = 0; j < counts.size(); ++j) {
-                    ++hists_map[j][counts[j]];
-                }
-            }
-        });
+        bool parallel = get_num_threads() > 1;
 
         return mask_nodes_by_label_dual<value_type, PValStorage>(
             graph_ptr,
-            [&](const std::vector<size_t> &, sdsl::bit_vector *) -> std::vector<VectorMap<uint64_t, size_t>> { throw std::runtime_error("not imf"); return {}; },
-            [&](const auto &callback, bool ordered) {
-                if (ordered) {
-                    utils::call_rows<std::unique_ptr<const bit_vector>,
+            [&](const std::vector<size_t> &min_counts, sdsl::bit_vector *kept) -> std::vector<VectorMap<uint64_t, size_t>> {
+                common::logger->trace("Calculating count histograms");
+                std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
+                utils::call_rows<std::unique_ptr<const bit_vector>,
                                  std::unique_ptr<const ValuesContainer>,
-                                 PairContainer, true>(columns_all, column_values_all,
-                                                      [&](uint64_t row_i, const auto &row, size_t) {
-                        callback(row_i, row);
-                    });
-                } else {
-                    utils::call_rows<std::unique_ptr<const bit_vector>,
-                                     std::unique_ptr<const ValuesContainer>,
-                                     PairContainer, false>(columns_all, column_values_all,
-                                                           [&](uint64_t row_i, const auto &row, size_t) {
-                        callback(row_i, row);
-                    });
-                }
+                                 PairContainer, false>(columns_all, column_values_all,
+                                                       [&](uint64_t row_i, const auto &row, size_t) {
+                    if (row.empty()) {
+                        if (kept)
+                            unset_bit(kept->data(), row_i, parallel, std::memory_order_relaxed);
+
+                        return;
+                    }
+
+                    bool found = false;
+                    Vector<uint64_t> counts(groups.size());
+                    for (const auto &[j, raw_c] : row) {
+                        if (min_counts.empty() || raw_c >= min_counts[j]) {
+                            counts[j] = raw_c;
+                            found = true;
+                        }
+                    }
+
+                    if (!found) {
+                        if (kept)
+                            unset_bit(kept->data(), row_i, parallel, std::memory_order_relaxed);
+
+                        return;
+                    }
+
+                    #pragma omp critical
+                    {
+                        for (size_t j = 0; j < counts.size(); ++j) {
+                            ++hists_map[j][counts[j]];
+                        }
+                    }
+                });
+
+                return hists_map;
             },
+            generate_rows,
             groups,
             config, num_threads, tmp_dir, num_parallel_files,
             [&]() {
@@ -1104,54 +1125,64 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
             config, num_threads, tmp_dir, num_threads
         );
     } else {
-        common::logger->trace("Calculating count histograms");
-        std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
-        for (size_t j = 0; j < groups.size(); ++j) {
-            hists_map[j][0] = 0;
-            hists_map[j][1] = 0;
-        }
-        ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
-        #pragma omp parallel for num_threads(num_threads)
-        for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
-            ++progress_bar;
-            std::vector<Row> row_ids(1, row_i);
-            auto set_bits = matrix.get_rows(row_ids);
-            std::vector<bool> container(groups.size());
-            for (auto j : set_bits[0]) {
-                container[j] = true;
-            }
+        auto generate_rows = [&](const auto &callback, bool ordered) {
+            ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
+            #pragma omp parallel for num_threads(num_threads) ordered
+            for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
+                ++progress_bar;
+                std::vector<Row> row_ids(1, row_i);
+                auto set_bits = matrix.get_rows(row_ids);
+                Vector<std::pair<uint64_t, uint64_t>> container;
+                container.reserve(set_bits[0].size());
+                for (auto j : set_bits[0]) {
+                    container.emplace_back(j, 1);
+                }
 
-            #pragma omp critical
-            {
-            for (size_t j = 0; j < container.size(); ++j) {
-                ++hists_map[j][container[j]];
-            }
+                if (ordered) {
+                    #pragma omp ordered
+                    callback(row_i, container);
+                } else {
+                    callback(row_i, container);
+                }
             }
-        }
+        };
+
+        bool parallel = get_num_threads() > 1;
+
         return mask_nodes_by_label_dual<uint64_t, PValStorage>(
             std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
-            [&](const std::vector<size_t> &, sdsl::bit_vector *) -> std::vector<VectorMap<uint64_t, size_t>> { throw std::runtime_error("not imf"); return {}; },
-            [&](const auto &callback, bool ordered) {
+            [&](const std::vector<size_t> &, sdsl::bit_vector *kept) -> std::vector<VectorMap<uint64_t, size_t>> {
+                common::logger->trace("Calculating count histograms");
+                std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    hists_map[j][0] = 0;
+                    hists_map[j][1] = 0;
+                }
                 ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
-                #pragma omp parallel for num_threads(num_threads) ordered
+                #pragma omp parallel for num_threads(num_threads)
                 for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
                     ++progress_bar;
                     std::vector<Row> row_ids(1, row_i);
                     auto set_bits = matrix.get_rows(row_ids);
-                    Vector<std::pair<uint64_t, uint64_t>> container;
-                    container.reserve(set_bits[0].size());
+                    std::vector<bool> container(groups.size());
                     for (auto j : set_bits[0]) {
-                        container.emplace_back(j, 1);
+                        container[j] = true;
                     }
 
-                    if (ordered) {
-                        #pragma omp ordered
-                        callback(row_i, container);
-                    } else {
-                        callback(row_i, container);
+                    if (kept && set_bits.empty())
+                        unset_bit(kept->data(), row_i, parallel, std::memory_order_relaxed);
+
+                    #pragma omp critical
+                    {
+                    for (size_t j = 0; j < container.size(); ++j) {
+                        ++hists_map[j][container[j]];
+                    }
                     }
                 }
+
+                return hists_map;
             },
+            generate_rows,
             groups,
             config, num_threads, tmp_dir, num_threads
         );

From 0a1040e713eb0f826b63a6a7ac92c1a4ff2383c0 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 20 Jul 2024 19:01:24 +0200
Subject: [PATCH 240/410] fix

---
 metagraph/src/cli/assemble.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index 466c9a3f2b..fa6d982f55 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -114,7 +114,7 @@ void call_masked_graphs(std::shared_ptr<DeBruijnGraph> graph_ptr,
             std::shared_ptr<DeBruijnGraph> outgraph;
 
             std::variant<std::vector<uint64_t>, sdsl::int_vector_buffer<64>> pvals;
-            if (diff_config.test_by_unitig) {
+            if (!diff_config.test_by_unitig) {
                 pvals = sdsl::int_vector_buffer<64>();
             } else {
                 pvals = std::vector<uint64_t>();

From 1538a688a8819f6fa206ca8aee92c711d14c14e4 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 21 Jul 2024 00:32:27 +0200
Subject: [PATCH 241/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 60 +++++++++++++++----
 1 file changed, 48 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index bd53e75313..9c4499706c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -180,22 +180,32 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Allocating p-value storage");
     auto nullpval = bit_cast<uint64_t>(double(1.1));
-    std::unique_ptr<utils::TempFile> tmp_file;
-    std::unique_ptr<utils::TempFile> tmp_file_min;
-    PValStorage pvals;
-    if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-        tmp_file = std::make_unique<utils::TempFile>(tmp_dir);
-        pvals = sdsl::int_vector_buffer<64>(tmp_file->name(), std::ios::out);
-    }
 
-    std::unique_ptr<PValStorage> pvals_min;
+    std::vector<PValStorage> pvals_buckets;
+    std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
+    if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+        pvals_buckets.emplace_back(graph_ptr->max_index() + 1, nullpval);
+    }
 
-    for (size_t i = 0; i <= graph_ptr->max_index(); ++i) {
-        pvals.push_back(nullpval);
+    uint64_t bucket_size = (graph_ptr->max_index() + 1) / num_threads;
+    if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+        for (size_t j = 0; j < num_threads; ++j) {
+            auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+            auto &pvals = pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
+            uint64_t cur_bucket_size = bucket_size;
+            if (j + 1 == num_threads)
+                cur_bucket_size -= (graph_ptr->max_index() + 1) % num_threads;
+
+            for (size_t i = 0; i < cur_bucket_size; ++i) {
+                pvals.push_back(nullpval);
+            }
+        }
     }
 
     std::unique_ptr<PValStorage> eff_size;
+    std::unique_ptr<PValStorage> pvals_min;
     std::unique_ptr<utils::TempFile> tmp_file_eff;
+    std::unique_ptr<utils::TempFile> tmp_file_min;
     if (config.test_by_unitig) {
         common::logger->trace("Allocating p-value storage");
         pvals_min = std::make_unique<PValStorage>();
@@ -764,9 +774,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         if (config.test_by_unitig) {
             assert(pvals_min);
+            size_t bucket_idx = std::min(node / bucket_size, pvals_buckets.size() - 1);
+            auto &pvals = pvals_buckets[bucket_idx];
+
             std::lock_guard<std::mutex> lock(pval_mu);
+            pvals[node - bucket_size * bucket_idx] = bit_cast<uint64_t>(pval);
             (*pvals_min)[node] = bit_cast<uint64_t>(pval_min);
-            pvals[node] = bit_cast<uint64_t>(pval);
             (*eff_size)[node] = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
         } else {
             uint64_t k = std::numeric_limits<uint64_t>::max();
@@ -788,15 +801,38 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             if (config.test_type != "notest") {
+                size_t bucket_idx = std::min(node / bucket_size, pvals_buckets.size() - 1);
+                auto &pvals = pvals_buckets[bucket_idx];
                 std::lock_guard<std::mutex> lock(pval_mu);
                 ++m[k];
-                pvals[node] = bit_cast<uint64_t>(pval);
+                pvals[node - bucket_size * bucket_idx] = bit_cast<uint64_t>(pval);
             }
         }
     }, false);
 
     std::atomic_thread_fence(std::memory_order_acquire);
 
+    std::unique_ptr<utils::TempFile> tmp_file;
+    PValStorage pvals;
+
+    if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+        pvals = std::move(pvals_buckets[0]);
+    }
+
+    if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+        common::logger->trace("Merging buckets");
+        for (size_t i = 1; i < pvals_buckets.size(); ++i) {
+            for (size_t j = 0; j < pvals_buckets[i].size(); ++j) {
+                pvals_buckets[0].push_back(pvals_buckets[i][j]);
+            }
+            pvals_buckets[i].close();
+        }
+        pvals = std::move(pvals_buckets[0]);
+        pvals_buckets.resize(0);
+        tmp_file = std::move(tmp_buckets[0]);
+        tmp_buckets.resize(0);
+    }
+
     if (ex)
         std::rethrow_exception(ex);
 

From 0cc67683a9398f2ad48355768e5ae36e598f4fa7 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 21 Jul 2024 14:48:12 +0200
Subject: [PATCH 242/410] start

---
 .../annotation/int_matrix/base/int_matrix.cpp |  8 ++--
 .../annotation/int_matrix/base/int_matrix.hpp |  2 +-
 metagraph/src/common/vectors/transpose.hpp    |  4 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 44 +++++++++++--------
 4 files changed, 33 insertions(+), 25 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
index 057edecfca..afc5f84968 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
@@ -14,7 +14,7 @@ namespace matrix {
 using Row = BinaryMatrix::Row;
 using Column = BinaryMatrix::Column;
 
-void IntMatrix::call_row_values(const std::function<void(uint64_t, RowValues&&)> &callback,
+void IntMatrix::call_row_values(const std::function<void(uint64_t, RowValues&&, size_t)> &callback,
                                 bool ordered) const {
     size_t n = get_binary_matrix().num_rows();
     ProgressBar progress_bar(n, "Streaming rows", std::cerr, !common::get_verbose());
@@ -26,9 +26,9 @@ void IntMatrix::call_row_values(const std::function<void(uint64_t, RowValues&&)>
 
         if (ordered) {
             #pragma omp ordered
-            callback(row_i, std::move(row_vals[0]));
+            callback(row_i, std::move(row_vals[0]), row_i);
         } else {
-            callback(row_i, std::move(row_vals[0]));
+            callback(row_i, std::move(row_vals[0]), row_i);
         }
     }
 }
@@ -39,7 +39,7 @@ std::vector<VectorMap<uint64_t, size_t>> IntMatrix::get_histograms(const std::ve
     bool parallel = get_num_threads() > 0;
     std::vector<VectorMap<uint64_t, size_t>> hists_map(get_binary_matrix().num_columns());
     std::atomic_thread_fence(std::memory_order_release);
-    call_row_values([&](uint64_t row_i, const auto &row) {
+    call_row_values([&](uint64_t row_i, const auto &row, size_t) {
         if (min_counts.size()) {
             bool keep_row = false;
             for (const auto &[j, c] : row) {
diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
index 7a2a399495..bb366cd80f 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
@@ -23,7 +23,7 @@ class IntMatrix {
     // |row| is in [0, num_rows), |column| is in [0, num_columns)
     virtual std::vector<RowValues> get_row_values(const std::vector<BinaryMatrix::Row> &rows) const = 0;
 
-    virtual void call_row_values(const std::function<void(uint64_t, RowValues&&)> &callback,
+    virtual void call_row_values(const std::function<void(uint64_t, RowValues&&, size_t)> &callback,
                                  bool ordered = true) const;
 
     virtual std::vector<VectorMap<uint64_t, size_t>> get_histograms(const std::vector<size_t> &min_counts = {},
diff --git a/metagraph/src/common/vectors/transpose.hpp b/metagraph/src/common/vectors/transpose.hpp
index 94a6dfdcab..819956f6f4 100644
--- a/metagraph/src/common/vectors/transpose.hpp
+++ b/metagraph/src/common/vectors/transpose.hpp
@@ -119,14 +119,14 @@ void call_rows(const std::vector<BitmapPtr> &columns,
             #pragma omp ordered
             {
                 for (uint64_t j = 0; j < rows.size(); ++j) {
-                    callback(j + begin, rows[j], begin);
+                    callback(j + begin, rows[j], begin / kNumRowsInBlock);
                     ++progress_bar;
                     rows[j].resize(0);
                 }
             }
         } else {
             for (uint64_t j = 0; j < rows.size(); ++j) {
-                callback(j + begin, rows[j], begin);
+                callback(j + begin, rows[j], begin / kNumRowsInBlock);
                 ++progress_bar;
                 rows[j].resize(0);
             }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9c4499706c..113ec8584d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -187,19 +187,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         pvals_buckets.emplace_back(graph_ptr->max_index() + 1, nullpval);
     }
 
-    uint64_t bucket_size = (graph_ptr->max_index() + 1) / num_threads;
+    uint64_t bucket_size = graph_ptr->max_index() / num_threads;
     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-        for (size_t j = 0; j < num_threads; ++j) {
+        for (size_t total_size = 0; total_size < graph_ptr->max_index(); total_size += bucket_size) {
             auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
             auto &pvals = pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
             uint64_t cur_bucket_size = bucket_size;
-            if (j + 1 == num_threads)
-                cur_bucket_size -= (graph_ptr->max_index() + 1) % num_threads;
+            if (total_size + bucket_size > graph_ptr->max_index())
+                cur_bucket_size -= total_size + bucket_size - graph_ptr->max_index();
 
             for (size_t i = 0; i < cur_bucket_size; ++i) {
                 pvals.push_back(nullpval);
             }
         }
+        pvals_buckets[0].push_back(nullpval);
     }
 
     std::unique_ptr<PValStorage> eff_size;
@@ -233,7 +234,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
     }
 
-    VectorMap<size_t, size_t> m;
     std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
     std::function<double(int64_t)> compute_min_pval;
 
@@ -704,9 +704,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::exception_ptr ex = nullptr;
 
     std::mutex pval_mu;
+
+    std::vector<VectorMap<size_t, size_t>> ms(pvals_buckets.size());
     std::atomic_thread_fence(std::memory_order_release);
 
-    generate_rows([&](uint64_t row_i, const auto &raw_row) {
+    generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
         if (ex)
             return;
 
@@ -774,11 +776,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         if (config.test_by_unitig) {
             assert(pvals_min);
-            size_t bucket_idx = std::min(node / bucket_size, pvals_buckets.size() - 1);
             auto &pvals = pvals_buckets[bucket_idx];
+            pvals[row_i - bucket_size * bucket_idx + (bucket_idx == 0)] = bit_cast<uint64_t>(pval);
 
             std::lock_guard<std::mutex> lock(pval_mu);
-            pvals[node - bucket_size * bucket_idx] = bit_cast<uint64_t>(pval);
             (*pvals_min)[node] = bit_cast<uint64_t>(pval_min);
             (*eff_size)[node] = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
         } else {
@@ -801,17 +802,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             if (config.test_type != "notest") {
-                size_t bucket_idx = std::min(node / bucket_size, pvals_buckets.size() - 1);
                 auto &pvals = pvals_buckets[bucket_idx];
-                std::lock_guard<std::mutex> lock(pval_mu);
-                ++m[k];
-                pvals[node - bucket_size * bucket_idx] = bit_cast<uint64_t>(pval);
+                pvals[row_i - bucket_size * bucket_idx + (bucket_idx == 0)] = bit_cast<uint64_t>(pval);
+                ++ms[bucket_idx][k];
             }
         }
     }, false);
 
     std::atomic_thread_fence(std::memory_order_acquire);
 
+    for (size_t i = 1; i < ms.size(); ++i) {
+        for (const auto &[k, c] : ms[i]) {
+            ms[0][k] += c;
+        }
+    }
+    VectorMap<size_t, size_t> m = std::move(ms[0]);
+    ms.resize(0);
+
     std::unique_ptr<utils::TempFile> tmp_file;
     PValStorage pvals;
 
@@ -1034,15 +1041,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 utils::call_rows<std::unique_ptr<const bit_vector>,
                                  std::unique_ptr<const ValuesContainer>,
                                  PairContainer, true>(columns_all, column_values_all,
-                                                      [&](uint64_t row_i, const auto &row, size_t) {
-                    callback(row_i, row);
+                                                      [&](uint64_t row_i, const auto &row, size_t thread_id) {
+                    callback(row_i, row, thread_id);
                 });
             } else {
                 utils::call_rows<std::unique_ptr<const bit_vector>,
                                  std::unique_ptr<const ValuesContainer>,
                                  PairContainer, false>(columns_all, column_values_all,
-                                                       [&](uint64_t row_i, const auto &row, size_t) {
-                    callback(row_i, row);
+                                                       [&](uint64_t row_i, const auto &row, size_t thread_id) {
+                    callback(row_i, row, thread_id);
                 });
             }
         };
@@ -1162,6 +1169,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         );
     } else {
         auto generate_rows = [&](const auto &callback, bool ordered) {
+            size_t bucket_size = matrix.num_rows() / num_threads;
             ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
             #pragma omp parallel for num_threads(num_threads) ordered
             for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
@@ -1176,9 +1184,9 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
 
                 if (ordered) {
                     #pragma omp ordered
-                    callback(row_i, container);
+                    callback(row_i, container, row_i / bucket_size);
                 } else {
-                    callback(row_i, container);
+                    callback(row_i, container, row_i / bucket_size);
                 }
             }
         };

From 36301efbf517f5b0f73c308c60920a0a08152c2f Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 21 Jul 2024 18:47:05 +0200
Subject: [PATCH 243/410] efficient

---
 .../src/graph/annotated_graph_algorithm.cpp   |  9 ++-
 .../tests/annotation/test_annotated_dbg.cpp   | 59 +++++++++----------
 2 files changed, 34 insertions(+), 34 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 113ec8584d..598e4c82e7 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -187,7 +187,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         pvals_buckets.emplace_back(graph_ptr->max_index() + 1, nullpval);
     }
 
-    uint64_t bucket_size = graph_ptr->max_index() / num_threads;
+    uint64_t bucket_size = graph_ptr->max_index() / std::max(size_t(1), num_threads - 1);
+    bucket_size -= bucket_size % 64;
     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
         for (size_t total_size = 0; total_size < graph_ptr->max_index(); total_size += bucket_size) {
             auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
@@ -797,13 +798,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
+            size_t pval_i = row_i - bucket_size * bucket_idx + (bucket_idx == 0);
             if (in_kmer != out_kmer && (pval < config.family_wise_error_rate || config.test_by_unitig)) {
-                set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
+                bool use_atomic = parallel && pval_i + 1 == pvals_buckets[bucket_idx].size();
+                set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
             }
 
             if (config.test_type != "notest") {
                 auto &pvals = pvals_buckets[bucket_idx];
-                pvals[row_i - bucket_size * bucket_idx + (bucket_idx == 0)] = bit_cast<uint64_t>(pval);
+                pvals[pval_i] = bit_cast<uint64_t>(pval);
                 ++ms[bucket_idx][k];
             }
         }
diff --git a/metagraph/tests/annotation/test_annotated_dbg.cpp b/metagraph/tests/annotation/test_annotated_dbg.cpp
index 278f01f1f0..2fd48c1426 100644
--- a/metagraph/tests/annotation/test_annotated_dbg.cpp
+++ b/metagraph/tests/annotation/test_annotated_dbg.cpp
@@ -4,21 +4,18 @@
 #include "gtest/gtest.h"
 
 #include "../test_helpers.hpp"
+#include "test_annotated_dbg_helpers.hpp"
 
 #include "common/threads/threading.hpp"
 #include "common/vectors/bit_vector_dyn.hpp"
 #include "common/vectors/vector_algorithm.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
+#include "annotation/annotation_converters.hpp"
 #include "graph/representation/bitmap/dbg_bitmap.hpp"
 #include "graph/representation/hash/dbg_hash_string.hpp"
 #include "graph/representation/hash/dbg_hash_ordered.hpp"
 #include "graph/representation/hash/dbg_hash_fast.hpp"
 
-#define protected public
-#define private public
-#include "annotation/annotation_converters.hpp"
-#include "test_annotated_dbg_helpers.hpp"
-
 
 namespace {
 
@@ -81,14 +78,14 @@ TEST(AnnotatedDBG, ExtendGraphWithSimplePath) {
         std::string sequence(100, 'A');
 
         bit_vector_dyn inserted_nodes(anno_graph.get_graph().max_index() + 1, 0);
-        anno_graph.graph_->add_sequence(sequence,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(sequence,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
 
         ASSERT_EQ(k + 2, anno_graph.get_graph().num_nodes());
         EXPECT_EQ(1u, anno_graph.get_annotator().num_objects());
 
-        anno_graph.annotator_->insert_rows(edge_to_row_idx(inserted_nodes));
+        const_cast<AnnotatedDBG::Annotator&>(anno_graph.get_annotator()).insert_rows(edge_to_row_idx(inserted_nodes));
         EXPECT_EQ(anno_graph.get_graph().num_nodes() + 1, inserted_nodes.size());
 
         EXPECT_FALSE(anno_graph.label_exists("Label"));
@@ -142,11 +139,11 @@ TEST(AnnotatedDBG, ExtendGraphAddPath) {
         check_labels(anno_graph, seq_first, { "First" }, { "Second", "Third" });
 
         bit_vector_dyn inserted_nodes(anno_graph.get_graph().max_index() + 1, 0);
-        anno_graph.graph_->add_sequence(seq_second,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_second,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
 
-        anno_graph.annotator_->insert_rows(edge_to_row_idx(inserted_nodes));
+        const_cast<AnnotatedDBG::Annotator&>(anno_graph.get_annotator()).insert_rows(edge_to_row_idx(inserted_nodes));
         EXPECT_EQ(anno_graph.get_graph().num_nodes() + 1, inserted_nodes.size());
 
         ASSERT_EQ(std::vector<std::string> { "First" },
@@ -211,11 +208,11 @@ TEST(AnnotatedDBG, Transform) {
         check_labels(*anno_graph, seq_first, { "First" }, { "Second", "Third" });
 
         bit_vector_dyn inserted_nodes(anno_graph->get_graph().max_index() + 1, 0);
-        anno_graph->graph_->add_sequence(seq_second,
+        const_cast<DeBruijnGraph&>(anno_graph->get_graph()).add_sequence(seq_second,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
 
-        anno_graph->annotator_->insert_rows(edge_to_row_idx(inserted_nodes));
+        const_cast<AnnotatedDBG::Annotator&>(anno_graph->get_annotator()).insert_rows(edge_to_row_idx(inserted_nodes));
         EXPECT_EQ(anno_graph->get_graph().num_nodes() + 1, inserted_nodes.size());
 
         ASSERT_EQ(std::vector<std::string> { "First" },
@@ -229,8 +226,8 @@ TEST(AnnotatedDBG, Transform) {
             graph,
             std::unique_ptr<AnnotatedDBG::Annotator>(
                 annot::convert<annot::RowFlatAnnotator>(
-                    std::move(dynamic_cast<annot::ColumnCompressed<>&>(
-                        *anno_graph->annotator_
+                    const_cast<annot::ColumnCompressed<>&&>(dynamic_cast<const annot::ColumnCompressed<>&>(
+                        anno_graph->get_annotator()
                     )
                 )).release()
             )
@@ -295,14 +292,14 @@ TEST(AnnotatedDBG, ExtendGraphAddTwoPaths) {
         EXPECT_FALSE(anno_graph.label_exists("Fourth"));
 
         bit_vector_dyn inserted_nodes(anno_graph.get_graph().max_index() + 1, 0);
-        anno_graph.graph_->add_sequence(seq_second,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_second,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
-        anno_graph.graph_->add_sequence(seq_third,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_third,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
 
-        anno_graph.annotator_->insert_rows(edge_to_row_idx(inserted_nodes));
+        const_cast<AnnotatedDBG::Annotator&>(anno_graph.get_annotator()).insert_rows(edge_to_row_idx(inserted_nodes));
         EXPECT_EQ(anno_graph.get_graph().num_nodes() + 1, inserted_nodes.size());
 
         ASSERT_EQ(std::vector<std::string> { "First" },
@@ -412,14 +409,14 @@ TEST(AnnotatedDBG, ExtendGraphAddTwoPathsParallel) {
                   anno_graph.get_labels(seq_first, 1));
 
         bit_vector_dyn inserted_nodes(anno_graph.get_graph().max_index() + 1, 0);
-        anno_graph.graph_->add_sequence(seq_second,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_second,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
-        anno_graph.graph_->add_sequence(seq_third,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_third,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
 
-        anno_graph.annotator_->insert_rows(edge_to_row_idx(inserted_nodes));
+        const_cast<AnnotatedDBG::Annotator&>(anno_graph.get_annotator()).insert_rows(edge_to_row_idx(inserted_nodes));
         EXPECT_EQ(anno_graph.get_graph().num_nodes() + 1, inserted_nodes.size());
 
         ASSERT_EQ(std::vector<std::string> { "First" },
@@ -532,14 +529,14 @@ TEST(AnnotatedDBG, ExtendGraphAddTwoPathsWithoutDummy) {
                   anno_graph.get_labels(seq_first, 1));
 
         bit_vector_dyn inserted_nodes(anno_graph.get_graph().max_index() + 1, 0);
-        anno_graph.graph_->add_sequence(seq_second,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_second,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
-        anno_graph.graph_->add_sequence(seq_third,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_third,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
 
-        anno_graph.annotator_->insert_rows(edge_to_row_idx(inserted_nodes));
+        const_cast<AnnotatedDBG::Annotator&>(anno_graph.get_annotator()).insert_rows(edge_to_row_idx(inserted_nodes));
         EXPECT_EQ(anno_graph.get_graph().num_nodes() + 1, inserted_nodes.size());
 
         ASSERT_EQ(std::vector<std::string> { "First" },
@@ -656,14 +653,14 @@ TEST(AnnotatedDBG, ExtendGraphAddTwoPathsWithoutDummyParallel) {
                   anno_graph.get_labels(seq_first, 1));
 
         bit_vector_dyn inserted_nodes(anno_graph.get_graph().max_index() + 1, 0);
-        anno_graph.graph_->add_sequence(seq_second,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_second,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
-        anno_graph.graph_->add_sequence(seq_third,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_third,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
 
-        anno_graph.annotator_->insert_rows(edge_to_row_idx(inserted_nodes));
+        const_cast<AnnotatedDBG::Annotator&>(anno_graph.get_annotator()).insert_rows(edge_to_row_idx(inserted_nodes));
         EXPECT_EQ(anno_graph.get_graph().num_nodes() + 1, inserted_nodes.size());
 
         ASSERT_EQ(std::vector<std::string> { "First" },
@@ -778,14 +775,14 @@ TEST(AnnotatedDBG, ExtendGraphAddTwoPathsPruneDummy) {
                   anno_graph.get_labels(seq_first, 1));
 
         bit_vector_dyn inserted_nodes(anno_graph.get_graph().max_index() + 1, 0);
-        anno_graph.graph_->add_sequence(seq_second,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_second,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
-        anno_graph.graph_->add_sequence(seq_third,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_third,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
 
-        anno_graph.annotator_->insert_rows(edge_to_row_idx(inserted_nodes));
+        const_cast<AnnotatedDBG::Annotator&>(anno_graph.get_annotator()).insert_rows(edge_to_row_idx(inserted_nodes));
         EXPECT_EQ(anno_graph.get_graph().num_nodes() + 1, inserted_nodes.size());
 
         ASSERT_EQ(std::vector<std::string> { "First" },
@@ -901,14 +898,14 @@ TEST(AnnotatedDBG, ExtendGraphAddTwoPathsPruneDummyParallel) {
                   anno_graph.get_labels(seq_first, 1));
 
         bit_vector_dyn inserted_nodes(anno_graph.get_graph().max_index() + 1, 0);
-        anno_graph.graph_->add_sequence(seq_second,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_second,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
-        anno_graph.graph_->add_sequence(seq_third,
+        const_cast<DeBruijnGraph&>(anno_graph.get_graph()).add_sequence(seq_third,
             [&](auto new_node) { inserted_nodes.insert_bit(new_node, true); }
         );
 
-        anno_graph.annotator_->insert_rows(edge_to_row_idx(inserted_nodes));
+        const_cast<AnnotatedDBG::Annotator&>(anno_graph.get_annotator()).insert_rows(edge_to_row_idx(inserted_nodes));
         EXPECT_EQ(anno_graph.get_graph().num_nodes() + 1, inserted_nodes.size());
 
         ASSERT_EQ(std::vector<std::string> { "First" },

From 1ed1d5f06f6fb092402fb57a5ef74e9d4320e26c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 22 Jul 2024 02:56:04 +0200
Subject: [PATCH 244/410] ttt

---
 .../int_matrix/csr_matrix/csr_matrix_flat.cpp | 124 ++++++++
 .../int_matrix/csr_matrix/csr_matrix_flat.hpp | 281 ++++++++++++++++++
 2 files changed, 405 insertions(+)
 create mode 100644 metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
 create mode 100644 metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp

diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
new file mode 100644
index 0000000000..7a03e1046d
--- /dev/null
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
@@ -0,0 +1,124 @@
+#include "csr_matrix_flat.hpp"
+
+#include <mutex>
+
+#include "common/vectors/vector_algorithm.hpp"
+
+namespace mtg::annot::matrix {
+
+constexpr size_t buffer_size = 1024*1024;
+
+CSRMatrixFlat::CSRMatrixFlat(const std::function<void(const std::function<void(const Vector<std::pair<uint64_t, uint64_t>>&)>&)> &row_generator,
+                             size_t num_columns,
+                             size_t num_rows,
+                             size_t num_nonzeros,
+                             uint8_t max_width) : num_columns_(num_columns) {
+    sdsl::int_vector<> vals(num_nonzeros, 0, max_width);
+    uint64_t r = 0;
+    std::mutex mu;
+    matrix_ = RowFlat<>([&](const auto &bin_row_callback) {
+        row_generator([&](const auto &row) {
+            std::lock_guard<std::mutex> lock(mu);
+            SetBitPositions binrow;
+            binrow.reserve(row.size());
+            for (const auto &[j, v] : row) {
+                binrow.emplace_back(j);
+                vals[r++] = v;
+            }
+
+            bin_row_callback(binrow);
+        });
+    }, num_columns, num_rows, num_nonzeros);
+
+    if (!max_width)
+        sdsl::util::bit_compress(vals);
+
+    values_ = std::move(vals);
+}
+
+auto CSRMatrixFlat::get_row_values(const std::vector<BinaryMatrix::Row> &rows) const -> std::vector<RowValues> {
+    std::vector<RowValues> row_values(rows.size());
+    std::visit([&](const auto &binmat) {
+        using T = std::decay_t<decltype(binmat)>;
+        static_assert(std::is_same_v<T, RowFlat<>>);
+        const auto &flat = binmat.data();
+
+        std::visit([&](const auto &v) {
+            for (size_t i = 0; i < rows.size(); ++i) {
+                uint64_t idx_begin = rows[i] * num_columns_;
+                uint64_t idx_end = idx_begin + num_columns_;
+                uint64_t r = flat.rank1(idx_begin) - flat[idx_begin];
+                flat.call_ones_in_range(idx_begin, idx_end, [&](uint64_t j) {
+                    row_values[i].emplace_back(j % num_columns_, v[r++]);
+                });
+            }
+        }, values_);
+    }, matrix_);
+    return row_values;
+}
+
+bool CSRMatrixFlat::load(const std::string &fname, std::streampos offset) {
+    std::ifstream fin(fname, std::ios::binary);
+    fin.seekg(offset, std::ios_base::beg);
+    num_columns_ = load_number(fin);
+    uint64_t m_index = load_number(fin);
+    uint64_t v_index = load_number(fin);
+    std::ignore = v_index;
+
+    switch (m_index) {
+        case 0: {
+            RowFlat<> binmat;
+            if (!binmat.load(fin))
+                return false;
+
+            matrix_ = std::move(binmat);
+        } break;
+        default: {
+            throw std::runtime_error("No other representations supported");
+        } break;
+    }
+
+    offset_ = fin.tellg();
+    values_ = sdsl::int_vector_buffer<>(fname, std::ios::in, buffer_size, 0, false, offset_);
+    return true;
+}
+
+bool CSRMatrixFlat::load(std::istream &in) {
+    num_columns_ = load_number(in);
+    uint64_t m_index = load_number(in);
+    uint64_t v_index = load_number(in);
+    std::ignore = v_index;
+
+    switch (m_index) {
+        case 0: {
+            RowFlat<> binmat;
+            if (!binmat.load(in))
+                return false;
+
+            matrix_ = std::move(binmat);
+        } break;
+        default: {
+            throw std::runtime_error("No other representations supported");
+        } break;
+    }
+
+    sdsl::int_vector<> v_load;
+    v_load.load(in);
+    values_ = v_load;
+    return true;
+}
+
+void CSRMatrixFlat::serialize(std::ostream &out) const {
+    serialize_number(out, num_columns_);
+    serialize_number(out, matrix_.index());
+    serialize_number(out, values_.index());
+    get_binary_matrix().serialize(out);
+    std::visit([&](const auto &v) {
+        using T = std::decay_t<decltype(v)>;
+        if constexpr(std::is_same_v<T, sdsl::int_vector<>>) {
+            v.serialize(out);
+        }
+    }, values_);
+}
+
+} // namespace mtg::annot::matrix
\ No newline at end of file
diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
new file mode 100644
index 0000000000..f8a081ccbe
--- /dev/null
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -0,0 +1,281 @@
+#ifndef __CSR_MATRIX_FLAT_HPP__
+#define __CSR_MATRIX_FLAT_HPP__
+
+#include <variant>
+#include <omp.h>
+
+#include <sdsl/int_vector.hpp>
+#include <sdsl/int_vector_buffer.hpp>
+#include <tsl/hopscotch_map.h>
+#include <progress_bar.hpp>
+
+#include "annotation/int_matrix/base/int_matrix.hpp"
+#include "annotation/binary_matrix/row_flat/flat_matrix.hpp"
+#include "annotation/binary_matrix/row_disk/row_disk.hpp"
+#include "common/serialization.hpp"
+
+
+namespace mtg::annot::matrix {
+
+class CSRMatrixFlat : public RowMajor, public IntMatrix {
+  public:
+    CSRMatrixFlat() {}
+
+    CSRMatrixFlat(const std::function<void(const std::function<void(const Vector<std::pair<uint64_t, uint64_t>>&)>&)> &row_generator,
+                  size_t num_columns,
+                  size_t num_rows,
+                  size_t num_nonzeros,
+                  uint8_t max_width = 0);
+
+    template <class BinMat, class Values>
+    CSRMatrixFlat(BinMat&& binmat, Values&& values)
+        : matrix_(std::move(binmat)), values_(std::move(values)) {}
+
+    std::vector<RowValues> get_row_values(const std::vector<BinaryMatrix::Row> &rows) const;
+
+    void call_row_values(const std::function<void(uint64_t, RowValues&&, size_t)> &callback,
+                         bool ordered = true) const override {
+        constexpr size_t kNumRowsInBlock = 50'000;
+        std::visit([&](const auto &binmat) {
+            using T = std::decay_t<decltype(binmat)>;
+            static_assert(std::is_same_v<T, RowFlat<>>);
+            const auto &flat = binmat.data();
+
+            std::visit([&](const auto &v) {
+                using S = std::decay_t<decltype(v)>;
+                uint64_t n = num_rows();
+
+                if (ordered) {
+                    ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
+                    uint64_t block_size = kNumRowsInBlock - (kNumRowsInBlock % num_columns_);
+                    std::vector<RowValues> rows;
+                    #pragma omp parallel for ordered num_threads(get_num_threads()) schedule(dynamic) private(rows)
+                    for (uint64_t begin = 0; begin < n; begin += block_size) {
+                        size_t thread_id = begin / block_size;
+                        std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
+                        if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+                            v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
+                        }
+
+                        uint64_t end = std::min(begin + block_size, n);
+                        assert(begin <= end);
+                        rows.resize(end - begin);
+
+                        uint64_t flat_begin = begin * num_columns_;
+                        uint64_t flat_end = end * num_columns_;
+                        uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
+
+                        auto set_bit_callback = [&](uint64_t b) __attribute__((always_inline)) {
+                            uint64_t i = b / num_columns_;
+                            uint64_t j = b % num_columns_;
+                            uint64_t val = 0;
+
+                            if constexpr(std::is_same_v<S, sdsl::int_vector<>>) {
+                                val = v[r++];
+                            }
+
+                            if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+                                val = (*v_copy)[r++];
+                            }
+
+                            rows[i - begin].emplace_back(j, val);
+                        };
+
+                        flat.call_ones_in_range(flat_begin, flat_end, set_bit_callback);
+
+                        #pragma omp ordered
+                        {
+                            uint64_t i = begin;
+                            progress_bar += rows.size();
+                            for (auto &row : rows) {
+                                callback(i++, std::move(row), thread_id);
+                                row = RowValues();
+                            }
+                        }
+                    }
+                } else {
+                    ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
+                    uint64_t block_size = n / std::max(1u, get_num_threads() - 1);
+                    block_size -= block_size % 64;
+                    #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
+                    for (uint64_t begin = 0; begin < n; begin += block_size) {
+                        size_t thread_id = begin / block_size;
+                        RowValues row;
+
+                        const S *v_use = &v;
+
+                        std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
+                        if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+                            v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
+                            v_use = v_copy.get();
+                        }
+
+                        uint64_t end = std::min(begin + block_size, n);
+                        assert(begin <= end);
+                        uint64_t flat_begin = begin * num_columns_;
+                        uint64_t flat_end = end * num_columns_;
+                        uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
+                        auto it = const_cast<S*>(v_use)->begin() + r;
+
+                        uint64_t last_i = begin;
+                        auto set_bit_callback = [&](uint64_t b) __attribute__((always_inline)) {
+                            uint64_t i = b / num_columns_;
+                            if (i > last_i) {
+                                callback(last_i, std::move(row), thread_id);
+
+                                ++last_i;
+                                row = RowValues();
+
+                                for ( ; last_i < i; ++last_i) {
+                                    callback(last_i, RowValues(), thread_id);
+                                }
+                            }
+
+                            uint64_t j = b % num_columns_;
+                            uint64_t val = *it;
+                            ++it;
+
+                            row.emplace_back(j, val);
+                        };
+
+                        flat.call_ones_in_range(flat_begin, flat_end, set_bit_callback);
+
+                        callback(last_i, std::move(row), thread_id);
+                        ++last_i;
+
+                        for ( ; last_i < end; ++last_i) {
+                            callback(last_i, RowValues(), thread_id);
+                        }
+
+                        progress_bar += end - begin;
+                    }
+                }
+            }, values_);
+        }, matrix_);
+    }
+
+    std::vector<VectorMap<uint64_t, size_t>> get_histograms(const std::vector<size_t> &min_counts = {},
+                                                            sdsl::bit_vector *unmark_discarded = nullptr) const override {
+        constexpr uint8_t HIST_CUTOFF = 64;
+        // bool parallel = get_num_threads() > 1;
+        uint64_t n = num_rows();
+        uint64_t block_size = n / std::max(1u, get_num_threads() - 1);
+        block_size -= block_size % 64;
+        uint64_t num_tasks = (n + block_size - 1) / block_size;
+        std::vector<std::vector<std::vector<size_t>>> hists_maps_base;
+        std::vector<std::vector<tsl::hopscotch_map<uint64_t, size_t>>> hists_maps;
+        for (size_t j = 0; j < num_tasks; ++j) {
+            uint64_t cur_block_size = block_size;
+            if ((j + 1) * block_size > n)
+                cur_block_size -= (j + 1) * block_size - n;
+
+            hists_maps.emplace_back(get_binary_matrix().num_columns());
+            hists_maps_base.emplace_back(get_binary_matrix().num_columns());
+            for (size_t i = 0; i < num_columns_; ++i) {
+                hists_maps_base.back()[i].resize(HIST_CUTOFF);
+                hists_maps_base.back()[i][0] = cur_block_size;
+            }
+        }
+
+        std::atomic<uint64_t> num_empty_rows{0};
+
+        std::atomic_thread_fence(std::memory_order_release);
+
+        auto row_callback = [&](uint64_t row_i, auto&& row, size_t bucket_id) __attribute__((always_inline)) {
+            bool keep = min_counts.empty();
+
+            if (min_counts.size()) {
+                for (const auto &[j, c] : row) {
+                    if (c >= min_counts[j]) {
+                        keep = true;
+                        break;
+                    }
+                }
+            }
+
+            if (!keep) {
+                // TODO: are these bottlenecks?
+                if (unmark_discarded)
+                    unset_bit(unmark_discarded->data(), row_i, false);
+                    // unset_bit(unmark_discarded->data(), row_i, parallel, std::memory_order_relaxed);
+
+                num_empty_rows.fetch_add(1, std::memory_order_relaxed);
+            } else {
+                auto &hists_map = hists_maps[bucket_id];
+                auto &hists_map_base = hists_maps_base[bucket_id];
+                for (const auto &[j, c] : row) {
+                    if (c < HIST_CUTOFF) {
+                        ++hists_map_base[j][c];
+                    } else {
+                        ++hists_map[j][c];
+                    }
+                    --hists_map_base[j][0];
+                }
+            }
+        };
+
+        call_row_values(row_callback, false);
+
+        std::atomic_thread_fence(std::memory_order_acquire);
+
+        std::vector<VectorMap<uint64_t, size_t>> hist_maps(num_columns_);
+        common::logger->trace("Merging histograms");
+        for (size_t j = 0; j < num_columns_; ++j) {
+            for (const auto &[k, c] : hists_maps[0][j]) {
+                hist_maps[j][k] += c;
+            }
+
+            for (size_t i = 1; i < hists_maps_base.size(); ++i) {
+                for (size_t k = 0; k < HIST_CUTOFF; ++k) {
+                    hists_maps_base[0][j][k] += hists_maps_base[i][j][k];
+                }
+
+                for (const auto &[k, c] : hists_maps[i][j]) {
+                    hist_maps[j][k] += c;
+                }
+            }
+        }
+
+        for (size_t j = 0; j < num_columns_; ++j) {
+            hists_maps_base[0][j][0] -= num_empty_rows;
+            for (size_t k = 0; k < HIST_CUTOFF; ++k) {
+                if (hists_maps_base[0][j][k] > 0)
+                    hist_maps[j][k] += hists_maps_base[0][j][k];
+            }
+        }
+
+        return hist_maps;
+    }
+
+    uint64_t num_columns() const { return num_columns_; }
+    uint64_t num_rows() const { return get_row_major_binary_matrix().num_rows(); }
+    uint64_t num_relations() const { return get_row_major_binary_matrix().num_relations(); }
+    SetBitPositions get_row(Row row) const { return get_row_major_binary_matrix().get_row(row); }
+    std::vector<Row> get_column(Column column) const { return get_row_major_binary_matrix().get_column(column); }
+
+    const BinaryMatrix& get_binary_matrix() const { return get_row_major_binary_matrix(); }
+
+    bool load(std::istream &in);
+    bool load(const std::string &fname, std::streampos offset = 0);
+    void serialize(std::ostream &out) const;
+
+  private:
+    size_t num_columns_ = 0;
+
+    // using BinMatStorage = std::variant<RowFlat<>, RowDisk>;
+    using BinMatStorage = std::variant<RowFlat<>>;
+    using ValueStorage = std::variant<sdsl::int_vector<>, sdsl::int_vector_buffer<>>;
+
+    BinMatStorage matrix_;
+    ValueStorage values_;
+
+    std::streampos offset_;
+
+    const RowMajor& get_row_major_binary_matrix() const {
+        return std::visit([&](const auto &m) -> const RowMajor& { return m; }, matrix_);
+    }
+};
+
+} // namespace mtg::annot::matrix
+
+#endif // __CSR_MATRIX_FLAT_HPP__
\ No newline at end of file

From 99084cf69f1718d1545d53b0de694a7b8b292ac4 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 22 Jul 2024 19:40:23 +0200
Subject: [PATCH 245/410] check

---
 .../src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp     | 1 -
 1 file changed, 1 deletion(-)

diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
index f8a081ccbe..46919d5493 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -194,7 +194,6 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
             }
 
             if (!keep) {
-                // TODO: are these bottlenecks?
                 if (unmark_discarded)
                     unset_bit(unmark_discarded->data(), row_i, false);
                     // unset_bit(unmark_discarded->data(), row_i, parallel, std::memory_order_relaxed);

From 8c34845293637660a196cdd2011797df6d6f99db Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 22 Jul 2024 19:50:20 +0200
Subject: [PATCH 246/410] push back

---
 .../src/graph/annotated_graph_algorithm.cpp   | 73 ++++++++-----------
 1 file changed, 31 insertions(+), 42 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 598e4c82e7..2f9abe1c41 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -184,22 +184,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<PValStorage> pvals_buckets;
     std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
     if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-        pvals_buckets.emplace_back(graph_ptr->max_index() + 1, nullpval);
+        auto &pvals = pvals_buckets.emplace_back();
+        pvals.emplace_back(nullpval);
     }
 
-    uint64_t bucket_size = graph_ptr->max_index() / std::max(size_t(1), num_threads - 1);
-    bucket_size -= bucket_size % 64;
     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-        for (size_t total_size = 0; total_size < graph_ptr->max_index(); total_size += bucket_size) {
+        for (size_t i = 0; i < get_num_threads() + 1; ++i) {
             auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-            auto &pvals = pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
-            uint64_t cur_bucket_size = bucket_size;
-            if (total_size + bucket_size > graph_ptr->max_index())
-                cur_bucket_size -= total_size + bucket_size - graph_ptr->max_index();
-
-            for (size_t i = 0; i < cur_bucket_size; ++i) {
-                pvals.push_back(nullpval);
-            }
+            pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
         }
         pvals_buckets[0].push_back(nullpval);
     }
@@ -752,25 +744,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
 
-        double pval;
-        double pval_min;
+        double pval = 1.1;
+        double pval_min = 1.1;
         try {
             pval_min = compute_min_pval(in_sum + out_sum);
 
-            if (pval_min >= 1.1)
-                return;
+            if (pval_min < 1.1)
+                pval = compute_pval(in_sum, out_sum, row);
 
-            pval = compute_pval(in_sum, out_sum, row);
         } catch (...) {
             std::lock_guard<std::mutex> lock(pval_mu);
             ex = std::current_exception();
             return;
         }
 
-        if (pval >= 1.1)
-            return;
-
-        if (pval_min - pval > 1e-10) {
+        if (pval < 1.1 && pval_min - pval > 1e-10) {
             common::logger->error("Min p-val estimate too high: min {} > cur {}\ttest: {}", pval_min, pval, config.test_type);
             throw std::runtime_error("Test failed");
         }
@@ -778,35 +766,36 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (config.test_by_unitig) {
             assert(pvals_min);
             auto &pvals = pvals_buckets[bucket_idx];
-            pvals[row_i - bucket_size * bucket_idx + (bucket_idx == 0)] = bit_cast<uint64_t>(pval);
+            pvals.push_back(bit_cast<uint64_t>(pval));
 
             std::lock_guard<std::mutex> lock(pval_mu);
             (*pvals_min)[node] = bit_cast<uint64_t>(pval_min);
             (*eff_size)[node] = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
         } else {
-            uint64_t k = std::numeric_limits<uint64_t>::max();
-            if (pval_min >= config.family_wise_error_rate) {
-                k = 0;
-            } else if (pval_min > 0) {
-                double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
-                if (lkd <= 64)
-                    k = pow(2.0, lkd);
-
-                if (k == 0) {
-                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
-                    throw std::runtime_error("Min failed");
+            if (config.test_type != "notest")
+                pvals_buckets[bucket_idx].push_back(bit_cast<uint64_t>(pval));
+
+            if (pval < 1.1) {
+                uint64_t k = std::numeric_limits<uint64_t>::max();
+                if (pval_min >= config.family_wise_error_rate) {
+                    k = 0;
+                } else if (pval_min > 0) {
+                    double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
+                    if (lkd <= 64)
+                        k = pow(2.0, lkd);
+
+                    if (k == 0) {
+                        common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
+                        throw std::runtime_error("Min failed");
+                    }
                 }
-            }
 
-            size_t pval_i = row_i - bucket_size * bucket_idx + (bucket_idx == 0);
-            if (in_kmer != out_kmer && (pval < config.family_wise_error_rate || config.test_by_unitig)) {
-                bool use_atomic = parallel && pval_i + 1 == pvals_buckets[bucket_idx].size();
-                set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
-            }
+                if (in_kmer != out_kmer && (pval < config.family_wise_error_rate || config.test_by_unitig)) {
+                    // bool use_atomic = parallel && pval_i + 1 == pvals_buckets[bucket_idx].size();
+                    bool use_atomic = parallel;
+                    set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
+                }
 
-            if (config.test_type != "notest") {
-                auto &pvals = pvals_buckets[bucket_idx];
-                pvals[pval_i] = bit_cast<uint64_t>(pval);
                 ++ms[bucket_idx][k];
             }
         }

From ef79109ff8ab095fd4ba2a14e4c54612a077910a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 22 Jul 2024 20:04:43 +0200
Subject: [PATCH 247/410] rearrange

---
 .../int_matrix/csr_matrix/csr_matrix_flat.hpp | 26 ++++++++++---------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
index 46919d5493..5cad5ca29a 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -157,26 +157,25 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
     std::vector<VectorMap<uint64_t, size_t>> get_histograms(const std::vector<size_t> &min_counts = {},
                                                             sdsl::bit_vector *unmark_discarded = nullptr) const override {
         constexpr uint8_t HIST_CUTOFF = 64;
-        // bool parallel = get_num_threads() > 1;
+        bool parallel = get_num_threads() > 1;
         uint64_t n = num_rows();
-        uint64_t block_size = n / std::max(1u, get_num_threads() - 1);
-        block_size -= block_size % 64;
-        uint64_t num_tasks = (n + block_size - 1) / block_size;
+        size_t num_tasks = get_num_threads() + 1;
+        std::vector<std::vector<size_t>> num_nonzeros;
         std::vector<std::vector<std::vector<size_t>>> hists_maps_base;
         std::vector<std::vector<tsl::hopscotch_map<uint64_t, size_t>>> hists_maps;
         for (size_t j = 0; j < num_tasks; ++j) {
-            uint64_t cur_block_size = block_size;
-            if ((j + 1) * block_size > n)
-                cur_block_size -= (j + 1) * block_size - n;
-
             hists_maps.emplace_back(get_binary_matrix().num_columns());
             hists_maps_base.emplace_back(get_binary_matrix().num_columns());
+            num_nonzeros.emplace_back(get_binary_matrix().num_columns());
             for (size_t i = 0; i < num_columns_; ++i) {
                 hists_maps_base.back()[i].resize(HIST_CUTOFF);
-                hists_maps_base.back()[i][0] = cur_block_size;
             }
         }
 
+        for (size_t i = 0; i < num_columns_; ++i) {
+            hists_maps_base[0][i][0] = n;
+        }
+
         std::atomic<uint64_t> num_empty_rows{0};
 
         std::atomic_thread_fence(std::memory_order_release);
@@ -195,8 +194,8 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
 
             if (!keep) {
                 if (unmark_discarded)
-                    unset_bit(unmark_discarded->data(), row_i, false);
-                    // unset_bit(unmark_discarded->data(), row_i, parallel, std::memory_order_relaxed);
+                    // unset_bit(unmark_discarded->data(), row_i, false);
+                    unset_bit(unmark_discarded->data(), row_i, parallel, std::memory_order_relaxed);
 
                 num_empty_rows.fetch_add(1, std::memory_order_relaxed);
             } else {
@@ -208,7 +207,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                     } else {
                         ++hists_map[j][c];
                     }
-                    --hists_map_base[j][0];
+                    ++num_nonzeros[bucket_id][j];
                 }
             }
         };
@@ -237,6 +236,9 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
 
         for (size_t j = 0; j < num_columns_; ++j) {
             hists_maps_base[0][j][0] -= num_empty_rows;
+            for (size_t i = 0; i < num_nonzeros.size(); ++i) {
+                hists_maps_base[0][j][0] -= num_nonzeros[i][j];
+            }
             for (size_t k = 0; k < HIST_CUTOFF; ++k) {
                 if (hists_maps_base[0][j][k] > 0)
                     hist_maps[j][k] += hists_maps_base[0][j][k];

From 9397bbb605ad9ee167b06efca9fa23b56ea0d8b9 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 23 Jul 2024 15:48:31 +0200
Subject: [PATCH 248/410] minor

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 2f9abe1c41..3a34440522 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -80,6 +80,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
 
+    common::logger->trace("Computing histogram from uncleaned counts");
     auto hists_map = get_hist_map(std::vector<size_t>(groups.size(), 1), nullptr);
 
     sdsl::bit_vector kept(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), true);
@@ -117,7 +118,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
-    common::logger->trace("Marking discarded k-mers");
+    common::logger->trace("Updating histogram and marking discarded k-mers");
 
     if (groups.size())
         hists_map = get_hist_map(min_counts, &kept);
@@ -791,9 +792,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 if (in_kmer != out_kmer && (pval < config.family_wise_error_rate || config.test_by_unitig)) {
-                    // bool use_atomic = parallel && pval_i + 1 == pvals_buckets[bucket_idx].size();
-                    bool use_atomic = parallel;
-                    set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
+                    set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
                 }
 
                 ++ms[bucket_idx][k];

From fd1c8f8d1ae4bc08a855f99fdde57d2fcbb2d282 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 23 Jul 2024 22:08:08 +0200
Subject: [PATCH 249/410] test

---
 .../int_matrix/csr_matrix/csr_matrix_flat.hpp | 187 ++++++++++++++----
 1 file changed, 148 insertions(+), 39 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
index 5cad5ca29a..47d7fd9483 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -33,8 +33,8 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
 
     std::vector<RowValues> get_row_values(const std::vector<BinaryMatrix::Row> &rows) const;
 
-    void call_row_values(const std::function<void(uint64_t, RowValues&&, size_t)> &callback,
-                         bool ordered = true) const override {
+    inline void call_row_values(const std::function<void(uint64_t, RowValues&&, size_t)> &callback,
+                                bool ordered = true) const override final {
         constexpr size_t kNumRowsInBlock = 50'000;
         std::visit([&](const auto &binmat) {
             using T = std::decay_t<decltype(binmat)>;
@@ -94,12 +94,35 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                         }
                     }
                 } else {
+                    uint64_t num_set_bits = flat.num_set_bits();
+                    uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
+                    size_t row_block_size = num_columns_ * 64;
+                    std::vector<uint64_t> boundaries;
+                    boundaries.emplace_back(0);
+                    for (size_t r = 1; r < num_set_bits; r += block_size) {
+                        uint64_t b = flat.select1(r);
+                        uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
+                        if (b_round_ncols_64 > boundaries.back()) {
+                            boundaries.emplace_back(b_round_ncols_64);
+                            if (boundaries.back() >= flat.size()) {
+                                boundaries.back() = flat.size();
+                                break;
+                            }
+                        }
+                    }
+
+                    if (boundaries.back() != flat.size())
+                        boundaries.emplace_back(flat.size());
+
+                    common::logger->trace("Streaming rows");
                     ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
-                    uint64_t block_size = n / std::max(1u, get_num_threads() - 1);
-                    block_size -= block_size % 64;
                     #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
-                    for (uint64_t begin = 0; begin < n; begin += block_size) {
-                        size_t thread_id = begin / block_size;
+                    for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
+                        uint64_t flat_begin = boundaries[thread_id];
+                        uint64_t flat_end = boundaries[thread_id + 1];
+                        uint64_t begin = flat_begin / num_columns_;
+                        uint64_t end = flat_end / num_columns_;
+
                         RowValues row;
 
                         const S *v_use = &v;
@@ -110,10 +133,6 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                             v_use = v_copy.get();
                         }
 
-                        uint64_t end = std::min(begin + block_size, n);
-                        assert(begin <= end);
-                        uint64_t flat_begin = begin * num_columns_;
-                        uint64_t flat_end = end * num_columns_;
                         uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
                         auto it = const_cast<S*>(v_use)->begin() + r;
 
@@ -154,10 +173,10 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
         }, matrix_);
     }
 
-    std::vector<VectorMap<uint64_t, size_t>> get_histograms(const std::vector<size_t> &min_counts = {},
-                                                            sdsl::bit_vector *unmark_discarded = nullptr) const override {
+    inline std::vector<VectorMap<uint64_t, size_t>>
+    get_histograms(const std::vector<size_t> &min_counts = {},
+                   sdsl::bit_vector *unmark_discarded = nullptr) const override final {
         constexpr uint8_t HIST_CUTOFF = 64;
-        bool parallel = get_num_threads() > 1;
         uint64_t n = num_rows();
         size_t num_tasks = get_num_threads() + 1;
         std::vector<std::vector<size_t>> num_nonzeros;
@@ -180,39 +199,129 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
 
         std::atomic_thread_fence(std::memory_order_release);
 
-        auto row_callback = [&](uint64_t row_i, auto&& row, size_t bucket_id) __attribute__((always_inline)) {
-            bool keep = min_counts.empty();
+        std::visit([&](const auto &binmat) {
+            using T = std::decay_t<decltype(binmat)>;
+            static_assert(std::is_same_v<T, RowFlat<>>);
+            const auto &flat = binmat.data();
+
+            std::visit([&](const auto &v) {
+                using S = std::decay_t<decltype(v)>;
+                uint64_t n = num_rows();
 
-            if (min_counts.size()) {
-                for (const auto &[j, c] : row) {
-                    if (c >= min_counts[j]) {
-                        keep = true;
-                        break;
+                uint64_t num_set_bits = flat.num_set_bits();
+                uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
+                size_t row_block_size = num_columns_ * 64;
+                std::vector<uint64_t> boundaries;
+                boundaries.emplace_back(0);
+                for (size_t r = 1; r < num_set_bits; r += block_size) {
+                    uint64_t b = flat.select1(r);
+                    uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
+                    if (b_round_ncols_64 > boundaries.back()) {
+                        boundaries.emplace_back(b_round_ncols_64);
+                        if (boundaries.back() >= flat.size()) {
+                            boundaries.back() = flat.size();
+                            break;
+                        }
                     }
                 }
-            }
 
-            if (!keep) {
-                if (unmark_discarded)
-                    // unset_bit(unmark_discarded->data(), row_i, false);
-                    unset_bit(unmark_discarded->data(), row_i, parallel, std::memory_order_relaxed);
-
-                num_empty_rows.fetch_add(1, std::memory_order_relaxed);
-            } else {
-                auto &hists_map = hists_maps[bucket_id];
-                auto &hists_map_base = hists_maps_base[bucket_id];
-                for (const auto &[j, c] : row) {
-                    if (c < HIST_CUTOFF) {
-                        ++hists_map_base[j][c];
+                if (boundaries.back() != flat.size())
+                    boundaries.emplace_back(flat.size());
+
+                common::logger->trace("Streaming rows");
+                ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
+                #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
+                for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
+                    RowValues row;
+                    auto row_callback = [&](uint64_t row_i) __attribute__((always_inline)) {
+                        bool keep = min_counts.empty();
+
+                        if (min_counts.size()) {
+                            for (const auto &[j, c] : row) {
+                                if (c >= min_counts[j]) {
+                                    keep = true;
+                                    break;
+                                }
+                            }
+                        }
+
+                        if (!keep) {
+                            if (unmark_discarded)
+                                unset_bit(unmark_discarded->data(), row_i, false, std::memory_order_relaxed);
+
+                            num_empty_rows.fetch_add(1, std::memory_order_relaxed);
+                        } else {
+                            auto &hists_map = hists_maps[thread_id];
+                            auto &hists_map_base = hists_maps_base[thread_id];
+                            for (const auto &[j, c] : row) {
+                                if (c < HIST_CUTOFF) {
+                                    ++hists_map_base[j][c];
+                                } else {
+                                    ++hists_map[j][c];
+                                }
+                                ++num_nonzeros[thread_id][j];
+                            }
+                        }
+                    };
+                    uint64_t flat_begin = boundaries[thread_id];
+                    uint64_t flat_end = boundaries[thread_id + 1];
+                    uint64_t begin = flat_begin / num_columns_;
+                    uint64_t end = flat_end / num_columns_;
+
+
+                    const S *v_use = &v;
+
+                    std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
+                    if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+                        v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
+                        v_use = v_copy.get();
+                    }
+
+                    uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
+                    auto it = const_cast<S*>(v_use)->begin() + r;
+
+                    uint64_t last_i = begin;
+                    flat.call_ones_in_range(flat_begin, flat_end, [&](uint64_t b) __attribute__((always_inline)) {
+                        uint64_t i = b / num_columns_;
+                        if (i > last_i) {
+                            row_callback(last_i);
+
+                            ++last_i;
+                            row.clear();
+
+                            num_empty_rows.fetch_add(i - last_i, std::memory_order_relaxed);
+                            if (unmark_discarded) {
+                                for ( ; last_i < i; ++last_i) {
+                                    unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+                                }
+                            } else {
+                                last_i = i;
+                            }
+                        }
+
+                        uint64_t j = b % num_columns_;
+                        uint64_t val = *it;
+                        ++it;
+
+                        row.emplace_back(j, val);
+                    });
+
+                    row_callback(last_i);
+                    ++last_i;
+
+                    num_empty_rows.fetch_add(end - last_i, std::memory_order_relaxed);
+                    if (unmark_discarded) {
+                        for ( ; last_i < end; ++last_i) {
+                            unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+                        }
                     } else {
-                        ++hists_map[j][c];
+                        last_i = end;
                     }
-                    ++num_nonzeros[bucket_id][j];
-                }
-            }
-        };
 
-        call_row_values(row_callback, false);
+                    progress_bar += end - begin;
+                }
+            }, values_);
+        }, matrix_);
 
         std::atomic_thread_fence(std::memory_order_acquire);
 

From 488503c18bed6a3e832524126bbc91f5782d45a7 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 23 Jul 2024 23:25:21 +0200
Subject: [PATCH 250/410] refactor

---
 metagraph/CMakeLists.txt                                    | 2 +-
 .../annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp    | 6 ++++--
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/metagraph/CMakeLists.txt b/metagraph/CMakeLists.txt
index b1e170bcf0..b2ba23d8ca 100644
--- a/metagraph/CMakeLists.txt
+++ b/metagraph/CMakeLists.txt
@@ -208,7 +208,7 @@ endif()
 
 
 # Profile with gprof
-set(CMAKE_CXX_FLAGS_PROFILE "-pg -DNDEBUG -O2 -g")
+set(CMAKE_CXX_FLAGS_PROFILE "-pg -DNDEBUG -O3 -g")
 set(CMAKE_EXE_LINKER_FLAGS_PROFILE "-pg -g")
 set(CMAKE_SHARED_LINKER_FLAGS_PROFILE "-pg -g")
 
diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
index 47d7fd9483..3feeab2d99 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -281,7 +281,9 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                     auto it = const_cast<S*>(v_use)->begin() + r;
 
                     uint64_t last_i = begin;
-                    flat.call_ones_in_range(flat_begin, flat_end, [&](uint64_t b) __attribute__((always_inline)) {
+                    uint64_t num_ones = flat_end ? flat.rank1(flat_end - 1) : 0;
+                    for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
+                        uint64_t b = flat.select1(r);
                         uint64_t i = b / num_columns_;
                         if (i > last_i) {
                             row_callback(last_i);
@@ -304,7 +306,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                         ++it;
 
                         row.emplace_back(j, val);
-                    });
+                    }
 
                     row_callback(last_i);
                     ++last_i;

From 447639ac20555b17ba55f6f758f79a996fb1e679 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 24 Jul 2024 12:49:14 +0200
Subject: [PATCH 251/410] remove std::visit

---
 .../int_matrix/csr_matrix/csr_matrix_flat.cpp |  65 +--
 .../int_matrix/csr_matrix/csr_matrix_flat.hpp | 413 +++++++++---------
 .../src/graph/annotated_graph_algorithm.cpp   | 277 ++++++------
 3 files changed, 365 insertions(+), 390 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
index 7a03e1046d..c4af937a02 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
@@ -38,22 +38,19 @@ CSRMatrixFlat::CSRMatrixFlat(const std::function<void(const std::function<void(c
 
 auto CSRMatrixFlat::get_row_values(const std::vector<BinaryMatrix::Row> &rows) const -> std::vector<RowValues> {
     std::vector<RowValues> row_values(rows.size());
-    std::visit([&](const auto &binmat) {
-        using T = std::decay_t<decltype(binmat)>;
-        static_assert(std::is_same_v<T, RowFlat<>>);
-        const auto &flat = binmat.data();
-
-        std::visit([&](const auto &v) {
-            for (size_t i = 0; i < rows.size(); ++i) {
-                uint64_t idx_begin = rows[i] * num_columns_;
-                uint64_t idx_end = idx_begin + num_columns_;
-                uint64_t r = flat.rank1(idx_begin) - flat[idx_begin];
-                flat.call_ones_in_range(idx_begin, idx_end, [&](uint64_t j) {
-                    row_values[i].emplace_back(j % num_columns_, v[r++]);
-                });
-            }
-        }, values_);
-    }, matrix_);
+    const auto &flat = matrix_.data();
+
+    std::visit([&](const auto &v) {
+        for (size_t i = 0; i < rows.size(); ++i) {
+            uint64_t idx_begin = rows[i] * num_columns_;
+            uint64_t idx_end = idx_begin + num_columns_;
+            uint64_t r = flat.rank1(idx_begin) - flat[idx_begin];
+            flat.call_ones_in_range(idx_begin, idx_end, [&](uint64_t j) {
+                row_values[i].emplace_back(j % num_columns_, v[r++]);
+            });
+        }
+    }, values_);
+
     return row_values;
 }
 
@@ -63,21 +60,14 @@ bool CSRMatrixFlat::load(const std::string &fname, std::streampos offset) {
     num_columns_ = load_number(fin);
     uint64_t m_index = load_number(fin);
     uint64_t v_index = load_number(fin);
+    std::ignore = m_index;
     std::ignore = v_index;
 
-    switch (m_index) {
-        case 0: {
-            RowFlat<> binmat;
-            if (!binmat.load(fin))
-                return false;
-
-            matrix_ = std::move(binmat);
-        } break;
-        default: {
-            throw std::runtime_error("No other representations supported");
-        } break;
-    }
+    RowFlat<> binmat;
+    if (!binmat.load(fin))
+        return false;
 
+    matrix_ = std::move(binmat);
     offset_ = fin.tellg();
     values_ = sdsl::int_vector_buffer<>(fname, std::ios::in, buffer_size, 0, false, offset_);
     return true;
@@ -87,21 +77,14 @@ bool CSRMatrixFlat::load(std::istream &in) {
     num_columns_ = load_number(in);
     uint64_t m_index = load_number(in);
     uint64_t v_index = load_number(in);
+    std::ignore = m_index;
     std::ignore = v_index;
 
-    switch (m_index) {
-        case 0: {
-            RowFlat<> binmat;
-            if (!binmat.load(in))
-                return false;
-
-            matrix_ = std::move(binmat);
-        } break;
-        default: {
-            throw std::runtime_error("No other representations supported");
-        } break;
-    }
+    RowFlat<> binmat;
+    if (!binmat.load(in))
+        return false;
 
+    matrix_ = std::move(binmat);
     sdsl::int_vector<> v_load;
     v_load.load(in);
     values_ = v_load;
@@ -110,7 +93,7 @@ bool CSRMatrixFlat::load(std::istream &in) {
 
 void CSRMatrixFlat::serialize(std::ostream &out) const {
     serialize_number(out, num_columns_);
-    serialize_number(out, matrix_.index());
+    serialize_number(out, 0);
     serialize_number(out, values_.index());
     get_binary_matrix().serialize(out);
     std::visit([&](const auto &v) {
diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
index 3feeab2d99..b6f47b394c 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -36,141 +36,137 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
     inline void call_row_values(const std::function<void(uint64_t, RowValues&&, size_t)> &callback,
                                 bool ordered = true) const override final {
         constexpr size_t kNumRowsInBlock = 50'000;
-        std::visit([&](const auto &binmat) {
-            using T = std::decay_t<decltype(binmat)>;
-            static_assert(std::is_same_v<T, RowFlat<>>);
-            const auto &flat = binmat.data();
-
-            std::visit([&](const auto &v) {
-                using S = std::decay_t<decltype(v)>;
-                uint64_t n = num_rows();
-
-                if (ordered) {
-                    ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
-                    uint64_t block_size = kNumRowsInBlock - (kNumRowsInBlock % num_columns_);
-                    std::vector<RowValues> rows;
-                    #pragma omp parallel for ordered num_threads(get_num_threads()) schedule(dynamic) private(rows)
-                    for (uint64_t begin = 0; begin < n; begin += block_size) {
-                        size_t thread_id = begin / block_size;
-                        std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
-                        if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
-                            v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
-                        }
+        const auto &flat = matrix_.data();
 
-                        uint64_t end = std::min(begin + block_size, n);
-                        assert(begin <= end);
-                        rows.resize(end - begin);
+        std::visit([&](const auto &v) {
+            using S = std::decay_t<decltype(v)>;
+            uint64_t n = num_rows();
 
-                        uint64_t flat_begin = begin * num_columns_;
-                        uint64_t flat_end = end * num_columns_;
-                        uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
+            if (ordered) {
+                ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
+                uint64_t block_size = kNumRowsInBlock - (kNumRowsInBlock % num_columns_);
+                std::vector<RowValues> rows;
+                #pragma omp parallel for ordered num_threads(get_num_threads()) schedule(dynamic) private(rows)
+                for (uint64_t begin = 0; begin < n; begin += block_size) {
+                    size_t thread_id = begin / block_size;
+                    std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
+                    if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+                        v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
+                    }
 
-                        auto set_bit_callback = [&](uint64_t b) __attribute__((always_inline)) {
-                            uint64_t i = b / num_columns_;
-                            uint64_t j = b % num_columns_;
-                            uint64_t val = 0;
+                    uint64_t end = std::min(begin + block_size, n);
+                    assert(begin <= end);
+                    rows.resize(end - begin);
 
-                            if constexpr(std::is_same_v<S, sdsl::int_vector<>>) {
-                                val = v[r++];
-                            }
+                    uint64_t flat_begin = begin * num_columns_;
+                    uint64_t flat_end = end * num_columns_;
+                    uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
 
-                            if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
-                                val = (*v_copy)[r++];
-                            }
+                    auto set_bit_callback = [&](uint64_t b) __attribute__((always_inline)) {
+                        uint64_t i = b / num_columns_;
+                        uint64_t j = b % num_columns_;
+                        uint64_t val = 0;
 
-                            rows[i - begin].emplace_back(j, val);
-                        };
+                        if constexpr(std::is_same_v<S, sdsl::int_vector<>>) {
+                            val = v[r++];
+                        }
 
-                        flat.call_ones_in_range(flat_begin, flat_end, set_bit_callback);
+                        if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+                            val = (*v_copy)[r++];
+                        }
 
-                        #pragma omp ordered
-                        {
-                            uint64_t i = begin;
-                            progress_bar += rows.size();
-                            for (auto &row : rows) {
-                                callback(i++, std::move(row), thread_id);
-                                row = RowValues();
-                            }
+                        rows[i - begin].emplace_back(j, val);
+                    };
+
+                    flat.call_ones_in_range(flat_begin, flat_end, set_bit_callback);
+
+                    #pragma omp ordered
+                    {
+                        uint64_t i = begin;
+                        progress_bar += rows.size();
+                        for (auto &row : rows) {
+                            callback(i++, std::move(row), thread_id);
+                            row = RowValues();
                         }
                     }
-                } else {
-                    uint64_t num_set_bits = flat.num_set_bits();
-                    uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
-                    size_t row_block_size = num_columns_ * 64;
-                    std::vector<uint64_t> boundaries;
-                    boundaries.emplace_back(0);
-                    for (size_t r = 1; r < num_set_bits; r += block_size) {
-                        uint64_t b = flat.select1(r);
-                        uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
-                        if (b_round_ncols_64 > boundaries.back()) {
-                            boundaries.emplace_back(b_round_ncols_64);
-                            if (boundaries.back() >= flat.size()) {
-                                boundaries.back() = flat.size();
-                                break;
-                            }
+                }
+            } else {
+                uint64_t num_set_bits = flat.num_set_bits();
+                uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
+                size_t row_block_size = num_columns_ * 64;
+                std::vector<uint64_t> boundaries;
+                boundaries.emplace_back(0);
+                for (size_t r = 1; r < num_set_bits; r += block_size) {
+                    uint64_t b = flat.select1(r);
+                    uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
+                    if (b_round_ncols_64 > boundaries.back()) {
+                        boundaries.emplace_back(b_round_ncols_64);
+                        if (boundaries.back() >= flat.size()) {
+                            boundaries.back() = flat.size();
+                            break;
                         }
                     }
+                }
 
-                    if (boundaries.back() != flat.size())
-                        boundaries.emplace_back(flat.size());
+                if (boundaries.back() != flat.size())
+                    boundaries.emplace_back(flat.size());
 
-                    common::logger->trace("Streaming rows");
-                    ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
-                    #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
-                    for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
-                        uint64_t flat_begin = boundaries[thread_id];
-                        uint64_t flat_end = boundaries[thread_id + 1];
-                        uint64_t begin = flat_begin / num_columns_;
-                        uint64_t end = flat_end / num_columns_;
+                common::logger->trace("Streaming rows");
+                ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
+                #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
+                for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
+                    uint64_t flat_begin = boundaries[thread_id];
+                    uint64_t flat_end = boundaries[thread_id + 1];
+                    uint64_t begin = flat_begin / num_columns_;
+                    uint64_t end = flat_end / num_columns_;
 
-                        RowValues row;
+                    RowValues row;
 
-                        const S *v_use = &v;
+                    const S *v_use = &v;
 
-                        std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
-                        if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
-                            v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
-                            v_use = v_copy.get();
-                        }
+                    std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
+                    if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+                        v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
+                        v_use = v_copy.get();
+                    }
 
-                        uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
-                        auto it = const_cast<S*>(v_use)->begin() + r;
+                    uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
+                    auto it = const_cast<S*>(v_use)->begin() + r;
 
-                        uint64_t last_i = begin;
-                        auto set_bit_callback = [&](uint64_t b) __attribute__((always_inline)) {
-                            uint64_t i = b / num_columns_;
-                            if (i > last_i) {
-                                callback(last_i, std::move(row), thread_id);
+                    uint64_t last_i = begin;
+                    auto set_bit_callback = [&](uint64_t b) __attribute__((always_inline)) {
+                        uint64_t i = b / num_columns_;
+                        if (i > last_i) {
+                            callback(last_i, std::move(row), thread_id);
 
-                                ++last_i;
-                                row = RowValues();
+                            ++last_i;
+                            row = RowValues();
 
-                                for ( ; last_i < i; ++last_i) {
-                                    callback(last_i, RowValues(), thread_id);
-                                }
+                            for ( ; last_i < i; ++last_i) {
+                                callback(last_i, RowValues(), thread_id);
                             }
+                        }
 
-                            uint64_t j = b % num_columns_;
-                            uint64_t val = *it;
-                            ++it;
-
-                            row.emplace_back(j, val);
-                        };
+                        uint64_t j = b % num_columns_;
+                        uint64_t val = *it;
+                        ++it;
 
-                        flat.call_ones_in_range(flat_begin, flat_end, set_bit_callback);
+                        row.emplace_back(j, val);
+                    };
 
-                        callback(last_i, std::move(row), thread_id);
-                        ++last_i;
+                    flat.call_ones_in_range(flat_begin, flat_end, set_bit_callback);
 
-                        for ( ; last_i < end; ++last_i) {
-                            callback(last_i, RowValues(), thread_id);
-                        }
+                    callback(last_i, std::move(row), thread_id);
+                    ++last_i;
 
-                        progress_bar += end - begin;
+                    for ( ; last_i < end; ++last_i) {
+                        callback(last_i, RowValues(), thread_id);
                     }
+
+                    progress_bar += end - begin;
                 }
-            }, values_);
-        }, matrix_);
+            }
+        }, values_);
     }
 
     inline std::vector<VectorMap<uint64_t, size_t>>
@@ -199,131 +195,127 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
 
         std::atomic_thread_fence(std::memory_order_release);
 
-        std::visit([&](const auto &binmat) {
-            using T = std::decay_t<decltype(binmat)>;
-            static_assert(std::is_same_v<T, RowFlat<>>);
-            const auto &flat = binmat.data();
-
-            std::visit([&](const auto &v) {
-                using S = std::decay_t<decltype(v)>;
-                uint64_t n = num_rows();
-
-                uint64_t num_set_bits = flat.num_set_bits();
-                uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
-                size_t row_block_size = num_columns_ * 64;
-                std::vector<uint64_t> boundaries;
-                boundaries.emplace_back(0);
-                for (size_t r = 1; r < num_set_bits; r += block_size) {
-                    uint64_t b = flat.select1(r);
-                    uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
-                    if (b_round_ncols_64 > boundaries.back()) {
-                        boundaries.emplace_back(b_round_ncols_64);
-                        if (boundaries.back() >= flat.size()) {
-                            boundaries.back() = flat.size();
-                            break;
-                        }
+        const auto &flat = matrix_.data();
+
+        std::visit([&](const auto &v) {
+            using S = std::decay_t<decltype(v)>;
+            uint64_t n = num_rows();
+
+            uint64_t num_set_bits = flat.num_set_bits();
+            uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
+            size_t row_block_size = num_columns_ * 64;
+            std::vector<uint64_t> boundaries;
+            boundaries.emplace_back(0);
+            for (size_t r = 1; r < num_set_bits; r += block_size) {
+                uint64_t b = flat.select1(r);
+                uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
+                if (b_round_ncols_64 > boundaries.back()) {
+                    boundaries.emplace_back(b_round_ncols_64);
+                    if (boundaries.back() >= flat.size()) {
+                        boundaries.back() = flat.size();
+                        break;
                     }
                 }
+            }
 
-                if (boundaries.back() != flat.size())
-                    boundaries.emplace_back(flat.size());
-
-                common::logger->trace("Streaming rows");
-                ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
-                #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
-                for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
-                    RowValues row;
-                    auto row_callback = [&](uint64_t row_i) __attribute__((always_inline)) {
-                        bool keep = min_counts.empty();
-
-                        if (min_counts.size()) {
-                            for (const auto &[j, c] : row) {
-                                if (c >= min_counts[j]) {
-                                    keep = true;
-                                    break;
-                                }
+            if (boundaries.back() != flat.size())
+                boundaries.emplace_back(flat.size());
+
+            common::logger->trace("Streaming rows");
+            ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
+            #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
+            for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
+                RowValues row;
+                auto row_callback = [&](uint64_t row_i) __attribute__((always_inline)) {
+                    bool keep = min_counts.empty();
+
+                    if (min_counts.size()) {
+                        for (const auto &[j, c] : row) {
+                            if (c >= min_counts[j]) {
+                                keep = true;
+                                break;
                             }
                         }
+                    }
 
-                        if (!keep) {
-                            if (unmark_discarded)
-                                unset_bit(unmark_discarded->data(), row_i, false, std::memory_order_relaxed);
+                    if (!keep) {
+                        if (unmark_discarded)
+                            unset_bit(unmark_discarded->data(), row_i, false, std::memory_order_relaxed);
 
-                            num_empty_rows.fetch_add(1, std::memory_order_relaxed);
-                        } else {
-                            auto &hists_map = hists_maps[thread_id];
-                            auto &hists_map_base = hists_maps_base[thread_id];
-                            for (const auto &[j, c] : row) {
-                                if (c < HIST_CUTOFF) {
-                                    ++hists_map_base[j][c];
-                                } else {
-                                    ++hists_map[j][c];
-                                }
-                                ++num_nonzeros[thread_id][j];
+                        num_empty_rows.fetch_add(1, std::memory_order_relaxed);
+                    } else {
+                        auto &hists_map = hists_maps[thread_id];
+                        auto &hists_map_base = hists_maps_base[thread_id];
+                        for (const auto &[j, c] : row) {
+                            if (c < HIST_CUTOFF) {
+                                ++hists_map_base[j][c];
+                            } else {
+                                ++hists_map[j][c];
                             }
+                            ++num_nonzeros[thread_id][j];
                         }
-                    };
-                    uint64_t flat_begin = boundaries[thread_id];
-                    uint64_t flat_end = boundaries[thread_id + 1];
-                    uint64_t begin = flat_begin / num_columns_;
-                    uint64_t end = flat_end / num_columns_;
+                    }
+                };
+                uint64_t flat_begin = boundaries[thread_id];
+                uint64_t flat_end = boundaries[thread_id + 1];
+                uint64_t begin = flat_begin / num_columns_;
+                uint64_t end = flat_end / num_columns_;
 
 
-                    const S *v_use = &v;
+                const S *v_use = &v;
 
-                    std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
-                    if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
-                        v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
-                        v_use = v_copy.get();
-                    }
+                std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
+                if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+                    v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
+                    v_use = v_copy.get();
+                }
 
-                    uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
-                    auto it = const_cast<S*>(v_use)->begin() + r;
+                uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
+                auto it = const_cast<S*>(v_use)->begin() + r;
 
-                    uint64_t last_i = begin;
-                    uint64_t num_ones = flat_end ? flat.rank1(flat_end - 1) : 0;
-                    for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
-                        uint64_t b = flat.select1(r);
-                        uint64_t i = b / num_columns_;
-                        if (i > last_i) {
-                            row_callback(last_i);
+                uint64_t last_i = begin;
+                uint64_t num_ones = flat_end ? flat.rank1(flat_end - 1) : 0;
+                for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
+                    uint64_t b = flat.select1(r);
+                    uint64_t i = b / num_columns_;
+                    if (i > last_i) {
+                        row_callback(last_i);
 
-                            ++last_i;
-                            row.clear();
+                        ++last_i;
+                        row.clear();
 
-                            num_empty_rows.fetch_add(i - last_i, std::memory_order_relaxed);
-                            if (unmark_discarded) {
-                                for ( ; last_i < i; ++last_i) {
-                                    unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
-                                }
-                            } else {
-                                last_i = i;
+                        num_empty_rows.fetch_add(i - last_i, std::memory_order_relaxed);
+                        if (unmark_discarded) {
+                            for ( ; last_i < i; ++last_i) {
+                                unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
                             }
+                        } else {
+                            last_i = i;
                         }
+                    }
 
-                        uint64_t j = b % num_columns_;
-                        uint64_t val = *it;
-                        ++it;
+                    uint64_t j = b % num_columns_;
+                    uint64_t val = *it;
+                    ++it;
 
-                        row.emplace_back(j, val);
-                    }
+                    row.emplace_back(j, val);
+                }
 
-                    row_callback(last_i);
-                    ++last_i;
+                row_callback(last_i);
+                ++last_i;
 
-                    num_empty_rows.fetch_add(end - last_i, std::memory_order_relaxed);
-                    if (unmark_discarded) {
-                        for ( ; last_i < end; ++last_i) {
-                            unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
-                        }
-                    } else {
-                        last_i = end;
+                num_empty_rows.fetch_add(end - last_i, std::memory_order_relaxed);
+                if (unmark_discarded) {
+                    for ( ; last_i < end; ++last_i) {
+                        unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
                     }
-
-                    progress_bar += end - begin;
+                } else {
+                    last_i = end;
                 }
-            }, values_);
-        }, matrix_);
+
+                progress_bar += end - begin;
+            }
+        }, values_);
 
         std::atomic_thread_fence(std::memory_order_acquire);
 
@@ -374,17 +366,16 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
   private:
     size_t num_columns_ = 0;
 
-    // using BinMatStorage = std::variant<RowFlat<>, RowDisk>;
-    using BinMatStorage = std::variant<RowFlat<>>;
+    using BitMatStorage = RowFlat<>;
     using ValueStorage = std::variant<sdsl::int_vector<>, sdsl::int_vector_buffer<>>;
 
-    BinMatStorage matrix_;
+    BitMatStorage matrix_;
     ValueStorage values_;
 
     std::streampos offset_;
 
     const RowMajor& get_row_major_binary_matrix() const {
-        return std::visit([&](const auto &m) -> const RowMajor& { return m; }, matrix_);
+        return matrix_;
     }
 };
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 3a34440522..96f9c7314a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -228,140 +228,141 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
     }
 
-    std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
-    std::function<double(int64_t)> compute_min_pval;
+    // std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
+    // std::function<double(int64_t)> compute_min_pval;
 
     std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps;
 
     common::logger->trace("Test: {}", config.test_type);
-    if (config.test_type == "poisson_exact") {
-        compute_min_pval = [&](int64_t n) {
-            if (n == 0)
-                return 1.1;
-
-            double p = static_cast<double>(in_kmers) / total_kmers;
-            auto bdist = boost::math::binomial(n, p);
-            auto get_deviance = [&](double y, double mu) {
-                y += 1e-8;
-                mu += 1e-8;
-                return 2 * (y * log(y/mu) - y + mu);
-            };
-
-            double mu1 = static_cast<double>(in_kmers) / nelem;
-            double mu2 = static_cast<double>(out_kmers) / nelem;
-
-            std::vector<double> devs;
-            devs.reserve(n + 1);
-            for (int64_t s = 0; s <= n; ++s) {
-                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-            }
-            double max_d = *std::max_element(devs.begin(), devs.end());
-            double pval = 0.0;
-
-            int64_t s = 0;
-            for ( ; s <= n; ++s) {
-                if (devs[s] != max_d)
-                    break;
-            }
-            if (s > 0)
-                pval += boost::math::cdf(bdist, s - 1);
-
-            int64_t sp = n;
-            for ( ; sp >= s; --sp) {
-                if (devs[sp] != max_d)
-                    break;
-            }
-
-            if (sp < n)
-                pval += boost::math::cdf(boost::math::complement(bdist, sp));
-
-            return pval;
-        };
-
-        compute_pval = [&](int64_t in_sum, int64_t out_sum, const auto &row) {
-            if (row.empty())
-                return 1.1;
-
-            int64_t n = in_sum + out_sum;
-            double p = static_cast<double>(in_kmers) / total_kmers;
-            auto bdist = boost::math::binomial(n, p);
-            auto get_deviance = [&](double y, double mu) {
-                y += 1e-8;
-                mu += 1e-8;
-                return 2 * (y * log(y/mu) - y + mu);
-            };
-
-            double mu1 = static_cast<double>(in_kmers) / nelem;
-            double mu2 = static_cast<double>(out_kmers) / nelem;
-
-            std::vector<double> devs;
-            devs.reserve(n + 1);
-            for (int64_t s = 0; s <= n; ++s) {
-                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-            }
-            double pval = 0.0;
-
-            int64_t s = 0;
-            for ( ; s <= n; ++s) {
-                if (devs[s] < devs[in_sum])
-                    break;
-            }
-            if (s > 0)
-                pval += boost::math::cdf(bdist, s - 1);
-
-            int64_t sp = n;
-            for ( ; sp >= s; --sp) {
-                if (devs[sp] < devs[in_sum])
-                    break;
-            }
-
-            if (sp < n)
-                pval += boost::math::cdf(boost::math::complement(bdist, sp));
-
-            return pval;
-        };
-    } else if (config.test_type == "fisher") {
-        double lbase_shared = lgamma(in_kmers + 1) + lgamma(out_kmers + 1) - lgamma(total_kmers + 1);
-
-        compute_min_pval = [&,lbase_shared](int64_t m1) {
-            if (m1 == 0)
-                return 1.1;
-
-            int64_t m2 = total_kmers - m1;
-
-            auto get_pval = [&](int64_t a) {
-                double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
-
-                int64_t b = in_kmers - a;
-                int64_t c = m1 - a;
-                int64_t d = m2 - b;
-                assert(d == out_kmers - c);
-
-                return out_kmers < c
-                    ? 1.0
-                    : exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
-            };
-
-            return std::min(get_pval(0), get_pval(std::min(in_kmers, m1)));
-        };
-
-        compute_pval = [&,lbase_shared](int64_t in_sum, int64_t out_sum, const auto &row) {
-            if (row.empty())
-                return 1.1;
-
-            int64_t m1 = in_sum + out_sum;
-            int64_t m2 = total_kmers - m1;
-
-            double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
-
-            int64_t a = in_sum;
-            int64_t b = in_kmers - in_sum;
-            int64_t c = out_sum;
-            int64_t d = out_kmers - out_sum;
-
-            return exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
-        };
-    } else if (config.test_type == "nbinom_exact") {
+    // if (config.test_type == "poisson_exact") {
+    //     compute_min_pval = [&](int64_t n) {
+    //         if (n == 0)
+    //             return 1.1;
+
+    //         double p = static_cast<double>(in_kmers) / total_kmers;
+    //         auto bdist = boost::math::binomial(n, p);
+    //         auto get_deviance = [&](double y, double mu) {
+    //             y += 1e-8;
+    //             mu += 1e-8;
+    //             return 2 * (y * log(y/mu) - y + mu);
+    //         };
+
+    //         double mu1 = static_cast<double>(in_kmers) / nelem;
+    //         double mu2 = static_cast<double>(out_kmers) / nelem;
+
+    //         std::vector<double> devs;
+    //         devs.reserve(n + 1);
+    //         for (int64_t s = 0; s <= n; ++s) {
+    //             devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+    //         }
+    //         double max_d = *std::max_element(devs.begin(), devs.end());
+    //         double pval = 0.0;
+
+    //         int64_t s = 0;
+    //         for ( ; s <= n; ++s) {
+    //             if (devs[s] != max_d)
+    //                 break;
+    //         }
+    //         if (s > 0)
+    //             pval += boost::math::cdf(bdist, s - 1);
+
+    //         int64_t sp = n;
+    //         for ( ; sp >= s; --sp) {
+    //             if (devs[sp] != max_d)
+    //                 break;
+    //         }
+
+    //         if (sp < n)
+    //             pval += boost::math::cdf(boost::math::complement(bdist, sp));
+
+    //         return pval;
+    //     };
+
+    //     compute_pval = [&](int64_t in_sum, int64_t out_sum, const auto &row) {
+    //         if (row.empty())
+    //             return 1.1;
+
+    //         int64_t n = in_sum + out_sum;
+    //         double p = static_cast<double>(in_kmers) / total_kmers;
+    //         auto bdist = boost::math::binomial(n, p);
+    //         auto get_deviance = [&](double y, double mu) {
+    //             y += 1e-8;
+    //             mu += 1e-8;
+    //             return 2 * (y * log(y/mu) - y + mu);
+    //         };
+
+    //         double mu1 = static_cast<double>(in_kmers) / nelem;
+    //         double mu2 = static_cast<double>(out_kmers) / nelem;
+
+    //         std::vector<double> devs;
+    //         devs.reserve(n + 1);
+    //         for (int64_t s = 0; s <= n; ++s) {
+    //             devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+    //         }
+    //         double pval = 0.0;
+
+    //         int64_t s = 0;
+    //         for ( ; s <= n; ++s) {
+    //             if (devs[s] < devs[in_sum])
+    //                 break;
+    //         }
+    //         if (s > 0)
+    //             pval += boost::math::cdf(bdist, s - 1);
+
+    //         int64_t sp = n;
+    //         for ( ; sp >= s; --sp) {
+    //             if (devs[sp] < devs[in_sum])
+    //                 break;
+    //         }
+
+    //         if (sp < n)
+    //             pval += boost::math::cdf(boost::math::complement(bdist, sp));
+
+    //         return pval;
+    //     };
+    // } else if (config.test_type == "fisher") {
+    //     double lbase_shared = lgamma(in_kmers + 1) + lgamma(out_kmers + 1) - lgamma(total_kmers + 1);
+
+    //     compute_min_pval = [&,lbase_shared](int64_t m1) {
+    //         if (m1 == 0)
+    //             return 1.1;
+
+    //         int64_t m2 = total_kmers - m1;
+
+    //         auto get_pval = [&](int64_t a) {
+    //             double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
+
+    //             int64_t b = in_kmers - a;
+    //             int64_t c = m1 - a;
+    //             int64_t d = m2 - b;
+    //             assert(d == out_kmers - c);
+
+    //             return out_kmers < c
+    //                 ? 1.0
+    //                 : exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
+    //         };
+
+    //         return std::min(get_pval(0), get_pval(std::min(in_kmers, m1)));
+    //     };
+
+    //     compute_pval = [&,lbase_shared](int64_t in_sum, int64_t out_sum, const auto &row) {
+    //         if (row.empty())
+    //             return 1.1;
+
+    //         int64_t m1 = in_sum + out_sum;
+    //         int64_t m2 = total_kmers - m1;
+
+    //         double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
+
+    //         int64_t a = in_sum;
+    //         int64_t b = in_kmers - in_sum;
+    //         int64_t c = out_sum;
+    //         int64_t d = out_kmers - out_sum;
+
+    //         return exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
+    //     };
+    // } else if (config.test_type == "nbinom_exact") {
+    // {
         common::logger->trace("Fitting per-sample negative binomial distributions");
         auto get_rp = [&](size_t j, auto begin, auto end) {
             double mu = 0;
@@ -525,7 +526,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (fit_var / fit_mu - 1.0 < 1e-5)
             common::logger->warn("Fit parameters are close to a Poisson distribution");
 
-        compute_min_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t n) {
+        auto compute_min_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t n) {
             if (n == 0)
                 return 1.1;
 
@@ -572,7 +573,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::min(1.0, pval);
         };
 
-        compute_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t in_sum, int64_t out_sum, const auto &row) {
+        auto compute_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
                 return 1.1;
 
@@ -687,12 +688,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->error("Best achievable p-value is too big. Use poisson_exact instead");
             throw std::domain_error("Too few samples");
         }
-    } else if (config.test_type == "notest") {
-        compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
-        compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };
-    } else {
-        throw std::runtime_error("Test not implemented");
-    }
+    // } else if (config.test_type == "notest") {
+    //     compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
+    //     compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };
+    // } else {
+    //     throw std::runtime_error("Test not implemented");
+    // }
 
     common::logger->trace("Running differential tests");
     std::exception_ptr ex = nullptr;

From 00d615de62b208172db697bbae3c8fe29401bf50 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 24 Jul 2024 15:12:33 +0200
Subject: [PATCH 252/410] test

---
 metagraph/CMakeLists.txt                      |   2 +-
 .../int_matrix/csr_matrix/csr_matrix_flat.hpp | 402 +++++++++---------
 2 files changed, 199 insertions(+), 205 deletions(-)

diff --git a/metagraph/CMakeLists.txt b/metagraph/CMakeLists.txt
index b2ba23d8ca..b1e170bcf0 100644
--- a/metagraph/CMakeLists.txt
+++ b/metagraph/CMakeLists.txt
@@ -208,7 +208,7 @@ endif()
 
 
 # Profile with gprof
-set(CMAKE_CXX_FLAGS_PROFILE "-pg -DNDEBUG -O3 -g")
+set(CMAKE_CXX_FLAGS_PROFILE "-pg -DNDEBUG -O2 -g")
 set(CMAKE_EXE_LINKER_FLAGS_PROFILE "-pg -g")
 set(CMAKE_SHARED_LINKER_FLAGS_PROFILE "-pg -g")
 
diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
index b6f47b394c..7ee25bc2e3 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -38,135 +38,127 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
         constexpr size_t kNumRowsInBlock = 50'000;
         const auto &flat = matrix_.data();
 
+        sdsl::int_vector_buffer<> *v_main;
         std::visit([&](const auto &v) {
             using S = std::decay_t<decltype(v)>;
-            uint64_t n = num_rows();
-
-            if (ordered) {
-                ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
-                uint64_t block_size = kNumRowsInBlock - (kNumRowsInBlock % num_columns_);
-                std::vector<RowValues> rows;
-                #pragma omp parallel for ordered num_threads(get_num_threads()) schedule(dynamic) private(rows)
-                for (uint64_t begin = 0; begin < n; begin += block_size) {
-                    size_t thread_id = begin / block_size;
-                    std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
-                    if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
-                        v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
-                    }
-
-                    uint64_t end = std::min(begin + block_size, n);
-                    assert(begin <= end);
-                    rows.resize(end - begin);
-
-                    uint64_t flat_begin = begin * num_columns_;
-                    uint64_t flat_end = end * num_columns_;
-                    uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
-
-                    auto set_bit_callback = [&](uint64_t b) __attribute__((always_inline)) {
-                        uint64_t i = b / num_columns_;
-                        uint64_t j = b % num_columns_;
-                        uint64_t val = 0;
-
-                        if constexpr(std::is_same_v<S, sdsl::int_vector<>>) {
-                            val = v[r++];
-                        }
-
-                        if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
-                            val = (*v_copy)[r++];
-                        }
+            if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+                v_main = const_cast<S*>(&v);
+            } else {
+                throw std::runtime_error("Failed");
+            }
+        }, values_);
 
-                        rows[i - begin].emplace_back(j, val);
-                    };
+        uint64_t n = num_rows();
 
-                    flat.call_ones_in_range(flat_begin, flat_end, set_bit_callback);
+        if (ordered) {
+            ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
+            uint64_t block_size = kNumRowsInBlock - (kNumRowsInBlock % num_columns_);
+            std::vector<RowValues> rows;
+            #pragma omp parallel for ordered num_threads(get_num_threads()) schedule(dynamic) private(rows)
+            for (uint64_t begin = 0; begin < n; begin += block_size) {
+                size_t thread_id = begin / block_size;
+                std::unique_ptr<sdsl::int_vector_buffer<>> v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v_main->filename(), std::ios::in, v_main->buffersize(), 0, false, offset_);
+
+                uint64_t end = std::min(begin + block_size, n);
+                assert(begin <= end);
+                rows.resize(end - begin);
+
+                uint64_t flat_begin = begin * num_columns_;
+                uint64_t flat_end = end * num_columns_;
+                uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
+                auto it = v_copy->begin() + r;
 
-                    #pragma omp ordered
-                    {
-                        uint64_t i = begin;
-                        progress_bar += rows.size();
-                        for (auto &row : rows) {
-                            callback(i++, std::move(row), thread_id);
-                            row = RowValues();
-                        }
+                flat.call_ones_in_range(flat_begin, flat_end, [&](uint64_t b) __attribute__((always_inline)) {
+                    uint64_t i = b / num_columns_;
+                    uint64_t j = b % num_columns_;
+                    uint64_t val = *it;
+                    ++it;
+                    rows[i - begin].emplace_back(j, val);
+                });
+
+                #pragma omp ordered
+                {
+                    uint64_t i = begin;
+                    progress_bar += rows.size();
+                    for (auto &row : rows) {
+                        callback(i++, std::move(row), thread_id);
+                        row = RowValues();
                     }
                 }
-            } else {
-                uint64_t num_set_bits = flat.num_set_bits();
-                uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
-                size_t row_block_size = num_columns_ * 64;
-                std::vector<uint64_t> boundaries;
-                boundaries.emplace_back(0);
-                for (size_t r = 1; r < num_set_bits; r += block_size) {
-                    uint64_t b = flat.select1(r);
-                    uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
-                    if (b_round_ncols_64 > boundaries.back()) {
-                        boundaries.emplace_back(b_round_ncols_64);
-                        if (boundaries.back() >= flat.size()) {
-                            boundaries.back() = flat.size();
-                            break;
-                        }
+            }
+        } else {
+            uint64_t num_set_bits = flat.num_set_bits();
+            uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
+            size_t row_block_size = num_columns_ * 64;
+            std::vector<uint64_t> boundaries;
+            boundaries.emplace_back(0);
+            for (size_t r = 1; r < num_set_bits; r += block_size) {
+                uint64_t b = flat.select1(r);
+                uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
+                if (b_round_ncols_64 > boundaries.back()) {
+                    boundaries.emplace_back(b_round_ncols_64);
+                    if (boundaries.back() >= flat.size()) {
+                        boundaries.back() = flat.size();
+                        break;
                     }
                 }
+            }
 
-                if (boundaries.back() != flat.size())
-                    boundaries.emplace_back(flat.size());
+            if (boundaries.back() != flat.size())
+                boundaries.emplace_back(flat.size());
 
-                common::logger->trace("Streaming rows");
-                ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
-                #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
-                for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
-                    uint64_t flat_begin = boundaries[thread_id];
-                    uint64_t flat_end = boundaries[thread_id + 1];
-                    uint64_t begin = flat_begin / num_columns_;
-                    uint64_t end = flat_end / num_columns_;
+            common::logger->trace("Streaming rows");
+            ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
+            #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
+            for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
+                uint64_t flat_begin = boundaries[thread_id];
+                uint64_t flat_end = boundaries[thread_id + 1];
+                uint64_t begin = flat_begin / num_columns_;
+                uint64_t end = flat_end / num_columns_;
 
-                    RowValues row;
+                RowValues row;
 
-                    const S *v_use = &v;
+                auto *v_use = v_main;
 
-                    std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
-                    if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
-                        v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
-                        v_use = v_copy.get();
-                    }
+                std::unique_ptr<sdsl::int_vector_buffer<>> v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v_use->filename(), std::ios::in, v_use->buffersize(), 0, false, offset_);
+                v_use = v_copy.get();
 
-                    uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
-                    auto it = const_cast<S*>(v_use)->begin() + r;
+                uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
+                auto it = v_use->begin() + r;
 
-                    uint64_t last_i = begin;
-                    auto set_bit_callback = [&](uint64_t b) __attribute__((always_inline)) {
-                        uint64_t i = b / num_columns_;
-                        if (i > last_i) {
-                            callback(last_i, std::move(row), thread_id);
+                uint64_t last_i = begin;
+                auto set_bit_callback = [&](uint64_t b) __attribute__((always_inline)) {
+                    uint64_t i = b / num_columns_;
+                    if (i > last_i) {
+                        callback(last_i, std::move(row), thread_id);
 
-                            ++last_i;
-                            row = RowValues();
+                        ++last_i;
+                        row = RowValues();
 
-                            for ( ; last_i < i; ++last_i) {
-                                callback(last_i, RowValues(), thread_id);
-                            }
+                        for ( ; last_i < i; ++last_i) {
+                            callback(last_i, RowValues(), thread_id);
                         }
+                    }
 
-                        uint64_t j = b % num_columns_;
-                        uint64_t val = *it;
-                        ++it;
-
-                        row.emplace_back(j, val);
-                    };
+                    uint64_t j = b % num_columns_;
+                    uint64_t val = *it;
+                    ++it;
 
-                    flat.call_ones_in_range(flat_begin, flat_end, set_bit_callback);
+                    row.emplace_back(j, val);
+                };
 
-                    callback(last_i, std::move(row), thread_id);
-                    ++last_i;
+                flat.call_ones_in_range(flat_begin, flat_end, set_bit_callback);
 
-                    for ( ; last_i < end; ++last_i) {
-                        callback(last_i, RowValues(), thread_id);
-                    }
+                callback(last_i, std::move(row), thread_id);
+                ++last_i;
 
-                    progress_bar += end - begin;
+                for ( ; last_i < end; ++last_i) {
+                    callback(last_i, RowValues(), thread_id);
                 }
+
+                progress_bar += end - begin;
             }
-        }, values_);
+        }
     }
 
     inline std::vector<VectorMap<uint64_t, size_t>>
@@ -177,9 +169,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
         size_t num_tasks = get_num_threads() + 1;
         std::vector<std::vector<size_t>> num_nonzeros;
         std::vector<std::vector<std::vector<size_t>>> hists_maps_base;
-        std::vector<std::vector<tsl::hopscotch_map<uint64_t, size_t>>> hists_maps;
         for (size_t j = 0; j < num_tasks; ++j) {
-            hists_maps.emplace_back(get_binary_matrix().num_columns());
             hists_maps_base.emplace_back(get_binary_matrix().num_columns());
             num_nonzeros.emplace_back(get_binary_matrix().num_columns());
             for (size_t i = 0; i < num_columns_; ++i) {
@@ -197,143 +187,147 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
 
         const auto &flat = matrix_.data();
 
+        sdsl::int_vector_buffer<> *v_main;
         std::visit([&](const auto &v) {
             using S = std::decay_t<decltype(v)>;
-            uint64_t n = num_rows();
+            if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+                v_main = const_cast<S*>(&v);
+            } else {
+                throw std::runtime_error("Failed");
+            }
+        }, values_);
 
-            uint64_t num_set_bits = flat.num_set_bits();
-            uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
-            size_t row_block_size = num_columns_ * 64;
-            std::vector<uint64_t> boundaries;
-            boundaries.emplace_back(0);
-            for (size_t r = 1; r < num_set_bits; r += block_size) {
-                uint64_t b = flat.select1(r);
-                uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
-                if (b_round_ncols_64 > boundaries.back()) {
-                    boundaries.emplace_back(b_round_ncols_64);
-                    if (boundaries.back() >= flat.size()) {
-                        boundaries.back() = flat.size();
-                        break;
-                    }
+        uint64_t num_set_bits = flat.num_set_bits();
+        uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
+        size_t row_block_size = num_columns_ * 64;
+        std::vector<uint64_t> boundaries;
+        boundaries.emplace_back(0);
+        for (size_t r = 1; r < num_set_bits; r += block_size) {
+            uint64_t b = flat.select1(r);
+            uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
+            if (b_round_ncols_64 > boundaries.back()) {
+                boundaries.emplace_back(b_round_ncols_64);
+                if (boundaries.back() >= flat.size()) {
+                    boundaries.back() = flat.size();
+                    break;
                 }
             }
+        }
 
-            if (boundaries.back() != flat.size())
-                boundaries.emplace_back(flat.size());
+        if (boundaries.back() != flat.size())
+            boundaries.emplace_back(flat.size());
 
-            common::logger->trace("Streaming rows");
-            ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
-            #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
-            for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
-                RowValues row;
-                auto row_callback = [&](uint64_t row_i) __attribute__((always_inline)) {
-                    bool keep = min_counts.empty();
-
-                    if (min_counts.size()) {
-                        for (const auto &[j, c] : row) {
-                            if (c >= min_counts[j]) {
-                                keep = true;
-                                break;
-                            }
+        std::vector<VectorMap<uint64_t, size_t>> hist_maps(num_columns_);
+
+        common::logger->trace("Streaming rows");
+        ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
+        #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
+        for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
+            std::vector<tsl::hopscotch_map<uint64_t, size_t>> hists_map(get_binary_matrix().num_columns());
+            std::vector<std::pair<uint64_t, size_t>> row;
+            auto row_callback = [&](uint64_t row_i) __attribute__((always_inline)) {
+                bool keep = min_counts.empty();
+
+                if (min_counts.size()) {
+                    for (const auto &[j, c] : row) {
+                        if (c >= min_counts[j]) {
+                            keep = true;
+                            break;
                         }
                     }
+                }
 
-                    if (!keep) {
-                        if (unmark_discarded)
-                            unset_bit(unmark_discarded->data(), row_i, false, std::memory_order_relaxed);
+                if (!keep) {
+                    if (unmark_discarded)
+                        unset_bit(unmark_discarded->data(), row_i, false, std::memory_order_relaxed);
 
-                        num_empty_rows.fetch_add(1, std::memory_order_relaxed);
-                    } else {
-                        auto &hists_map = hists_maps[thread_id];
-                        auto &hists_map_base = hists_maps_base[thread_id];
-                        for (const auto &[j, c] : row) {
-                            if (c < HIST_CUTOFF) {
-                                ++hists_map_base[j][c];
-                            } else {
-                                ++hists_map[j][c];
-                            }
-                            ++num_nonzeros[thread_id][j];
+                    num_empty_rows.fetch_add(1, std::memory_order_relaxed);
+                } else {
+                    auto &hists_map_base = hists_maps_base[thread_id];
+                    for (const auto &[j, c] : row) {
+                        if (c < HIST_CUTOFF) {
+                            ++hists_map_base[j][c];
+                        } else {
+                            ++hists_map[j][c];
                         }
+                        ++num_nonzeros[thread_id][j];
                     }
-                };
-                uint64_t flat_begin = boundaries[thread_id];
-                uint64_t flat_end = boundaries[thread_id + 1];
-                uint64_t begin = flat_begin / num_columns_;
-                uint64_t end = flat_end / num_columns_;
+                }
+            };
+            uint64_t flat_begin = boundaries[thread_id];
+            uint64_t flat_end = boundaries[thread_id + 1];
+            uint64_t begin = flat_begin / num_columns_;
+            uint64_t end = flat_end / num_columns_;
 
+            auto *v_use = v_main;
 
-                const S *v_use = &v;
+            std::unique_ptr<sdsl::int_vector_buffer<>> v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v_use->filename(), std::ios::in, v_use->buffersize(), 0, false, offset_);
+            v_use = v_copy.get();
 
-                std::unique_ptr<sdsl::int_vector_buffer<>> v_copy;
-                if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
-                    v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
-                    v_use = v_copy.get();
-                }
+            uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
+            auto it = v_use->begin() + r;
 
-                uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
-                auto it = const_cast<S*>(v_use)->begin() + r;
-
-                uint64_t last_i = begin;
-                uint64_t num_ones = flat_end ? flat.rank1(flat_end - 1) : 0;
-                for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
-                    uint64_t b = flat.select1(r);
-                    uint64_t i = b / num_columns_;
-                    if (i > last_i) {
-                        row_callback(last_i);
+            uint64_t last_i = begin;
+            uint64_t num_ones = flat_end ? flat.rank1(flat_end - 1) : 0;
+            for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
+                uint64_t b = flat.select1(r);
+                uint64_t i = b / num_columns_;
+                if (i > last_i) {
+                    row_callback(last_i);
 
-                        ++last_i;
-                        row.clear();
+                    ++last_i;
+                    row.clear();
 
-                        num_empty_rows.fetch_add(i - last_i, std::memory_order_relaxed);
-                        if (unmark_discarded) {
-                            for ( ; last_i < i; ++last_i) {
-                                unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
-                            }
-                        } else {
-                            last_i = i;
+                    num_empty_rows.fetch_add(i - last_i, std::memory_order_relaxed);
+                    if (unmark_discarded) {
+                        for ( ; last_i < i; ++last_i) {
+                            unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
                         }
+                    } else {
+                        last_i = i;
                     }
+                }
 
-                    uint64_t j = b % num_columns_;
-                    uint64_t val = *it;
-                    ++it;
+                uint64_t j = b % num_columns_;
+                uint64_t val = *it;
+                ++it;
 
-                    row.emplace_back(j, val);
+                row.emplace_back(j, val);
+            }
+
+            row_callback(last_i);
+            ++last_i;
+
+            num_empty_rows.fetch_add(end - last_i, std::memory_order_relaxed);
+            if (unmark_discarded) {
+                for ( ; last_i < end; ++last_i) {
+                    unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
                 }
+            } else {
+                last_i = end;
+            }
 
-                row_callback(last_i);
-                ++last_i;
+            progress_bar += end - begin;
 
-                num_empty_rows.fetch_add(end - last_i, std::memory_order_relaxed);
-                if (unmark_discarded) {
-                    for ( ; last_i < end; ++last_i) {
-                        unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+            #pragma omp critical
+            {
+                for (size_t j = 0; j < num_columns_; ++j) {
+                    for (const auto &[k, c] : hists_map[j]) {
+                        hist_maps[j][k] += c;
                     }
-                } else {
-                    last_i = end;
                 }
-
-                progress_bar += end - begin;
             }
-        }, values_);
+        }
 
         std::atomic_thread_fence(std::memory_order_acquire);
 
-        std::vector<VectorMap<uint64_t, size_t>> hist_maps(num_columns_);
+
         common::logger->trace("Merging histograms");
         for (size_t j = 0; j < num_columns_; ++j) {
-            for (const auto &[k, c] : hists_maps[0][j]) {
-                hist_maps[j][k] += c;
-            }
-
             for (size_t i = 1; i < hists_maps_base.size(); ++i) {
                 for (size_t k = 0; k < HIST_CUTOFF; ++k) {
                     hists_maps_base[0][j][k] += hists_maps_base[i][j][k];
                 }
-
-                for (const auto &[k, c] : hists_maps[i][j]) {
-                    hist_maps[j][k] += c;
-                }
             }
         }
 

From d98d613eaf5c9af1bf0f141d6fc9393017ae8d22 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 25 Jul 2024 00:21:34 +0200
Subject: [PATCH 253/410] test

---
 .../int_matrix/csr_matrix/csr_matrix_flat.cpp | 221 ++++++++++++++++++
 .../int_matrix/csr_matrix/csr_matrix_flat.hpp | 196 +---------------
 .../src/graph/annotated_graph_algorithm.cpp   |   7 +-
 metagraph/src/graph/graph_cleaning.cpp        |   8 +-
 metagraph/src/graph/graph_cleaning.hpp        |   2 +-
 5 files changed, 237 insertions(+), 197 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
index c4af937a02..034bc8022a 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
@@ -36,6 +36,227 @@ CSRMatrixFlat::CSRMatrixFlat(const std::function<void(const std::function<void(c
     values_ = std::move(vals);
 }
 
+std::vector<VectorMap<uint64_t, size_t>>
+CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
+                              sdsl::bit_vector *unmark_discarded) const {
+    bool keep_all_nonzeros = std::all_of(min_counts.begin(), min_counts.end(), [](size_t s) { return s; });
+    constexpr uint8_t HIST_CUTOFF = 64;
+    uint64_t n = num_rows();
+    size_t num_tasks = get_num_threads() + 1;
+    std::vector<std::vector<size_t>> num_nonzeros;
+    std::vector<std::vector<std::vector<size_t>>> hists_maps_base;
+    for (size_t j = 0; j < num_tasks; ++j) {
+        hists_maps_base.emplace_back(get_binary_matrix().num_columns());
+        num_nonzeros.emplace_back(get_binary_matrix().num_columns());
+        for (size_t i = 0; i < num_columns_; ++i) {
+            hists_maps_base.back()[i].resize(HIST_CUTOFF);
+        }
+    }
+
+    for (size_t j = 0; j < num_columns_; ++j) {
+        hists_maps_base[0][j][0] = n;
+    }
+
+    std::atomic<uint64_t> num_empty_rows{0};
+
+    std::atomic_thread_fence(std::memory_order_release);
+
+    const auto &flat = matrix_.data();
+
+    sdsl::int_vector_buffer<> *v_main;
+    std::visit([&](const auto &v) {
+        using S = std::decay_t<decltype(v)>;
+        if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+            v_main = const_cast<S*>(&v);
+        } else {
+            throw std::runtime_error("Failed");
+        }
+    }, values_);
+
+    uint64_t num_set_bits = flat.num_set_bits();
+    uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
+    size_t row_block_size = num_columns_ * 64;
+    std::vector<uint64_t> boundaries;
+    boundaries.emplace_back(0);
+    for (size_t r = 1; r < num_set_bits; r += block_size) {
+        uint64_t b = flat.select1(r);
+        uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
+        if (b_round_ncols_64 > boundaries.back()) {
+            boundaries.emplace_back(b_round_ncols_64);
+            if (boundaries.back() >= flat.size()) {
+                boundaries.back() = flat.size();
+                break;
+            }
+        }
+    }
+
+    if (boundaries.back() != flat.size())
+        boundaries.emplace_back(flat.size());
+
+    std::vector<VectorMap<uint64_t, size_t>> hist_maps(num_columns_);
+
+    common::logger->trace("Streaming rows");
+    ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
+    #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
+    for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
+        std::vector<tsl::hopscotch_map<uint64_t, size_t>> hists_map(get_binary_matrix().num_columns());
+        auto &hists_map_base = hists_maps_base[thread_id];
+        uint64_t flat_begin = boundaries[thread_id];
+        uint64_t flat_end = boundaries[thread_id + 1];
+        uint64_t begin = flat_begin / num_columns_;
+        uint64_t end = flat_end / num_columns_;
+
+        auto *v_use = v_main;
+
+        std::unique_ptr<sdsl::int_vector_buffer<>> v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v_use->filename(), std::ios::in, v_use->buffersize(), 0, false, offset_);
+        v_use = v_copy.get();
+
+        uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
+        auto it = v_use->begin() + r;
+
+        uint64_t last_i = begin;
+        uint64_t num_ones = flat_end ? flat.rank1(flat_end - 1) : 0;
+
+        if (false && keep_all_nonzeros) {
+            for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
+                uint64_t b = flat.select1(r);
+                uint64_t i = b / num_columns_;
+                if (i > last_i) {
+                    if (last_i == begin) {
+                        if (unmark_discarded)
+                            unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+
+                        num_empty_rows.fetch_add(1, std::memory_order_relaxed);
+                    }
+                    ++last_i;
+                    num_empty_rows.fetch_add(i - last_i, std::memory_order_relaxed);
+                    if (unmark_discarded) {
+                        for ( ; last_i < i; ++last_i) {
+                            unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+                        }
+                    } else {
+                        last_i = i;
+                    }
+                }
+
+                uint64_t j = b % num_columns_;
+                uint64_t val = *it;
+                ++it;
+
+                if (val < HIST_CUTOFF) {
+                    ++hists_map_base[j][val];
+                } else {
+                    ++hists_map[j][val];
+                }
+                ++num_nonzeros[thread_id][j];
+            }
+        } else {
+            std::vector<std::pair<uint64_t, size_t>> row;
+            auto row_callback = [&](uint64_t row_i) __attribute__((always_inline)) {
+                bool keep = min_counts.empty();
+
+                if (min_counts.size()) {
+                    for (const auto &[j, c] : row) {
+                        if (c >= min_counts[j]) {
+                            keep = true;
+                            break;
+                        }
+                    }
+                }
+
+                if (!keep) {
+                    if (unmark_discarded)
+                        unset_bit(unmark_discarded->data(), row_i, false, std::memory_order_relaxed);
+
+                    num_empty_rows.fetch_add(1, std::memory_order_relaxed);
+                } else {
+                    for (const auto &[j, c] : row) {
+                        if (c < HIST_CUTOFF) {
+                            ++hists_map_base[j][c];
+                        } else {
+                            ++hists_map[j][c];
+                        }
+                        ++num_nonzeros[thread_id][j];
+                    }
+                }
+            };
+
+            for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
+                uint64_t b = flat.select1(r);
+                uint64_t i = b / num_columns_;
+                if (i > last_i) {
+                    row_callback(last_i);
+
+                    ++last_i;
+                    row.clear();
+
+                    num_empty_rows.fetch_add(i - last_i, std::memory_order_relaxed);
+                    if (unmark_discarded) {
+                        for ( ; last_i < i; ++last_i) {
+                            unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+                        }
+                    } else {
+                        last_i = i;
+                    }
+                }
+
+                uint64_t j = b % num_columns_;
+                uint64_t val = *it;
+                ++it;
+
+                row.emplace_back(j, val);
+            }
+
+            row_callback(last_i);
+        }
+
+        ++last_i;
+
+        num_empty_rows.fetch_add(end - last_i, std::memory_order_relaxed);
+        if (unmark_discarded) {
+            for ( ; last_i < end; ++last_i) {
+                unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+            }
+        }
+
+        progress_bar += end - begin;
+
+        #pragma omp critical
+        {
+            for (size_t j = 0; j < num_columns_; ++j) {
+                for (const auto &[k, c] : hists_map[j]) {
+                    hist_maps[j][k] += c;
+                }
+            }
+        }
+    }
+
+    std::atomic_thread_fence(std::memory_order_acquire);
+
+    common::logger->trace("Merging histograms");
+    for (size_t j = 0; j < num_columns_; ++j) {
+        for (size_t i = 1; i < hists_maps_base.size(); ++i) {
+            for (size_t k = 0; k < HIST_CUTOFF; ++k) {
+                hists_maps_base[0][j][k] += hists_maps_base[i][j][k];
+            }
+        }
+    }
+
+    for (size_t j = 0; j < num_columns_; ++j) {
+        hists_maps_base[0][j][0] -= num_empty_rows;
+        for (size_t i = 0; i < num_nonzeros.size(); ++i) {
+            common::logger->trace("NZ: {}: {}", i, fmt::join(num_nonzeros[i], ","));
+            hists_maps_base[0][j][0] -= num_nonzeros[i][j];
+        }
+        for (size_t k = 0; k < HIST_CUTOFF; ++k) {
+            if (hists_maps_base[0][j][k] > 0)
+                hist_maps[j][k] += hists_maps_base[0][j][k];
+        }
+    }
+
+    return hist_maps;
+}
+
 auto CSRMatrixFlat::get_row_values(const std::vector<BinaryMatrix::Row> &rows) const -> std::vector<RowValues> {
     std::vector<RowValues> row_values(rows.size());
     const auto &flat = matrix_.data();
diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
index 7ee25bc2e3..c570cb2157 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -92,7 +92,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
             size_t row_block_size = num_columns_ * 64;
             std::vector<uint64_t> boundaries;
             boundaries.emplace_back(0);
-            for (size_t r = 1; r < num_set_bits; r += block_size) {
+            for (size_t r = 1; r <= num_set_bits; r += block_size) {
                 uint64_t b = flat.select1(r);
                 uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
                 if (b_round_ncols_64 > boundaries.back()) {
@@ -117,6 +117,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                 uint64_t end = flat_end / num_columns_;
 
                 RowValues row;
+                row.reserve(2);
 
                 auto *v_use = v_main;
 
@@ -127,13 +128,16 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                 auto it = v_use->begin() + r;
 
                 uint64_t last_i = begin;
-                auto set_bit_callback = [&](uint64_t b) __attribute__((always_inline)) {
+                uint64_t num_ones = flat_end ? flat.rank1(flat_end - 1) : 0;
+                for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
+                    uint64_t b = flat.select1(r);
                     uint64_t i = b / num_columns_;
                     if (i > last_i) {
                         callback(last_i, std::move(row), thread_id);
 
                         ++last_i;
                         row = RowValues();
+                        row.reserve(2);
 
                         for ( ; last_i < i; ++last_i) {
                             callback(last_i, RowValues(), thread_id);
@@ -145,9 +149,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                     ++it;
 
                     row.emplace_back(j, val);
-                };
-
-                flat.call_ones_in_range(flat_begin, flat_end, set_bit_callback);
+                }
 
                 callback(last_i, std::move(row), thread_id);
                 ++last_i;
@@ -161,189 +163,9 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
         }
     }
 
-    inline std::vector<VectorMap<uint64_t, size_t>>
+    std::vector<VectorMap<uint64_t, size_t>>
     get_histograms(const std::vector<size_t> &min_counts = {},
-                   sdsl::bit_vector *unmark_discarded = nullptr) const override final {
-        constexpr uint8_t HIST_CUTOFF = 64;
-        uint64_t n = num_rows();
-        size_t num_tasks = get_num_threads() + 1;
-        std::vector<std::vector<size_t>> num_nonzeros;
-        std::vector<std::vector<std::vector<size_t>>> hists_maps_base;
-        for (size_t j = 0; j < num_tasks; ++j) {
-            hists_maps_base.emplace_back(get_binary_matrix().num_columns());
-            num_nonzeros.emplace_back(get_binary_matrix().num_columns());
-            for (size_t i = 0; i < num_columns_; ++i) {
-                hists_maps_base.back()[i].resize(HIST_CUTOFF);
-            }
-        }
-
-        for (size_t i = 0; i < num_columns_; ++i) {
-            hists_maps_base[0][i][0] = n;
-        }
-
-        std::atomic<uint64_t> num_empty_rows{0};
-
-        std::atomic_thread_fence(std::memory_order_release);
-
-        const auto &flat = matrix_.data();
-
-        sdsl::int_vector_buffer<> *v_main;
-        std::visit([&](const auto &v) {
-            using S = std::decay_t<decltype(v)>;
-            if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
-                v_main = const_cast<S*>(&v);
-            } else {
-                throw std::runtime_error("Failed");
-            }
-        }, values_);
-
-        uint64_t num_set_bits = flat.num_set_bits();
-        uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
-        size_t row_block_size = num_columns_ * 64;
-        std::vector<uint64_t> boundaries;
-        boundaries.emplace_back(0);
-        for (size_t r = 1; r < num_set_bits; r += block_size) {
-            uint64_t b = flat.select1(r);
-            uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
-            if (b_round_ncols_64 > boundaries.back()) {
-                boundaries.emplace_back(b_round_ncols_64);
-                if (boundaries.back() >= flat.size()) {
-                    boundaries.back() = flat.size();
-                    break;
-                }
-            }
-        }
-
-        if (boundaries.back() != flat.size())
-            boundaries.emplace_back(flat.size());
-
-        std::vector<VectorMap<uint64_t, size_t>> hist_maps(num_columns_);
-
-        common::logger->trace("Streaming rows");
-        ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
-        #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
-        for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
-            std::vector<tsl::hopscotch_map<uint64_t, size_t>> hists_map(get_binary_matrix().num_columns());
-            std::vector<std::pair<uint64_t, size_t>> row;
-            auto row_callback = [&](uint64_t row_i) __attribute__((always_inline)) {
-                bool keep = min_counts.empty();
-
-                if (min_counts.size()) {
-                    for (const auto &[j, c] : row) {
-                        if (c >= min_counts[j]) {
-                            keep = true;
-                            break;
-                        }
-                    }
-                }
-
-                if (!keep) {
-                    if (unmark_discarded)
-                        unset_bit(unmark_discarded->data(), row_i, false, std::memory_order_relaxed);
-
-                    num_empty_rows.fetch_add(1, std::memory_order_relaxed);
-                } else {
-                    auto &hists_map_base = hists_maps_base[thread_id];
-                    for (const auto &[j, c] : row) {
-                        if (c < HIST_CUTOFF) {
-                            ++hists_map_base[j][c];
-                        } else {
-                            ++hists_map[j][c];
-                        }
-                        ++num_nonzeros[thread_id][j];
-                    }
-                }
-            };
-            uint64_t flat_begin = boundaries[thread_id];
-            uint64_t flat_end = boundaries[thread_id + 1];
-            uint64_t begin = flat_begin / num_columns_;
-            uint64_t end = flat_end / num_columns_;
-
-            auto *v_use = v_main;
-
-            std::unique_ptr<sdsl::int_vector_buffer<>> v_copy = std::make_unique<sdsl::int_vector_buffer<>>(v_use->filename(), std::ios::in, v_use->buffersize(), 0, false, offset_);
-            v_use = v_copy.get();
-
-            uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
-            auto it = v_use->begin() + r;
-
-            uint64_t last_i = begin;
-            uint64_t num_ones = flat_end ? flat.rank1(flat_end - 1) : 0;
-            for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
-                uint64_t b = flat.select1(r);
-                uint64_t i = b / num_columns_;
-                if (i > last_i) {
-                    row_callback(last_i);
-
-                    ++last_i;
-                    row.clear();
-
-                    num_empty_rows.fetch_add(i - last_i, std::memory_order_relaxed);
-                    if (unmark_discarded) {
-                        for ( ; last_i < i; ++last_i) {
-                            unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
-                        }
-                    } else {
-                        last_i = i;
-                    }
-                }
-
-                uint64_t j = b % num_columns_;
-                uint64_t val = *it;
-                ++it;
-
-                row.emplace_back(j, val);
-            }
-
-            row_callback(last_i);
-            ++last_i;
-
-            num_empty_rows.fetch_add(end - last_i, std::memory_order_relaxed);
-            if (unmark_discarded) {
-                for ( ; last_i < end; ++last_i) {
-                    unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
-                }
-            } else {
-                last_i = end;
-            }
-
-            progress_bar += end - begin;
-
-            #pragma omp critical
-            {
-                for (size_t j = 0; j < num_columns_; ++j) {
-                    for (const auto &[k, c] : hists_map[j]) {
-                        hist_maps[j][k] += c;
-                    }
-                }
-            }
-        }
-
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-
-        common::logger->trace("Merging histograms");
-        for (size_t j = 0; j < num_columns_; ++j) {
-            for (size_t i = 1; i < hists_maps_base.size(); ++i) {
-                for (size_t k = 0; k < HIST_CUTOFF; ++k) {
-                    hists_maps_base[0][j][k] += hists_maps_base[i][j][k];
-                }
-            }
-        }
-
-        for (size_t j = 0; j < num_columns_; ++j) {
-            hists_maps_base[0][j][0] -= num_empty_rows;
-            for (size_t i = 0; i < num_nonzeros.size(); ++i) {
-                hists_maps_base[0][j][0] -= num_nonzeros[i][j];
-            }
-            for (size_t k = 0; k < HIST_CUTOFF; ++k) {
-                if (hists_maps_base[0][j][k] > 0)
-                    hist_maps[j][k] += hists_maps_base[0][j][k];
-            }
-        }
-
-        return hist_maps;
-    }
+                   sdsl::bit_vector *unmark_discarded = nullptr) const override final;
 
     uint64_t num_columns() const { return num_columns_; }
     uint64_t num_rows() const { return get_row_major_binary_matrix().num_rows(); }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 96f9c7314a..5802982a23 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -100,11 +100,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto [mean_est, nzeros_est] = estimate_ztp_mean(
                 [&](const auto &callback) {
                     for (const auto &[k, c] : hists_map[j]) {
-                        if (k > 0 && k <= N_BUCKETS_FOR_ESTIMATION) {
-                            for (size_t i = 0; i < c; ++i) {
-                                callback(k);
-                            }
-                        }
+                        if (k > 0 && k <= N_BUCKETS_FOR_ESTIMATION)
+                            callback(k, c);
                     }
                 },
                 max_width,
diff --git a/metagraph/src/graph/graph_cleaning.cpp b/metagraph/src/graph/graph_cleaning.cpp
index 9d2dab7ea2..51c6841f24 100644
--- a/metagraph/src/graph/graph_cleaning.cpp
+++ b/metagraph/src/graph/graph_cleaning.cpp
@@ -75,7 +75,7 @@ uint64_t estimate_min_kmer_abundance_ztp(const DeBruijnGraph &graph,
     auto [mean_est, nzeros_est] = estimate_ztp_mean(
         [&](const auto &callback) {
             graph.call_nodes([&](DeBruijnGraph::node_index i) {
-                callback(node_weights[i]);
+                callback(node_weights[i], 1);
             });
         },
         node_weights.get_data().width(),
@@ -100,17 +100,17 @@ uint64_t estimate_min_kmer_abundance_ztp(const DeBruijnGraph &graph,
     return min_count + 1;
 }
 
-std::pair<double, double> estimate_ztp_mean(const std::function<void(const std::function<void(uint64_t)>&)> &generator,
+std::pair<double, double> estimate_ztp_mean(const std::function<void(const std::function<void(uint64_t, size_t)>&)> &generator,
                                             uint8_t width,
                                             uint64_t num_singleton_kmers,
                                             size_t touse) {
     std::vector<uint64_t> hist;
-    generator([&](uint64_t kmer_count) {
+    generator([&](uint64_t kmer_count, size_t occ) {
         assert(kmer_count && "All k-mers in graph must have non-zero counts");
         while (kmer_count >= hist.size()) {
             hist.push_back(0);
         }
-        hist[kmer_count]++;
+        hist[kmer_count] += occ;
     });
 
     if (num_singleton_kmers) {
diff --git a/metagraph/src/graph/graph_cleaning.hpp b/metagraph/src/graph/graph_cleaning.hpp
index a0d20aab15..e1e153f40f 100644
--- a/metagraph/src/graph/graph_cleaning.hpp
+++ b/metagraph/src/graph/graph_cleaning.hpp
@@ -17,7 +17,7 @@ uint64_t estimate_min_kmer_abundance(const DeBruijnGraph &graph,
                                      const NodeWeights &node_weights,
                                      uint64_t num_singleton_kmers = 0);
 
-std::pair<double, double> estimate_ztp_mean(const std::function<void(const std::function<void(uint64_t)>&)> &generator,
+std::pair<double, double> estimate_ztp_mean(const std::function<void(const std::function<void(uint64_t, size_t)>&)> &generator,
                                             uint8_t width = 64,
                                             uint64_t num_singleton_kmers = 0,
                                             size_t touse = std::numeric_limits<size_t>::max());

From 91976e90930c5350883a1204cd785fc64064b9f2 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 25 Jul 2024 15:24:42 +0200
Subject: [PATCH 254/410] fixes

---
 .../int_matrix/csr_matrix/csr_matrix_flat.cpp | 68 ++++++++++---------
 1 file changed, 37 insertions(+), 31 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
index 034bc8022a..023328a0a2 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
@@ -39,7 +39,8 @@ CSRMatrixFlat::CSRMatrixFlat(const std::function<void(const std::function<void(c
 std::vector<VectorMap<uint64_t, size_t>>
 CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
                               sdsl::bit_vector *unmark_discarded) const {
-    bool keep_all_nonzeros = std::all_of(min_counts.begin(), min_counts.end(), [](size_t s) { return s; });
+    bool keep_all_nonzeros = min_counts.size() == num_columns_
+                                && std::all_of(min_counts.begin(), min_counts.end(), [](size_t s) { return s == 1; });
     constexpr uint8_t HIST_CUTOFF = 64;
     uint64_t n = num_rows();
     size_t num_tasks = get_num_threads() + 1;
@@ -104,7 +105,7 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
         uint64_t flat_begin = boundaries[thread_id];
         uint64_t flat_end = boundaries[thread_id + 1];
         uint64_t begin = flat_begin / num_columns_;
-        uint64_t end = flat_end / num_columns_;
+        int64_t end = flat_end / num_columns_;
 
         auto *v_use = v_main;
 
@@ -114,43 +115,50 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
         uint64_t r = flat.rank1(flat_begin) - flat[flat_begin];
         auto it = v_use->begin() + r;
 
-        uint64_t last_i = begin;
         uint64_t num_ones = flat_end ? flat.rank1(flat_end - 1) : 0;
 
-        if (false && keep_all_nonzeros) {
+        if (keep_all_nonzeros) {
+            int64_t last_i = static_cast<int64_t>(begin) - 1;
             for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
                 uint64_t b = flat.select1(r);
-                uint64_t i = b / num_columns_;
-                if (i > last_i) {
-                    if (last_i == begin) {
-                        if (unmark_discarded)
-                            unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+                int64_t i = b / num_columns_;
+                uint64_t j = b % num_columns_;
+                uint64_t val = *it;
+                ++it;
 
-                        num_empty_rows.fetch_add(1, std::memory_order_relaxed);
-                    }
+                if (val < HIST_CUTOFF) {
+                    ++hists_map_base[j][val];
+                } else {
+                    ++hists_map[j][val];
+                }
+                ++num_nonzeros[thread_id][j];
+
+                if (i - last_i > 1) {
                     ++last_i;
-                    num_empty_rows.fetch_add(i - last_i, std::memory_order_relaxed);
+                    uint64_t num_added = i - last_i;
+                    num_empty_rows.fetch_add(num_added, std::memory_order_relaxed);
                     if (unmark_discarded) {
                         for ( ; last_i < i; ++last_i) {
                             unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
                         }
-                    } else {
-                        last_i = i;
                     }
                 }
 
-                uint64_t j = b % num_columns_;
-                uint64_t val = *it;
-                ++it;
+                last_i = i;
+            }
 
-                if (val < HIST_CUTOFF) {
-                    ++hists_map_base[j][val];
-                } else {
-                    ++hists_map[j][val];
+            if (end - last_i > 1) {
+                ++last_i;
+                uint64_t num_added = end - last_i;
+                num_empty_rows.fetch_add(num_added, std::memory_order_relaxed);
+                if (unmark_discarded) {
+                    for ( ; last_i < end; ++last_i) {
+                        unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+                    }
                 }
-                ++num_nonzeros[thread_id][j];
             }
         } else {
+            int64_t last_i = begin;
             std::vector<std::pair<uint64_t, size_t>> row;
             auto row_callback = [&](uint64_t row_i) __attribute__((always_inline)) {
                 bool keep = min_counts.empty();
@@ -183,7 +191,7 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
 
             for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
                 uint64_t b = flat.select1(r);
-                uint64_t i = b / num_columns_;
+                int64_t i = b / num_columns_;
                 if (i > last_i) {
                     row_callback(last_i);
 
@@ -208,14 +216,13 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
             }
 
             row_callback(last_i);
-        }
-
-        ++last_i;
+            ++last_i;
 
-        num_empty_rows.fetch_add(end - last_i, std::memory_order_relaxed);
-        if (unmark_discarded) {
-            for ( ; last_i < end; ++last_i) {
-                unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+            num_empty_rows.fetch_add(end - last_i, std::memory_order_relaxed);
+            if (unmark_discarded) {
+                for ( ; last_i < end; ++last_i) {
+                    unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+                }
             }
         }
 
@@ -245,7 +252,6 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
     for (size_t j = 0; j < num_columns_; ++j) {
         hists_maps_base[0][j][0] -= num_empty_rows;
         for (size_t i = 0; i < num_nonzeros.size(); ++i) {
-            common::logger->trace("NZ: {}: {}", i, fmt::join(num_nonzeros[i], ","));
             hists_maps_base[0][j][0] -= num_nonzeros[i][j];
         }
         for (size_t k = 0; k < HIST_CUTOFF; ++k) {

From d826a26304b3dc5da529d77484ca5248fe2f909d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 26 Jul 2024 14:20:57 +0200
Subject: [PATCH 255/410] better parallel

---
 .../int_matrix/csr_matrix/csr_matrix_flat.cpp |  37 +-
 .../int_matrix/csr_matrix/csr_matrix_flat.hpp |   2 +-
 metagraph/src/cli/assemble.cpp                |   1 +
 .../src/graph/annotated_graph_algorithm.cpp   | 406 ++++++++++--------
 .../src/graph/annotated_graph_algorithm.hpp   |   1 +
 5 files changed, 236 insertions(+), 211 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
index 023328a0a2..4804d5bee2 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
@@ -58,10 +58,6 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
         hists_maps_base[0][j][0] = n;
     }
 
-    std::atomic<uint64_t> num_empty_rows{0};
-
-    std::atomic_thread_fence(std::memory_order_release);
-
     const auto &flat = matrix_.data();
 
     sdsl::int_vector_buffer<> *v_main;
@@ -75,11 +71,11 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
     }, values_);
 
     uint64_t num_set_bits = flat.num_set_bits();
-    uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
+    uint64_t block_size = num_set_bits / get_num_threads();
     size_t row_block_size = num_columns_ * 64;
     std::vector<uint64_t> boundaries;
     boundaries.emplace_back(0);
-    for (size_t r = 1; r < num_set_bits; r += block_size) {
+    for (size_t r = block_size; r <= num_set_bits; r += block_size) {
         uint64_t b = flat.select1(r);
         uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
         if (b_round_ncols_64 > boundaries.back()) {
@@ -97,9 +93,11 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
     std::vector<VectorMap<uint64_t, size_t>> hist_maps(num_columns_);
 
     common::logger->trace("Streaming rows");
+    uint64_t num_empty_rows = 0;
     ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
-    #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
+    #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic) reduction(+ : num_empty_rows)
     for (size_t thread_id = 0; thread_id < boundaries.size() - 1; ++thread_id) {
+        uint64_t local_num_empty_rows = 0;
         std::vector<tsl::hopscotch_map<uint64_t, size_t>> hists_map(get_binary_matrix().num_columns());
         auto &hists_map_base = hists_maps_base[thread_id];
         uint64_t flat_begin = boundaries[thread_id];
@@ -136,10 +134,10 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
                 if (i - last_i > 1) {
                     ++last_i;
                     uint64_t num_added = i - last_i;
-                    num_empty_rows.fetch_add(num_added, std::memory_order_relaxed);
+                    local_num_empty_rows += num_added;
                     if (unmark_discarded) {
                         for ( ; last_i < i; ++last_i) {
-                            unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+                            unset_bit(unmark_discarded->data(), last_i, false);
                         }
                     }
                 }
@@ -150,10 +148,10 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
             if (end - last_i > 1) {
                 ++last_i;
                 uint64_t num_added = end - last_i;
-                num_empty_rows.fetch_add(num_added, std::memory_order_relaxed);
+                local_num_empty_rows += num_added;
                 if (unmark_discarded) {
                     for ( ; last_i < end; ++last_i) {
-                        unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+                        unset_bit(unmark_discarded->data(), last_i, false);
                     }
                 }
             }
@@ -174,9 +172,9 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
 
                 if (!keep) {
                     if (unmark_discarded)
-                        unset_bit(unmark_discarded->data(), row_i, false, std::memory_order_relaxed);
+                        unset_bit(unmark_discarded->data(), row_i, false);
 
-                    num_empty_rows.fetch_add(1, std::memory_order_relaxed);
+                    ++local_num_empty_rows;
                 } else {
                     for (const auto &[j, c] : row) {
                         if (c < HIST_CUTOFF) {
@@ -198,10 +196,10 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
                     ++last_i;
                     row.clear();
 
-                    num_empty_rows.fetch_add(i - last_i, std::memory_order_relaxed);
+                    local_num_empty_rows += i - last_i;
                     if (unmark_discarded) {
                         for ( ; last_i < i; ++last_i) {
-                            unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+                            unset_bit(unmark_discarded->data(), last_i, false);
                         }
                     } else {
                         last_i = i;
@@ -218,16 +216,19 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
             row_callback(last_i);
             ++last_i;
 
-            num_empty_rows.fetch_add(end - last_i, std::memory_order_relaxed);
+            local_num_empty_rows += end - last_i;
             if (unmark_discarded) {
                 for ( ; last_i < end; ++last_i) {
-                    unset_bit(unmark_discarded->data(), last_i, false, std::memory_order_relaxed);
+                    unset_bit(unmark_discarded->data(), last_i, false);
                 }
             }
         }
 
         progress_bar += end - begin;
 
+        #pragma omp atomic
+        num_empty_rows += local_num_empty_rows;
+
         #pragma omp critical
         {
             for (size_t j = 0; j < num_columns_; ++j) {
@@ -238,8 +239,6 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
         }
     }
 
-    std::atomic_thread_fence(std::memory_order_acquire);
-
     common::logger->trace("Merging histograms");
     for (size_t j = 0; j < num_columns_; ++j) {
         for (size_t i = 1; i < hists_maps_base.size(); ++i) {
diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
index c570cb2157..2598bfcf2d 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -92,7 +92,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
             size_t row_block_size = num_columns_ * 64;
             std::vector<uint64_t> boundaries;
             boundaries.emplace_back(0);
-            for (size_t r = 1; r <= num_set_bits; r += block_size) {
+            for (size_t r = block_size; r <= num_set_bits; r += block_size) {
                 uint64_t b = flat.select1(r);
                 uint64_t b_round_ncols_64 = (b + row_block_size - 1) / row_block_size * row_block_size;
                 if (b_round_ncols_64 > boundaries.back()) {
diff --git a/metagraph/src/cli/assemble.cpp b/metagraph/src/cli/assemble.cpp
index fa6d982f55..59fcb476c9 100644
--- a/metagraph/src/cli/assemble.cpp
+++ b/metagraph/src/cli/assemble.cpp
@@ -96,6 +96,7 @@ void call_masked_graphs(std::shared_ptr<DeBruijnGraph> graph_ptr,
 
             DifferentialAssemblyConfig diff_config = diff_assembly_config(experiment);
             diff_config.outfbase = config->outfbase;
+            diff_config.output_pvals = config->output_pvals;
 
             std::string exp_name = experiment["name"].asString()
                                     + (config->enumerate_out_sequences ? "." : "");
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5802982a23..7d1b1b39ee 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -225,141 +225,140 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
     }
 
-    // std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
-    // std::function<double(int64_t)> compute_min_pval;
+    std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
+    std::function<double(int64_t)> compute_min_pval;
 
     std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps;
 
     common::logger->trace("Test: {}", config.test_type);
-    // if (config.test_type == "poisson_exact") {
-    //     compute_min_pval = [&](int64_t n) {
-    //         if (n == 0)
-    //             return 1.1;
-
-    //         double p = static_cast<double>(in_kmers) / total_kmers;
-    //         auto bdist = boost::math::binomial(n, p);
-    //         auto get_deviance = [&](double y, double mu) {
-    //             y += 1e-8;
-    //             mu += 1e-8;
-    //             return 2 * (y * log(y/mu) - y + mu);
-    //         };
-
-    //         double mu1 = static_cast<double>(in_kmers) / nelem;
-    //         double mu2 = static_cast<double>(out_kmers) / nelem;
-
-    //         std::vector<double> devs;
-    //         devs.reserve(n + 1);
-    //         for (int64_t s = 0; s <= n; ++s) {
-    //             devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-    //         }
-    //         double max_d = *std::max_element(devs.begin(), devs.end());
-    //         double pval = 0.0;
-
-    //         int64_t s = 0;
-    //         for ( ; s <= n; ++s) {
-    //             if (devs[s] != max_d)
-    //                 break;
-    //         }
-    //         if (s > 0)
-    //             pval += boost::math::cdf(bdist, s - 1);
-
-    //         int64_t sp = n;
-    //         for ( ; sp >= s; --sp) {
-    //             if (devs[sp] != max_d)
-    //                 break;
-    //         }
-
-    //         if (sp < n)
-    //             pval += boost::math::cdf(boost::math::complement(bdist, sp));
-
-    //         return pval;
-    //     };
-
-    //     compute_pval = [&](int64_t in_sum, int64_t out_sum, const auto &row) {
-    //         if (row.empty())
-    //             return 1.1;
-
-    //         int64_t n = in_sum + out_sum;
-    //         double p = static_cast<double>(in_kmers) / total_kmers;
-    //         auto bdist = boost::math::binomial(n, p);
-    //         auto get_deviance = [&](double y, double mu) {
-    //             y += 1e-8;
-    //             mu += 1e-8;
-    //             return 2 * (y * log(y/mu) - y + mu);
-    //         };
-
-    //         double mu1 = static_cast<double>(in_kmers) / nelem;
-    //         double mu2 = static_cast<double>(out_kmers) / nelem;
-
-    //         std::vector<double> devs;
-    //         devs.reserve(n + 1);
-    //         for (int64_t s = 0; s <= n; ++s) {
-    //             devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-    //         }
-    //         double pval = 0.0;
-
-    //         int64_t s = 0;
-    //         for ( ; s <= n; ++s) {
-    //             if (devs[s] < devs[in_sum])
-    //                 break;
-    //         }
-    //         if (s > 0)
-    //             pval += boost::math::cdf(bdist, s - 1);
-
-    //         int64_t sp = n;
-    //         for ( ; sp >= s; --sp) {
-    //             if (devs[sp] < devs[in_sum])
-    //                 break;
-    //         }
-
-    //         if (sp < n)
-    //             pval += boost::math::cdf(boost::math::complement(bdist, sp));
-
-    //         return pval;
-    //     };
-    // } else if (config.test_type == "fisher") {
-    //     double lbase_shared = lgamma(in_kmers + 1) + lgamma(out_kmers + 1) - lgamma(total_kmers + 1);
-
-    //     compute_min_pval = [&,lbase_shared](int64_t m1) {
-    //         if (m1 == 0)
-    //             return 1.1;
-
-    //         int64_t m2 = total_kmers - m1;
-
-    //         auto get_pval = [&](int64_t a) {
-    //             double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
-
-    //             int64_t b = in_kmers - a;
-    //             int64_t c = m1 - a;
-    //             int64_t d = m2 - b;
-    //             assert(d == out_kmers - c);
-
-    //             return out_kmers < c
-    //                 ? 1.0
-    //                 : exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
-    //         };
-
-    //         return std::min(get_pval(0), get_pval(std::min(in_kmers, m1)));
-    //     };
-
-    //     compute_pval = [&,lbase_shared](int64_t in_sum, int64_t out_sum, const auto &row) {
-    //         if (row.empty())
-    //             return 1.1;
-
-    //         int64_t m1 = in_sum + out_sum;
-    //         int64_t m2 = total_kmers - m1;
-
-    //         double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
-
-    //         int64_t a = in_sum;
-    //         int64_t b = in_kmers - in_sum;
-    //         int64_t c = out_sum;
-    //         int64_t d = out_kmers - out_sum;
-
-    //         return exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
-    //     };
-    // } else if (config.test_type == "nbinom_exact") {
-    // {
+    if (config.test_type == "poisson_exact") {
+        compute_min_pval = [&](int64_t n) {
+            if (n == 0)
+                return 1.1;
+
+            double p = static_cast<double>(in_kmers) / total_kmers;
+            auto bdist = boost::math::binomial(n, p);
+            auto get_deviance = [&](double y, double mu) {
+                y += 1e-8;
+                mu += 1e-8;
+                return 2 * (y * log(y/mu) - y + mu);
+            };
+
+            double mu1 = static_cast<double>(in_kmers) / nelem;
+            double mu2 = static_cast<double>(out_kmers) / nelem;
+
+            std::vector<double> devs;
+            devs.reserve(n + 1);
+            for (int64_t s = 0; s <= n; ++s) {
+                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+            }
+            double max_d = *std::max_element(devs.begin(), devs.end());
+            double pval = 0.0;
+
+            int64_t s = 0;
+            for ( ; s <= n; ++s) {
+                if (devs[s] != max_d)
+                    break;
+            }
+            if (s > 0)
+                pval += boost::math::cdf(bdist, s - 1);
+
+            int64_t sp = n;
+            for ( ; sp >= s; --sp) {
+                if (devs[sp] != max_d)
+                    break;
+            }
+
+            if (sp < n)
+                pval += boost::math::cdf(boost::math::complement(bdist, sp));
+
+            return pval;
+        };
+
+        compute_pval = [&](int64_t in_sum, int64_t out_sum, const auto &row) {
+            if (row.empty())
+                return 1.1;
+
+            int64_t n = in_sum + out_sum;
+            double p = static_cast<double>(in_kmers) / total_kmers;
+            auto bdist = boost::math::binomial(n, p);
+            auto get_deviance = [&](double y, double mu) {
+                y += 1e-8;
+                mu += 1e-8;
+                return 2 * (y * log(y/mu) - y + mu);
+            };
+
+            double mu1 = static_cast<double>(in_kmers) / nelem;
+            double mu2 = static_cast<double>(out_kmers) / nelem;
+
+            std::vector<double> devs;
+            devs.reserve(n + 1);
+            for (int64_t s = 0; s <= n; ++s) {
+                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+            }
+            double pval = 0.0;
+
+            int64_t s = 0;
+            for ( ; s <= n; ++s) {
+                if (devs[s] < devs[in_sum])
+                    break;
+            }
+            if (s > 0)
+                pval += boost::math::cdf(bdist, s - 1);
+
+            int64_t sp = n;
+            for ( ; sp >= s; --sp) {
+                if (devs[sp] < devs[in_sum])
+                    break;
+            }
+
+            if (sp < n)
+                pval += boost::math::cdf(boost::math::complement(bdist, sp));
+
+            return pval;
+        };
+    } else if (config.test_type == "fisher") {
+        double lbase_shared = lgamma(in_kmers + 1) + lgamma(out_kmers + 1) - lgamma(total_kmers + 1);
+
+        compute_min_pval = [&,lbase_shared](int64_t m1) {
+            if (m1 == 0)
+                return 1.1;
+
+            int64_t m2 = total_kmers - m1;
+
+            auto get_pval = [&](int64_t a) {
+                double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
+
+                int64_t b = in_kmers - a;
+                int64_t c = m1 - a;
+                int64_t d = m2 - b;
+                assert(d == out_kmers - c);
+
+                return out_kmers < c
+                    ? 1.0
+                    : exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
+            };
+
+            return std::min(get_pval(0), get_pval(std::min(in_kmers, m1)));
+        };
+
+        compute_pval = [&,lbase_shared](int64_t in_sum, int64_t out_sum, const auto &row) {
+            if (row.empty())
+                return 1.1;
+
+            int64_t m1 = in_sum + out_sum;
+            int64_t m2 = total_kmers - m1;
+
+            double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
+
+            int64_t a = in_sum;
+            int64_t b = in_kmers - in_sum;
+            int64_t c = out_sum;
+            int64_t d = out_kmers - out_sum;
+
+            return exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
+        };
+    } else if (config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
         auto get_rp = [&](size_t j, auto begin, auto end) {
             double mu = 0;
@@ -523,7 +522,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (fit_var / fit_mu - 1.0 < 1e-5)
             common::logger->warn("Fit parameters are close to a Poisson distribution");
 
-        auto compute_min_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t n) {
+        compute_min_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t n) {
             if (n == 0)
                 return 1.1;
 
@@ -570,7 +569,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::min(1.0, pval);
         };
 
-        auto compute_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t in_sum, int64_t out_sum, const auto &row) {
+        compute_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
                 return 1.1;
 
@@ -685,12 +684,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->error("Best achievable p-value is too big. Use poisson_exact instead");
             throw std::domain_error("Too few samples");
         }
-    // } else if (config.test_type == "notest") {
-    //     compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
-    //     compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };
-    // } else {
-    //     throw std::runtime_error("Test not implemented");
-    // }
+    } else if (config.test_type == "notest") {
+        compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
+        compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };
+    } else {
+        throw std::runtime_error("Test not implemented");
+    }
 
     common::logger->trace("Running differential tests");
     std::exception_ptr ex = nullptr;
@@ -762,9 +761,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             throw std::runtime_error("Test failed");
         }
 
+        auto &pvals = pvals_buckets[std::min(bucket_idx, pvals_buckets.size() - 1)];
         if (config.test_by_unitig) {
             assert(pvals_min);
-            auto &pvals = pvals_buckets[bucket_idx];
             pvals.push_back(bit_cast<uint64_t>(pval));
 
             std::lock_guard<std::mutex> lock(pval_mu);
@@ -772,7 +771,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             (*eff_size)[node] = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
         } else {
             if (config.test_type != "notest")
-                pvals_buckets[bucket_idx].push_back(bit_cast<uint64_t>(pval));
+                pvals.push_back(bit_cast<uint64_t>(pval));
 
             if (pval < 1.1) {
                 uint64_t k = std::numeric_limits<uint64_t>::max();
@@ -790,7 +789,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 if (in_kmer != out_kmer && (pval < config.family_wise_error_rate || config.test_by_unitig)) {
-                    set_bit((in_kmer ? indicator_in : indicator_out).data(), node, parallel, MO_RELAXED);
+                    bool use_atomic = parallel && (node % 64 == 0);
+                    set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
                 }
 
                 ++ms[bucket_idx][k];
@@ -800,40 +800,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     std::atomic_thread_fence(std::memory_order_acquire);
 
-    for (size_t i = 1; i < ms.size(); ++i) {
-        for (const auto &[k, c] : ms[i]) {
-            ms[0][k] += c;
-        }
-    }
-    VectorMap<size_t, size_t> m = std::move(ms[0]);
-    ms.resize(0);
-
-    std::unique_ptr<utils::TempFile> tmp_file;
-    PValStorage pvals;
-
-    if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-        pvals = std::move(pvals_buckets[0]);
-    }
-
-    if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-        common::logger->trace("Merging buckets");
-        for (size_t i = 1; i < pvals_buckets.size(); ++i) {
-            for (size_t j = 0; j < pvals_buckets[i].size(); ++j) {
-                pvals_buckets[0].push_back(pvals_buckets[i][j]);
-            }
-            pvals_buckets[i].close();
-        }
-        pvals = std::move(pvals_buckets[0]);
-        pvals_buckets.resize(0);
-        tmp_file = std::move(tmp_buckets[0]);
-        tmp_buckets.resize(0);
-    }
-
     if (ex)
         std::rethrow_exception(ex);
 
     deallocate();
 
+    std::vector<size_t> boundaries;
+    boundaries.reserve(pvals_buckets.size() + 1);
+    boundaries.emplace_back(0);
+    for (size_t i = 0; i < pvals_buckets.size(); ++i) {
+        boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
+    }
+
     if (config.test_by_unitig) {
         common::logger->trace("Allocating k-mer bitmasks");
         indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
@@ -857,11 +835,24 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double stat = 0;
             double stat_min = 0;
             int64_t comb_eff_size = 0;
+            std::vector<size_t> bucket_idxs;
+            bucket_idxs.reserve(path.size());
+            for (node_index node : path) {
+                for (size_t i = 0; i < boundaries.size(); ++i) {
+                    if (boundaries[i] > node) {
+                        bucket_idxs.emplace_back(i);
+                        break;
+                    }
+                }
+            }
+
             {
                 std::lock_guard<std::mutex> lock(pval_mu);
-                for (node_index node : path) {
+                for (size_t i = 0; i < path.size(); ++i) {
+                    node_index node = path[i];
+                    size_t bucket_idx = bucket_idxs[i];
                     comb_eff_size += bit_cast<int64_t, uint64_t>((*eff_size)[node]);
-                    double pval = bit_cast<double, uint64_t>(pvals[node]);
+                    double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node - boundaries[bucket_idx]]);
                     double pval_min = bit_cast<double, uint64_t>((*pvals_min)[node]);
                     stat += tan((0.5 - pval) * M_PI);
                     stat_min += tan((0.5 - pval_min) * M_PI);
@@ -894,13 +885,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
-
-            std::lock_guard<std::mutex> lock(pval_mu);
-            ++m[k];
-
-            pvals[path[0]] = comb_pval_enc;
+            size_t bucket_idx = bucket_idxs[0];
+            ++ms[bucket_idx][k];
+            pvals_buckets[bucket_idx][path[0] - boundaries[bucket_idx]] = comb_pval_enc;
             for (size_t i = 1; i < path.size(); ++i) {
-                pvals[path[i]] = nullpval;
+                size_t bucket_idx = bucket_idxs[i];
+                pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
             }
         }, num_threads);
         std::atomic_thread_fence(std::memory_order_acquire);
@@ -909,6 +899,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         pvals_min.reset();
     }
 
+    for (size_t i = 1; i < ms.size(); ++i) {
+        for (const auto &[k, c] : ms[i]) {
+            ms[0][k] += c;
+        }
+    }
+    VectorMap<size_t, size_t> m = std::move(ms[0]);
+    ms.resize(0);
+
     if (config.test_type != "notest") {
         uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
         auto m_data = const_cast<std::vector<std::pair<size_t, size_t>>&&>(m.values_container());
@@ -941,27 +939,53 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 common::logger->trace("k: {}\talpha_corr: {}", k, config.family_wise_error_rate / k);
 
                 std::atomic_thread_fence(std::memory_order_release);
-                #pragma omp parallel for ordered num_threads(num_threads)
-                for (size_t i = 0; i < indicator_in.size(); ++i) {
-                    if (fetch_bit(indicator_in.data(), i, parallel, MO_RELAXED)
-                            || fetch_bit(indicator_out.data(), i, parallel, MO_RELAXED)) {
-                        double p = 1.0;
-
-                        #pragma omp ordered
-                        p = bit_cast<double, uint64_t>(pvals[i]);
-
+                #pragma omp parallel for num_threads(num_threads)
+                for (size_t j = 0; j < boundaries.size() - 1; ++j) {
+                    size_t begin = boundaries[j];
+                    size_t end = boundaries[j + 1];
+                    call_ones(indicator_in, begin, end, [&](uint64_t i) {
+                        double p = bit_cast<double, uint64_t>(pvals_buckets[j][i - begin]);
                         if (p < config.family_wise_error_rate && p * k >= config.family_wise_error_rate) {
-                            unset_bit(indicator_in.data(), i, parallel, MO_RELAXED);
-                            unset_bit(indicator_out.data(), i, parallel, MO_RELAXED);
+                            unset_bit(indicator_in.data(), i, parallel, std::memory_order_release);
                         }
-                    }
+                    }, parallel, std::memory_order_acquire);
+                    call_ones(indicator_out, begin, end, [&](uint64_t i) {
+                        double p = bit_cast<double, uint64_t>(pvals_buckets[j][i - begin]);
+                        if (p < config.family_wise_error_rate && p * k >= config.family_wise_error_rate) {
+                            unset_bit(indicator_out.data(), i, parallel, std::memory_order_release);
+                        }
+                    }, parallel, std::memory_order_acquire);
                 }
                 std::atomic_thread_fence(std::memory_order_acquire);
             }
         }
     }
 
+    std::unique_ptr<utils::TempFile> tmp_file;
+    PValStorage pvals;
+
+    if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+        pvals = std::move(pvals_buckets[0]);
+    }
+
+    if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+        if (config.output_pvals) {
+            common::logger->trace("Merging buckets");
+            for (size_t i = 1; i < pvals_buckets.size(); ++i) {
+                for (size_t j = 0; j < pvals_buckets[i].size(); ++j) {
+                    pvals_buckets[0].push_back(pvals_buckets[i][j]);
+                }
+                pvals_buckets[i].close();
+            }
+        }
+        pvals = std::move(pvals_buckets[0]);
+        pvals_buckets.resize(0);
+        tmp_file = std::move(tmp_buckets[0]);
+        tmp_buckets.resize(0);
+    }
+
     common::logger->trace("Done! Assembling contigs.");
+
     auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
         graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
diff --git a/metagraph/src/graph/annotated_graph_algorithm.hpp b/metagraph/src/graph/annotated_graph_algorithm.hpp
index 33f173bf14..a60a7771b8 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.hpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.hpp
@@ -22,6 +22,7 @@ struct DifferentialAssemblyConfig {
     bool clean = false;
     double family_wise_error_rate = 0.05;
     bool test_by_unitig = false;
+    bool output_pvals = false;
     std::string test_type = "nbinom_exact";
     bool assemble_shared = false;
     uint64_t min_count = 1;

From b654368e0eb13708537ecb729b2991f702568e6b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 26 Jul 2024 17:01:27 +0200
Subject: [PATCH 256/410] optimizations

---
 .../src/graph/annotated_graph_algorithm.cpp   | 68 ++++++++++++-------
 1 file changed, 43 insertions(+), 25 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 7d1b1b39ee..b9ec9101b3 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -526,12 +526,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (n == 0)
                 return 1.1;
 
-            double lscaling = lgamma(n + 1) + lscaling_base;
+            double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
             auto get_pmf = [&](int64_t s) {
                 int64_t t = n - s;
                 double rs = r_in + s;
                 double rt = r_out + t;
-                return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
+                return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt));
             };
 
             std::vector<double> devs;
@@ -575,34 +575,50 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             int64_t n = in_sum + out_sum;
 
-            double lscaling = lgamma(n + 1) + lscaling_base;
-            auto get_pmf = [&](int64_t s) {
-                int64_t t = n - s;
-                double rs = r_in + s;
-                double rt = r_out + t;
-                return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt) - lgamma(rs + rt));
-            };
+            double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
 
-            std::vector<double> devs;
-            devs.reserve(n + 1);
+            std::vector<double> devs(n + 1);
             for (int64_t s = 0; s <= n; ++s) {
-                devs.emplace_back(get_deviance(s, mu1, r_in) + get_deviance(n - s, mu2, r_out));
+                devs[s] = get_deviance(s, mu1, r_in) + get_deviance(n - s, mu2, r_out);
             }
             double pval = 0.0;
             int64_t s = 0;
-            for ( ; s <= n; ++s) {
-                if (devs[s] >= devs[in_sum]) {
-                    pval += get_pmf(s);
-                } else {
-                    break;
+            if (devs[s] >= devs[in_sum]) {
+                int64_t t = n;
+                double rs = r_in;
+                double rt = r_out + n;
+                double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                pval += exp(base);
+                for (++s; s <= n; ++s) {
+                    if (devs[s] >= devs[in_sum]) {
+                        --t;
+                        ++rs;
+                        --rt;
+                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                        pval += exp(base);
+                    } else {
+                        break;
+                    }
                 }
             }
 
-            for (int64_t sp = n; sp >= s; --sp) {
-                if (devs[sp] >= devs[in_sum]) {
-                    pval += get_pmf(sp);
-                } else {
-                    break;
+            int64_t sp = n;
+            if (devs[sp] >= devs[in_sum]) {
+                int64_t t = 0;
+                double rs = r_in + sp;
+                double rt = r_out;
+                double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                pval += exp(base);
+                for (--sp; sp >= s; --sp) {
+                    if (devs[sp] >= devs[in_sum]) {
+                        ++t;
+                        --rs;
+                        ++rt;
+                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                        pval += exp(base);
+                    } else {
+                        break;
+                    }
                 }
             }
 
@@ -888,9 +904,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             size_t bucket_idx = bucket_idxs[0];
             ++ms[bucket_idx][k];
             pvals_buckets[bucket_idx][path[0] - boundaries[bucket_idx]] = comb_pval_enc;
-            for (size_t i = 1; i < path.size(); ++i) {
-                size_t bucket_idx = bucket_idxs[i];
-                pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
+            if (config.output_pvals) {
+                for (size_t i = 1; i < path.size(); ++i) {
+                    size_t bucket_idx = bucket_idxs[i];
+                    pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
+                }
             }
         }, num_threads);
         std::atomic_thread_fence(std::memory_order_acquire);

From 7da05f69ea6985beeecc45ec49fb35579e9696ac Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 26 Jul 2024 21:30:39 +0200
Subject: [PATCH 257/410] optimize

---
 .../annotation/int_matrix/base/int_matrix.cpp |  6 ++--
 .../annotation/int_matrix/base/int_matrix.hpp |  2 +-
 .../int_matrix/csr_matrix/csr_matrix_flat.hpp | 16 ++++-----
 .../src/graph/annotated_graph_algorithm.cpp   | 33 +++++++++----------
 4 files changed, 26 insertions(+), 31 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
index afc5f84968..e3434da6bb 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
@@ -14,7 +14,7 @@ namespace matrix {
 using Row = BinaryMatrix::Row;
 using Column = BinaryMatrix::Column;
 
-void IntMatrix::call_row_values(const std::function<void(uint64_t, RowValues&&, size_t)> &callback,
+void IntMatrix::call_row_values(const std::function<void(uint64_t, const RowValues&, size_t)> &callback,
                                 bool ordered) const {
     size_t n = get_binary_matrix().num_rows();
     ProgressBar progress_bar(n, "Streaming rows", std::cerr, !common::get_verbose());
@@ -26,9 +26,9 @@ void IntMatrix::call_row_values(const std::function<void(uint64_t, RowValues&&,
 
         if (ordered) {
             #pragma omp ordered
-            callback(row_i, std::move(row_vals[0]), row_i);
+            callback(row_i, row_vals[0], row_i);
         } else {
-            callback(row_i, std::move(row_vals[0]), row_i);
+            callback(row_i, row_vals[0], row_i);
         }
     }
 }
diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
index bb366cd80f..e196a63a77 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.hpp
@@ -23,7 +23,7 @@ class IntMatrix {
     // |row| is in [0, num_rows), |column| is in [0, num_columns)
     virtual std::vector<RowValues> get_row_values(const std::vector<BinaryMatrix::Row> &rows) const = 0;
 
-    virtual void call_row_values(const std::function<void(uint64_t, RowValues&&, size_t)> &callback,
+    virtual void call_row_values(const std::function<void(uint64_t, const RowValues&, size_t)> &callback,
                                  bool ordered = true) const;
 
     virtual std::vector<VectorMap<uint64_t, size_t>> get_histograms(const std::vector<size_t> &min_counts = {},
diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
index 2598bfcf2d..a43fd0fd2c 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -33,7 +33,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
 
     std::vector<RowValues> get_row_values(const std::vector<BinaryMatrix::Row> &rows) const;
 
-    inline void call_row_values(const std::function<void(uint64_t, RowValues&&, size_t)> &callback,
+    inline void call_row_values(const std::function<void(uint64_t, const RowValues&, size_t)> &callback,
                                 bool ordered = true) const override final {
         constexpr size_t kNumRowsInBlock = 50'000;
         const auto &flat = matrix_.data();
@@ -81,14 +81,14 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                     uint64_t i = begin;
                     progress_bar += rows.size();
                     for (auto &row : rows) {
-                        callback(i++, std::move(row), thread_id);
+                        callback(i++, row, thread_id);
                         row = RowValues();
                     }
                 }
             }
         } else {
             uint64_t num_set_bits = flat.num_set_bits();
-            uint64_t block_size = num_set_bits / std::max(1u, get_num_threads() - 1);
+            uint64_t block_size = num_set_bits / get_num_threads();
             size_t row_block_size = num_columns_ * 64;
             std::vector<uint64_t> boundaries;
             boundaries.emplace_back(0);
@@ -117,7 +117,6 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                 uint64_t end = flat_end / num_columns_;
 
                 RowValues row;
-                row.reserve(2);
 
                 auto *v_use = v_main;
 
@@ -133,14 +132,13 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                     uint64_t b = flat.select1(r);
                     uint64_t i = b / num_columns_;
                     if (i > last_i) {
-                        callback(last_i, std::move(row), thread_id);
+                        callback(last_i, row, thread_id);
 
                         ++last_i;
                         row = RowValues();
-                        row.reserve(2);
 
                         for ( ; last_i < i; ++last_i) {
-                            callback(last_i, RowValues(), thread_id);
+                            callback(last_i, row, thread_id);
                         }
                     }
 
@@ -151,11 +149,11 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                     row.emplace_back(j, val);
                 }
 
-                callback(last_i, std::move(row), thread_id);
+                callback(last_i, row, thread_id);
                 ++last_i;
 
                 for ( ; last_i < end; ++last_i) {
-                    callback(last_i, RowValues(), thread_id);
+                    callback(last_i, row, thread_id);
                 }
 
                 progress_bar += end - begin;
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b9ec9101b3..6475034c11 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -451,12 +451,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             });
         }
 
-        auto get_deviance = [&](double y, double mu, double invphi) {
-            y += 1e-8;
-            mu += 1e-8;
-            return 2.0 * (y * log( y/mu ) + (y + invphi) * log( (mu + invphi)/(y + invphi) ) );
-        };
-
         double r_guess = 0.0;
         double target_mu = 0.0;
         double target_var = 0.0;
@@ -522,7 +516,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (fit_var / fit_mu - 1.0 < 1e-5)
             common::logger->warn("Fit parameters are close to a Poisson distribution");
 
-        compute_min_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t n) {
+        auto get_deviance1 = [mu=mu1+1e-8,invphi=r_in,logmuinvphi=log(mu1+1e-8+r_in),logmu=log(mu1+1e-8)](double y) {
+            y += 1e-8;
+            double logyshift = logmuinvphi - log(y + invphi);
+            return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
+        };
+        auto get_deviance2 = [mu=mu2+1e-8,invphi=r_out,logmuinvphi=log(mu2+1e-8+r_out),logmu=log(mu2+1e-8)](double y) {
+            y += 1e-8;
+            double logyshift = logmuinvphi - log(y + invphi);
+            return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
+        };
+
+        compute_min_pval = [lscaling_base,r_in,r_out,target_p,get_deviance1,get_deviance2](int64_t n) {
             if (n == 0)
                 return 1.1;
 
@@ -537,16 +542,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             std::vector<double> devs;
             devs.reserve(n + 1);
             for (int64_t s = 0; s <= n; ++s) {
-                devs.emplace_back(get_deviance(s, mu1, r_in) + get_deviance(n - s, mu2, r_out));
+                devs.emplace_back(get_deviance1(s) + get_deviance2(n - s));
             }
             double max_d = *std::max_element(devs.begin(), devs.end());
             double pval = 0.0;
             int64_t s = 0;
-            bool found = false;
             for ( ; s <= n; ++s) {
                 if (devs[s] == max_d) {
                     pval += get_pmf(s);
-                    found = true;
                 } else {
                     break;
                 }
@@ -555,21 +558,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             for (int64_t sp = n; sp >= s; --sp) {
                 if (devs[sp] == max_d) {
                     pval += get_pmf(sp);
-                    found = true;
                 } else {
                     break;
                 }
             }
 
-            if (!found) {
-                common::logger->error("Devs: {}", fmt::join(devs, ","));
-                throw std::runtime_error("Fail");
-            }
-
             return std::min(1.0, pval);
         };
 
-        compute_pval = [&,lscaling_base,r_in,r_out,target_p,get_deviance,mu1,mu2](int64_t in_sum, int64_t out_sum, const auto &row) {
+        compute_pval = [lscaling_base,r_in,r_out,target_p,get_deviance1,get_deviance2](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
                 return 1.1;
 
@@ -579,7 +576,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             std::vector<double> devs(n + 1);
             for (int64_t s = 0; s <= n; ++s) {
-                devs[s] = get_deviance(s, mu1, r_in) + get_deviance(n - s, mu2, r_out);
+                devs[s] = get_deviance1(s) + get_deviance2(n - s);
             }
             double pval = 0.0;
             int64_t s = 0;

From 1ea18abed2311c259985fe8d6d181b641206eabf Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 27 Jul 2024 13:30:29 +0200
Subject: [PATCH 258/410] deviance lookup table

---
 .../src/graph/annotated_graph_algorithm.cpp   | 207 ++++++++++--------
 1 file changed, 117 insertions(+), 90 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6475034c11..4fbd7557b2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -516,6 +516,75 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (fit_var / fit_mu - 1.0 < 1e-5)
             common::logger->warn("Fit parameters are close to a Poisson distribution");
 
+        count_maps.resize(groups.size());
+
+        common::logger->trace("Computing quantile maps");
+        #pragma omp parallel for num_threads(num_parallel_files)
+        for (size_t j = 0; j < groups.size(); ++j) {
+            const auto &[r, p] = nb_params[j];
+
+            common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
+                                  j, r, p, r_map, target_p);
+
+            boost::math::negative_binomial nb_out(r_map, target_p);
+            double scale = target_sum / sums[j];
+
+            auto compute_map = [&](const auto &nb) {
+                // ensure that 0 maps to 0
+                count_maps[j][0] = std::make_pair(0, 0);
+
+                const auto &hist = hists[j];
+                for (const auto &[k, c] : hist) {
+                    if (!count_maps[j].count(k)) {
+                        uint64_t new_k = map_value(k, scale, nb, nb_out);
+                        count_maps[j][k] = std::make_pair(std::max(new_k, uint64_t(1)), c);
+                    } else {
+                        count_maps[j][k].second += c;
+                    }
+                }
+            };
+
+            if (p < 1.0) {
+                compute_map(boost::math::negative_binomial(r, p));
+            } else {
+                compute_map(boost::math::poisson(static_cast<double>(sums[j]) * scale / nelem));
+            }
+        }
+
+        common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
+        in_kmers = 0;
+        out_kmers = 0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            for (const auto &[k, m] : count_maps[j]) {
+                const auto &[v, c] = m;
+                if (groups[j]) {
+                    out_kmers += v * c;
+                } else {
+                    in_kmers += v * c;
+                }
+            }
+        }
+        total_kmers = in_kmers + out_kmers;
+        common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
+
+        PairContainer best_in_row;
+        PairContainer best_out_row;
+        int64_t in_sum = 0;
+        int64_t out_sum = 0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            if (hists[j].size()) {
+                uint64_t k = count_maps[j].find(hists[j].back().first)->second.first;
+                if (groups[j]) {
+                    best_out_row.emplace_back(j, k);
+                    out_sum += k;
+                } else {
+                    best_in_row.emplace_back(j, k);
+                    in_sum += k;
+                }
+            }
+        }
+        int64_t total_sum = in_sum + out_sum;
+
         auto get_deviance1 = [mu=mu1+1e-8,invphi=r_in,logmuinvphi=log(mu1+1e-8+r_in),logmu=log(mu1+1e-8)](double y) {
             y += 1e-8;
             double logyshift = logmuinvphi - log(y + invphi);
@@ -527,46 +596,70 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
         };
 
-        compute_min_pval = [lscaling_base,r_in,r_out,target_p,get_deviance1,get_deviance2](int64_t n) {
+        std::vector<double> deviances1(total_sum + 1);
+        std::vector<double> deviances2(total_sum + 1);
+        for (int64_t s = 0; s <= total_sum; ++s) {
+            deviances1[s] = get_deviance1(s);
+            deviances2[s] = get_deviance2(s);
+        }
+
+        compute_min_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t n) {
             if (n == 0)
                 return 1.1;
 
             double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
-            auto get_pmf = [&](int64_t s) {
-                int64_t t = n - s;
-                double rs = r_in + s;
-                double rt = r_out + t;
-                return exp(lscaling - lgamma(s + 1) - lgamma(t + 1) + lgamma(rs) + lgamma(rt));
-            };
+            std::vector<double> devs(n + 1);
 
-            std::vector<double> devs;
-            devs.reserve(n + 1);
             for (int64_t s = 0; s <= n; ++s) {
-                devs.emplace_back(get_deviance1(s) + get_deviance2(n - s));
+                devs[s] = deviances1[s] + deviances2[n - s];
             }
+
             double max_d = *std::max_element(devs.begin(), devs.end());
             double pval = 0.0;
             int64_t s = 0;
-            for ( ; s <= n; ++s) {
-                if (devs[s] == max_d) {
-                    pval += get_pmf(s);
-                } else {
-                    break;
+            if (devs[s] == max_d) {
+                int64_t t = n;
+                double rs = r_in;
+                double rt = r_out + n;
+                double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                pval += exp(base);
+                for (++s; s <= n; ++s) {
+                    if (devs[s] == max_d) {
+                        --t;
+                        ++rs;
+                        --rt;
+                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                        pval += exp(base);
+                    } else {
+                        break;
+                    }
                 }
             }
 
-            for (int64_t sp = n; sp >= s; --sp) {
-                if (devs[sp] == max_d) {
-                    pval += get_pmf(sp);
-                } else {
-                    break;
+            int64_t sp = n;
+            if (devs[sp] == max_d) {
+                int64_t t = 0;
+                double rs = r_in + sp;
+                double rt = r_out;
+                double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                pval += exp(base);
+                for (--sp; sp >= s; --sp) {
+                    if (devs[sp] == max_d) {
+                        ++t;
+                        --rs;
+                        ++rt;
+                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                        pval += exp(base);
+                    } else {
+                        break;
+                    }
                 }
             }
 
             return std::min(1.0, pval);
         };
 
-        compute_pval = [lscaling_base,r_in,r_out,target_p,get_deviance1,get_deviance2](int64_t in_sum, int64_t out_sum, const auto &row) {
+        compute_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
                 return 1.1;
 
@@ -575,9 +668,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
 
             std::vector<double> devs(n + 1);
+
             for (int64_t s = 0; s <= n; ++s) {
-                devs[s] = get_deviance1(s) + get_deviance2(n - s);
+                devs[s] = deviances1[s] + deviances2[n - s];
             }
+
             double pval = 0.0;
             int64_t s = 0;
             if (devs[s] >= devs[in_sum]) {
@@ -622,74 +717,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::min(1.0, pval);
         };
 
-        count_maps.resize(groups.size());
-
-        common::logger->trace("Computing quantile maps");
-        #pragma omp parallel for num_threads(num_parallel_files)
-        for (size_t j = 0; j < groups.size(); ++j) {
-            const auto &[r, p] = nb_params[j];
-
-            common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
-                                  j, r, p, r_map, target_p);
-
-            boost::math::negative_binomial nb_out(r_map, target_p);
-            double scale = target_sum / sums[j];
-
-            auto compute_map = [&](const auto &nb) {
-                // ensure that 0 maps to 0
-                count_maps[j][0] = std::make_pair(0, 0);
-
-                const auto &hist = hists[j];
-                for (const auto &[k, c] : hist) {
-                    if (!count_maps[j].count(k)) {
-                        uint64_t new_k = map_value(k, scale, nb, nb_out);
-                        count_maps[j][k] = std::make_pair(std::max(new_k, uint64_t(1)), c);
-                    } else {
-                        count_maps[j][k].second += c;
-                    }
-                }
-            };
-
-            if (p < 1.0) {
-                compute_map(boost::math::negative_binomial(r, p));
-            } else {
-                compute_map(boost::math::poisson(static_cast<double>(sums[j]) * scale / nelem));
-            }
-        }
-
-        common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
-        in_kmers = 0;
-        out_kmers = 0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            for (const auto &[k, m] : count_maps[j]) {
-                const auto &[v, c] = m;
-                if (groups[j]) {
-                    out_kmers += v * c;
-                } else {
-                    in_kmers += v * c;
-                }
-            }
-        }
-        total_kmers = in_kmers + out_kmers;
-        common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
-
-        PairContainer best_in_row;
-        PairContainer best_out_row;
-        int64_t in_sum = 0;
-        int64_t out_sum = 0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            if (hists[j].size()) {
-                uint64_t k = count_maps[j].find(hists[j].back().first)->second.first;
-                if (groups[j]) {
-                    best_out_row.emplace_back(j, k);
-                    out_sum += k;
-                } else {
-                    best_in_row.emplace_back(j, k);
-                    in_sum += k;
-                }
-            }
-        }
-
         double pval = std::min(compute_pval(in_sum, 0, best_in_row),
                                compute_pval(0, out_sum, best_out_row));
         common::logger->trace("Best achievable p-value: {}\tin_sum: {}\tout_sum: {}", pval, in_sum, out_sum);

From d715d520a313b2a4bb8860fffcc47bfb516688aa Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 27 Jul 2024 14:14:15 +0200
Subject: [PATCH 259/410] optim

---
 .../src/graph/annotated_graph_algorithm.cpp   | 20 +++++++------------
 1 file changed, 7 insertions(+), 13 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 4fbd7557b2..2393eaf54c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -667,23 +667,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
 
-            std::vector<double> devs(n + 1);
-
-            for (int64_t s = 0; s <= n; ++s) {
-                devs[s] = deviances1[s] + deviances2[n - s];
-            }
+            double in_sum_total_deviance = deviances1[in_sum] + deviances2[out_sum];
 
             double pval = 0.0;
             int64_t s = 0;
-            if (devs[s] >= devs[in_sum]) {
-                int64_t t = n;
+            int64_t t = n;
+            if (deviances1[s] + deviances2[t] >= in_sum_total_deviance) {
                 double rs = r_in;
                 double rt = r_out + n;
                 double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
                 pval += exp(base);
                 for (++s; s <= n; ++s) {
-                    if (devs[s] >= devs[in_sum]) {
-                        --t;
+                    if (deviances1[s] + deviances2[--t] >= in_sum_total_deviance) {
                         ++rs;
                         --rt;
                         base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
@@ -695,15 +690,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             int64_t sp = n;
-            if (devs[sp] >= devs[in_sum]) {
-                int64_t t = 0;
+            t = 0;
+            if (deviances1[sp] + deviances2[t] >= in_sum_total_deviance) {
                 double rs = r_in + sp;
                 double rt = r_out;
                 double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
                 pval += exp(base);
                 for (--sp; sp >= s; --sp) {
-                    if (devs[sp] >= devs[in_sum]) {
-                        ++t;
+                    if (deviances1[sp] + deviances2[++t] >= in_sum_total_deviance) {
                         --rs;
                         ++rt;
                         base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);

From 9114af5071fd16ab8ad894e394fbf67f5f64719a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 27 Jul 2024 14:17:35 +0200
Subject: [PATCH 260/410] minor

---
 .../src/graph/annotated_graph_algorithm.cpp   | 34 ++++++++-----------
 1 file changed, 14 insertions(+), 20 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 2393eaf54c..3aa505b7f9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -232,20 +232,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Test: {}", config.test_type);
     if (config.test_type == "poisson_exact") {
-        compute_min_pval = [&](int64_t n) {
+        auto get_deviance = [&](double y, double mu) {
+            y += 1e-8;
+            mu += 1e-8;
+            return 2 * (y * log(y/mu) - y + mu);
+        };
+
+        compute_min_pval = [&,p=static_cast<double>(in_kmers)/total_kmers,
+                              mu1=static_cast<double>(in_kmers)/nelem,
+                              mu2=static_cast<double>(out_kmers)/nelem,
+                              get_deviance](int64_t n) {
             if (n == 0)
                 return 1.1;
 
-            double p = static_cast<double>(in_kmers) / total_kmers;
             auto bdist = boost::math::binomial(n, p);
-            auto get_deviance = [&](double y, double mu) {
-                y += 1e-8;
-                mu += 1e-8;
-                return 2 * (y * log(y/mu) - y + mu);
-            };
-
-            double mu1 = static_cast<double>(in_kmers) / nelem;
-            double mu2 = static_cast<double>(out_kmers) / nelem;
 
             std::vector<double> devs;
             devs.reserve(n + 1);
@@ -275,21 +275,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return pval;
         };
 
-        compute_pval = [&](int64_t in_sum, int64_t out_sum, const auto &row) {
+        compute_pval = [&,p=static_cast<double>(in_kmers)/total_kmers,
+                          mu1=static_cast<double>(in_kmers)/nelem,
+                          mu2=static_cast<double>(out_kmers)/nelem,
+                          get_deviance](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
                 return 1.1;
 
             int64_t n = in_sum + out_sum;
-            double p = static_cast<double>(in_kmers) / total_kmers;
             auto bdist = boost::math::binomial(n, p);
-            auto get_deviance = [&](double y, double mu) {
-                y += 1e-8;
-                mu += 1e-8;
-                return 2 * (y * log(y/mu) - y + mu);
-            };
-
-            double mu1 = static_cast<double>(in_kmers) / nelem;
-            double mu2 = static_cast<double>(out_kmers) / nelem;
 
             std::vector<double> devs;
             devs.reserve(n + 1);

From 0b918bba026643b8e6c3b4f076c8e0247ec436bc Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 27 Jul 2024 14:37:48 +0200
Subject: [PATCH 261/410] simplify logic

---
 .../src/graph/annotated_graph_algorithm.cpp   | 62 +++++++++----------
 1 file changed, 30 insertions(+), 32 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 3aa505b7f9..616302da18 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -404,7 +404,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::vector<std::vector<std::pair<uint64_t, size_t>>::const_iterator> hist_its(groups.size());
         size_t matrix_size = 0;
 
-        #pragma omp parallel for num_threads(num_parallel_files)
+        #pragma omp parallel for num_threads(num_parallel_files) reduction(+:matrix_size)
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto &hist = hists[j];
             size_t total = 0;
@@ -417,8 +417,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             hist_its[j] = it;
             nb_params[j] = get_rp(j, hist.begin(), it);
 
-            #pragma omp critical
-            matrix_size += total + (hist.size() && hist[0].first == 0 ? hist[0].second : 0);
+            total += (hist.size() && hist[0].first == 0 ? hist[0].second : 0);
+
+            #pragma omp atomic
+            matrix_size += total;
         }
 
         common::logger->trace("Scaling distributions");
@@ -772,11 +774,29 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         double pval = 1.1;
         double pval_min = 1.1;
+        uint64_t k = std::numeric_limits<uint64_t>::max();
         try {
             pval_min = compute_min_pval(in_sum + out_sum);
 
-            if (pval_min < 1.1)
+            if (pval_min < 1.1) {
+                if (!config.test_by_unitig) {
+                    if (pval_min >= config.family_wise_error_rate) {
+                        k = 0;
+                    } else if (pval_min > 0) {
+                        double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
+                        if (lkd <= 64)
+                            k = pow(2.0, lkd);
+
+                        if (k == 0) {
+                            common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
+                            throw std::runtime_error("Min failed");
+                        }
+                    }
+
+                    ++ms[bucket_idx][k];
+                }
                 pval = compute_pval(in_sum, out_sum, row);
+            }
 
         } catch (...) {
             std::lock_guard<std::mutex> lock(pval_mu);
@@ -790,39 +810,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         auto &pvals = pvals_buckets[std::min(bucket_idx, pvals_buckets.size() - 1)];
-        if (config.test_by_unitig) {
-            assert(pvals_min);
+        if (config.test_type != "notest")
             pvals.push_back(bit_cast<uint64_t>(pval));
 
+        if (config.test_by_unitig) {
+            assert(pvals_min);
             std::lock_guard<std::mutex> lock(pval_mu);
             (*pvals_min)[node] = bit_cast<uint64_t>(pval_min);
             (*eff_size)[node] = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
-        } else {
-            if (config.test_type != "notest")
-                pvals.push_back(bit_cast<uint64_t>(pval));
-
-            if (pval < 1.1) {
-                uint64_t k = std::numeric_limits<uint64_t>::max();
-                if (pval_min >= config.family_wise_error_rate) {
-                    k = 0;
-                } else if (pval_min > 0) {
-                    double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
-                    if (lkd <= 64)
-                        k = pow(2.0, lkd);
-
-                    if (k == 0) {
-                        common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
-                        throw std::runtime_error("Min failed");
-                    }
-                }
-
-                if (in_kmer != out_kmer && (pval < config.family_wise_error_rate || config.test_by_unitig)) {
-                    bool use_atomic = parallel && (node % 64 == 0);
-                    set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
-                }
-
-                ++ms[bucket_idx][k];
-            }
+        } else if (in_kmer != out_kmer && pval < config.family_wise_error_rate) {
+            bool use_atomic = parallel && (node % 64 == 0);
+            set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
         }
     }, false);
 

From b22f2a6c3a8b66016b9748d81e783ab2d3e79e53 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 27 Jul 2024 14:57:30 +0200
Subject: [PATCH 262/410] don't compute p-values if p-min is too high

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 616302da18..f21ab7367a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -775,10 +775,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         double pval = 1.1;
         double pval_min = 1.1;
         uint64_t k = std::numeric_limits<uint64_t>::max();
+        bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
         try {
             pval_min = compute_min_pval(in_sum + out_sum);
 
             if (pval_min < 1.1) {
+                size_t num_obs = 0;
                 if (!config.test_by_unitig) {
                     if (pval_min >= config.family_wise_error_rate) {
                         k = 0;
@@ -793,9 +795,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         }
                     }
 
-                    ++ms[bucket_idx][k];
+                    num_obs = ++ms[bucket_idx][k];
                 }
-                pval = compute_pval(in_sum, out_sum, row);
+
+                // only compute a p-value if it is needed for later, or if it
+                // has a chance of being significant after correction
+                if (config.output_pvals || config.test_by_unitig || num_obs < k)
+                    pval = compute_pval(in_sum, out_sum, row);
             }
 
         } catch (...) {
@@ -809,7 +815,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             throw std::runtime_error("Test failed");
         }
 
-        auto &pvals = pvals_buckets[std::min(bucket_idx, pvals_buckets.size() - 1)];
+        auto &pvals = pvals_buckets[bucket_idx];
         if (config.test_type != "notest")
             pvals.push_back(bit_cast<uint64_t>(pval));
 

From dba15c3c8480cd063f7ecc44b9304094eb209787 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 27 Jul 2024 19:44:33 +0200
Subject: [PATCH 263/410] test

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index f21ab7367a..26b00ee075 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -795,6 +795,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         }
                     }
 
+                    // std::lock_guard<std::mutex> lock(pval_mu);
+                    // num_obs = ++ms[log(bucket_idx + 1)][k];
+                    // num_obs = ++ms[0][k];
                     num_obs = ++ms[bucket_idx][k];
                 }
 
@@ -1215,7 +1218,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
             config, num_threads, tmp_dir, num_threads
         );
     } else {
-        auto generate_rows = [&](const auto &callback, bool ordered) {
+        auto generate_rows = [&](const auto &callback, bool) {
             size_t bucket_size = matrix.num_rows() / num_threads;
             ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
             #pragma omp parallel for num_threads(num_threads) ordered
@@ -1229,12 +1232,8 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
                     container.emplace_back(j, 1);
                 }
 
-                if (ordered) {
-                    #pragma omp ordered
-                    callback(row_i, container, row_i / bucket_size);
-                } else {
-                    callback(row_i, container, row_i / bucket_size);
-                }
+                #pragma omp ordered
+                callback(row_i, container, row_i / bucket_size);
             }
         };
 

From c195a1f1aaef8d0a51311c7b0ade966e7f1dc170 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 27 Jul 2024 22:24:59 +0200
Subject: [PATCH 264/410] initial two-pass implementation

---
 .../src/graph/annotated_graph_algorithm.cpp   | 464 +++++++++---------
 1 file changed, 244 insertions(+), 220 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 26b00ee075..8d8cfc7d5e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -721,12 +721,128 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         throw std::runtime_error("Test not implemented");
     }
 
+    common::logger->trace("Computing minimum p-values");
+    std::vector<std::pair<double, size_t>> m;
+    {
+        std::vector<std::vector<std::pair<double, size_t>>> ms(pvals_buckets.size());
+        generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+            int64_t out_stat_int = 0;
+            bool in_kmer = false;
+            bool out_kmer = false;
+            PairContainer row;
+            if (kept[row_i]) {
+                size_t count_in = 0;
+                size_t count_out = 0;
+                for (const auto &[j, raw_c] : raw_row) {
+                    uint64_t c = raw_c;
+                    if (count_maps.size())
+                        c = count_maps[j].find(c)->second.first;
+
+                    row.emplace_back(j, c);
+                    if (groups[j]) {
+                        out_sum += c;
+                        ++count_out;
+                    } else {
+                        in_sum += c;
+                        ++count_in;
+                    }
+                }
+
+                if (count_in + count_out >= config.min_recurrence) {
+                    double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+                    if (out_stat != 0)
+                        out_stat_int = out_stat > 0 ? ceil(out_stat) : floor(out_stat);
+
+                    in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+                    out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+                }
+            }
+
+            bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
+            size_t n = in_sum + out_sum;
+            if (!in_kmer && !out_kmer) {
+                row.clear();
+                n = 0;
+            }
+
+            if (n >= ms[bucket_idx].size())
+                ms[bucket_idx].resize(n + 1, std::make_pair(1.1, 0));
+
+            if (n > 0 && ms[bucket_idx][n].first == 1.1)
+                ms[bucket_idx][n].first = compute_min_pval(n);
+
+            ++ms[bucket_idx][n].second;
+
+            // test_by_unitig
+            std::ignore = out_stat_int;
+            // (*eff_size)[node] = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
+            // (*pvals_min)[node] = bit_cast<uint64_t>(pval_min);
+        }, false);
+
+        common::logger->trace("Merging min p-value tables");
+        for (size_t i = 1; i < ms.size(); ++i) {
+            size_t end = std::min(ms[0].size(), ms[i].size());
+            for (size_t j = 0; j < end; ++j) {
+                assert(ms[0][j].first == 1.1 || ms[i][j].first == 1.1 || ms[0][j] == ms[i][j]);
+                ms[0][j].first = std::min(ms[0][j].first, ms[i][j].first);
+                ms[0][j].second += ms[i][j].second;
+            }
+
+            if (ms[i].size() > ms[0].size()) {
+                std::copy(std::make_move_iterator(ms[i].begin() + ms[0].size()),
+                          std::make_move_iterator(ms[i].end()),
+                          std::back_inserter(ms[0]));
+            }
+        }
+
+        m = std::move(ms[0]);
+    }
+
+    size_t k = 0;
+    size_t n_cutoff = 1;
+    if (!config.test_by_unitig) {
+        size_t acc = std::accumulate(m.begin(), m.end(), size_t(0),
+                                     [](size_t sum, const auto &a) { return sum + a.second; });
+        common::logger->trace("Calculating cutoffs for {}/{} tests", acc, nelem);
+        size_t last_k = 0;
+        for (size_t n = 0; n < m.size(); ++n) {
+            auto [pval_min, s] = m[n];
+            size_t cur_k = std::numeric_limits<size_t>::max();
+            if (pval_min >= config.family_wise_error_rate) {
+                cur_k = 0;
+            } else if (pval_min > 0) {
+                double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
+                if (lkd <= 64)
+                    cur_k = pow(2.0, lkd);
+
+                if (cur_k == 0) {
+                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}", cur_k, lkd, pval_min);
+                    throw std::runtime_error("Min failed");
+                }
+            }
+            if (s > 0)
+                common::logger->trace("k: {}\ts: {}\tm(k): {}\tn: {}\tpval_min: {}", cur_k, s, acc, n, pval_min);
+
+            if (cur_k != last_k && acc <= cur_k) {
+                k = std::max(acc, last_k + 1);
+                n_cutoff = n;
+                common::logger->trace("Picked: k: {}\tn: {}\tpval_min: {}", k, n_cutoff, config.family_wise_error_rate / k);
+                break;
+            }
+
+            last_k = cur_k;
+            acc -= s;
+        }
+
+        if (k == 0)
+            common::logger->trace("No significant k-mers found");
+    }
+
     common::logger->trace("Running differential tests");
     std::exception_ptr ex = nullptr;
-
     std::mutex pval_mu;
-
-    std::vector<VectorMap<size_t, size_t>> ms(pvals_buckets.size());
     std::atomic_thread_fence(std::memory_order_release);
 
     generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
@@ -735,7 +851,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         int64_t in_sum = 0;
         int64_t out_sum = 0;
-        int64_t out_stat_int = 0;
         bool in_kmer = false;
         bool out_kmer = false;
         PairContainer row;
@@ -759,244 +874,153 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             if (count_in + count_out >= config.min_recurrence) {
                 double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-                if (out_stat != 0)
-                    out_stat_int = out_stat > 0 ? ceil(out_stat) : floor(out_stat);
-
                 in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
                 out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
             }
         }
 
-        if (!in_kmer && !out_kmer)
+        size_t n = in_sum + out_sum;
+        if (!in_kmer && !out_kmer) {
             row.clear();
-
-        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-
-        double pval = 1.1;
-        double pval_min = 1.1;
-        uint64_t k = std::numeric_limits<uint64_t>::max();
-        bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
-        try {
-            pval_min = compute_min_pval(in_sum + out_sum);
-
-            if (pval_min < 1.1) {
-                size_t num_obs = 0;
-                if (!config.test_by_unitig) {
-                    if (pval_min >= config.family_wise_error_rate) {
-                        k = 0;
-                    } else if (pval_min > 0) {
-                        double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
-                        if (lkd <= 64)
-                            k = pow(2.0, lkd);
-
-                        if (k == 0) {
-                            common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, pval_min, pval);
-                            throw std::runtime_error("Min failed");
-                        }
-                    }
-
-                    // std::lock_guard<std::mutex> lock(pval_mu);
-                    // num_obs = ++ms[log(bucket_idx + 1)][k];
-                    // num_obs = ++ms[0][k];
-                    num_obs = ++ms[bucket_idx][k];
-                }
-
-                // only compute a p-value if it is needed for later, or if it
-                // has a chance of being significant after correction
-                if (config.output_pvals || config.test_by_unitig || num_obs < k)
-                    pval = compute_pval(in_sum, out_sum, row);
-            }
-
-        } catch (...) {
-            std::lock_guard<std::mutex> lock(pval_mu);
-            ex = std::current_exception();
-            return;
-        }
-
-        if (pval < 1.1 && pval_min - pval > 1e-10) {
-            common::logger->error("Min p-val estimate too high: min {} > cur {}\ttest: {}", pval_min, pval, config.test_type);
-            throw std::runtime_error("Test failed");
+            n = 0;
         }
 
-        auto &pvals = pvals_buckets[bucket_idx];
-        if (config.test_type != "notest")
-            pvals.push_back(bit_cast<uint64_t>(pval));
-
-        if (config.test_by_unitig) {
-            assert(pvals_min);
-            std::lock_guard<std::mutex> lock(pval_mu);
-            (*pvals_min)[node] = bit_cast<uint64_t>(pval_min);
-            (*eff_size)[node] = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
-        } else if (in_kmer != out_kmer && pval < config.family_wise_error_rate) {
-            bool use_atomic = parallel && (node % 64 == 0);
-            set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
-        }
-    }, false);
-
-    std::atomic_thread_fence(std::memory_order_acquire);
-
-    if (ex)
-        std::rethrow_exception(ex);
-
-    deallocate();
-
-    std::vector<size_t> boundaries;
-    boundaries.reserve(pvals_buckets.size() + 1);
-    boundaries.emplace_back(0);
-    for (size_t i = 0; i < pvals_buckets.size(); ++i) {
-        boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
-    }
-
-    if (config.test_by_unitig) {
-        common::logger->trace("Allocating k-mer bitmasks");
-        indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-        indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-
-        common::logger->trace("Combining p-values within unitigs");
-        MaskedDeBruijnGraph clean_masked_graph(
-            graph_ptr,
-            [&](node_index node) {
-                return node != DeBruijnGraph::npos
-                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
-            },
-            true,
-            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-        );
-
-        auto dist = boost::math::cauchy();
-
-        std::atomic_thread_fence(std::memory_order_release);
-        clean_masked_graph.call_unitigs([&](const std::string&, const auto &path) {
-            double stat = 0;
-            double stat_min = 0;
-            int64_t comb_eff_size = 0;
-            std::vector<size_t> bucket_idxs;
-            bucket_idxs.reserve(path.size());
-            for (node_index node : path) {
-                for (size_t i = 0; i < boundaries.size(); ++i) {
-                    if (boundaries[i] > node) {
-                        bucket_idxs.emplace_back(i);
-                        break;
-                    }
-                }
+        if (config.output_pvals || config.test_by_unitig || n >= n_cutoff) {
+            if (m[n].first == 1.1) {
+                common::logger->error("Min p-val too small: {}\tn: {}\ts: {}", m[n].first, n, m[n].second);
+                throw std::runtime_error("Test failed");
             }
 
-            {
+            double pval = 1.1;
+            try {
+                pval = compute_pval(in_sum, out_sum, row);
+            } catch (...) {
                 std::lock_guard<std::mutex> lock(pval_mu);
-                for (size_t i = 0; i < path.size(); ++i) {
-                    node_index node = path[i];
-                    size_t bucket_idx = bucket_idxs[i];
-                    comb_eff_size += bit_cast<int64_t, uint64_t>((*eff_size)[node]);
-                    double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node - boundaries[bucket_idx]]);
-                    double pval_min = bit_cast<double, uint64_t>((*pvals_min)[node]);
-                    stat += tan((0.5 - pval) * M_PI);
-                    stat_min += tan((0.5 - pval_min) * M_PI);
-                }
-            }
-            stat /= path.size();
-            stat_min /= path.size();
-            double comb_pval = boost::math::cdf(boost::math::complement(dist, stat));
-            if (comb_pval < config.family_wise_error_rate && comb_eff_size != 0) {
-                auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
-                for (node_index node : path) {
-                    set_bit(data, node, parallel, MO_RELAXED);
-                }
+                ex = std::current_exception();
+                return;
             }
 
-            double comb_pval_min = boost::math::cdf(boost::math::complement(dist, stat_min));
-
-            uint64_t k = std::numeric_limits<uint64_t>::max();
-            if (comb_pval_min >= config.family_wise_error_rate) {
-                k = 0;
-            } else if (comb_pval_min > 0) {
-                double lkd = log2(config.family_wise_error_rate) - log2(comb_pval_min);
-                if (lkd <= 64)
-                    k = pow(2.0, lkd);
-
-                if (k == 0) {
-                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, comb_pval_min, comb_pval);
-                    throw std::runtime_error("Min failed");
-                }
+            if (pval < 1.1 && m[n].first - pval > 1e-10) {
+                common::logger->error("Min p-val estimate too high: min {} > cur {}\tn: {}\ttest: {}", m[n].first, pval, n, config.test_type);
+                throw std::runtime_error("Test failed");
             }
 
-            uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
-            size_t bucket_idx = bucket_idxs[0];
-            ++ms[bucket_idx][k];
-            pvals_buckets[bucket_idx][path[0] - boundaries[bucket_idx]] = comb_pval_enc;
-            if (config.output_pvals) {
-                for (size_t i = 1; i < path.size(); ++i) {
-                    size_t bucket_idx = bucket_idxs[i];
-                    pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
-                }
+            if (config.test_type != "notest" && (config.output_pvals || config.test_by_unitig)) {
+                bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
+                auto &pvals = pvals_buckets[bucket_idx];
+                pvals.push_back(bit_cast<uint64_t>(pval));
             }
-        }, num_threads);
-        std::atomic_thread_fence(std::memory_order_acquire);
 
-        eff_size.reset();
-        pvals_min.reset();
-    }
-
-    for (size_t i = 1; i < ms.size(); ++i) {
-        for (const auto &[k, c] : ms[i]) {
-            ms[0][k] += c;
+            if (!config.test_by_unitig && in_kmer != out_kmer && pval * k < config.family_wise_error_rate) {
+                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                bool use_atomic = parallel && (node % 64 == 0);
+                set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
+            }
         }
-    }
-    VectorMap<size_t, size_t> m = std::move(ms[0]);
-    ms.resize(0);
-
-    if (config.test_type != "notest") {
-        uint64_t total_sig = sdsl::util::cnt_one_bits(indicator_in) + sdsl::util::cnt_one_bits(indicator_out);
-        auto m_data = const_cast<std::vector<std::pair<size_t, size_t>>&&>(m.values_container());
+    }, false);
 
-        std::sort(m_data.begin(), m_data.end(), utils::LessFirst());
-        size_t acc = std::accumulate(m_data.begin(), m_data.end(), size_t(0),
-                                     [](size_t sum, const auto &a) { return sum + a.second; });
-        common::logger->trace("Performed {}/{} tests", acc, nelem);
-        common::logger->trace("Correcting {}/{} significant p-values", total_sig, acc);
-
-        if (total_sig) {
-            size_t k = 0;
-            size_t last_k = 0;
-            for (const auto &[cur_k, s] : m_data) {
-                common::logger->trace("k: {}\tm(k): {}", cur_k, acc);
-                if (acc <= cur_k) {
-                    k = std::max(acc, last_k + 1);
-                    break;
-                }
+    std::atomic_thread_fence(std::memory_order_acquire);
 
-                last_k = cur_k;
-                acc -= s;
-            }
+    if (ex)
+        std::rethrow_exception(ex);
 
-            if (k == 0) {
-                common::logger->trace("No significant k-mers found");
-                sdsl::util::set_to_value(indicator_in, false);
-                sdsl::util::set_to_value(indicator_out, false);
-            } else {
-                common::logger->trace("k: {}\talpha_corr: {}", k, config.family_wise_error_rate / k);
+    deallocate();
 
-                std::atomic_thread_fence(std::memory_order_release);
-                #pragma omp parallel for num_threads(num_threads)
-                for (size_t j = 0; j < boundaries.size() - 1; ++j) {
-                    size_t begin = boundaries[j];
-                    size_t end = boundaries[j + 1];
-                    call_ones(indicator_in, begin, end, [&](uint64_t i) {
-                        double p = bit_cast<double, uint64_t>(pvals_buckets[j][i - begin]);
-                        if (p < config.family_wise_error_rate && p * k >= config.family_wise_error_rate) {
-                            unset_bit(indicator_in.data(), i, parallel, std::memory_order_release);
-                        }
-                    }, parallel, std::memory_order_acquire);
-                    call_ones(indicator_out, begin, end, [&](uint64_t i) {
-                        double p = bit_cast<double, uint64_t>(pvals_buckets[j][i - begin]);
-                        if (p < config.family_wise_error_rate && p * k >= config.family_wise_error_rate) {
-                            unset_bit(indicator_out.data(), i, parallel, std::memory_order_release);
-                        }
-                    }, parallel, std::memory_order_acquire);
-                }
-                std::atomic_thread_fence(std::memory_order_acquire);
-            }
-        }
-    }
+    // std::vector<size_t> boundaries;
+    // boundaries.reserve(pvals_buckets.size() + 1);
+    // boundaries.emplace_back(0);
+    // for (size_t i = 0; i < pvals_buckets.size(); ++i) {
+    //     boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
+    // }
+
+    // if (config.test_by_unitig) {
+    //     common::logger->trace("Allocating k-mer bitmasks");
+    //     indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+    //     indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+
+    //     common::logger->trace("Combining p-values within unitigs");
+    //     MaskedDeBruijnGraph clean_masked_graph(
+    //         graph_ptr,
+    //         [&](node_index node) {
+    //             return node != DeBruijnGraph::npos
+    //                     && kept[AnnotatedDBG::graph_to_anno_index(node)];
+    //         },
+    //         true,
+    //         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+    //     );
+
+    //     auto dist = boost::math::cauchy();
+
+    //     std::atomic_thread_fence(std::memory_order_release);
+    //     clean_masked_graph.call_unitigs([&](const std::string&, const auto &path) {
+    //         double stat = 0;
+    //         double stat_min = 0;
+    //         int64_t comb_eff_size = 0;
+    //         std::vector<size_t> bucket_idxs;
+    //         bucket_idxs.reserve(path.size());
+    //         for (node_index node : path) {
+    //             for (size_t i = 0; i < boundaries.size(); ++i) {
+    //                 if (boundaries[i] > node) {
+    //                     bucket_idxs.emplace_back(i);
+    //                     break;
+    //                 }
+    //             }
+    //         }
+
+    //         {
+    //             std::lock_guard<std::mutex> lock(pval_mu);
+    //             for (size_t i = 0; i < path.size(); ++i) {
+    //                 node_index node = path[i];
+    //                 size_t bucket_idx = bucket_idxs[i];
+    //                 comb_eff_size += bit_cast<int64_t, uint64_t>((*eff_size)[node]);
+    //                 double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node - boundaries[bucket_idx]]);
+    //                 double pval_min = bit_cast<double, uint64_t>((*pvals_min)[node]);
+    //                 stat += tan((0.5 - pval) * M_PI);
+    //                 stat_min += tan((0.5 - pval_min) * M_PI);
+    //             }
+    //         }
+    //         stat /= path.size();
+    //         stat_min /= path.size();
+    //         double comb_pval = boost::math::cdf(boost::math::complement(dist, stat));
+    //         if (comb_pval < config.family_wise_error_rate && comb_eff_size != 0) {
+    //             auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
+    //             for (node_index node : path) {
+    //                 set_bit(data, node, parallel, MO_RELAXED);
+    //             }
+    //         }
+
+    //         double comb_pval_min = boost::math::cdf(boost::math::complement(dist, stat_min));
+
+    //         uint64_t k = std::numeric_limits<uint64_t>::max();
+    //         if (comb_pval_min >= config.family_wise_error_rate) {
+    //             k = 0;
+    //         } else if (comb_pval_min > 0) {
+    //             double lkd = log2(config.family_wise_error_rate) - log2(comb_pval_min);
+    //             if (lkd <= 64)
+    //                 k = pow(2.0, lkd);
+
+    //             if (k == 0) {
+    //                 common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, comb_pval_min, comb_pval);
+    //                 throw std::runtime_error("Min failed");
+    //             }
+    //         }
+
+    //         uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
+    //         size_t bucket_idx = bucket_idxs[0];
+    //         ++ms[bucket_idx][k];
+    //         pvals_buckets[bucket_idx][path[0] - boundaries[bucket_idx]] = comb_pval_enc;
+    //         if (config.output_pvals) {
+    //             for (size_t i = 1; i < path.size(); ++i) {
+    //                 size_t bucket_idx = bucket_idxs[i];
+    //                 pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
+    //             }
+    //         }
+    //     }, num_threads);
+    //     std::atomic_thread_fence(std::memory_order_acquire);
+
+    //     eff_size.reset();
+    //     pvals_min.reset();
+    // }
 
     std::unique_ptr<utils::TempFile> tmp_file;
     PValStorage pvals;

From b814d8cf31c957a5bde40e63ef2a76d0877f09f1 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 27 Jul 2024 23:04:49 +0200
Subject: [PATCH 265/410] minor

---
 .../src/graph/annotated_graph_algorithm.cpp   | 276 ++++++++++--------
 1 file changed, 156 insertions(+), 120 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 8d8cfc7d5e..1cfa9d2c09 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -194,25 +194,30 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         pvals_buckets[0].push_back(nullpval);
     }
 
-    std::unique_ptr<PValStorage> eff_size;
-    std::unique_ptr<PValStorage> pvals_min;
-    std::unique_ptr<utils::TempFile> tmp_file_eff;
-    std::unique_ptr<utils::TempFile> tmp_file_min;
+    std::vector<PValStorage> eff_size_buckets;
+    std::vector<std::unique_ptr<utils::TempFile>> tmp_eff_buckets;
+    std::vector<PValStorage> sum_buckets;
+    std::vector<std::unique_ptr<utils::TempFile>> tmp_sum_buckets;
     if (config.test_by_unitig) {
-        common::logger->trace("Allocating p-value storage");
-        pvals_min = std::make_unique<PValStorage>();
-        eff_size = std::make_unique<PValStorage>();
-        if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-            tmp_file_min = std::make_unique<utils::TempFile>(tmp_dir);
-            pvals_min = std::make_unique<sdsl::int_vector_buffer<64>>(tmp_file_min->name(), std::ios::out);
+        common::logger->trace("Allocating effect size and row sum storage");
+        if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+            auto &eff_size = eff_size_buckets.emplace_back();
+            eff_size.emplace_back(0);
 
-            tmp_file_eff = std::make_unique<utils::TempFile>(tmp_dir);
-            eff_size = std::make_unique<sdsl::int_vector_buffer<64>>(tmp_file_eff->name(), std::ios::out);
+            auto &sum = sum_buckets.emplace_back();
+            sum.emplace_back(0);
         }
 
-        for (size_t i = 0; i <= graph_ptr->max_index(); ++i) {
-            pvals_min->push_back(nullpval);
-            eff_size->push_back(0);
+        if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+            for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+                auto &tmp_file_eff = tmp_eff_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+                eff_size_buckets.emplace_back(tmp_file_eff->name(), std::ios::out);
+
+                auto &tmp_file_sum = tmp_sum_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+                sum_buckets.emplace_back(tmp_file_sum->name(), std::ios::out);
+            }
+            eff_size_buckets[0].push_back(0);
+            sum_buckets[0].push_back(0);
         }
     }
 
@@ -775,10 +780,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             ++ms[bucket_idx][n].second;
 
-            // test_by_unitig
-            std::ignore = out_stat_int;
-            // (*eff_size)[node] = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
-            // (*pvals_min)[node] = bit_cast<uint64_t>(pval_min);
+            if (config.test_by_unitig) {
+                eff_size_buckets[bucket_idx].push_back(bit_cast<uint64_t, int64_t>(in_sum - out_stat_int));
+                sum_buckets[bucket_idx].push_back(n);
+            }
+
         }, false);
 
         common::logger->trace("Merging min p-value tables");
@@ -798,11 +804,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         m = std::move(ms[0]);
+        ms.resize(0);
     }
 
-    size_t k = 0;
-    size_t n_cutoff = 1;
-    if (!config.test_by_unitig) {
+    auto correct_pvals = [&](const std::vector<std::pair<double, size_t>> &m) {
+        size_t k = 1;
+        size_t n_cutoff = 1;
         size_t acc = std::accumulate(m.begin(), m.end(), size_t(0),
                                      [](size_t sum, const auto &a) { return sum + a.second; });
         common::logger->trace("Calculating cutoffs for {}/{} tests", acc, nelem);
@@ -838,11 +845,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         if (k == 0)
             common::logger->trace("No significant k-mers found");
-    }
+
+        return std::make_pair(k, n_cutoff);
+    };
+
+    size_t k = 1;
+    size_t n_cutoff = 1;
+    if (!config.test_by_unitig)
+        std::tie(k, n_cutoff) = correct_pvals(m);
 
     common::logger->trace("Running differential tests");
     std::exception_ptr ex = nullptr;
-    std::mutex pval_mu;
+    std::mutex ex_mu;
     std::atomic_thread_fence(std::memory_order_release);
 
     generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
@@ -895,7 +909,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             try {
                 pval = compute_pval(in_sum, out_sum, row);
             } catch (...) {
-                std::lock_guard<std::mutex> lock(pval_mu);
+                std::lock_guard<std::mutex> lock(ex_mu);
                 ex = std::current_exception();
                 return;
             }
@@ -926,101 +940,123 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     deallocate();
 
-    // std::vector<size_t> boundaries;
-    // boundaries.reserve(pvals_buckets.size() + 1);
-    // boundaries.emplace_back(0);
-    // for (size_t i = 0; i < pvals_buckets.size(); ++i) {
-    //     boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
-    // }
-
-    // if (config.test_by_unitig) {
-    //     common::logger->trace("Allocating k-mer bitmasks");
-    //     indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-    //     indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-
-    //     common::logger->trace("Combining p-values within unitigs");
-    //     MaskedDeBruijnGraph clean_masked_graph(
-    //         graph_ptr,
-    //         [&](node_index node) {
-    //             return node != DeBruijnGraph::npos
-    //                     && kept[AnnotatedDBG::graph_to_anno_index(node)];
-    //         },
-    //         true,
-    //         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    //     );
-
-    //     auto dist = boost::math::cauchy();
-
-    //     std::atomic_thread_fence(std::memory_order_release);
-    //     clean_masked_graph.call_unitigs([&](const std::string&, const auto &path) {
-    //         double stat = 0;
-    //         double stat_min = 0;
-    //         int64_t comb_eff_size = 0;
-    //         std::vector<size_t> bucket_idxs;
-    //         bucket_idxs.reserve(path.size());
-    //         for (node_index node : path) {
-    //             for (size_t i = 0; i < boundaries.size(); ++i) {
-    //                 if (boundaries[i] > node) {
-    //                     bucket_idxs.emplace_back(i);
-    //                     break;
-    //                 }
-    //             }
-    //         }
-
-    //         {
-    //             std::lock_guard<std::mutex> lock(pval_mu);
-    //             for (size_t i = 0; i < path.size(); ++i) {
-    //                 node_index node = path[i];
-    //                 size_t bucket_idx = bucket_idxs[i];
-    //                 comb_eff_size += bit_cast<int64_t, uint64_t>((*eff_size)[node]);
-    //                 double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node - boundaries[bucket_idx]]);
-    //                 double pval_min = bit_cast<double, uint64_t>((*pvals_min)[node]);
-    //                 stat += tan((0.5 - pval) * M_PI);
-    //                 stat_min += tan((0.5 - pval_min) * M_PI);
-    //             }
-    //         }
-    //         stat /= path.size();
-    //         stat_min /= path.size();
-    //         double comb_pval = boost::math::cdf(boost::math::complement(dist, stat));
-    //         if (comb_pval < config.family_wise_error_rate && comb_eff_size != 0) {
-    //             auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
-    //             for (node_index node : path) {
-    //                 set_bit(data, node, parallel, MO_RELAXED);
-    //             }
-    //         }
-
-    //         double comb_pval_min = boost::math::cdf(boost::math::complement(dist, stat_min));
-
-    //         uint64_t k = std::numeric_limits<uint64_t>::max();
-    //         if (comb_pval_min >= config.family_wise_error_rate) {
-    //             k = 0;
-    //         } else if (comb_pval_min > 0) {
-    //             double lkd = log2(config.family_wise_error_rate) - log2(comb_pval_min);
-    //             if (lkd <= 64)
-    //                 k = pow(2.0, lkd);
-
-    //             if (k == 0) {
-    //                 common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, comb_pval_min, comb_pval);
-    //                 throw std::runtime_error("Min failed");
-    //             }
-    //         }
-
-    //         uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
-    //         size_t bucket_idx = bucket_idxs[0];
-    //         ++ms[bucket_idx][k];
-    //         pvals_buckets[bucket_idx][path[0] - boundaries[bucket_idx]] = comb_pval_enc;
-    //         if (config.output_pvals) {
-    //             for (size_t i = 1; i < path.size(); ++i) {
-    //                 size_t bucket_idx = bucket_idxs[i];
-    //                 pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
-    //             }
-    //         }
-    //     }, num_threads);
-    //     std::atomic_thread_fence(std::memory_order_acquire);
-
-    //     eff_size.reset();
-    //     pvals_min.reset();
-    // }
+    std::vector<size_t> boundaries;
+    boundaries.reserve(pvals_buckets.size() + 1);
+    boundaries.emplace_back(0);
+    for (size_t i = 0; i < pvals_buckets.size(); ++i) {
+        boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
+    }
+
+    if (config.test_by_unitig) {
+        std::vector<std::pair<double, size_t>> comb_m;
+        std::mutex pval_mu;
+
+        common::logger->trace("Allocating k-mer bitmasks");
+        indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+        indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+
+        common::logger->trace("Combining p-values within unitigs");
+        MaskedDeBruijnGraph clean_masked_graph(
+            graph_ptr,
+            [&](node_index node) {
+                return node != DeBruijnGraph::npos
+                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
+            },
+            true,
+            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+        );
+
+        auto dist = boost::math::cauchy();
+
+        std::atomic_thread_fence(std::memory_order_release);
+        clean_masked_graph.call_unitigs([&](const std::string&, const auto &path) {
+            double stat = 0;
+            double stat_min = 0;
+            int64_t comb_eff_size = 0;
+            size_t comb_n = 0;
+            std::vector<size_t> bucket_idxs;
+            bucket_idxs.reserve(path.size());
+            for (node_index node : path) {
+                for (size_t i = 0; i < boundaries.size(); ++i) {
+                    if (boundaries[i] > node) {
+                        bucket_idxs.emplace_back(i);
+                        break;
+                    }
+                }
+            }
+
+            {
+                std::lock_guard<std::mutex> lock(pval_mu);
+                for (size_t i = 0; i < path.size(); ++i) {
+                    node_index node = path[i];
+                    size_t bucket_idx = bucket_idxs[i];
+                    size_t node_shift = node - boundaries[bucket_idx];
+
+                    comb_eff_size += bit_cast<int64_t, uint64_t>(eff_size_buckets[bucket_idx][node_shift]);
+
+                    size_t n = sum_buckets[bucket_idx][node_shift];
+                    comb_n += n;
+
+                    double pval_min = m[n].first;
+                    stat_min += tan((0.5 - pval_min) * M_PI);
+
+                    double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node_shift]);
+                    stat += tan((0.5 - pval) * M_PI);
+                }
+            }
+            stat /= path.size();
+            stat_min /= path.size();
+            double comb_pval = boost::math::cdf(boost::math::complement(dist, stat));
+            if (comb_pval < config.family_wise_error_rate && comb_eff_size != 0) {
+                auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
+                for (node_index node : path) {
+                    set_bit(data, node, parallel, MO_RELAXED);
+                }
+            }
+
+            double comb_pval_min = boost::math::cdf(boost::math::complement(dist, stat_min));
+
+            uint64_t k = std::numeric_limits<uint64_t>::max();
+            if (comb_pval_min >= config.family_wise_error_rate) {
+                k = 0;
+            } else if (comb_pval_min > 0) {
+                double lkd = log2(config.family_wise_error_rate) - log2(comb_pval_min);
+                if (lkd <= 64)
+                    k = pow(2.0, lkd);
+
+                if (k == 0) {
+                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, comb_pval_min, comb_pval);
+                    throw std::runtime_error("Min failed");
+                }
+            }
+
+            uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
+            size_t bucket_idx = bucket_idxs[0];
+
+            std::lock_guard<std::mutex> lock(pval_mu);
+            if (comb_n >= comb_m.size())
+                comb_m.resize(comb_n + 1, std::make_pair(1.1, 0));
+
+            assert(comb_m[comb_n].first == 1.1 || comb_m[comb_n].first == comb_pval);
+            comb_m[comb_n].first = comb_pval;
+            ++comb_m[comb_n].second;
+            pvals_buckets[bucket_idx][path[0] - boundaries[bucket_idx]] = comb_pval_enc;
+            if (config.output_pvals) {
+                for (size_t i = 1; i < path.size(); ++i) {
+                    size_t bucket_idx = bucket_idxs[i];
+                    pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
+                }
+            }
+        }, num_threads);
+        std::atomic_thread_fence(std::memory_order_acquire);
+
+        eff_size_buckets.resize(0);
+        tmp_eff_buckets.resize(0);
+        sum_buckets.resize(0);
+        tmp_sum_buckets.resize(0);
+
+        std::tie(k, n_cutoff) = correct_pvals(comb_m);
+    }
 
     std::unique_ptr<utils::TempFile> tmp_file;
     PValStorage pvals;

From c6e4ee75760972982ab2f54c7594e2cc4fefc1f4 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 27 Jul 2024 23:47:10 +0200
Subject: [PATCH 266/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 41 +++++++++++++++----
 1 file changed, 32 insertions(+), 9 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 1cfa9d2c09..37abb895ea 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -977,11 +977,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             std::vector<size_t> bucket_idxs;
             bucket_idxs.reserve(path.size());
             for (node_index node : path) {
+                bucket_idxs.emplace_back(0);
                 for (size_t i = 0; i < boundaries.size(); ++i) {
-                    if (boundaries[i] > node) {
-                        bucket_idxs.emplace_back(i);
+                    if (boundaries[i] > node)
                         break;
-                    }
+
+                    ++bucket_idxs.back();
                 }
             }
 
@@ -1041,12 +1042,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             comb_m[comb_n].first = comb_pval;
             ++comb_m[comb_n].second;
             pvals_buckets[bucket_idx][path[0] - boundaries[bucket_idx]] = comb_pval_enc;
-            if (config.output_pvals) {
-                for (size_t i = 1; i < path.size(); ++i) {
-                    size_t bucket_idx = bucket_idxs[i];
-                    pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
-                }
-            }
+
+            // TODO: how do I only output one p-value per unitig?
+            // if (config.output_pvals) {
+            //     for (size_t i = 1; i < path.size(); ++i) {
+            //         size_t bucket_idx = bucket_idxs[i];
+            //         pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
+            //     }
+            // }
         }, num_threads);
         std::atomic_thread_fence(std::memory_order_acquire);
 
@@ -1056,6 +1059,26 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         tmp_sum_buckets.resize(0);
 
         std::tie(k, n_cutoff) = correct_pvals(comb_m);
+        auto b_it = boundaries.begin();
+        call_ones(indicator_in, [&](node_index node) {
+            for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
+            --b_it;
+            size_t bucket_idx = b_it - boundaries.begin();
+            size_t node_idx = node - *b_it;
+            const auto &pval = pvals_buckets[bucket_idx][node_idx];
+            if (pval * k >= config.family_wise_error_rate)
+                unset_bit(indicator_in.data(), node, parallel, MO_RELAXED);
+        });
+        b_it = boundaries.begin();
+        call_ones(indicator_out, [&](node_index node) {
+            for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
+            --b_it;
+            size_t bucket_idx = b_it - boundaries.begin();
+            size_t node_idx = node - *b_it;
+            const auto &pval = pvals_buckets[bucket_idx][node_idx];
+            if (pval * k >= config.family_wise_error_rate)
+                unset_bit(indicator_out.data(), node, parallel, MO_RELAXED);
+        });
     }
 
     std::unique_ptr<utils::TempFile> tmp_file;

From 8f6ec908cb8a1dac62227b57741e8d663d43b956 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 31 Jul 2024 13:24:56 +0200
Subject: [PATCH 267/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 432 +++++++++---------
 1 file changed, 217 insertions(+), 215 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 37abb895ea..bf39aa717e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -248,7 +248,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                               mu2=static_cast<double>(out_kmers)/nelem,
                               get_deviance](int64_t n) {
             if (n == 0)
-                return 1.1;
+                return 1.0;
 
             auto bdist = boost::math::binomial(n, p);
 
@@ -285,7 +285,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                           mu2=static_cast<double>(out_kmers)/nelem,
                           get_deviance](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
-                return 1.1;
+                return 1.0;
 
             int64_t n = in_sum + out_sum;
             auto bdist = boost::math::binomial(n, p);
@@ -321,7 +321,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         compute_min_pval = [&,lbase_shared](int64_t m1) {
             if (m1 == 0)
-                return 1.1;
+                return 1.0;
 
             int64_t m2 = total_kmers - m1;
 
@@ -343,7 +343,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         compute_pval = [&,lbase_shared](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
-                return 1.1;
+                return 1.0;
 
             int64_t m1 = in_sum + out_sum;
             int64_t m2 = total_kmers - m1;
@@ -538,7 +538,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 for (const auto &[k, c] : hist) {
                     if (!count_maps[j].count(k)) {
                         uint64_t new_k = map_value(k, scale, nb, nb_out);
-                        count_maps[j][k] = std::make_pair(std::max(new_k, uint64_t(1)), c);
+                        count_maps[j][k] = std::make_pair(new_k, c);
                     } else {
                         count_maps[j][k].second += c;
                     }
@@ -566,20 +566,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
         total_kmers = in_kmers + out_kmers;
-        common::logger->trace("Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
+        common::logger->trace("  Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
-        PairContainer best_in_row;
-        PairContainer best_out_row;
         int64_t in_sum = 0;
         int64_t out_sum = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
             if (hists[j].size()) {
                 uint64_t k = count_maps[j].find(hists[j].back().first)->second.first;
                 if (groups[j]) {
-                    best_out_row.emplace_back(j, k);
                     out_sum += k;
                 } else {
-                    best_in_row.emplace_back(j, k);
                     in_sum += k;
                 }
             }
@@ -606,7 +602,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         compute_min_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t n) {
             if (n == 0)
-                return 1.1;
+                return 1.0;
 
             double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
             std::vector<double> devs(n + 1);
@@ -662,7 +658,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         compute_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t in_sum, int64_t out_sum, const auto &row) {
             if (row.empty())
-                return 1.1;
+                return 1.0;
 
             int64_t n = in_sum + out_sum;
 
@@ -711,14 +707,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return std::min(1.0, pval);
         };
-
-        double pval = std::min(compute_pval(in_sum, 0, best_in_row),
-                               compute_pval(0, out_sum, best_out_row));
-        common::logger->trace("Best achievable p-value: {}\tin_sum: {}\tout_sum: {}", pval, in_sum, out_sum);
-        if (pval >= config.family_wise_error_rate) {
-            common::logger->error("Best achievable p-value is too big. Use poisson_exact instead");
-            throw std::domain_error("Too few samples");
-        }
     } else if (config.test_type == "notest") {
         compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
         compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };
@@ -763,6 +751,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
                     out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
                 }
+            } else {
+                return;
             }
 
             bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
@@ -775,7 +765,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (n >= ms[bucket_idx].size())
                 ms[bucket_idx].resize(n + 1, std::make_pair(1.1, 0));
 
-            if (n > 0 && ms[bucket_idx][n].first == 1.1)
+            if (ms[bucket_idx][n].first == 1.1)
                 ms[bucket_idx][n].first = compute_min_pval(n);
 
             ++ms[bucket_idx][n].second;
@@ -808,7 +798,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
 
     auto correct_pvals = [&](const std::vector<std::pair<double, size_t>> &m) {
-        size_t k = 1;
+        size_t k = 0;
         size_t n_cutoff = 1;
         size_t acc = std::accumulate(m.begin(), m.end(), size_t(0),
                                      [](size_t sum, const auto &a) { return sum + a.second; });
@@ -829,8 +819,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     throw std::runtime_error("Min failed");
                 }
             }
-            if (s > 0)
-                common::logger->trace("k: {}\ts: {}\tm(k): {}\tn: {}\tpval_min: {}", cur_k, s, acc, n, pval_min);
+
+            // if (s > 0)
+            //     common::logger->trace("k: {}\ts: {}\tm(k): {}\tn: {}\tpval_min: {}", cur_k, s, acc, n, pval_min);
 
             if (cur_k != last_k && acc <= cur_k) {
                 k = std::max(acc, last_k + 1);
@@ -844,241 +835,252 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         if (k == 0)
-            common::logger->trace("No significant k-mers found");
+            common::logger->warn("No significant p-values achievable");
 
         return std::make_pair(k, n_cutoff);
     };
 
-    size_t k = 1;
+    size_t k = 0;
     size_t n_cutoff = 1;
     if (!config.test_by_unitig)
         std::tie(k, n_cutoff) = correct_pvals(m);
 
-    common::logger->trace("Running differential tests");
-    std::exception_ptr ex = nullptr;
-    std::mutex ex_mu;
-    std::atomic_thread_fence(std::memory_order_release);
+    if (k > 0) {
+        common::logger->trace("Running differential tests");
+        std::exception_ptr ex = nullptr;
+        std::mutex ex_mu;
+        bool keep_all_pvals = config.output_pvals || config.test_by_unitig;
+        std::atomic_thread_fence(std::memory_order_release);
 
-    generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
-        if (ex)
-            return;
+        generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
+            if (ex)
+                return;
 
-        int64_t in_sum = 0;
-        int64_t out_sum = 0;
-        bool in_kmer = false;
-        bool out_kmer = false;
-        PairContainer row;
-        if (kept[row_i]) {
-            size_t count_in = 0;
-            size_t count_out = 0;
-            for (const auto &[j, raw_c] : raw_row) {
-                uint64_t c = raw_c;
-                if (count_maps.size())
-                    c = count_maps[j].find(c)->second.first;
-
-                row.emplace_back(j, c);
-                if (groups[j]) {
-                    out_sum += c;
-                    ++count_out;
-                } else {
-                    in_sum += c;
-                    ++count_in;
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+            bool in_kmer = false;
+            bool out_kmer = false;
+            PairContainer row;
+            bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
+            double pval = 1.1;
+            if (kept[row_i]) {
+                size_t count_in = 0;
+                size_t count_out = 0;
+                for (const auto &[j, raw_c] : raw_row) {
+                    uint64_t c = raw_c;
+                    if (count_maps.size())
+                        c = count_maps[j].find(c)->second.first;
+
+                    row.emplace_back(j, c);
+                    if (groups[j]) {
+                        out_sum += c;
+                        ++count_out;
+                    } else {
+                        in_sum += c;
+                        ++count_in;
+                    }
                 }
-            }
 
-            if (count_in + count_out >= config.min_recurrence) {
-                double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-                in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-                out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+                if (count_in + count_out >= config.min_recurrence) {
+                    double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+                    in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+                    out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+                }
+            } else {
+                if (keep_all_pvals) {
+                    auto &pvals = pvals_buckets[bucket_idx];
+                    pvals.push_back(bit_cast<uint64_t>(pval));
+                }
+                return;
             }
-        }
-
-        size_t n = in_sum + out_sum;
-        if (!in_kmer && !out_kmer) {
-            row.clear();
-            n = 0;
-        }
 
-        if (config.output_pvals || config.test_by_unitig || n >= n_cutoff) {
-            if (m[n].first == 1.1) {
-                common::logger->error("Min p-val too small: {}\tn: {}\ts: {}", m[n].first, n, m[n].second);
-                throw std::runtime_error("Test failed");
+            size_t n = in_sum + out_sum;
+            if (!in_kmer && !out_kmer) {
+                row.clear();
+                n = 0;
             }
 
-            double pval = 1.1;
-            try {
-                pval = compute_pval(in_sum, out_sum, row);
-            } catch (...) {
-                std::lock_guard<std::mutex> lock(ex_mu);
-                ex = std::current_exception();
-                return;
-            }
+            if (keep_all_pvals || n >= n_cutoff) {
+                if (m[n].first == 1.1) {
+                    common::logger->error("Min p-val too small: {}\tn: {}\ts: {}", m[n].first, n, m[n].second);
+                    throw std::runtime_error("Test failed");
+                }
 
-            if (pval < 1.1 && m[n].first - pval > 1e-10) {
-                common::logger->error("Min p-val estimate too high: min {} > cur {}\tn: {}\ttest: {}", m[n].first, pval, n, config.test_type);
-                throw std::runtime_error("Test failed");
-            }
+                try {
+                    pval = compute_pval(in_sum, out_sum, row);
+                } catch (...) {
+                    std::lock_guard<std::mutex> lock(ex_mu);
+                    ex = std::current_exception();
+                    return;
+                }
 
-            if (config.test_type != "notest" && (config.output_pvals || config.test_by_unitig)) {
-                bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
-                auto &pvals = pvals_buckets[bucket_idx];
-                pvals.push_back(bit_cast<uint64_t>(pval));
-            }
+                if (pval < 1.1 && m[n].first - pval > 1e-10) {
+                    common::logger->error("Min p-val estimate too high: min {} > cur {}\tn: {}\ttest: {}", m[n].first, pval, n, config.test_type);
+                    throw std::runtime_error("Test failed");
+                }
+
+                if (config.test_type != "notest" && keep_all_pvals) {
+                    auto &pvals = pvals_buckets[bucket_idx];
+                    pvals.push_back(bit_cast<uint64_t>(pval));
+                }
 
-            if (!config.test_by_unitig && in_kmer != out_kmer && pval * k < config.family_wise_error_rate) {
-                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                bool use_atomic = parallel && (node % 64 == 0);
-                set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
+                if (!config.test_by_unitig && in_kmer != out_kmer && pval * k < config.family_wise_error_rate) {
+                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                    bool use_atomic = parallel && (node % 64 == 0);
+                    set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
+                }
             }
-        }
-    }, false);
+        }, false);
 
-    std::atomic_thread_fence(std::memory_order_acquire);
+        std::atomic_thread_fence(std::memory_order_acquire);
 
-    if (ex)
-        std::rethrow_exception(ex);
+        if (ex)
+            std::rethrow_exception(ex);
 
-    deallocate();
+        deallocate();
 
-    std::vector<size_t> boundaries;
-    boundaries.reserve(pvals_buckets.size() + 1);
-    boundaries.emplace_back(0);
-    for (size_t i = 0; i < pvals_buckets.size(); ++i) {
-        boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
-    }
+        std::vector<size_t> boundaries;
+        boundaries.reserve(pvals_buckets.size() + 1);
+        boundaries.emplace_back(0);
+        for (size_t i = 0; i < pvals_buckets.size(); ++i) {
+            boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
+        }
 
-    if (config.test_by_unitig) {
-        std::vector<std::pair<double, size_t>> comb_m;
-        std::mutex pval_mu;
+        if (config.test_by_unitig) {
+            std::vector<std::pair<double, size_t>> comb_m;
+            std::mutex pval_mu;
 
-        common::logger->trace("Allocating k-mer bitmasks");
-        indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-        indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+            common::logger->trace("Allocating k-mer bitmasks");
+            indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+            indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
 
-        common::logger->trace("Combining p-values within unitigs");
-        MaskedDeBruijnGraph clean_masked_graph(
-            graph_ptr,
-            [&](node_index node) {
-                return node != DeBruijnGraph::npos
-                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
-            },
-            true,
-            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-        );
+            common::logger->trace("Combining p-values within unitigs");
+            MaskedDeBruijnGraph clean_masked_graph(
+                graph_ptr,
+                [&](node_index node) {
+                    return node != DeBruijnGraph::npos
+                            && kept[AnnotatedDBG::graph_to_anno_index(node)];
+                },
+                true,
+                is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+            );
 
-        auto dist = boost::math::cauchy();
+            auto dist = boost::math::cauchy();
 
-        std::atomic_thread_fence(std::memory_order_release);
-        clean_masked_graph.call_unitigs([&](const std::string&, const auto &path) {
-            double stat = 0;
-            double stat_min = 0;
-            int64_t comb_eff_size = 0;
-            size_t comb_n = 0;
-            std::vector<size_t> bucket_idxs;
-            bucket_idxs.reserve(path.size());
-            for (node_index node : path) {
-                bucket_idxs.emplace_back(0);
-                for (size_t i = 0; i < boundaries.size(); ++i) {
-                    if (boundaries[i] > node)
-                        break;
+            std::atomic_thread_fence(std::memory_order_release);
+            clean_masked_graph.call_unitigs([&](const std::string&, const auto &path) {
+                double stat = 0;
+                double stat_min = 0;
+                int64_t comb_eff_size = 0;
+                size_t comb_n = 0;
+                std::vector<size_t> bucket_idxs;
+                bucket_idxs.reserve(path.size());
+                for (node_index node : path) {
+                    bucket_idxs.emplace_back(0);
+                    for (size_t i = 0; i < boundaries.size(); ++i) {
+                        if (boundaries[i] > node)
+                            break;
 
-                    ++bucket_idxs.back();
+                        ++bucket_idxs.back();
+                    }
                 }
-            }
 
-            {
-                std::lock_guard<std::mutex> lock(pval_mu);
-                for (size_t i = 0; i < path.size(); ++i) {
-                    node_index node = path[i];
-                    size_t bucket_idx = bucket_idxs[i];
-                    size_t node_shift = node - boundaries[bucket_idx];
+                {
+                    std::lock_guard<std::mutex> lock(pval_mu);
+                    for (size_t i = 0; i < path.size(); ++i) {
+                        node_index node = path[i];
+                        size_t bucket_idx = bucket_idxs[i];
+                        size_t node_shift = node - boundaries[bucket_idx];
 
-                    comb_eff_size += bit_cast<int64_t, uint64_t>(eff_size_buckets[bucket_idx][node_shift]);
+                        comb_eff_size += bit_cast<int64_t, uint64_t>(eff_size_buckets[bucket_idx][node_shift]);
 
-                    size_t n = sum_buckets[bucket_idx][node_shift];
-                    comb_n += n;
+                        size_t n = sum_buckets[bucket_idx][node_shift];
+                        comb_n += n;
 
-                    double pval_min = m[n].first;
-                    stat_min += tan((0.5 - pval_min) * M_PI);
+                        double pval_min = m[n].first;
+                        stat_min += tan((0.5 - pval_min) * M_PI);
 
-                    double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node_shift]);
-                    stat += tan((0.5 - pval) * M_PI);
+                        double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node_shift]);
+                        stat += tan((0.5 - pval) * M_PI);
+                    }
                 }
-            }
-            stat /= path.size();
-            stat_min /= path.size();
-            double comb_pval = boost::math::cdf(boost::math::complement(dist, stat));
-            if (comb_pval < config.family_wise_error_rate && comb_eff_size != 0) {
-                auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
-                for (node_index node : path) {
-                    set_bit(data, node, parallel, MO_RELAXED);
+                stat /= path.size();
+                stat_min /= path.size();
+                double comb_pval = boost::math::cdf(boost::math::complement(dist, stat));
+                if (comb_pval < config.family_wise_error_rate && comb_eff_size != 0) {
+                    auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
+                    for (node_index node : path) {
+                        set_bit(data, node, parallel, MO_RELAXED);
+                    }
                 }
-            }
 
-            double comb_pval_min = boost::math::cdf(boost::math::complement(dist, stat_min));
+                double comb_pval_min = boost::math::cdf(boost::math::complement(dist, stat_min));
 
-            uint64_t k = std::numeric_limits<uint64_t>::max();
-            if (comb_pval_min >= config.family_wise_error_rate) {
-                k = 0;
-            } else if (comb_pval_min > 0) {
-                double lkd = log2(config.family_wise_error_rate) - log2(comb_pval_min);
-                if (lkd <= 64)
-                    k = pow(2.0, lkd);
+                uint64_t k = std::numeric_limits<uint64_t>::max();
+                if (comb_pval_min >= config.family_wise_error_rate) {
+                    k = 0;
+                } else if (comb_pval_min > 0) {
+                    double lkd = log2(config.family_wise_error_rate) - log2(comb_pval_min);
+                    if (lkd <= 64)
+                        k = pow(2.0, lkd);
 
-                if (k == 0) {
-                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, comb_pval_min, comb_pval);
-                    throw std::runtime_error("Min failed");
+                    if (k == 0) {
+                        common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, comb_pval_min, comb_pval);
+                        throw std::runtime_error("Min failed");
+                    }
                 }
-            }
 
-            uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
-            size_t bucket_idx = bucket_idxs[0];
-
-            std::lock_guard<std::mutex> lock(pval_mu);
-            if (comb_n >= comb_m.size())
-                comb_m.resize(comb_n + 1, std::make_pair(1.1, 0));
-
-            assert(comb_m[comb_n].first == 1.1 || comb_m[comb_n].first == comb_pval);
-            comb_m[comb_n].first = comb_pval;
-            ++comb_m[comb_n].second;
-            pvals_buckets[bucket_idx][path[0] - boundaries[bucket_idx]] = comb_pval_enc;
-
-            // TODO: how do I only output one p-value per unitig?
-            // if (config.output_pvals) {
-            //     for (size_t i = 1; i < path.size(); ++i) {
-            //         size_t bucket_idx = bucket_idxs[i];
-            //         pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
-            //     }
-            // }
-        }, num_threads);
-        std::atomic_thread_fence(std::memory_order_acquire);
+                uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
+                size_t bucket_idx = bucket_idxs[0];
 
-        eff_size_buckets.resize(0);
-        tmp_eff_buckets.resize(0);
-        sum_buckets.resize(0);
-        tmp_sum_buckets.resize(0);
-
-        std::tie(k, n_cutoff) = correct_pvals(comb_m);
-        auto b_it = boundaries.begin();
-        call_ones(indicator_in, [&](node_index node) {
-            for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
-            --b_it;
-            size_t bucket_idx = b_it - boundaries.begin();
-            size_t node_idx = node - *b_it;
-            const auto &pval = pvals_buckets[bucket_idx][node_idx];
-            if (pval * k >= config.family_wise_error_rate)
-                unset_bit(indicator_in.data(), node, parallel, MO_RELAXED);
-        });
-        b_it = boundaries.begin();
-        call_ones(indicator_out, [&](node_index node) {
-            for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
-            --b_it;
-            size_t bucket_idx = b_it - boundaries.begin();
-            size_t node_idx = node - *b_it;
-            const auto &pval = pvals_buckets[bucket_idx][node_idx];
-            if (pval * k >= config.family_wise_error_rate)
-                unset_bit(indicator_out.data(), node, parallel, MO_RELAXED);
-        });
+                std::lock_guard<std::mutex> lock(pval_mu);
+                if (comb_n >= comb_m.size())
+                    comb_m.resize(comb_n + 1, std::make_pair(1.1, 0));
+
+                assert(comb_m[comb_n].first == 1.1 || comb_m[comb_n].first == comb_pval);
+                comb_m[comb_n].first = comb_pval;
+                ++comb_m[comb_n].second;
+                pvals_buckets[bucket_idx][path[0] - boundaries[bucket_idx]] = comb_pval_enc;
+
+                // TODO: how do I only output one p-value per unitig?
+                // if (config.output_pvals) {
+                //     for (size_t i = 1; i < path.size(); ++i) {
+                //         size_t bucket_idx = bucket_idxs[i];
+                //         pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
+                //     }
+                // }
+            }, num_threads);
+            std::atomic_thread_fence(std::memory_order_acquire);
+
+            eff_size_buckets.resize(0);
+            tmp_eff_buckets.resize(0);
+            sum_buckets.resize(0);
+            tmp_sum_buckets.resize(0);
+
+            std::tie(k, n_cutoff) = correct_pvals(comb_m);
+            auto b_it = boundaries.begin();
+            call_ones(indicator_in, [&](node_index node) {
+                for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
+                --b_it;
+                size_t bucket_idx = b_it - boundaries.begin();
+                size_t node_idx = node - *b_it;
+                const auto &pval = pvals_buckets[bucket_idx][node_idx];
+                if (pval * k >= config.family_wise_error_rate)
+                    unset_bit(indicator_in.data(), node, parallel, MO_RELAXED);
+            });
+            b_it = boundaries.begin();
+            call_ones(indicator_out, [&](node_index node) {
+                for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
+                --b_it;
+                size_t bucket_idx = b_it - boundaries.begin();
+                size_t node_idx = node - *b_it;
+                const auto &pval = pvals_buckets[bucket_idx][node_idx];
+                if (pval * k >= config.family_wise_error_rate)
+                    unset_bit(indicator_out.data(), node, parallel, MO_RELAXED);
+            });
+        }
+    } else {
+        deallocate();
     }
 
     std::unique_ptr<utils::TempFile> tmp_file;

From 51ad45c968d485fe3c10915073d59e863d48251c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 1 Aug 2024 00:36:11 +0200
Subject: [PATCH 268/410] fixed unitig-based

---
 .../src/graph/annotated_graph_algorithm.cpp   | 134 +++++++++++-------
 1 file changed, 79 insertions(+), 55 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index bf39aa717e..eebecc6212 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -181,9 +181,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     std::vector<PValStorage> pvals_buckets;
     std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
+    constexpr bool preallocated = std::is_same_v<PValStorage, std::vector<uint64_t>>;
     if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
         auto &pvals = pvals_buckets.emplace_back();
-        pvals.emplace_back(nullpval);
+        pvals.resize(graph_ptr->max_index() + 1, nullpval);
     }
 
     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
@@ -202,10 +203,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Allocating effect size and row sum storage");
         if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
             auto &eff_size = eff_size_buckets.emplace_back();
-            eff_size.emplace_back(0);
+            eff_size.resize(graph_ptr->max_index() + 1);
 
             auto &sum = sum_buckets.emplace_back();
-            sum.emplace_back(0);
+            sum.resize(graph_ptr->max_index() + 1);
         }
 
         if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
@@ -717,7 +718,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Computing minimum p-values");
     std::vector<std::pair<double, size_t>> m;
     {
-        std::vector<std::vector<std::pair<double, size_t>>> ms(pvals_buckets.size());
+        std::vector<std::vector<std::pair<double, size_t>>> ms(num_threads + 1);
         generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
             int64_t in_sum = 0;
             int64_t out_sum = 0;
@@ -755,7 +756,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return;
             }
 
-            bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
             size_t n = in_sum + out_sum;
             if (!in_kmer && !out_kmer) {
                 row.clear();
@@ -771,8 +771,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             ++ms[bucket_idx][n].second;
 
             if (config.test_by_unitig) {
-                eff_size_buckets[bucket_idx].push_back(bit_cast<uint64_t, int64_t>(in_sum - out_stat_int));
-                sum_buckets[bucket_idx].push_back(n);
+                bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
+                uint64_t eff_size = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
+                if constexpr(preallocated) {
+                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                    eff_size_buckets[bucket_idx][node] = eff_size;
+                    sum_buckets[bucket_idx][node] = n;
+                } else {
+                    eff_size_buckets[bucket_idx].push_back(eff_size);
+                    sum_buckets[bucket_idx].push_back(n);
+                }
             }
 
         }, false);
@@ -797,7 +805,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         ms.resize(0);
     }
 
-    auto correct_pvals = [&](const std::vector<std::pair<double, size_t>> &m) {
+    auto correct_pvals = [&config,nelem](const std::vector<std::pair<double, size_t>> &m) {
         size_t k = 0;
         size_t n_cutoff = 1;
         size_t acc = std::accumulate(m.begin(), m.end(), size_t(0),
@@ -820,9 +828,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            // if (s > 0)
-            //     common::logger->trace("k: {}\ts: {}\tm(k): {}\tn: {}\tpval_min: {}", cur_k, s, acc, n, pval_min);
-
             if (cur_k != last_k && acc <= cur_k) {
                 k = std::max(acc, last_k + 1);
                 n_cutoff = n;
@@ -845,7 +850,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     if (!config.test_by_unitig)
         std::tie(k, n_cutoff) = correct_pvals(m);
 
-    if (k > 0) {
+    if (k > 0 || config.test_by_unitig) {
         common::logger->trace("Running differential tests");
         std::exception_ptr ex = nullptr;
         std::mutex ex_mu;
@@ -862,6 +867,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             bool out_kmer = false;
             PairContainer row;
             bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
             double pval = 1.1;
             if (kept[row_i]) {
                 size_t count_in = 0;
@@ -889,7 +895,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             } else {
                 if (keep_all_pvals) {
                     auto &pvals = pvals_buckets[bucket_idx];
-                    pvals.push_back(bit_cast<uint64_t>(pval));
+                    if constexpr(preallocated) {
+                        pvals[node] = bit_cast<uint64_t>(pval);
+                    } else {
+                        pvals.push_back(bit_cast<uint64_t>(pval));
+                    }
                 }
                 return;
             }
@@ -902,8 +912,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             if (keep_all_pvals || n >= n_cutoff) {
                 if (m[n].first == 1.1) {
-                    common::logger->error("Min p-val too small: {}\tn: {}\ts: {}", m[n].first, n, m[n].second);
-                    throw std::runtime_error("Test failed");
+                    common::logger->error("in: {}\tout: {}\tn: {}", in_sum, out_sum, n);
+                    throw std::runtime_error("Indexing invalid");
                 }
 
                 try {
@@ -921,11 +931,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 if (config.test_type != "notest" && keep_all_pvals) {
                     auto &pvals = pvals_buckets[bucket_idx];
-                    pvals.push_back(bit_cast<uint64_t>(pval));
+                    if constexpr(preallocated) {
+                        pvals[node] = bit_cast<uint64_t>(pval);
+                    } else {
+                        pvals.push_back(bit_cast<uint64_t>(pval));
+                    }
                 }
 
                 if (!config.test_by_unitig && in_kmer != out_kmer && pval * k < config.family_wise_error_rate) {
-                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
                     bool use_atomic = parallel && (node % 64 == 0);
                     set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
                 }
@@ -966,9 +979,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             );
 
             auto dist = boost::math::cauchy();
+            std::atomic<uint64_t> num_sig_unitigs{0};
+
+            sdsl::bit_vector unitig_start;
+            if (config.output_pvals) {
+                common::logger->trace("Allocating unitig start indicator");
+                unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+            }
 
             std::atomic_thread_fence(std::memory_order_release);
             clean_masked_graph.call_unitigs([&](const std::string&, const auto &path) {
+                if (config.output_pvals)
+                    set_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
+
                 double stat = 0;
                 double stat_min = 0;
                 int64_t comb_eff_size = 0;
@@ -977,7 +1000,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 bucket_idxs.reserve(path.size());
                 for (node_index node : path) {
                     bucket_idxs.emplace_back(0);
-                    for (size_t i = 0; i < boundaries.size(); ++i) {
+                    for (size_t i = 1; i < boundaries.size(); ++i) {
                         if (boundaries[i] > node)
                             break;
 
@@ -1008,6 +1031,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 stat_min /= path.size();
                 double comb_pval = boost::math::cdf(boost::math::complement(dist, stat));
                 if (comb_pval < config.family_wise_error_rate && comb_eff_size != 0) {
+                    ++num_sig_unitigs;
                     auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
                     for (node_index node : path) {
                         set_bit(data, node, parallel, MO_RELAXED);
@@ -1031,53 +1055,53 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
-                size_t bucket_idx = bucket_idxs[0];
 
                 std::lock_guard<std::mutex> lock(pval_mu);
-                if (comb_n >= comb_m.size())
-                    comb_m.resize(comb_n + 1, std::make_pair(1.1, 0));
-
-                assert(comb_m[comb_n].first == 1.1 || comb_m[comb_n].first == comb_pval);
-                comb_m[comb_n].first = comb_pval;
-                ++comb_m[comb_n].second;
-                pvals_buckets[bucket_idx][path[0] - boundaries[bucket_idx]] = comb_pval_enc;
-
-                // TODO: how do I only output one p-value per unitig?
-                // if (config.output_pvals) {
-                //     for (size_t i = 1; i < path.size(); ++i) {
-                //         size_t bucket_idx = bucket_idxs[i];
-                //         pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = nullpval;
-                //     }
-                // }
+                comb_m.emplace_back(comb_pval, 1);
+                for (size_t i = 0; i < path.size(); ++i) {
+                    size_t bucket_idx = bucket_idxs[i];
+                    pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = comb_pval_enc;
+                }
             }, num_threads);
             std::atomic_thread_fence(std::memory_order_acquire);
+            common::logger->trace("Found {} significant unitigs before correction", num_sig_unitigs);
 
             eff_size_buckets.resize(0);
             tmp_eff_buckets.resize(0);
             sum_buckets.resize(0);
             tmp_sum_buckets.resize(0);
 
-            std::tie(k, n_cutoff) = correct_pvals(comb_m);
-            auto b_it = boundaries.begin();
-            call_ones(indicator_in, [&](node_index node) {
-                for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
-                --b_it;
-                size_t bucket_idx = b_it - boundaries.begin();
-                size_t node_idx = node - *b_it;
-                const auto &pval = pvals_buckets[bucket_idx][node_idx];
-                if (pval * k >= config.family_wise_error_rate)
-                    unset_bit(indicator_in.data(), node, parallel, MO_RELAXED);
-            });
-            b_it = boundaries.begin();
-            call_ones(indicator_out, [&](node_index node) {
-                for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
-                --b_it;
-                size_t bucket_idx = b_it - boundaries.begin();
-                size_t node_idx = node - *b_it;
-                const auto &pval = pvals_buckets[bucket_idx][node_idx];
-                if (pval * k >= config.family_wise_error_rate)
-                    unset_bit(indicator_out.data(), node, parallel, MO_RELAXED);
-            });
+            if (num_sig_unitigs) {
+                std::sort(comb_m.begin(), comb_m.end(), utils::GreaterFirst());
+                std::tie(k, n_cutoff) = correct_pvals(comb_m);
+
+                auto b_it = boundaries.begin();
+                call_ones(indicator_in, [&](node_index node) {
+                    for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
+                    --b_it;
+                    size_t bucket_idx = b_it - boundaries.begin();
+                    size_t node_idx = node - *b_it;
+                    double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node_idx]);
+                    if (pval * k >= config.family_wise_error_rate)
+                        unset_bit(indicator_in.data(), node, parallel, MO_RELAXED);
+
+                    if (config.output_pvals && !unitig_start[node])
+                        pvals_buckets[bucket_idx][node_idx] = nullpval;
+                });
+                b_it = boundaries.begin();
+                call_ones(indicator_out, [&](node_index node) {
+                    for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
+                    --b_it;
+                    size_t bucket_idx = b_it - boundaries.begin();
+                    size_t node_idx = node - *b_it;
+                    double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node_idx]);
+                    if (pval * k >= config.family_wise_error_rate)
+                        unset_bit(indicator_out.data(), node, parallel, MO_RELAXED);
+
+                    if (config.output_pvals && !unitig_start[node])
+                        pvals_buckets[bucket_idx][node_idx] = nullpval;
+                });
+            }
         }
     } else {
         deallocate();

From 81f808cc0693826c4ccaf40ac821b0bd527a7ee2 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 9 Aug 2024 12:12:37 +0200
Subject: [PATCH 269/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 312 ++++++++++--------
 1 file changed, 178 insertions(+), 134 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index eebecc6212..807762194d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -115,6 +115,45 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
+    auto correct_pvals = [&config](const std::vector<std::pair<double, size_t>> &m, size_t nelem) {
+        size_t k = 0;
+        size_t n_cutoff = 1;
+        size_t acc = std::accumulate(m.begin(), m.end(), size_t(0),
+                                     [](size_t sum, const auto &a) { return sum + a.second; });
+        common::logger->trace("Calculating cutoffs for {}/{} tests", acc, nelem);
+        size_t last_k = 0;
+        for (size_t n = 0; n < m.size(); ++n) {
+            auto [pval_min, s] = m[n];
+            size_t cur_k = std::numeric_limits<size_t>::max();
+            if (pval_min >= config.family_wise_error_rate) {
+                cur_k = 0;
+            } else if (pval_min > 0) {
+                double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
+                if (lkd <= 64)
+                    cur_k = pow(2.0, lkd);
+
+                if (cur_k == 0) {
+                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}", cur_k, lkd, pval_min);
+                    throw std::runtime_error("Min failed");
+                }
+            }
+
+            if (cur_k != last_k && acc <= cur_k) {
+                k = std::max(acc, last_k + 1);
+                n_cutoff = n;
+                break;
+            }
+
+            last_k = cur_k;
+            acc -= s;
+        }
+
+        if (k == 0)
+            common::logger->warn("No significant p-values achievable");
+
+        return std::make_pair(k, n_cutoff);
+    };
+
     common::logger->trace("Updating histogram and marking discarded k-mers");
 
     if (groups.size())
@@ -145,7 +184,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Computing aggregate statistics");
     int64_t in_kmers = 0;
     int64_t out_kmers = 0;
-    std::vector<double> medians(groups.size());
     std::vector<uint64_t> max_obs_vals(groups.size());
     std::vector<size_t> n_kmers(groups.size());
     std::vector<uint64_t> sums(groups.size());
@@ -805,52 +843,128 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         ms.resize(0);
     }
 
-    auto correct_pvals = [&config,nelem](const std::vector<std::pair<double, size_t>> &m) {
-        size_t k = 0;
-        size_t n_cutoff = 1;
-        size_t acc = std::accumulate(m.begin(), m.end(), size_t(0),
-                                     [](size_t sum, const auto &a) { return sum + a.second; });
-        common::logger->trace("Calculating cutoffs for {}/{} tests", acc, nelem);
-        size_t last_k = 0;
-        for (size_t n = 0; n < m.size(); ++n) {
-            auto [pval_min, s] = m[n];
-            size_t cur_k = std::numeric_limits<size_t>::max();
-            if (pval_min >= config.family_wise_error_rate) {
-                cur_k = 0;
-            } else if (pval_min > 0) {
-                double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
-                if (lkd <= 64)
-                    cur_k = pow(2.0, lkd);
+    size_t k = 0;
+    size_t n_cutoff = 1;
 
-                if (cur_k == 0) {
-                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}", cur_k, lkd, pval_min);
-                    throw std::runtime_error("Min failed");
-                }
-            }
+    auto combine_pvals = [dist=boost::math::cauchy()](const std::vector<double> &pvals) {
+        double stat = 0.0;
+        for (double pval : pvals) {
+            stat += tan((0.5 - pval) * M_PI);
+        }
+        stat /= pvals.size();
+        return boost::math::cdf(boost::math::complement(dist, stat));
+    };
 
-            if (cur_k != last_k && acc <= cur_k) {
-                k = std::max(acc, last_k + 1);
-                n_cutoff = n;
-                common::logger->trace("Picked: k: {}\tn: {}\tpval_min: {}", k, n_cutoff, config.family_wise_error_rate / k);
-                break;
+    std::vector<size_t> boundaries;
+    if (config.test_by_unitig) {
+        boundaries.reserve(eff_size_buckets.size() + 1);
+        boundaries.emplace_back(0);
+        for (size_t i = 0; i < eff_size_buckets.size(); ++i) {
+            boundaries.emplace_back(boundaries.back() + eff_size_buckets[i].size());
+        }
+    }
+
+    std::mutex pval_mu;
+    std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
+    sdsl::bit_vector unitig_start;
+    if (!config.test_by_unitig) {
+        std::tie(k, n_cutoff) = correct_pvals(m, nelem);
+        common::logger->trace("Picked: k: {}\tn: {}\tpval_min: {}", k, n_cutoff, config.family_wise_error_rate / k);
+    } else {
+        clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
+            graph_ptr,
+            [&](node_index node) {
+                return node != DeBruijnGraph::npos
+                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
+            },
+            true,
+            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+        );
+
+        std::vector<std::pair<double, size_t>> comb_m;
+        common::logger->trace("Allocating updated indicator");
+        unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+
+        common::logger->trace("Combining min p-vals");
+        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+            std::vector<size_t> bucket_idxs;
+            bucket_idxs.reserve(path.size());
+            for (node_index node : path) {
+                bucket_idxs.emplace_back(0);
+                for (size_t i = 1; i < boundaries.size(); ++i) {
+                    if (boundaries[i] > node)
+                        break;
+
+                    ++bucket_idxs.back();
+                }
             }
 
-            last_k = cur_k;
-            acc -= s;
-        }
+            std::vector<double> pvals_min;
+            pvals_min.reserve(path.size());
 
-        if (k == 0)
-            common::logger->warn("No significant p-values achievable");
+            {
+                std::lock_guard<std::mutex> lock(pval_mu);
+                for (size_t i = 0; i < path.size(); ++i) {
+                    size_t bucket_idx = bucket_idxs[i];
+                    size_t n = sum_buckets[bucket_idx][path[i] - boundaries[bucket_idx]];
+                    pvals_min.emplace_back(m[n].first);
+                }
+            }
 
-        return std::make_pair(k, n_cutoff);
-    };
+            double comb_pval = combine_pvals(pvals_min);
+            if (comb_pval < config.family_wise_error_rate) {
+                for (node_index node : path) {
+                    set_bit(unitig_start.data(), node, parallel, MO_RELAXED);
+                }
+            }
 
-    size_t k = 0;
-    size_t n_cutoff = 1;
-    if (!config.test_by_unitig)
-        std::tie(k, n_cutoff) = correct_pvals(m);
+            std::lock_guard<std::mutex> lock(pval_mu);
+            comb_m.emplace_back(comb_pval, 1);
+        }, num_threads);
+
+        std::sort(comb_m.begin(), comb_m.end(), utils::GreaterFirst());
+        std::tie(k, n_cutoff) = correct_pvals(comb_m, nelem);
+        common::logger->trace("Picked: k: {}\tpval_min: {}", k, config.family_wise_error_rate / k);
+
+        // common::logger->trace("Marking testable unitigs");
+        // std::atomic_thread_fence(std::memory_order_release);
+        // clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+        //     std::vector<size_t> bucket_idxs;
+        //     bucket_idxs.reserve(path.size());
+        //     for (node_index node : path) {
+        //         bucket_idxs.emplace_back(0);
+        //         for (size_t i = 1; i < boundaries.size(); ++i) {
+        //             if (boundaries[i] > node)
+        //                 break;
+
+        //             ++bucket_idxs.back();
+        //         }
+        //     }
+
+        //     std::vector<double> pvals_min;
+        //     pvals_min.reserve(path.size());
+        //     {
+        //         std::lock_guard<std::mutex> lock(pval_mu);
+        //         for (size_t i = 0; i < path.size(); ++i) {
+        //             size_t bucket_idx = bucket_idxs[i];
+        //             size_t n = sum_buckets[bucket_idx][path[i] - boundaries[bucket_idx]];
+        //             pvals_min.emplace_back(m[n].first);
+        //         }
+        //     }
+
+        //     double comb_pval = combine_pvals(pvals_min);
+        //     if (comb_pval * k < config.family_wise_error_rate) {
+        //         for (node_index node : path) {
+        //             set_bit(unitig_start.data(), node, parallel, MO_RELAXED);
+        //         }
+        //     }
+        // }, num_threads);
+
+        sum_buckets.resize(0);
+        tmp_sum_buckets.resize(0);
+    }
 
-    if (k > 0 || config.test_by_unitig) {
+    if (k > 0) {
         common::logger->trace("Running differential tests");
         std::exception_ptr ex = nullptr;
         std::mutex ex_mu;
@@ -910,7 +1024,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 n = 0;
             }
 
-            if (keep_all_pvals || n >= n_cutoff) {
+            if (!config.output_pvals && config.test_by_unitig && !unitig_start[node]) {
+                auto &pvals = pvals_buckets[bucket_idx];
+                if constexpr(preallocated) {
+                    pvals[node] = bit_cast<uint64_t>(pval);
+                } else {
+                    pvals.push_back(bit_cast<uint64_t>(pval));
+                }
+            }
+
+            if (config.output_pvals
+                    || (!config.test_by_unitig && n >= n_cutoff)
+                    || (config.test_by_unitig && unitig_start[node])) {
                 if (m[n].first == 1.1) {
                     common::logger->error("in: {}\tout: {}\tn: {}", in_sum, out_sum, n);
                     throw std::runtime_error("Indexing invalid");
@@ -950,52 +1075,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (ex)
             std::rethrow_exception(ex);
 
+        unitig_start = sdsl::bit_vector();
         deallocate();
 
-        std::vector<size_t> boundaries;
-        boundaries.reserve(pvals_buckets.size() + 1);
-        boundaries.emplace_back(0);
-        for (size_t i = 0; i < pvals_buckets.size(); ++i) {
-            boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
+        if (!config.test_by_unitig) {
+            boundaries.reserve(pvals_buckets.size() + 1);
+            boundaries.emplace_back(0);
+            for (size_t i = 0; i < pvals_buckets.size(); ++i) {
+                boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
+            }
         }
 
         if (config.test_by_unitig) {
-            std::vector<std::pair<double, size_t>> comb_m;
-            std::mutex pval_mu;
-
             common::logger->trace("Allocating k-mer bitmasks");
             indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
             indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
 
             common::logger->trace("Combining p-values within unitigs");
-            MaskedDeBruijnGraph clean_masked_graph(
-                graph_ptr,
-                [&](node_index node) {
-                    return node != DeBruijnGraph::npos
-                            && kept[AnnotatedDBG::graph_to_anno_index(node)];
-                },
-                true,
-                is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-            );
-
-            auto dist = boost::math::cauchy();
             std::atomic<uint64_t> num_sig_unitigs{0};
 
-            sdsl::bit_vector unitig_start;
-            if (config.output_pvals) {
-                common::logger->trace("Allocating unitig start indicator");
-                unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-            }
-
             std::atomic_thread_fence(std::memory_order_release);
-            clean_masked_graph.call_unitigs([&](const std::string&, const auto &path) {
-                if (config.output_pvals)
-                    set_bit(unitig_start.data(), path[0], parallel, MO_RELAXED);
-
-                double stat = 0;
-                double stat_min = 0;
+            clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
                 int64_t comb_eff_size = 0;
-                size_t comb_n = 0;
                 std::vector<size_t> bucket_idxs;
                 bucket_idxs.reserve(path.size());
                 for (node_index node : path) {
@@ -1008,6 +1109,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 }
 
+                std::vector<double> pvals;
+                pvals.reserve(path.size());
                 {
                     std::lock_guard<std::mutex> lock(pval_mu);
                     for (size_t i = 0; i < path.size(); ++i) {
@@ -1017,20 +1120,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                         comb_eff_size += bit_cast<int64_t, uint64_t>(eff_size_buckets[bucket_idx][node_shift]);
 
-                        size_t n = sum_buckets[bucket_idx][node_shift];
-                        comb_n += n;
-
-                        double pval_min = m[n].first;
-                        stat_min += tan((0.5 - pval_min) * M_PI);
-
                         double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node_shift]);
-                        stat += tan((0.5 - pval) * M_PI);
+                        pvals.emplace_back(pval);
                     }
                 }
-                stat /= path.size();
-                stat_min /= path.size();
-                double comb_pval = boost::math::cdf(boost::math::complement(dist, stat));
-                if (comb_pval < config.family_wise_error_rate && comb_eff_size != 0) {
+                double comb_pval = combine_pvals(pvals);
+                if (comb_pval * k < config.family_wise_error_rate && comb_eff_size != 0) {
                     ++num_sig_unitigs;
                     auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
                     for (node_index node : path) {
@@ -1038,26 +1133,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 }
 
-                double comb_pval_min = boost::math::cdf(boost::math::complement(dist, stat_min));
-
-                uint64_t k = std::numeric_limits<uint64_t>::max();
-                if (comb_pval_min >= config.family_wise_error_rate) {
-                    k = 0;
-                } else if (comb_pval_min > 0) {
-                    double lkd = log2(config.family_wise_error_rate) - log2(comb_pval_min);
-                    if (lkd <= 64)
-                        k = pow(2.0, lkd);
-
-                    if (k == 0) {
-                        common::logger->error("k: {}\tlog k: {}\tpval_min: {}\tpval: {}", k, lkd, comb_pval_min, comb_pval);
-                        throw std::runtime_error("Min failed");
-                    }
-                }
-
                 uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
 
                 std::lock_guard<std::mutex> lock(pval_mu);
-                comb_m.emplace_back(comb_pval, 1);
                 for (size_t i = 0; i < path.size(); ++i) {
                     size_t bucket_idx = bucket_idxs[i];
                     pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = comb_pval_enc;
@@ -1068,40 +1146,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             eff_size_buckets.resize(0);
             tmp_eff_buckets.resize(0);
-            sum_buckets.resize(0);
-            tmp_sum_buckets.resize(0);
-
-            if (num_sig_unitigs) {
-                std::sort(comb_m.begin(), comb_m.end(), utils::GreaterFirst());
-                std::tie(k, n_cutoff) = correct_pvals(comb_m);
-
-                auto b_it = boundaries.begin();
-                call_ones(indicator_in, [&](node_index node) {
-                    for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
-                    --b_it;
-                    size_t bucket_idx = b_it - boundaries.begin();
-                    size_t node_idx = node - *b_it;
-                    double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node_idx]);
-                    if (pval * k >= config.family_wise_error_rate)
-                        unset_bit(indicator_in.data(), node, parallel, MO_RELAXED);
-
-                    if (config.output_pvals && !unitig_start[node])
-                        pvals_buckets[bucket_idx][node_idx] = nullpval;
-                });
-                b_it = boundaries.begin();
-                call_ones(indicator_out, [&](node_index node) {
-                    for ( ; b_it != boundaries.end() && *b_it <= node; ++b_it) {}
-                    --b_it;
-                    size_t bucket_idx = b_it - boundaries.begin();
-                    size_t node_idx = node - *b_it;
-                    double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node_idx]);
-                    if (pval * k >= config.family_wise_error_rate)
-                        unset_bit(indicator_out.data(), node, parallel, MO_RELAXED);
-
-                    if (config.output_pvals && !unitig_start[node])
-                        pvals_buckets[bucket_idx][node_idx] = nullpval;
-                });
-            }
         }
     } else {
         deallocate();

From 48a97db63e52ed916bbd791a1dbb25ea22e5052c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 9 Aug 2024 15:01:34 +0200
Subject: [PATCH 270/410] cleanup

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 807762194d..e21c571668 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -621,13 +621,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
         int64_t total_sum = in_sum + out_sum;
 
-        auto get_deviance1 = [mu=mu1+1e-8,invphi=r_in,logmuinvphi=log(mu1+1e-8+r_in),logmu=log(mu1+1e-8)](double y) {
-            y += 1e-8;
+        auto get_deviance1 = [mu=mu1,invphi=r_in,logmuinvphi=log(mu1+r_in),logmu=log(mu1),zv=2.0*r_in*log((r_in*mu1)/r_in)](double y) {
+            if (y == 0)
+                return zv;
+
             double logyshift = logmuinvphi - log(y + invphi);
             return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
         };
-        auto get_deviance2 = [mu=mu2+1e-8,invphi=r_out,logmuinvphi=log(mu2+1e-8+r_out),logmu=log(mu2+1e-8)](double y) {
-            y += 1e-8;
+        auto get_deviance2 = [mu=mu2,invphi=r_out,logmuinvphi=log(mu2+r_out),logmu=log(mu2),zv=2.0*r_out*log((r_out*mu2)/r_out)](double y) {
+            if (y == 0)
+                return zv;
+
             double logyshift = logmuinvphi - log(y + invphi);
             return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
         };

From 125005583eba8a7c269c0c81cfc00263e218a96c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 13 Aug 2024 19:26:22 +0200
Subject: [PATCH 271/410] first impl of zinb

---
 .../src/graph/annotated_graph_algorithm.cpp   | 440 ++++++++++++------
 1 file changed, 307 insertions(+), 133 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index e21c571668..19e794b6a3 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -11,6 +11,7 @@
 #include <boost/math/distributions/binomial.hpp>
 #include <boost/math/distributions/negative_binomial.hpp>
 #include <boost/math/tools/roots.hpp>
+#include <boost/math/tools/minima.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
 #include <boost/math/distributions/cauchy.hpp>
@@ -120,6 +121,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         size_t n_cutoff = 1;
         size_t acc = std::accumulate(m.begin(), m.end(), size_t(0),
                                      [](size_t sum, const auto &a) { return sum + a.second; });
+        size_t total = acc;
         common::logger->trace("Calculating cutoffs for {}/{} tests", acc, nelem);
         size_t last_k = 0;
         for (size_t n = 0; n < m.size(); ++n) {
@@ -151,6 +153,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (k == 0)
             common::logger->warn("No significant p-values achievable");
 
+        common::logger->trace("Kept {} / {}", acc, total);
         return std::make_pair(k, n_cutoff);
     };
 
@@ -396,7 +399,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
         };
-    } else if (config.test_type == "nbinom_exact") {
+    } else if (config.test_type == "nbinom_exact" || config.test_type == "zinb_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
         auto get_rp = [&](size_t j, auto begin, auto end) {
             double mu = 0;
@@ -621,135 +624,340 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
         int64_t total_sum = in_sum + out_sum;
 
-        auto get_deviance1 = [mu=mu1,invphi=r_in,logmuinvphi=log(mu1+r_in),logmu=log(mu1),zv=2.0*r_in*log((r_in*mu1)/r_in)](double y) {
-            if (y == 0)
-                return zv;
+        if (config.test_type == "nbinom_exact") {
+            auto get_deviance1 = [mu=mu1,invphi=r_in,logmuinvphi=log(mu1+r_in),logmu=log(mu1),zv=2.0*r_in*log((r_in*mu1)/r_in)](double y) {
+                if (y == 0)
+                    return zv;
 
-            double logyshift = logmuinvphi - log(y + invphi);
-            return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
-        };
-        auto get_deviance2 = [mu=mu2,invphi=r_out,logmuinvphi=log(mu2+r_out),logmu=log(mu2),zv=2.0*r_out*log((r_out*mu2)/r_out)](double y) {
-            if (y == 0)
-                return zv;
+                double logyshift = logmuinvphi - log(y + invphi);
+                return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
+            };
+            auto get_deviance2 = [mu=mu2,invphi=r_out,logmuinvphi=log(mu2+r_out),logmu=log(mu2),zv=2.0*r_out*log((r_out*mu2)/r_out)](double y) {
+                if (y == 0)
+                    return zv;
 
-            double logyshift = logmuinvphi - log(y + invphi);
-            return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
-        };
+                double logyshift = logmuinvphi - log(y + invphi);
+                return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
+            };
 
-        std::vector<double> deviances1(total_sum + 1);
-        std::vector<double> deviances2(total_sum + 1);
-        for (int64_t s = 0; s <= total_sum; ++s) {
-            deviances1[s] = get_deviance1(s);
-            deviances2[s] = get_deviance2(s);
-        }
+            std::vector<double> deviances1(total_sum + 1);
+            std::vector<double> deviances2(total_sum + 1);
+            for (int64_t s = 0; s <= total_sum; ++s) {
+                deviances1[s] = get_deviance1(s);
+                deviances2[s] = get_deviance2(s);
+            }
+            compute_min_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t n) {
+                if (n == 0)
+                    return 1.0;
 
-        compute_min_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t n) {
-            if (n == 0)
-                return 1.0;
+                double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
+                std::vector<double> devs(n + 1);
 
-            double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
-            std::vector<double> devs(n + 1);
+                for (int64_t s = 0; s <= n; ++s) {
+                    devs[s] = deviances1[s] + deviances2[n - s];
+                }
 
-            for (int64_t s = 0; s <= n; ++s) {
-                devs[s] = deviances1[s] + deviances2[n - s];
-            }
+                double max_d = *std::max_element(devs.begin(), devs.end());
+                double pval = 0.0;
+                int64_t s = 0;
+                if (devs[s] == max_d) {
+                    int64_t t = n;
+                    double rs = r_in;
+                    double rt = r_out + n;
+                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    pval += exp(base);
+                    for (++s; s <= n; ++s) {
+                        if (devs[s] == max_d) {
+                            --t;
+                            ++rs;
+                            --rt;
+                            base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                            pval += exp(base);
+                        } else {
+                            break;
+                        }
+                    }
+                }
 
-            double max_d = *std::max_element(devs.begin(), devs.end());
-            double pval = 0.0;
-            int64_t s = 0;
-            if (devs[s] == max_d) {
+                int64_t sp = n;
+                if (devs[sp] == max_d) {
+                    int64_t t = 0;
+                    double rs = r_in + sp;
+                    double rt = r_out;
+                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    pval += exp(base);
+                    for (--sp; sp >= s; --sp) {
+                        if (devs[sp] == max_d) {
+                            ++t;
+                            --rs;
+                            ++rt;
+                            base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                            pval += exp(base);
+                        } else {
+                            break;
+                        }
+                    }
+                }
+
+                return std::min(1.0, pval);
+            };
+
+            compute_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t in_sum, int64_t out_sum, const auto &row) {
+                if (row.empty())
+                    return 1.0;
+
+                int64_t n = in_sum + out_sum;
+
+                double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
+
+                double in_sum_total_deviance = deviances1[in_sum] + deviances2[out_sum];
+
+                double pval = 0.0;
+                int64_t s = 0;
                 int64_t t = n;
-                double rs = r_in;
-                double rt = r_out + n;
-                double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
-                pval += exp(base);
-                for (++s; s <= n; ++s) {
-                    if (devs[s] == max_d) {
-                        --t;
-                        ++rs;
-                        --rt;
-                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
-                        pval += exp(base);
-                    } else {
-                        break;
+                if (deviances1[s] + deviances2[t] >= in_sum_total_deviance) {
+                    double rs = r_in;
+                    double rt = r_out + n;
+                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    pval += exp(base);
+                    for (++s; s <= n; ++s) {
+                        if (deviances1[s] + deviances2[--t] >= in_sum_total_deviance) {
+                            ++rs;
+                            --rt;
+                            base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                            pval += exp(base);
+                        } else {
+                            break;
+                        }
                     }
                 }
-            }
 
-            int64_t sp = n;
-            if (devs[sp] == max_d) {
-                int64_t t = 0;
-                double rs = r_in + sp;
-                double rt = r_out;
-                double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
-                pval += exp(base);
-                for (--sp; sp >= s; --sp) {
-                    if (devs[sp] == max_d) {
-                        ++t;
-                        --rs;
-                        ++rt;
-                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
-                        pval += exp(base);
+                int64_t sp = n;
+                t = 0;
+                if (deviances1[sp] + deviances2[t] >= in_sum_total_deviance) {
+                    double rs = r_in + sp;
+                    double rt = r_out;
+                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    pval += exp(base);
+                    for (--sp; sp >= s; --sp) {
+                        if (deviances1[sp] + deviances2[++t] >= in_sum_total_deviance) {
+                            --rs;
+                            ++rt;
+                            base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                            pval += exp(base);
+                        } else {
+                            break;
+                        }
+                    }
+                }
+
+                return std::min(1.0, pval);
+            };
+        } else {
+            common::logger->trace("Computing merged histograms");
+            std::mutex hist_mu;
+            tsl::hopscotch_map<uint64_t, size_t> merged_hist_in;
+            tsl::hopscotch_map<uint64_t, size_t> merged_hist_out;
+            tsl::hopscotch_map<uint64_t, size_t> merged_hist_null;
+            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t) {
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                bool in_kmer = false;
+                bool out_kmer = false;
+                if (kept[row_i]) {
+                    size_t count_in = 0;
+                    size_t count_out = 0;
+                    for (const auto &[j, raw_c] : raw_row) {
+                        uint64_t c = raw_c;
+                        if (count_maps.size())
+                            c = count_maps[j].find(c)->second.first;
+
+                        if (groups[j]) {
+                            out_sum += c;
+                            ++count_out;
+                        } else {
+                            in_sum += c;
+                            ++count_in;
+                        }
+                    }
+
+                    if (count_in + count_out >= config.min_recurrence) {
+                        double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+                        in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+                        out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+                    }
+                } else {
+                    return;
+                }
+
+                if (!in_kmer && !out_kmer)
+                    return;
+
+                size_t n = in_sum + out_sum;
+                std::lock_guard<std::mutex> lock(hist_mu);
+                ++merged_hist_in[in_sum];
+                ++merged_hist_out[out_sum];
+                ++merged_hist_null[n];
+            }, false);
+
+            auto get_rp_zinb = [&](const tsl::hopscotch_map<uint64_t, size_t> &hist, double r_guess) {
+                size_t total_nzeros = 0;
+                size_t total_zeros = 0;
+                size_t sum = 0;
+                for (const auto &[k, c] : hist) {
+                    if (k != 0) {
+                        sum += k * c;
+                        total_nzeros += c;
                     } else {
-                        break;
+                        total_zeros += c;
                     }
                 }
+                size_t total = total_nzeros + total_zeros;
+
+                auto get_p = [&](double r) {
+                    return boost::math::tools::newton_raphson_iterate([&](double p) {
+                        double dl = r * total_nzeros * (1.0 - p) - p * (1 - pow(p, r)) * sum;
+                        double ddl = (r + 1) * pow(p, r) * sum - r * total_nzeros - sum;
+                        return std::make_pair(dl, ddl);
+                    }, 0.5, 0.0, 1.0, 30);
+                };
+
+                double r = boost::math::tools::newton_raphson_iterate([&](double r) {
+                    double p = get_p(r);
+                    double dl = (log(p) / (1.0 - pow(p, r)) - boost::math::digamma(r)) * total_nzeros;
+                    double ddl = (log(p) * r / pow(1.0 - pow(p, r), 2.0) * pow(p, r - 1.0) - boost::math::trigamma(r)) * total_nzeros;
+                    for (const auto &[k, c] : hist) {
+                        if (k != 0) {
+                            dl += boost::math::digamma(r + k) * c;
+                            ddl += boost::math::trigamma(r + k) * c;
+                        }
+                    }
+
+                    return std::make_pair(dl, ddl);
+                }, r_guess, std::numeric_limits<double>::min(), static_cast<double>(sum), 30);
+
+                double p = get_p(r);
+                double mu = static_cast<double>(sum) / total;
+
+                double pi = 1.0 - (mu * p / r / (1.0 - p));
+
+                size_t exp_extra_zeros = total * pi;
+                size_t exp_zeros = total * (1.0 - pi) * pow(p, r) + exp_extra_zeros;
+                common::logger->trace("r: {}\tp: {}\tpi: {}\tobs_total: {}\tobs_zeros: {}\texp_zeros: {}\texp_extra_zeros: {}\texp_nb_zeros: {}",
+                                      r, p, pi,
+                                      total, total_zeros,
+                                      exp_zeros, exp_extra_zeros, exp_zeros - exp_extra_zeros);
+
+                return std::make_tuple(r, p, pi);
+            };
+
+            auto [r_in, p_in, pi_in] = get_rp_zinb(merged_hist_in, r_map * num_labels_in);
+            auto [r_out, p_out, pi_out] = get_rp_zinb(merged_hist_out, r_map * num_labels_out);
+            auto [r_null, p_null, pi_null] = get_rp_zinb(merged_hist_null, r_map * (num_labels_in + num_labels_out));
+
+            double mu1 = r_in * (1.0 - p_in) / p_in * (1.0 - pi_in);
+            double mu2 = r_out * (1.0 - p_out) / p_out * (1.0 - pi_out);
+
+            auto get_zv = [](double r, double mu, double pi) {
+                return -2 * log(pi + (1.0 - pi) * pow((1.0-pi)*r/(mu + (1.0-pi)*r), r));
+            };
+
+            auto get_deviance1 = [r=r_in,mu=mu1,mpir=(1.0-pi_in)*r_in,zv=get_zv(r_in,mu1,pi_in)](double y) {
+                if (y == 0)
+                    return zv;
+
+                return 2.0 * ((r + y) * log((mu + mpir)/(y + mpir)) + y * log(y / mu));
+            };
+            auto get_deviance2 = [r=r_out,mu=mu2,mpir=(1.0-pi_out)*r_out,zv=get_zv(r_out,mu2,pi_out)](double y) {
+                if (y == 0)
+                    return zv;
+
+                return 2.0 * ((r + y) * log((mu + mpir)/(y + mpir)) + y * log(y / mu));
+            };
+
+            std::vector<double> deviances1(total_sum + 1);
+            std::vector<double> deviances2(total_sum + 1);
+            for (int64_t s = 0; s <= total_sum; ++s) {
+                deviances1[s] = get_deviance1(s);
+                deviances2[s] = get_deviance2(s);
             }
 
-            return std::min(1.0, pval);
-        };
+            auto get_lprob_zinb = [&](int64_t s, double r, double p, double pi) {
+                return s == 0
+                    ? log(pi + (1.0 - pi) * pow(p, r))
+                    : log1p(-pi) + lgamma(r + s) - lgamma(r) - lgamma(s + 1) + r * log(p) + log1p(-p) * s;
+            };
 
-        compute_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t in_sum, int64_t out_sum, const auto &row) {
-            if (row.empty())
-                return 1.0;
+            auto get_lprob_ztnb = [&](int64_t s, double r, double p) {
+                return lgamma(r + s) - lgamma(r) - lgamma(s + 1) + r * log(p) + log1p(-p) * s - log1p(pow(p, r));
+            };
 
-            int64_t n = in_sum + out_sum;
+            compute_min_pval = [r_in,p_in,pi_in,r_out,p_out,pi_out,r_null,p_null,get_lprob_zinb,get_lprob_ztnb,deviances1,deviances2](int64_t n) {
+                if (n == 0)
+                    return 1.0;
 
-            double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
+                std::vector<double> devs(n + 1);
 
-            double in_sum_total_deviance = deviances1[in_sum] + deviances2[out_sum];
+                for (int64_t s = 0; s <= n; ++s) {
+                    devs[s] = deviances1[s] + deviances2[n - s];
+                }
 
-            double pval = 0.0;
-            int64_t s = 0;
-            int64_t t = n;
-            if (deviances1[s] + deviances2[t] >= in_sum_total_deviance) {
-                double rs = r_in;
-                double rt = r_out + n;
-                double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
-                pval += exp(base);
-                for (++s; s <= n; ++s) {
-                    if (deviances1[s] + deviances2[--t] >= in_sum_total_deviance) {
-                        ++rs;
-                        --rt;
-                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
-                        pval += exp(base);
+                double max_d = *std::max_element(devs.begin(), devs.end());
+                double pval = 0.0;
+                double base_lprob = get_lprob_ztnb(n, r_null, p_null);
+                for (int64_t s = 0; s <= n; ++s) {
+                    if (devs[s] == max_d)
+                        pval += exp(get_lprob_zinb(s, r_in, p_in, pi_in) + get_lprob_zinb(n - s, r_out, p_out, pi_out) - base_lprob);
+                }
+
+                if (pval > 1.0) {
+                    common::logger->error("fail: {}", pval);
+                    throw std::runtime_error("FOO");
+                }
+
+                return pval;
+            };
+
+            compute_pval = [r_in,p_in,pi_in,r_out,p_out,pi_out,r_null,p_null,get_lprob_zinb,get_lprob_ztnb,deviances1,deviances2](int64_t in_sum, int64_t out_sum, const auto &) {
+                int64_t n = in_sum + out_sum;
+
+                if (n == 0)
+                    return 1.0;
+
+                std::vector<double> devs(n + 1);
+
+                for (int64_t s = 0; s <= n; ++s) {
+                    devs[s] = deviances1[s] + deviances2[n - s];
+                }
+
+                double target_d = devs[in_sum];
+                double pval = 0.0;
+                double base_lprob = get_lprob_ztnb(n, r_null, p_null);
+                int64_t s = 0;
+                for ( ; s <= n; ++s) {
+                    if (devs[s] >= target_d) {
+                        pval += exp(get_lprob_zinb(s, r_in, p_in, pi_in) + get_lprob_zinb(n - s, r_out, p_out, pi_out) - base_lprob);
                     } else {
                         break;
                     }
                 }
-            }
 
-            int64_t sp = n;
-            t = 0;
-            if (deviances1[sp] + deviances2[t] >= in_sum_total_deviance) {
-                double rs = r_in + sp;
-                double rt = r_out;
-                double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
-                pval += exp(base);
-                for (--sp; sp >= s; --sp) {
-                    if (deviances1[sp] + deviances2[++t] >= in_sum_total_deviance) {
-                        --rs;
-                        ++rt;
-                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
-                        pval += exp(base);
+                for (int64_t ns = n; ns > s; --ns) {
+                    if (devs[ns] >= target_d) {
+                        pval += exp(get_lprob_zinb(ns, r_in, p_in, pi_in) + get_lprob_zinb(n - ns, r_out, p_out, pi_out) - base_lprob);
                     } else {
                         break;
                     }
                 }
-            }
 
-            return std::min(1.0, pval);
-        };
+                if (pval > 1.0) {
+                    common::logger->error("fail: {}", pval);
+                    throw std::runtime_error("FOO");
+                }
+
+                return pval;
+            };
+        }
     } else if (config.test_type == "notest") {
         compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
         compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };
@@ -930,40 +1138,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::tie(k, n_cutoff) = correct_pvals(comb_m, nelem);
         common::logger->trace("Picked: k: {}\tpval_min: {}", k, config.family_wise_error_rate / k);
 
-        // common::logger->trace("Marking testable unitigs");
-        // std::atomic_thread_fence(std::memory_order_release);
-        // clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-        //     std::vector<size_t> bucket_idxs;
-        //     bucket_idxs.reserve(path.size());
-        //     for (node_index node : path) {
-        //         bucket_idxs.emplace_back(0);
-        //         for (size_t i = 1; i < boundaries.size(); ++i) {
-        //             if (boundaries[i] > node)
-        //                 break;
-
-        //             ++bucket_idxs.back();
-        //         }
-        //     }
-
-        //     std::vector<double> pvals_min;
-        //     pvals_min.reserve(path.size());
-        //     {
-        //         std::lock_guard<std::mutex> lock(pval_mu);
-        //         for (size_t i = 0; i < path.size(); ++i) {
-        //             size_t bucket_idx = bucket_idxs[i];
-        //             size_t n = sum_buckets[bucket_idx][path[i] - boundaries[bucket_idx]];
-        //             pvals_min.emplace_back(m[n].first);
-        //         }
-        //     }
-
-        //     double comb_pval = combine_pvals(pvals_min);
-        //     if (comb_pval * k < config.family_wise_error_rate) {
-        //         for (node_index node : path) {
-        //             set_bit(unitig_start.data(), node, parallel, MO_RELAXED);
-        //         }
-        //     }
-        // }, num_threads);
-
         sum_buckets.resize(0);
         tmp_sum_buckets.resize(0);
     }

From 39665dd48388969f14fe77c2aa02fb2814733d72 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 14 Aug 2024 14:05:09 +0200
Subject: [PATCH 272/410] zinb

---
 .../src/graph/annotated_graph_algorithm.cpp   | 118 ++++++++++--------
 1 file changed, 67 insertions(+), 51 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 19e794b6a3..f0df098cef 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -800,63 +800,70 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ++merged_hist_null[n];
             }, false);
 
-            auto get_rp_zinb = [&](const tsl::hopscotch_map<uint64_t, size_t> &hist, double r_guess) {
-                size_t total_nzeros = 0;
-                size_t total_zeros = 0;
-                size_t sum = 0;
-                for (const auto &[k, c] : hist) {
-                    if (k != 0) {
-                        sum += k * c;
-                        total_nzeros += c;
-                    } else {
-                        total_zeros += c;
+            auto get_rp_zinb = [&](const tsl::hopscotch_map<uint64_t, size_t> &hist_in,
+                                   const tsl::hopscotch_map<uint64_t, size_t> &hist_out) {
+                auto get_stats = [&](const auto &hist) {
+                    size_t total_nzeros = 0;
+                    size_t total_zeros = 0;
+                    size_t sum = 0;
+                    for (const auto &[k, c] : hist) {
+                        if (k != 0) {
+                            sum += k * c;
+                            total_nzeros += c;
+                        } else {
+                            total_zeros += c;
+                        }
                     }
-                }
-                size_t total = total_nzeros + total_zeros;
+                    size_t total = total_nzeros + total_zeros;
 
-                auto get_p = [&](double r) {
-                    return boost::math::tools::newton_raphson_iterate([&](double p) {
-                        double dl = r * total_nzeros * (1.0 - p) - p * (1 - pow(p, r)) * sum;
-                        double ddl = (r + 1) * pow(p, r) * sum - r * total_nzeros - sum;
-                        return std::make_pair(dl, ddl);
-                    }, 0.5, 0.0, 1.0, 30);
+                    return std::make_tuple(total_nzeros, total_zeros, sum, total);
                 };
 
-                double r = boost::math::tools::newton_raphson_iterate([&](double r) {
-                    double p = get_p(r);
-                    double dl = (log(p) / (1.0 - pow(p, r)) - boost::math::digamma(r)) * total_nzeros;
-                    double ddl = (log(p) * r / pow(1.0 - pow(p, r), 2.0) * pow(p, r - 1.0) - boost::math::trigamma(r)) * total_nzeros;
-                    for (const auto &[k, c] : hist) {
-                        if (k != 0) {
-                            dl += boost::math::digamma(r + k) * c;
-                            ddl += boost::math::trigamma(r + k) * c;
+                auto [total_nzeros_in, total_zeros_in, sum_in, total_in] = get_stats(hist_in);
+                auto [total_nzeros_out, total_zeros_out, sum_out, total_out] = get_stats(hist_out);
+
+                auto get_r = [&](const auto &hist, size_t total, double total_nzeros, double p, double r_guess, double sum) {
+                    return boost::math::tools::newton_raphson_iterate([&](double r) {
+                        double dl = (log(p) - boost::math::digamma(r)) * total_nzeros + log(total_nzeros / total) * (total - total_nzeros);
+                        double ddl = -boost::math::trigamma(r) * total_nzeros;
+                        for (const auto &[k, c] : hist) {
+                            if (k != 0) {
+                                dl += boost::math::digamma(r + k) * c;
+                                ddl += boost::math::trigamma(r + k) * c;
+                            }
                         }
-                    }
 
-                    return std::make_pair(dl, ddl);
-                }, r_guess, std::numeric_limits<double>::min(), static_cast<double>(sum), 30);
+                        return std::make_pair(dl, ddl);
+                    }, r_guess, std::numeric_limits<double>::min(), sum, 30);
+                };
 
-                double p = get_p(r);
-                double mu = static_cast<double>(sum) / total;
+                double p = boost::math::tools::newton_raphson_iterate([&](double p) {
+                    auto r_in = get_r(hist_in, total_in, total_nzeros_in, p, r_map * num_labels_in, sum_in);
+                    auto r_out = get_r(hist_out, total_out, total_nzeros_out, p, r_map * num_labels_out, sum_out);
 
-                double pi = 1.0 - (mu * p / r / (1.0 - p));
+                    double dl_in = r_in * total_nzeros_in / p / (1.0 - pow(p, r_in)) - sum_in / (1.0 - p);
+                    double dl_out = r_out * total_nzeros_out / p / (1.0 - pow(p, r_out)) - sum_out / (1.0 - p);
+                    double ddl_in = -r_in * total_nzeros_in / p / p / (1.0 - pow(p, r_in)) / (1.0 - pow(p, r_in)) * ((1.0-pow(p, r_in)) - p * r_in * pow(p, r_in-1)) - static_cast<double>(sum_in) / (1.0-p) / (1.0-p);
+                    double ddl_out = -r_out * total_nzeros_out / p / p / (1.0 - pow(p, r_out)) / (1.0 - pow(p, r_out)) * ((1.0-pow(p, r_out)) - p * r_out * pow(p, r_out-1)) - static_cast<double>(sum_out) / (1.0-p) / (1.0-p);
+                    return std::make_pair(dl_in + dl_out, ddl_in + ddl_out);
+                }, target_p, 0.0, 1.0, 30);
 
-                size_t exp_extra_zeros = total * pi;
-                size_t exp_zeros = total * (1.0 - pi) * pow(p, r) + exp_extra_zeros;
-                common::logger->trace("r: {}\tp: {}\tpi: {}\tobs_total: {}\tobs_zeros: {}\texp_zeros: {}\texp_extra_zeros: {}\texp_nb_zeros: {}",
-                                      r, p, pi,
-                                      total, total_zeros,
-                                      exp_zeros, exp_extra_zeros, exp_zeros - exp_extra_zeros);
+                double r_in = get_r(hist_in, total_in, total_nzeros_in, p, r_map * num_labels_in, sum_in);
+                double r_out = get_r(hist_out, total_out, total_nzeros_out, p, r_map * num_labels_out, sum_out);
 
-                return std::make_tuple(r, p, pi);
+                double pi_in = (static_cast<double>(total_in - total_nzeros_in) / total_in - pow(p, r_in)) / (1.0 - pow(p, r_in));
+                double pi_out = (static_cast<double>(total_out - total_nzeros_out) / total_out - pow(p, r_out)) / (1.0 - pow(p, r_out));
+
+                return std::make_tuple(r_in, pi_in, r_out, pi_out, p);
             };
 
-            auto [r_in, p_in, pi_in] = get_rp_zinb(merged_hist_in, r_map * num_labels_in);
-            auto [r_out, p_out, pi_out] = get_rp_zinb(merged_hist_out, r_map * num_labels_out);
-            auto [r_null, p_null, pi_null] = get_rp_zinb(merged_hist_null, r_map * (num_labels_in + num_labels_out));
+            auto [r_in, pi_in, r_out, pi_out, p] = get_rp_zinb(merged_hist_in, merged_hist_in);
+
+            common::logger->trace("r: ({}, {})\tp: {}\tpi: ({}, {})",
+                                  r_in, r_out, p, pi_in, pi_out);
 
-            double mu1 = r_in * (1.0 - p_in) / p_in * (1.0 - pi_in);
-            double mu2 = r_out * (1.0 - p_out) / p_out * (1.0 - pi_out);
+            double mu1 = r_in * (1.0 - p) / p * (1.0 - pi_in);
+            double mu2 = r_out * (1.0 - p) / p * (1.0 - pi_out);
 
             auto get_zv = [](double r, double mu, double pi) {
                 return -2 * log(pi + (1.0 - pi) * pow((1.0-pi)*r/(mu + (1.0-pi)*r), r));
@@ -882,17 +889,26 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 deviances2[s] = get_deviance2(s);
             }
 
-            auto get_lprob_zinb = [&](int64_t s, double r, double p, double pi) {
+            auto get_lprob_zinb = [](int64_t s, double r, double p, double pi) {
                 return s == 0
                     ? log(pi + (1.0 - pi) * pow(p, r))
                     : log1p(-pi) + lgamma(r + s) - lgamma(r) - lgamma(s + 1) + r * log(p) + log1p(-p) * s;
             };
 
-            auto get_lprob_ztnb = [&](int64_t s, double r, double p) {
-                return lgamma(r + s) - lgamma(r) - lgamma(s + 1) + r * log(p) + log1p(-p) * s - log1p(pow(p, r));
+            auto get_lprob_ztnb = [get_lprob_zinb](int64_t s, double r_in, double r_out, double p, double pi_in, double pi_out) {
+                double pval = 0.0;
+                for (int64_t ss = 0; ss <= s; ++ss) {
+                    int64_t tt = s - ss;
+                    pval += exp(get_lprob_zinb(ss, r_in, p, pi_in) + get_lprob_zinb(tt, r_out, p, pi_out));
+                }
+                return log(pval);
+                // double lp1 = get_lprob_zinb(0, r_in, p, pi_in) + get_lprob_zinb(s, r_out, p, pi_out);
+                // double lp2 = get_lprob_zinb(s, r_in, p, pi_in) + get_lprob_zinb(0, r_out, p, pi_out);
+                // double lp3 = log1p(-pi_in) + log1p(-pi_out) + get_lprob_zinb(s, r_in + r_out, p, 0.0);
+                // return log(exp(lp1) + exp(lp2) + exp(lp3));
             };
 
-            compute_min_pval = [r_in,p_in,pi_in,r_out,p_out,pi_out,r_null,p_null,get_lprob_zinb,get_lprob_ztnb,deviances1,deviances2](int64_t n) {
+            compute_min_pval = [r_in,p_in=p,pi_in,r_out,p_out=p,pi_out,get_lprob_zinb,get_lprob_ztnb,deviances1,deviances2](int64_t n) {
                 if (n == 0)
                     return 1.0;
 
@@ -904,7 +920,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 double max_d = *std::max_element(devs.begin(), devs.end());
                 double pval = 0.0;
-                double base_lprob = get_lprob_ztnb(n, r_null, p_null);
+                double base_lprob = get_lprob_ztnb(n, r_in, r_out, p_in, pi_in, pi_out);
                 for (int64_t s = 0; s <= n; ++s) {
                     if (devs[s] == max_d)
                         pval += exp(get_lprob_zinb(s, r_in, p_in, pi_in) + get_lprob_zinb(n - s, r_out, p_out, pi_out) - base_lprob);
@@ -918,7 +934,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return pval;
             };
 
-            compute_pval = [r_in,p_in,pi_in,r_out,p_out,pi_out,r_null,p_null,get_lprob_zinb,get_lprob_ztnb,deviances1,deviances2](int64_t in_sum, int64_t out_sum, const auto &) {
+            compute_pval = [r_in,p_in=p,pi_in,r_out,p_out=p,pi_out,get_lprob_zinb,get_lprob_ztnb,deviances1,deviances2](int64_t in_sum, int64_t out_sum, const auto &) {
                 int64_t n = in_sum + out_sum;
 
                 if (n == 0)
@@ -932,7 +948,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 double target_d = devs[in_sum];
                 double pval = 0.0;
-                double base_lprob = get_lprob_ztnb(n, r_null, p_null);
+                double base_lprob = get_lprob_ztnb(n, r_in, r_out, p_in, pi_in, pi_out);
                 int64_t s = 0;
                 for ( ; s <= n; ++s) {
                     if (devs[s] >= target_d) {

From 9bfb6e767553c04299012abd544044a16f3e5243 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 15 Aug 2024 11:55:32 +0200
Subject: [PATCH 273/410] use em algo

---
 .../src/graph/annotated_graph_algorithm.cpp   | 304 +++++++++++++++++-
 1 file changed, 294 insertions(+), 10 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index f0df098cef..91c333859b 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -273,7 +273,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
 
     std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
-    std::function<double(int64_t)> compute_min_pval;
+    std::function<double(int64_t, const PairContainer&)> compute_min_pval;
 
     std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps;
 
@@ -288,7 +288,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         compute_min_pval = [&,p=static_cast<double>(in_kmers)/total_kmers,
                               mu1=static_cast<double>(in_kmers)/nelem,
                               mu2=static_cast<double>(out_kmers)/nelem,
-                              get_deviance](int64_t n) {
+                              get_deviance](int64_t n, const PairContainer&) {
             if (n == 0)
                 return 1.0;
 
@@ -361,7 +361,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     } else if (config.test_type == "fisher") {
         double lbase_shared = lgamma(in_kmers + 1) + lgamma(out_kmers + 1) - lgamma(total_kmers + 1);
 
-        compute_min_pval = [&,lbase_shared](int64_t m1) {
+        compute_min_pval = [&,lbase_shared](int64_t m1, const PairContainer&) {
             if (m1 == 0)
                 return 1.0;
 
@@ -399,7 +399,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
         };
-    } else if (config.test_type == "nbinom_exact" || config.test_type == "zinb_exact") {
+    } else if (config.test_type == "nbinom_exact" || config.test_type == "zinb_exact" || config.test_type == "gnb_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
         auto get_rp = [&](size_t j, auto begin, auto end) {
             double mu = 0;
@@ -529,6 +529,26 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             throw std::domain_error("Fit failed");
         }
 
+        double p_min = std::min(target_mu / target_var, double(1.0));
+        double p_max = std::min(target_mu / (target_var - target_mu2), double(1.0));
+
+        if (p_min == 1.0) {
+            common::logger->error("Invalid bounds: {} < p < {}", p_min, p_max);
+            throw std::runtime_error("FOo");
+        }
+
+        if (p_max == 1.0) {
+            p_max = std::max(real_sum_sq / (real_sum_sq + target_mu), p_min);
+        }
+
+        // (p_min * target_mu)/(1-p_min) = r_map
+        double r_min = p_min * target_mu / (1.0 - p_min);
+        double r_max = p_max * target_mu / (1.0 - p_max);
+
+        common::logger->trace("Bounds: min: {},{}\tmax: {},{}", r_min,p_min, r_max,p_max);
+        // if (r_guess <= r_min || r_guess >= r_max)
+        //     r_guess = (r_min + r_max) / 2;
+
         double r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
             double dl = (log(r) - log(r + target_mu) - boost::math::digamma(r)) * matrix_size;
             double ddl = (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r)) * matrix_size;
@@ -541,7 +561,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 });
             }
             return std::make_pair(dl, ddl);
-        }, r_guess, std::numeric_limits<double>::min(), real_sum, 30);
+        }, r_guess, 0.0, real_sum, 30);
+        // }, r_guess, r_min, r_max, 30);
+
         double target_p = r_map / (r_map + target_mu);
         double fit_mu = r_map * (1.0 - target_p) / target_p;
         double fit_var = fit_mu / target_p;
@@ -646,7 +668,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 deviances1[s] = get_deviance1(s);
                 deviances2[s] = get_deviance2(s);
             }
-            compute_min_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t n) {
+            compute_min_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t n, const PairContainer&) {
                 if (n == 0)
                     return 1.0;
 
@@ -753,12 +775,26 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 return std::min(1.0, pval);
             };
-        } else {
+        } else if (config.test_type == "zinb_exact") {
             common::logger->trace("Computing merged histograms");
             std::mutex hist_mu;
             tsl::hopscotch_map<uint64_t, size_t> merged_hist_in;
             tsl::hopscotch_map<uint64_t, size_t> merged_hist_out;
             tsl::hopscotch_map<uint64_t, size_t> merged_hist_null;
+            int64_t p12_sum = 0;
+            int64_t p11_sum = 0;
+            double m0 = 0;
+            double m1 = 0;
+            for (const auto &[k, m] : count_maps[0]) {
+                const auto &[v, c] = m;
+                m0 += v * c;
+            }
+            for (const auto &[k, m] : count_maps[1]) {
+                const auto &[v, c] = m;
+                m1 += v * c;
+            }
+            m0 /= nelem;
+            m1 /= nelem;
             generate_rows([&](uint64_t row_i, const auto &raw_row, size_t) {
                 int64_t in_sum = 0;
                 int64_t out_sum = 0;
@@ -767,11 +803,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (kept[row_i]) {
                     size_t count_in = 0;
                     size_t count_out = 0;
+                    double p0 = -m0;
+                    double p1 = -m1;
                     for (const auto &[j, raw_c] : raw_row) {
                         uint64_t c = raw_c;
                         if (count_maps.size())
                             c = count_maps[j].find(c)->second.first;
 
+                        if (j == 0) {
+                            p0 = c - m0;
+                        } else if (j == 1) {
+                            p1 = c - m1;
+                        }
+
                         if (groups[j]) {
                             out_sum += c;
                             ++count_out;
@@ -781,6 +825,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         }
                     }
 
+                    p12_sum += p0 * p1;
+                    p11_sum += p0 * p0;
+
                     if (count_in + count_out >= config.min_recurrence) {
                         double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
                         in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
@@ -800,6 +847,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ++merged_hist_null[n];
             }, false);
 
+            double obs_cov = static_cast<double>(p12_sum) / (nelem - 1);
+            double obs_11_cov = static_cast<double>(p11_sum) / (nelem - 1);
+            common::logger->trace("Var: {}\tExp cov: {}\tObs11 cov: {}\tobs12 cov: {}",
+                                  r_map * (1.0 - target_p) / target_p / target_p,
+                                  (1.0 - target_p) / target_p / target_p,
+                                  obs_11_cov,
+                                  obs_cov);
+
             auto get_rp_zinb = [&](const tsl::hopscotch_map<uint64_t, size_t> &hist_in,
                                    const tsl::hopscotch_map<uint64_t, size_t> &hist_out) {
                 auto get_stats = [&](const auto &hist) {
@@ -908,7 +963,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 // return log(exp(lp1) + exp(lp2) + exp(lp3));
             };
 
-            compute_min_pval = [r_in,p_in=p,pi_in,r_out,p_out=p,pi_out,get_lprob_zinb,get_lprob_ztnb,deviances1,deviances2](int64_t n) {
+            compute_min_pval = [r_in,p_in=p,pi_in,r_out,p_out=p,pi_out,get_lprob_zinb,get_lprob_ztnb,deviances1,deviances2](int64_t n, const PairContainer&) {
                 if (n == 0)
                     return 1.0;
 
@@ -973,9 +1028,238 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 return pval;
             };
+        } else {
+            size_t total = nelem * (num_labels_in + num_labels_out);
+            double mu = static_cast<double>(total_kmers) / total;
+            int64_t ssq = 0;
+            for (size_t j = 0; j < groups.size(); ++j) {
+                for (const auto &[k, m] : count_maps[j]) {
+                    const auto &[v, c] = m;
+                    ssq += v * v * c;
+                }
+            }
+            double mu2 = mu * mu;
+            double var = static_cast<double>(ssq) / total - mu2;
+            if (mu >= var) {
+                throw std::runtime_error("Data is underdispersed");
+            }
+
+            double p = target_p;
+            auto get_beta = [&](double alpha, double old_beta) {
+                return boost::math::tools::newton_raphson_iterate([&](double beta) {
+                    double dl = 0;
+                    double ddl = 0;
+                    for (size_t j = 0; j < groups.size(); ++j) {
+                        for (const auto &[k, m] : count_maps[j]) {
+                            const auto &[v, c] = m;
+                            dl -= boost::math::digamma(alpha / beta + v) * c;
+                            ddl += boost::math::trigamma(alpha / beta + v) * c;
+                        }
+                    }
+                    dl *= alpha;
+                    ddl *= alpha * alpha / beta / beta;
+
+                    dl += (beta - alpha * log(p)) * total;
+                    ddl += total;
+                    return std::make_pair(dl, ddl);
+                }, old_beta, 0.0, real_sum, 30);
+            };
+            auto get_alpha = [&](double old_alpha, double beta) {
+                return boost::math::tools::newton_raphson_iterate([&](double alpha) {
+                    double dl = 0;
+                    double ddl = 0;
+                    for (size_t j = 0; j < groups.size(); ++j) {
+                        for (const auto &[k, m] : count_maps[j]) {
+                            const auto &[v, c] = m;
+                            dl += boost::math::digamma(alpha / beta + v) * c;
+                            ddl += boost::math::trigamma(alpha / beta + v) * c;
+                        }
+                    }
+                    dl /= beta;
+                    ddl /= beta * beta;
+
+                    dl += (-boost::math::digamma(alpha) + log(p) / beta - 1.0/alpha + log(alpha) - 0.5/alpha/alpha) * total;
+                    ddl += (-boost::math::trigamma(alpha) + 1.0/alpha/alpha + 1.0/alpha + 1.0/alpha/alpha/alpha) * total;
+                    return std::make_pair(dl, ddl);
+                }, old_alpha, 0.0, real_sum, 30);
+            };
+
+            double beta = (1.0 - p) * mu / (p * var - mu);
+            if (beta <= 0)
+                beta = 1.0;
+
+            double alpha = beta * p / (1.0 - p) * mu;
+            common::logger->trace("Initial guess: a: {}\tb: {}", alpha, beta);
+            while (true) {
+                double old_alpha = alpha;
+                double old_beta = beta;
+                alpha = get_alpha(old_alpha, beta);
+                common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
+                beta = get_beta(alpha, old_beta);
+                common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
+                if ((alpha - old_alpha) / alpha < 1e-5 && (beta - old_beta) / beta < 1e-5)
+                    break;
+            }
+
+            double gamma_mu = alpha / beta;
+            common::logger->trace("Gamma-NB EM fit: p: {}\ta: {}\tb: {}\tE[r]: {}\tVar(r): {}",
+                                  p, alpha, beta, gamma_mu, gamma_mu / beta);
+
+            auto get_r = [alpha,beta,lp=log(p),sum=static_cast<double>(total_kmers),gamma_mu,gs=groups.size()](const PairContainer &row) {
+                return boost::math::tools::newton_raphson_iterate([&](double r) {
+                    double dl = (lp + (alpha - 1)/r - beta - boost::math::digamma(r)) * gs;
+                    double ddl = (-(alpha - 1)/r/r - boost::math::trigamma(r)) * gs;
+                    for (const auto &[j, c] : row) {
+                        dl += boost::math::digamma(r + c);
+                        ddl += boost::math::trigamma(r + c);
+                    }
+                    dl += boost::math::digamma(r) * (gs - row.size());
+                    ddl += boost::math::trigamma(r) * (gs - row.size());
+                    return std::make_pair(dl, ddl);
+                }, gamma_mu, 0.0, sum, 30);
+            };
+
+            auto get_deviance = [p](double invphi, double y) {
+                double mu = invphi * (1.0 - p) / p;
+                if (y == 0)
+                    return 2.0*invphi*log((invphi*mu)/invphi);
+
+                double logmuinvphi = log(mu+invphi);
+                double logmu = log(mu);
+                double logyshift = logmuinvphi - log(y + invphi);
+                return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
+            };
+
+            compute_min_pval = [get_r,num_labels_in,num_labels_out,get_deviance](int64_t n, const auto &row) {
+                if (n == 0)
+                    return 1.0;
+
+                double r = get_r(row);
+                double r_in = r * num_labels_in;
+                double r_out = r * num_labels_out;
+                double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+
+                double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
+
+                std::vector<double> devs(n + 1);
+
+                for (int64_t s = 0; s <= n; ++s) {
+                    devs[s] = get_deviance(r_in, s) + get_deviance(r_out, n - s);
+                }
+
+                double max_d = *std::max_element(devs.begin(), devs.end());
+
+                double pval = 0.0;
+                int64_t s = 0;
+                int64_t t = n;
+                if (devs[s] == max_d) {
+                    double rs = r_in;
+                    double rt = r_out + n;
+                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    pval += exp(base);
+                    for (++s; s <= n; ++s) {
+                        --t;
+                        if (devs[s] == max_d) {
+                            ++rs;
+                            --rt;
+                            base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                            pval += exp(base);
+                        } else {
+                            break;
+                        }
+                    }
+                }
+
+                int64_t sp = n;
+                t = 0;
+                if (devs[sp] == max_d) {
+                    double rs = r_in + sp;
+                    double rt = r_out;
+                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    pval += exp(base);
+                    for (--sp; sp >= s; --sp) {
+                        ++t;
+                        if (devs[sp] == max_d) {
+                            --rs;
+                            ++rt;
+                            base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                            pval += exp(base);
+                        } else {
+                            break;
+                        }
+                    }
+                }
+
+                return std::min(1.0, pval);
+            };
+
+            compute_pval = [get_r,num_labels_in,num_labels_out,get_deviance](int64_t in_sum, int64_t out_sum, const auto &row) {
+                if (row.empty())
+                    return 1.0;
+
+                double r = get_r(row);
+                double r_in = r * num_labels_in;
+                double r_out = r * num_labels_out;
+                double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+
+                int64_t n = in_sum + out_sum;
+
+                double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
+
+                std::vector<double> devs(n + 1);
+
+                for (int64_t s = 0; s <= n; ++s) {
+                    devs[s] = get_deviance(r_in, s) + get_deviance(r_out, n - s);
+                }
+
+                double in_sum_total_deviance = devs[in_sum];
+
+                double pval = 0.0;
+                int64_t s = 0;
+                int64_t t = n;
+                if (devs[s] >= in_sum_total_deviance) {
+                    double rs = r_in;
+                    double rt = r_out + n;
+                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    pval += exp(base);
+                    for (++s; s <= n; ++s) {
+                        --t;
+                        if (devs[s] >= in_sum_total_deviance) {
+                            ++rs;
+                            --rt;
+                            base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                            pval += exp(base);
+                        } else {
+                            break;
+                        }
+                    }
+                }
+
+                int64_t sp = n;
+                t = 0;
+                if (devs[sp] >= in_sum_total_deviance) {
+                    double rs = r_in + sp;
+                    double rt = r_out;
+                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    pval += exp(base);
+                    for (--sp; sp >= s; --sp) {
+                        ++t;
+                        if (devs[sp] >= in_sum_total_deviance) {
+                            --rs;
+                            ++rt;
+                            base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                            pval += exp(base);
+                        } else {
+                            break;
+                        }
+                    }
+                }
+
+                return std::min(1.0, pval);
+            };
         }
     } else if (config.test_type == "notest") {
-        compute_min_pval = [&](int64_t n) { return n > 0 ? 0.0 : 1.1; };
+        compute_min_pval = [&](int64_t n, const PairContainer&) { return n > 0 ? 0.0 : 1.1; };
         compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };
     } else {
         throw std::runtime_error("Test not implemented");
@@ -1032,7 +1316,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ms[bucket_idx].resize(n + 1, std::make_pair(1.1, 0));
 
             if (ms[bucket_idx][n].first == 1.1)
-                ms[bucket_idx][n].first = compute_min_pval(n);
+                ms[bucket_idx][n].first = compute_min_pval(n, row);
 
             ++ms[bucket_idx][n].second;
 

From cbcc6f18c967af61f182948ba2e884b7729642d6 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 15 Aug 2024 14:09:26 +0200
Subject: [PATCH 274/410] reduce number of tests

---
 .../src/graph/annotated_graph_algorithm.cpp   | 145 ++++++++++++------
 1 file changed, 94 insertions(+), 51 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 91c333859b..76d96817be 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -20,6 +20,7 @@
 #include "common/vectors/bitmap.hpp"
 #include "common/vector_map.hpp"
 #include "common/vectors/transpose.hpp"
+#include "common/hashers/hash.hpp"
 #include "graph/representation/masked_graph.hpp"
 #include "graph/graph_cleaning.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
@@ -116,7 +117,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
-    auto correct_pvals = [&config](const std::vector<std::pair<double, size_t>> &m, size_t nelem) {
+    auto correct_pvals = [&config](const std::vector<std::pair<double, size_t>> &m,
+                                   size_t nelem,
+                                   const std::vector<int64_t> &m_sums = {}) {
         size_t k = 0;
         size_t n_cutoff = 1;
         size_t acc = std::accumulate(m.begin(), m.end(), size_t(0),
@@ -142,7 +145,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             if (cur_k != last_k && acc <= cur_k) {
                 k = std::max(acc, last_k + 1);
-                n_cutoff = n;
+                if (m_sums.empty()) {
+                    n_cutoff = n;
+                } else {
+                    n_cutoff = *std::min_element(m_sums.begin() + n, m_sums.end());
+                }
                 break;
             }
 
@@ -529,26 +536,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             throw std::domain_error("Fit failed");
         }
 
-        double p_min = std::min(target_mu / target_var, double(1.0));
-        double p_max = std::min(target_mu / (target_var - target_mu2), double(1.0));
-
-        if (p_min == 1.0) {
-            common::logger->error("Invalid bounds: {} < p < {}", p_min, p_max);
-            throw std::runtime_error("FOo");
-        }
-
-        if (p_max == 1.0) {
-            p_max = std::max(real_sum_sq / (real_sum_sq + target_mu), p_min);
-        }
-
-        // (p_min * target_mu)/(1-p_min) = r_map
-        double r_min = p_min * target_mu / (1.0 - p_min);
-        double r_max = p_max * target_mu / (1.0 - p_max);
-
-        common::logger->trace("Bounds: min: {},{}\tmax: {},{}", r_min,p_min, r_max,p_max);
-        // if (r_guess <= r_min || r_guess >= r_max)
-        //     r_guess = (r_min + r_max) / 2;
-
         double r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
             double dl = (log(r) - log(r + target_mu) - boost::math::digamma(r)) * matrix_size;
             double ddl = (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r)) * matrix_size;
@@ -562,7 +549,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
             return std::make_pair(dl, ddl);
         }, r_guess, 0.0, real_sum, 30);
-        // }, r_guess, r_min, r_max, 30);
 
         double target_p = r_map / (r_map + target_mu);
         double fit_mu = r_map * (1.0 - target_p) / target_p;
@@ -1094,9 +1080,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double old_alpha = alpha;
                 double old_beta = beta;
                 alpha = get_alpha(old_alpha, beta);
-                common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
                 beta = get_beta(alpha, old_beta);
-                common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
                 if ((alpha - old_alpha) / alpha < 1e-5 && (beta - old_beta) / beta < 1e-5)
                     break;
             }
@@ -1131,7 +1115,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             };
 
             compute_min_pval = [get_r,num_labels_in,num_labels_out,get_deviance](int64_t n, const auto &row) {
-                if (n == 0)
+                if (row.empty())
                     return 1.0;
 
                 double r = get_r(row);
@@ -1267,8 +1251,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Computing minimum p-values");
     std::vector<std::pair<double, size_t>> m;
+    std::vector<int64_t> m_sums;
     {
         std::vector<std::vector<std::pair<double, size_t>>> ms(num_threads + 1);
+        std::vector<tsl::hopscotch_map<std::vector<int64_t>, std::pair<double, size_t>, utils::VectorHash>> ms_vecs(num_threads + 1);
         generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
             int64_t in_sum = 0;
             int64_t out_sum = 0;
@@ -1276,6 +1262,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             bool in_kmer = false;
             bool out_kmer = false;
             PairContainer row;
+            std::vector<int64_t> vals;
             if (kept[row_i]) {
                 size_t count_in = 0;
                 size_t count_out = 0;
@@ -1285,6 +1272,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         c = count_maps[j].find(c)->second.first;
 
                     row.emplace_back(j, c);
+                    vals.emplace_back(c);
                     if (groups[j]) {
                         out_sum += c;
                         ++count_out;
@@ -1312,13 +1300,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 n = 0;
             }
 
-            if (n >= ms[bucket_idx].size())
-                ms[bucket_idx].resize(n + 1, std::make_pair(1.1, 0));
+            if (config.test_type != "gnb_exact") {
+                if (n >= ms[bucket_idx].size())
+                    ms[bucket_idx].resize(n + 1, std::make_pair(1.1, 0));
 
-            if (ms[bucket_idx][n].first == 1.1)
-                ms[bucket_idx][n].first = compute_min_pval(n, row);
+                if (ms[bucket_idx][n].first == 1.1)
+                    ms[bucket_idx][n].first = compute_min_pval(n, row);
 
-            ++ms[bucket_idx][n].second;
+                ++ms[bucket_idx][n].second;
+            } else {
+                std::sort(vals.begin(), vals.end());
+                auto find = ms_vecs[bucket_idx].find(vals);
+                if (find == ms_vecs[bucket_idx].end()) {
+                    ms_vecs[bucket_idx][vals] = std::make_pair(compute_min_pval(n, row), 1);
+                } else {
+                    ++find.value().second;
+                }
+            }
 
             if (config.test_by_unitig) {
                 bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
@@ -1336,23 +1334,56 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }, false);
 
         common::logger->trace("Merging min p-value tables");
-        for (size_t i = 1; i < ms.size(); ++i) {
-            size_t end = std::min(ms[0].size(), ms[i].size());
-            for (size_t j = 0; j < end; ++j) {
-                assert(ms[0][j].first == 1.1 || ms[i][j].first == 1.1 || ms[0][j] == ms[i][j]);
-                ms[0][j].first = std::min(ms[0][j].first, ms[i][j].first);
-                ms[0][j].second += ms[i][j].second;
+        if (config.test_type != "gnb_exact") {
+            for (size_t i = 1; i < ms.size(); ++i) {
+                size_t end = std::min(ms[0].size(), ms[i].size());
+                for (size_t j = 0; j < end; ++j) {
+                    assert(ms[0][j].first == 1.1 || ms[i][j].first == 1.1 || ms[0][j] == ms[i][j]);
+                    ms[0][j].first = std::min(ms[0][j].first, ms[i][j].first);
+                    ms[0][j].second += ms[i][j].second;
+                }
+
+                if (ms[i].size() > ms[0].size()) {
+                    std::copy(std::make_move_iterator(ms[i].begin() + ms[0].size()),
+                            std::make_move_iterator(ms[i].end()),
+                            std::back_inserter(ms[0]));
+                }
+            }
+            m = std::move(ms[0]);
+            ms.resize(0);
+        } else {
+            for (size_t i = 1; i < ms_vecs.size(); ++i) {
+                for (const auto &[vec, v] : ms_vecs[i]) {
+                    auto find = ms_vecs[0].find(vec);
+                    if (find == ms_vecs[0].end()) {
+                        ms_vecs[0][vec] = v;
+                    } else {
+                        find.value().second += v.second;
+                    }
+                }
             }
+            ms_vecs.resize(1);
+
+            std::vector<std::tuple<double, int64_t, size_t>> mn;
+            mn.reserve(ms_vecs[0].size());
+            for (const auto &[vec, v] : ms_vecs[0]) {
+                mn.emplace_back(
+                    v.first,
+                    std::accumulate(vec.begin(), vec.end(), int64_t(0)),
+                    v.second
+                );
+            }
+            std::sort(mn.begin(), mn.end(), utils::GreaterFirst());
 
-            if (ms[i].size() > ms[0].size()) {
-                std::copy(std::make_move_iterator(ms[i].begin() + ms[0].size()),
-                          std::make_move_iterator(ms[i].end()),
-                          std::back_inserter(ms[0]));
+            m.reserve(ms_vecs[0].size());
+            m_sums.reserve(ms_vecs[0].size());
+            for (const auto &[p, s, c] : mn) {
+                m.emplace_back(p, c);
+                m_sums.emplace_back(s);
             }
-        }
 
-        m = std::move(ms[0]);
-        ms.resize(0);
+            ms_vecs.resize(0);
+        }
     }
 
     size_t k = 0;
@@ -1380,7 +1411,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
     sdsl::bit_vector unitig_start;
     if (!config.test_by_unitig) {
-        std::tie(k, n_cutoff) = correct_pvals(m, nelem);
+        std::tie(k, n_cutoff) = correct_pvals(m, nelem, m_sums);
         common::logger->trace("Picked: k: {}\tn: {}\tpval_min: {}", k, n_cutoff, config.family_wise_error_rate / k);
     } else {
         clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
@@ -1393,7 +1424,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
         );
 
-        std::vector<std::pair<double, size_t>> comb_m;
+        std::vector<std::pair<double, int64_t>> comb_msums;
         common::logger->trace("Allocating updated indicator");
         unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
 
@@ -1414,11 +1445,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             std::vector<double> pvals_min;
             pvals_min.reserve(path.size());
 
+            int64_t n_sum = 0;
             {
                 std::lock_guard<std::mutex> lock(pval_mu);
                 for (size_t i = 0; i < path.size(); ++i) {
                     size_t bucket_idx = bucket_idxs[i];
                     size_t n = sum_buckets[bucket_idx][path[i] - boundaries[bucket_idx]];
+                    n_sum += n;
                     pvals_min.emplace_back(m[n].first);
                 }
             }
@@ -1431,11 +1464,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             std::lock_guard<std::mutex> lock(pval_mu);
-            comb_m.emplace_back(comb_pval, 1);
+            comb_msums.emplace_back(comb_pval, n_sum);
         }, num_threads);
 
-        std::sort(comb_m.begin(), comb_m.end(), utils::GreaterFirst());
-        std::tie(k, n_cutoff) = correct_pvals(comb_m, nelem);
+        std::sort(comb_msums.begin(), comb_msums.end(), utils::GreaterFirst());
+        std::vector<std::pair<double, size_t>> comb_m;
+        std::vector<int64_t> comb_m_sums;
+        comb_m.reserve(comb_msums.size());
+        comb_m_sums.reserve(comb_msums.size());
+        for (const auto &[p, s] : comb_msums) {
+            comb_m.emplace_back(p, 1);
+            comb_m_sums.emplace_back(s);
+        }
+
+        std::tie(k, n_cutoff) = correct_pvals(comb_m, nelem, comb_m_sums);
         common::logger->trace("Picked: k: {}\tpval_min: {}", k, config.family_wise_error_rate / k);
 
         sum_buckets.resize(0);
@@ -1514,7 +1556,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (config.output_pvals
                     || (!config.test_by_unitig && n >= n_cutoff)
                     || (config.test_by_unitig && unitig_start[node])) {
-                if (m[n].first == 1.1) {
+                if (config.test_type != "gnb_exact" && m[n].first == 1.1) {
                     common::logger->error("in: {}\tout: {}\tn: {}", in_sum, out_sum, n);
                     throw std::runtime_error("Indexing invalid");
                 }
@@ -1527,7 +1569,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return;
                 }
 
-                if (pval < 1.1 && m[n].first - pval > 1e-10) {
+                if (config.test_type != "gnb_exact" && pval < 1.1 && m[n].first - pval > 1e-10) {
                     common::logger->error("Min p-val estimate too high: min {} > cur {}\tn: {}\ttest: {}", m[n].first, pval, n, config.test_type);
                     throw std::runtime_error("Test failed");
                 }
@@ -1587,6 +1629,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 }
 
+                // TODO: don't bother combining if the row sum is too small
                 std::vector<double> pvals;
                 pvals.reserve(path.size());
                 {

From 3e336fcb9a1373f37fa643104b91634a026c2111 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 15 Aug 2024 14:15:40 +0200
Subject: [PATCH 275/410] cleanup

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 76d96817be..e234ca030a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -4,14 +4,11 @@
 #include <mutex>
 #include <variant>
 
-#include <sdust.h>
-
 #include <boost/math/distributions/chi_squared.hpp>
 #include <boost/math/distributions/poisson.hpp>
 #include <boost/math/distributions/binomial.hpp>
 #include <boost/math/distributions/negative_binomial.hpp>
 #include <boost/math/tools/roots.hpp>
-#include <boost/math/tools/minima.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
 #include <boost/math/distributions/cauchy.hpp>

From 3d4439a5df8030eefb487c45946e6de434f08e03 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 15 Aug 2024 17:32:50 +0200
Subject: [PATCH 276/410] first try of fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 21 ++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index e234ca030a..2b242cd365 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1013,12 +1013,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             };
         } else {
             size_t total = nelem * (num_labels_in + num_labels_out);
+            size_t total_zeros = 0;
             double mu = static_cast<double>(total_kmers) / total;
             int64_t ssq = 0;
             for (size_t j = 0; j < groups.size(); ++j) {
                 for (const auto &[k, m] : count_maps[j]) {
                     const auto &[v, c] = m;
                     ssq += v * v * c;
+                    if (v == 0)
+                        total_zeros += c;
                 }
             }
             double mu2 = mu * mu;
@@ -1042,8 +1045,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     dl *= alpha;
                     ddl *= alpha * alpha / beta / beta;
 
-                    dl += (beta - alpha * log(p)) * total;
-                    ddl += total;
+                    dl += (beta - alpha * log(p) + alpha * boost::math::digamma(alpha / beta)) * total;
+                    ddl += (1.0 - alpha * alpha / beta / beta * boost::math::trigamma(alpha / beta)) * total;
                     return std::make_pair(dl, ddl);
                 }, old_beta, 0.0, real_sum, 30);
             };
@@ -1061,8 +1064,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     dl /= beta;
                     ddl /= beta * beta;
 
-                    dl += (-boost::math::digamma(alpha) + log(p) / beta - 1.0/alpha + log(alpha) - 0.5/alpha/alpha) * total;
-                    ddl += (-boost::math::trigamma(alpha) + 1.0/alpha/alpha + 1.0/alpha + 1.0/alpha/alpha/alpha) * total;
+                    dl += 0.5 * total_kmers / alpha / alpha + (-boost::math::digamma(alpha) + log(p) / beta - 1.0/alpha - 0.5/alpha/alpha + log(alpha) - boost::math::digamma(alpha/beta)/beta) * total;
+                    ddl += -1.0 / alpha/alpha/alpha * total_kmers + (-boost::math::trigamma(alpha) - boost::math::trigamma(alpha/beta)/beta/beta + 1.0/alpha/alpha + 1.0/alpha/alpha/alpha + 1.0/alpha) * total;
                     return std::make_pair(dl, ddl);
                 }, old_alpha, 0.0, real_sum, 30);
             };
@@ -1072,12 +1075,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 beta = 1.0;
 
             double alpha = beta * p / (1.0 - p) * mu;
-            common::logger->trace("Initial guess: a: {}\tb: {}", alpha, beta);
+            common::logger->trace("Initial MoM guess: a: {}\tb: {}", alpha, beta);
             while (true) {
                 double old_alpha = alpha;
                 double old_beta = beta;
-                alpha = get_alpha(old_alpha, beta);
-                beta = get_beta(alpha, old_beta);
+
+                alpha = get_alpha(alpha, beta);
+                common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
+                beta = get_beta(alpha, beta);
+                common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
+
                 if ((alpha - old_alpha) / alpha < 1e-5 && (beta - old_beta) / beta < 1e-5)
                     break;
             }

From f5587fcb80240c22805796a79b61955bcfde0191 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 16 Aug 2024 11:32:33 +0200
Subject: [PATCH 277/410] backup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 125 ++++++++++++++----
 1 file changed, 98 insertions(+), 27 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 2b242cd365..fc6d296be4 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -11,6 +11,7 @@
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
+#include <boost/math/special_functions/polygamma.hpp>
 #include <boost/math/distributions/cauchy.hpp>
 
 #include "common/logger.hpp"
@@ -1016,14 +1017,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             size_t total_zeros = 0;
             double mu = static_cast<double>(total_kmers) / total;
             int64_t ssq = 0;
+            VectorMap<int64_t, size_t> joint_map;
             for (size_t j = 0; j < groups.size(); ++j) {
                 for (const auto &[k, m] : count_maps[j]) {
                     const auto &[v, c] = m;
+                    joint_map[v] += c;
                     ssq += v * v * c;
                     if (v == 0)
                         total_zeros += c;
                 }
             }
+            auto joint_counts = const_cast<std::vector<std::pair<int64_t, size_t>>&&>(joint_map.values_container());
+            std::sort(joint_counts.begin(), joint_counts.end());
+
             double mu2 = mu * mu;
             double var = static_cast<double>(ssq) / total - mu2;
             if (mu >= var) {
@@ -1031,42 +1037,105 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             double p = target_p;
-            auto get_beta = [&](double alpha, double old_beta) {
-                return boost::math::tools::newton_raphson_iterate([&](double beta) {
+            auto get_beta = [&](const double alpha, const double old_beta) {
+                return boost::math::tools::schroder_iterate([alpha,total,p,&joint_counts](const double beta) {
+                    common::logger->trace("B: a: {}\tb: {}", alpha, beta);
                     double dl = 0;
                     double ddl = 0;
-                    for (size_t j = 0; j < groups.size(); ++j) {
-                        for (const auto &[k, m] : count_maps[j]) {
-                            const auto &[v, c] = m;
-                            dl -= boost::math::digamma(alpha / beta + v) * c;
-                            ddl += boost::math::trigamma(alpha / beta + v) * c;
+                    double dddl = 0;
+                    for (const auto &[v, c] : joint_counts) {
+                        dl -= boost::math::digamma(alpha / beta + v) * c;
+                        ddl += boost::math::trigamma(alpha / beta + v) * c;
+                        dddl -= boost::math::polygamma(2, alpha / beta + v) * c;
+                    }
+                    dddl *= alpha * alpha / beta / beta / beta / beta;
+                    dddl -= 2.0 * alpha * alpha / beta / beta / beta * ddl;
+
+                    size_t cur_total = total;
+                    auto it = joint_counts.begin();
+                    if (it->first == 0) {
+                        cur_total -= it->second;
+                        ++it;
+                    }
+
+                    double ddl_counts = 0.0;
+                    double dddl_counts = 0.0;
+                    for (int64_t j = 0; j < joint_counts.back().first; ++j) {
+                        dl += static_cast<double>(j * cur_total) / pow(alpha + beta * j, 3.0);
+                        ddl_counts += static_cast<double>(j * j * cur_total) / pow(alpha + beta * j, 4.0);
+                        dddl_counts += static_cast<double>(j*j*j * cur_total) / pow(alpha + beta * j, 5.0);
+                        if (j + 1 == it->first) {
+                            if (it == joint_counts.end() || it->second > cur_total)
+                                throw std::runtime_error("GGGGGG");
+                            cur_total -= it->second;
+                            ++it;
                         }
                     }
+
                     dl *= alpha;
                     ddl *= alpha * alpha / beta / beta;
+                    ddl -= 3.0 * alpha * ddl_counts;
+
+                    dddl += 12.0 * alpha + dddl_counts + (2.0 * alpha * alpha / beta / beta / beta * boost::math::trigamma(alpha/beta) + alpha * alpha * alpha/beta/beta/beta/beta*boost::math::polygamma(2, alpha/beta)) * total;
 
                     dl += (beta - alpha * log(p) + alpha * boost::math::digamma(alpha / beta)) * total;
                     ddl += (1.0 - alpha * alpha / beta / beta * boost::math::trigamma(alpha / beta)) * total;
-                    return std::make_pair(dl, ddl);
+                    return std::make_tuple(dl, ddl, dddl);
+                    // return std::make_pair(dl, ddl);
                 }, old_beta, 0.0, real_sum, 30);
             };
-            auto get_alpha = [&](double old_alpha, double beta) {
-                return boost::math::tools::newton_raphson_iterate([&](double alpha) {
+
+            auto get_alpha = [&](const double old_alpha, double &beta) {
+                return boost::math::tools::schroder_iterate([&,total,p](const double alpha) {
+                    beta = get_beta(alpha, beta);
+                    common::logger->trace("A: a: {}\tb: {}", alpha, beta);
                     double dl = 0;
                     double ddl = 0;
-                    for (size_t j = 0; j < groups.size(); ++j) {
-                        for (const auto &[k, m] : count_maps[j]) {
-                            const auto &[v, c] = m;
-                            dl += boost::math::digamma(alpha / beta + v) * c;
-                            ddl += boost::math::trigamma(alpha / beta + v) * c;
-                        }
+                    double dddl = 0;
+                    for (const auto &[v, c] : joint_counts) {
+                        dl += boost::math::digamma(alpha / beta + v) * c;
+                        ddl += boost::math::trigamma(alpha / beta + v) * c;
+                        dddl += boost::math::trigamma(alpha / beta + v) * c;
                     }
                     dl /= beta;
                     ddl /= beta * beta;
+                    dddl /= beta * beta * beta;
 
-                    dl += 0.5 * total_kmers / alpha / alpha + (-boost::math::digamma(alpha) + log(p) / beta - 1.0/alpha - 0.5/alpha/alpha + log(alpha) - boost::math::digamma(alpha/beta)/beta) * total;
-                    ddl += -1.0 / alpha/alpha/alpha * total_kmers + (-boost::math::trigamma(alpha) - boost::math::trigamma(alpha/beta)/beta/beta + 1.0/alpha/alpha + 1.0/alpha/alpha/alpha + 1.0/alpha) * total;
-                    return std::make_pair(dl, ddl);
+                    size_t cur_total = total;
+                    auto it = joint_counts.begin();
+                    if (it->first == 0) {
+                        cur_total -= it->second;
+                        ++it;
+                    }
+
+                    double dl_counts = 0.0;
+                    double ddl_counts = 0.0;
+                    double dddl_counts = 0.0;
+                    for (int64_t j = 0; j < joint_counts.back().first; ++j) {
+                        double v2 = static_cast<double>(cur_total) / pow(alpha + beta * j, 2.0);
+                        double v3 = v2 / (alpha + beta * j);
+                        double v4 = v3 / (alpha + beta * j);
+                        double v5 = v4 / (alpha + beta * j);
+                        dl_counts += -v2 / 2 + alpha * v3;
+                        ddl_counts += v3 * 2 - 3.0 * alpha * v4;
+                        dddl_counts += -v4 * 9 + 12.0 * alpha * v5;
+                        if (j + 1 == it->first) {
+                            if (it == joint_counts.end() || it->second > cur_total)
+                                throw std::runtime_error("FFFFFF");
+                            cur_total -= it->second;
+                            ++it;
+                        }
+                    }
+
+                    dl += dl_counts;
+                    ddl += ddl_counts;
+                    dddl += dddl_counts;
+
+                    dl += (-boost::math::digamma(alpha) + log(p) / beta - 1.0/alpha - 0.5/alpha/alpha + log(alpha) - boost::math::digamma(alpha/beta)/beta) * total;
+                    ddl += (-boost::math::trigamma(alpha) - boost::math::trigamma(alpha/beta)/beta/beta + 1.0/alpha/alpha + 1.0/alpha/alpha/alpha + 1.0/alpha) * total;
+                    dddl += (-boost::math::polygamma(2, alpha) - boost::math::polygamma(2, alpha/beta)/beta/beta/beta - 2.0/alpha/alpha/alpha - 3.0/alpha/alpha/alpha/alpha - 1.0/alpha/alpha) * total;
+                    return std::make_tuple(dl, ddl, dddl);
+                    // return std::make_pair(dl, ddl);
                 }, old_alpha, 0.0, real_sum, 30);
             };
 
@@ -1076,18 +1145,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double alpha = beta * p / (1.0 - p) * mu;
             common::logger->trace("Initial MoM guess: a: {}\tb: {}", alpha, beta);
-            while (true) {
-                double old_alpha = alpha;
-                double old_beta = beta;
+
+            // while (true) {
+            //     double old_alpha = alpha;
+            //     double old_beta = beta;
 
                 alpha = get_alpha(alpha, beta);
-                common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
+            //     common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
+
                 beta = get_beta(alpha, beta);
-                common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
+            //     common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
 
-                if ((alpha - old_alpha) / alpha < 1e-5 && (beta - old_beta) / beta < 1e-5)
-                    break;
-            }
+            //     if ((alpha - old_alpha) / alpha < 1e-5 && (beta - old_beta) / beta < 1e-5)
+            //         break;
+            // }
 
             double gamma_mu = alpha / beta;
             common::logger->trace("Gamma-NB EM fit: p: {}\ta: {}\tb: {}\tE[r]: {}\tVar(r): {}",

From a935f889aff38ca1b12698994fecb017e381337d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 18 Aug 2024 21:31:09 +0200
Subject: [PATCH 278/410] ttt

---
 .../src/graph/annotated_graph_algorithm.cpp   | 384 +++++++++++-------
 1 file changed, 245 insertions(+), 139 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fc6d296be4..9421885d2d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -9,6 +9,7 @@
 #include <boost/math/distributions/binomial.hpp>
 #include <boost/math/distributions/negative_binomial.hpp>
 #include <boost/math/tools/roots.hpp>
+#include <boost/math/tools/minima.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
 #include <boost/math/special_functions/polygamma.hpp>
@@ -429,7 +430,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double p_guess = mu / var;
             double r = r_guess;
             try {
-                r = boost::math::tools::newton_raphson_iterate([&](double r) {
+                auto get_dl_ddl = [&](double r) {
                     double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
                     double ddl = (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r)) * total;
                     std::for_each(begin, end, [&](const auto &a) {
@@ -438,7 +439,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         ddl += boost::math::trigamma(k + r) * c;
                     });
                     return std::make_pair(dl, ddl);
-                }, r_guess, std::numeric_limits<double>::min(), mu * total, 30);
+                };
+                r = boost::math::tools::newton_raphson_iterate(get_dl_ddl, r_guess,
+                                                               std::numeric_limits<double>::min(), mu * total, 30);
+                auto [dl, ddl] = get_dl_ddl(r);
+                if (ddl > 0) {
+                    common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r, dl, ddl);
+                    throw std::domain_error("FFF");
+                }
             } catch (std::exception &e) {
                 common::logger->warn("Caught exception for sample {}: Falling back to initial guess", j);
                 common::logger->warn("{}", e.what());
@@ -534,7 +542,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             throw std::domain_error("Fit failed");
         }
 
-        double r_map = boost::math::tools::newton_raphson_iterate([&](double r) {
+        auto get_dl_ddl = [&](double r) {
             double dl = (log(r) - log(r + target_mu) - boost::math::digamma(r)) * matrix_size;
             double ddl = (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r)) * matrix_size;
             for (size_t j = 0; j < groups.size(); ++j) {
@@ -546,7 +554,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 });
             }
             return std::make_pair(dl, ddl);
-        }, r_guess, 0.0, real_sum, 30);
+        };
+        double r_map = boost::math::tools::newton_raphson_iterate(get_dl_ddl, r_guess, 0.0, real_sum, 30);
+        auto [dl, ddl] = get_dl_ddl(r_map);
+        if (ddl > 0) {
+            common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r_map, dl, ddl);
+            throw std::domain_error("GGG");
+        }
 
         double target_p = r_map / (r_map + target_mu);
         double fit_mu = r_map * (1.0 - target_p) / target_p;
@@ -1013,169 +1027,261 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return pval;
             };
         } else {
-            size_t total = nelem * (num_labels_in + num_labels_out);
-            size_t total_zeros = 0;
-            double mu = static_cast<double>(total_kmers) / total;
-            int64_t ssq = 0;
-            VectorMap<int64_t, size_t> joint_map;
-            for (size_t j = 0; j < groups.size(); ++j) {
-                for (const auto &[k, m] : count_maps[j]) {
-                    const auto &[v, c] = m;
-                    joint_map[v] += c;
-                    ssq += v * v * c;
-                    if (v == 0)
-                        total_zeros += c;
-                }
-            }
-            auto joint_counts = const_cast<std::vector<std::pair<int64_t, size_t>>&&>(joint_map.values_container());
-            std::sort(joint_counts.begin(), joint_counts.end());
-
-            double mu2 = mu * mu;
-            double var = static_cast<double>(ssq) / total - mu2;
-            if (mu >= var) {
-                throw std::runtime_error("Data is underdispersed");
-            }
-
             double p = target_p;
-            auto get_beta = [&](const double alpha, const double old_beta) {
-                return boost::math::tools::schroder_iterate([alpha,total,p,&joint_counts](const double beta) {
-                    common::logger->trace("B: a: {}\tb: {}", alpha, beta);
-                    double dl = 0;
-                    double ddl = 0;
-                    double dddl = 0;
-                    for (const auto &[v, c] : joint_counts) {
-                        dl -= boost::math::digamma(alpha / beta + v) * c;
-                        ddl += boost::math::trigamma(alpha / beta + v) * c;
-                        dddl -= boost::math::polygamma(2, alpha / beta + v) * c;
-                    }
-                    dddl *= alpha * alpha / beta / beta / beta / beta;
-                    dddl -= 2.0 * alpha * alpha / beta / beta / beta * ddl;
-
-                    size_t cur_total = total;
-                    auto it = joint_counts.begin();
-                    if (it->first == 0) {
-                        cur_total -= it->second;
-                        ++it;
-                    }
+            double p1p = exp(log(p) - log1p(-p));
+            common::logger->trace("Computing merged histograms");
+            std::mutex hist_mu;
+            tsl::hopscotch_map<uint64_t, size_t> merged_hist;
+            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t) {
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                bool in_kmer = false;
+                bool out_kmer = false;
+                if (kept[row_i]) {
+                    size_t count_in = 0;
+                    size_t count_out = 0;
+                    for (const auto &[j, raw_c] : raw_row) {
+                        uint64_t c = raw_c;
+                        if (count_maps.size())
+                            c = count_maps[j].find(c)->second.first;
 
-                    double ddl_counts = 0.0;
-                    double dddl_counts = 0.0;
-                    for (int64_t j = 0; j < joint_counts.back().first; ++j) {
-                        dl += static_cast<double>(j * cur_total) / pow(alpha + beta * j, 3.0);
-                        ddl_counts += static_cast<double>(j * j * cur_total) / pow(alpha + beta * j, 4.0);
-                        dddl_counts += static_cast<double>(j*j*j * cur_total) / pow(alpha + beta * j, 5.0);
-                        if (j + 1 == it->first) {
-                            if (it == joint_counts.end() || it->second > cur_total)
-                                throw std::runtime_error("GGGGGG");
-                            cur_total -= it->second;
-                            ++it;
+                        if (groups[j]) {
+                            out_sum += c;
+                            ++count_out;
+                        } else {
+                            in_sum += c;
+                            ++count_in;
                         }
                     }
 
-                    dl *= alpha;
-                    ddl *= alpha * alpha / beta / beta;
-                    ddl -= 3.0 * alpha * ddl_counts;
+                    if (count_in + count_out >= config.min_recurrence) {
+                        double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+                        in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+                        out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+                    }
+                } else {
+                    return;
+                }
 
-                    dddl += 12.0 * alpha + dddl_counts + (2.0 * alpha * alpha / beta / beta / beta * boost::math::trigamma(alpha/beta) + alpha * alpha * alpha/beta/beta/beta/beta*boost::math::polygamma(2, alpha/beta)) * total;
+                if (!in_kmer && !out_kmer)
+                    return;
 
-                    dl += (beta - alpha * log(p) + alpha * boost::math::digamma(alpha / beta)) * total;
-                    ddl += (1.0 - alpha * alpha / beta / beta * boost::math::trigamma(alpha / beta)) * total;
-                    return std::make_tuple(dl, ddl, dddl);
-                    // return std::make_pair(dl, ddl);
-                }, old_beta, 0.0, real_sum, 30);
-            };
+                size_t n = in_sum + out_sum;
+                std::lock_guard<std::mutex> lock(hist_mu);
+                ++merged_hist[n];
+            }, false);
 
-            auto get_alpha = [&](const double old_alpha, double &beta) {
-                return boost::math::tools::schroder_iterate([&,total,p](const double alpha) {
-                    beta = get_beta(alpha, beta);
-                    common::logger->trace("A: a: {}\tb: {}", alpha, beta);
-                    double dl = 0;
-                    double ddl = 0;
-                    double dddl = 0;
-                    for (const auto &[v, c] : joint_counts) {
-                        dl += boost::math::digamma(alpha / beta + v) * c;
-                        ddl += boost::math::trigamma(alpha / beta + v) * c;
-                        dddl += boost::math::trigamma(alpha / beta + v) * c;
-                    }
-                    dl /= beta;
-                    ddl /= beta * beta;
-                    dddl /= beta * beta * beta;
-
-                    size_t cur_total = total;
-                    auto it = joint_counts.begin();
-                    if (it->first == 0) {
-                        cur_total -= it->second;
-                        ++it;
-                    }
+            double rsum = 0;
+            double lrsum = 0;
+            double rlrsum = 0;
+            for (const auto &[n, c] : merged_hist) {
+                double r = p1p * n / groups.size();
+                r = 1.0 / r;
+                rsum += r * c;
+                lrsum += log(r) * c;
+                rlrsum += r * log(r) * c;
+            }
 
-                    double dl_counts = 0.0;
-                    double ddl_counts = 0.0;
-                    double dddl_counts = 0.0;
-                    for (int64_t j = 0; j < joint_counts.back().first; ++j) {
-                        double v2 = static_cast<double>(cur_total) / pow(alpha + beta * j, 2.0);
-                        double v3 = v2 / (alpha + beta * j);
-                        double v4 = v3 / (alpha + beta * j);
-                        double v5 = v4 / (alpha + beta * j);
-                        dl_counts += -v2 / 2 + alpha * v3;
-                        ddl_counts += v3 * 2 - 3.0 * alpha * v4;
-                        dddl_counts += -v4 * 9 + 12.0 * alpha * v5;
-                        if (j + 1 == it->first) {
-                            if (it == joint_counts.end() || it->second > cur_total)
-                                throw std::runtime_error("FFFFFF");
-                            cur_total -= it->second;
-                            ++it;
-                        }
-                    }
+            size_t total = nelem * groups.size();
+            double mu = rsum / total;
+            double mul = lrsum / total;
+
+            double theta = rlrsum / total - mu * mul;
+            double alpha = mu / theta;
+
+            theta = theta * total / (total - 1);
+            alpha -= (alpha * 3 - alpha * 2 / 3 / (alpha + 1) - alpha * 4 / 5 / (alpha + 1) / alpha + 1) / total;
+
+            double beta = 1.0 / theta;
+
+            // size_t total = nelem * (num_labels_in + num_labels_out);
+            // size_t total_zeros = 0;
+            // double mu = static_cast<double>(total_kmers) / total;
+            // int64_t ssq = 0;
+            // VectorMap<int64_t, size_t> joint_map;
+            // size_t meas_total = 0;
+            // int64_t meas_total_kmers = 0;
+            // for (size_t j = 0; j < groups.size(); ++j) {
+            //     for (const auto &[k, m] : count_maps[j]) {
+            //         const auto &[v, c] = m;
+            //         joint_map[v] += c;
+            //         ssq += v * v * c;
+            //         meas_total_kmers += v * c;
+            //         if (v == 0)
+            //             total_zeros += c;
+            //         meas_total += c;
+            //     }
+            // }
+            // if (meas_total != total) {
+            //     common::logger->error("{} != {}", meas_total, total);
+            //     throw std::runtime_error("internal");
+            // }
+            // if (meas_total_kmers != total_kmers) {
+            //     common::logger->error("{} != {}", meas_total_kmers, total_kmers);
+            //     throw std::runtime_error("internal2");
+            // }
+            // auto joint_counts = const_cast<std::vector<std::pair<int64_t, size_t>>&&>(joint_map.values_container());
+            // std::sort(joint_counts.begin(), joint_counts.end());
+
+            // double mu2 = mu * mu;
+            // double var = static_cast<double>(ssq) / total - mu2;
+            // if (mu >= var) {
+            //     common::logger->error("{} >= {}", mu, var);
+            //     throw std::runtime_error("Data is underdispersed");
+            // }
 
-                    dl += dl_counts;
-                    ddl += ddl_counts;
-                    dddl += dddl_counts;
+            // double p = target_p;
 
-                    dl += (-boost::math::digamma(alpha) + log(p) / beta - 1.0/alpha - 0.5/alpha/alpha + log(alpha) - boost::math::digamma(alpha/beta)/beta) * total;
-                    ddl += (-boost::math::trigamma(alpha) - boost::math::trigamma(alpha/beta)/beta/beta + 1.0/alpha/alpha + 1.0/alpha/alpha/alpha + 1.0/alpha) * total;
-                    dddl += (-boost::math::polygamma(2, alpha) - boost::math::polygamma(2, alpha/beta)/beta/beta/beta - 2.0/alpha/alpha/alpha - 3.0/alpha/alpha/alpha/alpha - 1.0/alpha/alpha) * total;
-                    return std::make_tuple(dl, ddl, dddl);
-                    // return std::make_pair(dl, ddl);
-                }, old_alpha, 0.0, real_sum, 30);
-            };
+            // double beta = (1.0 - p) * mu / (p * var - mu);
+            // if (beta <= 0) {
+            //     common::logger->warn("Underdispersed data: b: {}", beta);
+            //     beta = 1.0;
+            // }
 
-            double beta = (1.0 - p) * mu / (p * var - mu);
-            if (beta <= 0)
-                beta = 1.0;
+            // double alpha = beta * p / (1.0 - p) * mu;
 
-            double alpha = beta * p / (1.0 - p) * mu;
-            common::logger->trace("Initial MoM guess: a: {}\tb: {}", alpha, beta);
+            // double base_l = 0;
+            // for (const auto &[k, c] : joint_counts) {
+            //     base_l += (log1p(-p) * k - lgamma(k + 1)) * c;
+            // }
 
+            // auto get_nb_l = [&](double r) {
+            //     double l = base_l - lgamma(r) * total + r * log(p);
+            //     for (const auto &[k, c] : joint_counts) {
+            //         l += lgamma(r + k) * c;
+            //     }
+
+            //     return l;
+            // };
+
+            // auto get_gamma_l = [&](double r, double alpha, double beta) {
+            //     return alpha * log(beta) - lgamma(alpha) + (alpha-1) * log(r) - beta * r;
+            // };
+
+            // auto get_base_exp = [&](double alpha, double beta) {
+            //     return base_l + (alpha * log(beta) - lgamma(alpha)) * total;
+            // };
+
+            // auto get_exp = [&](double l, double alpha, double beta) {
+            //     double r_mu = alpha / beta;
+            //     double lgm1 = lgamma(r_mu + 1);
+            //     double tg1 = boost::math::trigamma(1);
+            //     for (const auto &[k, c] : joint_counts) {
+            //         if (k > 1) {
+            //             l += (lgamma(r_mu + k) - lgm1) * c;
+            //             l += (boost::math::trigamma(k) - tg1) * r_mu / 2 / beta * c;
+            //         }
+            //     }
+            //     l += (r_mu * log(p) - alpha) * total + (boost::math::digamma(alpha) - log(beta)) * ((alpha-1) * total + total - total_zeros);
+            //     return l;
+            // };
+            // common::logger->trace("Initial MoM guess: a: {}\tb: {}\tE[r]: {}\tVar(r): {}\tE[log(r)]: {}\tl: {}",
+            //                       alpha, beta,
+            //                       alpha/beta, alpha/beta/beta,
+            //                       boost::math::digamma(alpha) - log(beta),
+            //                       get_exp(get_base_exp(alpha, beta), alpha, beta));
+
+            // auto get_alpha_dl_ddl = [&](double alpha, double beta) {
+            //     double dl = 0;
+            //     double ddl = 0;
+            //     for (const auto &[k, c] : joint_counts) {
+            //         if (k > 1) {
+            //             dl += (boost::math::digamma(alpha/beta + k) - boost::math::digamma(alpha/beta+1) + boost::math::trigamma(k)/2/beta) * c;
+            //             ddl += (boost::math::trigamma(alpha/beta + k) - boost::math::trigamma(alpha/beta+1)) * c;
+            //         }
+            //     }
+            //     dl = dl / beta + (log(beta) - boost::math::digamma(alpha) + log(p)/beta - 1 + (alpha-1)*boost::math::trigamma(alpha) + boost::math::digamma(alpha)) * total;
+            //     ddl += dl / beta / beta + ((alpha-1) * boost::math::polygamma(2, alpha) + boost::math::trigamma(alpha)) * total;
+            //     return std::make_pair(dl, ddl);
+            // };
+            // uintmax_t max_iter = 10;
+            // auto get_alpha = [&](double alpha, double beta) {
+            //     double old_alpha = alpha;
+            //     alpha = boost::math::tools::newton_raphson_iterate([&](double alpha) {
+            //         return get_alpha_dl_ddl(alpha, beta);
+            //     }, alpha, 0.0, real_sum, 30);
+            //     auto [dl, ddl] = get_alpha_dl_ddl(alpha, beta);
+            //     if (ddl > 0) {
+            //         common::logger->warn("Found local minimum: a: {}\tb: {}\tdl: {}\tddl: {}", alpha, beta, dl, ddl);
+            //         return boost::math::tools::brent_find_minima([&](double alpha) {
+            //             return -get_exp(get_base_exp(alpha, beta), alpha, beta);
+            //         }, old_alpha / 1.05, old_alpha * 1.05, 10, max_iter);
+            //         // throw std::runtime_error("Fail");
+            //     }
+            //     return std::make_pair(alpha, -get_exp(get_base_exp(alpha, beta), alpha, beta));
+            // };
+
+            // double nl = 0;
             // while (true) {
             //     double old_alpha = alpha;
             //     double old_beta = beta;
 
-                alpha = get_alpha(alpha, beta);
-            //     common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
-
-                beta = get_beta(alpha, beta);
-            //     common::logger->trace("Cur: a: {}\tb: {}", alpha, beta);
-
-            //     if ((alpha - old_alpha) / alpha < 1e-5 && (beta - old_beta) / beta < 1e-5)
+            //     // maximize log likelihood (using terms dependent on r)
+            //     while (true) {
+            //         double old_alpha = alpha;
+            //         double old_beta = beta;
+            //         std::tie(alpha, nl) = get_alpha(alpha, beta);
+            //         // std::tie(alpha, nl) = boost::math::tools::brent_find_minima([&](double alpha) {
+            //         //     return -get_exp(get_base_exp(alpha, beta), alpha, beta);
+            //         // }, old_alpha / 1.05, old_alpha * 1.05, 10, max_iter);
+            //         common::logger->trace("A: a: {}\tb: {}\tE[r]: {}\tVar(r): {}\tl: {}", alpha, beta, alpha/beta, alpha/beta/beta, -nl);
+
+            //         std::tie(beta, nl) = boost::math::tools::brent_find_minima([&](double beta) {
+            //             return -get_exp(get_base_exp(alpha, beta), alpha, beta);
+            //         }, old_beta / 1.05, old_beta * 1.05, 10, max_iter);
+            //         common::logger->trace("B: a: {}\tb: {}\tE[r]: {}\tVar(r): {}\tl: {}", alpha, beta, alpha/beta, alpha/beta/beta, -nl);
+
+            //         if (abs(alpha - old_alpha) / alpha < 1e-5 && abs(beta - old_beta) / beta < 1e-5)
+            //             break;
+            //     }
+
+            //     if (abs(alpha - old_alpha) / alpha < 1e-5 && abs(beta - old_beta) / beta < 1e-5)
             //         break;
             // }
 
             double gamma_mu = alpha / beta;
             common::logger->trace("Gamma-NB EM fit: p: {}\ta: {}\tb: {}\tE[r]: {}\tVar(r): {}",
-                                  p, alpha, beta, gamma_mu, gamma_mu / beta);
+                                  p, alpha, beta,
+                                  gamma_mu, gamma_mu/beta);
 
             auto get_r = [alpha,beta,lp=log(p),sum=static_cast<double>(total_kmers),gamma_mu,gs=groups.size()](const PairContainer &row) {
-                return boost::math::tools::newton_raphson_iterate([&](double r) {
-                    double dl = (lp + (alpha - 1)/r - beta - boost::math::digamma(r)) * gs;
-                    double ddl = (-(alpha - 1)/r/r - boost::math::trigamma(r)) * gs;
+                auto get_dl_ddl = [&](double a) {
+                    double r = 1.0/a;
+                    double dl = (a * (alpha-1) - beta * a * a - lp) * gs;
+                    double ddl = (alpha-1 - 2.0 * beta * a) * gs;
                     for (const auto &[j, c] : row) {
-                        dl += boost::math::digamma(r + c);
-                        ddl += boost::math::trigamma(r + c);
+                        dl -= boost::math::digamma(r + c) - boost::math::digamma(r);
+                        ddl -= (boost::math::trigamma(r + c) - boost::math::trigamma(r)) * r * r;
                     }
-                    dl += boost::math::digamma(r) * (gs - row.size());
-                    ddl += boost::math::trigamma(r) * (gs - row.size());
+                    // double r = 1.0/a;
+                    // double dl = (lp - (alpha-1)/r - beta/r/r - boost::math::digamma(r)) * gs;
+                    // double ddl = ((alpha-1)/r/r + 2.0*beta/r/r/r - boost::math::trigamma(r)) * gs;
+                    // for (const auto &[j, c] : row) {
+                    //     dl += boost::math::digamma(r + c);
+                    //     ddl += boost::math::trigamma(r + c);
+                    // }
+                // auto get_dl_ddl = [&](double r) {
+                    // r = 1.0 / r;
+                    // double dl = (lp + (alpha - 1)/r - beta - boost::math::digamma(r)) * gs;
+                    // double ddl = (-(alpha - 1)/r/r - boost::math::trigamma(r)) * gs;
+                    // for (const auto &[j, c] : row) {
+                    //     dl += boost::math::digamma(r + c);
+                    //     ddl += boost::math::trigamma(r + c);
+                    // }
+                    // dl += boost::math::digamma(r) * (gs - row.size());
+                    // ddl += boost::math::trigamma(r) * (gs - row.size());
                     return std::make_pair(dl, ddl);
-                }, gamma_mu, 0.0, sum, 30);
+                };
+
+                double a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, gamma_mu, 1.0 / sum, 10000.0, 30);
+                double r = 1.0/a;
+                auto [dl, ddl] = get_dl_ddl(a);
+                if (ddl > 0) {
+                    common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r, dl, ddl);
+                    throw std::domain_error("GGG");
+                }
+
+                return r;
             };
 
             auto get_deviance = [p](double invphi, double y) {

From b6128537ab32550cbc16c9d25bf29c3fa9c34703 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 18 Aug 2024 22:54:56 +0200
Subject: [PATCH 279/410] this works better: use log-normal

---
 .../src/graph/annotated_graph_algorithm.cpp   | 237 +++---------------
 1 file changed, 40 insertions(+), 197 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9421885d2d..0501179ff2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1071,217 +1071,60 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ++merged_hist[n];
             }, false);
 
-            double rsum = 0;
-            double lrsum = 0;
-            double rlrsum = 0;
+            size_t total = nelem * groups.size();
+            double ln_mu = 0.0;
+            double ln_var = 0.0;
             for (const auto &[n, c] : merged_hist) {
                 double r = p1p * n / groups.size();
-                r = 1.0 / r;
-                rsum += r * c;
-                lrsum += log(r) * c;
-                rlrsum += r * log(r) * c;
+                double a = 1.0 / r;
+                ln_mu += log(a) * c;
+                ln_var += log(a) * log(a) * c;
             }
+            ln_mu /= total;
+            ln_var = ln_var / total - ln_mu * ln_mu;
+            common::logger->trace("Log-normal fit: mu: {}\tvar: {}\texp(-mu): {}", ln_mu, ln_var, exp(-ln_mu));
+
+            auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
+                double a_guess = 0.0;
+                for (const auto &[j, c] : row) {
+                    a_guess += c;
+                }
+                a_guess /= total;
+                a_guess = p1p * a_guess;
 
-            size_t total = nelem * groups.size();
-            double mu = rsum / total;
-            double mul = lrsum / total;
-
-            double theta = rlrsum / total - mu * mul;
-            double alpha = mu / theta;
-
-            theta = theta * total / (total - 1);
-            alpha -= (alpha * 3 - alpha * 2 / 3 / (alpha + 1) - alpha * 4 / 5 / (alpha + 1) / alpha + 1) / total;
-
-            double beta = 1.0 / theta;
-
-            // size_t total = nelem * (num_labels_in + num_labels_out);
-            // size_t total_zeros = 0;
-            // double mu = static_cast<double>(total_kmers) / total;
-            // int64_t ssq = 0;
-            // VectorMap<int64_t, size_t> joint_map;
-            // size_t meas_total = 0;
-            // int64_t meas_total_kmers = 0;
-            // for (size_t j = 0; j < groups.size(); ++j) {
-            //     for (const auto &[k, m] : count_maps[j]) {
-            //         const auto &[v, c] = m;
-            //         joint_map[v] += c;
-            //         ssq += v * v * c;
-            //         meas_total_kmers += v * c;
-            //         if (v == 0)
-            //             total_zeros += c;
-            //         meas_total += c;
-            //     }
-            // }
-            // if (meas_total != total) {
-            //     common::logger->error("{} != {}", meas_total, total);
-            //     throw std::runtime_error("internal");
-            // }
-            // if (meas_total_kmers != total_kmers) {
-            //     common::logger->error("{} != {}", meas_total_kmers, total_kmers);
-            //     throw std::runtime_error("internal2");
-            // }
-            // auto joint_counts = const_cast<std::vector<std::pair<int64_t, size_t>>&&>(joint_map.values_container());
-            // std::sort(joint_counts.begin(), joint_counts.end());
-
-            // double mu2 = mu * mu;
-            // double var = static_cast<double>(ssq) / total - mu2;
-            // if (mu >= var) {
-            //     common::logger->error("{} >= {}", mu, var);
-            //     throw std::runtime_error("Data is underdispersed");
-            // }
-
-            // double p = target_p;
-
-            // double beta = (1.0 - p) * mu / (p * var - mu);
-            // if (beta <= 0) {
-            //     common::logger->warn("Underdispersed data: b: {}", beta);
-            //     beta = 1.0;
-            // }
-
-            // double alpha = beta * p / (1.0 - p) * mu;
-
-            // double base_l = 0;
-            // for (const auto &[k, c] : joint_counts) {
-            //     base_l += (log1p(-p) * k - lgamma(k + 1)) * c;
-            // }
-
-            // auto get_nb_l = [&](double r) {
-            //     double l = base_l - lgamma(r) * total + r * log(p);
-            //     for (const auto &[k, c] : joint_counts) {
-            //         l += lgamma(r + k) * c;
-            //     }
-
-            //     return l;
-            // };
-
-            // auto get_gamma_l = [&](double r, double alpha, double beta) {
-            //     return alpha * log(beta) - lgamma(alpha) + (alpha-1) * log(r) - beta * r;
-            // };
-
-            // auto get_base_exp = [&](double alpha, double beta) {
-            //     return base_l + (alpha * log(beta) - lgamma(alpha)) * total;
-            // };
-
-            // auto get_exp = [&](double l, double alpha, double beta) {
-            //     double r_mu = alpha / beta;
-            //     double lgm1 = lgamma(r_mu + 1);
-            //     double tg1 = boost::math::trigamma(1);
-            //     for (const auto &[k, c] : joint_counts) {
-            //         if (k > 1) {
-            //             l += (lgamma(r_mu + k) - lgm1) * c;
-            //             l += (boost::math::trigamma(k) - tg1) * r_mu / 2 / beta * c;
-            //         }
-            //     }
-            //     l += (r_mu * log(p) - alpha) * total + (boost::math::digamma(alpha) - log(beta)) * ((alpha-1) * total + total - total_zeros);
-            //     return l;
-            // };
-            // common::logger->trace("Initial MoM guess: a: {}\tb: {}\tE[r]: {}\tVar(r): {}\tE[log(r)]: {}\tl: {}",
-            //                       alpha, beta,
-            //                       alpha/beta, alpha/beta/beta,
-            //                       boost::math::digamma(alpha) - log(beta),
-            //                       get_exp(get_base_exp(alpha, beta), alpha, beta));
-
-            // auto get_alpha_dl_ddl = [&](double alpha, double beta) {
-            //     double dl = 0;
-            //     double ddl = 0;
-            //     for (const auto &[k, c] : joint_counts) {
-            //         if (k > 1) {
-            //             dl += (boost::math::digamma(alpha/beta + k) - boost::math::digamma(alpha/beta+1) + boost::math::trigamma(k)/2/beta) * c;
-            //             ddl += (boost::math::trigamma(alpha/beta + k) - boost::math::trigamma(alpha/beta+1)) * c;
-            //         }
-            //     }
-            //     dl = dl / beta + (log(beta) - boost::math::digamma(alpha) + log(p)/beta - 1 + (alpha-1)*boost::math::trigamma(alpha) + boost::math::digamma(alpha)) * total;
-            //     ddl += dl / beta / beta + ((alpha-1) * boost::math::polygamma(2, alpha) + boost::math::trigamma(alpha)) * total;
-            //     return std::make_pair(dl, ddl);
-            // };
-            // uintmax_t max_iter = 10;
-            // auto get_alpha = [&](double alpha, double beta) {
-            //     double old_alpha = alpha;
-            //     alpha = boost::math::tools::newton_raphson_iterate([&](double alpha) {
-            //         return get_alpha_dl_ddl(alpha, beta);
-            //     }, alpha, 0.0, real_sum, 30);
-            //     auto [dl, ddl] = get_alpha_dl_ddl(alpha, beta);
-            //     if (ddl > 0) {
-            //         common::logger->warn("Found local minimum: a: {}\tb: {}\tdl: {}\tddl: {}", alpha, beta, dl, ddl);
-            //         return boost::math::tools::brent_find_minima([&](double alpha) {
-            //             return -get_exp(get_base_exp(alpha, beta), alpha, beta);
-            //         }, old_alpha / 1.05, old_alpha * 1.05, 10, max_iter);
-            //         // throw std::runtime_error("Fail");
-            //     }
-            //     return std::make_pair(alpha, -get_exp(get_base_exp(alpha, beta), alpha, beta));
-            // };
-
-            // double nl = 0;
-            // while (true) {
-            //     double old_alpha = alpha;
-            //     double old_beta = beta;
-
-            //     // maximize log likelihood (using terms dependent on r)
-            //     while (true) {
-            //         double old_alpha = alpha;
-            //         double old_beta = beta;
-            //         std::tie(alpha, nl) = get_alpha(alpha, beta);
-            //         // std::tie(alpha, nl) = boost::math::tools::brent_find_minima([&](double alpha) {
-            //         //     return -get_exp(get_base_exp(alpha, beta), alpha, beta);
-            //         // }, old_alpha / 1.05, old_alpha * 1.05, 10, max_iter);
-            //         common::logger->trace("A: a: {}\tb: {}\tE[r]: {}\tVar(r): {}\tl: {}", alpha, beta, alpha/beta, alpha/beta/beta, -nl);
-
-            //         std::tie(beta, nl) = boost::math::tools::brent_find_minima([&](double beta) {
-            //             return -get_exp(get_base_exp(alpha, beta), alpha, beta);
-            //         }, old_beta / 1.05, old_beta * 1.05, 10, max_iter);
-            //         common::logger->trace("B: a: {}\tb: {}\tE[r]: {}\tVar(r): {}\tl: {}", alpha, beta, alpha/beta, alpha/beta/beta, -nl);
-
-            //         if (abs(alpha - old_alpha) / alpha < 1e-5 && abs(beta - old_beta) / beta < 1e-5)
-            //             break;
-            //     }
-
-            //     if (abs(alpha - old_alpha) / alpha < 1e-5 && abs(beta - old_beta) / beta < 1e-5)
-            //         break;
-            // }
-
-            double gamma_mu = alpha / beta;
-            common::logger->trace("Gamma-NB EM fit: p: {}\ta: {}\tb: {}\tE[r]: {}\tVar(r): {}",
-                                  p, alpha, beta,
-                                  gamma_mu, gamma_mu/beta);
-
-            auto get_r = [alpha,beta,lp=log(p),sum=static_cast<double>(total_kmers),gamma_mu,gs=groups.size()](const PairContainer &row) {
                 auto get_dl_ddl = [&](double a) {
-                    double r = 1.0/a;
-                    double dl = (a * (alpha-1) - beta * a * a - lp) * gs;
-                    double ddl = (alpha-1 - 2.0 * beta * a) * gs;
+                    double r = 1.0 / a;
+                    double dl = (-lp + boost::math::digamma(r) + a - (a*(log(a)-ln_mu))/ln_var) * gs;
+                    double ddl = (-r*r*boost::math::trigamma(r) + 1 - (log(a)-ln_mu)/ln_var - 1.0/ln_var) * gs;
+
                     for (const auto &[j, c] : row) {
-                        dl -= boost::math::digamma(r + c) - boost::math::digamma(r);
-                        ddl -= (boost::math::trigamma(r + c) - boost::math::trigamma(r)) * r * r;
+                        dl -= boost::math::digamma(r + c);
+                        ddl += boost::math::trigamma(r + c) * r * r;
                     }
-                    // double r = 1.0/a;
-                    // double dl = (lp - (alpha-1)/r - beta/r/r - boost::math::digamma(r)) * gs;
-                    // double ddl = ((alpha-1)/r/r + 2.0*beta/r/r/r - boost::math::trigamma(r)) * gs;
-                    // for (const auto &[j, c] : row) {
-                    //     dl += boost::math::digamma(r + c);
-                    //     ddl += boost::math::trigamma(r + c);
-                    // }
-                // auto get_dl_ddl = [&](double r) {
-                    // r = 1.0 / r;
-                    // double dl = (lp + (alpha - 1)/r - beta - boost::math::digamma(r)) * gs;
-                    // double ddl = (-(alpha - 1)/r/r - boost::math::trigamma(r)) * gs;
-                    // for (const auto &[j, c] : row) {
-                    //     dl += boost::math::digamma(r + c);
-                    //     ddl += boost::math::trigamma(r + c);
-                    // }
-                    // dl += boost::math::digamma(r) * (gs - row.size());
-                    // ddl += boost::math::trigamma(r) * (gs - row.size());
+
+                    dl -= boost::math::digamma(r) * (gs - row.size());
+                    ddl += boost::math::trigamma(r) * r * r * (gs - row.size());
+
                     return std::make_pair(dl, ddl);
                 };
 
-                double a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, gamma_mu, 1.0 / sum, 10000.0, 30);
-                double r = 1.0/a;
+                double a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, a_guess, 1.0 / real_sum, real_sum, 30);
                 auto [dl, ddl] = get_dl_ddl(a);
                 if (ddl > 0) {
-                    common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r, dl, ddl);
-                    throw std::domain_error("GGG");
+                    a = boost::math::tools::brent_find_minima([&](double a) {
+                        double r = 1.0 / a;
+                        double l = (lp * r - lgamma(r) - log(a) - pow(log(a)-ln_mu, 2.0)/(2*ln_var)) * gs;
+                        for (const auto &[j, c] : row) {
+                            l += lgamma(r + c);
+                        }
+                        l += lgamma(r) * (gs - row.size());
+                        return -l;
+                    }, 1.0 / real_sum, real_sum, 30).first;
+                    // common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r, dl, ddl);
+                    // throw std::domain_error("GGG");
                 }
 
-                return r;
+                return 1.0 / a;
             };
 
             auto get_deviance = [p](double invphi, double y) {

From fe639b66696cb7732a3b7d3effea53d34662802f Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 19 Aug 2024 03:00:19 +0200
Subject: [PATCH 280/410] lognormal test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 125 ++++++++++++++----
 1 file changed, 99 insertions(+), 26 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0501179ff2..799101c7d1 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1085,45 +1085,111 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->trace("Log-normal fit: mu: {}\tvar: {}\texp(-mu): {}", ln_mu, ln_var, exp(-ln_mu));
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
-                double a_guess = 0.0;
-                for (const auto &[j, c] : row) {
-                    a_guess += c;
-                }
-                a_guess /= total;
-                a_guess = p1p * a_guess;
-
                 auto get_dl_ddl = [&](double a) {
                     double r = 1.0 / a;
-                    double dl = (-lp + boost::math::digamma(r) + a - (a*(log(a)-ln_mu))/ln_var) * gs;
-                    double ddl = (-r*r*boost::math::trigamma(r) + 1 - (log(a)-ln_mu)/ln_var - 1.0/ln_var) * gs;
+                    double dl = (-lp + a - (a*(log(a)-ln_mu))/ln_var) * gs + boost::math::digamma(r) * row.size();
+                    double ddl = (1.0 - (log(a)-ln_mu)/ln_var - 1.0/ln_var) * gs - r*r*boost::math::trigamma(r) * row.size();
 
                     for (const auto &[j, c] : row) {
                         dl -= boost::math::digamma(r + c);
                         ddl += boost::math::trigamma(r + c) * r * r;
                     }
 
-                    dl -= boost::math::digamma(r) * (gs - row.size());
-                    ddl += boost::math::trigamma(r) * r * r * (gs - row.size());
-
                     return std::make_pair(dl, ddl);
                 };
-
-                double a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, a_guess, 1.0 / real_sum, real_sum, 30);
+                double a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, ln_mu, 1.0 / real_sum, real_sum, 30);
                 auto [dl, ddl] = get_dl_ddl(a);
                 if (ddl > 0) {
-                    a = boost::math::tools::brent_find_minima([&](double a) {
-                        double r = 1.0 / a;
-                        double l = (lp * r - lgamma(r) - log(a) - pow(log(a)-ln_mu, 2.0)/(2*ln_var)) * gs;
-                        for (const auto &[j, c] : row) {
-                            l += lgamma(r + c);
-                        }
-                        l += lgamma(r) * (gs - row.size());
-                        return -l;
-                    }, 1.0 / real_sum, real_sum, 30).first;
-                    // common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r, dl, ddl);
+                    return 0.0;
+                    // std::vector<int64_t> counts(gs);
+                    // for (const auto &[j, c] : row) {
+                    //     counts[j] = c;
+                    // }
+                    // common::logger->warn("Found local minimum instead: r: {}\tdl: {}\tddl: {}\tc: {}", 1/a, dl, ddl, fmt::join(counts,","));
+                    // // common::logger->warn("Found local minimum instead: r: {}\tl: {}\tdl: {}\tddl: {}", 1/a, get_l(a), dl, ddl);
                     // throw std::domain_error("GGG");
                 }
 
+                // std::vector<int64_t> counts(gs);
+                // double a_guess = 0.0;
+                // for (const auto &[j, c] : row) {
+                //     a_guess += c;
+                //     counts[j] = c;
+                // }
+                // a_guess /= total;
+                // a_guess = p1p * a_guess;
+
+                // auto get_l = [&](double a) {
+                //     double r = 1.0 / a;
+                //     double l = (lp * r - lgamma(r) - log(a) - pow(log(a)-ln_mu, 2.0)/(2*ln_var)) * gs;
+                //     for (const auto &[j, c] : row) {
+                //         l += lgamma(r + c);
+                //     }
+                //     l += lgamma(r) * (gs - row.size());
+                //     return l;
+                // };
+
+                // auto get_dl_ddl = [&](double a) {
+                //     double r = 1.0 / a;
+                //     double dl = (-lp + boost::math::digamma(r) + a - (a*(log(a)-ln_mu))/ln_var) * gs;
+                //     double ddl = (-r*r*boost::math::trigamma(r) + 1 - (log(a)-ln_mu)/ln_var - 1.0/ln_var) * gs;
+
+                //     for (const auto &[j, c] : row) {
+                //         dl -= boost::math::digamma(r + c);
+                //         ddl += boost::math::trigamma(r + c) * r * r;
+                //     }
+
+                //     dl -= boost::math::digamma(r) * (gs - row.size());
+                //     ddl += boost::math::trigamma(r) * r * r * (gs - row.size());
+
+                //     return std::make_pair(dl, ddl);
+                // };
+
+                // double a = a_guess;
+                // double l = -std::numeric_limits<double>::max();
+                // uintmax_t max_iter = 100;
+                // double margin = 1.1;
+                // while (true) {
+                //     double old_a = a;
+                //     double old_l = l;
+                //     try {
+                //         std::tie(a, l) = boost::math::tools::brent_find_minima([&](double a) { return -get_l(a); },
+                //                                                             a_guess / margin, a_guess * margin, 30, max_iter);
+                //         common::logger->trace("a: {}\tl: {}\tc: {}", a, l, fmt::join(counts, ","));
+                //         if (l < old_l) {
+                //             auto [dl, ddl] = get_dl_ddl(a);
+                //             common::logger->warn("Moving down: r: {}\tl: {}\tdl: {}\tddl: {}", 1/a, l, dl, ddl);
+                //             throw std::domain_error("FFF");
+                //         }
+                //         if (abs(a - old_a) / a < 1e-5) {
+                //             auto [dl, ddl] = get_dl_ddl(a);
+                //             if (ddl > 0) {
+                //                 common::logger->warn("Found local minimum instead: r: {}\tl: {}\tdl: {}\tddl: {}", 1/a, l, dl, ddl);
+                //                 throw std::domain_error("GGG");
+                //             }
+                //             break;
+                //         }
+                //     } catch (...) {
+                //         common::logger->error("Something happened: r: {}", 1/a);
+                //         throw std::runtime_error("FOFOF");
+                //     }
+                //     margin /= 2;
+                // }
+                // try {
+                //     a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, a_guess, 1.0 / real_sum, real_sum, 30);
+                //     auto [dl, ddl] = get_dl_ddl(a);
+                //     if (ddl > 0) {
+                //         common::logger->warn("Found local minimum instead: r: {}\tl: {}\tdl: {}\tddl: {}", 1/a, get_l(a), dl, ddl);
+                //         throw std::domain_error("GGG");
+                //     }
+                // } catch (std::domain_error &e) {
+                //     double l;
+                //     std::tie(a, l) = boost::math::tools::brent_find_minima([&](double a) { return -get_l(a); },
+                //                                               1.0 / real_sum, real_sum, 30);
+                //     auto [dl, ddl] = get_dl_ddl(a);
+                //     common::logger->warn("Best values: r: {}\tl: {}\tdl: {}\tddl: {}", 1/a, l, dl, ddl);
+                // }
+
                 return 1.0 / a;
             };
 
@@ -1138,11 +1204,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
             };
 
-            compute_min_pval = [get_r,num_labels_in,num_labels_out,get_deviance](int64_t n, const auto &row) {
+            compute_min_pval = [get_r,num_labels_in,num_labels_out,get_deviance,gs=groups.size()](int64_t n, const auto &row) {
                 if (row.empty())
                     return 1.0;
 
                 double r = get_r(row);
+                if (r == 0.0)
+                    return 1.0;
+
                 double r_in = r * num_labels_in;
                 double r_out = r * num_labels_out;
                 double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
@@ -1201,11 +1270,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return std::min(1.0, pval);
             };
 
-            compute_pval = [get_r,num_labels_in,num_labels_out,get_deviance](int64_t in_sum, int64_t out_sum, const auto &row) {
+            compute_pval = [get_r,num_labels_in,num_labels_out,get_deviance,gs=groups.size()](int64_t in_sum, int64_t out_sum, const auto &row) {
                 if (row.empty())
                     return 1.0;
 
                 double r = get_r(row);
+
+                if (r == 0.0)
+                    return 1.0;
+
                 double r_in = r * num_labels_in;
                 double r_out = r * num_labels_out;
                 double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);

From 7cccc9fdc72356c6a96029f87b920c70ff525512 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 19 Aug 2024 11:03:01 +0200
Subject: [PATCH 281/410] tt

---
 .../src/graph/annotated_graph_algorithm.cpp   | 21 ++++++++++++-------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 799101c7d1..b4a1548272 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1085,6 +1085,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->trace("Log-normal fit: mu: {}\tvar: {}\texp(-mu): {}", ln_mu, ln_var, exp(-ln_mu));
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
+                int64_t n = 0;
+                for (const auto &[j, c] : row) {
+                    n += c;
+                }
+                double r_guess = p1p * n / gs;
                 auto get_dl_ddl = [&](double a) {
                     double r = 1.0 / a;
                     double dl = (-lp + a - (a*(log(a)-ln_mu))/ln_var) * gs + boost::math::digamma(r) * row.size();
@@ -1097,17 +1102,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                     return std::make_pair(dl, ddl);
                 };
-                double a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, ln_mu, 1.0 / real_sum, real_sum, 30);
+                double a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, 1.0 / r_guess, 1.0 / real_sum, real_sum, 30);
                 auto [dl, ddl] = get_dl_ddl(a);
                 if (ddl > 0) {
-                    return 0.0;
-                    // std::vector<int64_t> counts(gs);
-                    // for (const auto &[j, c] : row) {
-                    //     counts[j] = c;
-                    // }
-                    // common::logger->warn("Found local minimum instead: r: {}\tdl: {}\tddl: {}\tc: {}", 1/a, dl, ddl, fmt::join(counts,","));
+                    // return 0.0;
+                    std::vector<int64_t> counts(gs);
+                    for (const auto &[j, c] : row) {
+                        counts[j] = c;
+                    }
+                    common::logger->warn("Found local minimum instead: r_guess: {}\tr: {}\tdl: {}\tddl: {}\tc: {}", r_guess, 1/a, dl, ddl, fmt::join(counts,","));
                     // // common::logger->warn("Found local minimum instead: r: {}\tl: {}\tdl: {}\tddl: {}", 1/a, get_l(a), dl, ddl);
-                    // throw std::domain_error("GGG");
+                    throw std::domain_error("GGG");
                 }
 
                 // std::vector<int64_t> counts(gs);

From 3c5e1e8e0d9f076f3b52c3d1c715cffbb00a6b41 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 19 Aug 2024 11:43:41 +0200
Subject: [PATCH 282/410] this works

---
 .../src/graph/annotated_graph_algorithm.cpp   | 20 +++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b4a1548272..206e1178e8 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1090,6 +1090,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     n += c;
                 }
                 double r_guess = p1p * n / gs;
+                auto get_l = [&](double a) {
+                    double r = 1.0 / a;
+                    double l = (lp * r - log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var) * gs - lgamma(r) * row.size();
+                    for (const auto &[j, c] : row) {
+                        l += lgamma(r + c);
+                    }
+                    return l;
+                };
                 auto get_dl_ddl = [&](double a) {
                     double r = 1.0 / a;
                     double dl = (-lp + a - (a*(log(a)-ln_mu))/ln_var) * gs + boost::math::digamma(r) * row.size();
@@ -1102,16 +1110,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                     return std::make_pair(dl, ddl);
                 };
-                double a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, 1.0 / r_guess, 1.0 / real_sum, real_sum, 30);
+
+                auto [a, l] = boost::math::tools::brent_find_minima(
+                    [&](double a) { return -get_l(a); },
+                    1.0 / real_sum, real_sum, 30
+                );
+                l *= -1;
+                // double a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, 1.0 / r_guess, 1.0 / real_sum, real_sum, 30);
                 auto [dl, ddl] = get_dl_ddl(a);
                 if (ddl > 0) {
-                    // return 0.0;
                     std::vector<int64_t> counts(gs);
                     for (const auto &[j, c] : row) {
                         counts[j] = c;
                     }
-                    common::logger->warn("Found local minimum instead: r_guess: {}\tr: {}\tdl: {}\tddl: {}\tc: {}", r_guess, 1/a, dl, ddl, fmt::join(counts,","));
-                    // // common::logger->warn("Found local minimum instead: r: {}\tl: {}\tdl: {}\tddl: {}", 1/a, get_l(a), dl, ddl);
+                    common::logger->warn("Found local minimum instead: r_guess: {}\tr: {}\tl: {}\tdl: {}\tddl: {}\tc: {}", r_guess, 1/a, l, dl, ddl, fmt::join(counts,","));
                     throw std::domain_error("GGG");
                 }
 

From bd8b2aa6014c9b87b61b568f964abef73ab75221 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 19 Aug 2024 12:44:35 +0200
Subject: [PATCH 283/410] this works

---
 .../src/graph/annotated_graph_algorithm.cpp   | 115 ++----------------
 1 file changed, 11 insertions(+), 104 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 206e1178e8..543b7bc9d8 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1085,11 +1085,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->trace("Log-normal fit: mu: {}\tvar: {}\texp(-mu): {}", ln_mu, ln_var, exp(-ln_mu));
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
-                int64_t n = 0;
-                for (const auto &[j, c] : row) {
-                    n += c;
-                }
-                double r_guess = p1p * n / gs;
                 auto get_l = [&](double a) {
                     double r = 1.0 / a;
                     double l = (lp * r - log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var) * gs - lgamma(r) * row.size();
@@ -1098,114 +1093,26 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                     return l;
                 };
-                auto get_dl_ddl = [&](double a) {
-                    double r = 1.0 / a;
-                    double dl = (-lp + a - (a*(log(a)-ln_mu))/ln_var) * gs + boost::math::digamma(r) * row.size();
-                    double ddl = (1.0 - (log(a)-ln_mu)/ln_var - 1.0/ln_var) * gs - r*r*boost::math::trigamma(r) * row.size();
-
-                    for (const auto &[j, c] : row) {
-                        dl -= boost::math::digamma(r + c);
-                        ddl += boost::math::trigamma(r + c) * r * r;
-                    }
-
-                    return std::make_pair(dl, ddl);
-                };
-
-                auto [a, l] = boost::math::tools::brent_find_minima(
-                    [&](double a) { return -get_l(a); },
-                    1.0 / real_sum, real_sum, 30
-                );
-                l *= -1;
-                // double a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, 1.0 / r_guess, 1.0 / real_sum, real_sum, 30);
-                auto [dl, ddl] = get_dl_ddl(a);
-                if (ddl > 0) {
-                    std::vector<int64_t> counts(gs);
-                    for (const auto &[j, c] : row) {
-                        counts[j] = c;
-                    }
-                    common::logger->warn("Found local minimum instead: r_guess: {}\tr: {}\tl: {}\tdl: {}\tddl: {}\tc: {}", r_guess, 1/a, l, dl, ddl, fmt::join(counts,","));
-                    throw std::domain_error("GGG");
-                }
-
-                // std::vector<int64_t> counts(gs);
-                // double a_guess = 0.0;
-                // for (const auto &[j, c] : row) {
-                //     a_guess += c;
-                //     counts[j] = c;
-                // }
-                // a_guess /= total;
-                // a_guess = p1p * a_guess;
-
-                // auto get_l = [&](double a) {
-                //     double r = 1.0 / a;
-                //     double l = (lp * r - lgamma(r) - log(a) - pow(log(a)-ln_mu, 2.0)/(2*ln_var)) * gs;
-                //     for (const auto &[j, c] : row) {
-                //         l += lgamma(r + c);
-                //     }
-                //     l += lgamma(r) * (gs - row.size());
-                //     return l;
-                // };
-
-                // auto get_dl_ddl = [&](double a) {
+                // auto get_dl_ddl_dddl = [&](double a) {
                 //     double r = 1.0 / a;
-                //     double dl = (-lp + boost::math::digamma(r) + a - (a*(log(a)-ln_mu))/ln_var) * gs;
-                //     double ddl = (-r*r*boost::math::trigamma(r) + 1 - (log(a)-ln_mu)/ln_var - 1.0/ln_var) * gs;
+                //     double dl = (-lp + a - (a*(log(a)-ln_mu))/ln_var) * gs + boost::math::digamma(r) * row.size();
+                //     double ddl = (1.0 - (log(a)-ln_mu)/ln_var - 1.0/ln_var) * gs - r*r*boost::math::trigamma(r) * row.size();
+                //     double dddl = -gs * r / ln_var + (2.0*r*r*boost::math::trigamma(r)+r*r*r*boost::math::polygamma(2,r)) * r * row.size();
 
                 //     for (const auto &[j, c] : row) {
                 //         dl -= boost::math::digamma(r + c);
                 //         ddl += boost::math::trigamma(r + c) * r * r;
+                //         dddl -= boost::math::trigamma(r + c) * 2 * r * r * r + boost::math::polygamma(2.0, r + c) * r * r * r * r;
                 //     }
 
-                //     dl -= boost::math::digamma(r) * (gs - row.size());
-                //     ddl += boost::math::trigamma(r) * r * r * (gs - row.size());
-
-                //     return std::make_pair(dl, ddl);
+                //     return std::make_tuple(dl, ddl, dddl);
                 // };
 
-                // double a = a_guess;
-                // double l = -std::numeric_limits<double>::max();
-                // uintmax_t max_iter = 100;
-                // double margin = 1.1;
-                // while (true) {
-                //     double old_a = a;
-                //     double old_l = l;
-                //     try {
-                //         std::tie(a, l) = boost::math::tools::brent_find_minima([&](double a) { return -get_l(a); },
-                //                                                             a_guess / margin, a_guess * margin, 30, max_iter);
-                //         common::logger->trace("a: {}\tl: {}\tc: {}", a, l, fmt::join(counts, ","));
-                //         if (l < old_l) {
-                //             auto [dl, ddl] = get_dl_ddl(a);
-                //             common::logger->warn("Moving down: r: {}\tl: {}\tdl: {}\tddl: {}", 1/a, l, dl, ddl);
-                //             throw std::domain_error("FFF");
-                //         }
-                //         if (abs(a - old_a) / a < 1e-5) {
-                //             auto [dl, ddl] = get_dl_ddl(a);
-                //             if (ddl > 0) {
-                //                 common::logger->warn("Found local minimum instead: r: {}\tl: {}\tdl: {}\tddl: {}", 1/a, l, dl, ddl);
-                //                 throw std::domain_error("GGG");
-                //             }
-                //             break;
-                //         }
-                //     } catch (...) {
-                //         common::logger->error("Something happened: r: {}", 1/a);
-                //         throw std::runtime_error("FOFOF");
-                //     }
-                //     margin /= 2;
-                // }
-                // try {
-                //     a = boost::math::tools::newton_raphson_iterate(get_dl_ddl, a_guess, 1.0 / real_sum, real_sum, 30);
-                //     auto [dl, ddl] = get_dl_ddl(a);
-                //     if (ddl > 0) {
-                //         common::logger->warn("Found local minimum instead: r: {}\tl: {}\tdl: {}\tddl: {}", 1/a, get_l(a), dl, ddl);
-                //         throw std::domain_error("GGG");
-                //     }
-                // } catch (std::domain_error &e) {
-                //     double l;
-                //     std::tie(a, l) = boost::math::tools::brent_find_minima([&](double a) { return -get_l(a); },
-                //                                               1.0 / real_sum, real_sum, 30);
-                //     auto [dl, ddl] = get_dl_ddl(a);
-                //     common::logger->warn("Best values: r: {}\tl: {}\tdl: {}\tddl: {}", 1/a, l, dl, ddl);
-                // }
+                auto [a, l] = boost::math::tools::brent_find_minima(
+                    [&](double a) { return -get_l(a); },
+                    1.0 / real_sum, real_sum, 30
+                );
+                l *= -1;
 
                 return 1.0 / a;
             };

From bc82d3749098b5ab2d238597f4049d94713dae63 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 19 Aug 2024 16:09:17 +0200
Subject: [PATCH 284/410] optim

---
 .../src/graph/annotated_graph_algorithm.cpp   | 44 ++++++++++++++-----
 1 file changed, 33 insertions(+), 11 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 543b7bc9d8..5b88d16f66 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1030,9 +1030,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double p = target_p;
             double p1p = exp(log(p) - log1p(-p));
             common::logger->trace("Computing merged histograms");
-            std::mutex hist_mu;
-            tsl::hopscotch_map<uint64_t, size_t> merged_hist;
-            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t) {
+            size_t total = nelem * groups.size();
+            std::vector<std::vector<size_t>> sum_counts_pt(num_threads + 1);
+
+            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
                 int64_t in_sum = 0;
                 int64_t out_sum = 0;
                 bool in_kmer = false;
@@ -1067,21 +1068,42 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return;
 
                 size_t n = in_sum + out_sum;
-                std::lock_guard<std::mutex> lock(hist_mu);
-                ++merged_hist[n];
+
+                if (sum_counts_pt[bucket_idx].size() <= n)
+                    sum_counts_pt[bucket_idx].resize(n + 1);
+
+                ++sum_counts_pt[bucket_idx][n];
             }, false);
 
-            size_t total = nelem * groups.size();
+            std::vector<size_t> sum_counts = std::move(sum_counts_pt[0]);
+            for (size_t j = 1; j < sum_counts_pt.size(); ++j) {
+                size_t overlap_n = std::min(sum_counts_pt[j].size(), sum_counts.size());
+                for (size_t n = 0; n < overlap_n; ++n) {
+                    sum_counts[n] += sum_counts_pt[j][n];
+                }
+
+                if (sum_counts_pt[j].size() > sum_counts.size()) {
+                    std::copy(sum_counts_pt[j].begin() + sum_counts.size(),
+                              sum_counts_pt[j].end(),
+                              std::back_inserter(sum_counts));
+                }
+            }
+
             double ln_mu = 0.0;
             double ln_var = 0.0;
-            for (const auto &[n, c] : merged_hist) {
-                double r = p1p * n / groups.size();
-                double a = 1.0 / r;
-                ln_mu += log(a) * c;
-                ln_var += log(a) * log(a) * c;
+            for (size_t n = 0; n < sum_counts.size(); ++n) {
+                size_t c = sum_counts[n];
+                if (c > 0) {
+                    double r = p1p * n / groups.size();
+                    double a = 1.0 / r;
+                    ln_mu += log(a) * c;
+                    ln_var += log(a) * log(a) * c;
+                }
             }
+
             ln_mu /= total;
             ln_var = ln_var / total - ln_mu * ln_mu;
+
             common::logger->trace("Log-normal fit: mu: {}\tvar: {}\texp(-mu): {}", ln_mu, ln_var, exp(-ln_mu));
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {

From f392dd19dacce0c83ae0e2653c2b06bc45ee86f1 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 19 Aug 2024 22:15:12 +0200
Subject: [PATCH 285/410] this works better

---
 .../src/graph/annotated_graph_algorithm.cpp   | 110 +++++-------------
 1 file changed, 27 insertions(+), 83 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5b88d16f66..5cf95885c2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -617,6 +617,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
         in_kmers = 0;
         out_kmers = 0;
+        int64_t total_kmers_sq = 0;
+        size_t total_nonzeros = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
             for (const auto &[k, m] : count_maps[j]) {
                 const auto &[v, c] = m;
@@ -625,6 +627,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 } else {
                     in_kmers += v * c;
                 }
+
+                if (v > 0)
+                    total_nonzeros += c;
+
+                total_kmers_sq += v * v * c;
             }
         }
         total_kmers = in_kmers + out_kmers;
@@ -1029,82 +1036,33 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         } else {
             double p = target_p;
             double p1p = exp(log(p) - log1p(-p));
-            common::logger->trace("Computing merged histograms");
             size_t total = nelem * groups.size();
-            std::vector<std::vector<size_t>> sum_counts_pt(num_threads + 1);
 
-            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
-                bool in_kmer = false;
-                bool out_kmer = false;
-                if (kept[row_i]) {
-                    size_t count_in = 0;
-                    size_t count_out = 0;
-                    for (const auto &[j, raw_c] : raw_row) {
-                        uint64_t c = raw_c;
-                        if (count_maps.size())
-                            c = count_maps[j].find(c)->second.first;
+            double mu = static_cast<double>(total_kmers) / total;
+            double mu2 = static_cast<double>(total_kmers_sq) / total;
 
-                        if (groups[j]) {
-                            out_sum += c;
-                            ++count_out;
-                        } else {
-                            in_sum += c;
-                            ++count_in;
-                        }
-                    }
+            double ln_var = log(p * mu2 - mu) - log(p) - 2.0 * log(mu);
+            double ln_mu = log(mu) + log(p) - log1p(-p) - ln_var / 2;
 
-                    if (count_in + count_out >= config.min_recurrence) {
-                        double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-                        in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-                        out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
-                    }
-                } else {
-                    return;
-                }
-
-                if (!in_kmer && !out_kmer)
-                    return;
-
-                size_t n = in_sum + out_sum;
-
-                if (sum_counts_pt[bucket_idx].size() <= n)
-                    sum_counts_pt[bucket_idx].resize(n + 1);
-
-                ++sum_counts_pt[bucket_idx][n];
-            }, false);
-
-            std::vector<size_t> sum_counts = std::move(sum_counts_pt[0]);
-            for (size_t j = 1; j < sum_counts_pt.size(); ++j) {
-                size_t overlap_n = std::min(sum_counts_pt[j].size(), sum_counts.size());
-                for (size_t n = 0; n < overlap_n; ++n) {
-                    sum_counts[n] += sum_counts_pt[j][n];
-                }
-
-                if (sum_counts_pt[j].size() > sum_counts.size()) {
-                    std::copy(sum_counts_pt[j].begin() + sum_counts.size(),
-                              sum_counts_pt[j].end(),
-                              std::back_inserter(sum_counts));
-                }
-            }
+            auto get_l = [&](double mu, double var) {
+                return (exp(-mu + var/2)*log(p)-log(sqrt(var))) * total - mu * (total + total_nonzeros);
+            };
 
-            double ln_mu = 0.0;
-            double ln_var = 0.0;
-            for (size_t n = 0; n < sum_counts.size(); ++n) {
-                size_t c = sum_counts[n];
-                if (c > 0) {
-                    double r = p1p * n / groups.size();
-                    double a = 1.0 / r;
-                    ln_mu += log(a) * c;
-                    ln_var += log(a) * log(a) * c;
-                }
+            common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C", ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
+
+            while (true) {
+                double old_ln_mu = ln_mu;
+                double old_ln_var = ln_var;
+                ln_mu = boost::math::tools::brent_find_minima([&](double ln_mu) { return -get_l(ln_mu, old_ln_var); },
+                                                              0.0, real_sum, 30).first;
+                ln_var = boost::math::tools::brent_find_minima([&](double ln_var) { return -get_l(ln_mu, ln_var); },
+                                                               0.0, real_sum, 30).first;
+                common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C", ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
+                if (abs(ln_mu - old_ln_mu)/ln_mu < 1e-5 && abs(ln_var - old_ln_var)/ln_var < 1e-5)
+                    break;
             }
 
-            ln_mu /= total;
-            ln_var = ln_var / total - ln_mu * ln_mu;
-
-            common::logger->trace("Log-normal fit: mu: {}\tvar: {}\texp(-mu): {}", ln_mu, ln_var, exp(-ln_mu));
+            // common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C", ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
                 auto get_l = [&](double a) {
@@ -1115,20 +1073,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                     return l;
                 };
-                // auto get_dl_ddl_dddl = [&](double a) {
-                //     double r = 1.0 / a;
-                //     double dl = (-lp + a - (a*(log(a)-ln_mu))/ln_var) * gs + boost::math::digamma(r) * row.size();
-                //     double ddl = (1.0 - (log(a)-ln_mu)/ln_var - 1.0/ln_var) * gs - r*r*boost::math::trigamma(r) * row.size();
-                //     double dddl = -gs * r / ln_var + (2.0*r*r*boost::math::trigamma(r)+r*r*r*boost::math::polygamma(2,r)) * r * row.size();
-
-                //     for (const auto &[j, c] : row) {
-                //         dl -= boost::math::digamma(r + c);
-                //         ddl += boost::math::trigamma(r + c) * r * r;
-                //         dddl -= boost::math::trigamma(r + c) * 2 * r * r * r + boost::math::polygamma(2.0, r + c) * r * r * r * r;
-                //     }
-
-                //     return std::make_tuple(dl, ddl, dddl);
-                // };
 
                 auto [a, l] = boost::math::tools::brent_find_minima(
                     [&](double a) { return -get_l(a); },

From 72fbe97152ab54c955857a8d75414d1706df5964 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 20 Aug 2024 17:11:30 +0200
Subject: [PATCH 286/410] try

---
 .../src/graph/annotated_graph_algorithm.cpp   | 126 ++++++++++++++++--
 1 file changed, 115 insertions(+), 11 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5cf95885c2..58f369e4dd 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1034,37 +1034,141 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return pval;
             };
         } else {
+            tsl::hopscotch_map<int64_t, std::vector<size_t>> row_sums;
+            std::mutex nmu;
+            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t) {
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                bool in_kmer = false;
+                bool out_kmer = false;
+                size_t count_in = 0;
+                size_t count_out = 0;
+                if (kept[row_i]) {
+                    for (const auto &[j, raw_c] : raw_row) {
+                        uint64_t c = raw_c;
+                        if (count_maps.size())
+                            c = count_maps[j].find(c)->second.first;
+
+                        if (groups[j]) {
+                            out_sum += c;
+                            ++count_out;
+                        } else {
+                            in_sum += c;
+                            ++count_in;
+                        }
+                    }
+
+                    if (count_in + count_out >= config.min_recurrence) {
+                        double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+                        in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+                        out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+                    }
+                } else {
+                    return;
+                }
+
+                if (!in_kmer && !out_kmer)
+                    return;
+
+                std::lock_guard<std::mutex> lock(nmu);
+                auto &bucket = row_sums[in_sum + out_sum];
+                if (bucket.empty())
+                    bucket.resize(groups.size());
+
+                ++bucket[count_in + count_out - 1];
+            }, false);
+
             double p = target_p;
+
             double p1p = exp(log(p) - log1p(-p));
             size_t total = nelem * groups.size();
 
             double mu = static_cast<double>(total_kmers) / total;
             double mu2 = static_cast<double>(total_kmers_sq) / total;
 
+            if (mu >= p * (mu2 - mu*mu))
+                common::logger->warn("Data is underdispersed: {} >= {}", mu, p * (mu2 - mu*mu));
+
             double ln_var = log(p * mu2 - mu) - log(p) - 2.0 * log(mu);
             double ln_mu = log(mu) + log(p) - log1p(-p) - ln_var / 2;
 
+            if (ln_var < 0) {
+                common::logger->warn("Flipping sign of var");
+                ln_var *= -1;
+            }
+
             auto get_l = [&](double mu, double var) {
-                return (exp(-mu + var/2)*log(p)-log(sqrt(var))) * total - mu * (total + total_nonzeros);
+                double l = (-log(sqrt(var))-mu*mu/2/var) * total;
+                for (const auto &[n, bucket] : row_sums) {
+                    for (size_t j = 0; j < groups.size(); ++j) {
+                        auto [phi, nl] = boost::math::tools::brent_find_minima([&](double phi) {
+                            size_t adj = phi < 1 ? n : j + 1;
+                            return -(log(p)/phi - static_cast<double>(groups.size() + adj)/groups.size()*log(phi) - log(phi)*(log(phi)-2*mu)/(2*var));
+                        }, 0.0, real_sum, 30);
+                        l -= nl * bucket[j] * groups.size();
+                    }
+                }
+
+                return l;
             };
 
-            common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C", ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
+            double nl = -get_l(ln_mu, ln_var);
+            common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
 
             while (true) {
-                double old_ln_mu = ln_mu;
-                double old_ln_var = ln_var;
-                ln_mu = boost::math::tools::brent_find_minima([&](double ln_mu) { return -get_l(ln_mu, old_ln_var); },
-                                                              0.0, real_sum, 30).first;
-                ln_var = boost::math::tools::brent_find_minima([&](double ln_var) { return -get_l(ln_mu, ln_var); },
-                                                               0.0, real_sum, 30).first;
-                common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C", ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
-                if (abs(ln_mu - old_ln_mu)/ln_mu < 1e-5 && abs(ln_var - old_ln_var)/ln_var < 1e-5)
+                double old_nl = nl;
+                std::tie(ln_mu, nl) = boost::math::tools::brent_find_minima([&](double ln_mu) { return -get_l(ln_mu, ln_var); }, -real_sum, real_sum, 30);
+                common::logger->trace("Lognormal fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+                                      ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
+                std::tie(ln_var, nl) = boost::math::tools::brent_find_minima([&](double ln_var) { return -get_l(ln_mu, ln_var); }, 0.0, real_sum, 30);
+                common::logger->trace("Lognormal fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+                                      ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
+                if (abs(nl - old_nl) / abs(nl) < 1e-5)
                     break;
             }
 
-            // common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C", ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
+            // // common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\texp(-mu): {}\tN: {}\tNnz: {}\tl: {} + C",
+            // //                       ln_mu, ln_var, exp(-ln_mu), total, total_nonzeros, get_l(ln_mu, ln_var));
+
+            // // while (true) {
+            // //     double old_ln_mu = ln_mu;
+            // //     double old_ln_var = ln_var;
+            // //     ln_mu = old_ln_var / 2 - log(-static_cast<double>(total+total_nonzeros)/total/log(p));
+            // //     common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C", ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
+            // //     ln_var = boost::math::tools::brent_find_minima([&](double ln_var) { return -get_l(ln_mu, ln_var); },
+            // //                                                    0.0, real_sum, 30).first;
+            // //     common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C", ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
+            // //     if (abs(ln_mu - old_ln_mu)/ln_mu < 1e-5 && abs(ln_var - old_ln_var)/ln_var < 1e-5)
+            // //         break;
+            // // }
+            // // ln_mu = - log(-static_cast<double>(total+total_nonzeros)/total/log(p));
+            // // ln_var = 0;
+
+            // while (true) {
+            //     double old_ln_mu = ln_mu;
+            //     double old_ln_var = ln_var;
+            //     auto get_l = [&](double mu, double var) {
+            //         double l = 0;
+            //         for (size_t j = 0; j < groups.size(); ++j) {
+            //             for (const auto &[k, m] : count_maps[j]) {
+            //                 const auto &[v, c] = m;
+            //                 l -= log(sqrt(var))
+            //             }
+            //         }
+            //     };
+            // }
+
+            // common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}",
+            //                       ln_mu, ln_var, exp(-ln_mu));
+
+            // common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C",
+            //                       ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
+                if (ln_var == 0)
+                    return exp(ln_mu);
+
                 auto get_l = [&](double a) {
                     double r = 1.0 / a;
                     double l = (lp * r - log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var) * gs - lgamma(r) * row.size();

From 34aefd6339d0764ed574ea85ad36bc2de76438f8 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 20 Aug 2024 17:14:44 +0200
Subject: [PATCH 287/410] revert to MoM estimators

---
 .../src/graph/annotated_graph_algorithm.cpp   | 121 +-----------------
 1 file changed, 4 insertions(+), 117 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 58f369e4dd..d2d0ed89e2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1034,50 +1034,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return pval;
             };
         } else {
-            tsl::hopscotch_map<int64_t, std::vector<size_t>> row_sums;
-            std::mutex nmu;
-            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t) {
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
-                bool in_kmer = false;
-                bool out_kmer = false;
-                size_t count_in = 0;
-                size_t count_out = 0;
-                if (kept[row_i]) {
-                    for (const auto &[j, raw_c] : raw_row) {
-                        uint64_t c = raw_c;
-                        if (count_maps.size())
-                            c = count_maps[j].find(c)->second.first;
-
-                        if (groups[j]) {
-                            out_sum += c;
-                            ++count_out;
-                        } else {
-                            in_sum += c;
-                            ++count_in;
-                        }
-                    }
-
-                    if (count_in + count_out >= config.min_recurrence) {
-                        double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-                        in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-                        out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
-                    }
-                } else {
-                    return;
-                }
-
-                if (!in_kmer && !out_kmer)
-                    return;
-
-                std::lock_guard<std::mutex> lock(nmu);
-                auto &bucket = row_sums[in_sum + out_sum];
-                if (bucket.empty())
-                    bucket.resize(groups.size());
-
-                ++bucket[count_in + count_out - 1];
-            }, false);
-
             double p = target_p;
 
             double p1p = exp(log(p) - log1p(-p));
@@ -1089,81 +1045,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (mu >= p * (mu2 - mu*mu))
                 common::logger->warn("Data is underdispersed: {} >= {}", mu, p * (mu2 - mu*mu));
 
-            double ln_var = log(p * mu2 - mu) - log(p) - 2.0 * log(mu);
+            double ln_var = std::max(log(p * mu2 - mu) - log(p) - 2.0 * log(mu),
+                                     std::numeric_limits<double>::min());
             double ln_mu = log(mu) + log(p) - log1p(-p) - ln_var / 2;
 
-            if (ln_var < 0) {
-                common::logger->warn("Flipping sign of var");
-                ln_var *= -1;
-            }
-
-            auto get_l = [&](double mu, double var) {
-                double l = (-log(sqrt(var))-mu*mu/2/var) * total;
-                for (const auto &[n, bucket] : row_sums) {
-                    for (size_t j = 0; j < groups.size(); ++j) {
-                        auto [phi, nl] = boost::math::tools::brent_find_minima([&](double phi) {
-                            size_t adj = phi < 1 ? n : j + 1;
-                            return -(log(p)/phi - static_cast<double>(groups.size() + adj)/groups.size()*log(phi) - log(phi)*(log(phi)-2*mu)/(2*var));
-                        }, 0.0, real_sum, 30);
-                        l -= nl * bucket[j] * groups.size();
-                    }
-                }
-
-                return l;
-            };
-
-            double nl = -get_l(ln_mu, ln_var);
-            common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
-
-            while (true) {
-                double old_nl = nl;
-                std::tie(ln_mu, nl) = boost::math::tools::brent_find_minima([&](double ln_mu) { return -get_l(ln_mu, ln_var); }, -real_sum, real_sum, 30);
-                common::logger->trace("Lognormal fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-                                      ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
-                std::tie(ln_var, nl) = boost::math::tools::brent_find_minima([&](double ln_var) { return -get_l(ln_mu, ln_var); }, 0.0, real_sum, 30);
-                common::logger->trace("Lognormal fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-                                      ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
-                if (abs(nl - old_nl) / abs(nl) < 1e-5)
-                    break;
-            }
-
-            // // common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\texp(-mu): {}\tN: {}\tNnz: {}\tl: {} + C",
-            // //                       ln_mu, ln_var, exp(-ln_mu), total, total_nonzeros, get_l(ln_mu, ln_var));
-
-            // // while (true) {
-            // //     double old_ln_mu = ln_mu;
-            // //     double old_ln_var = ln_var;
-            // //     ln_mu = old_ln_var / 2 - log(-static_cast<double>(total+total_nonzeros)/total/log(p));
-            // //     common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C", ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
-            // //     ln_var = boost::math::tools::brent_find_minima([&](double ln_var) { return -get_l(ln_mu, ln_var); },
-            // //                                                    0.0, real_sum, 30).first;
-            // //     common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C", ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
-            // //     if (abs(ln_mu - old_ln_mu)/ln_mu < 1e-5 && abs(ln_var - old_ln_var)/ln_var < 1e-5)
-            // //         break;
-            // // }
-            // // ln_mu = - log(-static_cast<double>(total+total_nonzeros)/total/log(p));
-            // // ln_var = 0;
-
-            // while (true) {
-            //     double old_ln_mu = ln_mu;
-            //     double old_ln_var = ln_var;
-            //     auto get_l = [&](double mu, double var) {
-            //         double l = 0;
-            //         for (size_t j = 0; j < groups.size(); ++j) {
-            //             for (const auto &[k, m] : count_maps[j]) {
-            //                 const auto &[v, c] = m;
-            //                 l -= log(sqrt(var))
-            //             }
-            //         }
-            //     };
-            // }
-
-            // common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}",
-            //                       ln_mu, ln_var, exp(-ln_mu));
-
-            // common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\texp(-mu): {}\tl: {} + C",
-            //                       ln_mu, ln_var, exp(-ln_mu), get_l(ln_mu, ln_var));
+            common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
+                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2));
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
                 if (ln_var == 0)

From 2d309b5e455e71562a202e98cb2a3f4c2d75e0a4 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 20 Aug 2024 17:25:41 +0200
Subject: [PATCH 288/410] fixes

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index d2d0ed89e2..ecaf6b62c7 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1045,8 +1045,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (mu >= p * (mu2 - mu*mu))
                 common::logger->warn("Data is underdispersed: {} >= {}", mu, p * (mu2 - mu*mu));
 
-            double ln_var = std::max(log(p * mu2 - mu) - log(p) - 2.0 * log(mu),
-                                     std::numeric_limits<double>::min());
+            double ln_var = std::max(log(p * mu2 - mu) - log(p) - 2.0 * log(mu), 0);
             double ln_mu = log(mu) + log(p) - log1p(-p) - ln_var / 2;
 
             common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
@@ -1054,7 +1053,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
                 if (ln_var == 0)
-                    return exp(ln_mu);
+                    return exp(-ln_mu);
 
                 auto get_l = [&](double a) {
                     double r = 1.0 / a;

From b08fb16aba506840e652f8c366b4d5d8becaf8dd Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 20 Aug 2024 17:40:18 +0200
Subject: [PATCH 289/410] course-grained adding

---
 .../int_matrix/csr_matrix/csr_matrix_flat.cpp | 19 +++++++++++++++++--
 .../int_matrix/csr_matrix/csr_matrix_flat.hpp |  9 ++++++++-
 .../src/graph/annotated_graph_algorithm.cpp   |  5 +++--
 3 files changed, 28 insertions(+), 5 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
index 4804d5bee2..d1b667f0c8 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
@@ -115,6 +115,8 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
 
         uint64_t num_ones = flat_end ? flat.rank1(flat_end - 1) : 0;
 
+        size_t rows_per_update = 10000;
+        size_t last_output_i = begin;
         if (keep_all_nonzeros) {
             int64_t last_i = static_cast<int64_t>(begin) - 1;
             for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
@@ -133,6 +135,7 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
 
                 if (i - last_i > 1) {
                     ++last_i;
+
                     uint64_t num_added = i - last_i;
                     local_num_empty_rows += num_added;
                     if (unmark_discarded) {
@@ -143,6 +146,11 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
                 }
 
                 last_i = i;
+
+                if (i - last_output_i > rows_per_update) {
+                    progress_bar += i - last_output_i;
+                    last_output_i = i;
+                }
             }
 
             if (end - last_i > 1) {
@@ -155,10 +163,17 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
                     }
                 }
             }
+
+            progress_bar += end - last_output_i;;
         } else {
             int64_t last_i = begin;
             std::vector<std::pair<uint64_t, size_t>> row;
             auto row_callback = [&](uint64_t row_i) __attribute__((always_inline)) {
+                if (row_i - last_output_i > rows_per_update) {
+                    progress_bar += row_i - last_output_i;
+                    last_output_i = row_i;
+                }
+
                 bool keep = min_counts.empty();
 
                 if (min_counts.size()) {
@@ -222,9 +237,9 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
                     unset_bit(unmark_discarded->data(), last_i, false);
                 }
             }
-        }
 
-        progress_bar += end - begin;
+            progress_bar += end - last_output_i;
+        }
 
         #pragma omp atomic
         num_empty_rows += local_num_empty_rows;
diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
index a43fd0fd2c..6767e775d2 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -107,6 +107,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
             if (boundaries.back() != flat.size())
                 boundaries.emplace_back(flat.size());
 
+            size_t rows_per_update = 10000;
             common::logger->trace("Streaming rows");
             ProgressBar progress_bar(n, "Constructed rows", std::cerr, !common::get_verbose());
             #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
@@ -127,6 +128,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                 auto it = v_use->begin() + r;
 
                 uint64_t last_i = begin;
+                size_t last_output_i = begin;
                 uint64_t num_ones = flat_end ? flat.rank1(flat_end - 1) : 0;
                 for (uint64_t r = flat_begin ? flat.rank1(flat_begin - 1) + 1 : 1; r <= num_ones; ++r) {
                     uint64_t b = flat.select1(r);
@@ -147,6 +149,11 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                     ++it;
 
                     row.emplace_back(j, val);
+
+                    if (last_i - last_output_i > rows_per_update) {
+                        progress_bar += last_i - last_output_i;
+                        last_output_i = last_i;
+                    }
                 }
 
                 callback(last_i, row, thread_id);
@@ -156,7 +163,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
                     callback(last_i, row, thread_id);
                 }
 
-                progress_bar += end - begin;
+                progress_bar += end - last_output_i;
             }
         }
     }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ecaf6b62c7..f06307c7c3 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1045,7 +1045,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (mu >= p * (mu2 - mu*mu))
                 common::logger->warn("Data is underdispersed: {} >= {}", mu, p * (mu2 - mu*mu));
 
-            double ln_var = std::max(log(p * mu2 - mu) - log(p) - 2.0 * log(mu), 0);
+            double ln_var = std::max(log(p * mu2 - mu) - log(p) - 2.0 * log(mu), 0.0);
             double ln_mu = log(mu) + log(p) - log1p(-p) - ln_var / 2;
 
             common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
@@ -1064,9 +1064,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return l;
                 };
 
+                uintmax_t max_iter = 100;
                 auto [a, l] = boost::math::tools::brent_find_minima(
                     [&](double a) { return -get_l(a); },
-                    1.0 / real_sum, real_sum, 30
+                    1.0 / real_sum, real_sum, 30, max_iter
                 );
                 l *= -1;
 

From c39f87bc88b540dae6a022fc34afc6edff1af5c9 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 20 Aug 2024 18:58:16 +0200
Subject: [PATCH 290/410] fix sign error

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index f06307c7c3..1bbde5f369 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1046,7 +1046,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 common::logger->warn("Data is underdispersed: {} >= {}", mu, p * (mu2 - mu*mu));
 
             double ln_var = std::max(log(p * mu2 - mu) - log(p) - 2.0 * log(mu), 0.0);
-            double ln_mu = log(mu) + log(p) - log1p(-p) - ln_var / 2;
+            double ln_mu = ln_var / 2 - log(mu) - log(p) + log1p(-p);
 
             common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
                                   ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2));
@@ -1064,10 +1064,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return l;
                 };
 
-                uintmax_t max_iter = 100;
                 auto [a, l] = boost::math::tools::brent_find_minima(
                     [&](double a) { return -get_l(a); },
-                    1.0 / real_sum, real_sum, 30, max_iter
+                    1.0 / real_sum, real_sum, 30
                 );
                 l *= -1;
 

From 860d99554df486202f0e986bffdb860a9abf6dd1 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 20 Aug 2024 21:34:10 +0200
Subject: [PATCH 291/410] em algo

---
 .../src/graph/annotated_graph_algorithm.cpp   | 46 +++++++++++++++++--
 1 file changed, 43 insertions(+), 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 1bbde5f369..64d692dc06 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -618,10 +618,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         in_kmers = 0;
         out_kmers = 0;
         int64_t total_kmers_sq = 0;
-        size_t total_nonzeros = 0;
+        int64_t total_nonzeros = 0;
+        std::vector<int64_t> counts;
         for (size_t j = 0; j < groups.size(); ++j) {
             for (const auto &[k, m] : count_maps[j]) {
                 const auto &[v, c] = m;
+                if (v >= counts.size())
+                    counts.resize(v + 1);
+
+                counts[v] += c;
+
                 if (groups[j]) {
                     out_kmers += v * c;
                 } else {
@@ -634,7 +640,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 total_kmers_sq += v * v * c;
             }
         }
+
         total_kmers = in_kmers + out_kmers;
+
+        std::partial_sum(counts.rbegin(), counts.rend(), counts.rbegin());
+        if (counts[0] != static_cast<int64_t>(nelem * groups.size()) || counts[1] != total_nonzeros) {
+            throw std::runtime_error("FSFJKHJFSL");
+        }
+
         common::logger->trace("  Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
         int64_t in_sum = 0;
@@ -1048,8 +1061,35 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double ln_var = std::max(log(p * mu2 - mu) - log(p) - 2.0 * log(mu), 0.0);
             double ln_mu = ln_var / 2 - log(mu) - log(p) + log1p(-p);
 
-            common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
-                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2));
+            auto get_exp_l = [&](double mu, double var) {
+                double ex = exp(-mu + var/2);
+                double vr = exp(-mu * 2 + var) * (exp(var) - 1);
+                double l = (-log(sqrt(var)) + ex*log(p) - mu) * total - mu * total_nonzeros;
+                for (size_t j = 1; j < counts.size() - 1; ++j) {
+                    l += (log(j + ex) - vr/pow(j + ex, 2.0)) * counts[j + 1];
+                }
+                // common::logger->trace("{}\t{}\t{}", mu, var, l);
+                return l;
+            };
+
+            double nl = -get_exp_l(ln_mu, ln_var);
+
+            common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
+
+            double range = 1.5;
+            while (true) {
+                double old_nl = nl;
+                std::tie(ln_mu, nl) = boost::math::tools::brent_find_minima([&](double ln_mu) { return -get_exp_l(ln_mu, ln_var); }, -abs(ln_mu) / range, abs(ln_mu) * range, 30);
+                common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+                                ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
+                std::tie(ln_var, nl) = boost::math::tools::brent_find_minima([&](double ln_var) { return -get_exp_l(ln_mu, ln_var); }, ln_var / range, ln_var * range, 30);
+                common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+                                ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
+
+                if (abs(nl - old_nl) / abs(nl) < 1e-3)
+                    break;
+            }
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
                 if (ln_var == 0)

From aaad9bc67f559a01beb3c807d0c7bf5644d197d2 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 21 Aug 2024 02:13:52 +0200
Subject: [PATCH 292/410] fix posterior

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 64d692dc06..ccc6867091 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1064,11 +1064,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto get_exp_l = [&](double mu, double var) {
                 double ex = exp(-mu + var/2);
                 double vr = exp(-mu * 2 + var) * (exp(var) - 1);
-                double l = (-log(sqrt(var)) + ex*log(p) - mu) * total - mu * total_nonzeros;
+                double l = -log(sqrt(var)) - mu + ex*log(p) * total - mu * total_nonzeros;
                 for (size_t j = 1; j < counts.size() - 1; ++j) {
                     l += (log(j + ex) - vr/pow(j + ex, 2.0)) * counts[j + 1];
                 }
-                // common::logger->trace("{}\t{}\t{}", mu, var, l);
                 return l;
             };
 
@@ -1077,9 +1076,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                   ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
 
-            double range = 1.5;
+            double range = 1.1;
             while (true) {
-                double old_nl = nl;
+                double old_exp = exp(ln_mu + ln_var/2);
                 std::tie(ln_mu, nl) = boost::math::tools::brent_find_minima([&](double ln_mu) { return -get_exp_l(ln_mu, ln_var); }, -abs(ln_mu) / range, abs(ln_mu) * range, 30);
                 common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                 ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
@@ -1087,7 +1086,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                 ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
 
-                if (abs(nl - old_nl) / abs(nl) < 1e-3)
+                if (ln_var == 0 || abs(exp(ln_mu + ln_var/2) - old_exp) / exp(ln_mu + ln_var/2) < 1e-5)
                     break;
             }
 
@@ -1097,18 +1096,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 auto get_l = [&](double a) {
                     double r = 1.0 / a;
-                    double l = (lp * r - log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var) * gs - lgamma(r) * row.size();
+                    double l = -log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var + lp * r * gs - lgamma(r) * row.size();
                     for (const auto &[j, c] : row) {
                         l += lgamma(r + c);
                     }
                     return l;
                 };
 
-                auto [a, l] = boost::math::tools::brent_find_minima(
+                auto [a, nl] = boost::math::tools::brent_find_minima(
                     [&](double a) { return -get_l(a); },
                     1.0 / real_sum, real_sum, 30
                 );
-                l *= -1;
 
                 return 1.0 / a;
             };

From 3d697549e6755c99b37113f1f909963fa646c6df Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 21 Aug 2024 18:41:22 +0200
Subject: [PATCH 293/410] approx mle

---
 .../src/graph/annotated_graph_algorithm.cpp   | 50 +++++++++++--------
 1 file changed, 28 insertions(+), 22 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ccc6867091..66dad68330 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1049,47 +1049,53 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         } else {
             double p = target_p;
 
-            double p1p = exp(log(p) - log1p(-p));
             size_t total = nelem * groups.size();
 
             double mu = static_cast<double>(total_kmers) / total;
             double mu2 = static_cast<double>(total_kmers_sq) / total;
 
-            if (mu >= p * (mu2 - mu*mu))
-                common::logger->warn("Data is underdispersed: {} >= {}", mu, p * (mu2 - mu*mu));
-
             double ln_var = std::max(log(p * mu2 - mu) - log(p) - 2.0 * log(mu), 0.0);
             double ln_mu = ln_var / 2 - log(mu) - log(p) + log1p(-p);
 
-            auto get_exp_l = [&](double mu, double var) {
-                double ex = exp(-mu + var/2);
-                double vr = exp(-mu * 2 + var) * (exp(var) - 1);
-                double l = -log(sqrt(var)) - mu + ex*log(p) * total - mu * total_nonzeros;
+            if (mu >= p * (mu2 - mu*mu)) {
+                common::logger->warn("Data is underdispersed: {} >= {}", mu, p * (mu2 - mu*mu));
+                ln_var = 1;
+            }
+
+            auto get_exp = [&](double mu, double var) { return exp(mu + var/2); };
+            auto get_var = [&](double mu, double var) { return exp(mu*2 + var) * (exp(var)-1); };
+
+            auto get_l = [&](double mu, double var) {
+                double l = get_exp(-mu, var) * log(p) * total - mu * total_nonzeros;
                 for (size_t j = 1; j < counts.size() - 1; ++j) {
-                    l += (log(j + ex) - vr/pow(j + ex, 2.0)) * counts[j + 1];
+                    l += (log(j + get_exp(-mu, var)) - get_var(-mu, var)/pow(j + get_exp(-mu, var), 2.0)) * counts[j + 1];
                 }
                 return l;
             };
 
-            double nl = -get_exp_l(ln_mu, ln_var);
-
+            double l = get_l(ln_mu, ln_var);
             common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
+                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
 
-            double range = 1.1;
+            double factor = 1.1;
             while (true) {
-                double old_exp = exp(ln_mu + ln_var/2);
-                std::tie(ln_mu, nl) = boost::math::tools::brent_find_minima([&](double ln_mu) { return -get_exp_l(ln_mu, ln_var); }, -abs(ln_mu) / range, abs(ln_mu) * range, 30);
-                common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-                                ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
-                std::tie(ln_var, nl) = boost::math::tools::brent_find_minima([&](double ln_var) { return -get_exp_l(ln_mu, ln_var); }, ln_var / range, ln_var * range, 30);
-                common::logger->trace("Lognormal EM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-                                ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), -nl);
-
-                if (ln_var == 0 || abs(exp(ln_mu + ln_var/2) - old_exp) / exp(ln_mu + ln_var/2) < 1e-5)
+                double old_l = l;
+                std::tie(ln_mu, l) = boost::math::tools::brent_find_minima([&](double mu) { return -get_l(mu, ln_var); },
+                                                                           -abs(ln_mu) / factor, abs(ln_mu) * factor, 30);
+                l *= -1;
+                common::logger->trace("Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+                std::tie(ln_var, l) = boost::math::tools::brent_find_minima([&](double var) { return -get_l(ln_mu, var); },
+                                                                           0.0, ln_var * factor, 30);
+                l *= -1;
+                common::logger->trace("Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+                if (abs(l - old_l) / abs(l) < 1e-5)
                     break;
             }
 
+            double p1p = exp(log(p) - log1p(-p));
+
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
                 if (ln_var == 0)
                     return exp(-ln_mu);

From 123d61550e5c2ad55ec75ee05545b86a7016ea05 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 22 Aug 2024 14:31:44 +0200
Subject: [PATCH 294/410] check

---
 .../src/graph/annotated_graph_algorithm.cpp   | 76 ++++++++++++++++---
 1 file changed, 66 insertions(+), 10 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 66dad68330..f42b6b6f91 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1066,13 +1066,40 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto get_var = [&](double mu, double var) { return exp(mu*2 + var) * (exp(var)-1); };
 
             auto get_l = [&](double mu, double var) {
-                double l = get_exp(-mu, var) * log(p) * total - mu * total_nonzeros;
+                double ev = get_exp(-mu, var);
+                double vr = get_var(-mu, var);
+                double l = ev * log(p) * total - mu * total_nonzeros;
                 for (size_t j = 1; j < counts.size() - 1; ++j) {
-                    l += (log(j + get_exp(-mu, var)) - get_var(-mu, var)/pow(j + get_exp(-mu, var), 2.0)) * counts[j + 1];
+                    l += (log(j + ev) - vr/2/j/j) * counts[j + 1];
                 }
                 return l;
             };
 
+            auto get_dl_ddl_mu = [&](double mu, double var) {
+                double ev = get_exp(-mu, var);
+                double vr = get_var(-mu, var);
+                double ddl = ev * log(p) * total;
+                double dl = -ddl - total_nonzeros;
+                for (size_t j = 1; j < counts.size() - 1; ++j) {
+                    dl += (vr / j / j - ev / (ev + j)) * counts[j + 1];
+                    ddl -= (vr * 2 / j / j - ev * j/pow(ev + j, 2.0)) * counts[j + 1];
+                }
+                return std::make_pair(dl, ddl);
+            };
+
+            auto get_dl_ddl_var = [&](double mu, double var) {
+                double ev = get_exp(-mu, var);
+                double vr = get_var(-mu, var);
+                double dl = 0;
+                double ddl = 0;
+                for (size_t j = 1; j < counts.size() - 1; ++j) {
+                    dl += (ev/(ev+j)-(vr*(1+0.5/ev)+ev*ev)/j/j) * counts[j + 1];
+                    ddl += (0.5*ev/(ev+j) - ev*ev/2/pow(ev+j, 2.0) - ((vr*2+ev*ev)*(1+0.5/ev)-vr/4/ev + ev*ev)/j/j) * counts[j + 1];
+                }
+
+                return std::make_pair(dl, ddl);
+            };
+
             double l = get_l(ln_mu, ln_var);
             common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                   ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
@@ -1080,15 +1107,44 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double factor = 1.1;
             while (true) {
                 double old_l = l;
-                std::tie(ln_mu, l) = boost::math::tools::brent_find_minima([&](double mu) { return -get_l(mu, ln_var); },
-                                                                           -abs(ln_mu) / factor, abs(ln_mu) * factor, 30);
-                l *= -1;
-                common::logger->trace("Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+
+                double old_ln_mu = ln_mu;
+                ln_mu = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, old_ln_mu, -real_sum, real_sum, 30);
+                auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
+                if (ddl >= 0) {
+                    l = -std::numeric_limits<double>::max();
+                } else {
+                    l = get_l(ln_mu, ln_var);
+                }
+
+                auto [b_ln_mu, b_l] = boost::math::tools::brent_find_minima([&](double mu) { return -get_l(mu, ln_var); },
+                                                                             -abs(old_ln_mu) / factor, abs(old_ln_mu) * factor, 30);
+                b_l *= -1;
+                if (b_l > l) {
+                    l = b_l;
+                    ln_mu = b_ln_mu;
+                }
+                common::logger->trace("M: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                   ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
-                std::tie(ln_var, l) = boost::math::tools::brent_find_minima([&](double var) { return -get_l(ln_mu, var); },
-                                                                           0.0, ln_var * factor, 30);
-                l *= -1;
-                common::logger->trace("Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+
+                double old_ln_var = ln_var;
+                ln_var = boost::math::tools::newton_raphson_iterate([&](double var) { return get_dl_ddl_mu(ln_mu, var); }, old_ln_var, 0.0, real_sum, 30);
+                std::tie(dl, ddl) = get_dl_ddl_var(ln_mu, ln_var);
+                if (ddl >= 0) {
+                    l = -std::numeric_limits<double>::max();
+                } else {
+                    l = get_l(ln_mu, ln_var);
+                }
+
+                double b_ln_var;
+                std::tie(b_ln_var, b_l) = boost::math::tools::brent_find_minima([&](double var) { return -get_l(ln_mu, var); },
+                                                                                0.0, old_ln_var * factor, 30);
+                b_l *= -1;
+                if (b_l > l) {
+                    l = b_l;
+                    ln_var = b_ln_var;
+                }
+                common::logger->trace("V: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                   ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
                 if (abs(l - old_l) / abs(l) < 1e-5)
                     break;

From 3b14b25b5f92312a4f3855c74a5333471b1b7552 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 22 Aug 2024 14:35:29 +0200
Subject: [PATCH 295/410] finds better maxima

---
 .../src/graph/annotated_graph_algorithm.cpp   | 48 ++++++++++---------
 1 file changed, 26 insertions(+), 22 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index f42b6b6f91..2ae0a77424 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1109,41 +1109,45 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double old_l = l;
 
                 double old_ln_mu = ln_mu;
-                ln_mu = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, old_ln_mu, -real_sum, real_sum, 30);
-                auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
-                if (ddl >= 0) {
-                    l = -std::numeric_limits<double>::max();
-                } else {
-                    l = get_l(ln_mu, ln_var);
-                }
+                // ln_mu = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, old_ln_mu, -real_sum, real_sum, 30);
+                // auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
+                // if (ddl >= 0) {
+                //     l = -std::numeric_limits<double>::max();
+                // } else {
+                //     l = get_l(ln_mu, ln_var);
+                // }
 
                 auto [b_ln_mu, b_l] = boost::math::tools::brent_find_minima([&](double mu) { return -get_l(mu, ln_var); },
                                                                              -abs(old_ln_mu) / factor, abs(old_ln_mu) * factor, 30);
                 b_l *= -1;
-                if (b_l > l) {
-                    l = b_l;
-                    ln_mu = b_ln_mu;
-                }
+                ln_mu = b_ln_mu;
+                l = b_l;
+                // if (b_l > l) {
+                //     l = b_l;
+                //     ln_mu = b_ln_mu;
+                // }
                 common::logger->trace("M: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                   ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
 
                 double old_ln_var = ln_var;
-                ln_var = boost::math::tools::newton_raphson_iterate([&](double var) { return get_dl_ddl_mu(ln_mu, var); }, old_ln_var, 0.0, real_sum, 30);
-                std::tie(dl, ddl) = get_dl_ddl_var(ln_mu, ln_var);
-                if (ddl >= 0) {
-                    l = -std::numeric_limits<double>::max();
-                } else {
-                    l = get_l(ln_mu, ln_var);
-                }
+                // ln_var = boost::math::tools::newton_raphson_iterate([&](double var) { return get_dl_ddl_mu(ln_mu, var); }, old_ln_var, 0.0, real_sum, 30);
+                // std::tie(dl, ddl) = get_dl_ddl_var(ln_mu, ln_var);
+                // if (ddl >= 0) {
+                //     l = -std::numeric_limits<double>::max();
+                // } else {
+                //     l = get_l(ln_mu, ln_var);
+                // }
 
                 double b_ln_var;
                 std::tie(b_ln_var, b_l) = boost::math::tools::brent_find_minima([&](double var) { return -get_l(ln_mu, var); },
                                                                                 0.0, old_ln_var * factor, 30);
                 b_l *= -1;
-                if (b_l > l) {
-                    l = b_l;
-                    ln_var = b_ln_var;
-                }
+                // if (b_l > l) {
+                //     l = b_l;
+                //     ln_var = b_ln_var;
+                // }
+                ln_var = b_ln_var;
+                l = b_l;
                 common::logger->trace("V: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                   ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
                 if (abs(l - old_l) / abs(l) < 1e-5)

From 748bc0454c32d1e6eb7680ee96ff43fa34e2e1b3 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 22 Aug 2024 21:17:48 +0200
Subject: [PATCH 296/410] t

---
 .../src/graph/annotated_graph_algorithm.cpp   | 208 +++++++++++++-----
 1 file changed, 155 insertions(+), 53 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 2ae0a77424..513df4c40a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1049,6 +1049,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         } else {
             double p = target_p;
 
+            double c = 0;
+            for (size_t j = 1; j < counts.size() - 1; ++j) {
+                c += static_cast<double>(counts[j + 1]) / 2 / j / j;
+            }
+
             size_t total = nelem * groups.size();
 
             double mu = static_cast<double>(total_kmers) / total;
@@ -1068,9 +1073,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto get_l = [&](double mu, double var) {
                 double ev = get_exp(-mu, var);
                 double vr = get_var(-mu, var);
-                double l = ev * log(p) * total - mu * total_nonzeros;
+                double l = ev * log(p) * total - mu * total_nonzeros - c * vr;
                 for (size_t j = 1; j < counts.size() - 1; ++j) {
-                    l += (log(j + ev) - vr/2/j/j) * counts[j + 1];
+                    l += log(ev + j) * counts[j + 1];
                 }
                 return l;
             };
@@ -1078,82 +1083,179 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto get_dl_ddl_mu = [&](double mu, double var) {
                 double ev = get_exp(-mu, var);
                 double vr = get_var(-mu, var);
-                double ddl = ev * log(p) * total;
-                double dl = -ddl - total_nonzeros;
+                double dl = -ev * log(p) * total - total_nonzeros + c * 2 * vr;
+                double ddl = ev * log(p) * total - c * 4 * vr;
                 for (size_t j = 1; j < counts.size() - 1; ++j) {
-                    dl += (vr / j / j - ev / (ev + j)) * counts[j + 1];
-                    ddl -= (vr * 2 / j / j - ev * j/pow(ev + j, 2.0)) * counts[j + 1];
+                    dl -= ev / (ev + j) * counts[j + 1];
+                    ddl += ev / pow(ev + j, 2.0) * j * counts[j + 1];
                 }
                 return std::make_pair(dl, ddl);
             };
 
-            auto get_dl_ddl_var = [&](double mu, double var) {
+            auto get_dl_ddl_sd = [&](double mu, double sd) {
+                double var = sd * sd;
                 double ev = get_exp(-mu, var);
-                double vr = get_var(-mu, var);
-                double dl = 0;
-                double ddl = 0;
+                double dl = log(p) * total - c * exp(-mu) * (3.0 * exp(var) - 1);
+                double ddl = c * -6 * sd * exp(-mu + var);
                 for (size_t j = 1; j < counts.size() - 1; ++j) {
-                    dl += (ev/(ev+j)-(vr*(1+0.5/ev)+ev*ev)/j/j) * counts[j + 1];
-                    ddl += (0.5*ev/(ev+j) - ev*ev/2/pow(ev+j, 2.0) - ((vr*2+ev*ev)*(1+0.5/ev)-vr/4/ev + ev*ev)/j/j) * counts[j + 1];
+                    dl += static_cast<double>(counts[j + 1])/(ev + j);
+                    ddl -= sd * ev * counts[j + 1] / pow(ev + j, 2.0);
                 }
-
                 return std::make_pair(dl, ddl);
             };
 
             double l = get_l(ln_mu, ln_var);
             common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                   ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+            // common::logger->trace("Lognormal MoM test fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+            //                       ln_mu, 0, exp(ln_mu), 0, exp(-ln_mu), get_l(ln_mu, 0));
 
-            double factor = 1.1;
             while (true) {
                 double old_l = l;
+                ln_mu = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, ln_mu, -real_sum, real_sum, 30);
+                auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
+                if (ddl >= 0)
+                    throw std::domain_error("");
 
-                double old_ln_mu = ln_mu;
-                // ln_mu = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, old_ln_mu, -real_sum, real_sum, 30);
-                // auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
-                // if (ddl >= 0) {
-                //     l = -std::numeric_limits<double>::max();
-                // } else {
-                //     l = get_l(ln_mu, ln_var);
-                // }
-
-                auto [b_ln_mu, b_l] = boost::math::tools::brent_find_minima([&](double mu) { return -get_l(mu, ln_var); },
-                                                                             -abs(old_ln_mu) / factor, abs(old_ln_mu) * factor, 30);
-                b_l *= -1;
-                ln_mu = b_ln_mu;
-                l = b_l;
-                // if (b_l > l) {
-                //     l = b_l;
-                //     ln_mu = b_ln_mu;
-                // }
+                l = get_l(ln_mu, ln_var);
                 common::logger->trace("M: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+                                ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
 
-                double old_ln_var = ln_var;
-                // ln_var = boost::math::tools::newton_raphson_iterate([&](double var) { return get_dl_ddl_mu(ln_mu, var); }, old_ln_var, 0.0, real_sum, 30);
-                // std::tie(dl, ddl) = get_dl_ddl_var(ln_mu, ln_var);
-                // if (ddl >= 0) {
-                //     l = -std::numeric_limits<double>::max();
-                // } else {
-                //     l = get_l(ln_mu, ln_var);
-                // }
-
-                double b_ln_var;
-                std::tie(b_ln_var, b_l) = boost::math::tools::brent_find_minima([&](double var) { return -get_l(ln_mu, var); },
-                                                                                0.0, old_ln_var * factor, 30);
-                b_l *= -1;
-                // if (b_l > l) {
-                //     l = b_l;
-                //     ln_var = b_ln_var;
-                // }
-                ln_var = b_ln_var;
-                l = b_l;
+                l = -std::numeric_limits<double>::max();
+                double ln_sd_try = 0;
+                try {
+                    double ln_sd_try1 = boost::math::tools::newton_raphson_iterate([&](double sd) { return get_dl_ddl_sd(ln_mu, sd); }, sqrt(ln_var), -real_sum, real_sum, 30);
+                    auto [dl_sd, ddl_sd] = get_dl_ddl_sd(ln_mu, ln_sd_try1);
+                    if (ddl_sd >= 0)
+                        throw std::domain_error("");
+
+                    double l_try1 = get_l(ln_mu, ln_sd_try1 * ln_sd_try1);
+                    double l_try2 = get_l(ln_mu, 0);
+                    if (l_try1 >= l_try2) {
+                        ln_sd_try = ln_sd_try1;
+                        l = l_try1;
+                    } else {
+                        l = l_try2;
+                    }
+                } catch (boost::wrapexcept<boost::math::evaluation_error> &) {
+                    l = get_l(ln_mu, 0);
+                } catch (std::domain_error &) {
+                    l = get_l(ln_mu, 0);
+                }
+
+                ln_var = ln_sd_try * ln_sd_try;
                 common::logger->trace("V: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+                                ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+
                 if (abs(l - old_l) / abs(l) < 1e-5)
                     break;
             }
 
+            // double factor = 1.1;
+            // while (true) {
+            //     double old_l = l;
+
+            //     double old_ln_mu = ln_mu;
+            //     // ln_mu = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, old_ln_mu, -real_sum, real_sum, 30);
+            //     // auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
+            //     // if (ddl >= 0) {
+            //     //     l = -std::numeric_limits<double>::max();
+            //     // } else {
+            //     //     l = get_l(ln_mu, ln_var);
+            //     // }
+
+            //     auto [b_ln_mu, b_l] = boost::math::tools::brent_find_minima([&](double mu) { return -get_l(mu, ln_var); },
+            //                                                                  -abs(old_ln_mu) / factor, abs(old_ln_mu) * factor, 30);
+            //     b_l *= -1;
+            //     ln_mu = b_ln_mu;
+            //     l = b_l;
+            //     // if (b_l > l) {
+            //     //     l = b_l;
+            //     //     ln_mu = b_ln_mu;
+            //     // }
+            //     common::logger->trace("M: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+            //                       ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+
+            //     double old_ln_var = ln_var;
+            //     // ln_var = boost::math::tools::newton_raphson_iterate([&](double var) { return get_dl_ddl_mu(ln_mu, var); }, old_ln_var, 0.0, real_sum, 30);
+            //     // std::tie(dl, ddl) = get_dl_ddl_var(ln_mu, ln_var);
+            //     // if (ddl >= 0) {
+            //     //     l = -std::numeric_limits<double>::max();
+            //     // } else {
+            //     //     l = get_l(ln_mu, ln_var);
+            //     // }
+
+            //     double b_ln_var;
+            //     std::tie(b_ln_var, b_l) = boost::math::tools::brent_find_minima([&](double var) { return -get_l(ln_mu, var); },
+            //                                                                     0.0, old_ln_var * factor, 30);
+            //     b_l *= -1;
+            //     // if (b_l > l) {
+            //     //     l = b_l;
+            //     //     ln_var = b_ln_var;
+            //     // }
+            //     ln_var = b_ln_var;
+            //     l = b_l;
+            //     common::logger->trace("V: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+            //                       ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+
+            //     try {
+            //         double ln_mu_try = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, ln_mu, -real_sum, real_sum, 30);
+            //         auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
+            //         if (ddl >= 0)
+            //             throw std::domain_error("");
+
+            //         double ln_sd_try1 = boost::math::tools::newton_raphson_iterate([&](double sd) { return get_dl_ddl_sd(ln_mu_try, sd); }, sqrt(ln_var), -real_sum, real_sum, 30);
+            //         auto [dl_sd, ddl_sd] = get_dl_ddl_sd(ln_mu, ln_sd_try1);
+            //         double ln_sd_try2 = 0;
+            //         double l_try1 = ddl_sd < 0 ? get_l(ln_mu, ln_sd_try1 * ln_sd_try2) : -std::numeric_limits<double>::max();
+            //         double l_try2 = get_l(ln_mu, 0);
+            //         double ln_sd_try = l_try1 >= l_try2 ? ln_sd_try1 : ln_sd_try2;
+
+            //         ln_mu = ln_mu_try;
+            //         ln_var = ln_sd_try * ln_sd_try;
+
+            //         l = std::max(l_try1, l_try2);
+            //         common::logger->trace("G: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+            //                         ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+            //     } catch (std::domain_error &e) {
+            //         l = old_l;
+            //         ln_mu = old_ln_mu;
+            //         ln_var = old_ln_var;
+            //         factor *= factor;
+            //         common::logger->warn("I'm lost :'(. Increasing factor to: {}", factor);
+            //         // if (factor == 1.5)
+            //         //     throw std::runtime_error("I'm lost :'(");
+
+            //         continue;
+            //     }
+
+
+            //     if (abs(l - old_l) / abs(l) < 1e-5)
+            //         break;
+            // }
+
+            // while (true) {
+            //     double old_l = l;
+            //     double ln_mu_try = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, ln_mu, -real_sum, real_sum, 30);
+            //     auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
+            //     if (ddl >= 0)
+            //         break;
+
+            //     double ln_var_try = boost::math::tools::newton_raphson_iterate([&](double var) { return get_dl_ddl_mu(ln_mu_try, var); }, ln_var, 0.0, real_sum, 30);
+            //     std::tie(dl, ddl) = get_dl_ddl_var(ln_mu, ln_var);
+            //     if (ddl >= 0)
+            //         break;
+
+            //     ln_mu = ln_mu_try;
+            //     ln_var = ln_var_try;
+
+            //     l = get_l(ln_mu, ln_var);
+            //     common::logger->trace("Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+            //                       ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+            //     if (abs(l - old_l) / abs(l) < 1e-5)
+            //         break;
+            // }
+
             double p1p = exp(log(p) - log1p(-p));
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {

From 5a2974d9f0de89536e077effb72aed6807119ea0 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 22 Aug 2024 21:22:14 +0200
Subject: [PATCH 297/410] fix

---
 .../src/graph/annotated_graph_algorithm.cpp   | 141 ++----------------
 1 file changed, 9 insertions(+), 132 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 513df4c40a..c43c3073e0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1095,11 +1095,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto get_dl_ddl_sd = [&](double mu, double sd) {
                 double var = sd * sd;
                 double ev = get_exp(-mu, var);
-                double dl = log(p) * total - c * exp(-mu) * (3.0 * exp(var) - 1);
-                double ddl = c * -6 * sd * exp(-mu + var);
+                double dl = sd * log(p) * total - c * exp(-mu) * sd * (3.0 * exp(var) - 1);
+                double ddl = log(p) * total - c * exp(-mu) * (exp(var)*3 - 1) - c*6*exp(-mu)*var*exp(var);
                 for (size_t j = 1; j < counts.size() - 1; ++j) {
-                    dl += static_cast<double>(counts[j + 1])/(ev + j);
-                    ddl -= sd * ev * counts[j + 1] / pow(ev + j, 2.0);
+                    dl += sd * counts[j + 1] / (ev + j);
+                    ddl -= (ev * (1-var) + j) / pow(ev + j, 2.0);
                 }
                 return std::make_pair(dl, ddl);
             };
@@ -1107,8 +1107,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double l = get_l(ln_mu, ln_var);
             common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                   ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
-            // common::logger->trace("Lognormal MoM test fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-            //                       ln_mu, 0, exp(ln_mu), 0, exp(-ln_mu), get_l(ln_mu, 0));
 
             while (true) {
                 double old_l = l;
@@ -1121,29 +1119,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 common::logger->trace("M: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                 ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
 
-                l = -std::numeric_limits<double>::max();
-                double ln_sd_try = 0;
-                try {
-                    double ln_sd_try1 = boost::math::tools::newton_raphson_iterate([&](double sd) { return get_dl_ddl_sd(ln_mu, sd); }, sqrt(ln_var), -real_sum, real_sum, 30);
-                    auto [dl_sd, ddl_sd] = get_dl_ddl_sd(ln_mu, ln_sd_try1);
-                    if (ddl_sd >= 0)
-                        throw std::domain_error("");
-
-                    double l_try1 = get_l(ln_mu, ln_sd_try1 * ln_sd_try1);
-                    double l_try2 = get_l(ln_mu, 0);
-                    if (l_try1 >= l_try2) {
-                        ln_sd_try = ln_sd_try1;
-                        l = l_try1;
-                    } else {
-                        l = l_try2;
-                    }
-                } catch (boost::wrapexcept<boost::math::evaluation_error> &) {
-                    l = get_l(ln_mu, 0);
-                } catch (std::domain_error &) {
-                    l = get_l(ln_mu, 0);
-                }
+                double ln_sd_try = boost::math::tools::newton_raphson_iterate([&](double sd) { return get_dl_ddl_sd(ln_mu, sd); }, sqrt(ln_var), -real_sum, real_sum, 30);
+                auto [dl_sd, ddl_sd] = get_dl_ddl_sd(ln_mu, ln_sd_try);
+                if (ddl_sd >= 0)
+                    throw std::domain_error("");
 
                 ln_var = ln_sd_try * ln_sd_try;
+                l = get_l(ln_mu, ln_var);
                 common::logger->trace("V: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
                                 ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
 
@@ -1151,111 +1133,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     break;
             }
 
-            // double factor = 1.1;
-            // while (true) {
-            //     double old_l = l;
-
-            //     double old_ln_mu = ln_mu;
-            //     // ln_mu = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, old_ln_mu, -real_sum, real_sum, 30);
-            //     // auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
-            //     // if (ddl >= 0) {
-            //     //     l = -std::numeric_limits<double>::max();
-            //     // } else {
-            //     //     l = get_l(ln_mu, ln_var);
-            //     // }
-
-            //     auto [b_ln_mu, b_l] = boost::math::tools::brent_find_minima([&](double mu) { return -get_l(mu, ln_var); },
-            //                                                                  -abs(old_ln_mu) / factor, abs(old_ln_mu) * factor, 30);
-            //     b_l *= -1;
-            //     ln_mu = b_ln_mu;
-            //     l = b_l;
-            //     // if (b_l > l) {
-            //     //     l = b_l;
-            //     //     ln_mu = b_ln_mu;
-            //     // }
-            //     common::logger->trace("M: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-            //                       ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
-
-            //     double old_ln_var = ln_var;
-            //     // ln_var = boost::math::tools::newton_raphson_iterate([&](double var) { return get_dl_ddl_mu(ln_mu, var); }, old_ln_var, 0.0, real_sum, 30);
-            //     // std::tie(dl, ddl) = get_dl_ddl_var(ln_mu, ln_var);
-            //     // if (ddl >= 0) {
-            //     //     l = -std::numeric_limits<double>::max();
-            //     // } else {
-            //     //     l = get_l(ln_mu, ln_var);
-            //     // }
-
-            //     double b_ln_var;
-            //     std::tie(b_ln_var, b_l) = boost::math::tools::brent_find_minima([&](double var) { return -get_l(ln_mu, var); },
-            //                                                                     0.0, old_ln_var * factor, 30);
-            //     b_l *= -1;
-            //     // if (b_l > l) {
-            //     //     l = b_l;
-            //     //     ln_var = b_ln_var;
-            //     // }
-            //     ln_var = b_ln_var;
-            //     l = b_l;
-            //     common::logger->trace("V: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-            //                       ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
-
-            //     try {
-            //         double ln_mu_try = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, ln_mu, -real_sum, real_sum, 30);
-            //         auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
-            //         if (ddl >= 0)
-            //             throw std::domain_error("");
-
-            //         double ln_sd_try1 = boost::math::tools::newton_raphson_iterate([&](double sd) { return get_dl_ddl_sd(ln_mu_try, sd); }, sqrt(ln_var), -real_sum, real_sum, 30);
-            //         auto [dl_sd, ddl_sd] = get_dl_ddl_sd(ln_mu, ln_sd_try1);
-            //         double ln_sd_try2 = 0;
-            //         double l_try1 = ddl_sd < 0 ? get_l(ln_mu, ln_sd_try1 * ln_sd_try2) : -std::numeric_limits<double>::max();
-            //         double l_try2 = get_l(ln_mu, 0);
-            //         double ln_sd_try = l_try1 >= l_try2 ? ln_sd_try1 : ln_sd_try2;
-
-            //         ln_mu = ln_mu_try;
-            //         ln_var = ln_sd_try * ln_sd_try;
-
-            //         l = std::max(l_try1, l_try2);
-            //         common::logger->trace("G: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-            //                         ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
-            //     } catch (std::domain_error &e) {
-            //         l = old_l;
-            //         ln_mu = old_ln_mu;
-            //         ln_var = old_ln_var;
-            //         factor *= factor;
-            //         common::logger->warn("I'm lost :'(. Increasing factor to: {}", factor);
-            //         // if (factor == 1.5)
-            //         //     throw std::runtime_error("I'm lost :'(");
-
-            //         continue;
-            //     }
-
-
-            //     if (abs(l - old_l) / abs(l) < 1e-5)
-            //         break;
-            // }
-
-            // while (true) {
-            //     double old_l = l;
-            //     double ln_mu_try = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, ln_mu, -real_sum, real_sum, 30);
-            //     auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
-            //     if (ddl >= 0)
-            //         break;
-
-            //     double ln_var_try = boost::math::tools::newton_raphson_iterate([&](double var) { return get_dl_ddl_mu(ln_mu_try, var); }, ln_var, 0.0, real_sum, 30);
-            //     std::tie(dl, ddl) = get_dl_ddl_var(ln_mu, ln_var);
-            //     if (ddl >= 0)
-            //         break;
-
-            //     ln_mu = ln_mu_try;
-            //     ln_var = ln_var_try;
-
-            //     l = get_l(ln_mu, ln_var);
-            //     common::logger->trace("Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-            //                       ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
-            //     if (abs(l - old_l) / abs(l) < 1e-5)
-            //         break;
-            // }
-
             double p1p = exp(log(p) - log1p(-p));
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {

From 1dd70e94bc0dc257c48abba123d216a1cb1ee03a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 23 Aug 2024 01:33:40 +0200
Subject: [PATCH 298/410] this works best

---
 .../src/graph/annotated_graph_algorithm.cpp   | 220 ++++++++++++------
 1 file changed, 147 insertions(+), 73 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c43c3073e0..b36e3cf034 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1048,92 +1048,166 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             };
         } else {
             double p = target_p;
+            double p1p = exp(log(p) - log1p(-p));
 
-            double c = 0;
-            for (size_t j = 1; j < counts.size() - 1; ++j) {
-                c += static_cast<double>(counts[j + 1]) / 2 / j / j;
-            }
+            double ln_mu = 0.0;
+            double ln_var = 0.0;
 
-            size_t total = nelem * groups.size();
+            std::vector<std::vector<size_t>> row_sum_counts_ms(num_threads + 1);
 
-            double mu = static_cast<double>(total_kmers) / total;
-            double mu2 = static_cast<double>(total_kmers_sq) / total;
+            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                bool in_kmer = false;
+                bool out_kmer = false;
+                if (kept[row_i]) {
+                    size_t count_in = 0;
+                    size_t count_out = 0;
+                    for (const auto &[j, raw_c] : raw_row) {
+                        uint64_t c = raw_c;
+                        if (count_maps.size())
+                            c = count_maps[j].find(c)->second.first;
 
-            double ln_var = std::max(log(p * mu2 - mu) - log(p) - 2.0 * log(mu), 0.0);
-            double ln_mu = ln_var / 2 - log(mu) - log(p) + log1p(-p);
+                        if (groups[j]) {
+                            out_sum += c;
+                            ++count_out;
+                        } else {
+                            in_sum += c;
+                            ++count_in;
+                        }
+                    }
 
-            if (mu >= p * (mu2 - mu*mu)) {
-                common::logger->warn("Data is underdispersed: {} >= {}", mu, p * (mu2 - mu*mu));
-                ln_var = 1;
-            }
+                    if (count_in + count_out >= config.min_recurrence) {
+                        double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+                        in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+                        out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+                    }
+                } else {
+                    return;
+                }
 
-            auto get_exp = [&](double mu, double var) { return exp(mu + var/2); };
-            auto get_var = [&](double mu, double var) { return exp(mu*2 + var) * (exp(var)-1); };
+                if (!in_kmer && !out_kmer)
+                    return;
 
-            auto get_l = [&](double mu, double var) {
-                double ev = get_exp(-mu, var);
-                double vr = get_var(-mu, var);
-                double l = ev * log(p) * total - mu * total_nonzeros - c * vr;
-                for (size_t j = 1; j < counts.size() - 1; ++j) {
-                    l += log(ev + j) * counts[j + 1];
-                }
-                return l;
-            };
+                size_t n = in_sum + out_sum;
+                if (row_sum_counts_ms[bucket_idx].size() <= n)
+                    row_sum_counts_ms[bucket_idx].resize(n + 1);
+
+                ++row_sum_counts_ms[bucket_idx][n];
+                // double r = p1p * n / groups.size();
+                // std::lock_guard<std::mutex> lock(hist_mu);
+                // ln_mu += -log(r);
+                // ln_var += log(r) * log(r);
+            }, false);
 
-            auto get_dl_ddl_mu = [&](double mu, double var) {
-                double ev = get_exp(-mu, var);
-                double vr = get_var(-mu, var);
-                double dl = -ev * log(p) * total - total_nonzeros + c * 2 * vr;
-                double ddl = ev * log(p) * total - c * 4 * vr;
-                for (size_t j = 1; j < counts.size() - 1; ++j) {
-                    dl -= ev / (ev + j) * counts[j + 1];
-                    ddl += ev / pow(ev + j, 2.0) * j * counts[j + 1];
+            std::vector<size_t> row_sum_counts = std::move(row_sum_counts_ms[0]);
+            for (size_t j = 1; j < row_sum_counts_ms.size(); ++j) {
+                size_t ms = std::min(row_sum_counts_ms[j].size(), row_sum_counts.size());
+                for (size_t i = 0; i < ms; ++i) {
+                    row_sum_counts[i] += row_sum_counts_ms[j][i];
                 }
-                return std::make_pair(dl, ddl);
-            };
-
-            auto get_dl_ddl_sd = [&](double mu, double sd) {
-                double var = sd * sd;
-                double ev = get_exp(-mu, var);
-                double dl = sd * log(p) * total - c * exp(-mu) * sd * (3.0 * exp(var) - 1);
-                double ddl = log(p) * total - c * exp(-mu) * (exp(var)*3 - 1) - c*6*exp(-mu)*var*exp(var);
-                for (size_t j = 1; j < counts.size() - 1; ++j) {
-                    dl += sd * counts[j + 1] / (ev + j);
-                    ddl -= (ev * (1-var) + j) / pow(ev + j, 2.0);
+                if (row_sum_counts_ms[j].size() > row_sum_counts.size()) {
+                    std::copy(row_sum_counts_ms[j].begin() + row_sum_counts.size(),
+                              row_sum_counts_ms[j].end(),
+                              std::back_inserter(row_sum_counts));
                 }
-                return std::make_pair(dl, ddl);
-            };
-
-            double l = get_l(ln_mu, ln_var);
-            common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-                                  ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
-
-            while (true) {
-                double old_l = l;
-                ln_mu = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, ln_mu, -real_sum, real_sum, 30);
-                auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
-                if (ddl >= 0)
-                    throw std::domain_error("");
-
-                l = get_l(ln_mu, ln_var);
-                common::logger->trace("M: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-                                ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
-
-                double ln_sd_try = boost::math::tools::newton_raphson_iterate([&](double sd) { return get_dl_ddl_sd(ln_mu, sd); }, sqrt(ln_var), -real_sum, real_sum, 30);
-                auto [dl_sd, ddl_sd] = get_dl_ddl_sd(ln_mu, ln_sd_try);
-                if (ddl_sd >= 0)
-                    throw std::domain_error("");
-
-                ln_var = ln_sd_try * ln_sd_try;
-                l = get_l(ln_mu, ln_var);
-                common::logger->trace("V: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-                                ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+            }
+            row_sum_counts_ms.resize(0);
 
-                if (abs(l - old_l) / abs(l) < 1e-5)
-                    break;
+            for (size_t n = 1; n < row_sum_counts.size(); ++n) {
+                double r = p1p * n / groups.size();
+                ln_mu += -log(r) * row_sum_counts[n];
+                ln_var += log(r) * log(r) * row_sum_counts[n];
             }
 
-            double p1p = exp(log(p) - log1p(-p));
+            // double c = 0;
+            // for (size_t j = 1; j < counts.size() - 1; ++j) {
+            //     c += static_cast<double>(counts[j + 1]) / 2 / j / j;
+            // }
+
+            size_t total = nelem * groups.size();
+            ln_mu /= total;
+            ln_var = ln_var / total - ln_mu * ln_mu;
+
+            // double mu = static_cast<double>(total_kmers) / total;
+            // double mu2 = static_cast<double>(total_kmers_sq) / total;
+
+            // double ln_var = std::max(log(p * mu2 - mu) - log(p) - 2.0 * log(mu), 0.0);
+            // double ln_mu = ln_var / 2 - log(mu) - log(p) + log1p(-p);
+
+            // if (mu >= p * (mu2 - mu*mu)) {
+            //     common::logger->warn("Data is underdispersed: {} >= {}", mu, p * (mu2 - mu*mu));
+            //     ln_var = 1;
+            // }
+
+            // auto get_exp = [&](double mu, double var) { return exp(mu + var/2); };
+            // auto get_var = [&](double mu, double var) { return exp(mu*2 + var) * (exp(var)-1); };
+
+            // auto get_l = [&](double mu, double var) {
+            //     double ev = get_exp(-mu, var);
+            //     double vr = get_var(-mu, var);
+            //     double l = ev * log(p) * total - mu * total_nonzeros - c * vr;
+            //     for (size_t j = 1; j < counts.size() - 1; ++j) {
+            //         l += log(ev + j) * counts[j + 1];
+            //     }
+            //     return l;
+            // };
+
+            // auto get_dl_ddl_mu = [&](double mu, double var) {
+            //     double ev = get_exp(-mu, var);
+            //     double vr = get_var(-mu, var);
+            //     double dl = -ev * log(p) * total - total_nonzeros + c * 2 * vr;
+            //     double ddl = ev * log(p) * total - c * 4 * vr;
+            //     for (size_t j = 1; j < counts.size() - 1; ++j) {
+            //         dl -= ev / (ev + j) * counts[j + 1];
+            //         ddl += ev / pow(ev + j, 2.0) * j * counts[j + 1];
+            //     }
+            //     return std::make_pair(dl, ddl);
+            // };
+
+            // auto get_dl_ddl_sd = [&](double mu, double sd) {
+            //     double var = sd * sd;
+            //     double ev = get_exp(-mu, var);
+            //     double dl = sd * log(p) * total - c * exp(-mu) * sd * (3.0 * exp(var) - 1);
+            //     double ddl = log(p) * total - c * exp(-mu) * (exp(var)*3 - 1) - c*6*exp(-mu)*var*exp(var);
+            //     for (size_t j = 1; j < counts.size() - 1; ++j) {
+            //         dl += sd * counts[j + 1] / (ev + j);
+            //         ddl -= (ev * (1-var) + j) / pow(ev + j, 2.0);
+            //     }
+            //     return std::make_pair(dl, ddl);
+            // };
+
+            // double l = get_l(ln_mu, ln_var);
+            // common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+            //                       ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+
+            // while (true) {
+            //     double old_l = l;
+            //     ln_mu = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, ln_mu, -real_sum, real_sum, 30);
+            //     auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
+            //     if (ddl >= 0)
+            //         throw std::domain_error("");
+
+            //     l = get_l(ln_mu, ln_var);
+            //     common::logger->trace("M: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+            //                     ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+
+            //     double ln_sd_try = boost::math::tools::newton_raphson_iterate([&](double sd) { return get_dl_ddl_sd(ln_mu, sd); }, sqrt(ln_var), -real_sum, real_sum, 30);
+            //     auto [dl_sd, ddl_sd] = get_dl_ddl_sd(ln_mu, ln_sd_try);
+            //     if (ddl_sd >= 0)
+            //         throw std::domain_error("");
+
+            //     ln_var = ln_sd_try * ln_sd_try;
+            //     l = get_l(ln_mu, ln_var);
+            //     common::logger->trace("V: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
+            //                     ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
+
+            //     if (abs(l - old_l) / abs(l) < 1e-5)
+            //         break;
+            // }
+
+            common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
+                            ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2));
 
             auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
                 if (ln_var == 0)

From a56f19c8285feb580626ee61aff4c8309a20130d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 23 Aug 2024 01:50:58 +0200
Subject: [PATCH 299/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 86 -------------------
 1 file changed, 86 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b36e3cf034..dcd15e7fcf 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1094,10 +1094,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     row_sum_counts_ms[bucket_idx].resize(n + 1);
 
                 ++row_sum_counts_ms[bucket_idx][n];
-                // double r = p1p * n / groups.size();
-                // std::lock_guard<std::mutex> lock(hist_mu);
-                // ln_mu += -log(r);
-                // ln_var += log(r) * log(r);
             }, false);
 
             std::vector<size_t> row_sum_counts = std::move(row_sum_counts_ms[0]);
@@ -1120,92 +1116,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ln_var += log(r) * log(r) * row_sum_counts[n];
             }
 
-            // double c = 0;
-            // for (size_t j = 1; j < counts.size() - 1; ++j) {
-            //     c += static_cast<double>(counts[j + 1]) / 2 / j / j;
-            // }
-
             size_t total = nelem * groups.size();
             ln_mu /= total;
             ln_var = ln_var / total - ln_mu * ln_mu;
 
-            // double mu = static_cast<double>(total_kmers) / total;
-            // double mu2 = static_cast<double>(total_kmers_sq) / total;
-
-            // double ln_var = std::max(log(p * mu2 - mu) - log(p) - 2.0 * log(mu), 0.0);
-            // double ln_mu = ln_var / 2 - log(mu) - log(p) + log1p(-p);
-
-            // if (mu >= p * (mu2 - mu*mu)) {
-            //     common::logger->warn("Data is underdispersed: {} >= {}", mu, p * (mu2 - mu*mu));
-            //     ln_var = 1;
-            // }
-
-            // auto get_exp = [&](double mu, double var) { return exp(mu + var/2); };
-            // auto get_var = [&](double mu, double var) { return exp(mu*2 + var) * (exp(var)-1); };
-
-            // auto get_l = [&](double mu, double var) {
-            //     double ev = get_exp(-mu, var);
-            //     double vr = get_var(-mu, var);
-            //     double l = ev * log(p) * total - mu * total_nonzeros - c * vr;
-            //     for (size_t j = 1; j < counts.size() - 1; ++j) {
-            //         l += log(ev + j) * counts[j + 1];
-            //     }
-            //     return l;
-            // };
-
-            // auto get_dl_ddl_mu = [&](double mu, double var) {
-            //     double ev = get_exp(-mu, var);
-            //     double vr = get_var(-mu, var);
-            //     double dl = -ev * log(p) * total - total_nonzeros + c * 2 * vr;
-            //     double ddl = ev * log(p) * total - c * 4 * vr;
-            //     for (size_t j = 1; j < counts.size() - 1; ++j) {
-            //         dl -= ev / (ev + j) * counts[j + 1];
-            //         ddl += ev / pow(ev + j, 2.0) * j * counts[j + 1];
-            //     }
-            //     return std::make_pair(dl, ddl);
-            // };
-
-            // auto get_dl_ddl_sd = [&](double mu, double sd) {
-            //     double var = sd * sd;
-            //     double ev = get_exp(-mu, var);
-            //     double dl = sd * log(p) * total - c * exp(-mu) * sd * (3.0 * exp(var) - 1);
-            //     double ddl = log(p) * total - c * exp(-mu) * (exp(var)*3 - 1) - c*6*exp(-mu)*var*exp(var);
-            //     for (size_t j = 1; j < counts.size() - 1; ++j) {
-            //         dl += sd * counts[j + 1] / (ev + j);
-            //         ddl -= (ev * (1-var) + j) / pow(ev + j, 2.0);
-            //     }
-            //     return std::make_pair(dl, ddl);
-            // };
-
-            // double l = get_l(ln_mu, ln_var);
-            // common::logger->trace("Lognormal MoM fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-            //                       ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
-
-            // while (true) {
-            //     double old_l = l;
-            //     ln_mu = boost::math::tools::newton_raphson_iterate([&](double mu) { return get_dl_ddl_mu(mu, ln_var); }, ln_mu, -real_sum, real_sum, 30);
-            //     auto [dl, ddl] = get_dl_ddl_mu(ln_mu, ln_var);
-            //     if (ddl >= 0)
-            //         throw std::domain_error("");
-
-            //     l = get_l(ln_mu, ln_var);
-            //     common::logger->trace("M: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-            //                     ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
-
-            //     double ln_sd_try = boost::math::tools::newton_raphson_iterate([&](double sd) { return get_dl_ddl_sd(ln_mu, sd); }, sqrt(ln_var), -real_sum, real_sum, 30);
-            //     auto [dl_sd, ddl_sd] = get_dl_ddl_sd(ln_mu, ln_sd_try);
-            //     if (ddl_sd >= 0)
-            //         throw std::domain_error("");
-
-            //     ln_var = ln_sd_try * ln_sd_try;
-            //     l = get_l(ln_mu, ln_var);
-            //     common::logger->trace("V: Lognormal MLE fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}\tl: {}",
-            //                     ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2), l);
-
-            //     if (abs(l - old_l) / abs(l) < 1e-5)
-            //         break;
-            // }
-
             common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
                             ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2));
 

From bc35e2164c9dc2f75d9ddfdf51a4edd576a57c97 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 23 Aug 2024 02:06:28 +0200
Subject: [PATCH 300/410] precompute r values

---
 .../src/graph/annotated_graph_algorithm.cpp   | 69 +++++++++++--------
 1 file changed, 42 insertions(+), 27 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index dcd15e7fcf..c19a659d25 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1053,14 +1053,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double ln_mu = 0.0;
             double ln_var = 0.0;
 
-            std::vector<std::vector<size_t>> row_sum_counts_ms(num_threads + 1);
+            std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>> vector_counts_ms(num_threads + 1);
 
             generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
                 int64_t in_sum = 0;
                 int64_t out_sum = 0;
                 bool in_kmer = false;
                 bool out_kmer = false;
+                std::vector<int64_t> vec;
                 if (kept[row_i]) {
+                    vec.reserve(raw_row.size());
                     size_t count_in = 0;
                     size_t count_out = 0;
                     for (const auto &[j, raw_c] : raw_row) {
@@ -1075,6 +1077,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             in_sum += c;
                             ++count_in;
                         }
+
+                        vec.emplace_back(c);
                     }
 
                     if (count_in + count_out >= config.min_recurrence) {
@@ -1089,31 +1093,24 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (!in_kmer && !out_kmer)
                     return;
 
-                size_t n = in_sum + out_sum;
-                if (row_sum_counts_ms[bucket_idx].size() <= n)
-                    row_sum_counts_ms[bucket_idx].resize(n + 1);
-
-                ++row_sum_counts_ms[bucket_idx][n];
+                std::sort(vec.begin(), vec.end());
+                ++vector_counts_ms[bucket_idx][vec];
             }, false);
 
-            std::vector<size_t> row_sum_counts = std::move(row_sum_counts_ms[0]);
-            for (size_t j = 1; j < row_sum_counts_ms.size(); ++j) {
-                size_t ms = std::min(row_sum_counts_ms[j].size(), row_sum_counts.size());
-                for (size_t i = 0; i < ms; ++i) {
-                    row_sum_counts[i] += row_sum_counts_ms[j][i];
-                }
-                if (row_sum_counts_ms[j].size() > row_sum_counts.size()) {
-                    std::copy(row_sum_counts_ms[j].begin() + row_sum_counts.size(),
-                              row_sum_counts_ms[j].end(),
-                              std::back_inserter(row_sum_counts));
+            tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash> vector_counts = std::move(vector_counts_ms[0]);
+            for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
+                for (const auto &[v, c] : vector_counts_ms[j]) {
+                    vector_counts[v] += c;
                 }
             }
-            row_sum_counts_ms.resize(0);
 
-            for (size_t n = 1; n < row_sum_counts.size(); ++n) {
+            vector_counts_ms.resize(0);
+
+            for (const auto &[v, c] : vector_counts) {
+                int64_t n = std::accumulate(v.begin(), v.end(), int64_t(0));
                 double r = p1p * n / groups.size();
-                ln_mu += -log(r) * row_sum_counts[n];
-                ln_var += log(r) * log(r) * row_sum_counts[n];
+                ln_mu += -log(r) * c;
+                ln_var += log(r) * log(r) * c;
             }
 
             size_t total = nelem * groups.size();
@@ -1123,14 +1120,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
                             ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2));
 
-            auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const PairContainer &row) {
+            auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const auto &counts) {
                 if (ln_var == 0)
                     return exp(-ln_mu);
 
                 auto get_l = [&](double a) {
                     double r = 1.0 / a;
-                    double l = -log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var + lp * r * gs - lgamma(r) * row.size();
-                    for (const auto &[j, c] : row) {
+                    double l = -log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var + lp * r * gs - lgamma(r) * counts.size();
+                    for (int64_t c : counts) {
                         l += lgamma(r + c);
                     }
                     return l;
@@ -1144,6 +1141,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return 1.0 / a;
             };
 
+            for (auto it = vector_counts.begin(); it != vector_counts.end(); ++it) {
+                it.value() = get_r(it->first);
+            }
+
             auto get_deviance = [p](double invphi, double y) {
                 double mu = invphi * (1.0 - p) / p;
                 if (y == 0)
@@ -1155,11 +1156,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
             };
 
-            compute_min_pval = [get_r,num_labels_in,num_labels_out,get_deviance,gs=groups.size()](int64_t n, const auto &row) {
+            compute_min_pval = [vector_counts,num_labels_in,num_labels_out,get_deviance,gs=groups.size()](int64_t n, const auto &row) {
                 if (row.empty())
                     return 1.0;
 
-                double r = get_r(row);
+                std::vector<int64_t> counts;
+                counts.reserve(row.size());
+                for (const auto &[j, c] : row) {
+                    counts.emplace_back(c);
+                }
+                std::sort(counts.begin(), counts.end());
+                auto it = vector_counts.find(counts);
+                double r = it->second;
                 if (r == 0.0)
                     return 1.0;
 
@@ -1221,11 +1229,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return std::min(1.0, pval);
             };
 
-            compute_pval = [get_r,num_labels_in,num_labels_out,get_deviance,gs=groups.size()](int64_t in_sum, int64_t out_sum, const auto &row) {
+            compute_pval = [vector_counts,num_labels_in,num_labels_out,get_deviance,gs=groups.size()](int64_t in_sum, int64_t out_sum, const auto &row) {
                 if (row.empty())
                     return 1.0;
 
-                double r = get_r(row);
+                std::vector<int64_t> counts;
+                counts.reserve(row.size());
+                for (const auto &[j, c] : row) {
+                    counts.emplace_back(c);
+                }
+                std::sort(counts.begin(), counts.end());
+                auto it = vector_counts.find(counts);
+                double r = it->second;
 
                 if (r == 0.0)
                     return 1.0;

From bd44e66938d20177432cba034010ea6f2a2f1b1d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 23 Aug 2024 03:16:16 +0200
Subject: [PATCH 301/410] precompute

---
 .../src/graph/annotated_graph_algorithm.cpp   | 30 ++++++++++++++-----
 1 file changed, 23 insertions(+), 7 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c19a659d25..0c11256e09 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1097,14 +1097,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ++vector_counts_ms[bucket_idx][vec];
             }, false);
 
-            tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash> vector_counts = std::move(vector_counts_ms[0]);
+            size_t total_elems = 0;
             for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-                for (const auto &[v, c] : vector_counts_ms[j]) {
-                    vector_counts[v] += c;
-                }
+                total_elems += vector_counts_ms[j].size();
             }
 
-            vector_counts_ms.resize(0);
+            tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash> vector_counts = std::move(vector_counts_ms[0]);
+            {
+                ProgressBar progress_bar(total_elems, "Merging row", std::cerr, !common::get_verbose());
+                for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
+                    for (const auto &[v, c] : vector_counts_ms[j]) {
+                        vector_counts[v] += c;
+                        ++progress_bar;
+                    }
+                }
+
+                vector_counts_ms.resize(0);
+            }
 
             for (const auto &[v, c] : vector_counts) {
                 int64_t n = std::accumulate(v.begin(), v.end(), int64_t(0));
@@ -1141,8 +1150,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return 1.0 / a;
             };
 
-            for (auto it = vector_counts.begin(); it != vector_counts.end(); ++it) {
-                it.value() = get_r(it->first);
+            {
+                ProgressBar progress_bar(vector_counts.size(), "Precomputing r's", std::cerr, !common::get_verbose());
+                #pragma omp parallel
+                #pragma omp single
+                for (auto it = vector_counts.begin(); it != vector_counts.end(); ++it) {
+                    #pragma omp task
+                    it.value() = get_r(it->first);
+                    ++progress_bar;
+                }
             }
 
             auto get_deviance = [p](double invphi, double y) {

From dad2cfd0e9db197bd37bc70a05add7e928ba479d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 23 Aug 2024 11:40:49 +0200
Subject: [PATCH 302/410] optim

---
 .../src/graph/annotated_graph_algorithm.cpp   | 94 ++++++++++++-------
 1 file changed, 61 insertions(+), 33 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0c11256e09..0fa8966e54 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1053,7 +1053,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double ln_mu = 0.0;
             double ln_var = 0.0;
 
-            std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>> vector_counts_ms(num_threads + 1);
+            std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
 
             generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
                 int64_t in_sum = 0;
@@ -1093,33 +1093,47 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (!in_kmer && !out_kmer)
                     return;
 
+                size_t n = in_sum + out_sum;
+                if (n >= vector_counts_ms[bucket_idx].size())
+                    vector_counts_ms[bucket_idx].resize(n + 1);
+
                 std::sort(vec.begin(), vec.end());
-                ++vector_counts_ms[bucket_idx][vec];
+                ++vector_counts_ms[bucket_idx][n][vec];
             }, false);
 
-            size_t total_elems = 0;
+            std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>> vector_counts = std::move(vector_counts_ms[0]);
+            size_t max_size = vector_counts.size();
             for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-                total_elems += vector_counts_ms[j].size();
+                max_size = std::max(max_size, vector_counts_ms[j].size());
             }
+            vector_counts.resize(max_size);
 
-            tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash> vector_counts = std::move(vector_counts_ms[0]);
             {
-                ProgressBar progress_bar(total_elems, "Merging row", std::cerr, !common::get_verbose());
-                for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-                    for (const auto &[v, c] : vector_counts_ms[j]) {
-                        vector_counts[v] += c;
-                        ++progress_bar;
+                ProgressBar progress_bar(vector_counts.size() - 1, "Merging row", std::cerr, !common::get_verbose());
+                #pragma omp parallel for num_threads(num_threads)
+                for (size_t n = 1; n < vector_counts.size(); ++n) {
+                    for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
+                        if (n < vector_counts_ms[j].size()) {
+                            for (const auto &[v, c] : vector_counts_ms[j][n]) {
+                                vector_counts[n][v] += c;
+                            }
+                        }
                     }
+                    ++progress_bar;
                 }
 
                 vector_counts_ms.resize(0);
             }
 
-            for (const auto &[v, c] : vector_counts) {
-                int64_t n = std::accumulate(v.begin(), v.end(), int64_t(0));
+            for (size_t n = 1; n < vector_counts.size(); ++n) {
                 double r = p1p * n / groups.size();
-                ln_mu += -log(r) * c;
-                ln_var += log(r) * log(r) * c;
+                size_t total = 0;
+                for (const auto &[v, c] : vector_counts[n]) {
+                    total += c;
+                }
+
+                ln_mu += -log(r) * total;
+                ln_var += log(r) * log(r) * total;
             }
 
             size_t total = nelem * groups.size();
@@ -1151,14 +1165,22 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             };
 
             {
-                ProgressBar progress_bar(vector_counts.size(), "Precomputing r's", std::cerr, !common::get_verbose());
-                #pragma omp parallel
-                #pragma omp single
-                for (auto it = vector_counts.begin(); it != vector_counts.end(); ++it) {
-                    #pragma omp task
-                    it.value() = get_r(it->first);
-                    ++progress_bar;
+                std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>::iterator> its;
+                for (size_t n = 1; n < vector_counts.size(); ++n) {
+                    for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
+                        its.emplace_back(it);
+                    }
+                }
+
+                ProgressBar progress_bar(its.size(), "Precomputing r's", std::cerr, !common::get_verbose());
+                #pragma omp parallel for num_threads(num_threads)
+                for (size_t i = 0; i < its.size(); ++i) {
+                    if (i > 0 && i % 1000 == 0)
+                        progress_bar += 1000;
+
+                    its[i].value() = get_r(its[i]->first);
                 }
+                progress_bar += its.size() % 1000;
             }
 
             auto get_deviance = [p](double invphi, double y) {
@@ -1176,13 +1198,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (row.empty())
                     return 1.0;
 
-                std::vector<int64_t> counts;
-                counts.reserve(row.size());
-                for (const auto &[j, c] : row) {
-                    counts.emplace_back(c);
+                auto it = vector_counts[n].begin();
+                if (vector_counts[n].size() > 1) {
+                    std::vector<int64_t> counts;
+                    counts.reserve(row.size());
+                    for (const auto &[j, c] : row) {
+                        counts.emplace_back(c);
+                    }
+                    std::sort(counts.begin(), counts.end());
+                    it = vector_counts[n].find(counts);
                 }
-                std::sort(counts.begin(), counts.end());
-                auto it = vector_counts.find(counts);
                 double r = it->second;
                 if (r == 0.0)
                     return 1.0;
@@ -1249,13 +1274,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (row.empty())
                     return 1.0;
 
-                std::vector<int64_t> counts;
-                counts.reserve(row.size());
-                for (const auto &[j, c] : row) {
-                    counts.emplace_back(c);
+                auto it = vector_counts[in_sum + out_sum].begin();
+                if (vector_counts[in_sum + out_sum].size() > 1) {
+                    std::vector<int64_t> counts;
+                    counts.reserve(row.size());
+                    for (const auto &[j, c] : row) {
+                        counts.emplace_back(c);
+                    }
+                    std::sort(counts.begin(), counts.end());
+                    it = vector_counts[in_sum + out_sum].find(counts);
                 }
-                std::sort(counts.begin(), counts.end());
-                auto it = vector_counts.find(counts);
                 double r = it->second;
 
                 if (r == 0.0)

From a1b0397c1be01811ed5e7e9b655fb31af0c14733 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 25 Aug 2024 13:47:27 +0200
Subject: [PATCH 303/410] better fits

---
 .../src/graph/annotated_graph_algorithm.cpp   | 284 ++++++++++++++++--
 1 file changed, 251 insertions(+), 33 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0fa8966e54..72307672db 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -428,29 +428,88 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double r_guess = mu * mu / (var - mu);
             double p_guess = mu / var;
-            double r = r_guess;
-            try {
-                auto get_dl_ddl = [&](double r) {
-                    double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
-                    double ddl = (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r)) * total;
-                    std::for_each(begin, end, [&](const auto &a) {
-                        const auto &[k, c] = a;
-                        dl += boost::math::digamma(k + r) * c;
-                        ddl += boost::math::trigamma(k + r) * c;
-                    });
-                    return std::make_pair(dl, ddl);
-                };
-                r = boost::math::tools::newton_raphson_iterate(get_dl_ddl, r_guess,
-                                                               std::numeric_limits<double>::min(), mu * total, 30);
-                auto [dl, ddl] = get_dl_ddl(r);
-                if (ddl > 0) {
-                    common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r, dl, ddl);
-                    throw std::domain_error("FFF");
-                }
-            } catch (std::exception &e) {
-                common::logger->warn("Caught exception for sample {}: Falling back to initial guess", j);
-                common::logger->warn("{}", e.what());
-                r = r_guess;
+
+            auto get_dl = [&](double r) {
+                double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
+                std::for_each(begin, end, [&](const auto &a) {
+                    const auto &[k, c] = a;
+                    dl += boost::math::digamma(k + r) * c;
+                });
+                return dl;
+            };
+
+            auto get_ddl = [&](double r) {
+                double ddl = (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r)) * total;
+                std::for_each(begin, end, [&](const auto &a) {
+                    const auto &[k, c] = a;
+                    ddl += boost::math::trigamma(k + r) * c;
+                });
+                return ddl;
+            };
+
+            double r_min = r_guess;
+            double r_max = r_guess;
+
+            while (true) {
+                double dl_min = get_dl(r_min);
+                double dl_max = get_dl(r_max);
+
+                if (dl_min == 0) {
+                    r_max = r_min;
+                    break;
+                }
+
+                if (dl_max == 0) {
+                    r_min = r_max;
+                    break;
+                }
+
+                if (dl_min < 0)
+                    r_min /= 2;
+
+                if (dl_max > 0)
+                    r_max *= 2;
+
+                if (dl_min > 0 && dl_max < 0)
+                    break;
+            }
+
+            while (true) {
+                double r = (r_min + r_max) / 2;
+                double dl = get_dl(r);
+                if (dl > 0) {
+                    if (r == r_min) {
+                        break;
+                    }
+
+                    r_min = r;
+                }
+
+                if (dl < 0) {
+                    if (r == r_max) {
+                        break;
+                    }
+
+                    r_max = r;
+                }
+
+                if (abs(dl) <= 1e-4) {
+                    r_max = r;
+                    r_min = r;
+                    break;
+                }
+            }
+
+            double r = r_max;
+            double dl = get_dl(r);
+            double ddl = get_ddl(r);
+            // if (abs(dl) > 1e-4) {
+            //     common::logger->error("Not close enough to local maximum: r: [{}:{}]\tdl: [{},{}]", r_min, r_max, get_dl(r_min), get_dl(r_max));
+            //     throw std::domain_error("FFF");
+            // }
+            if (ddl > 0) {
+                common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r, dl, ddl);
+                throw std::domain_error("FFF");
             }
 
             double p = r / (r + mu);
@@ -542,26 +601,106 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             throw std::domain_error("Fit failed");
         }
 
-        auto get_dl_ddl = [&](double r) {
+        auto get_dl = [&](double r) {
             double dl = (log(r) - log(r + target_mu) - boost::math::digamma(r)) * matrix_size;
-            double ddl = (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r)) * matrix_size;
             for (size_t j = 0; j < groups.size(); ++j) {
                 double f = target_sum / sums[j];
                 std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
                     const auto &[k, c] = a;
                     dl += boost::math::digamma(f * k + r) * c;
+                });
+            }
+            return dl;
+        };
+
+        auto get_ddl = [&](double r) {
+            double ddl = (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r)) * matrix_size;
+            for (size_t j = 0; j < groups.size(); ++j) {
+                double f = target_sum / sums[j];
+                std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
+                    const auto &[k, c] = a;
                     ddl += boost::math::trigamma(f * k + r) * c;
                 });
             }
-            return std::make_pair(dl, ddl);
+            return ddl;
         };
-        double r_map = boost::math::tools::newton_raphson_iterate(get_dl_ddl, r_guess, 0.0, real_sum, 30);
-        auto [dl, ddl] = get_dl_ddl(r_map);
+
+        double r_min = r_guess;
+        double r_max = r_guess;
+
+        while (true) {
+            double dl_min = get_dl(r_min);
+            double dl_max = get_dl(r_max);
+
+            if (dl_min == 0) {
+                r_max = r_min;
+                break;
+            }
+
+            if (dl_max == 0) {
+                r_min = r_max;
+                break;
+            }
+
+            if (dl_min < 0)
+                r_min /= 2;
+
+            if (dl_max > 0)
+                r_max *= 2;
+
+            if (dl_min > 0 && dl_max < 0)
+                break;
+        }
+
+        while (true) {
+            double r = (r_min + r_max) / 2;
+            double dl = get_dl(r);
+            if (dl > 0) {
+                if (r == r_min) {
+                    break;
+                }
+
+                r_min = r;
+            }
+
+            if (dl < 0) {
+                if (r == r_max) {
+                    break;
+                }
+
+                r_max = r;
+            }
+
+            if (abs(dl) <= 1e-4) {
+                r_max = r;
+                r_min = r;
+                break;
+            }
+        }
+
+        double r_map = r_max;
+        double dl = get_dl(r_map);
+        double ddl = get_ddl(r_map);
+        // if (abs(dl) > 1e-4) {
+        //     common::logger->error("Not close enough to local maximum: r: [{}:{}]\tdl: [{},{}]", r_min, r_max, get_dl(r_min), get_dl(r_max));
+        //     throw std::domain_error("FFF");
+        // }
         if (ddl > 0) {
             common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r_map, dl, ddl);
-            throw std::domain_error("GGG");
+            throw std::domain_error("FFF");
         }
 
+        // double r_map = boost::math::tools::newton_raphson_iterate(get_dl_ddl, r_guess, 0.0, real_sum, 30);
+        // auto [dl, ddl] = get_dl_ddl(r_map);
+        // if (ddl > 0) {
+        //     common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r_map, dl, ddl);
+        //     throw std::domain_error("GGG");
+        // }
+        // if (abs(dl) > 1e-5) {
+        //     common::logger->error("Not close enough to local maximum: r: {}\tdl: {}\tddl: {}", r_map, dl, ddl);
+        //     throw std::domain_error("FFF");
+        // }
+
         double target_p = r_map / (r_map + target_mu);
         double fit_mu = r_map * (1.0 - target_p) / target_p;
         double fit_var = fit_mu / target_p;
@@ -1147,7 +1286,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (ln_var == 0)
                     return exp(-ln_mu);
 
+                double a_guess = p1p * std::accumulate(counts.begin(), counts.end(), int64_t(0)) / gs;
+
                 auto get_l = [&](double a) {
+                    if (a == 0)
+                        return -std::numeric_limits<double>::max();
+
                     double r = 1.0 / a;
                     double l = -log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var + lp * r * gs - lgamma(r) * counts.size();
                     for (int64_t c : counts) {
@@ -1156,12 +1300,86 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return l;
                 };
 
-                auto [a, nl] = boost::math::tools::brent_find_minima(
-                    [&](double a) { return -get_l(a); },
-                    1.0 / real_sum, real_sum, 30
-                );
+                auto get_dl_ddl = [&](double a) {
+                    double r = 1.0 / a;
+                    double dl = -a - a*(log(a)-ln_mu)/ln_var - gs*lp + boost::math::digamma(r)*counts.size();
+                    double ddl = a + a*(log(a)-ln_mu)/ln_var - a/ln_var + lp*gs*2 - boost::math::digamma(r)*counts.size()*2 - r*counts.size()*boost::math::trigamma(r);
+                    for (int64_t c : counts) {
+                        dl -= boost::math::digamma(r + c);
+                        ddl += 2.0*boost::math::digamma(r + c) + r*boost::math::trigamma(r + c);
+                    }
+
+                    return std::make_pair(r*r * dl, r*r*r * ddl);
+                };
+
+                double dl = 0;
+                double ddl = 0;
+                double a = a_guess;
+
+                double a_min = a_guess;
+                double a_max = a_guess;
+                while (true) {
+                    a_guess = (a_min + a_max) / 2;
+                    auto [dl_min, ddl_min] = get_dl_ddl(a_min);
+                    auto [dl_max, ddl_max] = get_dl_ddl(a_max);
+                    if (dl_min == 0) {
+                        a_max = a_min;
+                        break;
+                    }
+
+                    if (dl_max == 0) {
+                        a_min = a_max;
+                        break;
+                    }
+
+                    if (dl_min < 0)
+                        a_min /= 2;
+
+                    if (dl_max > 0)
+                        a_max *= 2;
+
+                    if (dl_min > 0 && dl_max < 0)
+                        break;
+                }
+
+                if (a_min < a_max) {
+                    std::tie(a_min, a_max) = boost::math::tools::bisect(
+                        [&](double a) { return get_dl_ddl(a).first; },
+                        a_min, a_max, boost::math::tools::eps_tolerance<double>(1e-5)
+                    );
+                    // a_min = boost::math::tools::newton_raphson_iterate(get_dl_ddl, a_guess, a_min, a_max, 30);
+                    // a_max = a_min;
+                }
+
+
+                // while (a_max - a_min >= 1e-5) {
+                //     double a_mid = (a_max + a_min) / 2;
+                //     auto [dl_mid, ddl_mid] = get_dl_ddl(a_mid);
+                //     if (dl_mid == 0) {
+                //         a_min = a_mid;
+                //         a_max = a_mid;
+                //     } else if (dl_mid > 0) {
+                //         a_min = a_mid;
+                //     } else {
+                //         a_max = a_mid;
+                //     }
+                // }
+                a = a_min;
+
+                std::tie(dl, ddl) = get_dl_ddl(a);
+                if (ddl >= 0) {
+                    common::logger->error("a_min: {}\ta_guess: {}\ta_max: {}", a_min, a_guess, a_max);
+                    common::logger->error("fa_min: {}\tfa_guess: {}\tfa_max: {}", get_dl_ddl(a_min).first, get_dl_ddl(a_guess).first, get_dl_ddl(a_max).first);
+                    common::logger->error("Failed to find maximum, eval error: a_guess: {}\tf'(a_guess): {}\tf''(a_guess): {}\ta: {}\tf'(a): {}\tf''(a): {}\t{}",
+                                          a_guess, get_dl_ddl(a_guess).first,get_dl_ddl(a_guess).second,
+                                          a,get_dl_ddl(a).first,get_dl_ddl(a).second,
+                                          fmt::join(counts, ","));
+                    throw std::domain_error("");
+                }
 
                 return 1.0 / a;
+                // a = boost::math::tools::brent_find_minima([&](double a) { return -get_l(a); },
+                //                                             0.0, real_sum, 30).first;
             };
 
             {

From 494194af2d4548386b0fec776d9a819b9b6a14de Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 25 Aug 2024 13:59:59 +0200
Subject: [PATCH 304/410] refactor

---
 .../src/graph/annotated_graph_algorithm.cpp   | 196 +++---------------
 1 file changed, 33 insertions(+), 163 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 72307672db..00aafa8db1 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -407,12 +407,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         };
     } else if (config.test_type == "nbinom_exact" || config.test_type == "zinb_exact" || config.test_type == "gnb_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
-        auto get_rp = [&](size_t j, auto begin, auto end) {
+        auto get_rp = [&](const auto &generate) {
             double mu = 0;
             double var = 0;
             size_t total = 0;
-            std::for_each(begin, end, [&](const auto &a) {
-                const auto &[k, c] = a;
+            generate([&](auto k, auto c) {
                 mu += k * c;
                 var += k * k * c;
                 total += c;
@@ -422,17 +421,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             var = (var - mu2 * total) / (total - 1);
 
             if (mu >= var) {
-                common::logger->warn("Fit {} failed, falling back to Poisson: mu: {} >= var: {}", j, mu, var);
+                common::logger->warn("Fit failed, falling back to Poisson: mu: {} >= var: {}", mu, var);
                 return std::make_pair(0.0, 1.0);
             }
 
             double r_guess = mu * mu / (var - mu);
-            double p_guess = mu / var;
 
             auto get_dl = [&](double r) {
                 double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
-                std::for_each(begin, end, [&](const auto &a) {
-                    const auto &[k, c] = a;
+                generate([&](auto k, auto c) {
                     dl += boost::math::digamma(k + r) * c;
                 });
                 return dl;
@@ -440,8 +437,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             auto get_ddl = [&](double r) {
                 double ddl = (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r)) * total;
-                std::for_each(begin, end, [&](const auto &a) {
-                    const auto &[k, c] = a;
+                generate([&](auto k, auto c) {
                     ddl += boost::math::trigamma(k + r) * c;
                 });
                 return ddl;
@@ -503,19 +499,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double r = r_max;
             double dl = get_dl(r);
             double ddl = get_ddl(r);
-            // if (abs(dl) > 1e-4) {
-            //     common::logger->error("Not close enough to local maximum: r: [{}:{}]\tdl: [{},{}]", r_min, r_max, get_dl(r_min), get_dl(r_max));
-            //     throw std::domain_error("FFF");
-            // }
             if (ddl > 0) {
                 common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r, dl, ddl);
                 throw std::domain_error("FFF");
             }
 
             double p = r / (r + mu);
-            common::logger->trace("{}: size: {}\tmax_val: {}\tmu: {}\tvar: {}\tmoment est:\tr: {}\tp: {}\tmle: r: {}\tp: {}",
-                                  j, sums[j], (end - 1)->first, mu, var, r_guess, p_guess, r, p);
-
             return std::make_pair(r, p);
         };
 
@@ -534,7 +523,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     total += it->second;
             }
             hist_its[j] = it;
-            nb_params[j] = get_rp(j, hist.begin(), it);
+            nb_params[j] = get_rp([&](const auto &callback) {
+                std::for_each(hist.begin(), it, [&](const auto &a) {
+                    const auto &[k, c] = a;
+                    callback(k, c);
+                });
+            });
+            const auto &[r, p] = nb_params[j];
+            common::logger->trace("{}: size: {}\tmax_val: {}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
+                                  j, sums[j], (it - 1)->first, r * (1-p)/p, r*(1-p)/p/p, r, p);
 
             total += (hist.size() && hist[0].first == 0 ? hist[0].second : 0);
 
@@ -551,161 +548,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         target_sum = exp(target_sum / groups.size());
 
         common::logger->trace("Finding common p and r.");
-        double real_sum = 0.0;
-        double real_sum_sq = 0.0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            double f = target_sum / sums[j];
-            common::logger->trace("{}: scale factor: {}", j, f);
-            std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
-                const auto &[k, c] = a;
-                if (k != 0) {
-                    double val = f * k;
-                    real_sum += val * c;
-                    real_sum_sq += val * val * c;
-                }
-            });
-        }
 
-        double r_guess = 0.0;
-        double target_mu = 0.0;
-        double target_var = 0.0;
-
-        auto map_value = [&](uint64_t k, double scale, const auto &old_dist, const auto &new_dist) {
-            double cdf = boost::math::cdf(old_dist, k);
-
-            if (cdf < 1.0)
-                return boost::math::quantile(new_dist, cdf);
-
-            double ccdf = boost::math::cdf(boost::math::complement(old_dist, k));
-
-            if (ccdf > 0.0)
-                return boost::math::quantile(boost::math::complement(new_dist, ccdf));
-
-            return ceil(scale * k);
-        };
-
-        // nelem * r * groups.size() / p - target_sum * groups.size() / (1 - p) = 0
-        // nelem * r / p = target_sum / (1 - p)
-        // r / target_mu = p / (1 - p)
-        // r / target_mu = p + p * r / target_mu
-        // p = r / target_mu / (r / target_mu + 1)
-        // p = r / (r + target_mu)
-        target_mu = real_sum / matrix_size;
-        double target_mu2 = target_mu * target_mu;
-        target_var = (real_sum_sq - target_mu2 * matrix_size) / (matrix_size - 1);
-
-        if (target_var > target_mu) {
-            r_guess = target_mu2 / (target_var - target_mu);
-        } else {
-            common::logger->error("Data is underdispersed: {} >= {}. Use poisson_exact instead", target_mu, target_var);
-            throw std::domain_error("Fit failed");
-        }
-
-        auto get_dl = [&](double r) {
-            double dl = (log(r) - log(r + target_mu) - boost::math::digamma(r)) * matrix_size;
-            for (size_t j = 0; j < groups.size(); ++j) {
-                double f = target_sum / sums[j];
-                std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
-                    const auto &[k, c] = a;
-                    dl += boost::math::digamma(f * k + r) * c;
-                });
-            }
-            return dl;
-        };
-
-        auto get_ddl = [&](double r) {
-            double ddl = (1.0 / r - 1.0 / (r + target_mu) - boost::math::trigamma(r)) * matrix_size;
+        auto [r_map, target_p] = get_rp([&](const auto &callback) {
             for (size_t j = 0; j < groups.size(); ++j) {
                 double f = target_sum / sums[j];
                 std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
                     const auto &[k, c] = a;
-                    ddl += boost::math::trigamma(f * k + r) * c;
+                    callback(f * k, c);
                 });
             }
-            return ddl;
-        };
-
-        double r_min = r_guess;
-        double r_max = r_guess;
+        });
 
-        while (true) {
-            double dl_min = get_dl(r_min);
-            double dl_max = get_dl(r_max);
-
-            if (dl_min == 0) {
-                r_max = r_min;
-                break;
-            }
-
-            if (dl_max == 0) {
-                r_min = r_max;
-                break;
-            }
-
-            if (dl_min < 0)
-                r_min /= 2;
-
-            if (dl_max > 0)
-                r_max *= 2;
-
-            if (dl_min > 0 && dl_max < 0)
-                break;
-        }
-
-        while (true) {
-            double r = (r_min + r_max) / 2;
-            double dl = get_dl(r);
-            if (dl > 0) {
-                if (r == r_min) {
-                    break;
-                }
-
-                r_min = r;
-            }
-
-            if (dl < 0) {
-                if (r == r_max) {
-                    break;
-                }
-
-                r_max = r;
-            }
-
-            if (abs(dl) <= 1e-4) {
-                r_max = r;
-                r_min = r;
-                break;
-            }
-        }
-
-        double r_map = r_max;
-        double dl = get_dl(r_map);
-        double ddl = get_ddl(r_map);
-        // if (abs(dl) > 1e-4) {
-        //     common::logger->error("Not close enough to local maximum: r: [{}:{}]\tdl: [{},{}]", r_min, r_max, get_dl(r_min), get_dl(r_max));
-        //     throw std::domain_error("FFF");
-        // }
-        if (ddl > 0) {
-            common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r_map, dl, ddl);
-            throw std::domain_error("FFF");
-        }
-
-        // double r_map = boost::math::tools::newton_raphson_iterate(get_dl_ddl, r_guess, 0.0, real_sum, 30);
-        // auto [dl, ddl] = get_dl_ddl(r_map);
-        // if (ddl > 0) {
-        //     common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r_map, dl, ddl);
-        //     throw std::domain_error("GGG");
-        // }
-        // if (abs(dl) > 1e-5) {
-        //     common::logger->error("Not close enough to local maximum: r: {}\tdl: {}\tddl: {}", r_map, dl, ddl);
-        //     throw std::domain_error("FFF");
-        // }
-
-        double target_p = r_map / (r_map + target_mu);
         double fit_mu = r_map * (1.0 - target_p) / target_p;
         double fit_var = fit_mu / target_p;
-        common::logger->trace("Common params: r_guess: {}\tr: {}\tp: {}\tmu: {}\tvar: {}",
-                              r_guess, r_map, target_p, fit_mu, fit_var);
+        common::logger->trace("Common params: r: {}\tp: {}\tmu: {}\tvar: {}",
+                              r_map, target_p, fit_mu, fit_var);
 
         double r_in = r_map * num_labels_in;
         double r_out = r_map * num_labels_out;
@@ -721,6 +578,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         count_maps.resize(groups.size());
 
         common::logger->trace("Computing quantile maps");
+        auto map_value = [&](uint64_t k, double scale, const auto &old_dist, const auto &new_dist) {
+            double cdf = boost::math::cdf(old_dist, k);
+
+            if (cdf < 1.0)
+                return boost::math::quantile(new_dist, cdf);
+
+            double ccdf = boost::math::cdf(boost::math::complement(old_dist, k));
+
+            if (ccdf > 0.0)
+                return boost::math::quantile(boost::math::complement(new_dist, ccdf));
+
+            return ceil(scale * k);
+        };
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto &[r, p] = nb_params[j];
@@ -1282,7 +1152,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
                             ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2));
 
-            auto get_r = [ln_mu,ln_var,real_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const auto &counts) {
+            auto get_r = [ln_mu,ln_var,real_sum=total_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const auto &counts) {
                 if (ln_var == 0)
                     return exp(-ln_mu);
 

From 72244fa903620ad5ea2956606748d6c68bbfb232 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 25 Aug 2024 15:49:06 +0200
Subject: [PATCH 305/410] per-k-mer prob

---
 .../src/graph/annotated_graph_algorithm.cpp   | 91 +++++++++++--------
 1 file changed, 55 insertions(+), 36 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 00aafa8db1..5ba61474c5 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1056,12 +1056,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return pval;
             };
         } else {
-            double p = target_p;
-            double p1p = exp(log(p) - log1p(-p));
-
-            double ln_mu = 0.0;
-            double ln_var = 0.0;
-
             std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
 
             generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
@@ -1134,14 +1128,34 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 vector_counts_ms.resize(0);
             }
 
+            common::logger->trace("Fitting LN prior distribution for dispersions");
+            double ln_mu = 0.0;
+            double ln_var = 0.0;
+
+            #pragma omp parallel for num_threads(num_threads) reduction(+:ln_mu,ln_var)
             for (size_t n = 1; n < vector_counts.size(); ++n) {
-                double r = p1p * n / groups.size();
                 size_t total = 0;
                 for (const auto &[v, c] : vector_counts[n]) {
                     total += c;
                 }
 
+                double r = target_p / (1.0 - target_p) * n / groups.size();
+
+                // auto [r, p] = get_rp([&](const auto &callback) {
+                //     for (const auto &[v, c] : vector_counts[n]) {
+                //         for (int64_t k : v) {
+                //             callback(k, c);
+                //         }
+
+                //         if (v.size() < groups.size())
+                //             callback(0, (groups.size() - v.size()) * c);
+                //     }
+                // });
+
+                #pragma omp atomic
                 ln_mu += -log(r) * total;
+
+                #pragma omp atomic
                 ln_var += log(r) * log(r) * total;
             }
 
@@ -1150,19 +1164,36 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             ln_var = ln_var / total - ln_mu * ln_mu;
 
             common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
-                            ln_mu, ln_var, exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1), exp(-ln_mu - ln_var/2));
+                                  ln_mu, ln_var,
+                                  exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1),
+                                  exp(-ln_mu - ln_var/2));
+
+            auto get_rp = [ln_mu,ln_var,real_sum=total_sum,total,sum=static_cast<double>(total_kmers),gs=groups.size()](const auto &counts) {
+                double mu = 0;
+                double var = 0;
+                for (int64_t c : counts) {
+                    mu += c;
+                    var += c * c;
+                }
+                mu /= gs;
+
+                if (ln_var == 0) {
+                    double r = exp(-ln_mu);
+                    return std::make_pair(r, r / (r + mu));
+                }
 
-            auto get_r = [ln_mu,ln_var,real_sum=total_sum,p1p,total,lp=log(p),sum=static_cast<double>(total_kmers),gs=groups.size()](const auto &counts) {
-                if (ln_var == 0)
-                    return exp(-ln_mu);
+                double mu2 = mu * mu;
+                var = var / gs - mu2;
 
-                double a_guess = p1p * std::accumulate(counts.begin(), counts.end(), int64_t(0)) / gs;
+                double a_guess = var > mu ? (var - mu) / mu2 : 1.0;
 
                 auto get_l = [&](double a) {
                     if (a == 0)
                         return -std::numeric_limits<double>::max();
 
                     double r = 1.0 / a;
+                    double p = r / (r + mu);
+                    double lp = log(p);
                     double l = -log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var + lp * r * gs - lgamma(r) * counts.size();
                     for (int64_t c : counts) {
                         l += lgamma(r + c);
@@ -1172,6 +1203,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 auto get_dl_ddl = [&](double a) {
                     double r = 1.0 / a;
+                    double p = r / (r + mu);
+                    double lp = log(p);
                     double dl = -a - a*(log(a)-ln_mu)/ln_var - gs*lp + boost::math::digamma(r)*counts.size();
                     double ddl = a + a*(log(a)-ln_mu)/ln_var - a/ln_var + lp*gs*2 - boost::math::digamma(r)*counts.size()*2 - r*counts.size()*boost::math::trigamma(r);
                     for (int64_t c : counts) {
@@ -1217,23 +1250,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         [&](double a) { return get_dl_ddl(a).first; },
                         a_min, a_max, boost::math::tools::eps_tolerance<double>(1e-5)
                     );
-                    // a_min = boost::math::tools::newton_raphson_iterate(get_dl_ddl, a_guess, a_min, a_max, 30);
-                    // a_max = a_min;
                 }
 
-
-                // while (a_max - a_min >= 1e-5) {
-                //     double a_mid = (a_max + a_min) / 2;
-                //     auto [dl_mid, ddl_mid] = get_dl_ddl(a_mid);
-                //     if (dl_mid == 0) {
-                //         a_min = a_mid;
-                //         a_max = a_mid;
-                //     } else if (dl_mid > 0) {
-                //         a_min = a_mid;
-                //     } else {
-                //         a_max = a_mid;
-                //     }
-                // }
                 a = a_min;
 
                 std::tie(dl, ddl) = get_dl_ddl(a);
@@ -1247,9 +1265,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     throw std::domain_error("");
                 }
 
-                return 1.0 / a;
-                // a = boost::math::tools::brent_find_minima([&](double a) { return -get_l(a); },
-                //                                             0.0, real_sum, 30).first;
+                double r = 1.0 / a;
+                double p = r / (r + mu);
+                return std::make_pair(r, p);
             };
 
             {
@@ -1266,12 +1284,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     if (i > 0 && i % 1000 == 0)
                         progress_bar += 1000;
 
-                    its[i].value() = get_r(its[i]->first);
+                    its[i].value() = get_rp(its[i]->first).first;
                 }
                 progress_bar += its.size() % 1000;
             }
 
-            auto get_deviance = [p](double invphi, double y) {
+            auto get_deviance = [](double invphi, double p, double y) {
                 double mu = invphi * (1.0 - p) / p;
                 if (y == 0)
                     return 2.0*invphi*log((invphi*mu)/invphi);
@@ -1300,6 +1318,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (r == 0.0)
                     return 1.0;
 
+                double p = r / (r + static_cast<double>(n) / gs);
                 double r_in = r * num_labels_in;
                 double r_out = r * num_labels_out;
                 double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
@@ -1309,7 +1328,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 std::vector<double> devs(n + 1);
 
                 for (int64_t s = 0; s <= n; ++s) {
-                    devs[s] = get_deviance(r_in, s) + get_deviance(r_out, n - s);
+                    devs[s] = get_deviance(r_in, p, s) + get_deviance(r_out, p, n - s);
                 }
 
                 double max_d = *std::max_element(devs.begin(), devs.end());
@@ -1377,18 +1396,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (r == 0.0)
                     return 1.0;
 
+                int64_t n = in_sum + out_sum;
+                double p = r / (r + static_cast<double>(n) / gs);
                 double r_in = r * num_labels_in;
                 double r_out = r * num_labels_out;
                 double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
 
-                int64_t n = in_sum + out_sum;
-
                 double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
 
                 std::vector<double> devs(n + 1);
 
                 for (int64_t s = 0; s <= n; ++s) {
-                    devs[s] = get_deviance(r_in, s) + get_deviance(r_out, n - s);
+                    devs[s] = get_deviance(r_in, p, s) + get_deviance(r_out, p, n - s);
                 }
 
                 double in_sum_total_deviance = devs[in_sum];

From f4b8e5b8efc9d1d412662c7cbdcdc61ee7317ed1 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 25 Aug 2024 16:23:57 +0200
Subject: [PATCH 306/410] refactor

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5ba61474c5..21f21fc3d8 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -407,7 +407,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         };
     } else if (config.test_type == "nbinom_exact" || config.test_type == "zinb_exact" || config.test_type == "gnb_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
-        auto get_rp = [&](const auto &generate) {
+        auto get_rp = [&](const auto &generate,
+                          const auto &add_dl,
+                          const auto &add_ddl) {
             double mu = 0;
             double var = 0;
             size_t total = 0;
@@ -432,7 +434,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 generate([&](auto k, auto c) {
                     dl += boost::math::digamma(k + r) * c;
                 });
-                return dl;
+                return dl + add_dl(r);
             };
 
             auto get_ddl = [&](double r) {
@@ -440,7 +442,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 generate([&](auto k, auto c) {
                     ddl += boost::math::trigamma(k + r) * c;
                 });
-                return ddl;
+                return ddl + add_ddl(r);
             };
 
             double r_min = r_guess;
@@ -528,7 +530,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const auto &[k, c] = a;
                     callback(k, c);
                 });
-            });
+            }, [](double) { return 0; }, [](double) { return 0; });
             const auto &[r, p] = nb_params[j];
             common::logger->trace("{}: size: {}\tmax_val: {}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
                                   j, sums[j], (it - 1)->first, r * (1-p)/p, r*(1-p)/p/p, r, p);
@@ -557,7 +559,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     callback(f * k, c);
                 });
             }
-        });
+        }, [](double) { return 0; }, [](double) { return 0; });
 
         double fit_mu = r_map * (1.0 - target_p) / target_p;
         double fit_var = fit_mu / target_p;

From 558759174cf0217ae6b28b853158e69e354bc70b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 25 Aug 2024 17:04:24 +0200
Subject: [PATCH 307/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 49 ++++++++++---------
 1 file changed, 27 insertions(+), 22 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 21f21fc3d8..c4b88bf9a9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -422,13 +422,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double mu2 = mu * mu;
             var = (var - mu2 * total) / (total - 1);
 
+            double r_guess = mu * mu / (var - mu);
+
             if (mu >= var) {
                 common::logger->warn("Fit failed, falling back to Poisson: mu: {} >= var: {}", mu, var);
                 return std::make_pair(0.0, 1.0);
             }
 
-            double r_guess = mu * mu / (var - mu);
-
             auto get_dl = [&](double r) {
                 double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
                 generate([&](auto k, auto c) {
@@ -1134,31 +1134,36 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double ln_mu = 0.0;
             double ln_var = 0.0;
 
-            #pragma omp parallel for num_threads(num_threads) reduction(+:ln_mu,ln_var)
-            for (size_t n = 1; n < vector_counts.size(); ++n) {
-                size_t total = 0;
-                for (const auto &[v, c] : vector_counts[n]) {
-                    total += c;
-                }
+            {
+                ProgressBar progress_bar(vector_counts.size() - 1, "Fitting", std::cerr, !common::get_verbose());
+                #pragma omp parallel for num_threads(num_threads) reduction(+:ln_mu,ln_var)
+                for (size_t n = 1; n < vector_counts.size(); ++n) {
+                    size_t total = 0;
+                    for (const auto &[v, c] : vector_counts[n]) {
+                        total += c;
+                    }
+
+                    double r = target_p / (1.0 - target_p) * n / groups.size();
 
-                double r = target_p / (1.0 - target_p) * n / groups.size();
+                    // auto [r, p] = get_rp([&](const auto &callback) {
+                    //     for (const auto &[v, c] : vector_counts[n]) {
+                    //         for (int64_t k : v) {
+                    //             callback(k, c);
+                    //         }
 
-                // auto [r, p] = get_rp([&](const auto &callback) {
-                //     for (const auto &[v, c] : vector_counts[n]) {
-                //         for (int64_t k : v) {
-                //             callback(k, c);
-                //         }
+                    //         if (v.size() < groups.size())
+                    //             callback(0, (groups.size() - v.size()) * c);
+                    //     }
+                    // }, [](double) { return 0; }, [](double) { return 0; });
 
-                //         if (v.size() < groups.size())
-                //             callback(0, (groups.size() - v.size()) * c);
-                //     }
-                // });
+                    #pragma omp atomic
+                    ln_mu += -log(r) * total;
 
-                #pragma omp atomic
-                ln_mu += -log(r) * total;
+                    #pragma omp atomic
+                    ln_var += log(r) * log(r) * total;
 
-                #pragma omp atomic
-                ln_var += log(r) * log(r) * total;
+                    ++progress_bar;
+                }
             }
 
             size_t total = nelem * groups.size();

From 07f07472aafb677d6309d0646fc41cb706c3622c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 25 Aug 2024 19:41:14 +0200
Subject: [PATCH 308/410] fix per-unitig tests for gnb

---
 .../src/graph/annotated_graph_algorithm.cpp   | 38 ++++++++++++++++++-
 1 file changed, 36 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c4b88bf9a9..7c6a5b4b5b 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -228,7 +228,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     std::vector<PValStorage> pvals_buckets;
     std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
+    std::vector<PValStorage> pvals_min_buckets;
+    std::vector<std::unique_ptr<utils::TempFile>> tmp_min_buckets;
     constexpr bool preallocated = std::is_same_v<PValStorage, std::vector<uint64_t>>;
+
     if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
         auto &pvals = pvals_buckets.emplace_back();
         pvals.resize(graph_ptr->max_index() + 1, nullpval);
@@ -242,6 +245,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         pvals_buckets[0].push_back(nullpval);
     }
 
+    if (config.test_type == "gnb_exact" && config.test_by_unitig) {
+        if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+            auto &pvals_min = pvals_min_buckets.emplace_back();
+            pvals_min.resize(graph_ptr->max_index() + 1, nullpval);
+        }
+
+        if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+            for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+                auto &tmp_file = tmp_min_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+                pvals_min_buckets.emplace_back(tmp_file->name(), std::ios::out);
+            }
+            pvals_min_buckets[0].push_back(nullpval);
+        }
+    }
+
     std::vector<PValStorage> eff_size_buckets;
     std::vector<std::unique_ptr<utils::TempFile>> tmp_eff_buckets;
     std::vector<PValStorage> sum_buckets;
@@ -1521,6 +1539,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 n = 0;
             }
 
+            double pval_min = 0;
             if (config.test_type != "gnb_exact") {
                 if (n >= ms[bucket_idx].size())
                     ms[bucket_idx].resize(n + 1, std::make_pair(1.1, 0));
@@ -1528,6 +1547,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (ms[bucket_idx][n].first == 1.1)
                     ms[bucket_idx][n].first = compute_min_pval(n, row);
 
+                pval_min = ms[bucket_idx][n].first;
                 ++ms[bucket_idx][n].second;
             } else {
                 std::sort(vals.begin(), vals.end());
@@ -1537,6 +1557,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 } else {
                     ++find.value().second;
                 }
+
+                pval_min = find->second.first;
             }
 
             if (config.test_by_unitig) {
@@ -1546,9 +1568,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
                     eff_size_buckets[bucket_idx][node] = eff_size;
                     sum_buckets[bucket_idx][node] = n;
+                    if (config.test_type == "gnb_exact")
+                        pvals_min_buckets[bucket_idx][node] = bit_cast<uint64_t>(pval_min);
                 } else {
                     eff_size_buckets[bucket_idx].push_back(eff_size);
                     sum_buckets[bucket_idx].push_back(n);
+                    if (config.test_type == "gnb_exact")
+                        pvals_min_buckets[bucket_idx].push_back(bit_cast<uint64_t>(pval_min));
                 }
             }
 
@@ -1671,9 +1697,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 std::lock_guard<std::mutex> lock(pval_mu);
                 for (size_t i = 0; i < path.size(); ++i) {
                     size_t bucket_idx = bucket_idxs[i];
-                    size_t n = sum_buckets[bucket_idx][path[i] - boundaries[bucket_idx]];
+                    size_t node_shift = path[i] - boundaries[bucket_idx];
+                    size_t n = sum_buckets[bucket_idx][node_shift];
                     n_sum += n;
-                    pvals_min.emplace_back(m[n].first);
+
+                    if (config.test_type == "gnb_exact") {
+                        pvals_min.emplace_back(bit_cast<double, uint64_t>(pvals_min_buckets[bucket_idx][node_shift]));
+                    } else {
+                        pvals_min.emplace_back(m[n].first);
+                    }
                 }
             }
 
@@ -1703,6 +1735,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         sum_buckets.resize(0);
         tmp_sum_buckets.resize(0);
+        pvals_min_buckets.resize(0);
+        tmp_min_buckets.resize(0);
     }
 
     if (k > 0) {

From 5856e77e2f7091a5a5c627954e54ac05bb39ea26 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 25 Aug 2024 21:02:17 +0200
Subject: [PATCH 309/410] less merging

---
 .../src/graph/annotated_graph_algorithm.cpp   | 41 +++++++------------
 1 file changed, 15 insertions(+), 26 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 7c6a5b4b5b..e63ae10f0e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1493,7 +1493,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<int64_t> m_sums;
     {
         std::vector<std::vector<std::pair<double, size_t>>> ms(num_threads + 1);
-        std::vector<tsl::hopscotch_map<std::vector<int64_t>, std::pair<double, size_t>, utils::VectorHash>> ms_vecs(num_threads + 1);
+        std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, std::pair<double, size_t>, utils::VectorHash>>> ms_vecs(num_threads + 1);
         generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
             int64_t in_sum = 0;
             int64_t out_sum = 0;
@@ -1551,9 +1551,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ++ms[bucket_idx][n].second;
             } else {
                 std::sort(vals.begin(), vals.end());
-                auto find = ms_vecs[bucket_idx].find(vals);
-                if (find == ms_vecs[bucket_idx].end()) {
-                    ms_vecs[bucket_idx][vals] = std::make_pair(compute_min_pval(n, row), 1);
+                if (ms_vecs[bucket_idx].size() <= n)
+                    ms_vecs[bucket_idx].resize(n + 1);
+
+                auto find = ms_vecs[bucket_idx][n].find(vals);
+                if (find == ms_vecs[bucket_idx][n].end()) {
+                    ms_vecs[bucket_idx][n][vals] = std::make_pair(compute_min_pval(n, row), 1);
                 } else {
                     ++find.value().second;
                 }
@@ -1599,37 +1602,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             m = std::move(ms[0]);
             ms.resize(0);
         } else {
-            for (size_t i = 1; i < ms_vecs.size(); ++i) {
-                for (const auto &[vec, v] : ms_vecs[i]) {
-                    auto find = ms_vecs[0].find(vec);
-                    if (find == ms_vecs[0].end()) {
-                        ms_vecs[0][vec] = v;
-                    } else {
-                        find.value().second += v.second;
+            std::vector<std::tuple<double, int64_t, size_t>> mn;
+            for (size_t i = 0; i < ms_vecs.size(); ++i) {
+                for (size_t n = 1; n < ms_vecs[i].size(); ++n) {
+                    for (const auto &[vec, v] : ms_vecs[i][n]) {
+                        mn.emplace_back(v.first, n, v.second);
                     }
                 }
             }
-            ms_vecs.resize(1);
-
-            std::vector<std::tuple<double, int64_t, size_t>> mn;
-            mn.reserve(ms_vecs[0].size());
-            for (const auto &[vec, v] : ms_vecs[0]) {
-                mn.emplace_back(
-                    v.first,
-                    std::accumulate(vec.begin(), vec.end(), int64_t(0)),
-                    v.second
-                );
-            }
+            ms_vecs.resize(0);
             std::sort(mn.begin(), mn.end(), utils::GreaterFirst());
 
-            m.reserve(ms_vecs[0].size());
-            m_sums.reserve(ms_vecs[0].size());
+            m.reserve(mn.size());
+            m_sums.reserve(mn.size());
             for (const auto &[p, s, c] : mn) {
                 m.emplace_back(p, c);
                 m_sums.emplace_back(s);
             }
-
-            ms_vecs.resize(0);
         }
     }
 

From 93c2a87e85efdac1950c6068c0f5af520ab98fb8 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 25 Aug 2024 21:30:43 +0200
Subject: [PATCH 310/410] simplify

---
 .../src/graph/annotated_graph_algorithm.cpp   | 157 ++++++++++--------
 1 file changed, 87 insertions(+), 70 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index e63ae10f0e..b52c12dff0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -455,13 +455,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return dl + add_dl(r);
             };
 
-            auto get_ddl = [&](double r) {
-                double ddl = (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r)) * total;
-                generate([&](auto k, auto c) {
-                    ddl += boost::math::trigamma(k + r) * c;
-                });
-                return ddl + add_ddl(r);
-            };
+            // auto get_ddl = [&](double r) {
+            //     double ddl = (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r)) * total;
+            //     generate([&](auto k, auto c) {
+            //         ddl += boost::math::trigamma(k + r) * c;
+            //     });
+            //     return ddl + add_ddl(r);
+            // };
 
             double r_min = r_guess;
             double r_max = r_guess;
@@ -490,39 +490,43 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     break;
             }
 
-            while (true) {
-                double r = (r_min + r_max) / 2;
-                double dl = get_dl(r);
-                if (dl > 0) {
-                    if (r == r_min) {
-                        break;
-                    }
+            std::tie(r_min, r_max) = boost::math::tools::bisect(
+                get_dl, r_min, r_max, boost::math::tools::eps_tolerance<double>(1e-5)
+            );
 
-                    r_min = r;
-                }
+            // while (true) {
+            //     double r = (r_min + r_max) / 2;
+            //     double dl = get_dl(r);
+            //     if (dl > 0) {
+            //         if (r == r_min) {
+            //             break;
+            //         }
 
-                if (dl < 0) {
-                    if (r == r_max) {
-                        break;
-                    }
+            //         r_min = r;
+            //     }
 
-                    r_max = r;
-                }
+            //     if (dl < 0) {
+            //         if (r == r_max) {
+            //             break;
+            //         }
 
-                if (abs(dl) <= 1e-4) {
-                    r_max = r;
-                    r_min = r;
-                    break;
-                }
-            }
+            //         r_max = r;
+            //     }
+
+            //     if (abs(dl) <= 1e-4) {
+            //         r_max = r;
+            //         r_min = r;
+            //         break;
+            //     }
+            // }
 
             double r = r_max;
-            double dl = get_dl(r);
-            double ddl = get_ddl(r);
-            if (ddl > 0) {
-                common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r, dl, ddl);
-                throw std::domain_error("FFF");
-            }
+            // double dl = get_dl(r);
+            // double ddl = get_ddl(r);
+            // if (ddl > 0) {
+            //     common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r, dl, ddl);
+            //     throw std::domain_error("FFF");
+            // }
 
             double p = r / (r + mu);
             return std::make_pair(r, p);
@@ -1212,44 +1216,57 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 double a_guess = var > mu ? (var - mu) / mu2 : 1.0;
 
-                auto get_l = [&](double a) {
-                    if (a == 0)
-                        return -std::numeric_limits<double>::max();
-
-                    double r = 1.0 / a;
-                    double p = r / (r + mu);
-                    double lp = log(p);
-                    double l = -log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var + lp * r * gs - lgamma(r) * counts.size();
-                    for (int64_t c : counts) {
-                        l += lgamma(r + c);
-                    }
-                    return l;
-                };
-
-                auto get_dl_ddl = [&](double a) {
+                // auto get_l = [&](double a) {
+                //     if (a == 0)
+                //         return -std::numeric_limits<double>::max();
+
+                //     double r = 1.0 / a;
+                //     double p = r / (r + mu);
+                //     double lp = log(p);
+                //     double l = -log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var + lp * r * gs - lgamma(r) * counts.size();
+                //     for (int64_t c : counts) {
+                //         l += lgamma(r + c);
+                //     }
+                //     return l;
+                // };
+
+                auto get_dl = [&](double a) {
                     double r = 1.0 / a;
                     double p = r / (r + mu);
                     double lp = log(p);
                     double dl = -a - a*(log(a)-ln_mu)/ln_var - gs*lp + boost::math::digamma(r)*counts.size();
-                    double ddl = a + a*(log(a)-ln_mu)/ln_var - a/ln_var + lp*gs*2 - boost::math::digamma(r)*counts.size()*2 - r*counts.size()*boost::math::trigamma(r);
                     for (int64_t c : counts) {
                         dl -= boost::math::digamma(r + c);
-                        ddl += 2.0*boost::math::digamma(r + c) + r*boost::math::trigamma(r + c);
                     }
-
-                    return std::make_pair(r*r * dl, r*r*r * ddl);
+                    return dl;
                 };
 
-                double dl = 0;
-                double ddl = 0;
-                double a = a_guess;
+                // auto get_dl_ddl = [&](double a) {
+                //     double r = 1.0 / a;
+                //     double p = r / (r + mu);
+                //     double lp = log(p);
+                //     double dl = -a - a*(log(a)-ln_mu)/ln_var - gs*lp + boost::math::digamma(r)*counts.size();
+                //     double ddl = a + a*(log(a)-ln_mu)/ln_var - a/ln_var + lp*gs*2 - boost::math::digamma(r)*counts.size()*2 - r*counts.size()*boost::math::trigamma(r);
+                //     for (int64_t c : counts) {
+                //         dl -= boost::math::digamma(r + c);
+                //         ddl += 2.0*boost::math::digamma(r + c) + r*boost::math::trigamma(r + c);
+                //     }
+
+                //     return std::make_pair(r*r * dl, r*r*r * ddl);
+                // };
+
+                // double dl = 0;
+                // double ddl = 0;
+                // double a = a_guess;
 
                 double a_min = a_guess;
                 double a_max = a_guess;
                 while (true) {
                     a_guess = (a_min + a_max) / 2;
-                    auto [dl_min, ddl_min] = get_dl_ddl(a_min);
-                    auto [dl_max, ddl_max] = get_dl_ddl(a_max);
+                    double dl_min = get_dl(a_min);
+                    double dl_max = get_dl(a_max);
+                    // auto [dl_min, ddl_min] = get_dl_ddl(a_min);
+                    // auto [dl_max, ddl_max] = get_dl_ddl(a_max);
                     if (dl_min == 0) {
                         a_max = a_min;
                         break;
@@ -1272,23 +1289,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 if (a_min < a_max) {
                     std::tie(a_min, a_max) = boost::math::tools::bisect(
-                        [&](double a) { return get_dl_ddl(a).first; },
+                        get_dl,
                         a_min, a_max, boost::math::tools::eps_tolerance<double>(1e-5)
                     );
                 }
 
-                a = a_min;
+                double a = a_min;
 
-                std::tie(dl, ddl) = get_dl_ddl(a);
-                if (ddl >= 0) {
-                    common::logger->error("a_min: {}\ta_guess: {}\ta_max: {}", a_min, a_guess, a_max);
-                    common::logger->error("fa_min: {}\tfa_guess: {}\tfa_max: {}", get_dl_ddl(a_min).first, get_dl_ddl(a_guess).first, get_dl_ddl(a_max).first);
-                    common::logger->error("Failed to find maximum, eval error: a_guess: {}\tf'(a_guess): {}\tf''(a_guess): {}\ta: {}\tf'(a): {}\tf''(a): {}\t{}",
-                                          a_guess, get_dl_ddl(a_guess).first,get_dl_ddl(a_guess).second,
-                                          a,get_dl_ddl(a).first,get_dl_ddl(a).second,
-                                          fmt::join(counts, ","));
-                    throw std::domain_error("");
-                }
+                // std::tie(dl, ddl) = get_dl_ddl(a);
+                // if (ddl >= 0) {
+                //     common::logger->error("a_min: {}\ta_guess: {}\ta_max: {}", a_min, a_guess, a_max);
+                //     common::logger->error("fa_min: {}\tfa_guess: {}\tfa_max: {}", get_dl_ddl(a_min).first, get_dl_ddl(a_guess).first, get_dl_ddl(a_max).first);
+                //     common::logger->error("Failed to find maximum, eval error: a_guess: {}\tf'(a_guess): {}\tf''(a_guess): {}\ta: {}\tf'(a): {}\tf''(a): {}\t{}",
+                //                           a_guess, get_dl_ddl(a_guess).first,get_dl_ddl(a_guess).second,
+                //                           a,get_dl_ddl(a).first,get_dl_ddl(a).second,
+                //                           fmt::join(counts, ","));
+                //     throw std::domain_error("");
+                // }
 
                 double r = 1.0 / a;
                 double p = r / (r + mu);

From b3751b7b02f99fdfbb20a7087f7e74140e64782a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 25 Aug 2024 21:31:33 +0200
Subject: [PATCH 311/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 98 -------------------
 1 file changed, 98 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b52c12dff0..09c6c73ad9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -455,14 +455,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return dl + add_dl(r);
             };
 
-            // auto get_ddl = [&](double r) {
-            //     double ddl = (1.0 / r - 1.0 / (r + mu) - boost::math::trigamma(r)) * total;
-            //     generate([&](auto k, auto c) {
-            //         ddl += boost::math::trigamma(k + r) * c;
-            //     });
-            //     return ddl + add_ddl(r);
-            // };
-
             double r_min = r_guess;
             double r_max = r_guess;
 
@@ -494,40 +486,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 get_dl, r_min, r_max, boost::math::tools::eps_tolerance<double>(1e-5)
             );
 
-            // while (true) {
-            //     double r = (r_min + r_max) / 2;
-            //     double dl = get_dl(r);
-            //     if (dl > 0) {
-            //         if (r == r_min) {
-            //             break;
-            //         }
-
-            //         r_min = r;
-            //     }
-
-            //     if (dl < 0) {
-            //         if (r == r_max) {
-            //             break;
-            //         }
-
-            //         r_max = r;
-            //     }
-
-            //     if (abs(dl) <= 1e-4) {
-            //         r_max = r;
-            //         r_min = r;
-            //         break;
-            //     }
-            // }
-
             double r = r_max;
-            // double dl = get_dl(r);
-            // double ddl = get_ddl(r);
-            // if (ddl > 0) {
-            //     common::logger->error("Found local minimum instead: r: {}\tdl: {}\tddl: {}", r, dl, ddl);
-            //     throw std::domain_error("FFF");
-            // }
-
             double p = r / (r + mu);
             return std::make_pair(r, p);
         };
@@ -1167,17 +1126,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                     double r = target_p / (1.0 - target_p) * n / groups.size();
 
-                    // auto [r, p] = get_rp([&](const auto &callback) {
-                    //     for (const auto &[v, c] : vector_counts[n]) {
-                    //         for (int64_t k : v) {
-                    //             callback(k, c);
-                    //         }
-
-                    //         if (v.size() < groups.size())
-                    //             callback(0, (groups.size() - v.size()) * c);
-                    //     }
-                    // }, [](double) { return 0; }, [](double) { return 0; });
-
                     #pragma omp atomic
                     ln_mu += -log(r) * total;
 
@@ -1216,20 +1164,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 double a_guess = var > mu ? (var - mu) / mu2 : 1.0;
 
-                // auto get_l = [&](double a) {
-                //     if (a == 0)
-                //         return -std::numeric_limits<double>::max();
-
-                //     double r = 1.0 / a;
-                //     double p = r / (r + mu);
-                //     double lp = log(p);
-                //     double l = -log(a) - pow(log(a)-ln_mu, 2.0)/2/ln_var + lp * r * gs - lgamma(r) * counts.size();
-                //     for (int64_t c : counts) {
-                //         l += lgamma(r + c);
-                //     }
-                //     return l;
-                // };
-
                 auto get_dl = [&](double a) {
                     double r = 1.0 / a;
                     double p = r / (r + mu);
@@ -1241,32 +1175,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return dl;
                 };
 
-                // auto get_dl_ddl = [&](double a) {
-                //     double r = 1.0 / a;
-                //     double p = r / (r + mu);
-                //     double lp = log(p);
-                //     double dl = -a - a*(log(a)-ln_mu)/ln_var - gs*lp + boost::math::digamma(r)*counts.size();
-                //     double ddl = a + a*(log(a)-ln_mu)/ln_var - a/ln_var + lp*gs*2 - boost::math::digamma(r)*counts.size()*2 - r*counts.size()*boost::math::trigamma(r);
-                //     for (int64_t c : counts) {
-                //         dl -= boost::math::digamma(r + c);
-                //         ddl += 2.0*boost::math::digamma(r + c) + r*boost::math::trigamma(r + c);
-                //     }
-
-                //     return std::make_pair(r*r * dl, r*r*r * ddl);
-                // };
-
-                // double dl = 0;
-                // double ddl = 0;
-                // double a = a_guess;
-
                 double a_min = a_guess;
                 double a_max = a_guess;
                 while (true) {
                     a_guess = (a_min + a_max) / 2;
                     double dl_min = get_dl(a_min);
                     double dl_max = get_dl(a_max);
-                    // auto [dl_min, ddl_min] = get_dl_ddl(a_min);
-                    // auto [dl_max, ddl_max] = get_dl_ddl(a_max);
                     if (dl_min == 0) {
                         a_max = a_min;
                         break;
@@ -1295,18 +1209,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 double a = a_min;
-
-                // std::tie(dl, ddl) = get_dl_ddl(a);
-                // if (ddl >= 0) {
-                //     common::logger->error("a_min: {}\ta_guess: {}\ta_max: {}", a_min, a_guess, a_max);
-                //     common::logger->error("fa_min: {}\tfa_guess: {}\tfa_max: {}", get_dl_ddl(a_min).first, get_dl_ddl(a_guess).first, get_dl_ddl(a_max).first);
-                //     common::logger->error("Failed to find maximum, eval error: a_guess: {}\tf'(a_guess): {}\tf''(a_guess): {}\ta: {}\tf'(a): {}\tf''(a): {}\t{}",
-                //                           a_guess, get_dl_ddl(a_guess).first,get_dl_ddl(a_guess).second,
-                //                           a,get_dl_ddl(a).first,get_dl_ddl(a).second,
-                //                           fmt::join(counts, ","));
-                //     throw std::domain_error("");
-                // }
-
                 double r = 1.0 / a;
                 double p = r / (r + mu);
                 return std::make_pair(r, p);

From 4f2ac90bd0ac763886a354098ab382b759d41133 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 25 Aug 2024 22:41:50 +0200
Subject: [PATCH 312/410] fix bug in computing deviance

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 09c6c73ad9..66f7e5d8b5 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -657,14 +657,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         int64_t total_sum = in_sum + out_sum;
 
         if (config.test_type == "nbinom_exact") {
-            auto get_deviance1 = [mu=mu1,invphi=r_in,logmuinvphi=log(mu1+r_in),logmu=log(mu1),zv=2.0*r_in*log((r_in*mu1)/r_in)](double y) {
+            auto get_deviance1 = [mu=mu1,invphi=r_in,logmuinvphi=log(mu1+r_in),logmu=log(mu1),zv=2.0*r_in*log((r_in+mu1)/r_in)](double y) {
                 if (y == 0)
                     return zv;
 
                 double logyshift = logmuinvphi - log(y + invphi);
                 return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
             };
-            auto get_deviance2 = [mu=mu2,invphi=r_out,logmuinvphi=log(mu2+r_out),logmu=log(mu2),zv=2.0*r_out*log((r_out*mu2)/r_out)](double y) {
+            auto get_deviance2 = [mu=mu2,invphi=r_out,logmuinvphi=log(mu2+r_out),logmu=log(mu2),zv=2.0*r_out*log((r_out+mu2)/r_out)](double y) {
                 if (y == 0)
                     return zv;
 
@@ -1236,7 +1236,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             auto get_deviance = [](double invphi, double p, double y) {
                 double mu = invphi * (1.0 - p) / p;
                 if (y == 0)
-                    return 2.0*invphi*log((invphi*mu)/invphi);
+                    return 2.0*invphi*log((invphi+mu)/invphi);
 
                 double logmuinvphi = log(mu+invphi);
                 double logmu = log(mu);

From 96d064903ffd136f291b6ee9408f980e380e97b7 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sun, 25 Aug 2024 23:05:36 +0200
Subject: [PATCH 313/410] cleanup

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 66f7e5d8b5..bb00ec58a2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -657,14 +657,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         int64_t total_sum = in_sum + out_sum;
 
         if (config.test_type == "nbinom_exact") {
-            auto get_deviance1 = [mu=mu1,invphi=r_in,logmuinvphi=log(mu1+r_in),logmu=log(mu1),zv=2.0*r_in*log((r_in+mu1)/r_in)](double y) {
+            auto get_deviance1 = [mu=mu1,invphi=r_in,logmuinvphi=log(mu1+r_in),logmu=log(mu1),zv=-2.0*r_in*log(target_p)](double y) {
                 if (y == 0)
                     return zv;
 
                 double logyshift = logmuinvphi - log(y + invphi);
                 return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
             };
-            auto get_deviance2 = [mu=mu2,invphi=r_out,logmuinvphi=log(mu2+r_out),logmu=log(mu2),zv=2.0*r_out*log((r_out+mu2)/r_out)](double y) {
+            auto get_deviance2 = [mu=mu2,invphi=r_out,logmuinvphi=log(mu2+r_out),logmu=log(mu2),zv=-2.0*r_out*log(target_p)](double y) {
                 if (y == 0)
                     return zv;
 

From 9f979f604a100b418379ddd31458b644bbd58556 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 00:58:54 +0200
Subject: [PATCH 314/410] optim

---
 .../src/graph/annotated_graph_algorithm.cpp   | 303 +-----------------
 1 file changed, 12 insertions(+), 291 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index bb00ec58a2..a0ba22fde3 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -552,8 +552,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
 
         double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
-        double mu1 = r_in * (1.0 - target_p) / target_p;
-        double mu2 = r_out * (1.0 - target_p) / target_p;
 
         if (fit_var / fit_mu - 1.0 < 1e-5)
             common::logger->warn("Fit parameters are close to a Poisson distribution");
@@ -609,37 +607,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
         in_kmers = 0;
         out_kmers = 0;
-        int64_t total_kmers_sq = 0;
-        int64_t total_nonzeros = 0;
-        std::vector<int64_t> counts;
         for (size_t j = 0; j < groups.size(); ++j) {
             for (const auto &[k, m] : count_maps[j]) {
                 const auto &[v, c] = m;
-                if (v >= counts.size())
-                    counts.resize(v + 1);
-
-                counts[v] += c;
 
                 if (groups[j]) {
                     out_kmers += v * c;
                 } else {
                     in_kmers += v * c;
                 }
-
-                if (v > 0)
-                    total_nonzeros += c;
-
-                total_kmers_sq += v * v * c;
             }
         }
 
         total_kmers = in_kmers + out_kmers;
 
-        std::partial_sum(counts.rbegin(), counts.rend(), counts.rbegin());
-        if (counts[0] != static_cast<int64_t>(nelem * groups.size()) || counts[1] != total_nonzeros) {
-            throw std::runtime_error("FSFJKHJFSL");
-        }
-
         common::logger->trace("  Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
         int64_t in_sum = 0;
@@ -657,19 +638,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         int64_t total_sum = in_sum + out_sum;
 
         if (config.test_type == "nbinom_exact") {
-            auto get_deviance1 = [mu=mu1,invphi=r_in,logmuinvphi=log(mu1+r_in),logmu=log(mu1),zv=-2.0*r_in*log(target_p)](double y) {
-                if (y == 0)
-                    return zv;
-
-                double logyshift = logmuinvphi - log(y + invphi);
-                return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
+            auto get_deviance1 = [p=target_p,r=r_in,l1pp=log1p(-target_p),rlogrp=r_in*(log(r_in)-log(target_p)),zv=-2.0*r_in*log(target_p)](double y) {
+                double yshift = y + r;
+                return y == 0 ? zv : 2.0 * (y * (log(y) - l1pp) - yshift * log(yshift) + rlogrp);
             };
-            auto get_deviance2 = [mu=mu2,invphi=r_out,logmuinvphi=log(mu2+r_out),logmu=log(mu2),zv=-2.0*r_out*log(target_p)](double y) {
-                if (y == 0)
-                    return zv;
-
-                double logyshift = logmuinvphi - log(y + invphi);
-                return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
+            auto get_deviance2 = [p=target_p,r=r_out,l1pp=log1p(-target_p),rlogrp=r_out*(log(r_out)-log(target_p)),zv=-2.0*r_out*log(target_p)](double y) {
+                double yshift = y + r;
+                return y == 0 ? zv : 2.0 * (y * (log(y) - l1pp) - yshift * log(yshift) + rlogrp);
             };
 
             std::vector<double> deviances1(total_sum + 1);
@@ -785,259 +760,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 return std::min(1.0, pval);
             };
-        } else if (config.test_type == "zinb_exact") {
-            common::logger->trace("Computing merged histograms");
-            std::mutex hist_mu;
-            tsl::hopscotch_map<uint64_t, size_t> merged_hist_in;
-            tsl::hopscotch_map<uint64_t, size_t> merged_hist_out;
-            tsl::hopscotch_map<uint64_t, size_t> merged_hist_null;
-            int64_t p12_sum = 0;
-            int64_t p11_sum = 0;
-            double m0 = 0;
-            double m1 = 0;
-            for (const auto &[k, m] : count_maps[0]) {
-                const auto &[v, c] = m;
-                m0 += v * c;
-            }
-            for (const auto &[k, m] : count_maps[1]) {
-                const auto &[v, c] = m;
-                m1 += v * c;
-            }
-            m0 /= nelem;
-            m1 /= nelem;
-            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t) {
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
-                bool in_kmer = false;
-                bool out_kmer = false;
-                if (kept[row_i]) {
-                    size_t count_in = 0;
-                    size_t count_out = 0;
-                    double p0 = -m0;
-                    double p1 = -m1;
-                    for (const auto &[j, raw_c] : raw_row) {
-                        uint64_t c = raw_c;
-                        if (count_maps.size())
-                            c = count_maps[j].find(c)->second.first;
-
-                        if (j == 0) {
-                            p0 = c - m0;
-                        } else if (j == 1) {
-                            p1 = c - m1;
-                        }
-
-                        if (groups[j]) {
-                            out_sum += c;
-                            ++count_out;
-                        } else {
-                            in_sum += c;
-                            ++count_in;
-                        }
-                    }
-
-                    p12_sum += p0 * p1;
-                    p11_sum += p0 * p0;
-
-                    if (count_in + count_out >= config.min_recurrence) {
-                        double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-                        in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-                        out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
-                    }
-                } else {
-                    return;
-                }
-
-                if (!in_kmer && !out_kmer)
-                    return;
-
-                size_t n = in_sum + out_sum;
-                std::lock_guard<std::mutex> lock(hist_mu);
-                ++merged_hist_in[in_sum];
-                ++merged_hist_out[out_sum];
-                ++merged_hist_null[n];
-            }, false);
-
-            double obs_cov = static_cast<double>(p12_sum) / (nelem - 1);
-            double obs_11_cov = static_cast<double>(p11_sum) / (nelem - 1);
-            common::logger->trace("Var: {}\tExp cov: {}\tObs11 cov: {}\tobs12 cov: {}",
-                                  r_map * (1.0 - target_p) / target_p / target_p,
-                                  (1.0 - target_p) / target_p / target_p,
-                                  obs_11_cov,
-                                  obs_cov);
-
-            auto get_rp_zinb = [&](const tsl::hopscotch_map<uint64_t, size_t> &hist_in,
-                                   const tsl::hopscotch_map<uint64_t, size_t> &hist_out) {
-                auto get_stats = [&](const auto &hist) {
-                    size_t total_nzeros = 0;
-                    size_t total_zeros = 0;
-                    size_t sum = 0;
-                    for (const auto &[k, c] : hist) {
-                        if (k != 0) {
-                            sum += k * c;
-                            total_nzeros += c;
-                        } else {
-                            total_zeros += c;
-                        }
-                    }
-                    size_t total = total_nzeros + total_zeros;
-
-                    return std::make_tuple(total_nzeros, total_zeros, sum, total);
-                };
-
-                auto [total_nzeros_in, total_zeros_in, sum_in, total_in] = get_stats(hist_in);
-                auto [total_nzeros_out, total_zeros_out, sum_out, total_out] = get_stats(hist_out);
-
-                auto get_r = [&](const auto &hist, size_t total, double total_nzeros, double p, double r_guess, double sum) {
-                    return boost::math::tools::newton_raphson_iterate([&](double r) {
-                        double dl = (log(p) - boost::math::digamma(r)) * total_nzeros + log(total_nzeros / total) * (total - total_nzeros);
-                        double ddl = -boost::math::trigamma(r) * total_nzeros;
-                        for (const auto &[k, c] : hist) {
-                            if (k != 0) {
-                                dl += boost::math::digamma(r + k) * c;
-                                ddl += boost::math::trigamma(r + k) * c;
-                            }
-                        }
-
-                        return std::make_pair(dl, ddl);
-                    }, r_guess, std::numeric_limits<double>::min(), sum, 30);
-                };
-
-                double p = boost::math::tools::newton_raphson_iterate([&](double p) {
-                    auto r_in = get_r(hist_in, total_in, total_nzeros_in, p, r_map * num_labels_in, sum_in);
-                    auto r_out = get_r(hist_out, total_out, total_nzeros_out, p, r_map * num_labels_out, sum_out);
-
-                    double dl_in = r_in * total_nzeros_in / p / (1.0 - pow(p, r_in)) - sum_in / (1.0 - p);
-                    double dl_out = r_out * total_nzeros_out / p / (1.0 - pow(p, r_out)) - sum_out / (1.0 - p);
-                    double ddl_in = -r_in * total_nzeros_in / p / p / (1.0 - pow(p, r_in)) / (1.0 - pow(p, r_in)) * ((1.0-pow(p, r_in)) - p * r_in * pow(p, r_in-1)) - static_cast<double>(sum_in) / (1.0-p) / (1.0-p);
-                    double ddl_out = -r_out * total_nzeros_out / p / p / (1.0 - pow(p, r_out)) / (1.0 - pow(p, r_out)) * ((1.0-pow(p, r_out)) - p * r_out * pow(p, r_out-1)) - static_cast<double>(sum_out) / (1.0-p) / (1.0-p);
-                    return std::make_pair(dl_in + dl_out, ddl_in + ddl_out);
-                }, target_p, 0.0, 1.0, 30);
-
-                double r_in = get_r(hist_in, total_in, total_nzeros_in, p, r_map * num_labels_in, sum_in);
-                double r_out = get_r(hist_out, total_out, total_nzeros_out, p, r_map * num_labels_out, sum_out);
-
-                double pi_in = (static_cast<double>(total_in - total_nzeros_in) / total_in - pow(p, r_in)) / (1.0 - pow(p, r_in));
-                double pi_out = (static_cast<double>(total_out - total_nzeros_out) / total_out - pow(p, r_out)) / (1.0 - pow(p, r_out));
-
-                return std::make_tuple(r_in, pi_in, r_out, pi_out, p);
-            };
-
-            auto [r_in, pi_in, r_out, pi_out, p] = get_rp_zinb(merged_hist_in, merged_hist_in);
-
-            common::logger->trace("r: ({}, {})\tp: {}\tpi: ({}, {})",
-                                  r_in, r_out, p, pi_in, pi_out);
-
-            double mu1 = r_in * (1.0 - p) / p * (1.0 - pi_in);
-            double mu2 = r_out * (1.0 - p) / p * (1.0 - pi_out);
-
-            auto get_zv = [](double r, double mu, double pi) {
-                return -2 * log(pi + (1.0 - pi) * pow((1.0-pi)*r/(mu + (1.0-pi)*r), r));
-            };
-
-            auto get_deviance1 = [r=r_in,mu=mu1,mpir=(1.0-pi_in)*r_in,zv=get_zv(r_in,mu1,pi_in)](double y) {
-                if (y == 0)
-                    return zv;
-
-                return 2.0 * ((r + y) * log((mu + mpir)/(y + mpir)) + y * log(y / mu));
-            };
-            auto get_deviance2 = [r=r_out,mu=mu2,mpir=(1.0-pi_out)*r_out,zv=get_zv(r_out,mu2,pi_out)](double y) {
-                if (y == 0)
-                    return zv;
-
-                return 2.0 * ((r + y) * log((mu + mpir)/(y + mpir)) + y * log(y / mu));
-            };
-
-            std::vector<double> deviances1(total_sum + 1);
-            std::vector<double> deviances2(total_sum + 1);
-            for (int64_t s = 0; s <= total_sum; ++s) {
-                deviances1[s] = get_deviance1(s);
-                deviances2[s] = get_deviance2(s);
-            }
-
-            auto get_lprob_zinb = [](int64_t s, double r, double p, double pi) {
-                return s == 0
-                    ? log(pi + (1.0 - pi) * pow(p, r))
-                    : log1p(-pi) + lgamma(r + s) - lgamma(r) - lgamma(s + 1) + r * log(p) + log1p(-p) * s;
-            };
-
-            auto get_lprob_ztnb = [get_lprob_zinb](int64_t s, double r_in, double r_out, double p, double pi_in, double pi_out) {
-                double pval = 0.0;
-                for (int64_t ss = 0; ss <= s; ++ss) {
-                    int64_t tt = s - ss;
-                    pval += exp(get_lprob_zinb(ss, r_in, p, pi_in) + get_lprob_zinb(tt, r_out, p, pi_out));
-                }
-                return log(pval);
-                // double lp1 = get_lprob_zinb(0, r_in, p, pi_in) + get_lprob_zinb(s, r_out, p, pi_out);
-                // double lp2 = get_lprob_zinb(s, r_in, p, pi_in) + get_lprob_zinb(0, r_out, p, pi_out);
-                // double lp3 = log1p(-pi_in) + log1p(-pi_out) + get_lprob_zinb(s, r_in + r_out, p, 0.0);
-                // return log(exp(lp1) + exp(lp2) + exp(lp3));
-            };
-
-            compute_min_pval = [r_in,p_in=p,pi_in,r_out,p_out=p,pi_out,get_lprob_zinb,get_lprob_ztnb,deviances1,deviances2](int64_t n, const PairContainer&) {
-                if (n == 0)
-                    return 1.0;
-
-                std::vector<double> devs(n + 1);
-
-                for (int64_t s = 0; s <= n; ++s) {
-                    devs[s] = deviances1[s] + deviances2[n - s];
-                }
-
-                double max_d = *std::max_element(devs.begin(), devs.end());
-                double pval = 0.0;
-                double base_lprob = get_lprob_ztnb(n, r_in, r_out, p_in, pi_in, pi_out);
-                for (int64_t s = 0; s <= n; ++s) {
-                    if (devs[s] == max_d)
-                        pval += exp(get_lprob_zinb(s, r_in, p_in, pi_in) + get_lprob_zinb(n - s, r_out, p_out, pi_out) - base_lprob);
-                }
-
-                if (pval > 1.0) {
-                    common::logger->error("fail: {}", pval);
-                    throw std::runtime_error("FOO");
-                }
-
-                return pval;
-            };
-
-            compute_pval = [r_in,p_in=p,pi_in,r_out,p_out=p,pi_out,get_lprob_zinb,get_lprob_ztnb,deviances1,deviances2](int64_t in_sum, int64_t out_sum, const auto &) {
-                int64_t n = in_sum + out_sum;
-
-                if (n == 0)
-                    return 1.0;
-
-                std::vector<double> devs(n + 1);
-
-                for (int64_t s = 0; s <= n; ++s) {
-                    devs[s] = deviances1[s] + deviances2[n - s];
-                }
-
-                double target_d = devs[in_sum];
-                double pval = 0.0;
-                double base_lprob = get_lprob_ztnb(n, r_in, r_out, p_in, pi_in, pi_out);
-                int64_t s = 0;
-                for ( ; s <= n; ++s) {
-                    if (devs[s] >= target_d) {
-                        pval += exp(get_lprob_zinb(s, r_in, p_in, pi_in) + get_lprob_zinb(n - s, r_out, p_out, pi_out) - base_lprob);
-                    } else {
-                        break;
-                    }
-                }
-
-                for (int64_t ns = n; ns > s; --ns) {
-                    if (devs[ns] >= target_d) {
-                        pval += exp(get_lprob_zinb(ns, r_in, p_in, pi_in) + get_lprob_zinb(n - ns, r_out, p_out, pi_out) - base_lprob);
-                    } else {
-                        break;
-                    }
-                }
-
-                if (pval > 1.0) {
-                    common::logger->error("fail: {}", pval);
-                    throw std::runtime_error("FOO");
-                }
-
-                return pval;
-            };
         } else {
             std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
 
@@ -1233,15 +955,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 progress_bar += its.size() % 1000;
             }
 
-            auto get_deviance = [](double invphi, double p, double y) {
-                double mu = invphi * (1.0 - p) / p;
+            auto get_deviance = [](double r, double p, double y) {
                 if (y == 0)
-                    return 2.0*invphi*log((invphi+mu)/invphi);
+                    return -2.0*r*log(p);
 
-                double logmuinvphi = log(mu+invphi);
-                double logmu = log(mu);
-                double logyshift = logmuinvphi - log(y + invphi);
-                return 2.0 * (y * (log(y) - logmu + logyshift) + invphi * logyshift);
+                double yshift = y + r;
+                double l1pp = log1p(-p);
+                double rlogrp = r * (log(r) - log(p));
+                return 2.0 * (y * (log(y) - l1pp) - yshift * log(yshift) + rlogrp);
             };
 
             compute_min_pval = [vector_counts,num_labels_in,num_labels_out,get_deviance,gs=groups.size()](int64_t n, const auto &row) {

From cd9b4568f7d7702e382189941c7679e8265cd0bf Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 01:37:41 +0200
Subject: [PATCH 315/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 96 +++++--------------
 1 file changed, 24 insertions(+), 72 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a0ba22fde3..8bf5149292 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -638,97 +638,49 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         int64_t total_sum = in_sum + out_sum;
 
         if (config.test_type == "nbinom_exact") {
-            auto get_deviance1 = [p=target_p,r=r_in,l1pp=log1p(-target_p),rlogrp=r_in*(log(r_in)-log(target_p)),zv=-2.0*r_in*log(target_p)](double y) {
-                double yshift = y + r;
-                return y == 0 ? zv : 2.0 * (y * (log(y) - l1pp) - yshift * log(yshift) + rlogrp);
-            };
-            auto get_deviance2 = [p=target_p,r=r_out,l1pp=log1p(-target_p),rlogrp=r_out*(log(r_out)-log(target_p)),zv=-2.0*r_out*log(target_p)](double y) {
-                double yshift = y + r;
-                return y == 0 ? zv : 2.0 * (y * (log(y) - l1pp) - yshift * log(yshift) + rlogrp);
-            };
-
-            std::vector<double> deviances1(total_sum + 1);
-            std::vector<double> deviances2(total_sum + 1);
-            for (int64_t s = 0; s <= total_sum; ++s) {
-                deviances1[s] = get_deviance1(s);
-                deviances2[s] = get_deviance2(s);
-            }
-            compute_min_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t n, const PairContainer&) {
+            compute_min_pval = [lscaling_base,r=r_in+r_out,r_in,r_out](int64_t n, const PairContainer&) {
                 if (n == 0)
                     return 1.0;
 
-                double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
-                std::vector<double> devs(n + 1);
+                double lscaling = lscaling_base - lgamma(r + n);
+                double argmin_d = r_in / r * n;
 
-                for (int64_t s = 0; s <= n; ++s) {
-                    devs[s] = deviances1[s] + deviances2[n - s];
-                }
+                double dist0 = argmin_d;
+                double distn = static_cast<double>(n) - argmin_d;
 
-                double max_d = *std::max_element(devs.begin(), devs.end());
                 double pval = 0.0;
-                int64_t s = 0;
-                if (devs[s] == max_d) {
-                    int64_t t = n;
-                    double rs = r_in;
-                    double rt = r_out + n;
-                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
-                    pval += exp(base);
-                    for (++s; s <= n; ++s) {
-                        if (devs[s] == max_d) {
-                            --t;
-                            ++rs;
-                            --rt;
-                            base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
-                            pval += exp(base);
-                        } else {
-                            break;
-                        }
-                    }
-                }
+                if (dist0 >= distn)
+                    pval += exp(lscaling + lgamma(r_in) + lgamma(r_out + n));
 
-                int64_t sp = n;
-                if (devs[sp] == max_d) {
-                    int64_t t = 0;
-                    double rs = r_in + sp;
-                    double rt = r_out;
-                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
-                    pval += exp(base);
-                    for (--sp; sp >= s; --sp) {
-                        if (devs[sp] == max_d) {
-                            ++t;
-                            --rs;
-                            ++rt;
-                            base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
-                            pval += exp(base);
-                        } else {
-                            break;
-                        }
-                    }
-                }
+                if (dist0 <= distn)
+                    pval += exp(lscaling + lgamma(r_in + n) + lgamma(r_out));
 
                 return std::min(1.0, pval);
             };
 
-            compute_pval = [lscaling_base,r_in,r_out,target_p,deviances1,deviances2](int64_t in_sum, int64_t out_sum, const auto &row) {
+            compute_pval = [lscaling_base,r=r_in+r_out,r_in,r_out](int64_t in_sum, int64_t out_sum, const auto &row) {
                 if (row.empty())
                     return 1.0;
 
-                int64_t n = in_sum + out_sum;
+                if (r_in == r_out && in_sum == out_sum)
+                    return 1.0;
 
-                double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
+                int64_t n = in_sum + out_sum;
+                double lscaling = lscaling_base - lgamma(r_in + r_out + n);
+                double argmin_d = r_in / r * n;
 
-                double in_sum_total_deviance = deviances1[in_sum] + deviances2[out_sum];
+                double dist_insum = abs(argmin_d - in_sum);
 
                 double pval = 0.0;
                 int64_t s = 0;
                 int64_t t = n;
-                if (deviances1[s] + deviances2[t] >= in_sum_total_deviance) {
+                if (argmin_d >= dist_insum) {
                     double rs = r_in;
                     double rt = r_out + n;
-                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    double base = lscaling + lgamma(rs) + lgamma(rt);
                     pval += exp(base);
-                    for (++s; s <= n; ++s) {
-                        if (deviances1[s] + deviances2[--t] >= in_sum_total_deviance) {
+                    for (++s,--t; s <= n; ++s,--t) {
+                        if (abs(s - argmin_d) >= dist_insum) {
                             ++rs;
                             --rt;
                             base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
@@ -741,13 +693,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 int64_t sp = n;
                 t = 0;
-                if (deviances1[sp] + deviances2[t] >= in_sum_total_deviance) {
+                if (abs(argmin_d - sp) >= dist_insum) {
                     double rs = r_in + sp;
                     double rt = r_out;
-                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    double base = lscaling + lgamma(rs) + lgamma(rt);
                     pval += exp(base);
-                    for (--sp; sp >= s; --sp) {
-                        if (deviances1[sp] + deviances2[++t] >= in_sum_total_deviance) {
+                    for (--sp,++t; sp >= s; --sp,++t) {
+                        if (abs(argmin_d - sp) >= dist_insum) {
                             --rs;
                             ++rt;
                             base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);

From 4d7c7b0e09969392f56d73d098733d26908f5256 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 01:56:50 +0200
Subject: [PATCH 316/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 185 ++++++------------
 1 file changed, 56 insertions(+), 129 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 8bf5149292..67cb51138a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -551,8 +551,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         double r_out = r_map * num_labels_out;
         common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
 
-        double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
-
         if (fit_var / fit_mu - 1.0 < 1e-5)
             common::logger->warn("Fit parameters are close to a Poisson distribution");
 
@@ -638,7 +636,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         int64_t total_sum = in_sum + out_sum;
 
         if (config.test_type == "nbinom_exact") {
-            compute_min_pval = [lscaling_base,r=r_in+r_out,r_in,r_out](int64_t n, const PairContainer&) {
+            double r = r_in + r_out;
+            double lscaling_base = lgamma(r);
+            compute_min_pval = [lscaling_base,r,r_in,r_out](int64_t n, const PairContainer&) {
                 if (n == 0)
                     return 1.0;
 
@@ -650,15 +650,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 double pval = 0.0;
                 if (dist0 >= distn)
-                    pval += exp(lscaling + lgamma(r_in) + lgamma(r_out + n));
+                    pval += exp(lscaling + lgamma(r_out + n) - lgamma(r_out));
 
                 if (dist0 <= distn)
-                    pval += exp(lscaling + lgamma(r_in + n) + lgamma(r_out));
+                    pval += exp(lscaling + lgamma(r_in + n) - lgamma(r_in));
 
                 return std::min(1.0, pval);
             };
 
-            compute_pval = [lscaling_base,r=r_in+r_out,r_in,r_out](int64_t in_sum, int64_t out_sum, const auto &row) {
+            compute_pval = [lscaling_base,r,r_in,r_out](int64_t in_sum, int64_t out_sum, const auto &row) {
                 if (row.empty())
                     return 1.0;
 
@@ -666,7 +666,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return 1.0;
 
                 int64_t n = in_sum + out_sum;
-                double lscaling = lscaling_base - lgamma(r_in + r_out + n);
+                double lscaling = lscaling_base - lgamma(r + n);
                 double argmin_d = r_in / r * n;
 
                 double dist_insum = abs(argmin_d - in_sum);
@@ -677,17 +677,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (argmin_d >= dist_insum) {
                     double rs = r_in;
                     double rt = r_out + n;
-                    double base = lscaling + lgamma(rs) + lgamma(rt);
+                    double base = lscaling + lgamma(rt) - lgamma(r_out);
                     pval += exp(base);
-                    for (++s,--t; s <= n; ++s,--t) {
-                        if (abs(s - argmin_d) >= dist_insum) {
-                            ++rs;
-                            --rt;
-                            base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
-                            pval += exp(base);
-                        } else {
-                            break;
-                        }
+                    for (++s,--t; s <= n && abs(s - argmin_d) >= dist_insum; ++s,--t) {
+                        ++rs;
+                        --rt;
+                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                        pval += exp(base);
                     }
                 }
 
@@ -696,17 +692,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (abs(argmin_d - sp) >= dist_insum) {
                     double rs = r_in + sp;
                     double rt = r_out;
-                    double base = lscaling + lgamma(rs) + lgamma(rt);
+                    double base = lscaling + lgamma(rs) - lgamma(r_in);
                     pval += exp(base);
-                    for (--sp,++t; sp >= s; --sp,++t) {
-                        if (abs(argmin_d - sp) >= dist_insum) {
-                            --rs;
-                            ++rt;
-                            base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
-                            pval += exp(base);
-                        } else {
-                            break;
-                        }
+                    for (--sp,++t; sp >= s && abs(argmin_d - sp) >= dist_insum; --sp,++t) {
+                        --rs;
+                        ++rt;
+                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                        pval += exp(base);
                     }
                 }
 
@@ -907,17 +899,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 progress_bar += its.size() % 1000;
             }
 
-            auto get_deviance = [](double r, double p, double y) {
-                if (y == 0)
-                    return -2.0*r*log(p);
-
-                double yshift = y + r;
-                double l1pp = log1p(-p);
-                double rlogrp = r * (log(r) - log(p));
-                return 2.0 * (y * (log(y) - l1pp) - yshift * log(yshift) + rlogrp);
-            };
-
-            compute_min_pval = [vector_counts,num_labels_in,num_labels_out,get_deviance,gs=groups.size()](int64_t n, const auto &row) {
+            compute_min_pval = [vector_counts,num_labels_in,num_labels_out](int64_t n, const auto &row) {
                 if (row.empty())
                     return 1.0;
 
@@ -931,70 +913,31 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     std::sort(counts.begin(), counts.end());
                     it = vector_counts[n].find(counts);
                 }
-                double r = it->second;
-                if (r == 0.0)
+                double r_base = it->second;
+                if (r_base == 0.0)
                     return 1.0;
 
-                double p = r / (r + static_cast<double>(n) / gs);
-                double r_in = r * num_labels_in;
-                double r_out = r * num_labels_out;
-                double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
-
-                double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
-
-                std::vector<double> devs(n + 1);
+                double r_in = r_base * num_labels_in;
+                double r_out = r_base * num_labels_out;
+                double r = r_in + r_out;
 
-                for (int64_t s = 0; s <= n; ++s) {
-                    devs[s] = get_deviance(r_in, p, s) + get_deviance(r_out, p, n - s);
-                }
+                double lscaling = lgamma(r) - lgamma(r + n);
+                double argmin_d = r_in / r * n;
 
-                double max_d = *std::max_element(devs.begin(), devs.end());
+                double dist0 = argmin_d;
+                double distn = static_cast<double>(n) - argmin_d;
 
                 double pval = 0.0;
-                int64_t s = 0;
-                int64_t t = n;
-                if (devs[s] == max_d) {
-                    double rs = r_in;
-                    double rt = r_out + n;
-                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
-                    pval += exp(base);
-                    for (++s; s <= n; ++s) {
-                        --t;
-                        if (devs[s] == max_d) {
-                            ++rs;
-                            --rt;
-                            base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
-                            pval += exp(base);
-                        } else {
-                            break;
-                        }
-                    }
-                }
+                if (dist0 >= distn)
+                    pval += exp(lscaling + lgamma(r_out + n) - lgamma(r_out));
 
-                int64_t sp = n;
-                t = 0;
-                if (devs[sp] == max_d) {
-                    double rs = r_in + sp;
-                    double rt = r_out;
-                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
-                    pval += exp(base);
-                    for (--sp; sp >= s; --sp) {
-                        ++t;
-                        if (devs[sp] == max_d) {
-                            --rs;
-                            ++rt;
-                            base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
-                            pval += exp(base);
-                        } else {
-                            break;
-                        }
-                    }
-                }
+                if (dist0 <= distn)
+                    pval += exp(lscaling + lgamma(r_in + n) - lgamma(r_in));
 
                 return std::min(1.0, pval);
             };
 
-            compute_pval = [vector_counts,num_labels_in,num_labels_out,get_deviance,gs=groups.size()](int64_t in_sum, int64_t out_sum, const auto &row) {
+            compute_pval = [vector_counts,num_labels_in,num_labels_out](int64_t in_sum, int64_t out_sum, const auto &row) {
                 if (row.empty())
                     return 1.0;
 
@@ -1008,65 +951,49 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     std::sort(counts.begin(), counts.end());
                     it = vector_counts[in_sum + out_sum].find(counts);
                 }
-                double r = it->second;
+                double r_base = it->second;
 
-                if (r == 0.0)
+                if (r_base == 0.0)
                     return 1.0;
 
                 int64_t n = in_sum + out_sum;
-                double p = r / (r + static_cast<double>(n) / gs);
-                double r_in = r * num_labels_in;
-                double r_out = r * num_labels_out;
-                double lscaling_base = lgamma(r_in + r_out) - lgamma(r_in) - lgamma(r_out);
+                double r_in = r_base * num_labels_in;
+                double r_out = r_base * num_labels_out;
+                double r = r_in + r_out;
 
-                double lscaling = lgamma(n + 1) - lgamma(r_in + r_out + n) + lscaling_base;
-
-                std::vector<double> devs(n + 1);
-
-                for (int64_t s = 0; s <= n; ++s) {
-                    devs[s] = get_deviance(r_in, p, s) + get_deviance(r_out, p, n - s);
-                }
+                double lscaling = lgamma(r) - lgamma(r + n);
+                double argmin_d = r_in / r * n;
 
-                double in_sum_total_deviance = devs[in_sum];
+                double dist_insum = abs(argmin_d - in_sum);
 
                 double pval = 0.0;
                 int64_t s = 0;
                 int64_t t = n;
-                if (devs[s] >= in_sum_total_deviance) {
+                if (argmin_d >= dist_insum) {
                     double rs = r_in;
                     double rt = r_out + n;
-                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    double base = lscaling + lgamma(rt) - lgamma(r_out);
                     pval += exp(base);
-                    for (++s; s <= n; ++s) {
-                        --t;
-                        if (devs[s] >= in_sum_total_deviance) {
-                            ++rs;
-                            --rt;
-                            base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
-                            pval += exp(base);
-                        } else {
-                            break;
-                        }
+                    for (++s,--t; s <= n && abs(s - argmin_d) >= dist_insum; ++s,--t) {
+                        ++rs;
+                        --rt;
+                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                        pval += exp(base);
                     }
                 }
 
                 int64_t sp = n;
                 t = 0;
-                if (devs[sp] >= in_sum_total_deviance) {
+                if (abs(argmin_d - sp) >= dist_insum) {
                     double rs = r_in + sp;
                     double rt = r_out;
-                    double base = lscaling - lgamma(n + 1) + lgamma(rs) + lgamma(rt);
+                    double base = lscaling + lgamma(rs) - lgamma(r_in);
                     pval += exp(base);
-                    for (--sp; sp >= s; --sp) {
-                        ++t;
-                        if (devs[sp] >= in_sum_total_deviance) {
-                            --rs;
-                            ++rt;
-                            base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
-                            pval += exp(base);
-                        } else {
-                            break;
-                        }
+                    for (--sp,++t; sp >= s && abs(argmin_d - sp) >= dist_insum; --sp,++t) {
+                        --rs;
+                        ++rt;
+                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                        pval += exp(base);
                     }
                 }
 

From 258a4266090a2760cebf9d3e8b6d992644356ec6 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 02:00:12 +0200
Subject: [PATCH 317/410] optim

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 67cb51138a..6955dad132 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -941,22 +941,25 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (row.empty())
                     return 1.0;
 
-                auto it = vector_counts[in_sum + out_sum].begin();
-                if (vector_counts[in_sum + out_sum].size() > 1) {
+                if (num_labels_in == num_labels_out && in_sum == out_sum)
+                    return 1.0;
+
+                int64_t n = in_sum + out_sum;
+                auto it = vector_counts[n].begin();
+                if (vector_counts[n].size() > 1) {
                     std::vector<int64_t> counts;
                     counts.reserve(row.size());
                     for (const auto &[j, c] : row) {
                         counts.emplace_back(c);
                     }
                     std::sort(counts.begin(), counts.end());
-                    it = vector_counts[in_sum + out_sum].find(counts);
+                    it = vector_counts[n].find(counts);
                 }
                 double r_base = it->second;
 
                 if (r_base == 0.0)
                     return 1.0;
 
-                int64_t n = in_sum + out_sum;
                 double r_in = r_base * num_labels_in;
                 double r_out = r_base * num_labels_out;
                 double r = r_in + r_out;

From b45a963e19296fc6ed4cdc1a8e320d0e6810a893 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 03:29:54 +0200
Subject: [PATCH 318/410] minor

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6955dad132..b83dc534b0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1058,6 +1058,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             size_t n = in_sum + out_sum;
             if (!in_kmer && !out_kmer) {
                 row.clear();
+                vals.clear();
                 n = 0;
             }
 
@@ -1073,12 +1074,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ++ms[bucket_idx][n].second;
             } else {
                 std::sort(vals.begin(), vals.end());
-                if (ms_vecs[bucket_idx].size() <= n)
+                if (n >= ms_vecs[bucket_idx].size())
                     ms_vecs[bucket_idx].resize(n + 1);
 
                 auto find = ms_vecs[bucket_idx][n].find(vals);
                 if (find == ms_vecs[bucket_idx][n].end()) {
-                    ms_vecs[bucket_idx][n][vals] = std::make_pair(compute_min_pval(n, row), 1);
+                    find = ms_vecs[bucket_idx][n].try_emplace(
+                        vals,
+                        std::make_pair(compute_min_pval(n, row), 1)
+                    ).first;
                 } else {
                     ++find.value().second;
                 }
@@ -1126,7 +1130,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         } else {
             std::vector<std::tuple<double, int64_t, size_t>> mn;
             for (size_t i = 0; i < ms_vecs.size(); ++i) {
-                for (size_t n = 1; n < ms_vecs[i].size(); ++n) {
+                for (size_t n = 0; n < ms_vecs[i].size(); ++n) {
                     for (const auto &[vec, v] : ms_vecs[i][n]) {
                         mn.emplace_back(v.first, n, v.second);
                     }

From 98db9a99c7ca9a76f2e522d1a60aa2fda72549c9 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 12:08:19 +0200
Subject: [PATCH 319/410] early exit

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b83dc534b0..eaefb40a1a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -966,6 +966,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 double lscaling = lgamma(r) - lgamma(r + n);
                 double argmin_d = r_in / r * n;
+                if (in_sum == argmin_d)
+                    return 1.0;
 
                 double dist_insum = abs(argmin_d - in_sum);
 

From cc2f7a04bdeb865e18d7bc30e3a091b96032c07a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 16:12:48 +0200
Subject: [PATCH 320/410] refactor

---
 .../src/graph/annotated_graph_algorithm.cpp   | 195 +++++++-----------
 1 file changed, 71 insertions(+), 124 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index eaefb40a1a..58a209a5d3 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -635,74 +635,76 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
         int64_t total_sum = in_sum + out_sum;
 
-        if (config.test_type == "nbinom_exact") {
-            double r = r_in + r_out;
-            double lscaling_base = lgamma(r);
-            compute_min_pval = [lscaling_base,r,r_in,r_out](int64_t n, const PairContainer&) {
-                if (n == 0)
-                    return 1.0;
+        auto compute_min_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t n) {
+            double lscaling = lscaling_base - lgamma(r + n);
+            double argmin_d = r_in / r * n;
 
-                double lscaling = lscaling_base - lgamma(r + n);
-                double argmin_d = r_in / r * n;
+            double dist0 = argmin_d;
+            double distn = static_cast<double>(n) - argmin_d;
 
-                double dist0 = argmin_d;
-                double distn = static_cast<double>(n) - argmin_d;
+            double pval = 0.0;
+            if (dist0 >= distn)
+                pval += exp(lscaling + lgamma(r_out + n) - lgamma(r_out));
 
-                double pval = 0.0;
-                if (dist0 >= distn)
-                    pval += exp(lscaling + lgamma(r_out + n) - lgamma(r_out));
+            if (dist0 <= distn)
+                pval += exp(lscaling + lgamma(r_in + n) - lgamma(r_in));
 
-                if (dist0 <= distn)
-                    pval += exp(lscaling + lgamma(r_in + n) - lgamma(r_in));
+            return std::min(1.0, pval);
+        };
 
-                return std::min(1.0, pval);
-            };
+        auto compute_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t in_sum, int64_t out_sum, const PairContainer &row) {
+            int64_t n = in_sum + out_sum;
+            if (in_sum == r_in / r * n)
+                return 1.0;
 
-            compute_pval = [lscaling_base,r,r_in,r_out](int64_t in_sum, int64_t out_sum, const auto &row) {
-                if (row.empty())
-                    return 1.0;
+            double lscaling = lscaling_base - lgamma(r + n);
+            double argmin_d = r_in / r * n;
 
-                if (r_in == r_out && in_sum == out_sum)
-                    return 1.0;
+            double dist_insum = abs(argmin_d - in_sum);
 
-                int64_t n = in_sum + out_sum;
-                double lscaling = lscaling_base - lgamma(r + n);
-                double argmin_d = r_in / r * n;
-
-                double dist_insum = abs(argmin_d - in_sum);
-
-                double pval = 0.0;
-                int64_t s = 0;
-                int64_t t = n;
-                if (argmin_d >= dist_insum) {
-                    double rs = r_in;
-                    double rt = r_out + n;
-                    double base = lscaling + lgamma(rt) - lgamma(r_out);
+            double pval = 0.0;
+            int64_t s = 0;
+            int64_t t = n;
+            if (argmin_d >= dist_insum) {
+                double rs = r_in;
+                double rt = r_out + n;
+                double base = lscaling + lgamma(rt) - lgamma(r_out);
+                pval += exp(base);
+                for (++s,--t; s <= n && abs(s - argmin_d) >= dist_insum; ++s,--t) {
+                    ++rs;
+                    --rt;
+                    base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
                     pval += exp(base);
-                    for (++s,--t; s <= n && abs(s - argmin_d) >= dist_insum; ++s,--t) {
-                        ++rs;
-                        --rt;
-                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
-                        pval += exp(base);
-                    }
                 }
+            }
 
-                int64_t sp = n;
-                t = 0;
-                if (abs(argmin_d - sp) >= dist_insum) {
-                    double rs = r_in + sp;
-                    double rt = r_out;
-                    double base = lscaling + lgamma(rs) - lgamma(r_in);
+            int64_t sp = n;
+            t = 0;
+            if (abs(argmin_d - sp) >= dist_insum) {
+                double rs = r_in + sp;
+                double rt = r_out;
+                double base = lscaling + lgamma(rs) - lgamma(r_in);
+                pval += exp(base);
+                for (--sp,++t; sp >= s && abs(argmin_d - sp) >= dist_insum; --sp,++t) {
+                    --rs;
+                    ++rt;
+                    base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
                     pval += exp(base);
-                    for (--sp,++t; sp >= s && abs(argmin_d - sp) >= dist_insum; --sp,++t) {
-                        --rs;
-                        ++rt;
-                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
-                        pval += exp(base);
-                    }
                 }
+            }
+
+            return std::min(1.0, pval);
+        };
 
-                return std::min(1.0, pval);
+        if (config.test_type == "nbinom_exact") {
+            double r = r_in + r_out;
+            double lscaling_base = lgamma(r);
+            compute_min_pval = [compute_min_pval_r,lscaling_base,r,r_in,r_out](int64_t n, const PairContainer&) {
+                return n > 0 ? compute_min_pval_r(r, r_in, r_out, lscaling_base, n) : 1.0;
+            };
+
+            compute_pval = [compute_pval_r,lscaling_base,r,r_in,r_out](int64_t in_sum, int64_t out_sum, const PairContainer &row) {
+                return row.size() ? compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row) : 1.0;
             };
         } else {
             std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
@@ -899,10 +901,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 progress_bar += its.size() % 1000;
             }
 
-            compute_min_pval = [vector_counts,num_labels_in,num_labels_out](int64_t n, const auto &row) {
-                if (row.empty())
-                    return 1.0;
-
+            auto get_r = [vector_counts](int64_t n, const auto &row) {
                 auto it = vector_counts[n].begin();
                 if (vector_counts[n].size() > 1) {
                     std::vector<int64_t> counts;
@@ -913,31 +912,26 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     std::sort(counts.begin(), counts.end());
                     it = vector_counts[n].find(counts);
                 }
-                double r_base = it->second;
+                return it->second;
+            };
+
+            compute_min_pval = [compute_min_pval_r,get_r,num_labels_in,num_labels_out](int64_t n, const PairContainer &row) {
+                if (row.empty())
+                    return 1.0;
+
+                double r_base = get_r(n, row);
                 if (r_base == 0.0)
                     return 1.0;
 
                 double r_in = r_base * num_labels_in;
                 double r_out = r_base * num_labels_out;
                 double r = r_in + r_out;
+                double lscaling_base = lgamma(r);
 
-                double lscaling = lgamma(r) - lgamma(r + n);
-                double argmin_d = r_in / r * n;
-
-                double dist0 = argmin_d;
-                double distn = static_cast<double>(n) - argmin_d;
-
-                double pval = 0.0;
-                if (dist0 >= distn)
-                    pval += exp(lscaling + lgamma(r_out + n) - lgamma(r_out));
-
-                if (dist0 <= distn)
-                    pval += exp(lscaling + lgamma(r_in + n) - lgamma(r_in));
-
-                return std::min(1.0, pval);
+                return compute_min_pval_r(r, r_in, r_out, lscaling_base, n);
             };
 
-            compute_pval = [vector_counts,num_labels_in,num_labels_out](int64_t in_sum, int64_t out_sum, const auto &row) {
+            compute_pval = [compute_pval_r,get_r,num_labels_in,num_labels_out](int64_t in_sum, int64_t out_sum, const PairContainer &row) {
                 if (row.empty())
                     return 1.0;
 
@@ -945,17 +939,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return 1.0;
 
                 int64_t n = in_sum + out_sum;
-                auto it = vector_counts[n].begin();
-                if (vector_counts[n].size() > 1) {
-                    std::vector<int64_t> counts;
-                    counts.reserve(row.size());
-                    for (const auto &[j, c] : row) {
-                        counts.emplace_back(c);
-                    }
-                    std::sort(counts.begin(), counts.end());
-                    it = vector_counts[n].find(counts);
-                }
-                double r_base = it->second;
+                double r_base = get_r(n, row);
 
                 if (r_base == 0.0)
                     return 1.0;
@@ -963,46 +947,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double r_in = r_base * num_labels_in;
                 double r_out = r_base * num_labels_out;
                 double r = r_in + r_out;
+                double lscaling_base = lgamma(r);
 
-                double lscaling = lgamma(r) - lgamma(r + n);
-                double argmin_d = r_in / r * n;
-                if (in_sum == argmin_d)
-                    return 1.0;
-
-                double dist_insum = abs(argmin_d - in_sum);
-
-                double pval = 0.0;
-                int64_t s = 0;
-                int64_t t = n;
-                if (argmin_d >= dist_insum) {
-                    double rs = r_in;
-                    double rt = r_out + n;
-                    double base = lscaling + lgamma(rt) - lgamma(r_out);
-                    pval += exp(base);
-                    for (++s,--t; s <= n && abs(s - argmin_d) >= dist_insum; ++s,--t) {
-                        ++rs;
-                        --rt;
-                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
-                        pval += exp(base);
-                    }
-                }
-
-                int64_t sp = n;
-                t = 0;
-                if (abs(argmin_d - sp) >= dist_insum) {
-                    double rs = r_in + sp;
-                    double rt = r_out;
-                    double base = lscaling + lgamma(rs) - lgamma(r_in);
-                    pval += exp(base);
-                    for (--sp,++t; sp >= s && abs(argmin_d - sp) >= dist_insum; --sp,++t) {
-                        --rs;
-                        ++rt;
-                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
-                        pval += exp(base);
-                    }
-                }
-
-                return std::min(1.0, pval);
+                return compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row);
             };
         }
     } else if (config.test_type == "notest") {

From 143a6993ae51a72cea1d0c3f137b394c10bb5426 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 16:20:39 +0200
Subject: [PATCH 321/410] cleanup

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 58a209a5d3..2e36e7b962 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -636,17 +636,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         int64_t total_sum = in_sum + out_sum;
 
         auto compute_min_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t n) {
-            double lscaling = lscaling_base - lgamma(r + n);
-            double argmin_d = r_in / r * n;
-
-            double dist0 = argmin_d;
-            double distn = static_cast<double>(n) - argmin_d;
+            double half_div0 = -r_in * log(r_in) - (n + r_out) * log(n + r_out);
+            double half_divn = -r_out * log(r_out) - (n + r_in) * log(n + r_in);
 
             double pval = 0.0;
-            if (dist0 >= distn)
+            double lscaling = lscaling_base - lgamma(r + n);
+            if (half_div0 >= half_divn)
                 pval += exp(lscaling + lgamma(r_out + n) - lgamma(r_out));
 
-            if (dist0 <= distn)
+            if (half_divn >= half_div0)
                 pval += exp(lscaling + lgamma(r_in + n) - lgamma(r_in));
 
             return std::min(1.0, pval);

From 3c8bf92f6024cd9649c734560b30ccb43481bf21 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 16:42:19 +0200
Subject: [PATCH 322/410] fixed deviance calculation

---
 .../src/graph/annotated_graph_algorithm.cpp   | 104 +++++++++++++-----
 1 file changed, 75 insertions(+), 29 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 2e36e7b962..ebb780b445 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -652,46 +652,92 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         auto compute_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t in_sum, int64_t out_sum, const PairContainer &row) {
             int64_t n = in_sum + out_sum;
-            if (in_sum == r_in / r * n)
+            double argmin_d = r_in / r * n;
+            if (in_sum == argmin_d)
                 return 1.0;
 
             double lscaling = lscaling_base - lgamma(r + n);
-            double argmin_d = r_in / r * n;
 
-            double dist_insum = abs(argmin_d - in_sum);
+            if (r_in == r_out) {
+                double dist_insum = abs(argmin_d - in_sum);
+                double pval = 0.0;
+                int64_t s = 0;
+                int64_t t = n;
+                if (argmin_d >= dist_insum) {
+                    double rs = r_in;
+                    double rt = r_out + n;
+                    double base = lscaling + lgamma(rt) - lgamma(r_out);
+                    pval += exp(base);
+                    for (++s,--t; s <= n && abs(s - argmin_d) >= dist_insum; ++s,--t) {
+                        ++rs;
+                        --rt;
+                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                        pval += exp(base);
+                    }
+                }
 
-            double pval = 0.0;
-            int64_t s = 0;
-            int64_t t = n;
-            if (argmin_d >= dist_insum) {
-                double rs = r_in;
-                double rt = r_out + n;
-                double base = lscaling + lgamma(rt) - lgamma(r_out);
-                pval += exp(base);
-                for (++s,--t; s <= n && abs(s - argmin_d) >= dist_insum; ++s,--t) {
-                    ++rs;
-                    --rt;
-                    base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                int64_t sp = n;
+                t = 0;
+                if (abs(argmin_d - sp) >= dist_insum) {
+                    double rs = r_in + sp;
+                    double rt = r_out;
+                    double base = lscaling + lgamma(rs) - lgamma(r_in);
                     pval += exp(base);
+                    for (--sp,++t; sp >= s && abs(argmin_d - sp) >= dist_insum; --sp,++t) {
+                        --rs;
+                        ++rt;
+                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                        pval += exp(base);
+                    }
                 }
-            }
 
-            int64_t sp = n;
-            t = 0;
-            if (abs(argmin_d - sp) >= dist_insum) {
-                double rs = r_in + sp;
-                double rt = r_out;
-                double base = lscaling + lgamma(rs) - lgamma(r_in);
-                pval += exp(base);
-                for (--sp,++t; sp >= s && abs(argmin_d - sp) >= dist_insum; --sp,++t) {
-                    --rs;
-                    ++rt;
-                    base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                return std::min(1.0, pval);
+            } else {
+                auto get_half_dev = [&](int64_t s) {
+                    if (s == 0)
+                        return -r_in * log(r_in) - (n + r_out) * log(n + r_out) + n * log(n);
+
+                    if (s == n)
+                        return -r_out * log(r_out) - (n + r_in) * log(n + r_out) + n * log(n);
+
+                    int64_t t = n - s;
+                    return log(s) * s - (r_in + s) * log(r_in + s) + log(t) * t - (t + r_out) * log(t + r_out);
+                };
+
+                double dev_insum = get_half_dev(in_sum);
+                double pval = 0.0;
+                int64_t s = 0;
+                int64_t t = n;
+                if (get_half_dev(s) >= dev_insum) {
+                    double rs = r_in;
+                    double rt = r_out + n;
+                    double base = lscaling + lgamma(rt) - lgamma(r_out);
                     pval += exp(base);
+                    for (++s,--t; s <= n && get_half_dev(s) >= dev_insum; ++s,--t) {
+                        ++rs;
+                        --rt;
+                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                        pval += exp(base);
+                    }
                 }
-            }
 
-            return std::min(1.0, pval);
+                int64_t sp = n;
+                t = 0;
+                if (get_half_dev(sp) >= dev_insum) {
+                    double rs = r_in + sp;
+                    double rt = r_out;
+                    double base = lscaling + lgamma(rs) - lgamma(r_in);
+                    pval += exp(base);
+                    for (--sp,++t; sp >= s && get_half_dev(sp) >= dev_insum; --sp,++t) {
+                        --rs;
+                        ++rt;
+                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                        pval += exp(base);
+                    }
+                }
+
+                return std::min(1.0, pval);
+            }
         };
 
         if (config.test_type == "nbinom_exact") {

From 8cde9ddbea743d6f4b48d3a33e05edb6882a76a3 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 16:44:23 +0200
Subject: [PATCH 323/410] refactor

---
 .../src/graph/annotated_graph_algorithm.cpp   | 54 +++++--------------
 1 file changed, 12 insertions(+), 42 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ebb780b445..71a57dbcca 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -658,17 +658,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double lscaling = lscaling_base - lgamma(r + n);
 
-            if (r_in == r_out) {
-                double dist_insum = abs(argmin_d - in_sum);
+            auto get_pval = [&](const auto &get_stat) {
+                double base_stat = get_stat(in_sum);
                 double pval = 0.0;
                 int64_t s = 0;
                 int64_t t = n;
-                if (argmin_d >= dist_insum) {
+                if (get_stat(s) >= base_stat) {
                     double rs = r_in;
                     double rt = r_out + n;
                     double base = lscaling + lgamma(rt) - lgamma(r_out);
                     pval += exp(base);
-                    for (++s,--t; s <= n && abs(s - argmin_d) >= dist_insum; ++s,--t) {
+                    for (++s,--t; s <= n && get_stat(s) >= base_stat; ++s,--t) {
                         ++rs;
                         --rt;
                         base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
@@ -678,12 +678,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 int64_t sp = n;
                 t = 0;
-                if (abs(argmin_d - sp) >= dist_insum) {
+                if (get_stat(sp) >= base_stat) {
                     double rs = r_in + sp;
                     double rt = r_out;
                     double base = lscaling + lgamma(rs) - lgamma(r_in);
                     pval += exp(base);
-                    for (--sp,++t; sp >= s && abs(argmin_d - sp) >= dist_insum; --sp,++t) {
+                    for (--sp,++t; sp >= s && get_stat(sp) >= base_stat; --sp,++t) {
                         --rs;
                         ++rt;
                         base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
@@ -692,8 +692,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 return std::min(1.0, pval);
+            };
+
+            if (r_in == r_out) {
+                return get_pval([&](int64_t s) { return abs(argmin_d - s); });
             } else {
-                auto get_half_dev = [&](int64_t s) {
+                return get_pval([&](int64_t s) {
                     if (s == 0)
                         return -r_in * log(r_in) - (n + r_out) * log(n + r_out) + n * log(n);
 
@@ -702,41 +706,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                     int64_t t = n - s;
                     return log(s) * s - (r_in + s) * log(r_in + s) + log(t) * t - (t + r_out) * log(t + r_out);
-                };
-
-                double dev_insum = get_half_dev(in_sum);
-                double pval = 0.0;
-                int64_t s = 0;
-                int64_t t = n;
-                if (get_half_dev(s) >= dev_insum) {
-                    double rs = r_in;
-                    double rt = r_out + n;
-                    double base = lscaling + lgamma(rt) - lgamma(r_out);
-                    pval += exp(base);
-                    for (++s,--t; s <= n && get_half_dev(s) >= dev_insum; ++s,--t) {
-                        ++rs;
-                        --rt;
-                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
-                        pval += exp(base);
-                    }
-                }
-
-                int64_t sp = n;
-                t = 0;
-                if (get_half_dev(sp) >= dev_insum) {
-                    double rs = r_in + sp;
-                    double rt = r_out;
-                    double base = lscaling + lgamma(rs) - lgamma(r_in);
-                    pval += exp(base);
-                    for (--sp,++t; sp >= s && get_half_dev(sp) >= dev_insum; --sp,++t) {
-                        --rs;
-                        ++rt;
-                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
-                        pval += exp(base);
-                    }
-                }
-
-                return std::min(1.0, pval);
+                });
             }
         };
 

From 81383b7cca9fd2daec5ecd7f116bb6c447bc5cd1 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 17:04:37 +0200
Subject: [PATCH 324/410] fix

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 71a57dbcca..8e09c00444 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -699,13 +699,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             } else {
                 return get_pval([&](int64_t s) {
                     if (s == 0)
-                        return -r_in * log(r_in) - (n + r_out) * log(n + r_out) + n * log(n);
+                        return n*log(n) - r_in*log(r_in) - (n + r_out)*log(n + r_out);
 
                     if (s == n)
-                        return -r_out * log(r_out) - (n + r_in) * log(n + r_out) + n * log(n);
+                        return n*log(n) - r_out*log(r_out) - (n + r_in)*log(n + r_in);
 
                     int64_t t = n - s;
-                    return log(s) * s - (r_in + s) * log(r_in + s) + log(t) * t - (t + r_out) * log(t + r_out);
+                    return log(s)*s - (r_in + s)*log(r_in + s) + log(t)*t - (t + r_out)*log(t + r_out);
                 });
             }
         };

From 33fb4211cfa473abc1c22689d95df514a0ca801b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 17:36:47 +0200
Subject: [PATCH 325/410] t

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 8e09c00444..a505dce5c9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -423,7 +423,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
         };
-    } else if (config.test_type == "nbinom_exact" || config.test_type == "zinb_exact" || config.test_type == "gnb_exact") {
+    } else if (config.test_type == "nbinom_exact" || config.test_type == "gnb_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
         auto get_rp = [&](const auto &generate,
                           const auto &add_dl,

From b57658875cd12e67345d9e7fdc40b8774f52b268 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 20:08:04 +0200
Subject: [PATCH 326/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 128 +++++++++---------
 1 file changed, 63 insertions(+), 65 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a505dce5c9..a8666acdcc 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -721,62 +721,64 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return row.size() ? compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row) : 1.0;
             };
         } else {
-            std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
-
-            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
-                bool in_kmer = false;
-                bool out_kmer = false;
-                std::vector<int64_t> vec;
-                if (kept[row_i]) {
-                    vec.reserve(raw_row.size());
-                    size_t count_in = 0;
-                    size_t count_out = 0;
-                    for (const auto &[j, raw_c] : raw_row) {
-                        uint64_t c = raw_c;
-                        if (count_maps.size())
-                            c = count_maps[j].find(c)->second.first;
-
-                        if (groups[j]) {
-                            out_sum += c;
-                            ++count_out;
-                        } else {
-                            in_sum += c;
-                            ++count_in;
-                        }
+            common::logger->trace("Enumerating row count distributions");
+            std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>> vector_counts;
+            {
+                std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
+
+                generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
+                    int64_t in_sum = 0;
+                    int64_t out_sum = 0;
+                    bool in_kmer = false;
+                    bool out_kmer = false;
+                    std::vector<int64_t> vec;
+                    if (kept[row_i]) {
+                        vec.reserve(raw_row.size());
+                        size_t count_in = 0;
+                        size_t count_out = 0;
+                        for (const auto &[j, raw_c] : raw_row) {
+                            uint64_t c = raw_c;
+                            if (count_maps.size())
+                                c = count_maps[j].find(c)->second.first;
+
+                            if (groups[j]) {
+                                out_sum += c;
+                                ++count_out;
+                            } else {
+                                in_sum += c;
+                                ++count_in;
+                            }
 
-                        vec.emplace_back(c);
-                    }
+                            vec.emplace_back(c);
+                        }
 
-                    if (count_in + count_out >= config.min_recurrence) {
-                        double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-                        in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-                        out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+                        if (count_in + count_out >= config.min_recurrence) {
+                            double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+                            in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+                            out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+                        }
+                    } else {
+                        return;
                     }
-                } else {
-                    return;
-                }
 
-                if (!in_kmer && !out_kmer)
-                    return;
+                    if (!in_kmer && !out_kmer)
+                        return;
 
-                size_t n = in_sum + out_sum;
-                if (n >= vector_counts_ms[bucket_idx].size())
-                    vector_counts_ms[bucket_idx].resize(n + 1);
+                    size_t n = in_sum + out_sum;
+                    if (n >= vector_counts_ms[bucket_idx].size())
+                        vector_counts_ms[bucket_idx].resize(n + 1);
 
-                std::sort(vec.begin(), vec.end());
-                ++vector_counts_ms[bucket_idx][n][vec];
-            }, false);
+                    std::sort(vec.begin(), vec.end());
+                    ++vector_counts_ms[bucket_idx][n][vec];
+                }, false);
 
-            std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>> vector_counts = std::move(vector_counts_ms[0]);
-            size_t max_size = vector_counts.size();
-            for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-                max_size = std::max(max_size, vector_counts_ms[j].size());
-            }
-            vector_counts.resize(max_size);
+                vector_counts = std::move(vector_counts_ms[0]);
+                size_t max_size = vector_counts.size();
+                for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
+                    max_size = std::max(max_size, vector_counts_ms[j].size());
+                }
+                vector_counts.resize(max_size);
 
-            {
                 ProgressBar progress_bar(vector_counts.size() - 1, "Merging row", std::cerr, !common::get_verbose());
                 #pragma omp parallel for num_threads(num_threads)
                 for (size_t n = 1; n < vector_counts.size(); ++n) {
@@ -789,33 +791,29 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                     ++progress_bar;
                 }
-
-                vector_counts_ms.resize(0);
             }
 
             common::logger->trace("Fitting LN prior distribution for dispersions");
             double ln_mu = 0.0;
             double ln_var = 0.0;
 
-            {
-                ProgressBar progress_bar(vector_counts.size() - 1, "Fitting", std::cerr, !common::get_verbose());
-                #pragma omp parallel for num_threads(num_threads) reduction(+:ln_mu,ln_var)
-                for (size_t n = 1; n < vector_counts.size(); ++n) {
-                    size_t total = 0;
-                    for (const auto &[v, c] : vector_counts[n]) {
-                        total += c;
-                    }
+            ProgressBar progress_bar(vector_counts.size() - 1, "Fitting", std::cerr, !common::get_verbose());
+            #pragma omp parallel for num_threads(num_threads) reduction(+:ln_mu,ln_var)
+            for (size_t n = 1; n < vector_counts.size(); ++n) {
+                size_t total = 0;
+                for (const auto &[v, c] : vector_counts[n]) {
+                    total += c;
+                }
 
-                    double r = target_p / (1.0 - target_p) * n / groups.size();
+                double r = target_p / (1.0 - target_p) * n / groups.size();
 
-                    #pragma omp atomic
-                    ln_mu += -log(r) * total;
+                #pragma omp atomic
+                ln_mu += -log(r) * total;
 
-                    #pragma omp atomic
-                    ln_var += log(r) * log(r) * total;
+                #pragma omp atomic
+                ln_var += log(r) * log(r) * total;
 
-                    ++progress_bar;
-                }
+                ++progress_bar;
             }
 
             size_t total = nelem * groups.size();

From 89f361c4ce72bdc074f763a766c28359634d6e0b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 26 Aug 2024 20:11:59 +0200
Subject: [PATCH 327/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp      | 16 +---------------
 1 file changed, 1 insertion(+), 15 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a8666acdcc..fcbbdd735e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -621,20 +621,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         common::logger->trace("  Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
-        int64_t in_sum = 0;
-        int64_t out_sum = 0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            if (hists[j].size()) {
-                uint64_t k = count_maps[j].find(hists[j].back().first)->second.first;
-                if (groups[j]) {
-                    out_sum += k;
-                } else {
-                    in_sum += k;
-                }
-            }
-        }
-        int64_t total_sum = in_sum + out_sum;
-
         auto compute_min_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t n) {
             double half_div0 = -r_in * log(r_in) - (n + r_out) * log(n + r_out);
             double half_divn = -r_out * log(r_out) - (n + r_in) * log(n + r_in);
@@ -825,7 +811,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                   exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1),
                                   exp(-ln_mu - ln_var/2));
 
-            auto get_rp = [ln_mu,ln_var,real_sum=total_sum,total,sum=static_cast<double>(total_kmers),gs=groups.size()](const auto &counts) {
+            auto get_rp = [ln_mu,ln_var,gs=groups.size()](const auto &counts) {
                 double mu = 0;
                 double var = 0;
                 for (int64_t c : counts) {

From 84315f4dc0e2102724ff4be1fc58bffbe746715d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 27 Aug 2024 15:56:58 +0200
Subject: [PATCH 328/410] optim

---
 .../src/graph/annotated_graph_algorithm.cpp   | 40 +++++++++----------
 1 file changed, 19 insertions(+), 21 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index fcbbdd735e..64d97b671e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -557,29 +557,27 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         count_maps.resize(groups.size());
 
         common::logger->trace("Computing quantile maps");
-        auto map_value = [&](uint64_t k, double scale, const auto &old_dist, const auto &new_dist) {
-            double cdf = boost::math::cdf(old_dist, k);
-
-            if (cdf < 1.0)
-                return boost::math::quantile(new_dist, cdf);
-
-            double ccdf = boost::math::cdf(boost::math::complement(old_dist, k));
-
-            if (ccdf > 0.0)
-                return boost::math::quantile(boost::math::complement(new_dist, ccdf));
-
-            return ceil(scale * k);
-        };
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto &[r, p] = nb_params[j];
 
-            common::logger->trace("{}\tApproximating NB({}, {}) with NB({}, {})",
-                                  j, r, p, r_map, target_p);
-
             boost::math::negative_binomial nb_out(r_map, target_p);
             double scale = target_sum / sums[j];
 
+            auto map_value = [&](uint64_t k, double scale, const auto &old_dist, const auto &new_dist) {
+                double cdf = boost::math::cdf(old_dist, k);
+
+                if (cdf < 1.0)
+                    return boost::math::quantile(new_dist, cdf);
+
+                double ccdf = boost::math::cdf(boost::math::complement(old_dist, k));
+
+                if (ccdf > 0.0)
+                    return boost::math::quantile(boost::math::complement(new_dist, ccdf));
+
+                return ceil(scale * k);
+            };
+
             auto compute_map = [&](const auto &nb) {
                 // ensure that 0 maps to 0
                 count_maps[j][0] = std::make_pair(0, 0);
@@ -766,12 +764,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 vector_counts.resize(max_size);
 
                 ProgressBar progress_bar(vector_counts.size() - 1, "Merging row", std::cerr, !common::get_verbose());
-                #pragma omp parallel for num_threads(num_threads)
-                for (size_t n = 1; n < vector_counts.size(); ++n) {
+                #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+                for (size_t n = vector_counts.size(); n > 0; --n) {
                     for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-                        if (n < vector_counts_ms[j].size()) {
-                            for (const auto &[v, c] : vector_counts_ms[j][n]) {
-                                vector_counts[n][v] += c;
+                        if (n <= vector_counts_ms[j].size()) {
+                            for (const auto &[v, c] : vector_counts_ms[j][n - 1]) {
+                                vector_counts[n - 1][v] += c;
                             }
                         }
                     }

From 106fc6ebc169a1e7ddb9424e2a08166bdbb48849 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 28 Aug 2024 10:13:22 +0200
Subject: [PATCH 329/410] optim

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 64d97b671e..e11ac4dff6 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -887,7 +887,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 ProgressBar progress_bar(its.size(), "Precomputing r's", std::cerr, !common::get_verbose());
-                #pragma omp parallel for num_threads(num_threads)
+                #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
                 for (size_t i = 0; i < its.size(); ++i) {
                     if (i > 0 && i % 1000 == 0)
                         progress_bar += 1000;

From 221e6a7d7afb21fcd77cf88dc6052f28154a59f3 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 28 Aug 2024 10:53:31 +0200
Subject: [PATCH 330/410] optim

---
 .../src/graph/annotated_graph_algorithm.cpp   | 59 +++++++++----------
 1 file changed, 27 insertions(+), 32 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index e11ac4dff6..930262320c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -781,8 +781,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double ln_mu = 0.0;
             double ln_var = 0.0;
 
-            ProgressBar progress_bar(vector_counts.size() - 1, "Fitting", std::cerr, !common::get_verbose());
-            #pragma omp parallel for num_threads(num_threads) reduction(+:ln_mu,ln_var)
+            // ProgressBar progress_bar(vector_counts.size() - 1, "Fitting", std::cerr, !common::get_verbose());
+            // #pragma omp parallel for num_threads(num_threads) reduction(+:ln_mu,ln_var)
             for (size_t n = 1; n < vector_counts.size(); ++n) {
                 size_t total = 0;
                 for (const auto &[v, c] : vector_counts[n]) {
@@ -797,7 +797,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 #pragma omp atomic
                 ln_var += log(r) * log(r) * total;
 
-                ++progress_bar;
+                // ++progress_bar;
             }
 
             size_t total = nelem * groups.size();
@@ -878,44 +878,39 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return std::make_pair(r, p);
             };
 
-            {
-                std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>::iterator> its;
-                for (size_t n = 1; n < vector_counts.size(); ++n) {
-                    for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
-                        its.emplace_back(it);
-                    }
-                }
-
-                ProgressBar progress_bar(its.size(), "Precomputing r's", std::cerr, !common::get_verbose());
-                #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
-                for (size_t i = 0; i < its.size(); ++i) {
-                    if (i > 0 && i % 1000 == 0)
-                        progress_bar += 1000;
+            ProgressBar progress_bar(vector_counts.size() - 1, "Precomputing r's", std::cerr, !common::get_verbose());
+            #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+            for (size_t n = 1; n < vector_counts.size(); ++n) {
+                if (n > 0 && n % 1000 == 0)
+                    progress_bar += 1000;
 
-                    its[i].value() = get_rp(its[i]->first).first;
+                for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
+                    it.value() = get_rp(it->first).first;
                 }
-                progress_bar += its.size() % 1000;
             }
-
-            auto get_r = [vector_counts](int64_t n, const auto &row) {
-                auto it = vector_counts[n].begin();
-                if (vector_counts[n].size() > 1) {
-                    std::vector<int64_t> counts;
-                    counts.reserve(row.size());
-                    for (const auto &[j, c] : row) {
-                        counts.emplace_back(c);
+            progress_bar += (vector_counts.size() - 1) % 1000;
+
+            auto get_r = std::make_shared<std::function<double(int64_t, const PairContainer&)>>(
+                [vc=std::move(vector_counts)](int64_t n, const auto &row) {
+                    auto it = vc[n].begin();
+                    if (vc[n].size() > 1) {
+                        std::vector<int64_t> counts;
+                        counts.reserve(row.size());
+                        for (const auto &[j, c] : row) {
+                            counts.emplace_back(c);
+                        }
+                        std::sort(counts.begin(), counts.end());
+                        it = vc[n].find(counts);
                     }
-                    std::sort(counts.begin(), counts.end());
-                    it = vector_counts[n].find(counts);
+                    return it->second;
                 }
-                return it->second;
-            };
+            );
 
             compute_min_pval = [compute_min_pval_r,get_r,num_labels_in,num_labels_out](int64_t n, const PairContainer &row) {
                 if (row.empty())
                     return 1.0;
 
-                double r_base = get_r(n, row);
+                double r_base = (*get_r)(n, row);
                 if (r_base == 0.0)
                     return 1.0;
 
@@ -935,7 +930,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return 1.0;
 
                 int64_t n = in_sum + out_sum;
-                double r_base = get_r(n, row);
+                double r_base = (*get_r)(n, row);
 
                 if (r_base == 0.0)
                     return 1.0;

From a0337c32deddc8123519c1ab6e061089449d220e Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 28 Aug 2024 10:53:56 +0200
Subject: [PATCH 331/410] cleanup

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 930262320c..a9c005b330 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -781,8 +781,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double ln_mu = 0.0;
             double ln_var = 0.0;
 
-            // ProgressBar progress_bar(vector_counts.size() - 1, "Fitting", std::cerr, !common::get_verbose());
-            // #pragma omp parallel for num_threads(num_threads) reduction(+:ln_mu,ln_var)
             for (size_t n = 1; n < vector_counts.size(); ++n) {
                 size_t total = 0;
                 for (const auto &[v, c] : vector_counts[n]) {
@@ -796,8 +794,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 #pragma omp atomic
                 ln_var += log(r) * log(r) * total;
-
-                // ++progress_bar;
             }
 
             size_t total = nelem * groups.size();

From b0fc9e910e7ea94d9affefef9dddb39c0bfcbda0 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 28 Aug 2024 13:02:55 +0200
Subject: [PATCH 332/410] fix cdf

---
 .../src/graph/annotated_graph_algorithm.cpp      | 16 ++++------------
 1 file changed, 4 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a9c005b330..50c190f290 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -648,14 +648,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 int64_t s = 0;
                 int64_t t = n;
                 if (get_stat(s) >= base_stat) {
-                    double rs = r_in;
-                    double rt = r_out + n;
-                    double base = lscaling + lgamma(rt) - lgamma(r_out);
+                    double base = lscaling + lgamma(n + r_out) - lgamma(r_out);
                     pval += exp(base);
                     for (++s,--t; s <= n && get_stat(s) >= base_stat; ++s,--t) {
-                        ++rs;
-                        --rt;
-                        base += log(t) - log(s) + log(rs - 1) - (rt > 1 ? log(rt - 1) : lgamma(rt + 1) - lgamma(rt));
+                        base += log1p(t) + log(s - 1 + r_in) - log(s) - log(t + r_out);
                         pval += exp(base);
                     }
                 }
@@ -663,14 +659,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 int64_t sp = n;
                 t = 0;
                 if (get_stat(sp) >= base_stat) {
-                    double rs = r_in + sp;
-                    double rt = r_out;
-                    double base = lscaling + lgamma(rs) - lgamma(r_in);
+                    double base = lscaling + lgamma(n + r_in) - lgamma(r_in);
                     pval += exp(base);
                     for (--sp,++t; sp >= s && get_stat(sp) >= base_stat; --sp,++t) {
-                        --rs;
-                        ++rt;
-                        base += log(sp) - log(t) - (rs > 1 ? log(rs - 1) : lgamma(rs + 1) - lgamma(rs)) + log(rt - 1);
+                        base += log1p(sp) + log(t - 1 + r_out) - log(t) - log(sp + r_in);
                         pval += exp(base);
                     }
                 }

From e0f116ddb38190383fc542f2539dca240bc5e087 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 28 Aug 2024 15:47:21 +0200
Subject: [PATCH 333/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 25 ++++++++-----------
 1 file changed, 10 insertions(+), 15 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 50c190f290..bd04359d56 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -643,14 +643,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double lscaling = lscaling_base - lgamma(r + n);
 
             auto get_pval = [&](const auto &get_stat) {
-                double base_stat = get_stat(in_sum);
+                double base_stat = get_stat(in_sum, out_sum);
                 double pval = 0.0;
                 int64_t s = 0;
                 int64_t t = n;
-                if (get_stat(s) >= base_stat) {
+                if (get_stat(s, t) >= base_stat) {
                     double base = lscaling + lgamma(n + r_out) - lgamma(r_out);
                     pval += exp(base);
-                    for (++s,--t; s <= n && get_stat(s) >= base_stat; ++s,--t) {
+                    for (++s,--t; s <= n && get_stat(s, t) >= base_stat; ++s,--t) {
                         base += log1p(t) + log(s - 1 + r_in) - log(s) - log(t + r_out);
                         pval += exp(base);
                     }
@@ -658,10 +658,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 int64_t sp = n;
                 t = 0;
-                if (get_stat(sp) >= base_stat) {
+                if (get_stat(sp, t) >= base_stat) {
                     double base = lscaling + lgamma(n + r_in) - lgamma(r_in);
                     pval += exp(base);
-                    for (--sp,++t; sp >= s && get_stat(sp) >= base_stat; --sp,++t) {
+                    for (--sp,++t; sp >= s && get_stat(sp, t) >= base_stat; --sp,++t) {
                         base += log1p(sp) + log(t - 1 + r_out) - log(t) - log(sp + r_in);
                         pval += exp(base);
                     }
@@ -671,17 +671,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             };
 
             if (r_in == r_out) {
-                return get_pval([&](int64_t s) { return abs(argmin_d - s); });
+                return get_pval([&](int64_t s, int64_t) { return abs(argmin_d - s); });
             } else {
-                return get_pval([&](int64_t s) {
-                    if (s == 0)
-                        return n*log(n) - r_in*log(r_in) - (n + r_out)*log(n + r_out);
-
-                    if (s == n)
-                        return n*log(n) - r_out*log(r_out) - (n + r_in)*log(n + r_in);
-
-                    int64_t t = n - s;
-                    return log(s)*s - (r_in + s)*log(r_in + s) + log(t)*t - (t + r_out)*log(t + r_out);
+                return get_pval([&](int64_t s, int64_t t) {
+                    double sls = s > 0 ? log(s) * s : 0;
+                    double tlt = t > 0 ? log(t) * t : 0;
+                    return sls - (r_in + s)*log(r_in + s) + tlt - (t + r_out)*log(t + r_out);
                 });
             }
         };

From 566cef8ac0b1bb29fe5eef0f36547fb2078e895b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 28 Aug 2024 16:32:15 +0200
Subject: [PATCH 334/410] fewer ops

---
 .../src/graph/annotated_graph_algorithm.cpp   | 38 +++++++++++++------
 1 file changed, 26 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index bd04359d56..5b99873bf6 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -643,26 +643,42 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             double lscaling = lscaling_base - lgamma(r + n);
 
             auto get_pval = [&](const auto &get_stat) {
-                double base_stat = get_stat(in_sum, out_sum);
+                double base_stat = get_stat(in_sum, out_sum,
+                                            r_in != r_out && in_sum > 0 ? log(in_sum) : 0.0,
+                                            r_in != r_out && out_sum > 0 ? log(out_sum) : 0.0);
                 double pval = 0.0;
                 int64_t s = 0;
                 int64_t t = n;
-                if (get_stat(s, t) >= base_stat) {
+                double ls = 0;
+                double lt = log(t);
+                if (get_stat(s, t, ls, lt) >= base_stat) {
                     double base = lscaling + lgamma(n + r_out) - lgamma(r_out);
                     pval += exp(base);
-                    for (++s,--t; s <= n && get_stat(s, t) >= base_stat; ++s,--t) {
-                        base += log1p(t) + log(s - 1 + r_in) - log(s) - log(t + r_out);
+                    for (++s,--t; s <= n; ++s,--t) {
+                        ls = log(s);
+                        lt = t > 0 ? log(t) : 0.0;
+                        if (get_stat(s, t, ls, lt) < base_stat)
+                            break;
+
+                        base += log1p(t) + log(s - 1 + r_in) - ls - log(t + r_out);
                         pval += exp(base);
                     }
                 }
 
                 int64_t sp = n;
                 t = 0;
-                if (get_stat(sp, t) >= base_stat) {
+                ls = log(sp);
+                lt = 0;
+                if (get_stat(sp, t, ls, lt) >= base_stat) {
                     double base = lscaling + lgamma(n + r_in) - lgamma(r_in);
                     pval += exp(base);
-                    for (--sp,++t; sp >= s && get_stat(sp, t) >= base_stat; --sp,++t) {
-                        base += log1p(sp) + log(t - 1 + r_out) - log(t) - log(sp + r_in);
+                    for (--sp,++t; sp >= s; --sp,++t) {
+                        ls = sp > 0 ? log(sp) : 0.0;
+                        lt = log(t);
+                        if (get_stat(sp, t, ls, lt) < base_stat)
+                            break;
+
+                        base += log1p(sp) + log(t - 1 + r_out) - lt - log(sp + r_in);
                         pval += exp(base);
                     }
                 }
@@ -671,12 +687,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             };
 
             if (r_in == r_out) {
-                return get_pval([&](int64_t s, int64_t) { return abs(argmin_d - s); });
+                return get_pval([&](int64_t s, int64_t, double, double) { return abs(argmin_d - s); });
             } else {
-                return get_pval([&](int64_t s, int64_t t) {
-                    double sls = s > 0 ? log(s) * s : 0;
-                    double tlt = t > 0 ? log(t) * t : 0;
-                    return sls - (r_in + s)*log(r_in + s) + tlt - (t + r_out)*log(t + r_out);
+                return get_pval([&](int64_t s, int64_t t, double ls, double lt) {
+                    return ls * s - (r_in + s)*log(r_in + s) + lt * t - (t + r_out)*log(t + r_out);
                 });
             }
         };

From a70cae57631d050c170f74c4cfcbb2e67f89d45b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 28 Aug 2024 16:52:21 +0200
Subject: [PATCH 335/410] optim: use log2 and exp2

---
 .../src/graph/annotated_graph_algorithm.cpp   | 34 +++++++++----------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5b99873bf6..51e57d61e8 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -644,42 +644,42 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             auto get_pval = [&](const auto &get_stat) {
                 double base_stat = get_stat(in_sum, out_sum,
-                                            r_in != r_out && in_sum > 0 ? log(in_sum) : 0.0,
-                                            r_in != r_out && out_sum > 0 ? log(out_sum) : 0.0);
+                                            r_in != r_out && in_sum > 0 ? log2(in_sum) : 0.0,
+                                            r_in != r_out && out_sum > 0 ? log2(out_sum) : 0.0);
                 double pval = 0.0;
                 int64_t s = 0;
                 int64_t t = n;
                 double ls = 0;
-                double lt = log(t);
+                double lt = log2(t);
                 if (get_stat(s, t, ls, lt) >= base_stat) {
-                    double base = lscaling + lgamma(n + r_out) - lgamma(r_out);
-                    pval += exp(base);
+                    double base = (lscaling + lgamma(n + r_out) - lgamma(r_out)) / log(2);
+                    pval += exp2(base);
                     for (++s,--t; s <= n; ++s,--t) {
-                        ls = log(s);
-                        lt = t > 0 ? log(t) : 0.0;
+                        ls = log2(s);
+                        lt = t > 0 ? log2(t) : 0.0;
                         if (get_stat(s, t, ls, lt) < base_stat)
                             break;
 
-                        base += log1p(t) + log(s - 1 + r_in) - ls - log(t + r_out);
-                        pval += exp(base);
+                        base += log2(t + 1) + log2(s - 1 + r_in) - ls - log2(t + r_out);
+                        pval += exp2(base);
                     }
                 }
 
                 int64_t sp = n;
                 t = 0;
-                ls = log(sp);
+                ls = log2(sp);
                 lt = 0;
                 if (get_stat(sp, t, ls, lt) >= base_stat) {
-                    double base = lscaling + lgamma(n + r_in) - lgamma(r_in);
-                    pval += exp(base);
+                    double base = (lscaling + lgamma(n + r_in) - lgamma(r_in)) / log(2);
+                    pval += exp2(base);
                     for (--sp,++t; sp >= s; --sp,++t) {
-                        ls = sp > 0 ? log(sp) : 0.0;
-                        lt = log(t);
+                        ls = sp > 0 ? log2(sp) : 0.0;
+                        lt = log2(t);
                         if (get_stat(sp, t, ls, lt) < base_stat)
                             break;
 
-                        base += log1p(sp) + log(t - 1 + r_out) - lt - log(sp + r_in);
-                        pval += exp(base);
+                        base += log2(sp + 1) + log2(t - 1 + r_out) - lt - log2(sp + r_in);
+                        pval += exp2(base);
                     }
                 }
 
@@ -690,7 +690,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return get_pval([&](int64_t s, int64_t, double, double) { return abs(argmin_d - s); });
             } else {
                 return get_pval([&](int64_t s, int64_t t, double ls, double lt) {
-                    return ls * s - (r_in + s)*log(r_in + s) + lt * t - (t + r_out)*log(t + r_out);
+                    return ls * s - (r_in + s)*log2(r_in + s) + lt * t - (t + r_out)*log2(t + r_out);
                 });
             }
         };

From 4c2ceb4c3626b43465d6c54839c0329e7833c093 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 28 Aug 2024 17:17:13 +0200
Subject: [PATCH 336/410] more optim

---
 .../src/graph/annotated_graph_algorithm.cpp   | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 51e57d61e8..193abe336a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -620,16 +620,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("  Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
         auto compute_min_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t n) {
-            double half_div0 = -r_in * log(r_in) - (n + r_out) * log(n + r_out);
-            double half_divn = -r_out * log(r_out) - (n + r_in) * log(n + r_in);
+            double half_div0 = -r_in * log2(r_in) - (n + r_out) * log2(n + r_out);
+            double half_divn = -r_out * log2(r_out) - (n + r_in) * log2(n + r_in);
 
             double pval = 0.0;
-            double lscaling = lscaling_base - lgamma(r + n);
+            double lscaling = lscaling_base - lgamma(r + n) / log(2);
             if (half_div0 >= half_divn)
-                pval += exp(lscaling + lgamma(r_out + n) - lgamma(r_out));
+                pval += exp2(lscaling + (lgamma(r_out + n) - lgamma(r_out)) / log(2));
 
             if (half_divn >= half_div0)
-                pval += exp(lscaling + lgamma(r_in + n) - lgamma(r_in));
+                pval += exp2(lscaling + (lgamma(r_in + n) - lgamma(r_in)) / log(2));
 
             return std::min(1.0, pval);
         };
@@ -640,7 +640,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (in_sum == argmin_d)
                 return 1.0;
 
-            double lscaling = lscaling_base - lgamma(r + n);
+            double lscaling = lscaling_base - lgamma(r + n) / log(2);
 
             auto get_pval = [&](const auto &get_stat) {
                 double base_stat = get_stat(in_sum, out_sum,
@@ -652,7 +652,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double ls = 0;
                 double lt = log2(t);
                 if (get_stat(s, t, ls, lt) >= base_stat) {
-                    double base = (lscaling + lgamma(n + r_out) - lgamma(r_out)) / log(2);
+                    double base = lscaling + (lgamma(n + r_out) - lgamma(r_out)) / log(2);
                     pval += exp2(base);
                     for (++s,--t; s <= n; ++s,--t) {
                         ls = log2(s);
@@ -670,7 +670,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ls = log2(sp);
                 lt = 0;
                 if (get_stat(sp, t, ls, lt) >= base_stat) {
-                    double base = (lscaling + lgamma(n + r_in) - lgamma(r_in)) / log(2);
+                    double base = lscaling + (lgamma(n + r_in) - lgamma(r_in)) / log(2);
                     pval += exp2(base);
                     for (--sp,++t; sp >= s; --sp,++t) {
                         ls = sp > 0 ? log2(sp) : 0.0;
@@ -697,7 +697,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         if (config.test_type == "nbinom_exact") {
             double r = r_in + r_out;
-            double lscaling_base = lgamma(r);
+            double lscaling_base = lgamma(r) / log(2);
             compute_min_pval = [compute_min_pval_r,lscaling_base,r,r_in,r_out](int64_t n, const PairContainer&) {
                 return n > 0 ? compute_min_pval_r(r, r_in, r_out, lscaling_base, n) : 1.0;
             };
@@ -914,7 +914,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double r_in = r_base * num_labels_in;
                 double r_out = r_base * num_labels_out;
                 double r = r_in + r_out;
-                double lscaling_base = lgamma(r);
+                double lscaling_base = lgamma(r) / log(2);
 
                 return compute_min_pval_r(r, r_in, r_out, lscaling_base, n);
             };
@@ -935,7 +935,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double r_in = r_base * num_labels_in;
                 double r_out = r_base * num_labels_out;
                 double r = r_in + r_out;
-                double lscaling_base = lgamma(r);
+                double lscaling_base = lgamma(r) / log(2);
 
                 return compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row);
             };

From d2bf95beffa1f2b49c605a1258b8a09be5bb7524 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Sat, 14 Sep 2024 15:11:57 +0200
Subject: [PATCH 337/410] added test for no counts

---
 .../src/graph/annotated_graph_algorithm.cpp   | 117 ++++++++++++++++--
 1 file changed, 110 insertions(+), 7 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 193abe336a..bed9fd3fe9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -157,7 +157,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         if (k == 0)
-            common::logger->warn("No significant p-values achievable");
+            common::logger->warn("No significant p-values achievable after correction");
 
         common::logger->trace("Kept {} / {}", acc, total);
         return std::make_pair(k, n_cutoff);
@@ -214,8 +214,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             in_kmers += sums[j];
         }
 
-        common::logger->trace("{}: sum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
-                              j, sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
+        common::logger->trace("{}: n_unique: {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
+                              j, n_kmers[j], sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
     }
 
     int64_t total_kmers = in_kmers + out_kmers;
@@ -940,9 +940,112 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row);
             };
         }
+    } else if (config.test_type == "poisson_binom") {
+        // fit distribution
+        std::vector<double> p;
+        p.reserve(groups.size());
+        for (size_t i = 0; i < groups.size(); ++i) {
+            p.emplace_back(static_cast<double>(n_kmers[i]) / nelem);
+        }
+
+        common::logger->trace("Precomputing PMFs");
+        std::vector<double> pmf_in { 1.0 };
+        std::vector<double> pmf_out { 1.0 };
+        std::vector<double> pmf_null { 1.0 };
+
+        for (size_t i = 1; i <= groups.size(); ++i) {
+            if (groups[i]) {
+                std::vector<double> pmf_out_cur(pmf_out.size() + 1);
+                pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
+                pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
+                for (size_t k = 1; k < pmf_out.size(); ++k) {
+                    pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1] + (1.0 - p[i - 1]) * pmf_out[k];
+                }
+                std::swap(pmf_out_cur, pmf_out);
+            } else {
+                std::vector<double> pmf_in_cur(pmf_in.size() + 1);
+                pmf_in_cur[0] = (1.0 - p[i - 1]) * pmf_in[0];
+                pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
+                for (size_t k = 1; k < pmf_in.size(); ++k) {
+                    pmf_in_cur[k] = p[i - 1] * pmf_in[k - 1] + (1.0 - p[i - 1]) * pmf_in[k];
+                }
+                std::swap(pmf_in_cur, pmf_in);
+            }
+            std::vector<double> pmf_null_cur(i + 1);
+            pmf_null_cur[0] = (1.0 - p[i - 1]) * pmf_null[0];
+            pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
+            for (size_t k = 1; k < pmf_null.size(); ++k) {
+                pmf_null_cur[k] = p[i - 1] * pmf_null[k - 1] + (1.0 - p[i - 1]) * pmf_null[k];
+            }
+            std::swap(pmf_null_cur, pmf_null);
+        }
+
+        common::logger->trace("Precomputing p-values");
+        std::vector<std::vector<double>> pvals(groups.size() + 1);
+        pvals[0].emplace_back(1.0);
+
+        double min_pval = 1.0;
+
+        for (size_t n = 1; n < pvals.size(); ++n) {
+            std::vector<double> probs;
+            for (uint64_t s = 0; s < pmf_in.size(); ++s) {
+                if (s > n)
+                    break;
+                uint64_t t = n - s;
+                if (t < pmf_out.size()) {
+                    probs.emplace_back(pmf_in[s] * pmf_out[t] / pmf_null[n]);
+                } else {
+                    probs.emplace_back(0.0);
+                }
+            }
+
+            for (uint64_t s = 0; s < probs.size(); ++s) {
+                pvals[n].emplace_back(0.0);
+                bool found = false;
+                for (uint64_t sp = 0; sp < probs.size(); ++sp) {
+                    if (pmf_in[sp] <= pmf_in[s]) {
+                        found = true;
+                        pvals[n][s] += probs[sp];
+                    }
+                }
+
+                if (found)
+                    min_pval = std::min(min_pval, pvals[n][s]);
+            }
+        }
+
+        common::logger->trace("Min. p-value: {}", min_pval);
+        if (min_pval >= config.family_wise_error_rate) {
+            common::logger->warn("No significant p-values achievable");
+            auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
+                graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
+                is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+            );
+
+            auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
+                graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_out)), true,
+                is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+            );
+
+            return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage{}, nullptr);
+        }
+
+        compute_min_pval = [pvals,num_labels_in](int64_t, const PairContainer &row) {
+            size_t front = row.size() - std::min(row.size(), num_labels_in);
+            return std::min(pvals[row.size()][front],
+                            pvals[row.size()].back());
+        };
+
+        compute_pval = [pvals,&groups](int64_t, int64_t, const auto &row) {
+            uint64_t s = std::count_if(row.begin(), row.end(),
+                                       [&](const auto &a) { return !groups[a.first]; });
+
+            return pvals[row.size()][s];
+        };
+
     } else if (config.test_type == "notest") {
-        compute_min_pval = [&](int64_t n, const PairContainer&) { return n > 0 ? 0.0 : 1.1; };
-        compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.1; };
+        compute_min_pval = [&](int64_t n, const PairContainer&) { return n > 0 ? 0.0 : 1.0; };
+        compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.0; };
     } else {
         throw std::runtime_error("Test not implemented");
     }
@@ -992,7 +1095,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return;
             }
 
-            size_t n = in_sum + out_sum;
+            size_t n = config.test_type != "poisson_binom" ? in_sum + out_sum : row.size();
             if (!in_kmer && !out_kmer) {
                 row.clear();
                 vals.clear();
@@ -1245,7 +1348,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return;
             }
 
-            size_t n = in_sum + out_sum;
+            size_t n = config.test_type != "poisson_binom" ? in_sum + out_sum : row.size();
             if (!in_kmer && !out_kmer) {
                 row.clear();
                 n = 0;

From afec3d04be78bc475e2a37513a36a73929a19b2f Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 24 Sep 2024 11:12:09 +0200
Subject: [PATCH 338/410] minor

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index bed9fd3fe9..814ce3aeb9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -993,7 +993,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     break;
                 uint64_t t = n - s;
                 if (t < pmf_out.size()) {
-                    probs.emplace_back(pmf_in[s] * pmf_out[t] / pmf_null[n]);
+                    probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t]) - log2(pmf_null[n])));
                 } else {
                     probs.emplace_back(0.0);
                 }

From 111d8953a6445ba7fc4226de2aa3c9e231448079 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 25 Sep 2024 11:28:45 +0200
Subject: [PATCH 339/410] fix histogram calculation for column-based annotation

---
 metagraph/src/common/vectors/transpose.hpp    | 116 +++++++++++++-----
 .../src/graph/annotated_graph_algorithm.cpp   |  71 +++++------
 2 files changed, 120 insertions(+), 67 deletions(-)

diff --git a/metagraph/src/common/vectors/transpose.hpp b/metagraph/src/common/vectors/transpose.hpp
index 819956f6f4..45b48dcdfe 100644
--- a/metagraph/src/common/vectors/transpose.hpp
+++ b/metagraph/src/common/vectors/transpose.hpp
@@ -77,45 +77,46 @@ void call_rows(const std::vector<BitmapPtr> &columns,
         return;
     }
 
+    using Container = std::remove_const_t<typename ValuesPtr::element_type>;
+
     if (column_values.empty())
         mtg::common::logger->warn("No column values provided, using default value of 1");
 
     num_rows = columns[0]->size();
-    std::vector<PairVector> rows;
 
     ProgressBar progress_bar(num_rows, "Constructed rows", std::cerr, !show_progress);
 
-    #pragma omp parallel for ordered num_threads(get_num_threads()) schedule(dynamic) private(rows)
-    for (uint64_t i = 0; i < num_rows; i += kNumRowsInBlock) {
-        uint64_t begin = i;
-        uint64_t end = std::min(i + kNumRowsInBlock, num_rows);
-        assert(begin <= end);
+    if constexpr(ordered) {
+        std::vector<PairVector> rows;
+        #pragma omp parallel for ordered num_threads(get_num_threads()) schedule(dynamic) private(rows)
+        for (uint64_t i = 0; i < num_rows; i += kNumRowsInBlock) {
+            uint64_t begin = i;
+            uint64_t end = std::min(i + kNumRowsInBlock, num_rows);
+            assert(begin <= end);
 
-        rows.resize(end - begin);
+            rows.resize(end - begin);
 
-        for (size_t j = 0; j < columns.size(); ++j) {
-            if (column_values.empty()) {
-                columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
-                    rows[i - begin].emplace_back(j, 1);
-                });
-            } else {
-                using Container = std::remove_const_t<typename ValuesPtr::element_type>;
-                const Container &col = *column_values[j];
-
-                if constexpr(std::is_same_v<Container, sdsl::int_vector_buffer<>>) {
-                    sdsl::int_vector_buffer<> cur_col(col.filename(), std::ios::in, col.buffersize());
+            for (size_t j = 0; j < columns.size(); ++j) {
+                if (column_values.empty()) {
                     columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
-                        rows[i - begin].emplace_back(j, cur_col[columns[j]->rank1(i) - 1]);
+                        rows[i - begin].emplace_back(j, 1);
                     });
                 } else {
-                    columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
-                        rows[i - begin].emplace_back(j, col[columns[j]->rank1(i) - 1]);
-                    });
+                    const Container &col = *column_values[j];
+
+                    if constexpr(std::is_same_v<Container, sdsl::int_vector_buffer<>>) {
+                        sdsl::int_vector_buffer<> cur_col(col.filename(), std::ios::in, col.buffersize());
+                        columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
+                            rows[i - begin].emplace_back(j, cur_col[columns[j]->rank1(i) - 1]);
+                        });
+                    } else {
+                        columns[j]->call_ones_in_range(begin, end, [&](uint64_t i) {
+                            rows[i - begin].emplace_back(j, col[columns[j]->rank1(i) - 1]);
+                        });
+                    }
                 }
             }
-        }
 
-        if constexpr(ordered) {
             #pragma omp ordered
             {
                 for (uint64_t j = 0; j < rows.size(); ++j) {
@@ -124,14 +125,71 @@ void call_rows(const std::vector<BitmapPtr> &columns,
                     rows[j].resize(0);
                 }
             }
-        } else {
-            for (uint64_t j = 0; j < rows.size(); ++j) {
-                callback(j + begin, rows[j], begin / kNumRowsInBlock);
-                ++progress_bar;
-                rows[j].resize(0);
+        }
+    } else {
+        size_t block_size = (num_rows + get_num_threads() - 1) / get_num_threads();
+        size_t rows_per_update = 10000;
+        #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
+        for (uint64_t i = 0; i < num_rows; i += block_size) {
+            uint64_t begin = i;
+            uint64_t end = std::min(i + block_size, num_rows);
+            assert(begin <= end);
+
+            std::vector<std::shared_ptr<const Container>> cur_cols;
+            cur_cols.reserve(column_values.size());
+            for (const auto &col : column_values) {
+                if constexpr(std::is_same_v<Container, sdsl::int_vector_buffer<>>) {
+                    cur_cols.emplace_back(std::make_shared<const Container>(col->filename(),
+                                                                            std::ios::in,
+                                                                            col->buffersize()));
+                } else {
+                    cur_cols.emplace_back(std::shared_ptr<const Container>{}, col.get());
+                }
+            }
+
+            std::vector<size_t> ranks(columns.size());
+            std::vector<uint64_t> next_set_bit(columns.size());
+            if (begin) {
+                for (size_t j = 0; j < columns.size(); ++j) {
+                    ranks[j] = columns[j]->rank1(begin - 1);
+                }
+            }
+
+            for (size_t j = 0; j < columns.size(); ++j) {
+                if (ranks[j] < columns[j]->num_set_bits()) {
+                    next_set_bit[j] = columns[j]->select1(++ranks[j]);
+                } else {
+                    next_set_bit[j] = end;
+                }
+            }
+
+            PairVector row;
+            row.reserve(columns.size());
+            for (size_t k = begin; k < end; ++k) {
+                for (size_t j = 0; j < columns.size(); ++j) {
+                    if (next_set_bit[j] == k) {
+                        uint64_t val = column_values.size()
+                            ? (*cur_cols[j])[ranks[j] - 1]
+                            : 1;
+
+                        row.emplace_back(j, val);
+                        if (ranks[j] < columns[j]->num_set_bits()) {
+                            next_set_bit[j] = columns[j]->select1(++ranks[j]);
+                        } else {
+                            next_set_bit[j] = end;
+                        }
+                    }
+                }
+
+                callback(k, row, i / block_size);
+                if (k && (k % rows_per_update) == 0)
+                    progress_bar += rows_per_update;
+
+                row.clear();
             }
         }
 
+        progress_bar += num_rows % rows_per_update;
     }
 }
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 814ce3aeb9..60746b8f6e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -72,7 +72,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          size_t num_parallel_files = std::numeric_limits<size_t>::max(),
                          const std::function<void()> &deallocate = []() {},
                          uint8_t max_width = 64) {
-    num_parallel_files = std::min(num_parallel_files, num_threads);
+    // num_parallel_files = std::min(num_parallel_files, num_threads);
+    num_parallel_files = get_num_threads();
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
     bool parallel = num_parallel_files > 1;
 
@@ -755,7 +756,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                     std::sort(vec.begin(), vec.end());
                     ++vector_counts_ms[bucket_idx][n][vec];
-                }, false);
+                });
 
                 vector_counts = std::move(vector_counts_ms[0]);
                 size_t max_size = vector_counts.size();
@@ -1147,7 +1148,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-        }, false);
+        });
 
         common::logger->trace("Merging min p-value tables");
         if (config.test_type != "gnb_exact") {
@@ -1398,7 +1399,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
                 }
             }
-        }, false);
+        });
 
         std::atomic_thread_fence(std::memory_order_acquire);
 
@@ -1530,6 +1531,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          size_t num_threads,
                          std::filesystem::path tmp_dir,
                          size_t num_parallel_files) {
+    num_parallel_files = get_num_threads();
     common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
     common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
 
@@ -1570,22 +1572,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             max_width = std::max(max_width, col_vals->width());
         }
 
-        auto generate_rows = [&](const auto &callback, bool ordered) {
-            if (ordered) {
-                utils::call_rows<std::unique_ptr<const bit_vector>,
-                                 std::unique_ptr<const ValuesContainer>,
-                                 PairContainer, true>(columns_all, column_values_all,
-                                                      [&](uint64_t row_i, const auto &row, size_t thread_id) {
-                    callback(row_i, row, thread_id);
-                });
-            } else {
-                utils::call_rows<std::unique_ptr<const bit_vector>,
-                                 std::unique_ptr<const ValuesContainer>,
-                                 PairContainer, false>(columns_all, column_values_all,
-                                                       [&](uint64_t row_i, const auto &row, size_t thread_id) {
-                    callback(row_i, row, thread_id);
-                });
-            }
+        auto generate_rows = [&](const auto &callback) {
+            utils::call_rows<std::unique_ptr<const bit_vector>,
+                             std::unique_ptr<const ValuesContainer>,
+                             PairContainer, false>(columns_all, column_values_all, callback);
         };
 
         bool parallel = get_num_threads() > 1;
@@ -1594,11 +1584,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             graph_ptr,
             [&](const std::vector<size_t> &min_counts, sdsl::bit_vector *kept) -> std::vector<VectorMap<uint64_t, size_t>> {
                 common::logger->trace("Calculating count histograms");
-                std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
-                utils::call_rows<std::unique_ptr<const bit_vector>,
-                                 std::unique_ptr<const ValuesContainer>,
-                                 PairContainer, false>(columns_all, column_values_all,
-                                                       [&](uint64_t row_i, const auto &row, size_t) {
+                std::vector<std::vector<VectorMap<uint64_t, size_t>>> hists_map_p(num_parallel_files);
+                for (auto &hists_map : hists_map_p) {
+                    hists_map.resize(groups.size());
+                }
+
+                generate_rows([&](uint64_t row_i, const auto &row, size_t thread_id) {
                     if (row.empty()) {
                         if (kept)
                             unset_bit(kept->data(), row_i, parallel, std::memory_order_relaxed);
@@ -1609,10 +1600,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     bool found = false;
                     Vector<uint64_t> counts(groups.size());
                     for (const auto &[j, raw_c] : row) {
-                        if (min_counts.empty() || raw_c >= min_counts[j]) {
-                            counts[j] = raw_c;
-                            found = true;
-                        }
+                        counts[j] = raw_c;
+                        found |= min_counts.empty() || raw_c >= min_counts[j];
                     }
 
                     if (!found) {
@@ -1622,15 +1611,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         return;
                     }
 
-                    #pragma omp critical
-                    {
-                        for (size_t j = 0; j < counts.size(); ++j) {
-                            ++hists_map[j][counts[j]];
-                        }
+                    for (size_t j = 0; j < counts.size(); ++j) {
+                        ++hists_map_p[thread_id][j][counts[j]];
                     }
                 });
 
-                return hists_map;
+                common::logger->trace("Merging histograms");
+                for (size_t thread_id = 1; thread_id < hists_map_p.size(); ++thread_id) {
+                    for (size_t j = 0; j < hists_map_p[thread_id].size(); ++j) {
+                        for (const auto &[k, c] : hists_map_p[thread_id][j]) {
+                            hists_map_p[0][j][k] += c;
+                        }
+                    }
+                }
+
+                return hists_map_p[0];
             },
             generate_rows,
             groups,
@@ -1695,14 +1690,14 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
             [&](const std::vector<size_t> &min_counts, sdsl::bit_vector *unmark_discarded) {
                 return int_matrix->get_histograms(min_counts, unmark_discarded);
             },
-            [&](const auto &callback, bool ordered) {
-                int_matrix->call_row_values(callback, ordered);
+            [&](const auto &callback) {
+                int_matrix->call_row_values(callback, false /* ordered */);
             },
             groups,
             config, num_threads, tmp_dir, num_threads
         );
     } else {
-        auto generate_rows = [&](const auto &callback, bool) {
+        auto generate_rows = [&](const auto &callback) {
             size_t bucket_size = matrix.num_rows() / num_threads;
             ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
             #pragma omp parallel for num_threads(num_threads) ordered

From 33116d5151da86cb0c714cf98f5b92ebce9d5261 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 25 Sep 2024 12:16:41 +0200
Subject: [PATCH 340/410] batch access binary annotations

---
 .../src/graph/annotated_graph_algorithm.cpp   | 85 ++++++++++++-------
 1 file changed, 53 insertions(+), 32 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 60746b8f6e..7c9d498b75 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1668,6 +1668,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads,
                          std::filesystem::path tmp_dir) {
+    num_threads = get_num_threads();
     common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
     common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
 
@@ -1697,59 +1698,79 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
             config, num_threads, tmp_dir, num_threads
         );
     } else {
-        auto generate_rows = [&](const auto &callback) {
-            size_t bucket_size = matrix.num_rows() / num_threads;
+        auto generate_bit_rows = [&](const auto &callback) {
+            size_t batch_size = (matrix.num_rows() + num_threads - 1) / num_threads;
+            size_t rows_per_update = 10000;
             ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
-            #pragma omp parallel for num_threads(num_threads) ordered
-            for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
-                ++progress_bar;
-                std::vector<Row> row_ids(1, row_i);
-                auto set_bits = matrix.get_rows(row_ids);
-                Vector<std::pair<uint64_t, uint64_t>> container;
-                container.reserve(set_bits[0].size());
-                for (auto j : set_bits[0]) {
-                    container.emplace_back(j, 1);
+            #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+            for (size_t k = 0; k < matrix.num_rows(); k += batch_size) {
+                size_t begin = k;
+                size_t end = std::min(begin + batch_size, matrix.num_rows());
+                size_t thread_id = k / batch_size;
+                std::vector<Row> row_ids;
+                for (size_t row_batch_i = begin; row_batch_i < end; row_batch_i += rows_per_update) {
+                    size_t row_batch_end = std::min(row_batch_i + rows_per_update, end);
+                    row_ids.resize(row_batch_end - row_batch_i);
+                    std::iota(row_ids.begin(), row_ids.end(), row_batch_i);
+                    auto set_bits = matrix.get_rows(row_ids);
+                    for (size_t i = 0; i < row_ids.size(); ++i) {
+                        callback(i + row_batch_i, set_bits[i], thread_id);
+                    }
+                    progress_bar += row_ids.size();
                 }
-
-                #pragma omp ordered
-                callback(row_i, container, row_i / bucket_size);
             }
         };
 
+        auto generate_rows = [&](const auto &callback) {
+            generate_bit_rows([&](uint64_t row_i, const auto &set_bits, size_t thread_id) {
+                Vector<std::pair<uint64_t, uint64_t>> row;
+                row.reserve(set_bits.size());
+                for (auto j : set_bits) {
+                    row.emplace_back(j, 1);
+                }
+
+                callback(row_i, row, thread_id);
+            });
+        };
+
         bool parallel = get_num_threads() > 1;
 
         return mask_nodes_by_label_dual<uint64_t, PValStorage>(
             std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
             [&](const std::vector<size_t> &, sdsl::bit_vector *kept) -> std::vector<VectorMap<uint64_t, size_t>> {
                 common::logger->trace("Calculating count histograms");
-                std::vector<VectorMap<uint64_t, size_t>> hists_map(groups.size());
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    hists_map[j][0] = 0;
-                    hists_map[j][1] = 0;
+                std::vector<std::vector<VectorMap<uint64_t, size_t>>> hists_map_p(num_threads);
+                for (auto &hists_map : hists_map_p) {
+                    hists_map.resize(groups.size());
                 }
-                ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
-                #pragma omp parallel for num_threads(num_threads)
-                for (size_t row_i = 0; row_i < matrix.num_rows(); ++row_i) {
-                    ++progress_bar;
-                    std::vector<Row> row_ids(1, row_i);
-                    auto set_bits = matrix.get_rows(row_ids);
+                generate_bit_rows([&](uint64_t row_i, const auto &set_bits, size_t thread_id) {
+                    if (set_bits.empty()) {
+                        if (kept)
+                            unset_bit(kept->data(), row_i, parallel, std::memory_order_relaxed);
+
+                        return;
+                    }
+
                     std::vector<bool> container(groups.size());
-                    for (auto j : set_bits[0]) {
+                    for (auto j : set_bits) {
                         container[j] = true;
                     }
 
-                    if (kept && set_bits.empty())
-                        unset_bit(kept->data(), row_i, parallel, std::memory_order_relaxed);
-
-                    #pragma omp critical
-                    {
                     for (size_t j = 0; j < container.size(); ++j) {
-                        ++hists_map[j][container[j]];
+                        ++hists_map_p[thread_id][j][container[j]];
                     }
+                });
+
+                common::logger->trace("Merging histograms");
+                for (size_t thread_id = 1; thread_id < hists_map_p.size(); ++thread_id) {
+                    for (size_t j = 0; j < hists_map_p[thread_id].size(); ++j) {
+                        for (const auto &[k, c] : hists_map_p[thread_id][j]) {
+                            hists_map_p[0][j][k] += c;
+                        }
                     }
                 }
 
-                return hists_map;
+                return hists_map_p[0];
             },
             generate_rows,
             groups,

From f2aa1f6664aecdbde44dfa604ad1cf06b31b228f Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 25 Sep 2024 12:19:05 +0200
Subject: [PATCH 341/410] minor

---
 .../src/graph/annotated_graph_algorithm.cpp   | 24 +++++++++----------
 1 file changed, 11 insertions(+), 13 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 7c9d498b75..7c0588d325 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1721,18 +1721,6 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
             }
         };
 
-        auto generate_rows = [&](const auto &callback) {
-            generate_bit_rows([&](uint64_t row_i, const auto &set_bits, size_t thread_id) {
-                Vector<std::pair<uint64_t, uint64_t>> row;
-                row.reserve(set_bits.size());
-                for (auto j : set_bits) {
-                    row.emplace_back(j, 1);
-                }
-
-                callback(row_i, row, thread_id);
-            });
-        };
-
         bool parallel = get_num_threads() > 1;
 
         return mask_nodes_by_label_dual<uint64_t, PValStorage>(
@@ -1772,7 +1760,17 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
 
                 return hists_map_p[0];
             },
-            generate_rows,
+            [&](const auto &callback) {
+                generate_bit_rows([&](uint64_t row_i, const auto &set_bits, size_t thread_id) {
+                    Vector<std::pair<uint64_t, uint64_t>> row;
+                    row.reserve(set_bits.size());
+                    for (auto j : set_bits) {
+                        row.emplace_back(j, 1);
+                    }
+
+                    callback(row_i, row, thread_id);
+                });
+            },
             groups,
             config, num_threads, tmp_dir, num_threads
         );

From bfdd6a7f85fe805aee26831def6210913cbf22a8 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 25 Sep 2024 14:40:57 +0200
Subject: [PATCH 342/410] fix segfault

---
 .../annotation/int_matrix/base/int_matrix.cpp | 31 ++++++++++++-------
 .../src/graph/annotated_graph_algorithm.cpp   |  7 ++++-
 2 files changed, 26 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
index e3434da6bb..78fbbc01cf 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
@@ -17,18 +17,27 @@ using Column = BinaryMatrix::Column;
 void IntMatrix::call_row_values(const std::function<void(uint64_t, const RowValues&, size_t)> &callback,
                                 bool ordered) const {
     size_t n = get_binary_matrix().num_rows();
+    size_t batch_size = (n + get_num_threads() - 1) / get_num_threads();
+    size_t rows_per_update = 10000;
     ProgressBar progress_bar(n, "Streaming rows", std::cerr, !common::get_verbose());
-    #pragma omp parallel for num_threads(get_num_threads()) ordered
-    for (size_t row_i = 0; row_i < n; ++row_i) {
-        ++progress_bar;
-        std::vector<Row> row_ids { row_i };
-        auto row_vals = get_row_values(row_ids);
-
-        if (ordered) {
-            #pragma omp ordered
-            callback(row_i, row_vals[0], row_i);
-        } else {
-            callback(row_i, row_vals[0], row_i);
+
+    std::ignore = ordered;
+
+    #pragma omp parallel for num_threads(get_num_threads()) schedule(dynamic)
+    for (size_t k = 0; k < n; k += batch_size) {
+        size_t begin = k;
+        size_t end = std::min(begin + batch_size, n);
+        size_t thread_id = k / batch_size;
+        std::vector<Row> row_ids;
+        for (size_t row_batch_i = begin; row_batch_i < end; row_batch_i += rows_per_update) {
+            size_t row_batch_end = std::min(row_batch_i + rows_per_update, end);
+            row_ids.resize(row_batch_end - row_batch_i);
+            std::iota(row_ids.begin(), row_ids.end(), row_batch_i);
+            auto row_vals = get_row_values(row_ids);
+            for (size_t i = 0; i < row_ids.size(); ++i) {
+                callback(i + row_batch_i, row_vals[i], thread_id);
+            }
+            progress_bar += row_ids.size();
         }
     }
 }
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 7c0588d325..43b8dd0fa6 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -74,6 +74,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          uint8_t max_width = 64) {
     // num_parallel_files = std::min(num_parallel_files, num_threads);
     num_parallel_files = get_num_threads();
+    num_threads = get_num_threads();
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
     bool parallel = num_parallel_files > 1;
 
@@ -1105,6 +1106,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             double pval_min = 0;
             if (config.test_type != "gnb_exact") {
+                assert(bucket_idx < ms.size());
                 if (n >= ms[bucket_idx].size())
                     ms[bucket_idx].resize(n + 1, std::make_pair(1.1, 0));
 
@@ -1147,7 +1149,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         pvals_min_buckets[bucket_idx].push_back(bit_cast<uint64_t>(pval_min));
                 }
             }
-
         });
 
         common::logger->trace("Merging min p-value tables");
@@ -1589,6 +1590,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     hists_map.resize(groups.size());
                 }
 
+                std::atomic_thread_fence(std::memory_order_release);
                 generate_rows([&](uint64_t row_i, const auto &row, size_t thread_id) {
                     if (row.empty()) {
                         if (kept)
@@ -1615,6 +1617,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         ++hists_map_p[thread_id][j][counts[j]];
                     }
                 });
+                std::atomic_thread_fence(std::memory_order_acquire);
 
                 common::logger->trace("Merging histograms");
                 for (size_t thread_id = 1; thread_id < hists_map_p.size(); ++thread_id) {
@@ -1731,6 +1734,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
                 for (auto &hists_map : hists_map_p) {
                     hists_map.resize(groups.size());
                 }
+                std::atomic_thread_fence(std::memory_order_release);
                 generate_bit_rows([&](uint64_t row_i, const auto &set_bits, size_t thread_id) {
                     if (set_bits.empty()) {
                         if (kept)
@@ -1748,6 +1752,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
                         ++hists_map_p[thread_id][j][container[j]];
                     }
                 });
+                std::atomic_thread_fence(std::memory_order_acquire);
 
                 common::logger->trace("Merging histograms");
                 for (size_t thread_id = 1; thread_id < hists_map_p.size(); ++thread_id) {

From 2d50666c32b9247541d6d534b872f5c530c8bca1 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 25 Sep 2024 16:02:46 +0200
Subject: [PATCH 343/410] fix off-by-one in poisson_binom test

---
 .../src/graph/annotated_graph_algorithm.cpp   |   8 +-
 .../test_annotated_graph_algorithm.cpp        | 161 ++++++++++++++++--
 2 files changed, 151 insertions(+), 18 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 43b8dd0fa6..8a9721cdc7 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -950,13 +950,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             p.emplace_back(static_cast<double>(n_kmers[i]) / nelem);
         }
 
+        common::logger->trace("p: {}", fmt::join(p, ","));
+
         common::logger->trace("Precomputing PMFs");
         std::vector<double> pmf_in { 1.0 };
         std::vector<double> pmf_out { 1.0 };
         std::vector<double> pmf_null { 1.0 };
 
         for (size_t i = 1; i <= groups.size(); ++i) {
-            if (groups[i]) {
+            if (groups[i - 1]) {
                 std::vector<double> pmf_out_cur(pmf_out.size() + 1);
                 pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
                 pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
@@ -1561,7 +1563,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         annot::ColumnCompressed<>::load_columns_and_values(
             files,
             [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector>&& column, ValuesContainer&& column_values) {
-                groups[offset] = labels_out.count(label);
+                groups[offset] = !labels_in.count(label);
                 columns_all[offset].reset(column.release());
                 column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
             },
@@ -1681,7 +1683,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
     std::vector<bool> groups;
     groups.reserve(total_labels);
     for (const auto &label : annotation.get_label_encoder().get_labels()) {
-        groups.emplace_back(labels_out.count(label));
+        groups.emplace_back(!labels_in.count(label));
     }
 
     const auto &matrix = annotation.get_matrix();
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index a1d50369a3..e9a2a5ef96 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -53,8 +53,6 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
             auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels);
             auto graph_ptr = std::static_pointer_cast<const Graph>(anno_graph->get_graph_ptr());
 
-            const auto &annotation = static_cast<const Annotation&>(anno_graph->get_annotator());
-
             std::unordered_set<std::string> obs_labels, obs_kmers;
             const std::unordered_set<std::string> ref_kmers {
                 std::string(k - 1, 'A') + "C"
@@ -72,21 +70,52 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
 
             // ingroup: B, C
             // outgroup: A
-            std::vector<bool> groups = { true, false, false };
             tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_in { "B", "C" };
             tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_out { "A" };
 
-            if constexpr(std::is_same_v<Annotation, ColumnCompressed<>>) {
-                const auto &in_columns = annotation.get_matrix().data();
-                std::vector<std::unique_ptr<const bit_vector>> columns;
-                columns.reserve(in_columns.size());
-                std::transform(in_columns.begin(), in_columns.end(), std::back_inserter(columns),
-                               [&](const auto &a) {
-                                  return std::make_unique<const bit_vector_stat>(a->to_vector());
-                               });
+            {
+                auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
+                    *anno_graph,
+                    labels_in, labels_out,
+                    config, num_threads, test_dump_basename
+                );
 
+                masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                    auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                    obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                    obs_kmers.insert(kmer);
+                });
 
-                columns.resize(groups.size());
+                EXPECT_EQ(ref_labels, obs_labels) << k;
+                EXPECT_EQ(ref_kmers, obs_kmers) << k;
+            }
+
+            if constexpr(std::is_same_v<Annotation, ColumnCompressed<>>) {
+                // std::vector<bool> groups = { true, false, false };
+                // const auto &annotation = static_cast<const Annotation&>(anno_graph->get_annotator());
+                // const auto &in_columns = annotation.get_matrix().data();
+                // std::vector<std::unique_ptr<const bit_vector>> columns;
+                // columns.reserve(in_columns.size());
+                // std::transform(in_columns.begin(), in_columns.end(), std::back_inserter(columns),
+                //                [&](const auto &a) {
+                //                   return std::make_unique<const bit_vector_stat>(a->to_vector());
+                //                });
+
+
+                // columns.resize(groups.size());
+
+                // // (std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                // //          const std::vector<std::string> &files,
+                // //          const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
+                // //          const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
+                // //          const DifferentialAssemblyConfig &config,
+                // //          size_t num_threads = 1,
+                // //          std::filesystem::path = "",
+                // //          size_t num_parallel_files = std::numeric_li
+                // // auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
+                // //     graph_ptr,
+
+                // // );
 
                 // using PairContainer = Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
                 // std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
@@ -113,7 +142,50 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
                 //     obs_labels.insert(cur_labels.begin(), cur_labels.end());
                 //     obs_kmers.insert(kmer);
                 // });
-            } else {
+                // EXPECT_EQ(ref_labels, obs_labels) << k;
+                // EXPECT_EQ(ref_kmers, obs_kmers) << k;
+            }
+        }
+    }
+}
+
+TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelTest) {
+    typedef typename TypeParam::first_type Graph;
+    typedef typename TypeParam::second_type Annotation;
+    for (size_t num_threads : { 1, 4 }) {
+        size_t max_k = std::min(max_test_k<Graph>(), (size_t)15);
+        for (size_t k = max_k-1; k < max_k; ++k) {
+            const std::vector<std::string> sequences {
+                std::string("T") + std::string(k - 1, 'A') + std::string(100, 'T'),
+                std::string("T") + std::string(k - 1, 'A') + "C",
+                std::string("T") + std::string(k - 1, 'A') + "C",
+                std::string("T") + std::string(k - 1, 'A') + "A",
+                std::string("T") + std::string(k - 1, 'A') + "G"
+            };
+            const std::vector<std::string> labels { "A", "B", "C", "D", "E" };
+
+            auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels);
+            auto graph_ptr = std::static_pointer_cast<const Graph>(anno_graph->get_graph_ptr());
+
+            std::unordered_set<std::string> obs_labels, obs_kmers;
+            const std::unordered_set<std::string> ref_kmers {
+                std::string(k - 1, 'A') + "C"
+            };
+            const std::unordered_set<std::string> ref_labels {
+                "B", "C"
+            };
+
+            DifferentialAssemblyConfig config {
+                .test_type = "poisson_binom",
+                .outfbase = "",
+            };
+
+            // ingroup: B, C
+            // outgroup: A
+            tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_in { "B", "C" };
+            tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_out { "A" };
+
+            {
                 auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
                     *anno_graph,
                     labels_in, labels_out,
@@ -125,10 +197,69 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
                     obs_labels.insert(cur_labels.begin(), cur_labels.end());
                     obs_kmers.insert(kmer);
                 });
+
+                EXPECT_EQ(ref_labels, obs_labels) << k;
+                EXPECT_EQ(ref_kmers, obs_kmers) << k;
             }
 
-            EXPECT_EQ(ref_labels, obs_labels) << k;
-            EXPECT_EQ(ref_kmers, obs_kmers) << k;
+            if constexpr(std::is_same_v<Annotation, ColumnCompressed<>>) {
+                // const auto &annotation = static_cast<const Annotation&>(anno_graph->get_annotator());
+                // std::vector<bool> groups(labels.size());
+                // for (const auto &label : labels) {
+                //     groups[annotation.get_label_encoder().encode(label)] = !labels_in.count(label);
+                // }
+                // const auto &in_columns = annotation.get_matrix().data();
+                // std::vector<std::unique_ptr<const bit_vector>> columns;
+                // columns.reserve(in_columns.size());
+                // std::transform(in_columns.begin(), in_columns.end(), std::back_inserter(columns),
+                //                [&](const auto &a) {
+                //                   return std::make_unique<const bit_vector_stat>(a->to_vector());
+                //                });
+
+
+                // columns.resize(groups.size());
+
+                // // (std::shared_ptr<const DeBruijnGraph> graph_ptr,
+                // //          const std::vector<std::string> &files,
+                // //          const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
+                // //          const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
+                // //          const DifferentialAssemblyConfig &config,
+                // //          size_t num_threads = 1,
+                // //          std::filesystem::path = "",
+                // //          size_t num_parallel_files = std::numeric_li
+                // // auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
+                // //     graph_ptr,
+
+                // // );
+
+                // using PairContainer = Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
+                // std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
+                // auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
+                //     graph_ptr,
+                //     [&](const auto &callback, bool) {
+                //         utils::call_rows<std::unique_ptr<const bit_vector>,
+                //                         std::unique_ptr<const sdsl::int_vector<>>,
+                //                         PairContainer, true>(columns, column_values,
+                //                                             [&](uint64_t row_i, const auto &row, size_t) {
+                //             callback(row_i, row);
+                //         });
+                //     },
+                //     groups,
+                //     config, num_threads, test_dump_basename, num_threads,
+                //     [&]() {
+                //         columns.clear();
+                //         column_values.clear();
+                //     }
+                // );
+
+                // masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                //     auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                //     obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                //     obs_kmers.insert(kmer);
+                // });
+                // EXPECT_EQ(ref_labels, obs_labels) << k;
+                // EXPECT_EQ(ref_kmers, obs_kmers) << k;
+            }
         }
     }
 }

From 31c8916dcfa6e9aa98dd6ff8b98da3a2e0122b42 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 25 Sep 2024 16:06:37 +0200
Subject: [PATCH 344/410] extend

---
 metagraph/tests/annotation/test_annotated_graph_algorithm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index e9a2a5ef96..81e0a07468 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -154,7 +154,7 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelTest) {
     typedef typename TypeParam::second_type Annotation;
     for (size_t num_threads : { 1, 4 }) {
         size_t max_k = std::min(max_test_k<Graph>(), (size_t)15);
-        for (size_t k = max_k-1; k < max_k; ++k) {
+        for (size_t k = 6; k < max_k; ++k) {
             const std::vector<std::string> sequences {
                 std::string("T") + std::string(k - 1, 'A') + std::string(100, 'T'),
                 std::string("T") + std::string(k - 1, 'A') + "C",

From 70b1c2a74782af30fa0b4aa353a8d863ec329990 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 25 Sep 2024 16:15:46 +0200
Subject: [PATCH 345/410] cleanup

---
 .../test_annotated_graph_algorithm.cpp        | 222 ++----------------
 1 file changed, 25 insertions(+), 197 deletions(-)

diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index 81e0a07468..dde4f0bcd9 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -91,59 +91,7 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabel) {
             }
 
             if constexpr(std::is_same_v<Annotation, ColumnCompressed<>>) {
-                // std::vector<bool> groups = { true, false, false };
-                // const auto &annotation = static_cast<const Annotation&>(anno_graph->get_annotator());
-                // const auto &in_columns = annotation.get_matrix().data();
-                // std::vector<std::unique_ptr<const bit_vector>> columns;
-                // columns.reserve(in_columns.size());
-                // std::transform(in_columns.begin(), in_columns.end(), std::back_inserter(columns),
-                //                [&](const auto &a) {
-                //                   return std::make_unique<const bit_vector_stat>(a->to_vector());
-                //                });
-
-
-                // columns.resize(groups.size());
-
-                // // (std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                // //          const std::vector<std::string> &files,
-                // //          const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
-                // //          const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
-                // //          const DifferentialAssemblyConfig &config,
-                // //          size_t num_threads = 1,
-                // //          std::filesystem::path = "",
-                // //          size_t num_parallel_files = std::numeric_li
-                // // auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
-                // //     graph_ptr,
-
-                // // );
-
-                // using PairContainer = Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
-                // std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
-                // auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
-                //     graph_ptr,
-                //     [&](const auto &callback, bool) {
-                //         utils::call_rows<std::unique_ptr<const bit_vector>,
-                //                         std::unique_ptr<const sdsl::int_vector<>>,
-                //                         PairContainer, true>(columns, column_values,
-                //                                             [&](uint64_t row_i, const auto &row, size_t) {
-                //             callback(row_i, row);
-                //         });
-                //     },
-                //     groups,
-                //     config, num_threads, test_dump_basename, num_threads,
-                //     [&]() {
-                //         columns.clear();
-                //         column_values.clear();
-                //     }
-                // );
-
-                // masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
-                //     auto cur_labels = anno_graph->get_labels(kmer, 0.0);
-                //     obs_labels.insert(cur_labels.begin(), cur_labels.end());
-                //     obs_kmers.insert(kmer);
-                // });
-                // EXPECT_EQ(ref_labels, obs_labels) << k;
-                // EXPECT_EQ(ref_kmers, obs_kmers) << k;
+                // TODO
             }
         }
     }
@@ -203,62 +151,7 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelTest) {
             }
 
             if constexpr(std::is_same_v<Annotation, ColumnCompressed<>>) {
-                // const auto &annotation = static_cast<const Annotation&>(anno_graph->get_annotator());
-                // std::vector<bool> groups(labels.size());
-                // for (const auto &label : labels) {
-                //     groups[annotation.get_label_encoder().encode(label)] = !labels_in.count(label);
-                // }
-                // const auto &in_columns = annotation.get_matrix().data();
-                // std::vector<std::unique_ptr<const bit_vector>> columns;
-                // columns.reserve(in_columns.size());
-                // std::transform(in_columns.begin(), in_columns.end(), std::back_inserter(columns),
-                //                [&](const auto &a) {
-                //                   return std::make_unique<const bit_vector_stat>(a->to_vector());
-                //                });
-
-
-                // columns.resize(groups.size());
-
-                // // (std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                // //          const std::vector<std::string> &files,
-                // //          const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
-                // //          const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
-                // //          const DifferentialAssemblyConfig &config,
-                // //          size_t num_threads = 1,
-                // //          std::filesystem::path = "",
-                // //          size_t num_parallel_files = std::numeric_li
-                // // auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
-                // //     graph_ptr,
-
-                // // );
-
-                // using PairContainer = Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
-                // std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
-                // auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
-                //     graph_ptr,
-                //     [&](const auto &callback, bool) {
-                //         utils::call_rows<std::unique_ptr<const bit_vector>,
-                //                         std::unique_ptr<const sdsl::int_vector<>>,
-                //                         PairContainer, true>(columns, column_values,
-                //                                             [&](uint64_t row_i, const auto &row, size_t) {
-                //             callback(row_i, row);
-                //         });
-                //     },
-                //     groups,
-                //     config, num_threads, test_dump_basename, num_threads,
-                //     [&]() {
-                //         columns.clear();
-                //         column_values.clear();
-                //     }
-                // );
-
-                // masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
-                //     auto cur_labels = anno_graph->get_labels(kmer, 0.0);
-                //     obs_labels.insert(cur_labels.begin(), cur_labels.end());
-                //     obs_kmers.insert(kmer);
-                // });
-                // EXPECT_EQ(ref_labels, obs_labels) << k;
-                // EXPECT_EQ(ref_kmers, obs_kmers) << k;
+                // TODO
             }
         }
     }
@@ -295,107 +188,42 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
 
             // ingroup: B, C
             // outgroup: A
-            std::vector<bool> groups { true, false, false };
             tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_in { "B", "C" };
             tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_out { "A" };
 
             std::vector<std::string> test_types = {
                 "poisson_exact",
                 "nbinom_exact",
+                "gnb_exact",
                 "fisher",
             };
 
             auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels, DeBruijnGraph::BASIC, true);
             auto graph_ptr = std::static_pointer_cast<const Graph>(anno_graph->get_graph_ptr());
 
+            for (const auto &test_type : test_types) {
+                DifferentialAssemblyConfig config {
+                    .test_type = test_type,
+                    .outfbase = "",
+                };
+
+                auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
+                    *anno_graph,
+                    labels_in, labels_out,
+                    config, num_threads, test_dump_basename
+                );
+
+                masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
+                    auto cur_labels = anno_graph->get_labels(kmer, 0.0);
+                    obs_labels.insert(cur_labels.begin(), cur_labels.end());
+                    obs_kmers.insert(kmer);
+                });
+                EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
+                EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
+            }
+
             if constexpr(std::is_same_v<Annotation, ColumnCompressed<>>) {
-                using BinMatType = matrix::ColumnMajor;
-                using CoordMatType = matrix::TupleCSCMatrix<BinMatType>;
-                const auto &annotation = static_cast<const ColumnCoordAnnotator&>(anno_graph->get_annotator());
-                const auto &count_matrix = static_cast<const CoordMatType&>(annotation.get_matrix());
-                const auto &in_binary_columns = static_cast<const BinMatType&>(count_matrix.get_binary_matrix()).data();
-                std::vector<std::unique_ptr<const bit_vector>> columns;
-                columns.reserve(in_binary_columns.size());
-                std::transform(in_binary_columns.begin(), in_binary_columns.end(), std::back_inserter(columns),
-                               [&](const auto &a) {
-                                  return std::make_unique<const bit_vector_stat>(a->to_vector());
-                               });
-
-                ASSERT_LT(0u, columns.size());
-
-                std::vector<std::unique_ptr<const sdsl::int_vector<>>> column_values;
-                std::vector<sdsl::int_vector<>::iterator> pos;
-                column_values.reserve(columns.size());
-                for (const auto &bin_col : columns) {
-                    column_values.emplace_back(std::make_unique<const sdsl::int_vector<>>(bin_col->num_set_bits()));
-                    pos.emplace_back(const_cast<sdsl::int_vector<>&>(*column_values.back()).begin());
-                }
-
-                std::vector<uint64_t> row_indices(columns[0]->size());
-                std::iota(row_indices.begin(), row_indices.end(), 0);
-                auto row_values = count_matrix.get_row_values(row_indices);
-                for (size_t i = 0; i < row_indices.size(); ++i) {
-                    for (const auto &[j, c] : row_values[i]) {
-                        *pos[j] = c;
-                        ++pos[j];
-                    }
-                }
-
-                columns.resize(groups.size());
-
-                ASSERT_GE(column_values.size(), groups.size());
-                column_values.resize(groups.size());
-
-                for (const auto &test_type : test_types) {
-                    DifferentialAssemblyConfig config {
-                        .test_type = test_type,
-                        .outfbase = "",
-                    };
-
-                    // using PairContainer = Vector<std::pair<uint64_t, typename sdsl::int_vector<>::value_type>>;
-                    // auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<typename sdsl::int_vector<>::value_type, std::vector<uint64_t>>(
-                    //     graph_ptr,
-                    //     [&](const auto &callback, bool) {
-                    //         utils::call_rows<std::unique_ptr<const bit_vector>,
-                    //                         std::unique_ptr<const sdsl::int_vector<>>,
-                    //                         PairContainer, true>(columns, column_values,
-                    //                                             [&](uint64_t row_i, const auto &row, size_t) {
-                    //             callback(row_i, row);
-                    //         });
-                    //     },
-                    //     groups,
-                    //     config, num_threads, test_dump_basename, num_threads
-                    // );
-
-                    // masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
-                    //     auto cur_labels = anno_graph->get_labels(kmer, 0.0);
-                    //     obs_labels.insert(cur_labels.begin(), cur_labels.end());
-                    //     obs_kmers.insert(kmer);
-                    // });
-                    // EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
-                    // EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
-                }
-            } else {
-                for (const auto &test_type : test_types) {
-                    DifferentialAssemblyConfig config {
-                        .test_type = test_type,
-                        .outfbase = "",
-                    };
-
-                    auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
-                        *anno_graph,
-                        labels_in, labels_out,
-                        config, num_threads, test_dump_basename
-                    );
-
-                    masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
-                        auto cur_labels = anno_graph->get_labels(kmer, 0.0);
-                        obs_labels.insert(cur_labels.begin(), cur_labels.end());
-                        obs_kmers.insert(kmer);
-                    });
-                    EXPECT_EQ(ref_labels, obs_labels) << k << " " << test_type;
-                    EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << test_type;
-                }
+                // TODO
             }
         }
     }

From 9d5bd6b80ed3704323878fee0c2bfb01482ece14 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 25 Sep 2024 17:09:07 +0200
Subject: [PATCH 346/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   |  10 +-
 .../test_annotated_graph_algorithm.cpp        | 390 +++++++-----------
 2 files changed, 147 insertions(+), 253 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 8a9721cdc7..10fe5cc7d3 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -22,6 +22,7 @@
 #include "common/hashers/hash.hpp"
 #include "graph/representation/masked_graph.hpp"
 #include "graph/graph_cleaning.hpp"
+#include "graph/representation/canonical_dbg.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 #include "annotation/int_matrix/base/int_matrix.hpp"
 
@@ -72,6 +73,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          size_t num_parallel_files = std::numeric_limits<size_t>::max(),
                          const std::function<void()> &deallocate = []() {},
                          uint8_t max_width = 64) {
+    if (auto canonical = std::dynamic_pointer_cast<const CanonicalDBG>(graph_ptr))
+        graph_ptr = canonical->get_graph_ptr();
+
     // num_parallel_files = std::min(num_parallel_files, num_threads);
     num_parallel_files = get_num_threads();
     num_threads = get_num_threads();
@@ -1686,13 +1690,15 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         groups.emplace_back(!labels_in.count(label));
     }
 
+    auto graph_ptr = std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr());
+
     const auto &matrix = annotation.get_matrix();
     using Row = annot::matrix::BinaryMatrix::Row;
 
     if (const auto *int_matrix = dynamic_cast<const annot::matrix::IntMatrix*>(&matrix)) {
         using value_type = annot::matrix::IntMatrix::Value;
         return mask_nodes_by_label_dual<value_type, PValStorage>(
-            std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
+            graph_ptr,
             [&](const std::vector<size_t> &min_counts, sdsl::bit_vector *unmark_discarded) {
                 return int_matrix->get_histograms(min_counts, unmark_discarded);
             },
@@ -1729,7 +1735,7 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         bool parallel = get_num_threads() > 1;
 
         return mask_nodes_by_label_dual<uint64_t, PValStorage>(
-            std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr()),
+            graph_ptr,
             [&](const std::vector<size_t> &, sdsl::bit_vector *kept) -> std::vector<VectorMap<uint64_t, size_t>> {
                 common::logger->trace("Calculating count histograms");
                 std::vector<std::vector<VectorMap<uint64_t, size_t>>> hists_map_p(num_threads);
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index dde4f0bcd9..2ba4d788ef 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -229,13 +229,14 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
     }
 }
 
+// TODO: tests not defined when there is overlap in the groups
 // TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelOverlap) {
 //     typedef typename TypeParam::first_type Graph;
 //     typedef typename TypeParam::second_type Annotation;
 //     for (size_t num_threads : { 1, 4 }) {
 //         size_t k = 5;
-//         const tsl::hopscotch_set<std::string> ingroup { "A", "B", "C" };
-//         const tsl::hopscotch_set<std::string> outgroup { "A", "B", "C" };
+//         const tsl::hopscotch_set<std::string> labels_in { "A", "B", "C" };
+//         const tsl::hopscotch_set<std::string> labels_out { "A", "B", "C" };
 
 //         const std::vector<std::string> sequences {
 //             "TTTTTAAAAATTTTTTTTTT",
@@ -253,20 +254,17 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
 //             "GGGGG", "GGGGA", "GGGAA", "GGAAA", "GAAAA", "AAAAG", "AAAGG", "AAGGG", "AGGGG",
 //         };
 //         DifferentialAssemblyConfig config {
-//             .label_mask_in_unitig_fraction = 0.0,
-//             .label_mask_in_kmer_fraction = 0.0,
-//             .label_mask_out_unitig_fraction = 1.0,
-//             .label_mask_out_kmer_fraction = 0.99,
-//             .label_mask_other_unitig_fraction = 1.0,
+//             .test_type = "poisson_binom",
 //             .outfbase = "",
 //         };
 
-//         auto masked_dbg = mask_nodes_by_label(*anno_graph,
-//                                               ingroup, outgroup,
-//                                               {}, {},
-//                                               config, num_threads);
+//         auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
+//             *anno_graph,
+//             labels_in, labels_out,
+//             config, num_threads, test_dump_basename
+//         );
 
-//         masked_dbg->call_kmers([&](auto, const std::string &kmer) {
+//         masked_dbg_in->call_kmers([&](auto, const std::string &kmer) {
 //             obs_kmers.insert(kmer);
 //         });
 
@@ -274,256 +272,146 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
 //     }
 // }
 
-// template <class Graph, class Annotation = ColumnCompressed<>>
-// void test_mask_unitigs(double inlabel_fraction,
-//                        double outlabel_fraction,
-//                        double other_label_fraction,
-//                        const std::unordered_set<std::string> &ref_kmers) {
-//     for (size_t num_threads : {1, 4}) {
-//         const tsl::hopscotch_set<std::string> ingroup { "B", "C" };
-//         const tsl::hopscotch_set<std::string> outgroup { "A" };
-//         size_t k = 3;
-
-//         {
-//             /*  B                  AB  AB
-//                CGA                 GCC-CCT
-//                   \ BC  BC  BC ABC/
-//                    GAA-AAT-ATG-TGC
-//                  C/               \  C   C
-//                GGA                 GCA-CAC
-//             */
-//             const std::vector<std::string> sequences {
-//                     "TGCCT",
-//                 "CGAATGCCT",
-//                 "GGAATGCAC",
-//                 "TTTTTTTTTTTTTT"
-//             };
-//             const std::vector<std::string> labels { "A", "B", "C", "D" };
-
-//             auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels);
-
-//             std::unordered_set<std::string> obs_kmers;
-
-//             DifferentialAssemblyConfig config {
-//                 .label_mask_in_unitig_fraction = inlabel_fraction,
-//                 .label_mask_in_kmer_fraction = 1.0,
-//                 .label_mask_out_unitig_fraction = outlabel_fraction,
-//                 .label_mask_out_kmer_fraction = 0.0,
-//                 .label_mask_other_unitig_fraction = other_label_fraction,
-//                 .outfbase = "",
-//             };
-
-//             auto masked_dbg = mask_nodes_by_label(*anno_graph,
-//                                                   ingroup, outgroup,
-//                                                   {}, {},
-//                                                   config, num_threads);
-
-//             masked_dbg->call_kmers([&](auto, const std::string &kmer) { obs_kmers.insert(kmer); });
-
-//             EXPECT_EQ(ref_kmers, obs_kmers)
-//                 << k << " "
-//                 << inlabel_fraction << " "
-//                 << outlabel_fraction << " "
-//                 << other_label_fraction;
-//         }
-//     }
-// }
+template <class Graph, class Annotation = ColumnCompressed<>>
+void test_mask_unitigs(const std::unordered_set<std::string> &ref_kmers) {
+    for (size_t num_threads : {1, 4}) {
+        const tsl::hopscotch_set<std::string> labels_in { "B", "C" };
+        const tsl::hopscotch_set<std::string> labels_out { "A" };
+        size_t k = 3;
+
+        {
+            /*  B                  AB  AB
+               CGA                 GCC-CCT
+                  \ BC  BC  BC ABC/
+                   GAA-AAT-ATG-TGC
+                 C/               \  C   C
+               GGA                 GCA-CAC
+            */
+            const std::vector<std::string> sequences {
+                    "TGCCT",
+                "CGAATGCCT",
+                "GGAATGCAC",
+                "TTTTTTTTTTTTTT"
+            };
+            const std::vector<std::string> labels { "A", "B", "C", "D" };
 
-// TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskUnitigsByLabel) {
-//     for (double other_frac : std::vector<double>{ 0.0, 1.0 }) {
-//         std::unordered_set<std::string> ref_kmers;
-//         test_mask_unitigs<typename TypeParam::first_type,
-//                           typename TypeParam::second_type>(1.0, 0.0, other_frac, ref_kmers);
+            auto anno_graph = build_anno_graph<Graph, Annotation>(k, sequences, labels);
 
-//         test_mask_unitigs<typename TypeParam::first_type,
-//                           typename TypeParam::second_type>(1.0, 0.24, other_frac, ref_kmers);
+            std::unordered_set<std::string> obs_kmers;
 
-//         ref_kmers.insert("GAA");
-//         ref_kmers.insert("AAT");
-//         ref_kmers.insert("ATG");
-//         ref_kmers.insert("TGC");
+            DifferentialAssemblyConfig config {
+                .test_by_unitig = true,
+                .test_type = "poisson_binom",
+                .outfbase = "",
+            };
 
-//         test_mask_unitigs<typename TypeParam::first_type,
-//                           typename TypeParam::second_type>(1.0, 0.25, other_frac, ref_kmers);
+            auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
+                *anno_graph,
+                labels_in, labels_out,
+                config, num_threads, test_dump_basename
+            );
 
-//         test_mask_unitigs<typename TypeParam::first_type,
-//                           typename TypeParam::second_type>(1.0, 0.50, other_frac, ref_kmers);
+            masked_dbg_in->call_kmers([&](auto, const std::string &kmer) { obs_kmers.insert(kmer); });
 
-//         test_mask_unitigs<typename TypeParam::first_type,
-//                           typename TypeParam::second_type>(1.0, 0.75, other_frac, ref_kmers);
+            EXPECT_EQ(ref_kmers, obs_kmers) << k;
+        }
+    }
+}
 
-//         test_mask_unitigs<typename TypeParam::first_type,
-//                           typename TypeParam::second_type>(1.0, 1.0, other_frac, ref_kmers);
-//     }
-// }
+TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskUnitigsByLabel) {
+    std::unordered_set<std::string> ref_kmers;
+    test_mask_unitigs<typename TypeParam::first_type, typename TypeParam::second_type>(ref_kmers);
 
-// #if ! _PROTEIN_GRAPH
-// template <class Graph, class Annotation = ColumnCompressed<>>
-// std::unordered_set<std::string>
-// test_mask_unitigs_canonical(double inlabel_fraction,
-//                             double outlabel_fraction,
-//                             double other_label_fraction,
-//                             const std::unordered_set<std::string> &ref_kmers,
-//                             bool add_complement,
-//                             DeBruijnGraph::Mode mode) {
-//     for (size_t num_threads : { 1, 4 }) {
-//         const tsl::hopscotch_set<std::string> ingroup { "B", "C" };
-//         const tsl::hopscotch_set<std::string> outgroup { "A" };
-//         size_t k = 5;
+    // TODO: make a test where some k-mers are significant
+}
 
-//         {
-//             /*
-//                 B                AB    AB
-//                CGAAT             ATGCC-TGCCT
-//                     \ BC   ABC  /
-//                      GAATG-AATGC
-//                  C  /           \  C     C
-//                GGAAT             ATGCA-TGCAC
-//                                    C  /  C                B
-//                                  GTGCA-TGCAT             ATTCG
-//                                             \ABC    BC  /
-//                                              GCATT-CATTC
-//                                  AB    AB   /           \  C
-//                                  AGGCA-GGCAT             ATTCC
-//             */
-//             const std::vector<std::string> sequences {
-//                   "AATGCCT",     // "AGGCATT"
-//                 "CGAATGCCT",     // "AGGCATTCG"
-//                 "GGAATGCAC",     // "GTGCATTCC"
-//                 "TTTTTTTTTTTTTT" // "AAAAAAAAAAAAAA"
-//             };
-//             const std::vector<std::string> labels { "A", "B", "C", "D" };
-
-//             // if add_complement, then the differential
-//             auto anno_graph = build_anno_graph<Graph, Annotation>(
-//                 k, sequences, labels, mode
-//             );
-
-//             std::unordered_set<std::string> obs_kmers;
-
-//             DifferentialAssemblyConfig config {
-//                 .label_mask_in_unitig_fraction = inlabel_fraction,
-//                 .label_mask_in_kmer_fraction = 1.0,
-//                 .label_mask_out_unitig_fraction = outlabel_fraction,
-//                 .label_mask_out_kmer_fraction = 0.0,
-//                 .label_mask_other_unitig_fraction = other_label_fraction,
-//                 .add_complement = add_complement,
-//                 .outfbase = "",
-//             };
-
-//             auto masked_dbg = mask_nodes_by_label(*anno_graph,
-//                                                   ingroup, outgroup,
-//                                                   {}, {},
-//                                                   config, num_threads);
-
-//             masked_dbg->call_kmers([&](auto, const std::string &kmer) { obs_kmers.insert(kmer); });
-
-//             if (ref_kmers != obs_kmers)
-//                 return obs_kmers;
-//         }
-//     }
+#if ! _PROTEIN_GRAPH
+template <class Graph, class Annotation = ColumnCompressed<>>
+std::unordered_set<std::string>
+test_mask_unitigs_canonical(const std::unordered_set<std::string> &ref_kmers,
+                            DeBruijnGraph::Mode mode,
+                            size_t k) {
+    for (size_t num_threads : { 1, 4 }) {
+        const tsl::hopscotch_set<std::string> labels_in { "B", "C" };
+        const tsl::hopscotch_set<std::string> labels_out { "A" };
+
+        {
+            /*
+                B                AB    AB
+               CGAAT             ATGCC-TGCCT
+                    \ BC   ABC  /
+                     GAATG-AATGC
+                 C  /           \  C     C
+               GGAAT             ATGCA-TGCAC
+                                   C  /  C                B
+                                 GTGCA-TGCAT             ATTCG
+                                            \ABC    BC  /
+                                             GCATT-CATTC
+                                 AB    AB   /           \  C
+                                 AGGCA-GGCAT             ATTCC
+            */
+            const std::vector<std::string> sequences {
+                  "AATGCCT",     // "AGGCATT"
+                "CGAATGCCT",     // "AGGCATTCG"
+                "GGAATGCAC",     // "GTGCATTCC"
+                "TTTTTTTTTTTTTT" // "AAAAAAAAAAAAAA"
+            };
+            const std::vector<std::string> labels { "A", "B", "C", "D" };
 
-//     return ref_kmers;
-// }
+            auto anno_graph = build_anno_graph<Graph, Annotation>(
+                k, sequences, labels, mode
+            );
 
-// TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskUnitigsByLabelAddCanonical) {
-//     for (DeBruijnGraph::Mode mode : { DeBruijnGraph::BASIC,
-//                                       DeBruijnGraph::CANONICAL,
-//                                       DeBruijnGraph::PRIMARY }) {
-//         std::string mode_str = mode == DeBruijnGraph::BASIC
-//             ? "BASIC"
-//             : (mode == DeBruijnGraph::CANONICAL ? "CANONICAL" : "PRIMARY");
-
-//         for (double other_frac : std::vector<double>{ 1.0, 0.0 }) {
-//             std::unordered_set<std::string> ref_kmers;
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 0.0, other_frac, ref_kmers, true, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 0.25, other_frac, ref_kmers, true, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 0.49, other_frac, ref_kmers, true, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-
-//             ref_kmers.insert("GAATG");
-//             ref_kmers.insert("AATGC");
-
-//             if (mode == DeBruijnGraph::CANONICAL || mode == DeBruijnGraph::PRIMARY) {
-//                 ref_kmers.insert("CATTC");
-//                 ref_kmers.insert("GCATT");
-//             }
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 0.50, other_frac, ref_kmers, true, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 0.75, other_frac, ref_kmers, true, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 1.0, other_frac, ref_kmers, true, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-//         }
-//     }
-// }
+            std::unordered_set<std::string> obs_kmers;
 
-// TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskUnitigsByLabelCanonical) {
-//     for (DeBruijnGraph::Mode mode : { DeBruijnGraph::CANONICAL,
-//                                       DeBruijnGraph::PRIMARY }) {
-//         std::string mode_str = mode == DeBruijnGraph::CANONICAL ? "CANONICAL" : "PRIMARY";
-//         for (double other_frac : std::vector<double>{ 1.0, 0.0 }) {
-//             std::unordered_set<std::string> ref_kmers;
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 0.0, other_frac, ref_kmers, false, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 0.25, other_frac, ref_kmers, false, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 0.49, other_frac, ref_kmers, false, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-
-//             ref_kmers.insert("GAATG");
-//             ref_kmers.insert("AATGC");
-//             ref_kmers.insert("CATTC");
-//             ref_kmers.insert("GCATT");
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 0.50, other_frac, ref_kmers, false, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 0.75, other_frac, ref_kmers, false, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-
-//             EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
-//                                                    typename TypeParam::second_type>(
-//                 1.0, 1.0, other_frac, ref_kmers, false, mode
-//             )), ref_kmers) << other_frac << " " << mode_str;
-//         }
-//     }
-// }
-// #endif
+            DifferentialAssemblyConfig config {
+                .test_by_unitig = true,
+                .test_type = "poisson_binom",
+                .outfbase = "",
+            };
+
+            auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
+                *anno_graph,
+                labels_in, labels_out,
+                config, num_threads, test_dump_basename
+            );
+
+            masked_dbg_in->call_kmers([&](auto, const std::string &kmer) { obs_kmers.insert(kmer); });
+
+            if (ref_kmers != obs_kmers)
+                return obs_kmers;
+        }
+    }
+
+    return ref_kmers;
+}
+
+TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskUnitigsByLabelAddCanonical) {
+    for (DeBruijnGraph::Mode mode : { DeBruijnGraph::BASIC,
+                                      DeBruijnGraph::PRIMARY }) {
+        std::string mode_str = mode == DeBruijnGraph::BASIC
+            ? "BASIC"
+            : (mode == DeBruijnGraph::CANONICAL ? "CANONICAL" : "PRIMARY");
+
+        std::unordered_set<std::string> ref_kmers;
+
+        EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+                                                typename TypeParam::second_type>(
+            ref_kmers, mode, 5
+        )), ref_kmers) << mode_str;
+        EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+                                                typename TypeParam::second_type>(
+            ref_kmers, mode, 6
+        )), ref_kmers) << mode_str;
+        EXPECT_EQ((test_mask_unitigs_canonical<typename TypeParam::first_type,
+                                                typename TypeParam::second_type>(
+            ref_kmers, mode, 7
+        )), ref_kmers) << mode_str;
+
+        // TODO: make a test where some k-mers are significant
+    }
+}
+
+#endif
 
 } // namespace

From 05ef139a2dc3eba3840a02e2f76c86e97f7aa7bd Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 26 Sep 2024 12:47:02 +0200
Subject: [PATCH 347/410] fix assertion fail

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 10fe5cc7d3..e68c212248 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -190,6 +190,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             total_c += c;
         }
 
+        assert(total_c == nelem);
+
         if (total_c != nelem) {
             common::logger->error("FAIL {}: {} != {}", j, total_c, nelem);
             throw std::runtime_error("GGG");
@@ -1026,12 +1028,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (min_pval >= config.family_wise_error_rate) {
             common::logger->warn("No significant p-values achievable");
             auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
-                graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
+                graph_ptr, [](node_index) { return false; }, true,
                 is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
             );
 
             auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
-                graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_out)), true,
+                graph_ptr, [](node_index) { return false; }, true,
                 is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
             );
 

From a9935d5b7f68a3801061f2721739d466ba3fed80 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 26 Sep 2024 16:47:46 +0200
Subject: [PATCH 348/410] skip sentinel in protein sequences

---
 metagraph/src/kmer/alphabets.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/kmer/alphabets.hpp b/metagraph/src/kmer/alphabets.hpp
index de55f8231c..5bdb01354d 100644
--- a/metagraph/src/kmer/alphabets.hpp
+++ b/metagraph/src/kmer/alphabets.hpp
@@ -30,7 +30,7 @@ constexpr uint8_t kBOSSBitsPerCharProtein = log2<kBOSSSigmaProtein - 1>::value +
 constexpr uint8_t kBOSSCharToProtein[128] = {
     26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
     26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
-    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
+    26, 26, 26, 26,  27, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
     26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
     26,  1,  2,  3,   4,  5,  6,  7,   8,  9, 10, 11,  12, 13, 14, 15,
     16, 17, 18, 19,  20, 21, 22, 23,  26, 24, 25, 26,  26, 26, 26, 26,
@@ -93,7 +93,7 @@ constexpr uint8_t kBitsPerCharProtein = log2<kSigmaProtein - 1>::value + 1;
 constexpr uint8_t kCharToProtein[128] = {
     25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
     25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
-    25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
+    25, 25, 25, 25,  26, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
     25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
     25,  0,  1,  2,   3,  4,  5,  6,   7,  8,  9, 10,  11, 12, 13, 14,
     15, 16, 17, 18,  19, 20, 21, 22,  25, 23, 24, 25,  25, 25, 25, 25,

From ce65ac3a1fc0f58efd625dfdbf61228039706134 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 26 Sep 2024 17:37:46 +0200
Subject: [PATCH 349/410] fix kmer unit test

---
 metagraph/tests/test_kmer.cpp | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/metagraph/tests/test_kmer.cpp b/metagraph/tests/test_kmer.cpp
index f84608f3de..347890e9e6 100644
--- a/metagraph/tests/test_kmer.cpp
+++ b/metagraph/tests/test_kmer.cpp
@@ -325,9 +325,7 @@ TYPED_TEST(Kmer, UpdateKmerVsConstruct) {
 TYPED_TEST(Kmer, InvertibleEndDol) {
 #if _DNA5_GRAPH || _DNA_CASE_SENSITIVE_GRAPH
     test_kmer_packed_codec<typename TypeParam::type>("ATG$", "ATGN");
-#elif _PROTEIN_GRAPH
-    test_kmer_packed_codec<typename TypeParam::type>("ATG$", "ATGX");
-#elif _DNA_GRAPH
+#elif _DNA_GRAPH || _PROTEIN_GRAPH
     ASSERT_DEBUG_DEATH(kmer_packed_codec<typename TypeParam::type>("ATG$"), "");
 #else
     static_assert(false,
@@ -339,9 +337,7 @@ TYPED_TEST(Kmer, InvertibleEndDol) {
 TYPED_TEST(Kmer, InvertibleStartDol) {
 #if _DNA5_GRAPH || _DNA_CASE_SENSITIVE_GRAPH
     test_kmer_packed_codec<typename TypeParam::type>("$ATGG", "NATGG");
-#elif _PROTEIN_GRAPH
-    test_kmer_packed_codec<typename TypeParam::type>("$ATGG", "XATGG");
-#elif _DNA_GRAPH
+#elif _DNA_GRAPH || _PROTEIN_GRAPH
     ASSERT_DEBUG_DEATH(kmer_packed_codec<typename TypeParam::type>("$ATGG"), "");
 #else
     static_assert(false,
@@ -353,9 +349,7 @@ TYPED_TEST(Kmer, InvertibleStartDol) {
 TYPED_TEST(Kmer, InvertibleBothDol) {
 #if _DNA5_GRAPH || _DNA_CASE_SENSITIVE_GRAPH
     test_kmer_packed_codec<typename TypeParam::type>("$ATG$", "NATGN");
-#elif _PROTEIN_GRAPH
-    test_kmer_packed_codec<typename TypeParam::type>("$ATG$", "XATGX");
-#elif _DNA_GRAPH
+#elif _DNA_GRAPH || _PROTEIN_GRAPH
     ASSERT_DEBUG_DEATH(kmer_packed_codec<typename TypeParam::type>("$ATG$"), "");
 #else
     static_assert(false,

From e27d44ced7c7440347837b92b932cb35ca13c5ba Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 26 Sep 2024 18:08:51 +0200
Subject: [PATCH 350/410] revert redundant changes

---
 .gitignore                      |  1 -
 metagraph/CMakeListsHelpers.txt | 48 ++++++++++++++++-----------------
 2 files changed, 24 insertions(+), 25 deletions(-)

diff --git a/.gitignore b/.gitignore
index de0ee6088f..d59a6a0b6f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -30,4 +30,3 @@ metagraph/**/build*
 tags
 **/cmake-build-debug
 .idea
-.vscode
diff --git a/metagraph/CMakeListsHelpers.txt b/metagraph/CMakeListsHelpers.txt
index fb955bd82c..973021c5b4 100644
--- a/metagraph/CMakeListsHelpers.txt
+++ b/metagraph/CMakeListsHelpers.txt
@@ -3,30 +3,30 @@
 
 
 function (findOpenMP)
-    # unset OpenMP_CXX_FLAGS. If OpenMP loading fails once, it will be set to NOT_FOUND
-    # and happily cached, thus messing up the compilation flags forever
-    unset(OpenMP_C_FLAGS CACHE)
-    unset(OpenMP_CXX_FLAGS CACHE)
-    if(APPLE AND NOT CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
-        execute_process(
-            COMMAND brew --prefix libomp
-            OUTPUT_VARIABLE BREW_OMP_PREFIX
-            OUTPUT_STRIP_TRAILING_WHITESPACE
-        )
-        set(OpenMP_C_FLAGS "-Xclang -fopenmp -I${BREW_OMP_PREFIX}/include")
-        set(OpenMP_CXX_FLAGS "-Xclang -fopenmp -I${BREW_OMP_PREFIX}/include")
-        set(OpenMP_C_LIB_NAMES libomp)
-        set(OpenMP_CXX_LIB_NAMES libomp)
-        set(OpenMP_libomp_LIBRARY ${BREW_OMP_PREFIX}/lib/libomp.dylib)
-    endif()
-    find_package(OpenMP REQUIRED)
-    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}" PARENT_SCOPE)
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}" PARENT_SCOPE)
-    set(OpenMP_CXX_LIBRARIES "${OpenMP_CXX_LIBRARIES}" PARENT_SCOPE)
-    set(OpenMP_CXX_VERSION "${OpenMP_CXX_VERSION}" PARENT_SCOPE)
-    if(CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
-        set(OpenMP_CXX_LIBRARIES ${OpenMP_CXX_LIBRARIES} -latomic PARENT_SCOPE)
-    endif()
+# unset OpenMP_CXX_FLAGS. If OpenMP loading fails once, it will be set to NOT_FOUND
+# and happily cached, thus messing up the compilation flags forever
+unset(OpenMP_C_FLAGS CACHE)
+unset(OpenMP_CXX_FLAGS CACHE)
+if(APPLE AND NOT CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+  execute_process(
+      COMMAND brew --prefix libomp
+      OUTPUT_VARIABLE BREW_OMP_PREFIX
+      OUTPUT_STRIP_TRAILING_WHITESPACE
+  )
+  set(OpenMP_C_FLAGS "-Xclang -fopenmp -I${BREW_OMP_PREFIX}/include")
+  set(OpenMP_CXX_FLAGS "-Xclang -fopenmp -I${BREW_OMP_PREFIX}/include")
+  set(OpenMP_C_LIB_NAMES libomp)
+  set(OpenMP_CXX_LIB_NAMES libomp)
+  set(OpenMP_libomp_LIBRARY ${BREW_OMP_PREFIX}/lib/libomp.dylib)
+endif()
+find_package(OpenMP REQUIRED)
+set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}" PARENT_SCOPE)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}" PARENT_SCOPE)
+set(OpenMP_CXX_LIBRARIES "${OpenMP_CXX_LIBRARIES}" PARENT_SCOPE)
+set(OpenMP_CXX_VERSION "${OpenMP_CXX_VERSION}" PARENT_SCOPE)
+if(CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+set(OpenMP_CXX_LIBRARIES ${OpenMP_CXX_LIBRARIES} -latomic PARENT_SCOPE)
+endif()
 endfunction()
 
 # check for std::filesystem::temp_directory_path

From 4be7fbfb83b55aefe1f6bdac3e0ad1f05b8a759b Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 26 Sep 2024 18:45:08 +0200
Subject: [PATCH 351/410] don't encode punctuation

---
 metagraph/src/kmer/alphabets.hpp | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/metagraph/src/kmer/alphabets.hpp b/metagraph/src/kmer/alphabets.hpp
index 5bdb01354d..e8217dfaf6 100644
--- a/metagraph/src/kmer/alphabets.hpp
+++ b/metagraph/src/kmer/alphabets.hpp
@@ -28,14 +28,14 @@ constexpr char kBOSSAlphabetProtein[] = "$ABCDEFGHIJKLMNOPQRSTUVWYZX";
 constexpr uint8_t kBOSSSigmaProtein = sizeof(kBOSSAlphabetProtein) - 1;
 constexpr uint8_t kBOSSBitsPerCharProtein = log2<kBOSSSigmaProtein - 1>::value + 1;
 constexpr uint8_t kBOSSCharToProtein[128] = {
-    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
-    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
-    26, 26, 26, 26,  27, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
-    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
-    26,  1,  2,  3,   4,  5,  6,  7,   8,  9, 10, 11,  12, 13, 14, 15,
-    16, 17, 18, 19,  20, 21, 22, 23,  26, 24, 25, 26,  26, 26, 26, 26,
-    26,  1,  2,  3,   4,  5,  6,  7,   8,  9, 10, 11,  12, 13, 14, 15,
-    16, 17, 18, 19,  20, 21, 22, 23,  26, 24, 25, 26,  26, 26, 26, 26
+    27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,
+    27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,
+    27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,
+    27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,
+    27,  1,  2,  3,   4,  5,  6,  7,   8,  9, 10, 11,  12, 13, 14, 15,
+    16, 17, 18, 19,  20, 21, 22, 23,  26, 24, 25, 27,  27, 27, 27, 27,
+    27,  1,  2,  3,   4,  5,  6,  7,   8,  9, 10, 11,  12, 13, 14, 15,
+    16, 17, 18, 19,  20, 21, 22, 23,  26, 24, 25, 27,  27, 27, 27, 27
 };
 const std::vector<uint8_t> kBOSSComplementMapProtein = {};
 static_assert(kBOSSSigmaProtein <= 1llu << kBOSSBitsPerCharProtein);
@@ -91,14 +91,14 @@ inline constexpr char kAlphabetProtein[] = "ABCDEFGHIJKLMNOPQRSTUVWYZX";
 constexpr uint8_t kSigmaProtein = sizeof(kAlphabetProtein) - 1;
 constexpr uint8_t kBitsPerCharProtein = log2<kSigmaProtein - 1>::value + 1;
 constexpr uint8_t kCharToProtein[128] = {
-    25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
-    25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
-    25, 25, 25, 25,  26, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
-    25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
-    25,  0,  1,  2,   3,  4,  5,  6,   7,  8,  9, 10,  11, 12, 13, 14,
-    15, 16, 17, 18,  19, 20, 21, 22,  25, 23, 24, 25,  25, 25, 25, 25,
-    25,  0,  1,  2,   3,  4,  5,  6,   7,  8,  9, 10,  11, 12, 13, 14,
-    15, 16, 17, 18,  19, 20, 21, 22,  25, 23, 24, 25,  25, 25, 25, 25
+    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
+    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
+    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
+    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
+    26,  0,  1,  2,   3,  4,  5,  6,   7,  8,  9, 10,  11, 12, 13, 14,
+    15, 16, 17, 18,  19, 20, 21, 22,  25, 23, 24, 26,  26, 26, 26, 26,
+    26,  0,  1,  2,   3,  4,  5,  6,   7,  8,  9, 10,  11, 12, 13, 14,
+    15, 16, 17, 18,  19, 20, 21, 22,  25, 23, 24, 26,  26, 26, 26, 26
 };
 const std::vector<uint8_t> kComplementMapProtein = {};
 static_assert(kSigmaProtein <= 1llu << kBitsPerCharProtein);

From 15c6c327c884a21704f43af0aea2abfece4c0d9f Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 26 Sep 2024 19:31:52 +0200
Subject: [PATCH 352/410] Revert "don't encode punctuation"

This reverts commit 4be7fbfb83b55aefe1f6bdac3e0ad1f05b8a759b.
---
 metagraph/src/kmer/alphabets.hpp | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/metagraph/src/kmer/alphabets.hpp b/metagraph/src/kmer/alphabets.hpp
index e8217dfaf6..5bdb01354d 100644
--- a/metagraph/src/kmer/alphabets.hpp
+++ b/metagraph/src/kmer/alphabets.hpp
@@ -28,14 +28,14 @@ constexpr char kBOSSAlphabetProtein[] = "$ABCDEFGHIJKLMNOPQRSTUVWYZX";
 constexpr uint8_t kBOSSSigmaProtein = sizeof(kBOSSAlphabetProtein) - 1;
 constexpr uint8_t kBOSSBitsPerCharProtein = log2<kBOSSSigmaProtein - 1>::value + 1;
 constexpr uint8_t kBOSSCharToProtein[128] = {
-    27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,
-    27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,
-    27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,
-    27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,  27, 27, 27, 27,
-    27,  1,  2,  3,   4,  5,  6,  7,   8,  9, 10, 11,  12, 13, 14, 15,
-    16, 17, 18, 19,  20, 21, 22, 23,  26, 24, 25, 27,  27, 27, 27, 27,
-    27,  1,  2,  3,   4,  5,  6,  7,   8,  9, 10, 11,  12, 13, 14, 15,
-    16, 17, 18, 19,  20, 21, 22, 23,  26, 24, 25, 27,  27, 27, 27, 27
+    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
+    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
+    26, 26, 26, 26,  27, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
+    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
+    26,  1,  2,  3,   4,  5,  6,  7,   8,  9, 10, 11,  12, 13, 14, 15,
+    16, 17, 18, 19,  20, 21, 22, 23,  26, 24, 25, 26,  26, 26, 26, 26,
+    26,  1,  2,  3,   4,  5,  6,  7,   8,  9, 10, 11,  12, 13, 14, 15,
+    16, 17, 18, 19,  20, 21, 22, 23,  26, 24, 25, 26,  26, 26, 26, 26
 };
 const std::vector<uint8_t> kBOSSComplementMapProtein = {};
 static_assert(kBOSSSigmaProtein <= 1llu << kBOSSBitsPerCharProtein);
@@ -91,14 +91,14 @@ inline constexpr char kAlphabetProtein[] = "ABCDEFGHIJKLMNOPQRSTUVWYZX";
 constexpr uint8_t kSigmaProtein = sizeof(kAlphabetProtein) - 1;
 constexpr uint8_t kBitsPerCharProtein = log2<kSigmaProtein - 1>::value + 1;
 constexpr uint8_t kCharToProtein[128] = {
-    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
-    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
-    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
-    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
-    26,  0,  1,  2,   3,  4,  5,  6,   7,  8,  9, 10,  11, 12, 13, 14,
-    15, 16, 17, 18,  19, 20, 21, 22,  25, 23, 24, 26,  26, 26, 26, 26,
-    26,  0,  1,  2,   3,  4,  5,  6,   7,  8,  9, 10,  11, 12, 13, 14,
-    15, 16, 17, 18,  19, 20, 21, 22,  25, 23, 24, 26,  26, 26, 26, 26
+    25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
+    25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
+    25, 25, 25, 25,  26, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
+    25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
+    25,  0,  1,  2,   3,  4,  5,  6,   7,  8,  9, 10,  11, 12, 13, 14,
+    15, 16, 17, 18,  19, 20, 21, 22,  25, 23, 24, 25,  25, 25, 25, 25,
+    25,  0,  1,  2,   3,  4,  5,  6,   7,  8,  9, 10,  11, 12, 13, 14,
+    15, 16, 17, 18,  19, 20, 21, 22,  25, 23, 24, 25,  25, 25, 25, 25
 };
 const std::vector<uint8_t> kComplementMapProtein = {};
 static_assert(kSigmaProtein <= 1llu << kBitsPerCharProtein);

From 756cfd5e962bc111af83d6b4ad7b7c79587ef7e8 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 26 Sep 2024 19:32:02 +0200
Subject: [PATCH 353/410] Revert "fix kmer unit test"

This reverts commit ce65ac3a1fc0f58efd625dfdbf61228039706134.
---
 metagraph/tests/test_kmer.cpp | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/metagraph/tests/test_kmer.cpp b/metagraph/tests/test_kmer.cpp
index 347890e9e6..f84608f3de 100644
--- a/metagraph/tests/test_kmer.cpp
+++ b/metagraph/tests/test_kmer.cpp
@@ -325,7 +325,9 @@ TYPED_TEST(Kmer, UpdateKmerVsConstruct) {
 TYPED_TEST(Kmer, InvertibleEndDol) {
 #if _DNA5_GRAPH || _DNA_CASE_SENSITIVE_GRAPH
     test_kmer_packed_codec<typename TypeParam::type>("ATG$", "ATGN");
-#elif _DNA_GRAPH || _PROTEIN_GRAPH
+#elif _PROTEIN_GRAPH
+    test_kmer_packed_codec<typename TypeParam::type>("ATG$", "ATGX");
+#elif _DNA_GRAPH
     ASSERT_DEBUG_DEATH(kmer_packed_codec<typename TypeParam::type>("ATG$"), "");
 #else
     static_assert(false,
@@ -337,7 +339,9 @@ TYPED_TEST(Kmer, InvertibleEndDol) {
 TYPED_TEST(Kmer, InvertibleStartDol) {
 #if _DNA5_GRAPH || _DNA_CASE_SENSITIVE_GRAPH
     test_kmer_packed_codec<typename TypeParam::type>("$ATGG", "NATGG");
-#elif _DNA_GRAPH || _PROTEIN_GRAPH
+#elif _PROTEIN_GRAPH
+    test_kmer_packed_codec<typename TypeParam::type>("$ATGG", "XATGG");
+#elif _DNA_GRAPH
     ASSERT_DEBUG_DEATH(kmer_packed_codec<typename TypeParam::type>("$ATGG"), "");
 #else
     static_assert(false,
@@ -349,7 +353,9 @@ TYPED_TEST(Kmer, InvertibleStartDol) {
 TYPED_TEST(Kmer, InvertibleBothDol) {
 #if _DNA5_GRAPH || _DNA_CASE_SENSITIVE_GRAPH
     test_kmer_packed_codec<typename TypeParam::type>("$ATG$", "NATGN");
-#elif _DNA_GRAPH || _PROTEIN_GRAPH
+#elif _PROTEIN_GRAPH
+    test_kmer_packed_codec<typename TypeParam::type>("$ATG$", "XATGX");
+#elif _DNA_GRAPH
     ASSERT_DEBUG_DEATH(kmer_packed_codec<typename TypeParam::type>("$ATG$"), "");
 #else
     static_assert(false,

From 4939bf7880a91c00ef8be0458bbd2dfb1c5dbfc2 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 26 Sep 2024 19:32:12 +0200
Subject: [PATCH 354/410] Revert "skip sentinel in protein sequences"

This reverts commit a9935d5b7f68a3801061f2721739d466ba3fed80.
---
 metagraph/src/kmer/alphabets.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/kmer/alphabets.hpp b/metagraph/src/kmer/alphabets.hpp
index 5bdb01354d..de55f8231c 100644
--- a/metagraph/src/kmer/alphabets.hpp
+++ b/metagraph/src/kmer/alphabets.hpp
@@ -30,7 +30,7 @@ constexpr uint8_t kBOSSBitsPerCharProtein = log2<kBOSSSigmaProtein - 1>::value +
 constexpr uint8_t kBOSSCharToProtein[128] = {
     26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
     26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
-    26, 26, 26, 26,  27, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
+    26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
     26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,  26, 26, 26, 26,
     26,  1,  2,  3,   4,  5,  6,  7,   8,  9, 10, 11,  12, 13, 14, 15,
     16, 17, 18, 19,  20, 21, 22, 23,  26, 24, 25, 26,  26, 26, 26, 26,
@@ -93,7 +93,7 @@ constexpr uint8_t kBitsPerCharProtein = log2<kSigmaProtein - 1>::value + 1;
 constexpr uint8_t kCharToProtein[128] = {
     25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
     25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
-    25, 25, 25, 25,  26, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
+    25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
     25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,  25, 25, 25, 25,
     25,  0,  1,  2,   3,  4,  5,  6,   7,  8,  9, 10,  11, 12, 13, 14,
     15, 16, 17, 18,  19, 20, 21, 22,  25, 23, 24, 25,  25, 25, 25, 25,

From 648a77a93e32363b1c59283a6f5194e79e1060f5 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 26 Sep 2024 19:41:34 +0200
Subject: [PATCH 355/410] fix unit test

---
 .../tests/annotation/test_annotated_graph_algorithm.cpp  | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index 2ba4d788ef..ca60b1f769 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -164,7 +164,7 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
     for (size_t num_threads : { 1, 4 }) {
         size_t max_k = std::min(max_test_k<Graph>(), (size_t)15);
         for (size_t k = 3; k < max_k; ++k) {
-            std::string sig = std::string(k - 1, 'A') + "C$";
+            std::string sig = std::string(k - 1, 'A') + "C";
             for (size_t i = 0; i < 7; ++i) {
                 sig += sig;
             }
@@ -179,9 +179,10 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelCounts) {
 
             std::vector<std::string> labels { "A", "B", "C", "D", "E" };
             std::unordered_set<std::string> obs_labels, obs_kmers;
-            const std::unordered_set<std::string> ref_kmers {
-                std::string(k - 1, 'A') + "C"
-            };
+            std::unordered_set<std::string> ref_kmers;
+            for (size_t j = 0; j < k; ++j) {
+                ref_kmers.emplace(std::string(j, 'A') + "C" + std::string(k - 1 - j, 'A'));
+            }
             const std::unordered_set<std::string> ref_labels {
                 "B", "C"
             };

From d5a82a6fbf4b6db5eb25e57031c34d4891ec967a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 26 Sep 2024 20:06:53 +0200
Subject: [PATCH 356/410] cleanup

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index e68c212248..2d3c95a4e0 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -12,7 +12,6 @@
 #include <boost/math/tools/minima.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
-#include <boost/math/special_functions/polygamma.hpp>
 #include <boost/math/distributions/cauchy.hpp>
 
 #include "common/logger.hpp"
@@ -433,9 +432,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         };
     } else if (config.test_type == "nbinom_exact" || config.test_type == "gnb_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
-        auto get_rp = [&](const auto &generate,
-                          const auto &add_dl,
-                          const auto &add_ddl) {
+        auto get_rp = [&](const auto &generate) {
             double mu = 0;
             double var = 0;
             size_t total = 0;
@@ -460,7 +457,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 generate([&](auto k, auto c) {
                     dl += boost::math::digamma(k + r) * c;
                 });
-                return dl + add_dl(r);
+                return dl;
             };
 
             double r_min = r_guess;
@@ -519,7 +516,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     const auto &[k, c] = a;
                     callback(k, c);
                 });
-            }, [](double) { return 0; }, [](double) { return 0; });
+            });
             const auto &[r, p] = nb_params[j];
             common::logger->trace("{}: size: {}\tmax_val: {}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
                                   j, sums[j], (it - 1)->first, r * (1-p)/p, r*(1-p)/p/p, r, p);
@@ -548,7 +545,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     callback(f * k, c);
                 });
             }
-        }, [](double) { return 0; }, [](double) { return 0; });
+        });
 
         double fit_mu = r_map * (1.0 - target_p) / target_p;
         double fit_var = fit_mu / target_p;

From 757fcdb62951d6021aab3d1dff03241daa393073 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 26 Sep 2024 20:37:58 +0200
Subject: [PATCH 357/410] fix compilation issues

---
 .../int_matrix/csr_matrix/csr_matrix_flat.hpp  | 18 +++++++++---------
 .../src/graph/annotated_graph_algorithm.cpp    | 17 ++++++++++-------
 2 files changed, 19 insertions(+), 16 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
index 6767e775d2..c9654a5b94 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.hpp
@@ -31,7 +31,7 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
     CSRMatrixFlat(BinMat&& binmat, Values&& values)
         : matrix_(std::move(binmat)), values_(std::move(values)) {}
 
-    std::vector<RowValues> get_row_values(const std::vector<BinaryMatrix::Row> &rows) const;
+    std::vector<RowValues> get_row_values(const std::vector<BinaryMatrix::Row> &rows) const override;
 
     inline void call_row_values(const std::function<void(uint64_t, const RowValues&, size_t)> &callback,
                                 bool ordered = true) const override final {
@@ -172,17 +172,17 @@ class CSRMatrixFlat : public RowMajor, public IntMatrix {
     get_histograms(const std::vector<size_t> &min_counts = {},
                    sdsl::bit_vector *unmark_discarded = nullptr) const override final;
 
-    uint64_t num_columns() const { return num_columns_; }
-    uint64_t num_rows() const { return get_row_major_binary_matrix().num_rows(); }
-    uint64_t num_relations() const { return get_row_major_binary_matrix().num_relations(); }
-    SetBitPositions get_row(Row row) const { return get_row_major_binary_matrix().get_row(row); }
-    std::vector<Row> get_column(Column column) const { return get_row_major_binary_matrix().get_column(column); }
+    uint64_t num_columns() const override { return num_columns_; }
+    uint64_t num_rows() const override { return get_row_major_binary_matrix().num_rows(); }
+    uint64_t num_relations() const override { return get_row_major_binary_matrix().num_relations(); }
+    SetBitPositions get_row(Row row) const override { return get_row_major_binary_matrix().get_row(row); }
+    std::vector<Row> get_column(Column column) const override { return get_row_major_binary_matrix().get_column(column); }
 
-    const BinaryMatrix& get_binary_matrix() const { return get_row_major_binary_matrix(); }
+    const BinaryMatrix& get_binary_matrix() const override { return get_row_major_binary_matrix(); }
 
-    bool load(std::istream &in);
+    bool load(std::istream &in) override;
     bool load(const std::string &fname, std::streampos offset = 0);
-    void serialize(std::ostream &out) const;
+    void serialize(std::ostream &out) const override;
 
   private:
     size_t num_columns_ = 0;
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 2d3c95a4e0..db09fc403c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -488,7 +488,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             std::tie(r_min, r_max) = boost::math::tools::bisect(
-                get_dl, r_min, r_max, boost::math::tools::eps_tolerance<double>(1e-5)
+                get_dl, r_min, r_max, boost::math::tools::eps_tolerance<double>(5)
             );
 
             double r = r_max;
@@ -537,7 +537,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         common::logger->trace("Finding common p and r.");
 
-        auto [r_map, target_p] = get_rp([&](const auto &callback) {
+        double r_map;
+        double target_p;
+        std::tie(r_map, target_p) = get_rp([&](const auto &callback) {
             for (size_t j = 0; j < groups.size(); ++j) {
                 double f = target_sum / sums[j];
                 std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
@@ -870,7 +872,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (a_min < a_max) {
                     std::tie(a_min, a_max) = boost::math::tools::bisect(
                         get_dl,
-                        a_min, a_max, boost::math::tools::eps_tolerance<double>(1e-5)
+                        a_min, a_max, boost::math::tools::eps_tolerance<double>(5)
                     );
                 }
 
@@ -1709,13 +1711,14 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         );
     } else {
         auto generate_bit_rows = [&](const auto &callback) {
-            size_t batch_size = (matrix.num_rows() + num_threads - 1) / num_threads;
+            size_t n = matrix.num_rows();
+            size_t batch_size = (n + num_threads - 1) / num_threads;
             size_t rows_per_update = 10000;
-            ProgressBar progress_bar(matrix.num_rows(), "Streaming rows", std::cerr, !common::get_verbose());
+            ProgressBar progress_bar(n, "Streaming rows", std::cerr, !common::get_verbose());
             #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
-            for (size_t k = 0; k < matrix.num_rows(); k += batch_size) {
+            for (size_t k = 0; k < n; k += batch_size) {
                 size_t begin = k;
-                size_t end = std::min(begin + batch_size, matrix.num_rows());
+                size_t end = std::min(begin + batch_size, n);
                 size_t thread_id = k / batch_size;
                 std::vector<Row> row_ids;
                 for (size_t row_batch_i = begin; row_batch_i < end; row_batch_i += rows_per_update) {

From da75095b79d37f3c0b62e65255ca417cbcaac317 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 27 Sep 2024 18:47:13 +0200
Subject: [PATCH 358/410] fix loading from columns without counts

---
 .../src/graph/annotated_graph_algorithm.cpp   | 43 +++++++++++++------
 1 file changed, 31 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index db09fc403c..9b6570ceb1 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1543,7 +1543,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
     common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
 
-    size_t total_labels = labels_in.size() + labels_out.size();
+    size_t total_labels = 0;
+    for (const auto &file : files) {
+        total_labels += annot::ColumnCompressed<>::read_num_labels(file);
+    }
 
     using ColumnValuesMem = std::vector<std::unique_ptr<const sdsl::int_vector<>>>;
     using ColumnValuesDisk = std::vector<std::unique_ptr<const sdsl::int_vector_buffer<>>>;
@@ -1551,9 +1554,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::variant<ColumnValuesMem, ColumnValuesDisk> column_values_all;
 
     if (config.test_by_unitig) {
-        column_values_all = ColumnValuesMem(total_labels);
+        column_values_all = ColumnValuesMem();
     } else {
-        column_values_all = ColumnValuesDisk(total_labels);
+        column_values_all = ColumnValuesDisk();
     }
 
     return std::visit([&](auto&& column_values_all) {
@@ -1565,15 +1568,31 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
         std::vector<bool> groups(total_labels);
-        annot::ColumnCompressed<>::load_columns_and_values(
-            files,
-            [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector>&& column, ValuesContainer&& column_values) {
-                groups[offset] = !labels_in.count(label);
-                columns_all[offset].reset(column.release());
-                column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
-            },
-            num_parallel_files
-        );
+        if (files.empty()) {
+            throw std::runtime_error("No files provided");
+        }
+
+        if (std::filesystem::exists(files[0] + ".counts")) {
+            column_values_all.resize(total_labels);
+            annot::ColumnCompressed<>::load_columns_and_values(
+                files,
+                [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector>&& column, ValuesContainer&& column_values) {
+                    groups[offset] = !labels_in.count(label);
+                    columns_all[offset].reset(column.release());
+                    column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
+                },
+                num_parallel_files
+            );
+        } else {
+            annot::ColumnCompressed<>::merge_load(
+                files,
+                [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector>&& column) {
+                    groups[offset] = !labels_in.count(label);
+                    columns_all[offset].reset(column.release());
+                },
+                num_parallel_files
+            );
+        }
 
         uint8_t max_width = 0;
         for (const auto &col_vals : column_values_all) {

From c34cbcd93f4812be5ddc3552334e7be4206584c0 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 27 Sep 2024 20:01:26 +0200
Subject: [PATCH 359/410] support independent labels and labels in both sets

---
 .../src/graph/annotated_graph_algorithm.cpp   | 157 ++++++++++++------
 .../test_annotated_graph_algorithm.cpp        |  26 +--
 2 files changed, 123 insertions(+), 60 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9b6570ceb1..98049a23fa 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -40,6 +40,13 @@ double CDF_CUTOFF = 0.95;
 double HIST_CUTOFF = 1.00;
 uint64_t N_BUCKETS_FOR_ESTIMATION = 3;
 
+enum Group {
+    IN,
+    OUT,
+    OTHER,
+    BOTH
+};
+
 
 template<class To, class From>
 std::enable_if_t<
@@ -65,7 +72,7 @@ std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValS
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                          const HistGetter &get_hist_map,
                          const Generator &generate_rows,
-                         const std::vector<bool> &groups,
+                         const std::vector<Group> &groups,
                          const DifferentialAssemblyConfig &config,
                          size_t num_threads = 1,
                          std::filesystem::path tmp_dir = "",
@@ -81,8 +88,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
     bool parallel = num_parallel_files > 1;
 
-    size_t num_labels_out = std::count(groups.begin(), groups.end(), true);
-    size_t num_labels_in = groups.size() - num_labels_out;
+    size_t num_labels_both = std::count(groups.begin(), groups.end(), Group::BOTH);
+    size_t num_labels_out = std::count(groups.begin(), groups.end(), Group::OUT);
+    size_t num_labels_in = std::count(groups.begin(), groups.end(), Group::IN);
+    size_t total_labels = num_labels_in + num_labels_out;
+    num_labels_in += num_labels_both;
+    num_labels_out += num_labels_both;
 
     common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
 
@@ -102,6 +113,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
+            if (groups[j] == Group::OTHER) {
+                min_counts[j] = std::numeric_limits<size_t>::max();
+                continue;
+            }
+
             // set cutoff for lower end of distribution
             auto [mean_est, nzeros_est] = estimate_ztp_mean(
                 [&](const auto &callback) {
@@ -178,8 +194,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
     for (size_t j = 0; j < hists.size(); ++j) {
         hists[j] = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(hists_map[j].values_container());
-        hists_map[j].clear();
         std::sort(hists[j].begin(), hists[j].end(), utils::LessFirst());
+        hists_map[j].clear();
     }
     hists_map.clear();
 
@@ -205,6 +221,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<uint64_t> sums(groups.size());
     std::vector<uint64_t> sums_of_squares(groups.size());
 
+    int64_t total_kmers = 0;
     for (size_t j = 0; j < groups.size(); ++j) {
         for (const auto &[k, c] : hists[j]) {
             sums[j] += k * c;
@@ -215,18 +232,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             max_obs_vals[j] = std::max(max_obs_vals[j], k);
         }
 
-        if (groups[j]) {
+        if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
             out_kmers += sums[j];
-        } else {
+
+        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
             in_kmers += sums[j];
-        }
+
+        total_kmers += sums[j];
 
         common::logger->trace("{}: n_unique: {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
                               j, n_kmers[j], sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
     }
 
-    int64_t total_kmers = in_kmers + out_kmers;
-
     common::logger->trace("Number of kept unique k-mers: {}\tNumber of kept k-mers: {}",
                           nelem, total_kmers);
 
@@ -511,20 +528,22 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     total += it->second;
             }
             hist_its[j] = it;
-            nb_params[j] = get_rp([&](const auto &callback) {
-                std::for_each(hist.begin(), it, [&](const auto &a) {
-                    const auto &[k, c] = a;
-                    callback(k, c);
+            if (hist.size()) {
+                nb_params[j] = get_rp([&](const auto &callback) {
+                    std::for_each(hist.begin(), it, [&](const auto &a) {
+                        const auto &[k, c] = a;
+                        callback(k, c);
+                    });
                 });
-            });
-            const auto &[r, p] = nb_params[j];
-            common::logger->trace("{}: size: {}\tmax_val: {}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
-                                  j, sums[j], (it - 1)->first, r * (1-p)/p, r*(1-p)/p/p, r, p);
+                const auto &[r, p] = nb_params[j];
+                common::logger->trace("{}: size: {}\tmax_val: {}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
+                                    j, sums[j], (it - 1)->first, r * (1-p)/p, r*(1-p)/p/p, r, p);
 
-            total += (hist.size() && hist[0].first == 0 ? hist[0].second : 0);
+                total += hist[0].first == 0 ? hist[0].second : 0;
 
-            #pragma omp atomic
-            matrix_size += total;
+                #pragma omp atomic
+                matrix_size += total;
+            }
         }
 
         common::logger->trace("Scaling distributions");
@@ -533,7 +552,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         for (size_t j = 0; j < groups.size(); ++j) {
             target_sum += log(static_cast<double>(sums[j]));
         }
-        target_sum = exp(target_sum / groups.size());
+        target_sum = exp(target_sum / total_labels);
 
         common::logger->trace("Finding common p and r.");
 
@@ -566,6 +585,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Computing quantile maps");
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
+            if (hists[j].empty())
+                continue;
+
             const auto &[r, p] = nb_params[j];
 
             boost::math::negative_binomial nb_out(r_map, target_p);
@@ -610,20 +632,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
         in_kmers = 0;
         out_kmers = 0;
+        total_kmers = 0;
         for (size_t j = 0; j < groups.size(); ++j) {
             for (const auto &[k, m] : count_maps[j]) {
                 const auto &[v, c] = m;
 
-                if (groups[j]) {
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
                     out_kmers += v * c;
-                } else {
+
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH)
                     in_kmers += v * c;
-                }
+
+                total_kmers += v * c;
             }
         }
 
-        total_kmers = in_kmers + out_kmers;
-
         common::logger->trace("  Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
         auto compute_min_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t n) {
@@ -733,10 +756,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             if (count_maps.size())
                                 c = count_maps[j].find(c)->second.first;
 
-                            if (groups[j]) {
+                            if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                                 out_sum += c;
                                 ++count_out;
-                            } else {
+                            }
+
+                            if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
                                 in_sum += c;
                                 ++count_in;
                             }
@@ -795,7 +820,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     total += c;
                 }
 
-                double r = target_p / (1.0 - target_p) * n / groups.size();
+                double r = target_p / (1.0 - target_p) * n / total_labels;
 
                 #pragma omp atomic
                 ln_mu += -log(r) * total;
@@ -804,7 +829,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 ln_var += log(r) * log(r) * total;
             }
 
-            size_t total = nelem * groups.size();
+            size_t total = nelem * total_labels;
             ln_mu /= total;
             ln_var = ln_var / total - ln_mu * ln_mu;
 
@@ -813,7 +838,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                   exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1),
                                   exp(-ln_mu - ln_var/2));
 
-            auto get_rp = [ln_mu,ln_var,gs=groups.size()](const auto &counts) {
+            auto get_rp = [ln_mu,ln_var,gs=total_labels](const auto &counts) {
                 double mu = 0;
                 double var = 0;
                 for (int64_t c : counts) {
@@ -963,7 +988,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::vector<double> pmf_null { 1.0 };
 
         for (size_t i = 1; i <= groups.size(); ++i) {
-            if (groups[i - 1]) {
+            if (groups[i - 1] == Group::OUT || groups[i - 1] == Group::BOTH) {
                 std::vector<double> pmf_out_cur(pmf_out.size() + 1);
                 pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
                 pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
@@ -971,7 +996,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1] + (1.0 - p[i - 1]) * pmf_out[k];
                 }
                 std::swap(pmf_out_cur, pmf_out);
-            } else {
+            }
+
+            if (groups[i - 1] == Group::IN || groups[i - 1] == Group::BOTH) {
                 std::vector<double> pmf_in_cur(pmf_in.size() + 1);
                 pmf_in_cur[0] = (1.0 - p[i - 1]) * pmf_in[0];
                 pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
@@ -990,7 +1017,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         common::logger->trace("Precomputing p-values");
-        std::vector<std::vector<double>> pvals(groups.size() + 1);
+        std::vector<std::vector<double>> pvals(num_labels_in + num_labels_out + 1);
         pvals[0].emplace_back(1.0);
 
         double min_pval = 1.0;
@@ -1039,17 +1066,25 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage{}, nullptr);
         }
 
-        compute_min_pval = [pvals,num_labels_in](int64_t, const PairContainer &row) {
-            size_t front = row.size() - std::min(row.size(), num_labels_in);
-            return std::min(pvals[row.size()][front],
-                            pvals[row.size()].back());
+        compute_min_pval = [pvals,num_labels_in,&groups](int64_t, const PairContainer &row) {
+            size_t n = 0;
+            for (const auto &[j, c] : row) {
+                n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
+            }
+            size_t front = n - std::min(n, num_labels_in);
+            return std::min(pvals[n][front],
+                            pvals[n].back());
         };
 
         compute_pval = [pvals,&groups](int64_t, int64_t, const auto &row) {
-            uint64_t s = std::count_if(row.begin(), row.end(),
-                                       [&](const auto &a) { return !groups[a.first]; });
+            int64_t n = 0;
+            uint64_t s = 0;
+            for (const auto &[j, c] : row) {
+                n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
+                s += (groups[j] == Group::IN);
+            }
 
-            return pvals[row.size()][s];
+            return pvals[n][s];
         };
 
     } else if (config.test_type == "notest") {
@@ -1083,10 +1118,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                     row.emplace_back(j, c);
                     vals.emplace_back(c);
-                    if (groups[j]) {
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                         out_sum += c;
                         ++count_out;
-                    } else {
+                    }
+
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
                         in_sum += c;
                         ++count_in;
                     }
@@ -1331,10 +1368,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         c = count_maps[j].find(c)->second.first;
 
                     row.emplace_back(j, c);
-                    if (groups[j]) {
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                         out_sum += c;
                         ++count_out;
-                    } else {
+                    }
+
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
                         in_sum += c;
                         ++count_in;
                     }
@@ -1567,7 +1606,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         using PairContainer = Vector<std::pair<uint64_t, value_type>>;
 
         std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
-        std::vector<bool> groups(total_labels);
+        std::vector<Group> groups(total_labels);
         if (files.empty()) {
             throw std::runtime_error("No files provided");
         }
@@ -1577,7 +1616,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             annot::ColumnCompressed<>::load_columns_and_values(
                 files,
                 [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector>&& column, ValuesContainer&& column_values) {
-                    groups[offset] = !labels_in.count(label);
+                    bool is_in = labels_in.count(label);
+                    bool is_out = labels_out.count(label);
+                    if (is_in) {
+                        groups[offset] = !is_out ? Group::IN : Group::BOTH;
+                    } else {
+                        groups[offset] = is_out ? Group::OUT : Group::OTHER;
+                    }
                     columns_all[offset].reset(column.release());
                     column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
                 },
@@ -1587,7 +1632,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             annot::ColumnCompressed<>::merge_load(
                 files,
                 [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector>&& column) {
-                    groups[offset] = !labels_in.count(label);
+                    bool is_in = labels_in.count(label);
+                    bool is_out = labels_out.count(label);
+                    if (is_in) {
+                        groups[offset] = !is_out ? Group::IN : Group::BOTH;
+                    } else {
+                        groups[offset] = is_out ? Group::OUT : Group::OTHER;
+                    }
                     columns_all[offset].reset(column.release());
                 },
                 num_parallel_files
@@ -1704,10 +1755,16 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
     size_t total_labels = labels_in.size() + labels_out.size();
     const auto &annotation = anno_graph.get_annotator();
 
-    std::vector<bool> groups;
+    std::vector<Group> groups;
     groups.reserve(total_labels);
     for (const auto &label : annotation.get_label_encoder().get_labels()) {
-        groups.emplace_back(!labels_in.count(label));
+        bool is_in = labels_in.count(label);
+        bool is_out = labels_out.count(label);
+        if (is_in) {
+            groups.emplace_back(!is_out ? Group::IN : Group::BOTH);
+        } else {
+            groups.emplace_back(is_out ? Group::OUT : Group::OTHER);
+        }
     }
 
     auto graph_ptr = std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr());
diff --git a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
index ca60b1f769..5b3b3c77dd 100644
--- a/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
+++ b/metagraph/tests/annotation/test_annotated_graph_algorithm.cpp
@@ -116,12 +116,7 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelTest) {
             auto graph_ptr = std::static_pointer_cast<const Graph>(anno_graph->get_graph_ptr());
 
             std::unordered_set<std::string> obs_labels, obs_kmers;
-            const std::unordered_set<std::string> ref_kmers {
-                std::string(k - 1, 'A') + "C"
-            };
-            const std::unordered_set<std::string> ref_labels {
-                "B", "C"
-            };
+            std::unordered_set<std::string> ref_kmers;
 
             DifferentialAssemblyConfig config {
                 .test_type = "poisson_binom",
@@ -133,7 +128,18 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelTest) {
             tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_in { "B", "C" };
             tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> labels_out { "A" };
 
-            {
+            std::unordered_set<std::string> ref_labels;
+
+            std::for_each(labels.begin() + 2, labels.end(), [&](const auto &label) {
+                if (label != "C")
+                    labels_out.emplace(label);
+
+                if (k >= 8 || label == "E" || (k >= 7 && label == "D")) {
+                    ref_labels.emplace("B");
+                    ref_labels.emplace("C");
+                    ref_kmers.emplace(std::string(k - 1, 'A') + "C");
+                }
+
                 auto [masked_dbg_in, masked_dbg_out, pvals, tmp_file] = mask_nodes_by_label_dual<std::vector<uint64_t>>(
                     *anno_graph,
                     labels_in, labels_out,
@@ -146,9 +152,9 @@ TYPED_TEST(MaskedDeBruijnGraphAlgorithm, MaskIndicesByLabelTest) {
                     obs_kmers.insert(kmer);
                 });
 
-                EXPECT_EQ(ref_labels, obs_labels) << k;
-                EXPECT_EQ(ref_kmers, obs_kmers) << k;
-            }
+                EXPECT_EQ(ref_labels, obs_labels) << k << " " << label;
+                EXPECT_EQ(ref_kmers, obs_kmers) << k << " " << label;
+            });
 
             if constexpr(std::is_same_v<Annotation, ColumnCompressed<>>) {
                 // TODO

From 22d472b4a82b0d355d4f926429766ed7a451fc05 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 27 Sep 2024 20:08:15 +0200
Subject: [PATCH 360/410] minor

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 98049a23fa..b49cb1a403 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -751,6 +751,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         vec.reserve(raw_row.size());
                         size_t count_in = 0;
                         size_t count_out = 0;
+                        size_t total_count = 0;
                         for (const auto &[j, raw_c] : raw_row) {
                             uint64_t c = raw_c;
                             if (count_maps.size())
@@ -766,10 +767,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                 ++count_in;
                             }
 
+                            total_count += groups[j] != Group::OTHER;
+
                             vec.emplace_back(c);
                         }
 
-                        if (count_in + count_out >= config.min_recurrence) {
+                        if (total_count >= config.min_recurrence) {
                             double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
                             in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
                             out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
@@ -1111,6 +1114,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (kept[row_i]) {
                 size_t count_in = 0;
                 size_t count_out = 0;
+                size_t total_count = 0;
                 for (const auto &[j, raw_c] : raw_row) {
                     uint64_t c = raw_c;
                     if (count_maps.size())
@@ -1127,9 +1131,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         in_sum += c;
                         ++count_in;
                     }
+
+                    total_count += groups[j] != Group::OTHER;
                 }
 
-                if (count_in + count_out >= config.min_recurrence) {
+                if (total_count >= config.min_recurrence) {
                     double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
                     if (out_stat != 0)
                         out_stat_int = out_stat > 0 ? ceil(out_stat) : floor(out_stat);
@@ -1362,6 +1368,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (kept[row_i]) {
                 size_t count_in = 0;
                 size_t count_out = 0;
+                size_t total_count = 0;
                 for (const auto &[j, raw_c] : raw_row) {
                     uint64_t c = raw_c;
                     if (count_maps.size())
@@ -1377,9 +1384,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         in_sum += c;
                         ++count_in;
                     }
+
+                    total_count += groups[j] != Group::OTHER;
                 }
 
-                if (count_in + count_out >= config.min_recurrence) {
+                if (total_count >= config.min_recurrence) {
                     double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
                     in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
                     out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;

From 4989e7079bb7572d5aebe1512f1e6414d5c4a36a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 25 Nov 2024 21:50:45 +0100
Subject: [PATCH 361/410] fixes

---
 metagraph/external-libraries/eigen            |  2 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 32 +++++++++++++------
 2 files changed, 24 insertions(+), 10 deletions(-)

diff --git a/metagraph/external-libraries/eigen b/metagraph/external-libraries/eigen
index 3147391d94..36b9596275 160000
--- a/metagraph/external-libraries/eigen
+++ b/metagraph/external-libraries/eigen
@@ -1 +1 @@
-Subproject commit 3147391d946bb4b6c68edd901f2add6ac1f31f8c
+Subproject commit 36b95962756c1fce8e29b1f8bc45967f30773c00
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b49cb1a403..96eadc36f5 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -918,6 +918,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
                     it.value() = get_rp(it->first).first;
+                    // #pragma omp critical
+                    // common::logger->info("N: {}\tr: {}", n, it->second);
                 }
             }
             progress_bar += (vector_counts.size() - 1) % 1000;
@@ -1040,16 +1042,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             for (uint64_t s = 0; s < probs.size(); ++s) {
                 pvals[n].emplace_back(0.0);
-                bool found = false;
                 for (uint64_t sp = 0; sp < probs.size(); ++sp) {
-                    if (pmf_in[sp] <= pmf_in[s]) {
-                        found = true;
+                    if (pmf_in[sp] <= pmf_in[s])
                         pvals[n][s] += probs[sp];
-                    }
                 }
+            }
 
-                if (found)
-                    min_pval = std::min(min_pval, pvals[n][s]);
+            size_t front = n - std::min(n, num_labels_out);
+            for (uint64_t s = 0; s < front; ++s) {
+                if (pvals[n][s] > 0) {
+                    common::logger->error("Non-zero p-value in impossible configuration: {},{}", n, s);
+                    throw std::domain_error("");
+                }
+            }
+            double local_min_pval = std::min(pvals[n][front],
+                                             pvals[n].back());
+            min_pval = std::min(min_pval, local_min_pval);
+
+            for (uint64_t s = front; s < pvals[n].size(); ++s) {
+                if (pvals[n][s] < local_min_pval) {
+                    common::logger->error("Min p-value not at boundary: {},{}: {} < {}", n, s, pvals[n][s], local_min_pval);
+                    throw std::domain_error("");
+                }
             }
         }
 
@@ -1069,12 +1083,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage{}, nullptr);
         }
 
-        compute_min_pval = [pvals,num_labels_in,&groups](int64_t, const PairContainer &row) {
+        compute_min_pval = [pvals,num_labels_out,&groups](int64_t, const PairContainer &row) {
             size_t n = 0;
             for (const auto &[j, c] : row) {
                 n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
             }
-            size_t front = n - std::min(n, num_labels_in);
+            size_t front = n - std::min(n, num_labels_out);
             return std::min(pvals[n][front],
                             pvals[n].back());
         };
@@ -1316,7 +1330,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            double comb_pval = combine_pvals(pvals_min);
+            double comb_pval = *std::min_element(pvals_min.begin(), pvals_min.end());
             if (comb_pval < config.family_wise_error_rate) {
                 for (node_index node : path) {
                     set_bit(unitig_start.data(), node, parallel, MO_RELAXED);

From 46f88b7bf181a2263d3d90267259346c096c2335 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 4 Dec 2024 17:38:07 +0100
Subject: [PATCH 362/410] minor

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 96eadc36f5..dff6a4ae6e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1029,11 +1029,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         for (size_t n = 1; n < pvals.size(); ++n) {
             std::vector<double> probs;
+            size_t front = n - std::min(n, num_labels_out);
             for (uint64_t s = 0; s < pmf_in.size(); ++s) {
                 if (s > n)
                     break;
                 uint64_t t = n - s;
-                if (t < pmf_out.size()) {
+                if (s < pmf_in.size() && t < pmf_out.size()) {
                     probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t]) - log2(pmf_null[n])));
                 } else {
                     probs.emplace_back(0.0);
@@ -1048,7 +1049,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            size_t front = n - std::min(n, num_labels_out);
             for (uint64_t s = 0; s < front; ++s) {
                 if (pvals[n][s] > 0) {
                     common::logger->error("Non-zero p-value in impossible configuration: {},{}", n, s);

From 0ec507742782ae2dbc2a5876f7e94af73c9dc3ca Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 4 Dec 2024 23:50:46 +0100
Subject: [PATCH 363/410] bug fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 29 +++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index dff6a4ae6e..edc7ebdc25 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1021,6 +1021,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             std::swap(pmf_null_cur, pmf_null);
         }
 
+        if (pmf_in.size() != num_labels_in + 1) {
+            common::logger->error("PMF in wrong: {} != {}", pmf_in.size(), num_labels_in + 1);
+            throw std::domain_error("");
+        }
+
+        if (pmf_out.size() != num_labels_out + 1) {
+            common::logger->error("PMF out wrong: {} != {}", pmf_out.size(), num_labels_out + 1);
+            throw std::domain_error("");
+        }
+
+        if (pmf_null.size() != num_labels_in + num_labels_out + 1) {
+            common::logger->error("PMF null wrong: {} != {}", pmf_null.size(), num_labels_in + num_labels_out + 1);
+            throw std::domain_error("");
+        }
+
         common::logger->trace("Precomputing p-values");
         std::vector<std::vector<double>> pvals(num_labels_in + num_labels_out + 1);
         pvals[0].emplace_back(1.0);
@@ -1035,6 +1050,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     break;
                 uint64_t t = n - s;
                 if (s < pmf_in.size() && t < pmf_out.size()) {
+                    if (s < front) {
+                        common::logger->error("Attempting non-zero p-value in impossible configuration: {},{}", n, s);
+                        throw std::domain_error("");
+                    }
                     probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t]) - log2(pmf_null[n])));
                 } else {
                     probs.emplace_back(0.0);
@@ -1043,7 +1062,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             for (uint64_t s = 0; s < probs.size(); ++s) {
                 pvals[n].emplace_back(0.0);
+                if (s > n)
+                    break;
+                uint64_t t = n - s;
+                if (s >= pmf_in.size() || t >= pmf_out.size())
+                    continue;
                 for (uint64_t sp = 0; sp < probs.size(); ++sp) {
+                    if (sp > n)
+                        break;
+                    uint64_t tp = n - sp;
+                    if (sp >= pmf_in.size() || tp >= pmf_out.size())
+                        continue;
                     if (pmf_in[sp] <= pmf_in[s])
                         pvals[n][s] += probs[sp];
                 }

From 8d0376a18f21e298a9ba3c8a69b2c781a6060e76 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 15 Jan 2025 10:11:57 +0100
Subject: [PATCH 364/410] no saving rows

---
 .../src/annotation/int_matrix/base/int_matrix.cpp    |  3 ++-
 .../int_matrix/csr_matrix/csr_matrix_flat.cpp        | 12 +++++++-----
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
index 78fbbc01cf..2fd889fc55 100644
--- a/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
+++ b/metagraph/src/annotation/int_matrix/base/int_matrix.cpp
@@ -68,7 +68,8 @@ std::vector<VectorMap<uint64_t, size_t>> IntMatrix::get_histograms(const std::ve
 
         Vector<uint64_t> counts(hists_map.size());
         for (const auto &[j, raw_c] : row) {
-            counts[j] = raw_c;
+            if (min_counts.empty() || raw_c >= min_counts[j])
+                counts[j] = raw_c;
         }
 
         #pragma omp critical
diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
index d1b667f0c8..74eb4b1ca5 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
@@ -192,12 +192,14 @@ CSRMatrixFlat::get_histograms(const std::vector<size_t> &min_counts,
                     ++local_num_empty_rows;
                 } else {
                     for (const auto &[j, c] : row) {
-                        if (c < HIST_CUTOFF) {
-                            ++hists_map_base[j][c];
-                        } else {
-                            ++hists_map[j][c];
+                        if (min_counts.empty() || c >= min_counts[j]) {
+                            if (c < HIST_CUTOFF) {
+                                ++hists_map_base[j][c];
+                            } else {
+                                ++hists_map[j][c];
+                            }
+                            ++num_nonzeros[thread_id][j];
                         }
-                        ++num_nonzeros[thread_id][j];
                     }
                 }
             };

From 4ae4b5aadc68fedef5c8d9c9bbccb14078435a06 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 15 Jan 2025 10:13:54 +0100
Subject: [PATCH 365/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 73 ++++++++++++++++++-
 1 file changed, 69 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index edc7ebdc25..5b505b46d1 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -753,6 +753,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         size_t count_out = 0;
                         size_t total_count = 0;
                         for (const auto &[j, raw_c] : raw_row) {
+                            if (min_counts.size() && raw_c < min_counts[j])
+                                continue;
+
                             uint64_t c = raw_c;
                             if (count_maps.size())
                                 c = count_maps[j].find(c)->second.first;
@@ -1038,6 +1041,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         common::logger->trace("Precomputing p-values");
         std::vector<std::vector<double>> pvals(num_labels_in + num_labels_out + 1);
+        std::vector<double> mid_points(num_labels_in + num_labels_out + 1);
+        mid_points.emplace_back(0);
         pvals[0].emplace_back(1.0);
 
         double min_pval = 1.0;
@@ -1073,7 +1078,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     uint64_t tp = n - sp;
                     if (sp >= pmf_in.size() || tp >= pmf_out.size())
                         continue;
-                    if (pmf_in[sp] <= pmf_in[s])
+                    if (probs[sp] <= probs[s])
                         pvals[n][s] += probs[sp];
                 }
             }
@@ -1088,12 +1093,63 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                              pvals[n].back());
             min_pval = std::min(min_pval, local_min_pval);
 
+            size_t max_prob_counts = 0;
+            double max_prob_pos = 0;
+            // double max_prob = -1;
+
             for (uint64_t s = front; s < pvals[n].size(); ++s) {
                 if (pvals[n][s] < local_min_pval) {
                     common::logger->error("Min p-value not at boundary: {},{}: {} < {}", n, s, pvals[n][s], local_min_pval);
                     throw std::domain_error("");
                 }
             }
+            if (front + 1 == pvals[n].size()) {
+                max_prob_counts = 1;
+                max_prob_pos = front;
+            } else if (front + 2 == pvals[n].size()) {
+                max_prob_counts = 2;
+                max_prob_pos = front + front + 1;
+            } else {
+                for (uint64_t s = front + 1; s < pvals[n].size(); ++s) {
+                    if (probs[s] == probs[s-1]) {
+                        max_prob_counts = 2;
+                        max_prob_pos = s + s;
+                        break;
+                    }
+
+                    if (s + 1 < pvals[n].size()) {
+                        if (probs[s-1] < probs[s] && probs[s] < probs[s+1])
+                            continue;
+
+                        if (probs[s-1] > probs[s] && probs[s] > probs[s+1])
+                            continue;
+
+                        if (probs[s] > probs[s-1] && probs[s] > probs[s+1]) {
+                            if (probs[s] - probs[s-1] == probs[s]-probs[s+1]) {
+                                max_prob_counts = 1;
+                                max_prob_pos = s;
+                            } else if (probs[s] - probs[s-1] > probs[s]-probs[s+1]) {
+                                max_prob_counts = 2;
+                                max_prob_pos = s + s + 1;
+                            } else {
+                                max_prob_counts = 2;
+                                max_prob_pos = s + s - 1;
+                            }
+                        }
+                    }
+                }
+            }
+
+            if (max_prob_counts == 0) {
+                max_prob_counts = 2;
+                max_prob_pos = front - 1 + front;
+            }
+
+            mid_points[n] = static_cast<double>(max_prob_pos) / max_prob_counts;
+            common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}",
+                                  n, mid_points[n],
+                                  fmt::join(probs,","),
+                                  fmt::join(pvals[n],","));
         }
 
         common::logger->trace("Min. p-value: {}", min_pval);
@@ -1122,7 +1178,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             pvals[n].back());
         };
 
-        compute_pval = [pvals,&groups](int64_t, int64_t, const auto &row) {
+        compute_pval = [pvals,mid_points,&groups](int64_t, int64_t, const auto &row) {
             int64_t n = 0;
             uint64_t s = 0;
             for (const auto &[j, c] : row) {
@@ -1159,6 +1215,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 size_t count_out = 0;
                 size_t total_count = 0;
                 for (const auto &[j, raw_c] : raw_row) {
+                    if (min_counts.size() && raw_c < min_counts[j])
+                        continue;
+
                     uint64_t c = raw_c;
                     if (count_maps.size())
                         c = count_maps[j].find(c)->second.first;
@@ -1413,6 +1472,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 size_t count_out = 0;
                 size_t total_count = 0;
                 for (const auto &[j, raw_c] : raw_row) {
+                    if (min_counts.size() && raw_c < min_counts[j])
+                        continue;
+
                     uint64_t c = raw_c;
                     if (count_maps.size())
                         c = count_maps[j].find(c)->second.first;
@@ -1449,6 +1511,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             size_t n = config.test_type != "poisson_binom" ? in_sum + out_sum : row.size();
+
             if (!in_kmer && !out_kmer) {
                 row.clear();
                 n = 0;
@@ -1731,8 +1794,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     bool found = false;
                     Vector<uint64_t> counts(groups.size());
                     for (const auto &[j, raw_c] : row) {
-                        counts[j] = raw_c;
-                        found |= min_counts.empty() || raw_c >= min_counts[j];
+                        if (min_counts.empty() || raw_c >= min_counts[j]) {
+                            counts[j] = raw_c;
+                            found = true;
+                        }
                     }
 
                     if (!found) {

From 10921c7086f56758fada276003efc79050ca2281 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 14 Feb 2025 11:16:27 +0100
Subject: [PATCH 366/410] cleanup and lnb

---
 .../src/graph/annotated_graph_algorithm.cpp   | 339 +++++++++++++++---
 1 file changed, 296 insertions(+), 43 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5b505b46d1..376aa01a05 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -219,13 +219,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<uint64_t> max_obs_vals(groups.size());
     std::vector<size_t> n_kmers(groups.size());
     std::vector<uint64_t> sums(groups.size());
-    std::vector<uint64_t> sums_of_squares(groups.size());
 
     int64_t total_kmers = 0;
     for (size_t j = 0; j < groups.size(); ++j) {
         for (const auto &[k, c] : hists[j]) {
             sums[j] += k * c;
-            sums_of_squares[j] += k * k * c;
             if (k != 0)
                 n_kmers[j] += c;
 
@@ -406,47 +404,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return pval;
         };
-    } else if (config.test_type == "fisher") {
-        double lbase_shared = lgamma(in_kmers + 1) + lgamma(out_kmers + 1) - lgamma(total_kmers + 1);
-
-        compute_min_pval = [&,lbase_shared](int64_t m1, const PairContainer&) {
-            if (m1 == 0)
-                return 1.0;
-
-            int64_t m2 = total_kmers - m1;
-
-            auto get_pval = [&](int64_t a) {
-                double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
-
-                int64_t b = in_kmers - a;
-                int64_t c = m1 - a;
-                int64_t d = m2 - b;
-                assert(d == out_kmers - c);
-
-                return out_kmers < c
-                    ? 1.0
-                    : exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
-            };
-
-            return std::min(get_pval(0), get_pval(std::min(in_kmers, m1)));
-        };
-
-        compute_pval = [&,lbase_shared](int64_t in_sum, int64_t out_sum, const auto &row) {
-            if (row.empty())
-                return 1.0;
-
-            int64_t m1 = in_sum + out_sum;
-            int64_t m2 = total_kmers - m1;
-
-            double lbase = lbase_shared + lgamma(m1 + 1) + lgamma(m2 + 1);
-
-            int64_t a = in_sum;
-            int64_t b = in_kmers - in_sum;
-            int64_t c = out_sum;
-            int64_t d = out_kmers - out_sum;
-
-            return exp(lbase - lgamma(a + 1) - lgamma(b + 1) - lgamma(c + 1) - lgamma(d + 1));
-        };
     } else if (config.test_type == "nbinom_exact" || config.test_type == "gnb_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
         auto get_rp = [&](const auto &generate) {
@@ -980,6 +937,302 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row);
             };
         }
+    } else if (config.test_type == "lnb_exact") {
+        double max_sum = *std::max_element(sums.begin(), sums.end());
+        std::vector<double> scale_factor;
+        scale_factor.reserve(sums.size());
+        std::transform(sums.begin(), sums.end(), std::back_inserter(scale_factor),
+                       [&](uint64_t sum) -> double { return max_sum / sum; });
+
+        common::logger->trace("Enumerating row count distributions");
+        std::vector<tsl::hopscotch_map<std::vector<double>, double, utils::VectorHash>> vector_counts;
+        {
+            std::vector<std::vector<tsl::hopscotch_map<std::vector<double>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
+
+            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
+                if (!kept[row_i])
+                    return;
+
+                double in_sum = 0;
+                double out_sum = 0;
+                bool in_kmer = false;
+                bool out_kmer = false;
+
+                std::vector<double> vec;
+                vec.reserve(raw_row.size());
+                size_t count_in = 0;
+                size_t count_out = 0;
+                size_t total_count = 0;
+                for (const auto &[j, raw_c] : raw_row) {
+                    if (min_counts.size() && raw_c < min_counts[j])
+                        continue;
+
+                    double c = raw_c * scale_factor[j];
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                        out_sum += c;
+                        ++count_out;
+                    }
+
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                        in_sum += c;
+                        ++count_in;
+                    }
+
+                    total_count += groups[j] != Group::OTHER;
+
+                    vec.emplace_back(c);
+                }
+
+                if (total_count >= config.min_recurrence) {
+                    in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence;
+                    out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence;
+                }
+
+                if (!in_kmer && !out_kmer)
+                    return;
+
+                double sum = in_sum + out_sum;
+
+                // if floating point error puts the sum slightly above the integer value, then round down
+                size_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
+                if (n >= vector_counts_ms[bucket_idx].size())
+                    vector_counts_ms[bucket_idx].resize(n + 1);
+
+                std::sort(vec.begin(), vec.end());
+                ++vector_counts_ms[bucket_idx][n][vec];
+            });
+
+            vector_counts = std::move(vector_counts_ms[0]);
+            size_t max_size = vector_counts.size();
+            for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
+                max_size = std::max(max_size, vector_counts_ms[j].size());
+            }
+            vector_counts.resize(max_size);
+
+            ProgressBar progress_bar(vector_counts.size() - 1, "Merging row", std::cerr, !common::get_verbose());
+            #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+            for (size_t n = vector_counts.size(); n > 0; --n) {
+                for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
+                    if (n <= vector_counts_ms[j].size()) {
+                        for (const auto &[v, c] : vector_counts_ms[j][n - 1]) {
+                            vector_counts[n - 1][v] += c;
+                        }
+                    }
+                }
+                ++progress_bar;
+            }
+        }
+
+        auto optim = [](const auto &f, auto x_min, auto x_max) {
+            while (x_max - x_min > 1e-5) {
+                double x_mid = (x_min + x_max) / 2;
+                double x_low_mid = (x_min + x_mid) / 2;
+                double x_high_mid = (x_mid + x_max) / 2;
+                if (f(x_low_mid) > f(x_high_mid)) {
+                    x_max = x_mid;
+                } else {
+                    x_min = x_mid;
+                }
+            }
+            return (x_min + x_max) / 2;
+        };
+
+        common::logger->trace("Fitting LN prior distribution for dispersions");
+        double ln_mu = 0.0;
+        double ln_var = 0.0;
+
+        for (size_t n = 0; n < vector_counts.size(); ++n) {
+            for (const auto &[v, c] : vector_counts[n]) {
+                double total = std::accumulate(v.begin(), v.end(), double(0));
+                double total2 = std::accumulate(v.begin(), v.end(), double(0),
+                                                [&](double sum, double vv) { return sum + vv * vv;});
+                double mu = total / total_labels;
+                double mu2 = mu * mu;
+                double var = total2 / total_labels - mu2;
+                if (var <= mu) {
+                    throw std::runtime_error("var <= mu when fitting lognormal");
+                }
+
+                double r = mu2 / (var - mu);
+                ln_mu += -log(r) * c;
+                ln_var += log(r) * log(r) * c;
+            }
+        }
+
+        ln_mu /= nelem;
+        ln_var = ln_var / nelem - ln_mu * ln_mu;
+
+        common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
+                              ln_mu, ln_var,
+                              exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1),
+                              exp(-ln_mu - ln_var/2));
+
+        double expr = exp(-ln_mu + ln_var / 2);
+        double target_p = expr / (max_sum / nelem + expr);
+
+        {
+            auto calc_r = [optim,ln_mu,ln_var,gs=total_labels,p=target_p](const auto &counts) {
+                return optim([&](double r) {
+                    double val = gs * log(p) * r - gs * lgamma(r) + log(r) - pow(-log(r)-ln_mu, 2.0)/2/ln_var;
+                    for (double c : counts) {
+                        val += lgamma(c + r);
+                    }
+                    return val;
+                }, 0, 100000);
+            };
+
+            ProgressBar progress_bar(vector_counts.size(), "Precomputing r's", std::cerr, !common::get_verbose());
+            #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+            for (size_t n = 0; n < vector_counts.size(); ++n) {
+                if (n > 0 && n % 1000 == 0)
+                    progress_bar += 1000;
+
+                for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
+                    it.value() = calc_r(it->first);
+                }
+            }
+            progress_bar += vector_counts.size() % 1000;
+        }
+
+        auto get_r = std::make_shared<std::function<double(int64_t, std::vector<double>&)>>(
+            [vc=std::move(vector_counts),scale_factor](int64_t n, auto &counts) {
+                auto it = vc[n].begin();
+                if (vc[n].size() > 1) {
+                    std::sort(counts.begin(), counts.end());
+                    it = vc[n].find(counts);
+                    assert(it != vc[n].end());
+                }
+                return it->second;
+            }
+        );
+
+        compute_min_pval = [get_r,num_labels_in,num_labels_out,scale_factor](int64_t, const PairContainer &row) {
+            if (row.empty())
+                return 1.0;
+
+            std::vector<double> counts;
+            double sum = 0;
+            counts.reserve(row.size());
+            for (const auto &[j, c] : row) {
+                sum += counts.emplace_back(c * scale_factor[j]);
+            }
+            // if floating point error puts the sum slightly above the integer value, then round down
+            int64_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
+
+            double r_base = (*get_r)(n, counts);
+            if (r_base == 0.0)
+                return 1.0;
+
+            double r_in = r_base * num_labels_in;
+            double r_out = r_base * num_labels_out;
+            double r = r_in + r_out;
+            double lscaling_base = lgamma(r) / log(2);
+
+            double half_div0 = -r_in * log2(r_in) - (n + r_out) * log2(n + r_out);
+            double half_divn = -r_out * log2(r_out) - (n + r_in) * log2(n + r_in);
+
+            double pval = 0.0;
+            double lscaling = lscaling_base - lgamma(r + n) / log(2);
+            if (half_div0 >= half_divn)
+                pval += exp2(lscaling + (lgamma(r_out + n) - lgamma(r_out)) / log(2));
+
+            if (half_divn >= half_div0)
+                pval += exp2(lscaling + (lgamma(r_in + n) - lgamma(r_in)) / log(2));
+
+            return std::min(1.0, pval);
+        };
+
+        compute_pval = [get_r,num_labels_in,num_labels_out,scale_factor,groups](int64_t, int64_t, const PairContainer &row) {
+            if (row.empty())
+                return 1.0;
+
+            std::vector<double> counts;
+            double sum = 0;
+            double in_sum = 0;
+            double out_sum = 0;
+            counts.reserve(row.size());
+            for (const auto &[j, c] : row) {
+                sum += counts.emplace_back(c * scale_factor[j]);
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                    out_sum += counts.back();
+                }
+
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                    in_sum += counts.back();
+                }
+            }
+            // if floating point error puts the sum slightly above the integer value, then round down
+            int64_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
+
+            double r_base = (*get_r)(n, counts);
+
+            if (r_base == 0.0)
+                return 1.0;
+
+            double r_in = r_base * num_labels_in;
+            double r_out = r_base * num_labels_out;
+            double r = r_in + r_out;
+            double lscaling_base = lgamma(r) / log(2);
+
+            double argmin_d = r_in / r * n;
+            if (in_sum == argmin_d)
+                return 1.0;
+
+            double lscaling = lscaling_base - lgamma(r + n) / log(2);
+
+            auto get_pval = [&](const auto &get_stat) {
+                double base_stat = get_stat(in_sum, out_sum,
+                                            r_in != r_out && in_sum > 0 ? log2(in_sum) : 0.0,
+                                            r_in != r_out && out_sum > 0 ? log2(out_sum) : 0.0);
+                double pval = 0.0;
+                int64_t s = 0;
+                int64_t t = n;
+                double ls = 0;
+                double lt = log2(t);
+                if (get_stat(s, t, ls, lt) >= base_stat) {
+                    double base = lscaling + (lgamma(n + r_out) - lgamma(r_out)) / log(2);
+                    pval += exp2(base);
+                    for (++s,--t; s <= n; ++s,--t) {
+                        ls = log2(s);
+                        lt = t > 0 ? log2(t) : 0.0;
+                        if (get_stat(s, t, ls, lt) < base_stat)
+                            break;
+
+                        base += log2(t + 1) + log2(s - 1 + r_in) - ls - log2(t + r_out);
+                        pval += exp2(base);
+                    }
+                }
+
+                int64_t sp = n;
+                t = 0;
+                ls = log2(sp);
+                lt = 0;
+                if (get_stat(sp, t, ls, lt) >= base_stat) {
+                    double base = lscaling + (lgamma(n + r_in) - lgamma(r_in)) / log(2);
+                    pval += exp2(base);
+                    for (--sp,++t; sp >= s; --sp,++t) {
+                        ls = sp > 0 ? log2(sp) : 0.0;
+                        lt = log2(t);
+                        if (get_stat(sp, t, ls, lt) < base_stat)
+                            break;
+
+                        base += log2(sp + 1) + log2(t - 1 + r_out) - lt - log2(sp + r_in);
+                        pval += exp2(base);
+                    }
+                }
+
+                return std::min(1.0, pval);
+            };
+
+            if (r_in == r_out) {
+                return get_pval([&](int64_t s, int64_t, double, double) { return abs(argmin_d - s); });
+            } else {
+                return get_pval([&](int64_t s, int64_t t, double ls, double lt) {
+                    return ls * s - (r_in + s)*log2(r_in + s) + lt * t - (t + r_out)*log2(t + r_out);
+                });
+            }
+        };
+
     } else if (config.test_type == "poisson_binom") {
         // fit distribution
         std::vector<double> p;

From 59c773d5a1db3b7fc84e450095f8fe75f5969390 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 17 Feb 2025 14:57:23 +0100
Subject: [PATCH 367/410] fix nbinom fit

---
 .../src/graph/annotated_graph_algorithm.cpp   | 189 +++++++++++-------
 1 file changed, 116 insertions(+), 73 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 376aa01a05..5261d2ab3c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -267,7 +267,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         pvals_buckets[0].push_back(nullpval);
     }
 
-    if (config.test_type == "gnb_exact" && config.test_by_unitig) {
+    if ((config.test_type == "gnb_exact" || config.test_type == "lnb_exact") && config.test_by_unitig) {
         if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
             auto &pvals_min = pvals_min_buckets.emplace_back();
             pvals_min.resize(graph_ptr->max_index() + 1, nullpval);
@@ -505,50 +505,71 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         common::logger->trace("Scaling distributions");
 
-        double target_sum = 0.0;
+        double target_p = 0.0;
         for (size_t j = 0; j < groups.size(); ++j) {
-            target_sum += log(static_cast<double>(sums[j]));
+            target_p += log(nb_params[j].second);
         }
-        target_sum = exp(target_sum / total_labels);
-
-        common::logger->trace("Finding common p and r.");
-
-        double r_map;
-        double target_p;
-        std::tie(r_map, target_p) = get_rp([&](const auto &callback) {
-            for (size_t j = 0; j < groups.size(); ++j) {
-                double f = target_sum / sums[j];
-                std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
-                    const auto &[k, c] = a;
-                    callback(f * k, c);
-                });
-            }
-        });
-
-        double fit_mu = r_map * (1.0 - target_p) / target_p;
-        double fit_var = fit_mu / target_p;
-        common::logger->trace("Common params: r: {}\tp: {}\tmu: {}\tvar: {}",
-                              r_map, target_p, fit_mu, fit_var);
-
-        double r_in = r_map * num_labels_in;
-        double r_out = r_map * num_labels_out;
-        common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
-
-        if (fit_var / fit_mu - 1.0 < 1e-5)
-            common::logger->warn("Fit parameters are close to a Poisson distribution");
+        target_p = exp(target_p / total_labels);
+        // double target_sum = 0.0;
+        // for (size_t j = 0; j < groups.size(); ++j) {
+        //     target_sum += log(static_cast<double>(sums[j]));
+        // }
+        // target_sum = exp(target_sum / total_labels);
+
+        // common::logger->trace("Finding common p and r.");
+
+        // double r_map;
+        // double target_p;
+        // std::tie(r_map, target_p) = get_rp([&](const auto &callback) {
+        //     for (size_t j = 0; j < groups.size(); ++j) {
+        //         double f = target_sum / sums[j];
+        //         std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
+        //             const auto &[k, c] = a;
+        //             callback(f * k, c);
+        //         });
+        //     }
+        // });
+
+        // common::logger->trace("Common p: {}", target_p);
+
+        // double fit_mu = r_map * (1.0 - target_p) / target_p;
+        // double fit_var = fit_mu / target_p;
+        // common::logger->trace("Common params: r: {}\tp: {}\tmu: {}\tvar: {}",
+        //                       r_map, target_p, fit_mu, fit_var);
+
+        // double r_in = r_map * num_labels_in;
+        // double r_out = r_map * num_labels_out;
+        // common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
+
+        // if (fit_var / fit_mu - 1.0 < 1e-5)
+        //     common::logger->warn("Fit parameters are close to a Poisson distribution");
 
         count_maps.resize(groups.size());
 
         common::logger->trace("Computing quantile maps");
+        double r_in = 0;
+        double r_out = 0;
         #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
             if (hists[j].empty())
                 continue;
 
             const auto &[r, p] = nb_params[j];
+            double r_map = r * target_p * (1.0 - p) / p / (1.0 - target_p);
+
+            if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                #pragma omp atomic
+                r_out += r_map;
+            }
+
+            if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                #pragma omp atomic
+                r_in += r_map;
+            }
 
             boost::math::negative_binomial nb_out(r_map, target_p);
-            double scale = target_sum / sums[j];
+            double scale = 1.0;
+            // double scale = target_sum / sums[j];
 
             auto map_value = [&](uint64_t k, double scale, const auto &old_dist, const auto &new_dist) {
                 double cdf = boost::math::cdf(old_dist, k);
@@ -586,6 +607,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
+        common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
+
         common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
         in_kmers = 0;
         out_kmers = 0;
@@ -945,29 +968,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                        [&](uint64_t sum) -> double { return max_sum / sum; });
 
         common::logger->trace("Enumerating row count distributions");
-        std::vector<tsl::hopscotch_map<std::vector<double>, double, utils::VectorHash>> vector_counts;
+        std::vector<tsl::hopscotch_map<std::vector<uint64_t>, double, utils::VectorHash>> vector_counts;
         {
-            std::vector<std::vector<tsl::hopscotch_map<std::vector<double>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
+            std::vector<std::vector<tsl::hopscotch_map<std::vector<uint64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
 
             generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
                 if (!kept[row_i])
                     return;
 
-                double in_sum = 0;
-                double out_sum = 0;
+                uint64_t in_sum = 0;
+                uint64_t out_sum = 0;
                 bool in_kmer = false;
                 bool out_kmer = false;
 
-                std::vector<double> vec;
-                vec.reserve(raw_row.size());
+                std::vector<uint64_t> vec;
+                vec.resize(total_labels);
                 size_t count_in = 0;
                 size_t count_out = 0;
                 size_t total_count = 0;
-                for (const auto &[j, raw_c] : raw_row) {
-                    if (min_counts.size() && raw_c < min_counts[j])
+                for (const auto &[j, c] : raw_row) {
+                    if (min_counts.size() && c < min_counts[j])
                         continue;
 
-                    double c = raw_c * scale_factor[j];
                     if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                         out_sum += c;
                         ++count_out;
@@ -980,7 +1002,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                     total_count += groups[j] != Group::OTHER;
 
-                    vec.emplace_back(c);
+                    vec[j] = c;
                 }
 
                 if (total_count >= config.min_recurrence) {
@@ -991,10 +1013,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (!in_kmer && !out_kmer)
                     return;
 
-                double sum = in_sum + out_sum;
-
                 // if floating point error puts the sum slightly above the integer value, then round down
-                size_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
+                size_t n = in_sum + out_sum;
                 if (n >= vector_counts_ms[bucket_idx].size())
                     vector_counts_ms[bucket_idx].resize(n + 1);
 
@@ -1024,8 +1044,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         auto optim = [](const auto &f, auto x_min, auto x_max) {
-            while (x_max - x_min > 1e-5) {
+            // common::logger->info("start");
+            while (x_max - x_min > 1e-3) {
                 double x_mid = (x_min + x_max) / 2;
+                // common::logger->info("({},{}) -> {}", x_min, x_max, f(x_mid));
                 double x_low_mid = (x_min + x_mid) / 2;
                 double x_high_mid = (x_mid + x_max) / 2;
                 if (f(x_low_mid) > f(x_high_mid)) {
@@ -1041,16 +1063,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         double ln_mu = 0.0;
         double ln_var = 0.0;
 
+        size_t skipped = 0;
         for (size_t n = 0; n < vector_counts.size(); ++n) {
             for (const auto &[v, c] : vector_counts[n]) {
-                double total = std::accumulate(v.begin(), v.end(), double(0));
-                double total2 = std::accumulate(v.begin(), v.end(), double(0),
-                                                [&](double sum, double vv) { return sum + vv * vv;});
+                double total = 0;
+                double total2 = 0;
+                for (size_t j = 0; j < v.size(); ++j) {
+                    total += v[j] * scale_factor[j];
+                    total2 += v[j] * scale_factor[j] * v[j] * scale_factor[j];
+                }
                 double mu = total / total_labels;
                 double mu2 = mu * mu;
                 double var = total2 / total_labels - mu2;
                 if (var <= mu) {
-                    throw std::runtime_error("var <= mu when fitting lognormal");
+                    ++skipped;
+                    continue;
                 }
 
                 double r = mu2 / (var - mu);
@@ -1059,8 +1086,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        ln_mu /= nelem;
-        ln_var = ln_var / nelem - ln_mu * ln_mu;
+        if (skipped)
+            common::logger->warn("Skipped {} / {} rows", skipped, nelem);
+
+        ln_mu /= (nelem - skipped);
+        ln_var = ln_var / (nelem - skipped) - ln_mu * ln_mu;
 
         common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
                               ln_mu, ln_var,
@@ -1073,12 +1103,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         {
             auto calc_r = [optim,ln_mu,ln_var,gs=total_labels,p=target_p](const auto &counts) {
                 return optim([&](double r) {
-                    double val = gs * log(p) * r - gs * lgamma(r) + log(r) - pow(-log(r)-ln_mu, 2.0)/2/ln_var;
+                    double val = log(p) * gs * r - lgamma(r) * counts.size() + log(r) - pow(-log(r)-ln_mu, 2.0)/2/ln_var;
                     for (double c : counts) {
                         val += lgamma(c + r);
                     }
                     return val;
-                }, 0, 100000);
+                }, 0.0, 100.0);
             };
 
             ProgressBar progress_bar(vector_counts.size(), "Precomputing r's", std::cerr, !common::get_verbose());
@@ -1088,17 +1118,22 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     progress_bar += 1000;
 
                 for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
-                    it.value() = calc_r(it->first);
+                    std::vector<double> scale_counts;
+                    scale_counts.reserve(it->first.size());
+                    for (size_t j = 0; j < it->first.size(); ++j) {
+                        if (it->first[j] > 0)
+                            scale_counts.emplace_back(it->first[j] * scale_factor[j]);
+                    }
+                    it.value() = calc_r(scale_counts);
                 }
             }
             progress_bar += vector_counts.size() % 1000;
         }
 
-        auto get_r = std::make_shared<std::function<double(int64_t, std::vector<double>&)>>(
-            [vc=std::move(vector_counts),scale_factor](int64_t n, auto &counts) {
+        auto get_r = std::make_shared<std::function<double(int64_t, std::vector<uint64_t>&)>>(
+            [vc=std::move(vector_counts),scale_factor](int64_t n, const auto &counts) {
                 auto it = vc[n].begin();
                 if (vc[n].size() > 1) {
-                    std::sort(counts.begin(), counts.end());
                     it = vc[n].find(counts);
                     assert(it != vc[n].end());
                 }
@@ -1106,20 +1141,24 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         );
 
-        compute_min_pval = [get_r,num_labels_in,num_labels_out,scale_factor](int64_t, const PairContainer &row) {
+        compute_min_pval = [get_r,num_labels_in,num_labels_out,scale_factor,total_labels](int64_t, const PairContainer &row) {
             if (row.empty())
                 return 1.0;
 
+            int64_t sum = 0;
+            double ssum = 0;
             std::vector<double> counts;
-            double sum = 0;
+            std::vector<uint64_t> row_int(total_labels);
             counts.reserve(row.size());
             for (const auto &[j, c] : row) {
-                sum += counts.emplace_back(c * scale_factor[j]);
+                row_int[j] = c;
+                sum += c;
+                ssum += counts.emplace_back(c * scale_factor[j]);
             }
             // if floating point error puts the sum slightly above the integer value, then round down
             int64_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
 
-            double r_base = (*get_r)(n, counts);
+            double r_base = (*get_r)(sum, row_int);
             if (r_base == 0.0)
                 return 1.0;
 
@@ -1142,17 +1181,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::min(1.0, pval);
         };
 
-        compute_pval = [get_r,num_labels_in,num_labels_out,scale_factor,groups](int64_t, int64_t, const PairContainer &row) {
+        compute_pval = [get_r,num_labels_in,num_labels_out,scale_factor,groups,total_labels](int64_t, int64_t, const PairContainer &row) {
             if (row.empty())
                 return 1.0;
 
-            std::vector<double> counts;
-            double sum = 0;
+            int64_t sum = 0;
+            double ssum = 0;
             double in_sum = 0;
             double out_sum = 0;
+            std::vector<double> counts;
+            std::vector<uint64_t> row_int(total_labels);
             counts.reserve(row.size());
             for (const auto &[j, c] : row) {
-                sum += counts.emplace_back(c * scale_factor[j]);
+                row_int[j] = c;
+                sum += c;
+                ssum += counts.emplace_back(c * scale_factor[j]);
                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                     out_sum += counts.back();
                 }
@@ -1164,7 +1207,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             // if floating point error puts the sum slightly above the integer value, then round down
             int64_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
 
-            double r_base = (*get_r)(n, counts);
+            double r_base = (*get_r)(sum, row_int);
 
             if (r_base == 0.0)
                 return 1.0;
@@ -1510,7 +1553,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             double pval_min = 0;
-            if (config.test_type != "gnb_exact") {
+            if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact") {
                 assert(bucket_idx < ms.size());
                 if (n >= ms[bucket_idx].size())
                     ms[bucket_idx].resize(n + 1, std::make_pair(1.1, 0));
@@ -1545,19 +1588,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
                     eff_size_buckets[bucket_idx][node] = eff_size;
                     sum_buckets[bucket_idx][node] = n;
-                    if (config.test_type == "gnb_exact")
+                    if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact")
                         pvals_min_buckets[bucket_idx][node] = bit_cast<uint64_t>(pval_min);
                 } else {
                     eff_size_buckets[bucket_idx].push_back(eff_size);
                     sum_buckets[bucket_idx].push_back(n);
-                    if (config.test_type == "gnb_exact")
+                    if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact")
                         pvals_min_buckets[bucket_idx].push_back(bit_cast<uint64_t>(pval_min));
                 }
             }
         });
 
         common::logger->trace("Merging min p-value tables");
-        if (config.test_type != "gnb_exact") {
+        if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact") {
             for (size_t i = 1; i < ms.size(); ++i) {
                 size_t end = std::min(ms[0].size(), ms[i].size());
                 for (size_t j = 0; j < end; ++j) {
@@ -1663,7 +1706,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     size_t n = sum_buckets[bucket_idx][node_shift];
                     n_sum += n;
 
-                    if (config.test_type == "gnb_exact") {
+                    if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact") {
                         pvals_min.emplace_back(bit_cast<double, uint64_t>(pvals_min_buckets[bucket_idx][node_shift]));
                     } else {
                         pvals_min.emplace_back(m[n].first);
@@ -1782,7 +1825,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (config.output_pvals
                     || (!config.test_by_unitig && n >= n_cutoff)
                     || (config.test_by_unitig && unitig_start[node])) {
-                if (config.test_type != "gnb_exact" && m[n].first == 1.1) {
+                if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact" && m[n].first == 1.1) {
                     common::logger->error("in: {}\tout: {}\tn: {}", in_sum, out_sum, n);
                     throw std::runtime_error("Indexing invalid");
                 }
@@ -1795,7 +1838,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     return;
                 }
 
-                if (config.test_type != "gnb_exact" && pval < 1.1 && m[n].first - pval > 1e-10) {
+                if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact" && pval < 1.1 && m[n].first - pval > 1e-10) {
                     common::logger->error("Min p-val estimate too high: min {} > cur {}\tn: {}\ttest: {}", m[n].first, pval, n, config.test_type);
                     throw std::runtime_error("Test failed");
                 }

From 38029eb2980d452ecd3718171aa6e51566090382 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 20 Feb 2025 12:44:49 +0100
Subject: [PATCH 368/410] rewrite

---
 metagraph/external-libraries/sshash           |    2 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 3698 ++++++++++-------
 2 files changed, 2139 insertions(+), 1561 deletions(-)

diff --git a/metagraph/external-libraries/sshash b/metagraph/external-libraries/sshash
index 72a92b62da..c62d1ae0d2 160000
--- a/metagraph/external-libraries/sshash
+++ b/metagraph/external-libraries/sshash
@@ -1 +1 @@
-Subproject commit 72a92b62da0538e18177ce0494832b315d103844
+Subproject commit c62d1ae0d2c194d36638b565d2dc488532d28613
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5261d2ab3c..ef835c649e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -67,6 +67,21 @@ bit_cast(const From& src) noexcept
     return dst;
 }
 
+template <class PValStorage>
+double get(typename PValStorage::const_reference &ref) {
+    return bit_cast<double>(static_cast<typename PValStorage::value_type>(ref));
+}
+
+template <class PValStorage>
+void set(typename PValStorage::reference &ref, double val) {
+    ref = bit_cast<PValStorage::value_type>(val);
+}
+
+template <class PValStorage>
+void push_back(PValStorage &v, double val) {
+    v.push_back(bit_cast<PValStorage::value_type>(val));
+}
+
 template <typename value_type, class PValStorage, typename HistGetter, typename Generator>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
@@ -86,12 +101,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     num_parallel_files = get_num_threads();
     num_threads = get_num_threads();
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
-    bool parallel = num_parallel_files > 1;
+    // bool parallel = num_parallel_files > 1;
 
     size_t num_labels_both = std::count(groups.begin(), groups.end(), Group::BOTH);
     size_t num_labels_out = std::count(groups.begin(), groups.end(), Group::OUT);
     size_t num_labels_in = std::count(groups.begin(), groups.end(), Group::IN);
-    size_t total_labels = num_labels_in + num_labels_out;
+    // size_t total_labels = num_labels_in + num_labels_out;
     num_labels_in += num_labels_both;
     num_labels_out += num_labels_both;
 
@@ -105,8 +120,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     std::vector<size_t> min_counts(groups.size(), config.min_count);
     std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
 
-    std::mutex agg_mu;
-    using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
+    // std::mutex agg_mu;
+    // using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
 
     if (config.clean) {
         common::logger->trace("Cleaning count columns");
@@ -137,51 +152,68 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
-    auto correct_pvals = [&config](const std::vector<std::pair<double, size_t>> &m,
-                                   size_t nelem,
-                                   const std::vector<int64_t> &m_sums = {}) {
-        size_t k = 0;
-        size_t n_cutoff = 1;
-        size_t acc = std::accumulate(m.begin(), m.end(), size_t(0),
-                                     [](size_t sum, const auto &a) { return sum + a.second; });
-        size_t total = acc;
-        common::logger->trace("Calculating cutoffs for {}/{} tests", acc, nelem);
-        size_t last_k = 0;
-        for (size_t n = 0; n < m.size(); ++n) {
-            auto [pval_min, s] = m[n];
-            size_t cur_k = std::numeric_limits<size_t>::max();
-            if (pval_min >= config.family_wise_error_rate) {
-                cur_k = 0;
-            } else if (pval_min > 0) {
-                double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
-                if (lkd <= 64)
-                    cur_k = pow(2.0, lkd);
-
-                if (cur_k == 0) {
-                    common::logger->error("k: {}\tlog k: {}\tpval_min: {}", cur_k, lkd, pval_min);
-                    throw std::runtime_error("Min failed");
-                }
-            }
+    auto correct_pvals = [&config](const std::vector<std::pair<double, size_t>> &m) {
+                                //    size_t nelem,
+                                //    const std::vector<int64_t> &m_sums = {}) {
+        // size_t k = 0;
+        // size_t n_cutoff = 1;
+        size_t total = std::accumulate(m.begin(), m.end(), size_t(0),
+                                       [](size_t sum, const auto &a) { return sum + a.second; });
+        size_t acc = 0;
+
+        for (const auto &[pval_min, s] : m) {
+            if (s == 0)
+                continue;
 
-            if (cur_k != last_k && acc <= cur_k) {
-                k = std::max(acc, last_k + 1);
-                if (m_sums.empty()) {
-                    n_cutoff = n;
-                } else {
-                    n_cutoff = *std::min_element(m_sums.begin() + n, m_sums.end());
-                }
-                break;
-            }
+            size_t c = floor(config.family_wise_error_rate / pval_min + 1);
+            size_t nsig = total - acc;
+            // there are nsig k-mers s.t. pval_min < alpha / c
+            if (nsig > 0 && c >= nsig)
+                return std::make_pair(c, nsig);
 
-            last_k = cur_k;
-            acc -= s;
+            acc += s;
         }
 
-        if (k == 0)
-            common::logger->warn("No significant p-values achievable after correction");
+        return std::make_pair<size_t, size_t>(std::numeric_limits<size_t>::max(),
+                                              std::numeric_limits<size_t>::max());
+        // size_t total = acc;
+        // common::logger->trace("Calculating cutoffs for {}/{} tests", acc, nelem);
+        // size_t last_k = 0;
+        // for (size_t n = 0; n < m.size(); ++n) {
+        //     auto [pval_min, s] = m[n];
+        //     size_t cur_k = std::numeric_limits<size_t>::max();
+        //     if (pval_min >= config.family_wise_error_rate) {
+        //         cur_k = 0;
+        //     } else if (pval_min > 0) {
+        //         double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
+        //         if (lkd <= 64)
+        //             cur_k = pow(2.0, lkd);
+
+        //         if (cur_k == 0) {
+        //             common::logger->error("k: {}\tlog k: {}\tpval_min: {}", cur_k, lkd, pval_min);
+        //             throw std::runtime_error("Min failed");
+        //         }
+        //     }
+
+        //     if (cur_k != last_k && acc <= cur_k) {
+        //         k = std::max(acc, last_k + 1);
+        //         if (m_sums.empty()) {
+        //             n_cutoff = n;
+        //         } else {
+        //             n_cutoff = *std::min_element(m_sums.begin() + n, m_sums.end());
+        //         }
+        //         break;
+        //     }
+
+        //     last_k = cur_k;
+        //     acc -= s;
+        // }
+
+        // if (k == 0)
+        //     common::logger->warn("No significant p-values achievable after correction");
 
-        common::logger->trace("Kept {} / {}", acc, total);
-        return std::make_pair(k, n_cutoff);
+        // common::logger->trace("Kept {} / {}", acc, total);
+        // return std::make_pair(k, n_cutoff);
     };
 
     common::logger->trace("Updating histogram and marking discarded k-mers");
@@ -245,166 +277,121 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Number of kept unique k-mers: {}\tNumber of kept k-mers: {}",
                           nelem, total_kmers);
 
-    common::logger->trace("Allocating p-value storage");
-    auto nullpval = bit_cast<uint64_t>(double(1.1));
-
-    std::vector<PValStorage> pvals_buckets;
-    std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
-    std::vector<PValStorage> pvals_min_buckets;
-    std::vector<std::unique_ptr<utils::TempFile>> tmp_min_buckets;
-    constexpr bool preallocated = std::is_same_v<PValStorage, std::vector<uint64_t>>;
-
-    if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-        auto &pvals = pvals_buckets.emplace_back();
-        pvals.resize(graph_ptr->max_index() + 1, nullpval);
-    }
-
-    if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-        for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-            auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-            pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
-        }
-        pvals_buckets[0].push_back(nullpval);
-    }
-
-    if ((config.test_type == "gnb_exact" || config.test_type == "lnb_exact") && config.test_by_unitig) {
-        if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-            auto &pvals_min = pvals_min_buckets.emplace_back();
-            pvals_min.resize(graph_ptr->max_index() + 1, nullpval);
-        }
-
-        if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-            for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-                auto &tmp_file = tmp_min_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-                pvals_min_buckets.emplace_back(tmp_file->name(), std::ios::out);
-            }
-            pvals_min_buckets[0].push_back(nullpval);
-        }
-    }
-
-    std::vector<PValStorage> eff_size_buckets;
-    std::vector<std::unique_ptr<utils::TempFile>> tmp_eff_buckets;
-    std::vector<PValStorage> sum_buckets;
-    std::vector<std::unique_ptr<utils::TempFile>> tmp_sum_buckets;
-    if (config.test_by_unitig) {
-        common::logger->trace("Allocating effect size and row sum storage");
-        if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-            auto &eff_size = eff_size_buckets.emplace_back();
-            eff_size.resize(graph_ptr->max_index() + 1);
-
-            auto &sum = sum_buckets.emplace_back();
-            sum.resize(graph_ptr->max_index() + 1);
-        }
-
-        if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-            for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-                auto &tmp_file_eff = tmp_eff_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-                eff_size_buckets.emplace_back(tmp_file_eff->name(), std::ios::out);
-
-                auto &tmp_file_sum = tmp_sum_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-                sum_buckets.emplace_back(tmp_file_sum->name(), std::ios::out);
-            }
-            eff_size_buckets[0].push_back(0);
-            sum_buckets[0].push_back(0);
-        }
-    }
+    // common::logger->trace("Allocating p-value storage");
+    // auto nullpval = bit_cast<uint64_t>(double(1.1));
+
+    // std::vector<PValStorage> pvals_buckets;
+    // std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
+    // std::vector<PValStorage> pvals_min_buckets;
+    // std::vector<std::unique_ptr<utils::TempFile>> tmp_min_buckets;
+    // constexpr bool preallocated = std::is_same_v<PValStorage, std::vector<uint64_t>>;
+
+    // if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+    //     auto &pvals = pvals_buckets.emplace_back();
+    //     pvals.resize(graph_ptr->max_index() + 1, nullpval);
+    // }
+
+    // if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+    //     for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+    //         auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+    //         pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
+    //     }
+    //     pvals_buckets[0].push_back(nullpval);
+    // }
+
+    // if ((config.test_type == "gnb_exact" || config.test_type == "lnb_exact") && config.test_by_unitig) {
+    //     if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+    //         auto &pvals_min = pvals_min_buckets.emplace_back();
+    //         pvals_min.resize(graph_ptr->max_index() + 1, nullpval);
+    //     }
+
+    //     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+    //         for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+    //             auto &tmp_file = tmp_min_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+    //             pvals_min_buckets.emplace_back(tmp_file->name(), std::ios::out);
+    //         }
+    //         pvals_min_buckets[0].push_back(nullpval);
+    //     }
+    // }
+
+    // std::vector<PValStorage> eff_size_buckets;
+    // std::vector<std::unique_ptr<utils::TempFile>> tmp_eff_buckets;
+    // std::vector<PValStorage> sum_buckets;
+    // std::vector<std::unique_ptr<utils::TempFile>> tmp_sum_buckets;
+    // if (config.test_by_unitig) {
+    //     common::logger->trace("Allocating effect size and row sum storage");
+    //     if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+    //         auto &eff_size = eff_size_buckets.emplace_back();
+    //         eff_size.resize(graph_ptr->max_index() + 1);
+
+    //         auto &sum = sum_buckets.emplace_back();
+    //         sum.resize(graph_ptr->max_index() + 1);
+    //     }
+
+    //     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+    //         for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+    //             auto &tmp_file_eff = tmp_eff_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+    //             eff_size_buckets.emplace_back(tmp_file_eff->name(), std::ios::out);
+
+    //             auto &tmp_file_sum = tmp_sum_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+    //             sum_buckets.emplace_back(tmp_file_sum->name(), std::ios::out);
+    //         }
+    //         eff_size_buckets[0].push_back(0);
+    //         sum_buckets[0].push_back(0);
+    //     }
+    // }
 
     sdsl::bit_vector indicator_in;
     sdsl::bit_vector indicator_out;
 
-    if (!config.test_by_unitig) {
+    // if (!config.test_by_unitig) {
         common::logger->trace("Allocating k-mer bitmasks");
         indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
         indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-    }
+    // }
 
-    std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
-    std::function<double(int64_t, const PairContainer&)> compute_min_pval;
+    // std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
+    // std::function<double(int64_t, const PairContainer&)> compute_min_pval;
 
-    std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps;
+    // std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps;
 
     common::logger->trace("Test: {}", config.test_type);
-    if (config.test_type == "poisson_exact") {
-        auto get_deviance = [&](double y, double mu) {
-            y += 1e-8;
-            mu += 1e-8;
-            return 2 * (y * log(y/mu) - y + mu);
-        };
 
-        compute_min_pval = [&,p=static_cast<double>(in_kmers)/total_kmers,
-                              mu1=static_cast<double>(in_kmers)/nelem,
-                              mu2=static_cast<double>(out_kmers)/nelem,
-                              get_deviance](int64_t n, const PairContainer&) {
-            if (n == 0)
-                return 1.0;
+    auto generate_clean_rows = [&](const auto &callback) {
+        generate_rows([&](uint64_t row_i, const auto &row, size_t bucket_idx) {
+            if (!kept[row_i])
+                return;
 
-            auto bdist = boost::math::binomial(n, p);
+            size_t count_in = 0;
+            size_t count_out = 0;
+            size_t total_count = 0;
+            for (const auto &[j, c] : row) {
+                if (min_counts.size() && c < min_counts[j])
+                    continue;
 
-            std::vector<double> devs;
-            devs.reserve(n + 1);
-            for (int64_t s = 0; s <= n; ++s) {
-                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-            }
-            double max_d = *std::max_element(devs.begin(), devs.end());
-            double pval = 0.0;
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                    ++count_out;
 
-            int64_t s = 0;
-            for ( ; s <= n; ++s) {
-                if (devs[s] != max_d)
-                    break;
-            }
-            if (s > 0)
-                pval += boost::math::cdf(bdist, s - 1);
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                    ++count_in;
 
-            int64_t sp = n;
-            for ( ; sp >= s; --sp) {
-                if (devs[sp] != max_d)
-                    break;
+                total_count += groups[j] != Group::OTHER;
             }
 
-            if (sp < n)
-                pval += boost::math::cdf(boost::math::complement(bdist, sp));
-
-            return pval;
-        };
-
-        compute_pval = [&,p=static_cast<double>(in_kmers)/total_kmers,
-                          mu1=static_cast<double>(in_kmers)/nelem,
-                          mu2=static_cast<double>(out_kmers)/nelem,
-                          get_deviance](int64_t in_sum, int64_t out_sum, const auto &row) {
-            if (row.empty())
-                return 1.0;
-
-            int64_t n = in_sum + out_sum;
-            auto bdist = boost::math::binomial(n, p);
-
-            std::vector<double> devs;
-            devs.reserve(n + 1);
-            for (int64_t s = 0; s <= n; ++s) {
-                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-            }
-            double pval = 0.0;
+            if (total_count < config.min_recurrence)
+                return;
 
-            int64_t s = 0;
-            for ( ; s <= n; ++s) {
-                if (devs[s] < devs[in_sum])
-                    break;
-            }
-            if (s > 0)
-                pval += boost::math::cdf(bdist, s - 1);
+            bool in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence;
+            bool out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence;
 
-            int64_t sp = n;
-            for ( ; sp >= s; --sp) {
-                if (devs[sp] < devs[in_sum])
-                    break;
-            }
+            if (in_kmer || out_kmer)
+                callback(row_i, row, bucket_idx);
+        });
+    };
 
-            if (sp < n)
-                pval += boost::math::cdf(boost::math::complement(bdist, sp));
+    std::vector<std::pair<double, double>> nb_params(groups.size());
 
-            return pval;
-        };
-    } else if (config.test_type == "nbinom_exact" || config.test_type == "gnb_exact") {
+    if (config.test_type == "poisson_bayes") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
         auto get_rp = [&](const auto &generate) {
             double mu = 0;
@@ -470,971 +457,180 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return std::make_pair(r, p);
         };
 
-        std::vector<std::pair<double, double>> nb_params(groups.size());
-        std::vector<std::vector<std::pair<uint64_t, size_t>>::const_iterator> hist_its(groups.size());
-        size_t matrix_size = 0;
-
-        #pragma omp parallel for num_threads(num_parallel_files) reduction(+:matrix_size)
+        #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto &hist = hists[j];
-            size_t total = 0;
-            size_t total_cutoff = n_kmers[j] * HIST_CUTOFF;
-            auto it = hist.begin();
-            for ( ; it != hist.end() && total < total_cutoff; ++it) {
-                if (it->first != 0)
-                    total += it->second;
-            }
-            hist_its[j] = it;
             if (hist.size()) {
                 nb_params[j] = get_rp([&](const auto &callback) {
-                    std::for_each(hist.begin(), it, [&](const auto &a) {
-                        const auto &[k, c] = a;
+                    for (const auto &[k, c] : hist) {
                         callback(k, c);
-                    });
+                    }
                 });
                 const auto &[r, p] = nb_params[j];
                 common::logger->trace("{}: size: {}\tmax_val: {}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
-                                    j, sums[j], (it - 1)->first, r * (1-p)/p, r*(1-p)/p/p, r, p);
-
-                total += hist[0].first == 0 ? hist[0].second : 0;
-
-                #pragma omp atomic
-                matrix_size += total;
+                                    j, sums[j], (hist.end() - 1)->first, r * (1-p)/p, r*(1-p)/p/p, r, p);
             }
         }
+    }
 
-        common::logger->trace("Scaling distributions");
-
-        double target_p = 0.0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            target_p += log(nb_params[j].second);
+    std::vector<std::vector<double>> pb_pvals(num_labels_in + num_labels_out + 1);
+    std::vector<double> mid_points(num_labels_in + num_labels_out + 1);
+    if (config.test_type == "poisson_binom") {
+        // fit distribution
+        std::vector<double> p;
+        p.reserve(groups.size());
+        for (size_t i = 0; i < groups.size(); ++i) {
+            p.emplace_back(static_cast<double>(n_kmers[i]) / nelem);
         }
-        target_p = exp(target_p / total_labels);
-        // double target_sum = 0.0;
-        // for (size_t j = 0; j < groups.size(); ++j) {
-        //     target_sum += log(static_cast<double>(sums[j]));
-        // }
-        // target_sum = exp(target_sum / total_labels);
-
-        // common::logger->trace("Finding common p and r.");
-
-        // double r_map;
-        // double target_p;
-        // std::tie(r_map, target_p) = get_rp([&](const auto &callback) {
-        //     for (size_t j = 0; j < groups.size(); ++j) {
-        //         double f = target_sum / sums[j];
-        //         std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
-        //             const auto &[k, c] = a;
-        //             callback(f * k, c);
-        //         });
-        //     }
-        // });
-
-        // common::logger->trace("Common p: {}", target_p);
-
-        // double fit_mu = r_map * (1.0 - target_p) / target_p;
-        // double fit_var = fit_mu / target_p;
-        // common::logger->trace("Common params: r: {}\tp: {}\tmu: {}\tvar: {}",
-        //                       r_map, target_p, fit_mu, fit_var);
-
-        // double r_in = r_map * num_labels_in;
-        // double r_out = r_map * num_labels_out;
-        // common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
 
-        // if (fit_var / fit_mu - 1.0 < 1e-5)
-        //     common::logger->warn("Fit parameters are close to a Poisson distribution");
-
-        count_maps.resize(groups.size());
-
-        common::logger->trace("Computing quantile maps");
-        double r_in = 0;
-        double r_out = 0;
-        #pragma omp parallel for num_threads(num_parallel_files)
-        for (size_t j = 0; j < groups.size(); ++j) {
-            if (hists[j].empty())
-                continue;
+        common::logger->trace("p: {}", fmt::join(p, ","));
 
-            const auto &[r, p] = nb_params[j];
-            double r_map = r * target_p * (1.0 - p) / p / (1.0 - target_p);
+        common::logger->trace("Precomputing PMFs");
+        std::vector<double> pmf_in { 1.0 };
+        std::vector<double> pmf_out { 1.0 };
+        std::vector<double> pmf_null { 1.0 };
 
-            if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                #pragma omp atomic
-                r_out += r_map;
+        for (size_t i = 1; i <= groups.size(); ++i) {
+            if (groups[i - 1] == Group::OUT || groups[i - 1] == Group::BOTH) {
+                std::vector<double> pmf_out_cur(pmf_out.size() + 1);
+                pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
+                pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
+                for (size_t k = 1; k < pmf_out.size(); ++k) {
+                    pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1] + (1.0 - p[i - 1]) * pmf_out[k];
+                }
+                std::swap(pmf_out_cur, pmf_out);
             }
 
-            if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                #pragma omp atomic
-                r_in += r_map;
+            if (groups[i - 1] == Group::IN || groups[i - 1] == Group::BOTH) {
+                std::vector<double> pmf_in_cur(pmf_in.size() + 1);
+                pmf_in_cur[0] = (1.0 - p[i - 1]) * pmf_in[0];
+                pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
+                for (size_t k = 1; k < pmf_in.size(); ++k) {
+                    pmf_in_cur[k] = p[i - 1] * pmf_in[k - 1] + (1.0 - p[i - 1]) * pmf_in[k];
+                }
+                std::swap(pmf_in_cur, pmf_in);
             }
+            std::vector<double> pmf_null_cur(i + 1);
+            pmf_null_cur[0] = (1.0 - p[i - 1]) * pmf_null[0];
+            pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
+            for (size_t k = 1; k < pmf_null.size(); ++k) {
+                pmf_null_cur[k] = p[i - 1] * pmf_null[k - 1] + (1.0 - p[i - 1]) * pmf_null[k];
+            }
+            std::swap(pmf_null_cur, pmf_null);
+        }
 
-            boost::math::negative_binomial nb_out(r_map, target_p);
-            double scale = 1.0;
-            // double scale = target_sum / sums[j];
-
-            auto map_value = [&](uint64_t k, double scale, const auto &old_dist, const auto &new_dist) {
-                double cdf = boost::math::cdf(old_dist, k);
-
-                if (cdf < 1.0)
-                    return boost::math::quantile(new_dist, cdf);
+        if (pmf_in.size() != num_labels_in + 1) {
+            common::logger->error("PMF in wrong: {} != {}", pmf_in.size(), num_labels_in + 1);
+            throw std::domain_error("");
+        }
 
-                double ccdf = boost::math::cdf(boost::math::complement(old_dist, k));
+        if (pmf_out.size() != num_labels_out + 1) {
+            common::logger->error("PMF out wrong: {} != {}", pmf_out.size(), num_labels_out + 1);
+            throw std::domain_error("");
+        }
 
-                if (ccdf > 0.0)
-                    return boost::math::quantile(boost::math::complement(new_dist, ccdf));
+        if (pmf_null.size() != num_labels_in + num_labels_out + 1) {
+            common::logger->error("PMF null wrong: {} != {}", pmf_null.size(), num_labels_in + num_labels_out + 1);
+            throw std::domain_error("");
+        }
 
-                return ceil(scale * k);
-            };
+        common::logger->trace("Precomputing p-values");
+        pb_pvals[0].emplace_back(1.0);
 
-            auto compute_map = [&](const auto &nb) {
-                // ensure that 0 maps to 0
-                count_maps[j][0] = std::make_pair(0, 0);
+        double min_pval = 1.0;
 
-                const auto &hist = hists[j];
-                for (const auto &[k, c] : hist) {
-                    if (!count_maps[j].count(k)) {
-                        uint64_t new_k = map_value(k, scale, nb, nb_out);
-                        count_maps[j][k] = std::make_pair(new_k, c);
-                    } else {
-                        count_maps[j][k].second += c;
+        for (size_t n = 1; n < pb_pvals.size(); ++n) {
+            std::vector<double> probs;
+            size_t front = n - std::min(n, num_labels_out);
+            for (uint64_t s = 0; s < pmf_in.size(); ++s) {
+                if (s > n)
+                    break;
+                uint64_t t = n - s;
+                if (s < pmf_in.size() && t < pmf_out.size()) {
+                    if (s < front) {
+                        common::logger->error("Attempting non-zero p-value in impossible configuration: {},{}", n, s);
+                        throw std::domain_error("");
                     }
+                    probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t]) - log2(pmf_null[n])));
+                } else {
+                    probs.emplace_back(0.0);
                 }
-            };
-
-            if (p < 1.0) {
-                compute_map(boost::math::negative_binomial(r, p));
-            } else {
-                compute_map(boost::math::poisson(static_cast<double>(sums[j]) * scale / nelem));
             }
-        }
-
-        common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
-
-        common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
-        in_kmers = 0;
-        out_kmers = 0;
-        total_kmers = 0;
-        for (size_t j = 0; j < groups.size(); ++j) {
-            for (const auto &[k, m] : count_maps[j]) {
-                const auto &[v, c] = m;
 
-                if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                    out_kmers += v * c;
-
-                if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                    in_kmers += v * c;
-
-                total_kmers += v * c;
+            for (uint64_t s = 0; s < probs.size(); ++s) {
+                pb_pvals[n].emplace_back(0.0);
+                if (s > n)
+                    break;
+                uint64_t t = n - s;
+                if (s >= pmf_in.size() || t >= pmf_out.size())
+                    continue;
+                for (uint64_t sp = 0; sp < probs.size(); ++sp) {
+                    if (sp > n)
+                        break;
+                    uint64_t tp = n - sp;
+                    if (sp >= pmf_in.size() || tp >= pmf_out.size())
+                        continue;
+                    if (probs[sp] <= probs[s])
+                        pb_pvals[n][s] += probs[sp];
+                }
             }
-        }
-
-        common::logger->trace("  Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
 
-        auto compute_min_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t n) {
-            double half_div0 = -r_in * log2(r_in) - (n + r_out) * log2(n + r_out);
-            double half_divn = -r_out * log2(r_out) - (n + r_in) * log2(n + r_in);
-
-            double pval = 0.0;
-            double lscaling = lscaling_base - lgamma(r + n) / log(2);
-            if (half_div0 >= half_divn)
-                pval += exp2(lscaling + (lgamma(r_out + n) - lgamma(r_out)) / log(2));
+            for (uint64_t s = 0; s < front; ++s) {
+                if (pb_pvals[n][s] > 0) {
+                    common::logger->error("Non-zero p-value in impossible configuration: {},{}", n, s);
+                    throw std::domain_error("");
+                }
+            }
+            double local_min_pval = std::min(pb_pvals[n][front],
+                                            pb_pvals[n].back());
+            min_pval = std::min(min_pval, local_min_pval);
 
-            if (half_divn >= half_div0)
-                pval += exp2(lscaling + (lgamma(r_in + n) - lgamma(r_in)) / log(2));
+            size_t max_prob_counts = 0;
+            double max_prob_pos = 0;
+            // double max_prob = -1;
 
-            return std::min(1.0, pval);
-        };
+            for (uint64_t s = front; s < pb_pvals[n].size(); ++s) {
+                if (pb_pvals[n][s] < local_min_pval) {
+                    common::logger->error("Min p-value not at boundary: {},{}: {} < {}", n, s, pb_pvals[n][s], local_min_pval);
+                    throw std::domain_error("");
+                }
+            }
+            if (front + 1 == pb_pvals[n].size()) {
+                max_prob_counts = 1;
+                max_prob_pos = front;
+            } else if (front + 2 == pb_pvals[n].size()) {
+                max_prob_counts = 2;
+                max_prob_pos = front + front + 1;
+            } else {
+                for (uint64_t s = front + 1; s < pb_pvals[n].size(); ++s) {
+                    if (probs[s] == probs[s-1]) {
+                        max_prob_counts = 2;
+                        max_prob_pos = s + s;
+                        break;
+                    }
 
-        auto compute_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t in_sum, int64_t out_sum, const PairContainer &row) {
-            int64_t n = in_sum + out_sum;
-            double argmin_d = r_in / r * n;
-            if (in_sum == argmin_d)
-                return 1.0;
+                    if (s + 1 < pb_pvals[n].size()) {
+                        if (probs[s-1] < probs[s] && probs[s] < probs[s+1])
+                            continue;
 
-            double lscaling = lscaling_base - lgamma(r + n) / log(2);
+                        if (probs[s-1] > probs[s] && probs[s] > probs[s+1])
+                            continue;
 
-            auto get_pval = [&](const auto &get_stat) {
-                double base_stat = get_stat(in_sum, out_sum,
-                                            r_in != r_out && in_sum > 0 ? log2(in_sum) : 0.0,
-                                            r_in != r_out && out_sum > 0 ? log2(out_sum) : 0.0);
-                double pval = 0.0;
-                int64_t s = 0;
-                int64_t t = n;
-                double ls = 0;
-                double lt = log2(t);
-                if (get_stat(s, t, ls, lt) >= base_stat) {
-                    double base = lscaling + (lgamma(n + r_out) - lgamma(r_out)) / log(2);
-                    pval += exp2(base);
-                    for (++s,--t; s <= n; ++s,--t) {
-                        ls = log2(s);
-                        lt = t > 0 ? log2(t) : 0.0;
-                        if (get_stat(s, t, ls, lt) < base_stat)
-                            break;
-
-                        base += log2(t + 1) + log2(s - 1 + r_in) - ls - log2(t + r_out);
-                        pval += exp2(base);
+                        if (probs[s] > probs[s-1] && probs[s] > probs[s+1]) {
+                            if (probs[s] - probs[s-1] == probs[s]-probs[s+1]) {
+                                max_prob_counts = 1;
+                                max_prob_pos = s;
+                            } else if (probs[s] - probs[s-1] > probs[s]-probs[s+1]) {
+                                max_prob_counts = 2;
+                                max_prob_pos = s + s + 1;
+                            } else {
+                                max_prob_counts = 2;
+                                max_prob_pos = s + s - 1;
+                            }
+                        }
                     }
                 }
-
-                int64_t sp = n;
-                t = 0;
-                ls = log2(sp);
-                lt = 0;
-                if (get_stat(sp, t, ls, lt) >= base_stat) {
-                    double base = lscaling + (lgamma(n + r_in) - lgamma(r_in)) / log(2);
-                    pval += exp2(base);
-                    for (--sp,++t; sp >= s; --sp,++t) {
-                        ls = sp > 0 ? log2(sp) : 0.0;
-                        lt = log2(t);
-                        if (get_stat(sp, t, ls, lt) < base_stat)
-                            break;
-
-                        base += log2(sp + 1) + log2(t - 1 + r_out) - lt - log2(sp + r_in);
-                        pval += exp2(base);
-                    }
-                }
-
-                return std::min(1.0, pval);
-            };
-
-            if (r_in == r_out) {
-                return get_pval([&](int64_t s, int64_t, double, double) { return abs(argmin_d - s); });
-            } else {
-                return get_pval([&](int64_t s, int64_t t, double ls, double lt) {
-                    return ls * s - (r_in + s)*log2(r_in + s) + lt * t - (t + r_out)*log2(t + r_out);
-                });
-            }
-        };
-
-        if (config.test_type == "nbinom_exact") {
-            double r = r_in + r_out;
-            double lscaling_base = lgamma(r) / log(2);
-            compute_min_pval = [compute_min_pval_r,lscaling_base,r,r_in,r_out](int64_t n, const PairContainer&) {
-                return n > 0 ? compute_min_pval_r(r, r_in, r_out, lscaling_base, n) : 1.0;
-            };
-
-            compute_pval = [compute_pval_r,lscaling_base,r,r_in,r_out](int64_t in_sum, int64_t out_sum, const PairContainer &row) {
-                return row.size() ? compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row) : 1.0;
-            };
-        } else {
-            common::logger->trace("Enumerating row count distributions");
-            std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>> vector_counts;
-            {
-                std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
-
-                generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
-                    int64_t in_sum = 0;
-                    int64_t out_sum = 0;
-                    bool in_kmer = false;
-                    bool out_kmer = false;
-                    std::vector<int64_t> vec;
-                    if (kept[row_i]) {
-                        vec.reserve(raw_row.size());
-                        size_t count_in = 0;
-                        size_t count_out = 0;
-                        size_t total_count = 0;
-                        for (const auto &[j, raw_c] : raw_row) {
-                            if (min_counts.size() && raw_c < min_counts[j])
-                                continue;
-
-                            uint64_t c = raw_c;
-                            if (count_maps.size())
-                                c = count_maps[j].find(c)->second.first;
-
-                            if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                                out_sum += c;
-                                ++count_out;
-                            }
-
-                            if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                                in_sum += c;
-                                ++count_in;
-                            }
-
-                            total_count += groups[j] != Group::OTHER;
-
-                            vec.emplace_back(c);
-                        }
-
-                        if (total_count >= config.min_recurrence) {
-                            double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-                            in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-                            out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
-                        }
-                    } else {
-                        return;
-                    }
-
-                    if (!in_kmer && !out_kmer)
-                        return;
-
-                    size_t n = in_sum + out_sum;
-                    if (n >= vector_counts_ms[bucket_idx].size())
-                        vector_counts_ms[bucket_idx].resize(n + 1);
-
-                    std::sort(vec.begin(), vec.end());
-                    ++vector_counts_ms[bucket_idx][n][vec];
-                });
-
-                vector_counts = std::move(vector_counts_ms[0]);
-                size_t max_size = vector_counts.size();
-                for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-                    max_size = std::max(max_size, vector_counts_ms[j].size());
-                }
-                vector_counts.resize(max_size);
-
-                ProgressBar progress_bar(vector_counts.size() - 1, "Merging row", std::cerr, !common::get_verbose());
-                #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
-                for (size_t n = vector_counts.size(); n > 0; --n) {
-                    for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-                        if (n <= vector_counts_ms[j].size()) {
-                            for (const auto &[v, c] : vector_counts_ms[j][n - 1]) {
-                                vector_counts[n - 1][v] += c;
-                            }
-                        }
-                    }
-                    ++progress_bar;
-                }
-            }
-
-            common::logger->trace("Fitting LN prior distribution for dispersions");
-            double ln_mu = 0.0;
-            double ln_var = 0.0;
-
-            for (size_t n = 1; n < vector_counts.size(); ++n) {
-                size_t total = 0;
-                for (const auto &[v, c] : vector_counts[n]) {
-                    total += c;
-                }
-
-                double r = target_p / (1.0 - target_p) * n / total_labels;
-
-                #pragma omp atomic
-                ln_mu += -log(r) * total;
-
-                #pragma omp atomic
-                ln_var += log(r) * log(r) * total;
-            }
-
-            size_t total = nelem * total_labels;
-            ln_mu /= total;
-            ln_var = ln_var / total - ln_mu * ln_mu;
-
-            common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
-                                  ln_mu, ln_var,
-                                  exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1),
-                                  exp(-ln_mu - ln_var/2));
-
-            auto get_rp = [ln_mu,ln_var,gs=total_labels](const auto &counts) {
-                double mu = 0;
-                double var = 0;
-                for (int64_t c : counts) {
-                    mu += c;
-                    var += c * c;
-                }
-                mu /= gs;
-
-                if (ln_var == 0) {
-                    double r = exp(-ln_mu);
-                    return std::make_pair(r, r / (r + mu));
-                }
-
-                double mu2 = mu * mu;
-                var = var / gs - mu2;
-
-                double a_guess = var > mu ? (var - mu) / mu2 : 1.0;
-
-                auto get_dl = [&](double a) {
-                    double r = 1.0 / a;
-                    double p = r / (r + mu);
-                    double lp = log(p);
-                    double dl = -a - a*(log(a)-ln_mu)/ln_var - gs*lp + boost::math::digamma(r)*counts.size();
-                    for (int64_t c : counts) {
-                        dl -= boost::math::digamma(r + c);
-                    }
-                    return dl;
-                };
-
-                double a_min = a_guess;
-                double a_max = a_guess;
-                while (true) {
-                    a_guess = (a_min + a_max) / 2;
-                    double dl_min = get_dl(a_min);
-                    double dl_max = get_dl(a_max);
-                    if (dl_min == 0) {
-                        a_max = a_min;
-                        break;
-                    }
-
-                    if (dl_max == 0) {
-                        a_min = a_max;
-                        break;
-                    }
-
-                    if (dl_min < 0)
-                        a_min /= 2;
-
-                    if (dl_max > 0)
-                        a_max *= 2;
-
-                    if (dl_min > 0 && dl_max < 0)
-                        break;
-                }
-
-                if (a_min < a_max) {
-                    std::tie(a_min, a_max) = boost::math::tools::bisect(
-                        get_dl,
-                        a_min, a_max, boost::math::tools::eps_tolerance<double>(5)
-                    );
-                }
-
-                double a = a_min;
-                double r = 1.0 / a;
-                double p = r / (r + mu);
-                return std::make_pair(r, p);
-            };
-
-            ProgressBar progress_bar(vector_counts.size() - 1, "Precomputing r's", std::cerr, !common::get_verbose());
-            #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
-            for (size_t n = 1; n < vector_counts.size(); ++n) {
-                if (n > 0 && n % 1000 == 0)
-                    progress_bar += 1000;
-
-                for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
-                    it.value() = get_rp(it->first).first;
-                    // #pragma omp critical
-                    // common::logger->info("N: {}\tr: {}", n, it->second);
-                }
-            }
-            progress_bar += (vector_counts.size() - 1) % 1000;
-
-            auto get_r = std::make_shared<std::function<double(int64_t, const PairContainer&)>>(
-                [vc=std::move(vector_counts)](int64_t n, const auto &row) {
-                    auto it = vc[n].begin();
-                    if (vc[n].size() > 1) {
-                        std::vector<int64_t> counts;
-                        counts.reserve(row.size());
-                        for (const auto &[j, c] : row) {
-                            counts.emplace_back(c);
-                        }
-                        std::sort(counts.begin(), counts.end());
-                        it = vc[n].find(counts);
-                    }
-                    return it->second;
-                }
-            );
-
-            compute_min_pval = [compute_min_pval_r,get_r,num_labels_in,num_labels_out](int64_t n, const PairContainer &row) {
-                if (row.empty())
-                    return 1.0;
-
-                double r_base = (*get_r)(n, row);
-                if (r_base == 0.0)
-                    return 1.0;
-
-                double r_in = r_base * num_labels_in;
-                double r_out = r_base * num_labels_out;
-                double r = r_in + r_out;
-                double lscaling_base = lgamma(r) / log(2);
-
-                return compute_min_pval_r(r, r_in, r_out, lscaling_base, n);
-            };
-
-            compute_pval = [compute_pval_r,get_r,num_labels_in,num_labels_out](int64_t in_sum, int64_t out_sum, const PairContainer &row) {
-                if (row.empty())
-                    return 1.0;
-
-                if (num_labels_in == num_labels_out && in_sum == out_sum)
-                    return 1.0;
-
-                int64_t n = in_sum + out_sum;
-                double r_base = (*get_r)(n, row);
-
-                if (r_base == 0.0)
-                    return 1.0;
-
-                double r_in = r_base * num_labels_in;
-                double r_out = r_base * num_labels_out;
-                double r = r_in + r_out;
-                double lscaling_base = lgamma(r) / log(2);
-
-                return compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row);
-            };
-        }
-    } else if (config.test_type == "lnb_exact") {
-        double max_sum = *std::max_element(sums.begin(), sums.end());
-        std::vector<double> scale_factor;
-        scale_factor.reserve(sums.size());
-        std::transform(sums.begin(), sums.end(), std::back_inserter(scale_factor),
-                       [&](uint64_t sum) -> double { return max_sum / sum; });
-
-        common::logger->trace("Enumerating row count distributions");
-        std::vector<tsl::hopscotch_map<std::vector<uint64_t>, double, utils::VectorHash>> vector_counts;
-        {
-            std::vector<std::vector<tsl::hopscotch_map<std::vector<uint64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
-
-            generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
-                if (!kept[row_i])
-                    return;
-
-                uint64_t in_sum = 0;
-                uint64_t out_sum = 0;
-                bool in_kmer = false;
-                bool out_kmer = false;
-
-                std::vector<uint64_t> vec;
-                vec.resize(total_labels);
-                size_t count_in = 0;
-                size_t count_out = 0;
-                size_t total_count = 0;
-                for (const auto &[j, c] : raw_row) {
-                    if (min_counts.size() && c < min_counts[j])
-                        continue;
-
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                        out_sum += c;
-                        ++count_out;
-                    }
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                        in_sum += c;
-                        ++count_in;
-                    }
-
-                    total_count += groups[j] != Group::OTHER;
-
-                    vec[j] = c;
-                }
-
-                if (total_count >= config.min_recurrence) {
-                    in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence;
-                    out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence;
-                }
-
-                if (!in_kmer && !out_kmer)
-                    return;
-
-                // if floating point error puts the sum slightly above the integer value, then round down
-                size_t n = in_sum + out_sum;
-                if (n >= vector_counts_ms[bucket_idx].size())
-                    vector_counts_ms[bucket_idx].resize(n + 1);
-
-                std::sort(vec.begin(), vec.end());
-                ++vector_counts_ms[bucket_idx][n][vec];
-            });
-
-            vector_counts = std::move(vector_counts_ms[0]);
-            size_t max_size = vector_counts.size();
-            for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-                max_size = std::max(max_size, vector_counts_ms[j].size());
-            }
-            vector_counts.resize(max_size);
-
-            ProgressBar progress_bar(vector_counts.size() - 1, "Merging row", std::cerr, !common::get_verbose());
-            #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
-            for (size_t n = vector_counts.size(); n > 0; --n) {
-                for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-                    if (n <= vector_counts_ms[j].size()) {
-                        for (const auto &[v, c] : vector_counts_ms[j][n - 1]) {
-                            vector_counts[n - 1][v] += c;
-                        }
-                    }
-                }
-                ++progress_bar;
-            }
-        }
-
-        auto optim = [](const auto &f, auto x_min, auto x_max) {
-            // common::logger->info("start");
-            while (x_max - x_min > 1e-3) {
-                double x_mid = (x_min + x_max) / 2;
-                // common::logger->info("({},{}) -> {}", x_min, x_max, f(x_mid));
-                double x_low_mid = (x_min + x_mid) / 2;
-                double x_high_mid = (x_mid + x_max) / 2;
-                if (f(x_low_mid) > f(x_high_mid)) {
-                    x_max = x_mid;
-                } else {
-                    x_min = x_mid;
-                }
-            }
-            return (x_min + x_max) / 2;
-        };
-
-        common::logger->trace("Fitting LN prior distribution for dispersions");
-        double ln_mu = 0.0;
-        double ln_var = 0.0;
-
-        size_t skipped = 0;
-        for (size_t n = 0; n < vector_counts.size(); ++n) {
-            for (const auto &[v, c] : vector_counts[n]) {
-                double total = 0;
-                double total2 = 0;
-                for (size_t j = 0; j < v.size(); ++j) {
-                    total += v[j] * scale_factor[j];
-                    total2 += v[j] * scale_factor[j] * v[j] * scale_factor[j];
-                }
-                double mu = total / total_labels;
-                double mu2 = mu * mu;
-                double var = total2 / total_labels - mu2;
-                if (var <= mu) {
-                    ++skipped;
-                    continue;
-                }
-
-                double r = mu2 / (var - mu);
-                ln_mu += -log(r) * c;
-                ln_var += log(r) * log(r) * c;
-            }
-        }
-
-        if (skipped)
-            common::logger->warn("Skipped {} / {} rows", skipped, nelem);
-
-        ln_mu /= (nelem - skipped);
-        ln_var = ln_var / (nelem - skipped) - ln_mu * ln_mu;
-
-        common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
-                              ln_mu, ln_var,
-                              exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1),
-                              exp(-ln_mu - ln_var/2));
-
-        double expr = exp(-ln_mu + ln_var / 2);
-        double target_p = expr / (max_sum / nelem + expr);
-
-        {
-            auto calc_r = [optim,ln_mu,ln_var,gs=total_labels,p=target_p](const auto &counts) {
-                return optim([&](double r) {
-                    double val = log(p) * gs * r - lgamma(r) * counts.size() + log(r) - pow(-log(r)-ln_mu, 2.0)/2/ln_var;
-                    for (double c : counts) {
-                        val += lgamma(c + r);
-                    }
-                    return val;
-                }, 0.0, 100.0);
-            };
-
-            ProgressBar progress_bar(vector_counts.size(), "Precomputing r's", std::cerr, !common::get_verbose());
-            #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
-            for (size_t n = 0; n < vector_counts.size(); ++n) {
-                if (n > 0 && n % 1000 == 0)
-                    progress_bar += 1000;
-
-                for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
-                    std::vector<double> scale_counts;
-                    scale_counts.reserve(it->first.size());
-                    for (size_t j = 0; j < it->first.size(); ++j) {
-                        if (it->first[j] > 0)
-                            scale_counts.emplace_back(it->first[j] * scale_factor[j]);
-                    }
-                    it.value() = calc_r(scale_counts);
-                }
-            }
-            progress_bar += vector_counts.size() % 1000;
-        }
-
-        auto get_r = std::make_shared<std::function<double(int64_t, std::vector<uint64_t>&)>>(
-            [vc=std::move(vector_counts),scale_factor](int64_t n, const auto &counts) {
-                auto it = vc[n].begin();
-                if (vc[n].size() > 1) {
-                    it = vc[n].find(counts);
-                    assert(it != vc[n].end());
-                }
-                return it->second;
-            }
-        );
-
-        compute_min_pval = [get_r,num_labels_in,num_labels_out,scale_factor,total_labels](int64_t, const PairContainer &row) {
-            if (row.empty())
-                return 1.0;
-
-            int64_t sum = 0;
-            double ssum = 0;
-            std::vector<double> counts;
-            std::vector<uint64_t> row_int(total_labels);
-            counts.reserve(row.size());
-            for (const auto &[j, c] : row) {
-                row_int[j] = c;
-                sum += c;
-                ssum += counts.emplace_back(c * scale_factor[j]);
-            }
-            // if floating point error puts the sum slightly above the integer value, then round down
-            int64_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
-
-            double r_base = (*get_r)(sum, row_int);
-            if (r_base == 0.0)
-                return 1.0;
-
-            double r_in = r_base * num_labels_in;
-            double r_out = r_base * num_labels_out;
-            double r = r_in + r_out;
-            double lscaling_base = lgamma(r) / log(2);
-
-            double half_div0 = -r_in * log2(r_in) - (n + r_out) * log2(n + r_out);
-            double half_divn = -r_out * log2(r_out) - (n + r_in) * log2(n + r_in);
-
-            double pval = 0.0;
-            double lscaling = lscaling_base - lgamma(r + n) / log(2);
-            if (half_div0 >= half_divn)
-                pval += exp2(lscaling + (lgamma(r_out + n) - lgamma(r_out)) / log(2));
-
-            if (half_divn >= half_div0)
-                pval += exp2(lscaling + (lgamma(r_in + n) - lgamma(r_in)) / log(2));
-
-            return std::min(1.0, pval);
-        };
-
-        compute_pval = [get_r,num_labels_in,num_labels_out,scale_factor,groups,total_labels](int64_t, int64_t, const PairContainer &row) {
-            if (row.empty())
-                return 1.0;
-
-            int64_t sum = 0;
-            double ssum = 0;
-            double in_sum = 0;
-            double out_sum = 0;
-            std::vector<double> counts;
-            std::vector<uint64_t> row_int(total_labels);
-            counts.reserve(row.size());
-            for (const auto &[j, c] : row) {
-                row_int[j] = c;
-                sum += c;
-                ssum += counts.emplace_back(c * scale_factor[j]);
-                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                    out_sum += counts.back();
-                }
-
-                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                    in_sum += counts.back();
-                }
-            }
-            // if floating point error puts the sum slightly above the integer value, then round down
-            int64_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
-
-            double r_base = (*get_r)(sum, row_int);
-
-            if (r_base == 0.0)
-                return 1.0;
-
-            double r_in = r_base * num_labels_in;
-            double r_out = r_base * num_labels_out;
-            double r = r_in + r_out;
-            double lscaling_base = lgamma(r) / log(2);
-
-            double argmin_d = r_in / r * n;
-            if (in_sum == argmin_d)
-                return 1.0;
-
-            double lscaling = lscaling_base - lgamma(r + n) / log(2);
-
-            auto get_pval = [&](const auto &get_stat) {
-                double base_stat = get_stat(in_sum, out_sum,
-                                            r_in != r_out && in_sum > 0 ? log2(in_sum) : 0.0,
-                                            r_in != r_out && out_sum > 0 ? log2(out_sum) : 0.0);
-                double pval = 0.0;
-                int64_t s = 0;
-                int64_t t = n;
-                double ls = 0;
-                double lt = log2(t);
-                if (get_stat(s, t, ls, lt) >= base_stat) {
-                    double base = lscaling + (lgamma(n + r_out) - lgamma(r_out)) / log(2);
-                    pval += exp2(base);
-                    for (++s,--t; s <= n; ++s,--t) {
-                        ls = log2(s);
-                        lt = t > 0 ? log2(t) : 0.0;
-                        if (get_stat(s, t, ls, lt) < base_stat)
-                            break;
-
-                        base += log2(t + 1) + log2(s - 1 + r_in) - ls - log2(t + r_out);
-                        pval += exp2(base);
-                    }
-                }
-
-                int64_t sp = n;
-                t = 0;
-                ls = log2(sp);
-                lt = 0;
-                if (get_stat(sp, t, ls, lt) >= base_stat) {
-                    double base = lscaling + (lgamma(n + r_in) - lgamma(r_in)) / log(2);
-                    pval += exp2(base);
-                    for (--sp,++t; sp >= s; --sp,++t) {
-                        ls = sp > 0 ? log2(sp) : 0.0;
-                        lt = log2(t);
-                        if (get_stat(sp, t, ls, lt) < base_stat)
-                            break;
-
-                        base += log2(sp + 1) + log2(t - 1 + r_out) - lt - log2(sp + r_in);
-                        pval += exp2(base);
-                    }
-                }
-
-                return std::min(1.0, pval);
-            };
-
-            if (r_in == r_out) {
-                return get_pval([&](int64_t s, int64_t, double, double) { return abs(argmin_d - s); });
-            } else {
-                return get_pval([&](int64_t s, int64_t t, double ls, double lt) {
-                    return ls * s - (r_in + s)*log2(r_in + s) + lt * t - (t + r_out)*log2(t + r_out);
-                });
-            }
-        };
-
-    } else if (config.test_type == "poisson_binom") {
-        // fit distribution
-        std::vector<double> p;
-        p.reserve(groups.size());
-        for (size_t i = 0; i < groups.size(); ++i) {
-            p.emplace_back(static_cast<double>(n_kmers[i]) / nelem);
-        }
-
-        common::logger->trace("p: {}", fmt::join(p, ","));
-
-        common::logger->trace("Precomputing PMFs");
-        std::vector<double> pmf_in { 1.0 };
-        std::vector<double> pmf_out { 1.0 };
-        std::vector<double> pmf_null { 1.0 };
-
-        for (size_t i = 1; i <= groups.size(); ++i) {
-            if (groups[i - 1] == Group::OUT || groups[i - 1] == Group::BOTH) {
-                std::vector<double> pmf_out_cur(pmf_out.size() + 1);
-                pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
-                pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
-                for (size_t k = 1; k < pmf_out.size(); ++k) {
-                    pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1] + (1.0 - p[i - 1]) * pmf_out[k];
-                }
-                std::swap(pmf_out_cur, pmf_out);
-            }
-
-            if (groups[i - 1] == Group::IN || groups[i - 1] == Group::BOTH) {
-                std::vector<double> pmf_in_cur(pmf_in.size() + 1);
-                pmf_in_cur[0] = (1.0 - p[i - 1]) * pmf_in[0];
-                pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
-                for (size_t k = 1; k < pmf_in.size(); ++k) {
-                    pmf_in_cur[k] = p[i - 1] * pmf_in[k - 1] + (1.0 - p[i - 1]) * pmf_in[k];
-                }
-                std::swap(pmf_in_cur, pmf_in);
-            }
-            std::vector<double> pmf_null_cur(i + 1);
-            pmf_null_cur[0] = (1.0 - p[i - 1]) * pmf_null[0];
-            pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
-            for (size_t k = 1; k < pmf_null.size(); ++k) {
-                pmf_null_cur[k] = p[i - 1] * pmf_null[k - 1] + (1.0 - p[i - 1]) * pmf_null[k];
-            }
-            std::swap(pmf_null_cur, pmf_null);
-        }
-
-        if (pmf_in.size() != num_labels_in + 1) {
-            common::logger->error("PMF in wrong: {} != {}", pmf_in.size(), num_labels_in + 1);
-            throw std::domain_error("");
-        }
-
-        if (pmf_out.size() != num_labels_out + 1) {
-            common::logger->error("PMF out wrong: {} != {}", pmf_out.size(), num_labels_out + 1);
-            throw std::domain_error("");
-        }
-
-        if (pmf_null.size() != num_labels_in + num_labels_out + 1) {
-            common::logger->error("PMF null wrong: {} != {}", pmf_null.size(), num_labels_in + num_labels_out + 1);
-            throw std::domain_error("");
-        }
-
-        common::logger->trace("Precomputing p-values");
-        std::vector<std::vector<double>> pvals(num_labels_in + num_labels_out + 1);
-        std::vector<double> mid_points(num_labels_in + num_labels_out + 1);
-        mid_points.emplace_back(0);
-        pvals[0].emplace_back(1.0);
-
-        double min_pval = 1.0;
-
-        for (size_t n = 1; n < pvals.size(); ++n) {
-            std::vector<double> probs;
-            size_t front = n - std::min(n, num_labels_out);
-            for (uint64_t s = 0; s < pmf_in.size(); ++s) {
-                if (s > n)
-                    break;
-                uint64_t t = n - s;
-                if (s < pmf_in.size() && t < pmf_out.size()) {
-                    if (s < front) {
-                        common::logger->error("Attempting non-zero p-value in impossible configuration: {},{}", n, s);
-                        throw std::domain_error("");
-                    }
-                    probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t]) - log2(pmf_null[n])));
-                } else {
-                    probs.emplace_back(0.0);
-                }
-            }
-
-            for (uint64_t s = 0; s < probs.size(); ++s) {
-                pvals[n].emplace_back(0.0);
-                if (s > n)
-                    break;
-                uint64_t t = n - s;
-                if (s >= pmf_in.size() || t >= pmf_out.size())
-                    continue;
-                for (uint64_t sp = 0; sp < probs.size(); ++sp) {
-                    if (sp > n)
-                        break;
-                    uint64_t tp = n - sp;
-                    if (sp >= pmf_in.size() || tp >= pmf_out.size())
-                        continue;
-                    if (probs[sp] <= probs[s])
-                        pvals[n][s] += probs[sp];
-                }
-            }
-
-            for (uint64_t s = 0; s < front; ++s) {
-                if (pvals[n][s] > 0) {
-                    common::logger->error("Non-zero p-value in impossible configuration: {},{}", n, s);
-                    throw std::domain_error("");
-                }
-            }
-            double local_min_pval = std::min(pvals[n][front],
-                                             pvals[n].back());
-            min_pval = std::min(min_pval, local_min_pval);
-
-            size_t max_prob_counts = 0;
-            double max_prob_pos = 0;
-            // double max_prob = -1;
-
-            for (uint64_t s = front; s < pvals[n].size(); ++s) {
-                if (pvals[n][s] < local_min_pval) {
-                    common::logger->error("Min p-value not at boundary: {},{}: {} < {}", n, s, pvals[n][s], local_min_pval);
-                    throw std::domain_error("");
-                }
-            }
-            if (front + 1 == pvals[n].size()) {
-                max_prob_counts = 1;
-                max_prob_pos = front;
-            } else if (front + 2 == pvals[n].size()) {
-                max_prob_counts = 2;
-                max_prob_pos = front + front + 1;
-            } else {
-                for (uint64_t s = front + 1; s < pvals[n].size(); ++s) {
-                    if (probs[s] == probs[s-1]) {
-                        max_prob_counts = 2;
-                        max_prob_pos = s + s;
-                        break;
-                    }
-
-                    if (s + 1 < pvals[n].size()) {
-                        if (probs[s-1] < probs[s] && probs[s] < probs[s+1])
-                            continue;
-
-                        if (probs[s-1] > probs[s] && probs[s] > probs[s+1])
-                            continue;
-
-                        if (probs[s] > probs[s-1] && probs[s] > probs[s+1]) {
-                            if (probs[s] - probs[s-1] == probs[s]-probs[s+1]) {
-                                max_prob_counts = 1;
-                                max_prob_pos = s;
-                            } else if (probs[s] - probs[s-1] > probs[s]-probs[s+1]) {
-                                max_prob_counts = 2;
-                                max_prob_pos = s + s + 1;
-                            } else {
-                                max_prob_counts = 2;
-                                max_prob_pos = s + s - 1;
-                            }
-                        }
-                    }
-                }
-            }
+            }
 
             if (max_prob_counts == 0) {
                 max_prob_counts = 2;
@@ -1443,9 +639,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             mid_points[n] = static_cast<double>(max_prob_pos) / max_prob_counts;
             common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}",
-                                  n, mid_points[n],
-                                  fmt::join(probs,","),
-                                  fmt::join(pvals[n],","));
+                                    n, mid_points[n],
+                                    fmt::join(probs,","),
+                                    fmt::join(pb_pvals[n],","));
         }
 
         common::logger->trace("Min. p-value: {}", min_pval);
@@ -1463,506 +659,1888 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage{}, nullptr);
         }
+    }
 
-        compute_min_pval = [pvals,num_labels_out,&groups](int64_t, const PairContainer &row) {
-            size_t n = 0;
-            for (const auto &[j, c] : row) {
-                n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
-            }
-            size_t front = n - std::min(n, num_labels_out);
-            return std::min(pvals[n][front],
-                            pvals[n].back());
-        };
-
-        compute_pval = [pvals,mid_points,&groups](int64_t, int64_t, const auto &row) {
-            int64_t n = 0;
-            uint64_t s = 0;
-            for (const auto &[j, c] : row) {
-                n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
-                s += (groups[j] == Group::IN);
-            }
-
-            return pvals[n][s];
-        };
 
-    } else if (config.test_type == "notest") {
-        compute_min_pval = [&](int64_t n, const PairContainer&) { return n > 0 ? 0.0 : 1.0; };
-        compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.0; };
-    } else {
-        throw std::runtime_error("Test not implemented");
-    }
+    auto get_deviance = [&](double y, double mu) {
+        y += 1e-8;
+        mu += 1e-8;
+        return 2 * (y * log(y/mu) - y + mu);
+    };
 
-    common::logger->trace("Computing minimum p-values");
     std::vector<std::pair<double, size_t>> m;
-    std::vector<int64_t> m_sums;
     {
-        std::vector<std::vector<std::pair<double, size_t>>> ms(num_threads + 1);
-        std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, std::pair<double, size_t>, utils::VectorHash>>> ms_vecs(num_threads + 1);
-        generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-            int64_t out_stat_int = 0;
-            bool in_kmer = false;
-            bool out_kmer = false;
-            PairContainer row;
-            std::vector<int64_t> vals;
-            if (kept[row_i]) {
-                size_t count_in = 0;
-                size_t count_out = 0;
-                size_t total_count = 0;
-                for (const auto &[j, raw_c] : raw_row) {
-                    if (min_counts.size() && raw_c < min_counts[j])
-                        continue;
-
-                    uint64_t c = raw_c;
-                    if (count_maps.size())
-                        c = count_maps[j].find(c)->second.first;
-
-                    row.emplace_back(j, c);
-                    vals.emplace_back(c);
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                        out_sum += c;
-                        ++count_out;
-                    }
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                        in_sum += c;
-                        ++count_in;
-                    }
-
-                    total_count += groups[j] != Group::OTHER;
-                }
-
-                if (total_count >= config.min_recurrence) {
-                    double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-                    if (out_stat != 0)
-                        out_stat_int = out_stat > 0 ? ceil(out_stat) : floor(out_stat);
-
-                    in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-                    out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
-                }
-            } else {
+        VectorMap<double, size_t> m_vec;
+        std::vector<VectorMap<double, size_t>> ms(num_threads + 1);
+        generate_clean_rows([&](uint64_t row_i, const auto &row, uint64_t bucket_idx) {
+            if (config.test_type == "poisson_binom") {
+                size_t n = 0;
+                for (const auto &[j, c] : row) {
+                    n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
+                }
+                size_t front = n - std::min(n, num_labels_out);
+                double pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+                ++ms[bucket_idx][pval];
                 return;
             }
 
-            size_t n = config.test_type != "poisson_binom" ? in_sum + out_sum : row.size();
-            if (!in_kmer && !out_kmer) {
-                row.clear();
-                vals.clear();
-                n = 0;
+            double mu1 = 0.0;
+            double mu2 = 0.0;
+            if (config.test_type == "poisson_exact") {
+                mu1 = static_cast<double>(in_kmers) / nelem;
+                mu2 = static_cast<double>(out_kmers) / nelem;
             }
+            int64_t n = 0;
+            for (const auto &[j, c] : row) {
+                n += c;
+                if (config.test_type == "poisson_bayes") {
+                    const auto &[r, p] = nb_params[j];
+                    double lambda_j = sqrt((1.0 - p) * (c + r - 1));
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                        mu2 += lambda_j;
 
-            double pval_min = 0;
-            if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact") {
-                assert(bucket_idx < ms.size());
-                if (n >= ms[bucket_idx].size())
-                    ms[bucket_idx].resize(n + 1, std::make_pair(1.1, 0));
-
-                if (ms[bucket_idx][n].first == 1.1)
-                    ms[bucket_idx][n].first = compute_min_pval(n, row);
-
-                pval_min = ms[bucket_idx][n].first;
-                ++ms[bucket_idx][n].second;
-            } else {
-                std::sort(vals.begin(), vals.end());
-                if (n >= ms_vecs[bucket_idx].size())
-                    ms_vecs[bucket_idx].resize(n + 1);
-
-                auto find = ms_vecs[bucket_idx][n].find(vals);
-                if (find == ms_vecs[bucket_idx][n].end()) {
-                    find = ms_vecs[bucket_idx][n].try_emplace(
-                        vals,
-                        std::make_pair(compute_min_pval(n, row), 1)
-                    ).first;
-                } else {
-                    ++find.value().second;
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                        mu1 += lambda_j;
                 }
-
-                pval_min = find->second.first;
             }
 
-            if (config.test_by_unitig) {
-                bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
-                uint64_t eff_size = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
-                if constexpr(preallocated) {
-                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                    eff_size_buckets[bucket_idx][node] = eff_size;
-                    sum_buckets[bucket_idx][node] = n;
-                    if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact")
-                        pvals_min_buckets[bucket_idx][node] = bit_cast<uint64_t>(pval_min);
-                } else {
-                    eff_size_buckets[bucket_idx].push_back(eff_size);
-                    sum_buckets[bucket_idx].push_back(n);
-                    if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact")
-                        pvals_min_buckets[bucket_idx].push_back(bit_cast<uint64_t>(pval_min));
-                }
+            double pval = 1.0;
+
+            if (n > 0) {
+                double p = mu1 / (mu1 + mu2);
+                auto bdist = boost::math::binomial(n, p);
+                double pval0 = boost::math::pdf(bdist, 0);
+                double pvaln = boost::math::pdf(bdist, n);
+                pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+
+                // std::vector<double> devs;
+                // devs.reserve(n + 1);
+                // for (int64_t s = 0; s <= n; ++s) {
+                //     devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+                // }
+                // double max_d = *std::max_element(devs.begin(), devs.end());
+                // pval = 0.0;
+
+                // int64_t s = 0;
+                // for ( ; s <= n; ++s) {
+                //     if (devs[s] != max_d)
+                //         break;
+                // }
+                // if (s > 0)
+                //     pval += boost::math::cdf(bdist, s - 1);
+
+                // int64_t sp = n;
+                // for ( ; sp >= s; --sp) {
+                //     if (devs[sp] != max_d)
+                //         break;
+                // }
+
+                // if (sp < n)
+                //     pval += boost::math::cdf(boost::math::complement(bdist, sp));
             }
-        });
 
-        common::logger->trace("Merging min p-value tables");
-        if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact") {
-            for (size_t i = 1; i < ms.size(); ++i) {
-                size_t end = std::min(ms[0].size(), ms[i].size());
-                for (size_t j = 0; j < end; ++j) {
-                    assert(ms[0][j].first == 1.1 || ms[i][j].first == 1.1 || ms[0][j] == ms[i][j]);
-                    ms[0][j].first = std::min(ms[0][j].first, ms[i][j].first);
-                    ms[0][j].second += ms[i][j].second;
-                }
+            ++ms[bucket_idx][pval];
+        });
 
-                if (ms[i].size() > ms[0].size()) {
-                    std::copy(std::make_move_iterator(ms[i].begin() + ms[0].size()),
-                            std::make_move_iterator(ms[i].end()),
-                            std::back_inserter(ms[0]));
-                }
+        std::swap(m_vec, ms[0]);
+        for (size_t i = 1; i < ms.size(); ++i) {
+            for (const auto &[pval, c] : ms[i]) {
+                m_vec[pval] += c;
             }
-            m = std::move(ms[0]);
-            ms.resize(0);
-        } else {
-            std::vector<std::tuple<double, int64_t, size_t>> mn;
-            for (size_t i = 0; i < ms_vecs.size(); ++i) {
-                for (size_t n = 0; n < ms_vecs[i].size(); ++n) {
-                    for (const auto &[vec, v] : ms_vecs[i][n]) {
-                        mn.emplace_back(v.first, n, v.second);
-                    }
-                }
-            }
-            ms_vecs.resize(0);
-            std::sort(mn.begin(), mn.end(), utils::GreaterFirst());
-
-            m.reserve(mn.size());
-            m_sums.reserve(mn.size());
-            for (const auto &[p, s, c] : mn) {
-                m.emplace_back(p, c);
-                m_sums.emplace_back(s);
-            }
-        }
-    }
-
-    size_t k = 0;
-    size_t n_cutoff = 1;
-
-    auto combine_pvals = [dist=boost::math::cauchy()](const std::vector<double> &pvals) {
-        double stat = 0.0;
-        for (double pval : pvals) {
-            stat += tan((0.5 - pval) * M_PI);
         }
-        stat /= pvals.size();
-        return boost::math::cdf(boost::math::complement(dist, stat));
-    };
 
-    std::vector<size_t> boundaries;
-    if (config.test_by_unitig) {
-        boundaries.reserve(eff_size_buckets.size() + 1);
-        boundaries.emplace_back(0);
-        for (size_t i = 0; i < eff_size_buckets.size(); ++i) {
-            boundaries.emplace_back(boundaries.back() + eff_size_buckets[i].size());
-        }
+        m = const_cast<std::vector<std::pair<double, size_t>>&&>(m_vec.values_container());
+        std::sort(m.rbegin(), m.rend());
     }
 
-    std::mutex pval_mu;
-    std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
-    sdsl::bit_vector unitig_start;
-    if (!config.test_by_unitig) {
-        std::tie(k, n_cutoff) = correct_pvals(m, nelem, m_sums);
-        common::logger->trace("Picked: k: {}\tn: {}\tpval_min: {}", k, n_cutoff, config.family_wise_error_rate / k);
-    } else {
-        clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
-            graph_ptr,
-            [&](node_index node) {
-                return node != DeBruijnGraph::npos
-                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
-            },
-            true,
-            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-        );
+    auto [k_min, k] = correct_pvals(m);
 
-        std::vector<std::pair<double, int64_t>> comb_msums;
-        common::logger->trace("Allocating updated indicator");
-        unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-
-        common::logger->trace("Combining min p-vals");
-        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-            std::vector<size_t> bucket_idxs;
-            bucket_idxs.reserve(path.size());
-            for (node_index node : path) {
-                bucket_idxs.emplace_back(0);
-                for (size_t i = 1; i < boundaries.size(); ++i) {
-                    if (boundaries[i] > node)
-                        break;
-
-                    ++bucket_idxs.back();
-                }
-            }
-
-            std::vector<double> pvals_min;
-            pvals_min.reserve(path.size());
-
-            int64_t n_sum = 0;
-            {
-                std::lock_guard<std::mutex> lock(pval_mu);
-                for (size_t i = 0; i < path.size(); ++i) {
-                    size_t bucket_idx = bucket_idxs[i];
-                    size_t node_shift = path[i] - boundaries[bucket_idx];
-                    size_t n = sum_buckets[bucket_idx][node_shift];
-                    n_sum += n;
-
-                    if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact") {
-                        pvals_min.emplace_back(bit_cast<double, uint64_t>(pvals_min_buckets[bucket_idx][node_shift]));
-                    } else {
-                        pvals_min.emplace_back(m[n].first);
-                    }
-                }
+    common::logger->trace("Picked: k: {}\tn: {} / {}\tpval_min: {}", k_min, k, nelem, config.family_wise_error_rate / k);
+    std::atomic_thread_fence(std::memory_order_release);
+    generate_clean_rows([&](uint64_t row_i, const auto &row, uint64_t bucket_idx) {
+        if (config.test_type == "poisson_binom") {
+            size_t n = 0;
+            uint64_t s = 0;
+            for (const auto &[j, c] : row) {
+                n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
+                s += (groups[j] == Group::IN);
             }
+            size_t front = n - std::min(n, num_labels_out);
+            double min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+            if (min_pval * k_min >= config.family_wise_error_rate)
+                return;
 
-            double comb_pval = *std::min_element(pvals_min.begin(), pvals_min.end());
-            if (comb_pval < config.family_wise_error_rate) {
-                for (node_index node : path) {
-                    set_bit(unitig_start.data(), node, parallel, MO_RELAXED);
+            double pval = pb_pvals[n][s];
+            if (pval * k < config.family_wise_error_rate) {
+                double eff_size = static_cast<double>(s) - mid_points[n];
+                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                if (eff_size > 0) {
+                    set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
+                } else if (eff_size < 0) {
+                    set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
                 }
             }
-
-            std::lock_guard<std::mutex> lock(pval_mu);
-            comb_msums.emplace_back(comb_pval, n_sum);
-        }, num_threads);
-
-        std::sort(comb_msums.begin(), comb_msums.end(), utils::GreaterFirst());
-        std::vector<std::pair<double, size_t>> comb_m;
-        std::vector<int64_t> comb_m_sums;
-        comb_m.reserve(comb_msums.size());
-        comb_m_sums.reserve(comb_msums.size());
-        for (const auto &[p, s] : comb_msums) {
-            comb_m.emplace_back(p, 1);
-            comb_m_sums.emplace_back(s);
+            return;
         }
 
-        std::tie(k, n_cutoff) = correct_pvals(comb_m, nelem, comb_m_sums);
-        common::logger->trace("Picked: k: {}\tpval_min: {}", k, config.family_wise_error_rate / k);
-
-        sum_buckets.resize(0);
-        tmp_sum_buckets.resize(0);
-        pvals_min_buckets.resize(0);
-        tmp_min_buckets.resize(0);
-    }
-
-    if (k > 0) {
-        common::logger->trace("Running differential tests");
-        std::exception_ptr ex = nullptr;
-        std::mutex ex_mu;
-        bool keep_all_pvals = config.output_pvals || config.test_by_unitig;
-        std::atomic_thread_fence(std::memory_order_release);
-
-        generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
-            if (ex)
-                return;
-
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-            bool in_kmer = false;
-            bool out_kmer = false;
-            PairContainer row;
-            bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            double pval = 1.1;
-            if (kept[row_i]) {
-                size_t count_in = 0;
-                size_t count_out = 0;
-                size_t total_count = 0;
-                for (const auto &[j, raw_c] : raw_row) {
-                    if (min_counts.size() && raw_c < min_counts[j])
-                        continue;
-
-                    uint64_t c = raw_c;
-                    if (count_maps.size())
-                        c = count_maps[j].find(c)->second.first;
-
-                    row.emplace_back(j, c);
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                        out_sum += c;
-                        ++count_out;
-                    }
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                        in_sum += c;
-                        ++count_in;
-                    }
-
-                    total_count += groups[j] != Group::OTHER;
-                }
+        double mu1 = 0.0;
+        double mu2 = 0.0;
+        if (config.test_type == "poisson_exact") {
+            mu1 = static_cast<double>(in_kmers) / nelem;
+            mu2 = static_cast<double>(out_kmers) / nelem;
+        }
+        int64_t n = 0;
+        int64_t in_sum = 0;
+        int64_t out_sum = 0;
+        for (const auto &[j, c] : row) {
+            n += c;
+            const auto &[r, p] = nb_params[j];
+            double lambda_j = config.test_type == "poisson_bayes"
+                ? sqrt((1.0 - p) * (r - 1.0 + c))
+                : 0.0;
+            if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                mu2 += lambda_j;
+                out_sum += c;
+            }
 
-                if (total_count >= config.min_recurrence) {
-                    double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-                    in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-                    out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
-                }
-            } else {
-                if (keep_all_pvals) {
-                    auto &pvals = pvals_buckets[bucket_idx];
-                    if constexpr(preallocated) {
-                        pvals[node] = bit_cast<uint64_t>(pval);
-                    } else {
-                        pvals.push_back(bit_cast<uint64_t>(pval));
-                    }
-                }
-                return;
+            if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                mu1 += lambda_j;
+                in_sum += c;
             }
+        }
 
-            size_t n = config.test_type != "poisson_binom" ? in_sum + out_sum : row.size();
+        double pval = 1.0;
 
-            if (!in_kmer && !out_kmer) {
-                row.clear();
-                n = 0;
-            }
+        double eff_size = 0;
+        if (n > 0) {
+            double p = mu1 / (mu1 + mu2);
+            auto bdist = boost::math::binomial(n, p);
+            eff_size = static_cast<double>(in_sum) - p * n;
+
+            // std::vector<double> pmfs;
+            // pmfs.reserve(n + 1);
+            // for (int64_t s = 0; s <= n; ++s) {
+            //     pmfs.emplace_back(boost::math::pdf(bdist, s));
+            // }
+
+            // pval = 0.0;
+            // for (int64_t s = 0; s <= n; ++s) {
+            //     if (pmfs[s] <= pmfs[in_sum])
+            //         pval += pmfs[s];
+            // }
+
+            // if (in_sum <= mean) {
+            //     pval = boost::math::cdf(bdist, in_sum);
+            //     int64_t t = boost::math::quantile(bdist, 1.0 - pval);
+            //     pval += boost::math::cdf(boost::math::complement(bdist, t - 1));
+            // } else {
+            //     pval = boost::math::cdf(boost::math::complement(bdist, in_sum - 1));
+            //     int64_t s = boost::math::quantile(bdist, pval);
+            //     pval += boost::math::cdf(bdist, s);
+            // }
 
-            if (!config.output_pvals && config.test_by_unitig && !unitig_start[node]) {
-                auto &pvals = pvals_buckets[bucket_idx];
-                if constexpr(preallocated) {
-                    pvals[node] = bit_cast<uint64_t>(pval);
-                } else {
-                    pvals.push_back(bit_cast<uint64_t>(pval));
-                }
+            std::vector<double> devs;
+            devs.reserve(n + 1);
+            for (int64_t s = 0; s <= n; ++s) {
+                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
             }
+            double max_d = *std::max_element(devs.begin(), devs.end());
 
-            if (config.output_pvals
-                    || (!config.test_by_unitig && n >= n_cutoff)
-                    || (config.test_by_unitig && unitig_start[node])) {
-                if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact" && m[n].first == 1.1) {
-                    common::logger->error("in: {}\tout: {}\tn: {}", in_sum, out_sum, n);
-                    throw std::runtime_error("Indexing invalid");
-                }
-
-                try {
-                    pval = compute_pval(in_sum, out_sum, row);
-                } catch (...) {
-                    std::lock_guard<std::mutex> lock(ex_mu);
-                    ex = std::current_exception();
-                    return;
+            {
+                double min_pval = 0.0;
+                int64_t s = 0;
+                for ( ; s <= n; ++s) {
+                    if (devs[s] != max_d)
+                        break;
                 }
+                if (s > 0)
+                    min_pval += boost::math::cdf(bdist, s - 1);
 
-                if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact" && pval < 1.1 && m[n].first - pval > 1e-10) {
-                    common::logger->error("Min p-val estimate too high: min {} > cur {}\tn: {}\ttest: {}", m[n].first, pval, n, config.test_type);
-                    throw std::runtime_error("Test failed");
+                int64_t sp = n;
+                for ( ; sp >= s; --sp) {
+                    if (devs[sp] != max_d)
+                        break;
                 }
 
-                if (config.test_type != "notest" && keep_all_pvals) {
-                    auto &pvals = pvals_buckets[bucket_idx];
-                    if constexpr(preallocated) {
-                        pvals[node] = bit_cast<uint64_t>(pval);
-                    } else {
-                        pvals.push_back(bit_cast<uint64_t>(pval));
-                    }
-                }
+                if (sp < n)
+                    min_pval += boost::math::cdf(boost::math::complement(bdist, sp));
 
-                if (!config.test_by_unitig && in_kmer != out_kmer && pval * k < config.family_wise_error_rate) {
-                    bool use_atomic = parallel && (node % 64 == 0);
-                    set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
-                }
+                if (min_pval * k_min >= config.family_wise_error_rate)
+                    return;
             }
-        });
 
-        std::atomic_thread_fence(std::memory_order_acquire);
+            pval = 0.0;
 
-        if (ex)
-            std::rethrow_exception(ex);
-
-        unitig_start = sdsl::bit_vector();
-        deallocate();
-
-        if (!config.test_by_unitig) {
-            boundaries.reserve(pvals_buckets.size() + 1);
-            boundaries.emplace_back(0);
-            for (size_t i = 0; i < pvals_buckets.size(); ++i) {
-                boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
+            int64_t s = 0;
+            for ( ; s <= n; ++s) {
+                if (devs[s] < devs[in_sum])
+                    break;
             }
-        }
-
-        if (config.test_by_unitig) {
-            common::logger->trace("Allocating k-mer bitmasks");
-            indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-            indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-
-            common::logger->trace("Combining p-values within unitigs");
-            std::atomic<uint64_t> num_sig_unitigs{0};
-
-            std::atomic_thread_fence(std::memory_order_release);
-            clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-                int64_t comb_eff_size = 0;
-                std::vector<size_t> bucket_idxs;
-                bucket_idxs.reserve(path.size());
-                for (node_index node : path) {
-                    bucket_idxs.emplace_back(0);
-                    for (size_t i = 1; i < boundaries.size(); ++i) {
-                        if (boundaries[i] > node)
-                            break;
-
-                        ++bucket_idxs.back();
-                    }
-                }
-
-                // TODO: don't bother combining if the row sum is too small
-                std::vector<double> pvals;
-                pvals.reserve(path.size());
-                {
-                    std::lock_guard<std::mutex> lock(pval_mu);
-                    for (size_t i = 0; i < path.size(); ++i) {
-                        node_index node = path[i];
-                        size_t bucket_idx = bucket_idxs[i];
-                        size_t node_shift = node - boundaries[bucket_idx];
-
-                        comb_eff_size += bit_cast<int64_t, uint64_t>(eff_size_buckets[bucket_idx][node_shift]);
+            if (s > 0)
+                pval += boost::math::cdf(bdist, s - 1);
 
-                        double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node_shift]);
-                        pvals.emplace_back(pval);
-                    }
-                }
-                double comb_pval = combine_pvals(pvals);
-                if (comb_pval * k < config.family_wise_error_rate && comb_eff_size != 0) {
-                    ++num_sig_unitigs;
-                    auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
-                    for (node_index node : path) {
-                        set_bit(data, node, parallel, MO_RELAXED);
-                    }
-                }
+            int64_t sp = n;
+            for ( ; sp >= s; --sp) {
+                if (devs[sp] < devs[in_sum])
+                    break;
+            }
 
-                uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
+            if (sp < n)
+                pval += boost::math::cdf(boost::math::complement(bdist, sp));
+        }
 
-                std::lock_guard<std::mutex> lock(pval_mu);
-                for (size_t i = 0; i < path.size(); ++i) {
-                    size_t bucket_idx = bucket_idxs[i];
-                    pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = comb_pval_enc;
-                }
-            }, num_threads);
-            std::atomic_thread_fence(std::memory_order_acquire);
-            common::logger->trace("Found {} significant unitigs before correction", num_sig_unitigs);
+        // common::logger->trace("test: {}/{}\t{}\t{}\t{} -> {}", in_sum, n, mu1, mu2, pval, pval * k);
 
-            eff_size_buckets.resize(0);
-            tmp_eff_buckets.resize(0);
+        if (pval * k < config.family_wise_error_rate) {
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+            if (eff_size > 0) {
+                set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
+            } else if (eff_size < 0) {
+                set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
+            }
         }
-    } else {
-        deallocate();
-    }
+    });
+    std::atomic_thread_fence(std::memory_order_acquire);
+
+    // if (config.test_type == "poisson_exact") {
+    //     auto get_deviance = [&](double y, double mu) {
+    //         y += 1e-8;
+    //         mu += 1e-8;
+    //         return 2 * (y * log(y/mu) - y + mu);
+    //     };
+
+    //     compute_min_pval = [&,p=static_cast<double>(in_kmers)/total_kmers,
+    //                           mu1=static_cast<double>(in_kmers)/nelem,
+    //                           mu2=static_cast<double>(out_kmers)/nelem,
+    //                           get_deviance](int64_t n, const PairContainer&) {
+    //         if (n == 0)
+    //             return 1.0;
+
+    //         auto bdist = boost::math::binomial(n, p);
+
+    //         std::vector<double> devs;
+    //         devs.reserve(n + 1);
+    //         for (int64_t s = 0; s <= n; ++s) {
+    //             devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+    //         }
+    //         double max_d = *std::max_element(devs.begin(), devs.end());
+    //         double pval = 0.0;
+
+    //         int64_t s = 0;
+    //         for ( ; s <= n; ++s) {
+    //             if (devs[s] != max_d)
+    //                 break;
+    //         }
+    //         if (s > 0)
+    //             pval += boost::math::cdf(bdist, s - 1);
+
+    //         int64_t sp = n;
+    //         for ( ; sp >= s; --sp) {
+    //             if (devs[sp] != max_d)
+    //                 break;
+    //         }
+
+    //         if (sp < n)
+    //             pval += boost::math::cdf(boost::math::complement(bdist, sp));
+
+    //         return pval;
+    //     };
+
+    //     compute_pval = [&,p=static_cast<double>(in_kmers)/total_kmers,
+    //                       mu1=static_cast<double>(in_kmers)/nelem,
+    //                       mu2=static_cast<double>(out_kmers)/nelem,
+    //                       get_deviance](int64_t in_sum, int64_t out_sum, const auto &row) {
+    //         if (row.empty())
+    //             return 1.0;
+
+    //         int64_t n = in_sum + out_sum;
+    //         auto bdist = boost::math::binomial(n, p);
+
+    //         std::vector<double> devs;
+    //         devs.reserve(n + 1);
+    //         for (int64_t s = 0; s <= n; ++s) {
+    //             devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+    //         }
+    //         double pval = 0.0;
+
+    //         int64_t s = 0;
+    //         for ( ; s <= n; ++s) {
+    //             if (devs[s] < devs[in_sum])
+    //                 break;
+    //         }
+    //         if (s > 0)
+    //             pval += boost::math::cdf(bdist, s - 1);
+
+    //         int64_t sp = n;
+    //         for ( ; sp >= s; --sp) {
+    //             if (devs[sp] < devs[in_sum])
+    //                 break;
+    //         }
+
+    //         if (sp < n)
+    //             pval += boost::math::cdf(boost::math::complement(bdist, sp));
+
+    //         return pval;
+    //     };
+    // } else if (config.test_type == "nbinom_exact" || config.test_type == "gnb_exact") {
+    //     common::logger->trace("Fitting per-sample negative binomial distributions");
+    //     auto get_rp = [&](const auto &generate) {
+    //         double mu = 0;
+    //         double var = 0;
+    //         size_t total = 0;
+    //         generate([&](auto k, auto c) {
+    //             mu += k * c;
+    //             var += k * k * c;
+    //             total += c;
+    //         });
+    //         mu /= total;
+    //         double mu2 = mu * mu;
+    //         var = (var - mu2 * total) / (total - 1);
+
+    //         double r_guess = mu * mu / (var - mu);
+
+    //         if (mu >= var) {
+    //             common::logger->warn("Fit failed, falling back to Poisson: mu: {} >= var: {}", mu, var);
+    //             return std::make_pair(0.0, 1.0);
+    //         }
+
+    //         auto get_dl = [&](double r) {
+    //             double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
+    //             generate([&](auto k, auto c) {
+    //                 dl += boost::math::digamma(k + r) * c;
+    //             });
+    //             return dl;
+    //         };
+
+    //         double r_min = r_guess;
+    //         double r_max = r_guess;
+
+    //         while (true) {
+    //             double dl_min = get_dl(r_min);
+    //             double dl_max = get_dl(r_max);
+
+    //             if (dl_min == 0) {
+    //                 r_max = r_min;
+    //                 break;
+    //             }
+
+    //             if (dl_max == 0) {
+    //                 r_min = r_max;
+    //                 break;
+    //             }
+
+    //             if (dl_min < 0)
+    //                 r_min /= 2;
+
+    //             if (dl_max > 0)
+    //                 r_max *= 2;
+
+    //             if (dl_min > 0 && dl_max < 0)
+    //                 break;
+    //         }
+
+    //         std::tie(r_min, r_max) = boost::math::tools::bisect(
+    //             get_dl, r_min, r_max, boost::math::tools::eps_tolerance<double>(5)
+    //         );
+
+    //         double r = r_max;
+    //         double p = r / (r + mu);
+    //         return std::make_pair(r, p);
+    //     };
+
+    //     std::vector<std::pair<double, double>> nb_params(groups.size());
+    //     std::vector<std::vector<std::pair<uint64_t, size_t>>::const_iterator> hist_its(groups.size());
+    //     size_t matrix_size = 0;
+
+    //     #pragma omp parallel for num_threads(num_parallel_files) reduction(+:matrix_size)
+    //     for (size_t j = 0; j < groups.size(); ++j) {
+    //         const auto &hist = hists[j];
+    //         size_t total = 0;
+    //         size_t total_cutoff = n_kmers[j] * HIST_CUTOFF;
+    //         auto it = hist.begin();
+    //         for ( ; it != hist.end() && total < total_cutoff; ++it) {
+    //             if (it->first != 0)
+    //                 total += it->second;
+    //         }
+    //         hist_its[j] = it;
+    //         if (hist.size()) {
+    //             nb_params[j] = get_rp([&](const auto &callback) {
+    //                 std::for_each(hist.begin(), it, [&](const auto &a) {
+    //                     const auto &[k, c] = a;
+    //                     callback(k, c);
+    //                 });
+    //             });
+    //             const auto &[r, p] = nb_params[j];
+    //             common::logger->trace("{}: size: {}\tmax_val: {}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
+    //                                 j, sums[j], (it - 1)->first, r * (1-p)/p, r*(1-p)/p/p, r, p);
+
+    //             total += hist[0].first == 0 ? hist[0].second : 0;
+
+    //             #pragma omp atomic
+    //             matrix_size += total;
+    //         }
+    //     }
+
+    //     common::logger->trace("Scaling distributions");
+
+    //     double target_p = 0.0;
+    //     for (size_t j = 0; j < groups.size(); ++j) {
+    //         target_p += log(nb_params[j].second);
+    //     }
+    //     target_p = exp(target_p / total_labels);
+    //     // double target_sum = 0.0;
+    //     // for (size_t j = 0; j < groups.size(); ++j) {
+    //     //     target_sum += log(static_cast<double>(sums[j]));
+    //     // }
+    //     // target_sum = exp(target_sum / total_labels);
+
+    //     // common::logger->trace("Finding common p and r.");
+
+    //     // double r_map;
+    //     // double target_p;
+    //     // std::tie(r_map, target_p) = get_rp([&](const auto &callback) {
+    //     //     for (size_t j = 0; j < groups.size(); ++j) {
+    //     //         double f = target_sum / sums[j];
+    //     //         std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
+    //     //             const auto &[k, c] = a;
+    //     //             callback(f * k, c);
+    //     //         });
+    //     //     }
+    //     // });
+
+    //     // common::logger->trace("Common p: {}", target_p);
+
+    //     // double fit_mu = r_map * (1.0 - target_p) / target_p;
+    //     // double fit_var = fit_mu / target_p;
+    //     // common::logger->trace("Common params: r: {}\tp: {}\tmu: {}\tvar: {}",
+    //     //                       r_map, target_p, fit_mu, fit_var);
+
+    //     // double r_in = r_map * num_labels_in;
+    //     // double r_out = r_map * num_labels_out;
+    //     // common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
+
+    //     // if (fit_var / fit_mu - 1.0 < 1e-5)
+    //     //     common::logger->warn("Fit parameters are close to a Poisson distribution");
+
+    //     count_maps.resize(groups.size());
+
+    //     common::logger->trace("Computing quantile maps");
+    //     double r_in = 0;
+    //     double r_out = 0;
+    //     #pragma omp parallel for num_threads(num_parallel_files)
+    //     for (size_t j = 0; j < groups.size(); ++j) {
+    //         if (hists[j].empty())
+    //             continue;
+
+    //         const auto &[r, p] = nb_params[j];
+    //         double r_map = r * target_p * (1.0 - p) / p / (1.0 - target_p);
+
+    //         if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+    //             #pragma omp atomic
+    //             r_out += r_map;
+    //         }
+
+    //         if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+    //             #pragma omp atomic
+    //             r_in += r_map;
+    //         }
+
+    //         boost::math::negative_binomial nb_out(r_map, target_p);
+    //         double scale = 1.0;
+    //         // double scale = target_sum / sums[j];
+
+    //         auto map_value = [&](uint64_t k, double scale, const auto &old_dist, const auto &new_dist) {
+    //             double cdf = boost::math::cdf(old_dist, k);
+
+    //             if (cdf < 1.0)
+    //                 return boost::math::quantile(new_dist, cdf);
+
+    //             double ccdf = boost::math::cdf(boost::math::complement(old_dist, k));
+
+    //             if (ccdf > 0.0)
+    //                 return boost::math::quantile(boost::math::complement(new_dist, ccdf));
+
+    //             return ceil(scale * k);
+    //         };
+
+    //         auto compute_map = [&](const auto &nb) {
+    //             // ensure that 0 maps to 0
+    //             count_maps[j][0] = std::make_pair(0, 0);
+
+    //             const auto &hist = hists[j];
+    //             for (const auto &[k, c] : hist) {
+    //                 if (!count_maps[j].count(k)) {
+    //                     uint64_t new_k = map_value(k, scale, nb, nb_out);
+    //                     count_maps[j][k] = std::make_pair(new_k, c);
+    //                 } else {
+    //                     count_maps[j][k].second += c;
+    //                 }
+    //             }
+    //         };
+
+    //         if (p < 1.0) {
+    //             compute_map(boost::math::negative_binomial(r, p));
+    //         } else {
+    //             compute_map(boost::math::poisson(static_cast<double>(sums[j]) * scale / nelem));
+    //         }
+    //     }
+
+    //     common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
+
+    //     common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
+    //     in_kmers = 0;
+    //     out_kmers = 0;
+    //     total_kmers = 0;
+    //     for (size_t j = 0; j < groups.size(); ++j) {
+    //         for (const auto &[k, m] : count_maps[j]) {
+    //             const auto &[v, c] = m;
+
+    //             if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+    //                 out_kmers += v * c;
+
+    //             if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+    //                 in_kmers += v * c;
+
+    //             total_kmers += v * c;
+    //         }
+    //     }
+
+    //     common::logger->trace("  Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
+
+    //     auto compute_min_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t n) {
+    //         double half_div0 = -r_in * log2(r_in) - (n + r_out) * log2(n + r_out);
+    //         double half_divn = -r_out * log2(r_out) - (n + r_in) * log2(n + r_in);
+
+    //         double pval = 0.0;
+    //         double lscaling = lscaling_base - lgamma(r + n) / log(2);
+    //         if (half_div0 >= half_divn)
+    //             pval += exp2(lscaling + (lgamma(r_out + n) - lgamma(r_out)) / log(2));
+
+    //         if (half_divn >= half_div0)
+    //             pval += exp2(lscaling + (lgamma(r_in + n) - lgamma(r_in)) / log(2));
+
+    //         return std::min(1.0, pval);
+    //     };
+
+    //     auto compute_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t in_sum, int64_t out_sum, const PairContainer &row) {
+    //         int64_t n = in_sum + out_sum;
+    //         double argmin_d = r_in / r * n;
+    //         if (in_sum == argmin_d)
+    //             return 1.0;
+
+    //         double lscaling = lscaling_base - lgamma(r + n) / log(2);
+
+    //         auto get_pval = [&](const auto &get_stat) {
+    //             double base_stat = get_stat(in_sum, out_sum,
+    //                                         r_in != r_out && in_sum > 0 ? log2(in_sum) : 0.0,
+    //                                         r_in != r_out && out_sum > 0 ? log2(out_sum) : 0.0);
+    //             double pval = 0.0;
+    //             int64_t s = 0;
+    //             int64_t t = n;
+    //             double ls = 0;
+    //             double lt = log2(t);
+    //             if (get_stat(s, t, ls, lt) >= base_stat) {
+    //                 double base = lscaling + (lgamma(n + r_out) - lgamma(r_out)) / log(2);
+    //                 pval += exp2(base);
+    //                 for (++s,--t; s <= n; ++s,--t) {
+    //                     ls = log2(s);
+    //                     lt = t > 0 ? log2(t) : 0.0;
+    //                     if (get_stat(s, t, ls, lt) < base_stat)
+    //                         break;
+
+    //                     base += log2(t + 1) + log2(s - 1 + r_in) - ls - log2(t + r_out);
+    //                     pval += exp2(base);
+    //                 }
+    //             }
+
+    //             int64_t sp = n;
+    //             t = 0;
+    //             ls = log2(sp);
+    //             lt = 0;
+    //             if (get_stat(sp, t, ls, lt) >= base_stat) {
+    //                 double base = lscaling + (lgamma(n + r_in) - lgamma(r_in)) / log(2);
+    //                 pval += exp2(base);
+    //                 for (--sp,++t; sp >= s; --sp,++t) {
+    //                     ls = sp > 0 ? log2(sp) : 0.0;
+    //                     lt = log2(t);
+    //                     if (get_stat(sp, t, ls, lt) < base_stat)
+    //                         break;
+
+    //                     base += log2(sp + 1) + log2(t - 1 + r_out) - lt - log2(sp + r_in);
+    //                     pval += exp2(base);
+    //                 }
+    //             }
+
+    //             return std::min(1.0, pval);
+    //         };
+
+    //         if (r_in == r_out) {
+    //             return get_pval([&](int64_t s, int64_t, double, double) { return abs(argmin_d - s); });
+    //         } else {
+    //             return get_pval([&](int64_t s, int64_t t, double ls, double lt) {
+    //                 return ls * s - (r_in + s)*log2(r_in + s) + lt * t - (t + r_out)*log2(t + r_out);
+    //             });
+    //         }
+    //     };
+
+    //     if (config.test_type == "nbinom_exact") {
+    //         double r = r_in + r_out;
+    //         double lscaling_base = lgamma(r) / log(2);
+    //         compute_min_pval = [compute_min_pval_r,lscaling_base,r,r_in,r_out](int64_t n, const PairContainer&) {
+    //             return n > 0 ? compute_min_pval_r(r, r_in, r_out, lscaling_base, n) : 1.0;
+    //         };
+
+    //         compute_pval = [compute_pval_r,lscaling_base,r,r_in,r_out](int64_t in_sum, int64_t out_sum, const PairContainer &row) {
+    //             return row.size() ? compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row) : 1.0;
+    //         };
+    //     } else {
+    //         common::logger->trace("Enumerating row count distributions");
+    //         std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>> vector_counts;
+    //         {
+    //             std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
+
+    //             generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
+    //                 int64_t in_sum = 0;
+    //                 int64_t out_sum = 0;
+    //                 bool in_kmer = false;
+    //                 bool out_kmer = false;
+    //                 std::vector<int64_t> vec;
+    //                 if (kept[row_i]) {
+    //                     vec.reserve(raw_row.size());
+    //                     size_t count_in = 0;
+    //                     size_t count_out = 0;
+    //                     size_t total_count = 0;
+    //                     for (const auto &[j, raw_c] : raw_row) {
+    //                         if (min_counts.size() && raw_c < min_counts[j])
+    //                             continue;
+
+    //                         uint64_t c = raw_c;
+    //                         if (count_maps.size())
+    //                             c = count_maps[j].find(c)->second.first;
+
+    //                         if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+    //                             out_sum += c;
+    //                             ++count_out;
+    //                         }
+
+    //                         if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+    //                             in_sum += c;
+    //                             ++count_in;
+    //                         }
+
+    //                         total_count += groups[j] != Group::OTHER;
+
+    //                         vec.emplace_back(c);
+    //                     }
+
+    //                     if (total_count >= config.min_recurrence) {
+    //                         double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+    //                         in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+    //                         out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+    //                     }
+    //                 } else {
+    //                     return;
+    //                 }
+
+    //                 if (!in_kmer && !out_kmer)
+    //                     return;
+
+    //                 size_t n = in_sum + out_sum;
+    //                 if (n >= vector_counts_ms[bucket_idx].size())
+    //                     vector_counts_ms[bucket_idx].resize(n + 1);
+
+    //                 std::sort(vec.begin(), vec.end());
+    //                 ++vector_counts_ms[bucket_idx][n][vec];
+    //             });
+
+    //             vector_counts = std::move(vector_counts_ms[0]);
+    //             size_t max_size = vector_counts.size();
+    //             for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
+    //                 max_size = std::max(max_size, vector_counts_ms[j].size());
+    //             }
+    //             vector_counts.resize(max_size);
+
+    //             ProgressBar progress_bar(vector_counts.size() - 1, "Merging row", std::cerr, !common::get_verbose());
+    //             #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+    //             for (size_t n = vector_counts.size(); n > 0; --n) {
+    //                 for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
+    //                     if (n <= vector_counts_ms[j].size()) {
+    //                         for (const auto &[v, c] : vector_counts_ms[j][n - 1]) {
+    //                             vector_counts[n - 1][v] += c;
+    //                         }
+    //                     }
+    //                 }
+    //                 ++progress_bar;
+    //             }
+    //         }
+
+    //         common::logger->trace("Fitting LN prior distribution for dispersions");
+    //         double ln_mu = 0.0;
+    //         double ln_var = 0.0;
+
+    //         for (size_t n = 1; n < vector_counts.size(); ++n) {
+    //             size_t total = 0;
+    //             for (const auto &[v, c] : vector_counts[n]) {
+    //                 total += c;
+    //             }
+
+    //             double r = target_p / (1.0 - target_p) * n / total_labels;
+
+    //             #pragma omp atomic
+    //             ln_mu += -log(r) * total;
+
+    //             #pragma omp atomic
+    //             ln_var += log(r) * log(r) * total;
+    //         }
+
+    //         size_t total = nelem * total_labels;
+    //         ln_mu /= total;
+    //         ln_var = ln_var / total - ln_mu * ln_mu;
+
+    //         common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
+    //                               ln_mu, ln_var,
+    //                               exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1),
+    //                               exp(-ln_mu - ln_var/2));
+
+    //         auto get_rp = [ln_mu,ln_var,gs=total_labels](const auto &counts) {
+    //             double mu = 0;
+    //             double var = 0;
+    //             for (int64_t c : counts) {
+    //                 mu += c;
+    //                 var += c * c;
+    //             }
+    //             mu /= gs;
+
+    //             if (ln_var == 0) {
+    //                 double r = exp(-ln_mu);
+    //                 return std::make_pair(r, r / (r + mu));
+    //             }
+
+    //             double mu2 = mu * mu;
+    //             var = var / gs - mu2;
+
+    //             double a_guess = var > mu ? (var - mu) / mu2 : 1.0;
+
+    //             auto get_dl = [&](double a) {
+    //                 double r = 1.0 / a;
+    //                 double p = r / (r + mu);
+    //                 double lp = log(p);
+    //                 double dl = -a - a*(log(a)-ln_mu)/ln_var - gs*lp + boost::math::digamma(r)*counts.size();
+    //                 for (int64_t c : counts) {
+    //                     dl -= boost::math::digamma(r + c);
+    //                 }
+    //                 return dl;
+    //             };
+
+    //             double a_min = a_guess;
+    //             double a_max = a_guess;
+    //             while (true) {
+    //                 a_guess = (a_min + a_max) / 2;
+    //                 double dl_min = get_dl(a_min);
+    //                 double dl_max = get_dl(a_max);
+    //                 if (dl_min == 0) {
+    //                     a_max = a_min;
+    //                     break;
+    //                 }
+
+    //                 if (dl_max == 0) {
+    //                     a_min = a_max;
+    //                     break;
+    //                 }
+
+    //                 if (dl_min < 0)
+    //                     a_min /= 2;
+
+    //                 if (dl_max > 0)
+    //                     a_max *= 2;
+
+    //                 if (dl_min > 0 && dl_max < 0)
+    //                     break;
+    //             }
+
+    //             if (a_min < a_max) {
+    //                 std::tie(a_min, a_max) = boost::math::tools::bisect(
+    //                     get_dl,
+    //                     a_min, a_max, boost::math::tools::eps_tolerance<double>(5)
+    //                 );
+    //             }
+
+    //             double a = a_min;
+    //             double r = 1.0 / a;
+    //             double p = r / (r + mu);
+    //             return std::make_pair(r, p);
+    //         };
+
+    //         ProgressBar progress_bar(vector_counts.size() - 1, "Precomputing r's", std::cerr, !common::get_verbose());
+    //         #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+    //         for (size_t n = 1; n < vector_counts.size(); ++n) {
+    //             if (n > 0 && n % 1000 == 0)
+    //                 progress_bar += 1000;
+
+    //             for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
+    //                 it.value() = get_rp(it->first).first;
+    //                 // #pragma omp critical
+    //                 // common::logger->info("N: {}\tr: {}", n, it->second);
+    //             }
+    //         }
+    //         progress_bar += (vector_counts.size() - 1) % 1000;
+
+    //         auto get_r = std::make_shared<std::function<double(int64_t, const PairContainer&)>>(
+    //             [vc=std::move(vector_counts)](int64_t n, const auto &row) {
+    //                 auto it = vc[n].begin();
+    //                 if (vc[n].size() > 1) {
+    //                     std::vector<int64_t> counts;
+    //                     counts.reserve(row.size());
+    //                     for (const auto &[j, c] : row) {
+    //                         counts.emplace_back(c);
+    //                     }
+    //                     std::sort(counts.begin(), counts.end());
+    //                     it = vc[n].find(counts);
+    //                 }
+    //                 return it->second;
+    //             }
+    //         );
+
+    //         compute_min_pval = [compute_min_pval_r,get_r,num_labels_in,num_labels_out](int64_t n, const PairContainer &row) {
+    //             if (row.empty())
+    //                 return 1.0;
+
+    //             double r_base = (*get_r)(n, row);
+    //             if (r_base == 0.0)
+    //                 return 1.0;
+
+    //             double r_in = r_base * num_labels_in;
+    //             double r_out = r_base * num_labels_out;
+    //             double r = r_in + r_out;
+    //             double lscaling_base = lgamma(r) / log(2);
+
+    //             return compute_min_pval_r(r, r_in, r_out, lscaling_base, n);
+    //         };
+
+    //         compute_pval = [compute_pval_r,get_r,num_labels_in,num_labels_out](int64_t in_sum, int64_t out_sum, const PairContainer &row) {
+    //             if (row.empty())
+    //                 return 1.0;
+
+    //             if (num_labels_in == num_labels_out && in_sum == out_sum)
+    //                 return 1.0;
+
+    //             int64_t n = in_sum + out_sum;
+    //             double r_base = (*get_r)(n, row);
+
+    //             if (r_base == 0.0)
+    //                 return 1.0;
+
+    //             double r_in = r_base * num_labels_in;
+    //             double r_out = r_base * num_labels_out;
+    //             double r = r_in + r_out;
+    //             double lscaling_base = lgamma(r) / log(2);
+
+    //             return compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row);
+    //         };
+    //     }
+    // } else if (config.test_type == "lnb_exact") {
+    //     double max_sum = *std::max_element(sums.begin(), sums.end());
+    //     std::vector<double> scale_factor;
+    //     scale_factor.reserve(sums.size());
+    //     std::transform(sums.begin(), sums.end(), std::back_inserter(scale_factor),
+    //                    [&](uint64_t sum) -> double { return max_sum / sum; });
+
+    //     common::logger->trace("Enumerating row count distributions");
+    //     std::vector<tsl::hopscotch_map<std::vector<uint64_t>, double, utils::VectorHash>> vector_counts;
+    //     {
+    //         std::vector<std::vector<tsl::hopscotch_map<std::vector<uint64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
+
+    //         generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
+    //             if (!kept[row_i])
+    //                 return;
+
+    //             uint64_t in_sum = 0;
+    //             uint64_t out_sum = 0;
+    //             bool in_kmer = false;
+    //             bool out_kmer = false;
+
+    //             std::vector<uint64_t> vec;
+    //             vec.resize(total_labels);
+    //             size_t count_in = 0;
+    //             size_t count_out = 0;
+    //             size_t total_count = 0;
+    //             for (const auto &[j, c] : raw_row) {
+    //                 if (min_counts.size() && c < min_counts[j])
+    //                     continue;
+
+    //                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+    //                     out_sum += c;
+    //                     ++count_out;
+    //                 }
+
+    //                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+    //                     in_sum += c;
+    //                     ++count_in;
+    //                 }
+
+    //                 total_count += groups[j] != Group::OTHER;
+
+    //                 vec[j] = c;
+    //             }
+
+    //             if (total_count >= config.min_recurrence) {
+    //                 in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence;
+    //                 out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence;
+    //             }
+
+    //             if (!in_kmer && !out_kmer)
+    //                 return;
+
+    //             // if floating point error puts the sum slightly above the integer value, then round down
+    //             size_t n = in_sum + out_sum;
+    //             if (n >= vector_counts_ms[bucket_idx].size())
+    //                 vector_counts_ms[bucket_idx].resize(n + 1);
+
+    //             std::sort(vec.begin(), vec.end());
+    //             ++vector_counts_ms[bucket_idx][n][vec];
+    //         });
+
+    //         vector_counts = std::move(vector_counts_ms[0]);
+    //         size_t max_size = vector_counts.size();
+    //         for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
+    //             max_size = std::max(max_size, vector_counts_ms[j].size());
+    //         }
+    //         vector_counts.resize(max_size);
+
+    //         ProgressBar progress_bar(vector_counts.size() - 1, "Merging row", std::cerr, !common::get_verbose());
+    //         #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+    //         for (size_t n = vector_counts.size(); n > 0; --n) {
+    //             for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
+    //                 if (n <= vector_counts_ms[j].size()) {
+    //                     for (const auto &[v, c] : vector_counts_ms[j][n - 1]) {
+    //                         vector_counts[n - 1][v] += c;
+    //                     }
+    //                 }
+    //             }
+    //             ++progress_bar;
+    //         }
+    //     }
+
+    //     auto optim = [](const auto &f, auto x_min, auto x_max) {
+    //         // common::logger->info("start");
+    //         while (x_max - x_min > 1e-3) {
+    //             double x_mid = (x_min + x_max) / 2;
+    //             // common::logger->info("({},{}) -> {}", x_min, x_max, f(x_mid));
+    //             double x_low_mid = (x_min + x_mid) / 2;
+    //             double x_high_mid = (x_mid + x_max) / 2;
+    //             if (f(x_low_mid) > f(x_high_mid)) {
+    //                 x_max = x_mid;
+    //             } else {
+    //                 x_min = x_mid;
+    //             }
+    //         }
+    //         return (x_min + x_max) / 2;
+    //     };
+
+    //     common::logger->trace("Fitting LN prior distribution for dispersions");
+    //     double ln_mu = 0.0;
+    //     double ln_var = 0.0;
+
+    //     size_t skipped = 0;
+    //     for (size_t n = 0; n < vector_counts.size(); ++n) {
+    //         for (const auto &[v, c] : vector_counts[n]) {
+    //             double total = 0;
+    //             double total2 = 0;
+    //             for (size_t j = 0; j < v.size(); ++j) {
+    //                 total += v[j] * scale_factor[j];
+    //                 total2 += v[j] * scale_factor[j] * v[j] * scale_factor[j];
+    //             }
+    //             double mu = total / total_labels;
+    //             double mu2 = mu * mu;
+    //             double var = total2 / total_labels - mu2;
+    //             if (var <= mu) {
+    //                 ++skipped;
+    //                 continue;
+    //             }
+
+    //             double r = mu2 / (var - mu);
+    //             ln_mu += -log(r) * c;
+    //             ln_var += log(r) * log(r) * c;
+    //         }
+    //     }
+
+    //     if (skipped)
+    //         common::logger->warn("Skipped {} / {} rows", skipped, nelem);
+
+    //     ln_mu /= (nelem - skipped);
+    //     ln_var = ln_var / (nelem - skipped) - ln_mu * ln_mu;
+
+    //     common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
+    //                           ln_mu, ln_var,
+    //                           exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1),
+    //                           exp(-ln_mu - ln_var/2));
+
+    //     double expr = exp(-ln_mu + ln_var / 2);
+    //     double target_p = expr / (max_sum / nelem + expr);
+
+    //     {
+    //         auto calc_r = [optim,ln_mu,ln_var,gs=total_labels,p=target_p](const auto &counts) {
+    //             return optim([&](double r) {
+    //                 double val = log(p) * gs * r - lgamma(r) * counts.size() + log(r) - pow(-log(r)-ln_mu, 2.0)/2/ln_var;
+    //                 for (double c : counts) {
+    //                     val += lgamma(c + r);
+    //                 }
+    //                 return val;
+    //             }, 0.0, 100.0);
+    //         };
+
+    //         ProgressBar progress_bar(vector_counts.size(), "Precomputing r's", std::cerr, !common::get_verbose());
+    //         #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+    //         for (size_t n = 0; n < vector_counts.size(); ++n) {
+    //             if (n > 0 && n % 1000 == 0)
+    //                 progress_bar += 1000;
+
+    //             for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
+    //                 std::vector<double> scale_counts;
+    //                 scale_counts.reserve(it->first.size());
+    //                 for (size_t j = 0; j < it->first.size(); ++j) {
+    //                     if (it->first[j] > 0)
+    //                         scale_counts.emplace_back(it->first[j] * scale_factor[j]);
+    //                 }
+    //                 it.value() = calc_r(scale_counts);
+    //             }
+    //         }
+    //         progress_bar += vector_counts.size() % 1000;
+    //     }
+
+    //     auto get_r = std::make_shared<std::function<double(int64_t, std::vector<uint64_t>&)>>(
+    //         [vc=std::move(vector_counts),scale_factor](int64_t n, const auto &counts) {
+    //             auto it = vc[n].begin();
+    //             if (vc[n].size() > 1) {
+    //                 it = vc[n].find(counts);
+    //                 assert(it != vc[n].end());
+    //             }
+    //             return it->second;
+    //         }
+    //     );
+
+    //     compute_min_pval = [get_r,num_labels_in,num_labels_out,scale_factor,total_labels](int64_t, const PairContainer &row) {
+    //         if (row.empty())
+    //             return 1.0;
+
+    //         int64_t sum = 0;
+    //         double ssum = 0;
+    //         std::vector<double> counts;
+    //         std::vector<uint64_t> row_int(total_labels);
+    //         counts.reserve(row.size());
+    //         for (const auto &[j, c] : row) {
+    //             row_int[j] = c;
+    //             sum += c;
+    //             ssum += counts.emplace_back(c * scale_factor[j]);
+    //         }
+    //         // if floating point error puts the sum slightly above the integer value, then round down
+    //         int64_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
+
+    //         double r_base = (*get_r)(sum, row_int);
+    //         if (r_base == 0.0)
+    //             return 1.0;
+
+    //         double r_in = r_base * num_labels_in;
+    //         double r_out = r_base * num_labels_out;
+    //         double r = r_in + r_out;
+    //         double lscaling_base = lgamma(r) / log(2);
+
+    //         double half_div0 = -r_in * log2(r_in) - (n + r_out) * log2(n + r_out);
+    //         double half_divn = -r_out * log2(r_out) - (n + r_in) * log2(n + r_in);
+
+    //         double pval = 0.0;
+    //         double lscaling = lscaling_base - lgamma(r + n) / log(2);
+    //         if (half_div0 >= half_divn)
+    //             pval += exp2(lscaling + (lgamma(r_out + n) - lgamma(r_out)) / log(2));
+
+    //         if (half_divn >= half_div0)
+    //             pval += exp2(lscaling + (lgamma(r_in + n) - lgamma(r_in)) / log(2));
+
+    //         return std::min(1.0, pval);
+    //     };
+
+    //     compute_pval = [get_r,num_labels_in,num_labels_out,scale_factor,groups,total_labels](int64_t, int64_t, const PairContainer &row) {
+    //         if (row.empty())
+    //             return 1.0;
+
+    //         int64_t sum = 0;
+    //         double ssum = 0;
+    //         double in_sum = 0;
+    //         double out_sum = 0;
+    //         std::vector<double> counts;
+    //         std::vector<uint64_t> row_int(total_labels);
+    //         counts.reserve(row.size());
+    //         for (const auto &[j, c] : row) {
+    //             row_int[j] = c;
+    //             sum += c;
+    //             ssum += counts.emplace_back(c * scale_factor[j]);
+    //             if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+    //                 out_sum += counts.back();
+    //             }
+
+    //             if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+    //                 in_sum += counts.back();
+    //             }
+    //         }
+    //         // if floating point error puts the sum slightly above the integer value, then round down
+    //         int64_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
+
+    //         double r_base = (*get_r)(sum, row_int);
+
+    //         if (r_base == 0.0)
+    //             return 1.0;
+
+    //         double r_in = r_base * num_labels_in;
+    //         double r_out = r_base * num_labels_out;
+    //         double r = r_in + r_out;
+    //         double lscaling_base = lgamma(r) / log(2);
+
+    //         double argmin_d = r_in / r * n;
+    //         if (in_sum == argmin_d)
+    //             return 1.0;
+
+    //         double lscaling = lscaling_base - lgamma(r + n) / log(2);
+
+    //         auto get_pval = [&](const auto &get_stat) {
+    //             double base_stat = get_stat(in_sum, out_sum,
+    //                                         r_in != r_out && in_sum > 0 ? log2(in_sum) : 0.0,
+    //                                         r_in != r_out && out_sum > 0 ? log2(out_sum) : 0.0);
+    //             double pval = 0.0;
+    //             int64_t s = 0;
+    //             int64_t t = n;
+    //             double ls = 0;
+    //             double lt = log2(t);
+    //             if (get_stat(s, t, ls, lt) >= base_stat) {
+    //                 double base = lscaling + (lgamma(n + r_out) - lgamma(r_out)) / log(2);
+    //                 pval += exp2(base);
+    //                 for (++s,--t; s <= n; ++s,--t) {
+    //                     ls = log2(s);
+    //                     lt = t > 0 ? log2(t) : 0.0;
+    //                     if (get_stat(s, t, ls, lt) < base_stat)
+    //                         break;
+
+    //                     base += log2(t + 1) + log2(s - 1 + r_in) - ls - log2(t + r_out);
+    //                     pval += exp2(base);
+    //                 }
+    //             }
+
+    //             int64_t sp = n;
+    //             t = 0;
+    //             ls = log2(sp);
+    //             lt = 0;
+    //             if (get_stat(sp, t, ls, lt) >= base_stat) {
+    //                 double base = lscaling + (lgamma(n + r_in) - lgamma(r_in)) / log(2);
+    //                 pval += exp2(base);
+    //                 for (--sp,++t; sp >= s; --sp,++t) {
+    //                     ls = sp > 0 ? log2(sp) : 0.0;
+    //                     lt = log2(t);
+    //                     if (get_stat(sp, t, ls, lt) < base_stat)
+    //                         break;
+
+    //                     base += log2(sp + 1) + log2(t - 1 + r_out) - lt - log2(sp + r_in);
+    //                     pval += exp2(base);
+    //                 }
+    //             }
+
+    //             return std::min(1.0, pval);
+    //         };
+
+    //         if (r_in == r_out) {
+    //             return get_pval([&](int64_t s, int64_t, double, double) { return abs(argmin_d - s); });
+    //         } else {
+    //             return get_pval([&](int64_t s, int64_t t, double ls, double lt) {
+    //                 return ls * s - (r_in + s)*log2(r_in + s) + lt * t - (t + r_out)*log2(t + r_out);
+    //             });
+    //         }
+    //     };
+
+    // } else if (config.test_type == "poisson_binom") {
+    //     // fit distribution
+    //     std::vector<double> p;
+    //     p.reserve(groups.size());
+    //     for (size_t i = 0; i < groups.size(); ++i) {
+    //         p.emplace_back(static_cast<double>(n_kmers[i]) / nelem);
+    //     }
+
+    //     common::logger->trace("p: {}", fmt::join(p, ","));
+
+    //     common::logger->trace("Precomputing PMFs");
+    //     std::vector<double> pmf_in { 1.0 };
+    //     std::vector<double> pmf_out { 1.0 };
+    //     std::vector<double> pmf_null { 1.0 };
+
+    //     for (size_t i = 1; i <= groups.size(); ++i) {
+    //         if (groups[i - 1] == Group::OUT || groups[i - 1] == Group::BOTH) {
+    //             std::vector<double> pmf_out_cur(pmf_out.size() + 1);
+    //             pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
+    //             pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
+    //             for (size_t k = 1; k < pmf_out.size(); ++k) {
+    //                 pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1] + (1.0 - p[i - 1]) * pmf_out[k];
+    //             }
+    //             std::swap(pmf_out_cur, pmf_out);
+    //         }
+
+    //         if (groups[i - 1] == Group::IN || groups[i - 1] == Group::BOTH) {
+    //             std::vector<double> pmf_in_cur(pmf_in.size() + 1);
+    //             pmf_in_cur[0] = (1.0 - p[i - 1]) * pmf_in[0];
+    //             pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
+    //             for (size_t k = 1; k < pmf_in.size(); ++k) {
+    //                 pmf_in_cur[k] = p[i - 1] * pmf_in[k - 1] + (1.0 - p[i - 1]) * pmf_in[k];
+    //             }
+    //             std::swap(pmf_in_cur, pmf_in);
+    //         }
+    //         std::vector<double> pmf_null_cur(i + 1);
+    //         pmf_null_cur[0] = (1.0 - p[i - 1]) * pmf_null[0];
+    //         pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
+    //         for (size_t k = 1; k < pmf_null.size(); ++k) {
+    //             pmf_null_cur[k] = p[i - 1] * pmf_null[k - 1] + (1.0 - p[i - 1]) * pmf_null[k];
+    //         }
+    //         std::swap(pmf_null_cur, pmf_null);
+    //     }
+
+    //     if (pmf_in.size() != num_labels_in + 1) {
+    //         common::logger->error("PMF in wrong: {} != {}", pmf_in.size(), num_labels_in + 1);
+    //         throw std::domain_error("");
+    //     }
+
+    //     if (pmf_out.size() != num_labels_out + 1) {
+    //         common::logger->error("PMF out wrong: {} != {}", pmf_out.size(), num_labels_out + 1);
+    //         throw std::domain_error("");
+    //     }
+
+    //     if (pmf_null.size() != num_labels_in + num_labels_out + 1) {
+    //         common::logger->error("PMF null wrong: {} != {}", pmf_null.size(), num_labels_in + num_labels_out + 1);
+    //         throw std::domain_error("");
+    //     }
+
+    //     common::logger->trace("Precomputing p-values");
+    //     std::vector<std::vector<double>> pvals(num_labels_in + num_labels_out + 1);
+    //     std::vector<double> mid_points(num_labels_in + num_labels_out + 1);
+    //     mid_points.emplace_back(0);
+    //     pvals[0].emplace_back(1.0);
+
+    //     double min_pval = 1.0;
+
+    //     for (size_t n = 1; n < pvals.size(); ++n) {
+    //         std::vector<double> probs;
+    //         size_t front = n - std::min(n, num_labels_out);
+    //         for (uint64_t s = 0; s < pmf_in.size(); ++s) {
+    //             if (s > n)
+    //                 break;
+    //             uint64_t t = n - s;
+    //             if (s < pmf_in.size() && t < pmf_out.size()) {
+    //                 if (s < front) {
+    //                     common::logger->error("Attempting non-zero p-value in impossible configuration: {},{}", n, s);
+    //                     throw std::domain_error("");
+    //                 }
+    //                 probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t]) - log2(pmf_null[n])));
+    //             } else {
+    //                 probs.emplace_back(0.0);
+    //             }
+    //         }
+
+    //         for (uint64_t s = 0; s < probs.size(); ++s) {
+    //             pvals[n].emplace_back(0.0);
+    //             if (s > n)
+    //                 break;
+    //             uint64_t t = n - s;
+    //             if (s >= pmf_in.size() || t >= pmf_out.size())
+    //                 continue;
+    //             for (uint64_t sp = 0; sp < probs.size(); ++sp) {
+    //                 if (sp > n)
+    //                     break;
+    //                 uint64_t tp = n - sp;
+    //                 if (sp >= pmf_in.size() || tp >= pmf_out.size())
+    //                     continue;
+    //                 if (probs[sp] <= probs[s])
+    //                     pvals[n][s] += probs[sp];
+    //             }
+    //         }
+
+    //         for (uint64_t s = 0; s < front; ++s) {
+    //             if (pvals[n][s] > 0) {
+    //                 common::logger->error("Non-zero p-value in impossible configuration: {},{}", n, s);
+    //                 throw std::domain_error("");
+    //             }
+    //         }
+    //         double local_min_pval = std::min(pvals[n][front],
+    //                                          pvals[n].back());
+    //         min_pval = std::min(min_pval, local_min_pval);
+
+    //         size_t max_prob_counts = 0;
+    //         double max_prob_pos = 0;
+    //         // double max_prob = -1;
+
+    //         for (uint64_t s = front; s < pvals[n].size(); ++s) {
+    //             if (pvals[n][s] < local_min_pval) {
+    //                 common::logger->error("Min p-value not at boundary: {},{}: {} < {}", n, s, pvals[n][s], local_min_pval);
+    //                 throw std::domain_error("");
+    //             }
+    //         }
+    //         if (front + 1 == pvals[n].size()) {
+    //             max_prob_counts = 1;
+    //             max_prob_pos = front;
+    //         } else if (front + 2 == pvals[n].size()) {
+    //             max_prob_counts = 2;
+    //             max_prob_pos = front + front + 1;
+    //         } else {
+    //             for (uint64_t s = front + 1; s < pvals[n].size(); ++s) {
+    //                 if (probs[s] == probs[s-1]) {
+    //                     max_prob_counts = 2;
+    //                     max_prob_pos = s + s;
+    //                     break;
+    //                 }
+
+    //                 if (s + 1 < pvals[n].size()) {
+    //                     if (probs[s-1] < probs[s] && probs[s] < probs[s+1])
+    //                         continue;
+
+    //                     if (probs[s-1] > probs[s] && probs[s] > probs[s+1])
+    //                         continue;
+
+    //                     if (probs[s] > probs[s-1] && probs[s] > probs[s+1]) {
+    //                         if (probs[s] - probs[s-1] == probs[s]-probs[s+1]) {
+    //                             max_prob_counts = 1;
+    //                             max_prob_pos = s;
+    //                         } else if (probs[s] - probs[s-1] > probs[s]-probs[s+1]) {
+    //                             max_prob_counts = 2;
+    //                             max_prob_pos = s + s + 1;
+    //                         } else {
+    //                             max_prob_counts = 2;
+    //                             max_prob_pos = s + s - 1;
+    //                         }
+    //                     }
+    //                 }
+    //             }
+    //         }
+
+    //         if (max_prob_counts == 0) {
+    //             max_prob_counts = 2;
+    //             max_prob_pos = front - 1 + front;
+    //         }
+
+    //         mid_points[n] = static_cast<double>(max_prob_pos) / max_prob_counts;
+    //         common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}",
+    //                               n, mid_points[n],
+    //                               fmt::join(probs,","),
+    //                               fmt::join(pvals[n],","));
+    //     }
+
+    //     common::logger->trace("Min. p-value: {}", min_pval);
+    //     if (min_pval >= config.family_wise_error_rate) {
+    //         common::logger->warn("No significant p-values achievable");
+    //         auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
+    //             graph_ptr, [](node_index) { return false; }, true,
+    //             is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+    //         );
+
+    //         auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
+    //             graph_ptr, [](node_index) { return false; }, true,
+    //             is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+    //         );
+
+    //         return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage{}, nullptr);
+    //     }
+
+    //     compute_min_pval = [pvals,num_labels_out,&groups](int64_t, const PairContainer &row) {
+    //         size_t n = 0;
+    //         for (const auto &[j, c] : row) {
+    //             n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
+    //         }
+    //         size_t front = n - std::min(n, num_labels_out);
+    //         return std::min(pvals[n][front],
+    //                         pvals[n].back());
+    //     };
+
+    //     compute_pval = [pvals,mid_points,&groups](int64_t, int64_t, const auto &row) {
+    //         int64_t n = 0;
+    //         uint64_t s = 0;
+    //         for (const auto &[j, c] : row) {
+    //             n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
+    //             s += (groups[j] == Group::IN);
+    //         }
+
+    //         return pvals[n][s];
+    //     };
+
+    // } else if (config.test_type == "notest") {
+    //     compute_min_pval = [&](int64_t n, const PairContainer&) { return n > 0 ? 0.0 : 1.0; };
+    //     compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.0; };
+    // } else {
+    //     throw std::runtime_error("Test not implemented");
+    // }
+
+    // common::logger->trace("Computing minimum p-values");
+    // std::vector<std::pair<double, size_t>> m;
+    // std::vector<int64_t> m_sums;
+    // {
+    //     std::vector<std::vector<std::pair<double, size_t>>> ms(num_threads + 1);
+    //     std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, std::pair<double, size_t>, utils::VectorHash>>> ms_vecs(num_threads + 1);
+    //     generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
+    //         int64_t in_sum = 0;
+    //         int64_t out_sum = 0;
+    //         int64_t out_stat_int = 0;
+    //         bool in_kmer = false;
+    //         bool out_kmer = false;
+    //         PairContainer row;
+    //         std::vector<int64_t> vals;
+    //         if (kept[row_i]) {
+    //             size_t count_in = 0;
+    //             size_t count_out = 0;
+    //             size_t total_count = 0;
+    //             for (const auto &[j, raw_c] : raw_row) {
+    //                 if (min_counts.size() && raw_c < min_counts[j])
+    //                     continue;
+
+    //                 uint64_t c = raw_c;
+    //                 if (count_maps.size())
+    //                     c = count_maps[j].find(c)->second.first;
+
+    //                 row.emplace_back(j, c);
+    //                 vals.emplace_back(c);
+    //                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+    //                     out_sum += c;
+    //                     ++count_out;
+    //                 }
+
+    //                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+    //                     in_sum += c;
+    //                     ++count_in;
+    //                 }
+
+    //                 total_count += groups[j] != Group::OTHER;
+    //             }
+
+    //             if (total_count >= config.min_recurrence) {
+    //                 double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+    //                 if (out_stat != 0)
+    //                     out_stat_int = out_stat > 0 ? ceil(out_stat) : floor(out_stat);
+
+    //                 in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+    //                 out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+    //             }
+    //         } else {
+    //             return;
+    //         }
+
+    //         size_t n = config.test_type != "poisson_binom" ? in_sum + out_sum : row.size();
+    //         if (!in_kmer && !out_kmer) {
+    //             row.clear();
+    //             vals.clear();
+    //             n = 0;
+    //         }
+
+    //         double pval_min = 0;
+    //         if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact") {
+    //             assert(bucket_idx < ms.size());
+    //             if (n >= ms[bucket_idx].size())
+    //                 ms[bucket_idx].resize(n + 1, std::make_pair(1.1, 0));
+
+    //             if (ms[bucket_idx][n].first == 1.1)
+    //                 ms[bucket_idx][n].first = compute_min_pval(n, row);
+
+    //             pval_min = ms[bucket_idx][n].first;
+    //             ++ms[bucket_idx][n].second;
+    //         } else {
+    //             std::sort(vals.begin(), vals.end());
+    //             if (n >= ms_vecs[bucket_idx].size())
+    //                 ms_vecs[bucket_idx].resize(n + 1);
+
+    //             auto find = ms_vecs[bucket_idx][n].find(vals);
+    //             if (find == ms_vecs[bucket_idx][n].end()) {
+    //                 find = ms_vecs[bucket_idx][n].try_emplace(
+    //                     vals,
+    //                     std::make_pair(compute_min_pval(n, row), 1)
+    //                 ).first;
+    //             } else {
+    //                 ++find.value().second;
+    //             }
+
+    //             pval_min = find->second.first;
+    //         }
+
+    //         if (config.test_by_unitig) {
+    //             bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
+    //             uint64_t eff_size = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
+    //             if constexpr(preallocated) {
+    //                 node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+    //                 eff_size_buckets[bucket_idx][node] = eff_size;
+    //                 sum_buckets[bucket_idx][node] = n;
+    //                 if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact")
+    //                     pvals_min_buckets[bucket_idx][node] = bit_cast<uint64_t>(pval_min);
+    //             } else {
+    //                 eff_size_buckets[bucket_idx].push_back(eff_size);
+    //                 sum_buckets[bucket_idx].push_back(n);
+    //                 if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact")
+    //                     pvals_min_buckets[bucket_idx].push_back(bit_cast<uint64_t>(pval_min));
+    //             }
+    //         }
+    //     });
+
+    //     common::logger->trace("Merging min p-value tables");
+    //     if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact") {
+    //         for (size_t i = 1; i < ms.size(); ++i) {
+    //             size_t end = std::min(ms[0].size(), ms[i].size());
+    //             for (size_t j = 0; j < end; ++j) {
+    //                 assert(ms[0][j].first == 1.1 || ms[i][j].first == 1.1 || ms[0][j] == ms[i][j]);
+    //                 ms[0][j].first = std::min(ms[0][j].first, ms[i][j].first);
+    //                 ms[0][j].second += ms[i][j].second;
+    //             }
+
+    //             if (ms[i].size() > ms[0].size()) {
+    //                 std::copy(std::make_move_iterator(ms[i].begin() + ms[0].size()),
+    //                         std::make_move_iterator(ms[i].end()),
+    //                         std::back_inserter(ms[0]));
+    //             }
+    //         }
+    //         m = std::move(ms[0]);
+    //         ms.resize(0);
+    //     } else {
+    //         std::vector<std::tuple<double, int64_t, size_t>> mn;
+    //         for (size_t i = 0; i < ms_vecs.size(); ++i) {
+    //             for (size_t n = 0; n < ms_vecs[i].size(); ++n) {
+    //                 for (const auto &[vec, v] : ms_vecs[i][n]) {
+    //                     mn.emplace_back(v.first, n, v.second);
+    //                 }
+    //             }
+    //         }
+    //         ms_vecs.resize(0);
+    //         std::sort(mn.begin(), mn.end(), utils::GreaterFirst());
+
+    //         m.reserve(mn.size());
+    //         m_sums.reserve(mn.size());
+    //         for (const auto &[p, s, c] : mn) {
+    //             m.emplace_back(p, c);
+    //             m_sums.emplace_back(s);
+    //         }
+    //     }
+    // }
+
+    // size_t k = 0;
+    // size_t n_cutoff = 1;
+
+    // auto combine_pvals = [dist=boost::math::cauchy()](const std::vector<double> &pvals) {
+    //     double stat = 0.0;
+    //     for (double pval : pvals) {
+    //         stat += tan((0.5 - pval) * M_PI);
+    //     }
+    //     stat /= pvals.size();
+    //     return boost::math::cdf(boost::math::complement(dist, stat));
+    // };
+
+    // std::vector<size_t> boundaries;
+    // if (config.test_by_unitig) {
+    //     boundaries.reserve(eff_size_buckets.size() + 1);
+    //     boundaries.emplace_back(0);
+    //     for (size_t i = 0; i < eff_size_buckets.size(); ++i) {
+    //         boundaries.emplace_back(boundaries.back() + eff_size_buckets[i].size());
+    //     }
+    // }
+
+    // std::mutex pval_mu;
+    // std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
+    // sdsl::bit_vector unitig_start;
+    // if (!config.test_by_unitig) {
+    //     std::tie(k, n_cutoff) = correct_pvals(m, nelem, m_sums);
+    //     common::logger->trace("Picked: k: {}\tn: {}\tpval_min: {}", k, n_cutoff, config.family_wise_error_rate / k);
+    // } else {
+    //     clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
+    //         graph_ptr,
+    //         [&](node_index node) {
+    //             return node != DeBruijnGraph::npos
+    //                     && kept[AnnotatedDBG::graph_to_anno_index(node)];
+    //         },
+    //         true,
+    //         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+    //     );
+
+    //     std::vector<std::pair<double, int64_t>> comb_msums;
+    //     common::logger->trace("Allocating updated indicator");
+    //     unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+
+    //     common::logger->trace("Combining min p-vals");
+    //     clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+    //         std::vector<size_t> bucket_idxs;
+    //         bucket_idxs.reserve(path.size());
+    //         for (node_index node : path) {
+    //             bucket_idxs.emplace_back(0);
+    //             for (size_t i = 1; i < boundaries.size(); ++i) {
+    //                 if (boundaries[i] > node)
+    //                     break;
+
+    //                 ++bucket_idxs.back();
+    //             }
+    //         }
+
+    //         std::vector<double> pvals_min;
+    //         pvals_min.reserve(path.size());
+
+    //         int64_t n_sum = 0;
+    //         {
+    //             std::lock_guard<std::mutex> lock(pval_mu);
+    //             for (size_t i = 0; i < path.size(); ++i) {
+    //                 size_t bucket_idx = bucket_idxs[i];
+    //                 size_t node_shift = path[i] - boundaries[bucket_idx];
+    //                 size_t n = sum_buckets[bucket_idx][node_shift];
+    //                 n_sum += n;
+
+    //                 if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact") {
+    //                     pvals_min.emplace_back(bit_cast<double, uint64_t>(pvals_min_buckets[bucket_idx][node_shift]));
+    //                 } else {
+    //                     pvals_min.emplace_back(m[n].first);
+    //                 }
+    //             }
+    //         }
+
+    //         double comb_pval = *std::min_element(pvals_min.begin(), pvals_min.end());
+    //         if (comb_pval < config.family_wise_error_rate) {
+    //             for (node_index node : path) {
+    //                 set_bit(unitig_start.data(), node, parallel, MO_RELAXED);
+    //             }
+    //         }
+
+    //         std::lock_guard<std::mutex> lock(pval_mu);
+    //         comb_msums.emplace_back(comb_pval, n_sum);
+    //     }, num_threads);
+
+    //     std::sort(comb_msums.begin(), comb_msums.end(), utils::GreaterFirst());
+    //     std::vector<std::pair<double, size_t>> comb_m;
+    //     std::vector<int64_t> comb_m_sums;
+    //     comb_m.reserve(comb_msums.size());
+    //     comb_m_sums.reserve(comb_msums.size());
+    //     for (const auto &[p, s] : comb_msums) {
+    //         comb_m.emplace_back(p, 1);
+    //         comb_m_sums.emplace_back(s);
+    //     }
+
+    //     std::tie(k, n_cutoff) = correct_pvals(comb_m, nelem, comb_m_sums);
+    //     common::logger->trace("Picked: k: {}\tpval_min: {}", k, config.family_wise_error_rate / k);
+
+    //     sum_buckets.resize(0);
+    //     tmp_sum_buckets.resize(0);
+    //     pvals_min_buckets.resize(0);
+    //     tmp_min_buckets.resize(0);
+    // }
+
+    // if (k > 0) {
+    //     common::logger->trace("Running differential tests");
+    //     std::exception_ptr ex = nullptr;
+    //     std::mutex ex_mu;
+    //     bool keep_all_pvals = config.output_pvals || config.test_by_unitig;
+    //     std::atomic_thread_fence(std::memory_order_release);
+
+    //     generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
+    //         if (ex)
+    //             return;
+
+    //         int64_t in_sum = 0;
+    //         int64_t out_sum = 0;
+    //         bool in_kmer = false;
+    //         bool out_kmer = false;
+    //         PairContainer row;
+    //         bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
+    //         node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+    //         double pval = 1.1;
+    //         if (kept[row_i]) {
+    //             size_t count_in = 0;
+    //             size_t count_out = 0;
+    //             size_t total_count = 0;
+    //             for (const auto &[j, raw_c] : raw_row) {
+    //                 if (min_counts.size() && raw_c < min_counts[j])
+    //                     continue;
+
+    //                 uint64_t c = raw_c;
+    //                 if (count_maps.size())
+    //                     c = count_maps[j].find(c)->second.first;
+
+    //                 row.emplace_back(j, c);
+    //                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+    //                     out_sum += c;
+    //                     ++count_out;
+    //                 }
+
+    //                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+    //                     in_sum += c;
+    //                     ++count_in;
+    //                 }
+
+    //                 total_count += groups[j] != Group::OTHER;
+    //             }
+
+    //             if (total_count >= config.min_recurrence) {
+    //                 double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
+    //                 in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
+    //                 out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+    //             }
+    //         } else {
+    //             if (keep_all_pvals) {
+    //                 auto &pvals = pvals_buckets[bucket_idx];
+    //                 if constexpr(preallocated) {
+    //                     pvals[node] = bit_cast<uint64_t>(pval);
+    //                 } else {
+    //                     pvals.push_back(bit_cast<uint64_t>(pval));
+    //                 }
+    //             }
+    //             return;
+    //         }
+
+    //         size_t n = config.test_type != "poisson_binom" ? in_sum + out_sum : row.size();
+
+    //         if (!in_kmer && !out_kmer) {
+    //             row.clear();
+    //             n = 0;
+    //         }
+
+    //         if (!config.output_pvals && config.test_by_unitig && !unitig_start[node]) {
+    //             auto &pvals = pvals_buckets[bucket_idx];
+    //             if constexpr(preallocated) {
+    //                 pvals[node] = bit_cast<uint64_t>(pval);
+    //             } else {
+    //                 pvals.push_back(bit_cast<uint64_t>(pval));
+    //             }
+    //         }
+
+    //         if (config.output_pvals
+    //                 || (!config.test_by_unitig && n >= n_cutoff)
+    //                 || (config.test_by_unitig && unitig_start[node])) {
+    //             if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact" && m[n].first == 1.1) {
+    //                 common::logger->error("in: {}\tout: {}\tn: {}", in_sum, out_sum, n);
+    //                 throw std::runtime_error("Indexing invalid");
+    //             }
+
+    //             try {
+    //                 pval = compute_pval(in_sum, out_sum, row);
+    //             } catch (...) {
+    //                 std::lock_guard<std::mutex> lock(ex_mu);
+    //                 ex = std::current_exception();
+    //                 return;
+    //             }
+
+    //             if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact" && pval < 1.1 && m[n].first - pval > 1e-10) {
+    //                 common::logger->error("Min p-val estimate too high: min {} > cur {}\tn: {}\ttest: {}", m[n].first, pval, n, config.test_type);
+    //                 throw std::runtime_error("Test failed");
+    //             }
+
+    //             if (config.test_type != "notest" && keep_all_pvals) {
+    //                 auto &pvals = pvals_buckets[bucket_idx];
+    //                 if constexpr(preallocated) {
+    //                     pvals[node] = bit_cast<uint64_t>(pval);
+    //                 } else {
+    //                     pvals.push_back(bit_cast<uint64_t>(pval));
+    //                 }
+    //             }
+
+    //             if (!config.test_by_unitig && in_kmer != out_kmer && pval * k < config.family_wise_error_rate) {
+    //                 bool use_atomic = parallel && (node % 64 == 0);
+    //                 set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
+    //             }
+    //         }
+    //     });
+
+    //     std::atomic_thread_fence(std::memory_order_acquire);
+
+    //     if (ex)
+    //         std::rethrow_exception(ex);
+
+    //     unitig_start = sdsl::bit_vector();
+    //     deallocate();
+
+    //     if (!config.test_by_unitig) {
+    //         boundaries.reserve(pvals_buckets.size() + 1);
+    //         boundaries.emplace_back(0);
+    //         for (size_t i = 0; i < pvals_buckets.size(); ++i) {
+    //             boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
+    //         }
+    //     }
+
+    //     if (config.test_by_unitig) {
+    //         common::logger->trace("Allocating k-mer bitmasks");
+    //         indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+    //         indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+
+    //         common::logger->trace("Combining p-values within unitigs");
+    //         std::atomic<uint64_t> num_sig_unitigs{0};
+
+    //         std::atomic_thread_fence(std::memory_order_release);
+    //         clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+    //             int64_t comb_eff_size = 0;
+    //             std::vector<size_t> bucket_idxs;
+    //             bucket_idxs.reserve(path.size());
+    //             for (node_index node : path) {
+    //                 bucket_idxs.emplace_back(0);
+    //                 for (size_t i = 1; i < boundaries.size(); ++i) {
+    //                     if (boundaries[i] > node)
+    //                         break;
+
+    //                     ++bucket_idxs.back();
+    //                 }
+    //             }
+
+    //             // TODO: don't bother combining if the row sum is too small
+    //             std::vector<double> pvals;
+    //             pvals.reserve(path.size());
+    //             {
+    //                 std::lock_guard<std::mutex> lock(pval_mu);
+    //                 for (size_t i = 0; i < path.size(); ++i) {
+    //                     node_index node = path[i];
+    //                     size_t bucket_idx = bucket_idxs[i];
+    //                     size_t node_shift = node - boundaries[bucket_idx];
+
+    //                     comb_eff_size += bit_cast<int64_t, uint64_t>(eff_size_buckets[bucket_idx][node_shift]);
+
+    //                     double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node_shift]);
+    //                     pvals.emplace_back(pval);
+    //                 }
+    //             }
+    //             double comb_pval = combine_pvals(pvals);
+    //             if (comb_pval * k < config.family_wise_error_rate && comb_eff_size != 0) {
+    //                 ++num_sig_unitigs;
+    //                 auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
+    //                 for (node_index node : path) {
+    //                     set_bit(data, node, parallel, MO_RELAXED);
+    //                 }
+    //             }
+
+    //             uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
+
+    //             std::lock_guard<std::mutex> lock(pval_mu);
+    //             for (size_t i = 0; i < path.size(); ++i) {
+    //                 size_t bucket_idx = bucket_idxs[i];
+    //                 pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = comb_pval_enc;
+    //             }
+    //         }, num_threads);
+    //         std::atomic_thread_fence(std::memory_order_acquire);
+    //         common::logger->trace("Found {} significant unitigs before correction", num_sig_unitigs);
+
+    //         eff_size_buckets.resize(0);
+    //         tmp_eff_buckets.resize(0);
+    //     }
+    // } else {
+    //     deallocate();
+    // }
 
     std::unique_ptr<utils::TempFile> tmp_file;
     PValStorage pvals;
 
-    if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-        pvals = std::move(pvals_buckets[0]);
-    }
-
-    if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-        if (config.output_pvals) {
-            common::logger->trace("Merging buckets");
-            for (size_t i = 1; i < pvals_buckets.size(); ++i) {
-                for (size_t j = 0; j < pvals_buckets[i].size(); ++j) {
-                    pvals_buckets[0].push_back(pvals_buckets[i][j]);
-                }
-                pvals_buckets[i].close();
-            }
-        }
-        pvals = std::move(pvals_buckets[0]);
-        pvals_buckets.resize(0);
-        tmp_file = std::move(tmp_buckets[0]);
-        tmp_buckets.resize(0);
-    }
+    // if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+    //     pvals = std::move(pvals_buckets[0]);
+    // }
+
+    // if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+    //     if (config.output_pvals) {
+    //         common::logger->trace("Merging buckets");
+    //         for (size_t i = 1; i < pvals_buckets.size(); ++i) {
+    //             for (size_t j = 0; j < pvals_buckets[i].size(); ++j) {
+    //                 pvals_buckets[0].push_back(pvals_buckets[i][j]);
+    //             }
+    //             pvals_buckets[i].close();
+    //         }
+    //     }
+    //     pvals = std::move(pvals_buckets[0]);
+    //     pvals_buckets.resize(0);
+    //     tmp_file = std::move(tmp_buckets[0]);
+    //     tmp_buckets.resize(0);
+    // }
 
     common::logger->trace("Done! Assembling contigs.");
 

From 110932cc6e11c63a8eadc3c4830c6026ecdd7170 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 20 Feb 2025 17:56:12 +0100
Subject: [PATCH 369/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 113 +++++-------------
 1 file changed, 32 insertions(+), 81 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ef835c649e..3ec5f9fc18 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -280,6 +280,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     // common::logger->trace("Allocating p-value storage");
     // auto nullpval = bit_cast<uint64_t>(double(1.1));
 
+    // std::vector<PValStorage> mean_buckets;
+    // std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
+    // if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+    //     auto &means = mean_buckets.emplace_back();
+    //     means.resize(graph_ptr->max_index() + 1, 0);
+    // }
+
+    // if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+    //     for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+    //         auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+    //         mean_buckets.emplace_back(tmp_file->name(), std::ios::out);
+    //     }
+    //     mean_buckets[0].push_back(0);
+    // }
+
     // std::vector<PValStorage> pvals_buckets;
     // std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
     // std::vector<PValStorage> pvals_min_buckets;
@@ -661,13 +676,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
-
-    auto get_deviance = [&](double y, double mu) {
-        y += 1e-8;
-        mu += 1e-8;
-        return 2 * (y * log(y/mu) - y + mu);
-    };
-
     std::vector<std::pair<double, size_t>> m;
     {
         VectorMap<double, size_t> m_vec;
@@ -712,31 +720,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 double pval0 = boost::math::pdf(bdist, 0);
                 double pvaln = boost::math::pdf(bdist, n);
                 pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-
-                // std::vector<double> devs;
-                // devs.reserve(n + 1);
-                // for (int64_t s = 0; s <= n; ++s) {
-                //     devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-                // }
-                // double max_d = *std::max_element(devs.begin(), devs.end());
-                // pval = 0.0;
-
-                // int64_t s = 0;
-                // for ( ; s <= n; ++s) {
-                //     if (devs[s] != max_d)
-                //         break;
-                // }
-                // if (s > 0)
-                //     pval += boost::math::cdf(bdist, s - 1);
-
-                // int64_t sp = n;
-                // for ( ; sp >= s; --sp) {
-                //     if (devs[sp] != max_d)
-                //         break;
-                // }
-
-                // if (sp < n)
-                //     pval += boost::math::cdf(boost::math::complement(bdist, sp));
             }
 
             ++ms[bucket_idx][pval];
@@ -815,58 +798,27 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (n > 0) {
             double p = mu1 / (mu1 + mu2);
             auto bdist = boost::math::binomial(n, p);
-            eff_size = static_cast<double>(in_sum) - p * n;
-
-            // std::vector<double> pmfs;
-            // pmfs.reserve(n + 1);
-            // for (int64_t s = 0; s <= n; ++s) {
-            //     pmfs.emplace_back(boost::math::pdf(bdist, s));
-            // }
-
-            // pval = 0.0;
-            // for (int64_t s = 0; s <= n; ++s) {
-            //     if (pmfs[s] <= pmfs[in_sum])
-            //         pval += pmfs[s];
-            // }
-
-            // if (in_sum <= mean) {
-            //     pval = boost::math::cdf(bdist, in_sum);
-            //     int64_t t = boost::math::quantile(bdist, 1.0 - pval);
-            //     pval += boost::math::cdf(boost::math::complement(bdist, t - 1));
-            // } else {
-            //     pval = boost::math::cdf(boost::math::complement(bdist, in_sum - 1));
-            //     int64_t s = boost::math::quantile(bdist, pval);
-            //     pval += boost::math::cdf(bdist, s);
-            // }
-
-            std::vector<double> devs;
-            devs.reserve(n + 1);
-            for (int64_t s = 0; s <= n; ++s) {
-                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-            }
-            double max_d = *std::max_element(devs.begin(), devs.end());
 
             {
-                double min_pval = 0.0;
-                int64_t s = 0;
-                for ( ; s <= n; ++s) {
-                    if (devs[s] != max_d)
-                        break;
-                }
-                if (s > 0)
-                    min_pval += boost::math::cdf(bdist, s - 1);
+                double pval0 = boost::math::pdf(bdist, 0);
+                double pvaln = boost::math::pdf(bdist, n);
+                double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                if (min_pval * k_min >= config.family_wise_error_rate)
+                    return;
+            }
 
-                int64_t sp = n;
-                for ( ; sp >= s; --sp) {
-                    if (devs[sp] != max_d)
-                        break;
-                }
+            eff_size = static_cast<double>(in_sum) - boost::math::mode(bdist);
 
-                if (sp < n)
-                    min_pval += boost::math::cdf(boost::math::complement(bdist, sp));
+            auto get_deviance = [&](double y, double mu) {
+                y += 1e-8;
+                mu += 1e-8;
+                return 2 * (y * log(y/mu) - y + mu);
+            };
 
-                if (min_pval * k_min >= config.family_wise_error_rate)
-                    return;
+            std::vector<double> devs;
+            devs.reserve(n + 1);
+            for (int64_t s = 0; s <= n; ++s) {
+                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
             }
 
             pval = 0.0;
@@ -889,8 +841,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 pval += boost::math::cdf(boost::math::complement(bdist, sp));
         }
 
-        // common::logger->trace("test: {}/{}\t{}\t{}\t{} -> {}", in_sum, n, mu1, mu2, pval, pval * k);
-
         if (pval * k < config.family_wise_error_rate) {
             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
             if (eff_size > 0) {
@@ -900,6 +850,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
     });
+
     std::atomic_thread_fence(std::memory_order_acquire);
 
     // if (config.test_type == "poisson_exact") {

From 66fc6065aba319fd2518eb1ff8b01eab9bcf0d36 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 20 Feb 2025 17:59:22 +0100
Subject: [PATCH 370/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 88 +++++++++----------
 1 file changed, 42 insertions(+), 46 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 3ec5f9fc18..275da5591c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -792,62 +792,58 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        double pval = 1.0;
+        if (n == 0)
+            return;
 
-        double eff_size = 0;
-        if (n > 0) {
-            double p = mu1 / (mu1 + mu2);
-            auto bdist = boost::math::binomial(n, p);
+        double p = mu1 / (mu1 + mu2);
+        auto bdist = boost::math::binomial(n, p);
 
-            {
-                double pval0 = boost::math::pdf(bdist, 0);
-                double pvaln = boost::math::pdf(bdist, n);
-                double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                if (min_pval * k_min >= config.family_wise_error_rate)
-                    return;
-            }
+        double pval0 = boost::math::pdf(bdist, 0);
+        double pvaln = boost::math::pdf(bdist, n);
+        double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+        if (min_pval * k_min >= config.family_wise_error_rate)
+            return;
 
-            eff_size = static_cast<double>(in_sum) - boost::math::mode(bdist);
+        auto get_deviance = [&](double y, double mu) {
+            y += 1e-8;
+            mu += 1e-8;
+            return 2 * (y * log(y/mu) - y + mu);
+        };
 
-            auto get_deviance = [&](double y, double mu) {
-                y += 1e-8;
-                mu += 1e-8;
-                return 2 * (y * log(y/mu) - y + mu);
-            };
+        std::vector<double> devs;
+        devs.reserve(n + 1);
+        for (int64_t s = 0; s <= n; ++s) {
+            devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+        }
 
-            std::vector<double> devs;
-            devs.reserve(n + 1);
-            for (int64_t s = 0; s <= n; ++s) {
-                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-            }
+        double pval = 0.0;
 
-            pval = 0.0;
+        int64_t s = 0;
+        for ( ; s <= n; ++s) {
+            if (devs[s] < devs[in_sum])
+                break;
+        }
+        if (s > 0)
+            pval += boost::math::cdf(bdist, s - 1);
 
-            int64_t s = 0;
-            for ( ; s <= n; ++s) {
-                if (devs[s] < devs[in_sum])
-                    break;
-            }
-            if (s > 0)
-                pval += boost::math::cdf(bdist, s - 1);
+        int64_t sp = n;
+        for ( ; sp >= s; --sp) {
+            if (devs[sp] < devs[in_sum])
+                break;
+        }
 
-            int64_t sp = n;
-            for ( ; sp >= s; --sp) {
-                if (devs[sp] < devs[in_sum])
-                    break;
-            }
+        if (sp < n)
+            pval += boost::math::cdf(boost::math::complement(bdist, sp));
 
-            if (sp < n)
-                pval += boost::math::cdf(boost::math::complement(bdist, sp));
-        }
+        if (pval * k >= config.family_wise_error_rate)
+            return;
 
-        if (pval * k < config.family_wise_error_rate) {
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            if (eff_size > 0) {
-                set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
-            } else if (eff_size < 0) {
-                set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
-            }
+        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+        double eff_size = static_cast<double>(in_sum) - boost::math::mode(bdist);
+        if (eff_size > 0) {
+            set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
+        } else if (eff_size < 0) {
+            set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
         }
     });
 

From baa3ffdd722240236306345dead04dd44c1da634 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 20 Feb 2025 19:01:01 +0100
Subject: [PATCH 371/410] added back unitigs

---
 .../src/graph/annotated_graph_algorithm.cpp   | 1534 +++--------------
 1 file changed, 221 insertions(+), 1313 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 275da5591c..b6c19f4c77 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -68,18 +68,18 @@ bit_cast(const From& src) noexcept
 }
 
 template <class PValStorage>
-double get(typename PValStorage::const_reference &ref) {
+double get(typename PValStorage::reference&& ref) {
     return bit_cast<double>(static_cast<typename PValStorage::value_type>(ref));
 }
 
 template <class PValStorage>
-void set(typename PValStorage::reference &ref, double val) {
-    ref = bit_cast<PValStorage::value_type>(val);
+void set(typename PValStorage::reference&& ref, double val) {
+    ref = bit_cast<typename PValStorage::value_type>(val);
 }
 
 template <class PValStorage>
 void push_back(PValStorage &v, double val) {
-    v.push_back(bit_cast<PValStorage::value_type>(val));
+    v.push_back(bit_cast<typename PValStorage::value_type>(val));
 }
 
 template <typename value_type, class PValStorage, typename HistGetter, typename Generator>
@@ -97,16 +97,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     if (auto canonical = std::dynamic_pointer_cast<const CanonicalDBG>(graph_ptr))
         graph_ptr = canonical->get_graph_ptr();
 
-    // num_parallel_files = std::min(num_parallel_files, num_threads);
     num_parallel_files = get_num_threads();
     num_threads = get_num_threads();
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
-    // bool parallel = num_parallel_files > 1;
 
     size_t num_labels_both = std::count(groups.begin(), groups.end(), Group::BOTH);
     size_t num_labels_out = std::count(groups.begin(), groups.end(), Group::OUT);
     size_t num_labels_in = std::count(groups.begin(), groups.end(), Group::IN);
-    // size_t total_labels = num_labels_in + num_labels_out;
     num_labels_in += num_labels_both;
     num_labels_out += num_labels_both;
 
@@ -153,10 +150,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
 
     auto correct_pvals = [&config](const std::vector<std::pair<double, size_t>> &m) {
-                                //    size_t nelem,
-                                //    const std::vector<int64_t> &m_sums = {}) {
-        // size_t k = 0;
-        // size_t n_cutoff = 1;
         size_t total = std::accumulate(m.begin(), m.end(), size_t(0),
                                        [](size_t sum, const auto &a) { return sum + a.second; });
         size_t acc = 0;
@@ -176,44 +169,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         return std::make_pair<size_t, size_t>(std::numeric_limits<size_t>::max(),
                                               std::numeric_limits<size_t>::max());
-        // size_t total = acc;
-        // common::logger->trace("Calculating cutoffs for {}/{} tests", acc, nelem);
-        // size_t last_k = 0;
-        // for (size_t n = 0; n < m.size(); ++n) {
-        //     auto [pval_min, s] = m[n];
-        //     size_t cur_k = std::numeric_limits<size_t>::max();
-        //     if (pval_min >= config.family_wise_error_rate) {
-        //         cur_k = 0;
-        //     } else if (pval_min > 0) {
-        //         double lkd = log2(config.family_wise_error_rate) - log2(pval_min);
-        //         if (lkd <= 64)
-        //             cur_k = pow(2.0, lkd);
-
-        //         if (cur_k == 0) {
-        //             common::logger->error("k: {}\tlog k: {}\tpval_min: {}", cur_k, lkd, pval_min);
-        //             throw std::runtime_error("Min failed");
-        //         }
-        //     }
-
-        //     if (cur_k != last_k && acc <= cur_k) {
-        //         k = std::max(acc, last_k + 1);
-        //         if (m_sums.empty()) {
-        //             n_cutoff = n;
-        //         } else {
-        //             n_cutoff = *std::min_element(m_sums.begin() + n, m_sums.end());
-        //         }
-        //         break;
-        //     }
-
-        //     last_k = cur_k;
-        //     acc -= s;
-        // }
-
-        // if (k == 0)
-        //     common::logger->warn("No significant p-values achievable after correction");
-
-        // common::logger->trace("Kept {} / {}", acc, total);
-        // return std::make_pair(k, n_cutoff);
     };
 
     common::logger->trace("Updating histogram and marking discarded k-mers");
@@ -223,6 +178,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     size_t nelem = sdsl::util::cnt_one_bits(kept);
 
+    std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
+    if (config.test_type != "notest" && config.test_by_unitig) {
+        clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
+            graph_ptr,
+            [&](node_index node) {
+                return node != DeBruijnGraph::npos
+                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
+            },
+            true,
+            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+        );
+    }
+
     std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
     for (size_t j = 0; j < hists.size(); ++j) {
         hists[j] = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(hists_map[j].values_container());
@@ -295,65 +263,57 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     //     mean_buckets[0].push_back(0);
     // }
 
-    // std::vector<PValStorage> pvals_buckets;
-    // std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
-    // std::vector<PValStorage> pvals_min_buckets;
-    // std::vector<std::unique_ptr<utils::TempFile>> tmp_min_buckets;
+    std::vector<PValStorage> pvals_buckets;
+    std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
+    std::vector<PValStorage> pvals_min_buckets;
+    std::vector<std::unique_ptr<utils::TempFile>> tmp_min_buckets;
+    std::vector<PValStorage> eff_size_buckets;
+    std::vector<std::unique_ptr<utils::TempFile>> tmp_eff_buckets;
     // constexpr bool preallocated = std::is_same_v<PValStorage, std::vector<uint64_t>>;
 
-    // if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-    //     auto &pvals = pvals_buckets.emplace_back();
-    //     pvals.resize(graph_ptr->max_index() + 1, nullpval);
-    // }
+    if (config.test_type != "notest" && config.test_by_unitig) {
+        if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+            auto &pvals = pvals_buckets.emplace_back();
+            pvals.resize(graph_ptr->max_index() + 1);
 
-    // if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-    //     for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-    //         auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-    //         pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
-    //     }
-    //     pvals_buckets[0].push_back(nullpval);
-    // }
+            auto &pvals_min = pvals_min_buckets.emplace_back();
+            pvals_min.resize(graph_ptr->max_index() + 1);
+
+            auto &eff_size = eff_size_buckets.emplace_back();
+            eff_size.resize(graph_ptr->max_index() + 1);
+        }
+
+        if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+            for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+                auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+                pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
+            }
+            pvals_buckets[0].push_back(0);
+
+            for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+                auto &tmp_file = tmp_min_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+                pvals_min_buckets.emplace_back(tmp_file->name(), std::ios::out);
+            }
+            pvals_min_buckets[0].push_back(0);
+
+            for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+                auto &tmp_file_eff = tmp_eff_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+                eff_size_buckets.emplace_back(tmp_file_eff->name(), std::ios::out);
+            }
+            eff_size_buckets[0].push_back(0);
+        }
+    }
 
     // if ((config.test_type == "gnb_exact" || config.test_type == "lnb_exact") && config.test_by_unitig) {
-    //     if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-    //         auto &pvals_min = pvals_min_buckets.emplace_back();
-    //         pvals_min.resize(graph_ptr->max_index() + 1, nullpval);
-    //     }
 
-    //     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-    //         for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-    //             auto &tmp_file = tmp_min_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-    //             pvals_min_buckets.emplace_back(tmp_file->name(), std::ios::out);
-    //         }
-    //         pvals_min_buckets[0].push_back(nullpval);
-    //     }
     // }
 
-    // std::vector<PValStorage> eff_size_buckets;
-    // std::vector<std::unique_ptr<utils::TempFile>> tmp_eff_buckets;
+
     // std::vector<PValStorage> sum_buckets;
     // std::vector<std::unique_ptr<utils::TempFile>> tmp_sum_buckets;
     // if (config.test_by_unitig) {
     //     common::logger->trace("Allocating effect size and row sum storage");
-    //     if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-    //         auto &eff_size = eff_size_buckets.emplace_back();
-    //         eff_size.resize(graph_ptr->max_index() + 1);
 
-    //         auto &sum = sum_buckets.emplace_back();
-    //         sum.resize(graph_ptr->max_index() + 1);
-    //     }
-
-    //     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-    //         for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-    //             auto &tmp_file_eff = tmp_eff_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-    //             eff_size_buckets.emplace_back(tmp_file_eff->name(), std::ios::out);
-
-    //             auto &tmp_file_sum = tmp_sum_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-    //             sum_buckets.emplace_back(tmp_file_sum->name(), std::ios::out);
-    //         }
-    //         eff_size_buckets[0].push_back(0);
-    //         sum_buckets[0].push_back(0);
-    //     }
     // }
 
     sdsl::bit_vector indicator_in;
@@ -370,8 +330,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     // std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps;
 
-    common::logger->trace("Test: {}", config.test_type);
+    common::logger->trace("Test: {}\tby unitig: {}", config.test_type, config.test_by_unitig);
 
+    // prefilter rows
     auto generate_clean_rows = [&](const auto &callback) {
         generate_rows([&](uint64_t row_i, const auto &row, size_t bucket_idx) {
             if (!kept[row_i])
@@ -396,16 +357,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (total_count < config.min_recurrence)
                 return;
 
-            bool in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence;
-            bool out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence;
+            bool in_kmer = count_in >= config.min_in_recurrence && count_in <= config.max_in_recurrence;
+            bool out_kmer = count_out >= config.min_out_recurrence && count_out <= config.max_out_recurrence;
 
             if (in_kmer || out_kmer)
                 callback(row_i, row, bucket_idx);
         });
     };
 
+    // precompute negative binomial fits for poisson_bayes test
     std::vector<std::pair<double, double>> nb_params(groups.size());
-
     if (config.test_type == "poisson_bayes") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
         auto get_rp = [&](const auto &generate) {
@@ -488,6 +449,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
+    // precompute p-values for poisson_binom test
     std::vector<std::vector<double>> pb_pvals(num_labels_in + num_labels_out + 1);
     std::vector<double> mid_points(num_labels_in + num_labels_out + 1);
     if (config.test_type == "poisson_binom") {
@@ -676,55 +638,94 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
+    auto combine_pvals = [dist=boost::math::cauchy()](const std::vector<double> &pvals) {
+        double stat = 0.0;
+        for (double pval : pvals) {
+            stat += tan((0.5 - pval) * M_PI);
+        }
+        stat /= pvals.size();
+        return boost::math::cdf(boost::math::complement(dist, stat));
+    };
+
+    // precompute minimal attainable p-values
     std::vector<std::pair<double, size_t>> m;
-    {
+    std::vector<size_t> offsets;
+    if (config.test_type != "notest") {
+        common::logger->trace("Computing minimum p-values");
         VectorMap<double, size_t> m_vec;
         std::vector<VectorMap<double, size_t>> ms(num_threads + 1);
         generate_clean_rows([&](uint64_t row_i, const auto &row, uint64_t bucket_idx) {
+            double pval = 1.0;
             if (config.test_type == "poisson_binom") {
                 size_t n = 0;
                 for (const auto &[j, c] : row) {
                     n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
                 }
                 size_t front = n - std::min(n, num_labels_out);
-                double pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
-                ++ms[bucket_idx][pval];
-                return;
-            }
+                pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+            } else {
+                double mu1 = 0.0;
+                double mu2 = 0.0;
+                if (config.test_type == "poisson_exact") {
+                    mu1 = static_cast<double>(in_kmers) / nelem;
+                    mu2 = static_cast<double>(out_kmers) / nelem;
+                }
+                int64_t n = 0;
+                for (const auto &[j, c] : row) {
+                    n += c;
+                    if (config.test_type == "poisson_bayes") {
+                        const auto &[r, p] = nb_params[j];
+                        double lambda_j = sqrt((1.0 - p) * (c + r - 1));
+                        if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                            mu2 += lambda_j;
+
+                        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                            mu1 += lambda_j;
+                    }
+                }
 
-            double mu1 = 0.0;
-            double mu2 = 0.0;
-            if (config.test_type == "poisson_exact") {
-                mu1 = static_cast<double>(in_kmers) / nelem;
-                mu2 = static_cast<double>(out_kmers) / nelem;
-            }
-            int64_t n = 0;
-            for (const auto &[j, c] : row) {
-                n += c;
-                if (config.test_type == "poisson_bayes") {
-                    const auto &[r, p] = nb_params[j];
-                    double lambda_j = sqrt((1.0 - p) * (c + r - 1));
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                        mu2 += lambda_j;
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                        mu1 += lambda_j;
+                if (n > 0) {
+                    double p = mu1 / (mu1 + mu2);
+                    auto bdist = boost::math::binomial(n, p);
+                    double pval0 = boost::math::pdf(bdist, 0);
+                    double pvaln = boost::math::pdf(bdist, n);
+                    pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
                 }
             }
 
-            double pval = 1.0;
-
-            if (n > 0) {
-                double p = mu1 / (mu1 + mu2);
-                auto bdist = boost::math::binomial(n, p);
-                double pval0 = boost::math::pdf(bdist, 0);
-                double pvaln = boost::math::pdf(bdist, n);
-                pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+            if (!config.test_by_unitig) {
+                ++ms[bucket_idx][pval];
+            } else {
+                push_back(pvals_min_buckets[bucket_idx], pval);
             }
-
-            ++ms[bucket_idx][pval];
         });
 
+        if (config.test_by_unitig) {
+            common::logger->trace("Combining minimal attainable p-values for unitigs");
+            offsets.reserve(num_threads + 1);
+            offsets.emplace_back(0);
+            for (const auto &bucket : pvals_min_buckets) {
+                offsets.emplace_back(offsets.back() + bucket.size());
+            }
+            std::mutex m_vec_mu;
+            clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+                std::vector<double> pvals;
+                pvals.reserve(path.size());
+                {
+                    std::lock_guard<std::mutex> lock(m_vec_mu);
+                    for (node_index node : path) {
+                        size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), node) - offsets.begin() - 1;
+                        pvals.emplace_back(get<PValStorage>(pvals_min_buckets[bucket_idx][node - offsets[bucket_idx]]));
+                    }
+                }
+                double comp_min_pval = combine_pvals(pvals);
+                size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), path[0]) - offsets.begin() - 1;
+                ++ms[bucket_idx][comp_min_pval];
+                std::lock_guard<std::mutex> lock(m_vec_mu);
+                set<PValStorage>(pvals_min_buckets[bucket_idx][path[0] - offsets[bucket_idx]], comp_min_pval);
+            });
+        }
+
         std::swap(m_vec, ms[0]);
         for (size_t i = 1; i < ms.size(); ++i) {
             for (const auto &[pval, c] : ms[i]) {
@@ -736,11 +737,54 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::sort(m.rbegin(), m.rend());
     }
 
+    // determine cutoffs for multiple testing correction
     auto [k_min, k] = correct_pvals(m);
 
     common::logger->trace("Picked: k: {}\tn: {} / {}\tpval_min: {}", k_min, k, nelem, config.family_wise_error_rate / k);
+
+    common::logger->trace("Running differential tests");
     std::atomic_thread_fence(std::memory_order_release);
     generate_clean_rows([&](uint64_t row_i, const auto &row, uint64_t bucket_idx) {
+        if (config.test_type == "notest") {
+            size_t count_in = 0;
+            size_t count_out = 0;
+            for (const auto &[j, c] : row) {
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                    ++count_out;
+
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                    ++count_in;
+            }
+
+            bool in_kmer = count_in >= config.min_in_recurrence && count_in <= config.max_in_recurrence;
+            bool out_kmer = count_out >= config.min_out_recurrence && count_out <= config.max_out_recurrence;
+
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+            if (in_kmer)
+                set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
+
+            if (out_kmer)
+                set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
+
+            return;
+        }
+
+        auto set_pval = [&](double pval, double eff_size) {
+            if (!config.test_by_unitig) {
+                if (pval * k < config.family_wise_error_rate) {
+                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                    if (eff_size > 0) {
+                        set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
+                    } else if (eff_size < 0) {
+                        set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
+                    }
+                }
+            } else {
+                push_back(pvals_buckets[bucket_idx], pval);
+                push_back(eff_size_buckets[bucket_idx], eff_size);
+            }
+        };
+
         if (config.test_type == "poisson_binom") {
             size_t n = 0;
             uint64_t s = 0;
@@ -750,19 +794,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
             size_t front = n - std::min(n, num_labels_out);
             double min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
-            if (min_pval * k_min >= config.family_wise_error_rate)
+
+            if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
                 return;
 
             double pval = pb_pvals[n][s];
-            if (pval * k < config.family_wise_error_rate) {
-                double eff_size = static_cast<double>(s) - mid_points[n];
-                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                if (eff_size > 0) {
-                    set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
-                } else if (eff_size < 0) {
-                    set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
-                }
-            }
+            double eff_size = static_cast<double>(s) - mid_points[n];
+            set_pval(pval, eff_size);
             return;
         }
 
@@ -801,7 +839,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         double pval0 = boost::math::pdf(bdist, 0);
         double pvaln = boost::math::pdf(bdist, n);
         double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-        if (min_pval * k_min >= config.family_wise_error_rate)
+        if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
             return;
 
         auto get_deviance = [&](double y, double mu) {
@@ -835,1187 +873,57 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (sp < n)
             pval += boost::math::cdf(boost::math::complement(bdist, sp));
 
-        if (pval * k >= config.family_wise_error_rate)
+        if (!config.test_by_unitig && pval * k >= config.family_wise_error_rate)
             return;
 
-        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
         double eff_size = static_cast<double>(in_sum) - boost::math::mode(bdist);
-        if (eff_size > 0) {
-            set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
-        } else if (eff_size < 0) {
-            set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
-        }
+        set_pval(pval, eff_size);
     });
 
-    std::atomic_thread_fence(std::memory_order_acquire);
-
-    // if (config.test_type == "poisson_exact") {
-    //     auto get_deviance = [&](double y, double mu) {
-    //         y += 1e-8;
-    //         mu += 1e-8;
-    //         return 2 * (y * log(y/mu) - y + mu);
-    //     };
-
-    //     compute_min_pval = [&,p=static_cast<double>(in_kmers)/total_kmers,
-    //                           mu1=static_cast<double>(in_kmers)/nelem,
-    //                           mu2=static_cast<double>(out_kmers)/nelem,
-    //                           get_deviance](int64_t n, const PairContainer&) {
-    //         if (n == 0)
-    //             return 1.0;
-
-    //         auto bdist = boost::math::binomial(n, p);
-
-    //         std::vector<double> devs;
-    //         devs.reserve(n + 1);
-    //         for (int64_t s = 0; s <= n; ++s) {
-    //             devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-    //         }
-    //         double max_d = *std::max_element(devs.begin(), devs.end());
-    //         double pval = 0.0;
-
-    //         int64_t s = 0;
-    //         for ( ; s <= n; ++s) {
-    //             if (devs[s] != max_d)
-    //                 break;
-    //         }
-    //         if (s > 0)
-    //             pval += boost::math::cdf(bdist, s - 1);
-
-    //         int64_t sp = n;
-    //         for ( ; sp >= s; --sp) {
-    //             if (devs[sp] != max_d)
-    //                 break;
-    //         }
-
-    //         if (sp < n)
-    //             pval += boost::math::cdf(boost::math::complement(bdist, sp));
-
-    //         return pval;
-    //     };
-
-    //     compute_pval = [&,p=static_cast<double>(in_kmers)/total_kmers,
-    //                       mu1=static_cast<double>(in_kmers)/nelem,
-    //                       mu2=static_cast<double>(out_kmers)/nelem,
-    //                       get_deviance](int64_t in_sum, int64_t out_sum, const auto &row) {
-    //         if (row.empty())
-    //             return 1.0;
-
-    //         int64_t n = in_sum + out_sum;
-    //         auto bdist = boost::math::binomial(n, p);
-
-    //         std::vector<double> devs;
-    //         devs.reserve(n + 1);
-    //         for (int64_t s = 0; s <= n; ++s) {
-    //             devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-    //         }
-    //         double pval = 0.0;
-
-    //         int64_t s = 0;
-    //         for ( ; s <= n; ++s) {
-    //             if (devs[s] < devs[in_sum])
-    //                 break;
-    //         }
-    //         if (s > 0)
-    //             pval += boost::math::cdf(bdist, s - 1);
-
-    //         int64_t sp = n;
-    //         for ( ; sp >= s; --sp) {
-    //             if (devs[sp] < devs[in_sum])
-    //                 break;
-    //         }
-
-    //         if (sp < n)
-    //             pval += boost::math::cdf(boost::math::complement(bdist, sp));
-
-    //         return pval;
-    //     };
-    // } else if (config.test_type == "nbinom_exact" || config.test_type == "gnb_exact") {
-    //     common::logger->trace("Fitting per-sample negative binomial distributions");
-    //     auto get_rp = [&](const auto &generate) {
-    //         double mu = 0;
-    //         double var = 0;
-    //         size_t total = 0;
-    //         generate([&](auto k, auto c) {
-    //             mu += k * c;
-    //             var += k * k * c;
-    //             total += c;
-    //         });
-    //         mu /= total;
-    //         double mu2 = mu * mu;
-    //         var = (var - mu2 * total) / (total - 1);
-
-    //         double r_guess = mu * mu / (var - mu);
-
-    //         if (mu >= var) {
-    //             common::logger->warn("Fit failed, falling back to Poisson: mu: {} >= var: {}", mu, var);
-    //             return std::make_pair(0.0, 1.0);
-    //         }
-
-    //         auto get_dl = [&](double r) {
-    //             double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
-    //             generate([&](auto k, auto c) {
-    //                 dl += boost::math::digamma(k + r) * c;
-    //             });
-    //             return dl;
-    //         };
-
-    //         double r_min = r_guess;
-    //         double r_max = r_guess;
-
-    //         while (true) {
-    //             double dl_min = get_dl(r_min);
-    //             double dl_max = get_dl(r_max);
-
-    //             if (dl_min == 0) {
-    //                 r_max = r_min;
-    //                 break;
-    //             }
-
-    //             if (dl_max == 0) {
-    //                 r_min = r_max;
-    //                 break;
-    //             }
-
-    //             if (dl_min < 0)
-    //                 r_min /= 2;
-
-    //             if (dl_max > 0)
-    //                 r_max *= 2;
-
-    //             if (dl_min > 0 && dl_max < 0)
-    //                 break;
-    //         }
-
-    //         std::tie(r_min, r_max) = boost::math::tools::bisect(
-    //             get_dl, r_min, r_max, boost::math::tools::eps_tolerance<double>(5)
-    //         );
-
-    //         double r = r_max;
-    //         double p = r / (r + mu);
-    //         return std::make_pair(r, p);
-    //     };
-
-    //     std::vector<std::pair<double, double>> nb_params(groups.size());
-    //     std::vector<std::vector<std::pair<uint64_t, size_t>>::const_iterator> hist_its(groups.size());
-    //     size_t matrix_size = 0;
-
-    //     #pragma omp parallel for num_threads(num_parallel_files) reduction(+:matrix_size)
-    //     for (size_t j = 0; j < groups.size(); ++j) {
-    //         const auto &hist = hists[j];
-    //         size_t total = 0;
-    //         size_t total_cutoff = n_kmers[j] * HIST_CUTOFF;
-    //         auto it = hist.begin();
-    //         for ( ; it != hist.end() && total < total_cutoff; ++it) {
-    //             if (it->first != 0)
-    //                 total += it->second;
-    //         }
-    //         hist_its[j] = it;
-    //         if (hist.size()) {
-    //             nb_params[j] = get_rp([&](const auto &callback) {
-    //                 std::for_each(hist.begin(), it, [&](const auto &a) {
-    //                     const auto &[k, c] = a;
-    //                     callback(k, c);
-    //                 });
-    //             });
-    //             const auto &[r, p] = nb_params[j];
-    //             common::logger->trace("{}: size: {}\tmax_val: {}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
-    //                                 j, sums[j], (it - 1)->first, r * (1-p)/p, r*(1-p)/p/p, r, p);
-
-    //             total += hist[0].first == 0 ? hist[0].second : 0;
-
-    //             #pragma omp atomic
-    //             matrix_size += total;
-    //         }
-    //     }
-
-    //     common::logger->trace("Scaling distributions");
-
-    //     double target_p = 0.0;
-    //     for (size_t j = 0; j < groups.size(); ++j) {
-    //         target_p += log(nb_params[j].second);
-    //     }
-    //     target_p = exp(target_p / total_labels);
-    //     // double target_sum = 0.0;
-    //     // for (size_t j = 0; j < groups.size(); ++j) {
-    //     //     target_sum += log(static_cast<double>(sums[j]));
-    //     // }
-    //     // target_sum = exp(target_sum / total_labels);
-
-    //     // common::logger->trace("Finding common p and r.");
-
-    //     // double r_map;
-    //     // double target_p;
-    //     // std::tie(r_map, target_p) = get_rp([&](const auto &callback) {
-    //     //     for (size_t j = 0; j < groups.size(); ++j) {
-    //     //         double f = target_sum / sums[j];
-    //     //         std::for_each(hists[j].cbegin(), hist_its[j], [&](const auto &a) {
-    //     //             const auto &[k, c] = a;
-    //     //             callback(f * k, c);
-    //     //         });
-    //     //     }
-    //     // });
-
-    //     // common::logger->trace("Common p: {}", target_p);
-
-    //     // double fit_mu = r_map * (1.0 - target_p) / target_p;
-    //     // double fit_var = fit_mu / target_p;
-    //     // common::logger->trace("Common params: r: {}\tp: {}\tmu: {}\tvar: {}",
-    //     //                       r_map, target_p, fit_mu, fit_var);
-
-    //     // double r_in = r_map * num_labels_in;
-    //     // double r_out = r_map * num_labels_out;
-    //     // common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
-
-    //     // if (fit_var / fit_mu - 1.0 < 1e-5)
-    //     //     common::logger->warn("Fit parameters are close to a Poisson distribution");
-
-    //     count_maps.resize(groups.size());
-
-    //     common::logger->trace("Computing quantile maps");
-    //     double r_in = 0;
-    //     double r_out = 0;
-    //     #pragma omp parallel for num_threads(num_parallel_files)
-    //     for (size_t j = 0; j < groups.size(); ++j) {
-    //         if (hists[j].empty())
-    //             continue;
-
-    //         const auto &[r, p] = nb_params[j];
-    //         double r_map = r * target_p * (1.0 - p) / p / (1.0 - target_p);
-
-    //         if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-    //             #pragma omp atomic
-    //             r_out += r_map;
-    //         }
-
-    //         if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-    //             #pragma omp atomic
-    //             r_in += r_map;
-    //         }
-
-    //         boost::math::negative_binomial nb_out(r_map, target_p);
-    //         double scale = 1.0;
-    //         // double scale = target_sum / sums[j];
-
-    //         auto map_value = [&](uint64_t k, double scale, const auto &old_dist, const auto &new_dist) {
-    //             double cdf = boost::math::cdf(old_dist, k);
-
-    //             if (cdf < 1.0)
-    //                 return boost::math::quantile(new_dist, cdf);
-
-    //             double ccdf = boost::math::cdf(boost::math::complement(old_dist, k));
-
-    //             if (ccdf > 0.0)
-    //                 return boost::math::quantile(boost::math::complement(new_dist, ccdf));
-
-    //             return ceil(scale * k);
-    //         };
-
-    //         auto compute_map = [&](const auto &nb) {
-    //             // ensure that 0 maps to 0
-    //             count_maps[j][0] = std::make_pair(0, 0);
-
-    //             const auto &hist = hists[j];
-    //             for (const auto &[k, c] : hist) {
-    //                 if (!count_maps[j].count(k)) {
-    //                     uint64_t new_k = map_value(k, scale, nb, nb_out);
-    //                     count_maps[j][k] = std::make_pair(new_k, c);
-    //                 } else {
-    //                     count_maps[j][k].second += c;
-    //                 }
-    //             }
-    //         };
-
-    //         if (p < 1.0) {
-    //             compute_map(boost::math::negative_binomial(r, p));
-    //         } else {
-    //             compute_map(boost::math::poisson(static_cast<double>(sums[j]) * scale / nelem));
-    //         }
-    //     }
-
-    //     common::logger->trace("Fits: in: {}\tout: {}\tp: {}", r_in, r_out, target_p);
-
-    //     common::logger->trace("Unscaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
-    //     in_kmers = 0;
-    //     out_kmers = 0;
-    //     total_kmers = 0;
-    //     for (size_t j = 0; j < groups.size(); ++j) {
-    //         for (const auto &[k, m] : count_maps[j]) {
-    //             const auto &[v, c] = m;
-
-    //             if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-    //                 out_kmers += v * c;
-
-    //             if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-    //                 in_kmers += v * c;
-
-    //             total_kmers += v * c;
-    //         }
-    //     }
-
-    //     common::logger->trace("  Scaled Totals: in: {}\tout: {}", in_kmers, out_kmers);
-
-    //     auto compute_min_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t n) {
-    //         double half_div0 = -r_in * log2(r_in) - (n + r_out) * log2(n + r_out);
-    //         double half_divn = -r_out * log2(r_out) - (n + r_in) * log2(n + r_in);
-
-    //         double pval = 0.0;
-    //         double lscaling = lscaling_base - lgamma(r + n) / log(2);
-    //         if (half_div0 >= half_divn)
-    //             pval += exp2(lscaling + (lgamma(r_out + n) - lgamma(r_out)) / log(2));
-
-    //         if (half_divn >= half_div0)
-    //             pval += exp2(lscaling + (lgamma(r_in + n) - lgamma(r_in)) / log(2));
-
-    //         return std::min(1.0, pval);
-    //     };
-
-    //     auto compute_pval_r = [](double r, double r_in, double r_out, double lscaling_base, int64_t in_sum, int64_t out_sum, const PairContainer &row) {
-    //         int64_t n = in_sum + out_sum;
-    //         double argmin_d = r_in / r * n;
-    //         if (in_sum == argmin_d)
-    //             return 1.0;
-
-    //         double lscaling = lscaling_base - lgamma(r + n) / log(2);
-
-    //         auto get_pval = [&](const auto &get_stat) {
-    //             double base_stat = get_stat(in_sum, out_sum,
-    //                                         r_in != r_out && in_sum > 0 ? log2(in_sum) : 0.0,
-    //                                         r_in != r_out && out_sum > 0 ? log2(out_sum) : 0.0);
-    //             double pval = 0.0;
-    //             int64_t s = 0;
-    //             int64_t t = n;
-    //             double ls = 0;
-    //             double lt = log2(t);
-    //             if (get_stat(s, t, ls, lt) >= base_stat) {
-    //                 double base = lscaling + (lgamma(n + r_out) - lgamma(r_out)) / log(2);
-    //                 pval += exp2(base);
-    //                 for (++s,--t; s <= n; ++s,--t) {
-    //                     ls = log2(s);
-    //                     lt = t > 0 ? log2(t) : 0.0;
-    //                     if (get_stat(s, t, ls, lt) < base_stat)
-    //                         break;
-
-    //                     base += log2(t + 1) + log2(s - 1 + r_in) - ls - log2(t + r_out);
-    //                     pval += exp2(base);
-    //                 }
-    //             }
-
-    //             int64_t sp = n;
-    //             t = 0;
-    //             ls = log2(sp);
-    //             lt = 0;
-    //             if (get_stat(sp, t, ls, lt) >= base_stat) {
-    //                 double base = lscaling + (lgamma(n + r_in) - lgamma(r_in)) / log(2);
-    //                 pval += exp2(base);
-    //                 for (--sp,++t; sp >= s; --sp,++t) {
-    //                     ls = sp > 0 ? log2(sp) : 0.0;
-    //                     lt = log2(t);
-    //                     if (get_stat(sp, t, ls, lt) < base_stat)
-    //                         break;
-
-    //                     base += log2(sp + 1) + log2(t - 1 + r_out) - lt - log2(sp + r_in);
-    //                     pval += exp2(base);
-    //                 }
-    //             }
-
-    //             return std::min(1.0, pval);
-    //         };
-
-    //         if (r_in == r_out) {
-    //             return get_pval([&](int64_t s, int64_t, double, double) { return abs(argmin_d - s); });
-    //         } else {
-    //             return get_pval([&](int64_t s, int64_t t, double ls, double lt) {
-    //                 return ls * s - (r_in + s)*log2(r_in + s) + lt * t - (t + r_out)*log2(t + r_out);
-    //             });
-    //         }
-    //     };
-
-    //     if (config.test_type == "nbinom_exact") {
-    //         double r = r_in + r_out;
-    //         double lscaling_base = lgamma(r) / log(2);
-    //         compute_min_pval = [compute_min_pval_r,lscaling_base,r,r_in,r_out](int64_t n, const PairContainer&) {
-    //             return n > 0 ? compute_min_pval_r(r, r_in, r_out, lscaling_base, n) : 1.0;
-    //         };
-
-    //         compute_pval = [compute_pval_r,lscaling_base,r,r_in,r_out](int64_t in_sum, int64_t out_sum, const PairContainer &row) {
-    //             return row.size() ? compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row) : 1.0;
-    //         };
-    //     } else {
-    //         common::logger->trace("Enumerating row count distributions");
-    //         std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>> vector_counts;
-    //         {
-    //             std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
-
-    //             generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
-    //                 int64_t in_sum = 0;
-    //                 int64_t out_sum = 0;
-    //                 bool in_kmer = false;
-    //                 bool out_kmer = false;
-    //                 std::vector<int64_t> vec;
-    //                 if (kept[row_i]) {
-    //                     vec.reserve(raw_row.size());
-    //                     size_t count_in = 0;
-    //                     size_t count_out = 0;
-    //                     size_t total_count = 0;
-    //                     for (const auto &[j, raw_c] : raw_row) {
-    //                         if (min_counts.size() && raw_c < min_counts[j])
-    //                             continue;
-
-    //                         uint64_t c = raw_c;
-    //                         if (count_maps.size())
-    //                             c = count_maps[j].find(c)->second.first;
-
-    //                         if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-    //                             out_sum += c;
-    //                             ++count_out;
-    //                         }
-
-    //                         if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-    //                             in_sum += c;
-    //                             ++count_in;
-    //                         }
-
-    //                         total_count += groups[j] != Group::OTHER;
-
-    //                         vec.emplace_back(c);
-    //                     }
-
-    //                     if (total_count >= config.min_recurrence) {
-    //                         double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-    //                         in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-    //                         out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
-    //                     }
-    //                 } else {
-    //                     return;
-    //                 }
-
-    //                 if (!in_kmer && !out_kmer)
-    //                     return;
-
-    //                 size_t n = in_sum + out_sum;
-    //                 if (n >= vector_counts_ms[bucket_idx].size())
-    //                     vector_counts_ms[bucket_idx].resize(n + 1);
-
-    //                 std::sort(vec.begin(), vec.end());
-    //                 ++vector_counts_ms[bucket_idx][n][vec];
-    //             });
-
-    //             vector_counts = std::move(vector_counts_ms[0]);
-    //             size_t max_size = vector_counts.size();
-    //             for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-    //                 max_size = std::max(max_size, vector_counts_ms[j].size());
-    //             }
-    //             vector_counts.resize(max_size);
-
-    //             ProgressBar progress_bar(vector_counts.size() - 1, "Merging row", std::cerr, !common::get_verbose());
-    //             #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
-    //             for (size_t n = vector_counts.size(); n > 0; --n) {
-    //                 for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-    //                     if (n <= vector_counts_ms[j].size()) {
-    //                         for (const auto &[v, c] : vector_counts_ms[j][n - 1]) {
-    //                             vector_counts[n - 1][v] += c;
-    //                         }
-    //                     }
-    //                 }
-    //                 ++progress_bar;
-    //             }
-    //         }
-
-    //         common::logger->trace("Fitting LN prior distribution for dispersions");
-    //         double ln_mu = 0.0;
-    //         double ln_var = 0.0;
-
-    //         for (size_t n = 1; n < vector_counts.size(); ++n) {
-    //             size_t total = 0;
-    //             for (const auto &[v, c] : vector_counts[n]) {
-    //                 total += c;
-    //             }
-
-    //             double r = target_p / (1.0 - target_p) * n / total_labels;
-
-    //             #pragma omp atomic
-    //             ln_mu += -log(r) * total;
-
-    //             #pragma omp atomic
-    //             ln_var += log(r) * log(r) * total;
-    //         }
-
-    //         size_t total = nelem * total_labels;
-    //         ln_mu /= total;
-    //         ln_var = ln_var / total - ln_mu * ln_mu;
-
-    //         common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
-    //                               ln_mu, ln_var,
-    //                               exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1),
-    //                               exp(-ln_mu - ln_var/2));
-
-    //         auto get_rp = [ln_mu,ln_var,gs=total_labels](const auto &counts) {
-    //             double mu = 0;
-    //             double var = 0;
-    //             for (int64_t c : counts) {
-    //                 mu += c;
-    //                 var += c * c;
-    //             }
-    //             mu /= gs;
-
-    //             if (ln_var == 0) {
-    //                 double r = exp(-ln_mu);
-    //                 return std::make_pair(r, r / (r + mu));
-    //             }
-
-    //             double mu2 = mu * mu;
-    //             var = var / gs - mu2;
-
-    //             double a_guess = var > mu ? (var - mu) / mu2 : 1.0;
-
-    //             auto get_dl = [&](double a) {
-    //                 double r = 1.0 / a;
-    //                 double p = r / (r + mu);
-    //                 double lp = log(p);
-    //                 double dl = -a - a*(log(a)-ln_mu)/ln_var - gs*lp + boost::math::digamma(r)*counts.size();
-    //                 for (int64_t c : counts) {
-    //                     dl -= boost::math::digamma(r + c);
-    //                 }
-    //                 return dl;
-    //             };
-
-    //             double a_min = a_guess;
-    //             double a_max = a_guess;
-    //             while (true) {
-    //                 a_guess = (a_min + a_max) / 2;
-    //                 double dl_min = get_dl(a_min);
-    //                 double dl_max = get_dl(a_max);
-    //                 if (dl_min == 0) {
-    //                     a_max = a_min;
-    //                     break;
-    //                 }
-
-    //                 if (dl_max == 0) {
-    //                     a_min = a_max;
-    //                     break;
-    //                 }
-
-    //                 if (dl_min < 0)
-    //                     a_min /= 2;
-
-    //                 if (dl_max > 0)
-    //                     a_max *= 2;
-
-    //                 if (dl_min > 0 && dl_max < 0)
-    //                     break;
-    //             }
-
-    //             if (a_min < a_max) {
-    //                 std::tie(a_min, a_max) = boost::math::tools::bisect(
-    //                     get_dl,
-    //                     a_min, a_max, boost::math::tools::eps_tolerance<double>(5)
-    //                 );
-    //             }
-
-    //             double a = a_min;
-    //             double r = 1.0 / a;
-    //             double p = r / (r + mu);
-    //             return std::make_pair(r, p);
-    //         };
-
-    //         ProgressBar progress_bar(vector_counts.size() - 1, "Precomputing r's", std::cerr, !common::get_verbose());
-    //         #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
-    //         for (size_t n = 1; n < vector_counts.size(); ++n) {
-    //             if (n > 0 && n % 1000 == 0)
-    //                 progress_bar += 1000;
-
-    //             for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
-    //                 it.value() = get_rp(it->first).first;
-    //                 // #pragma omp critical
-    //                 // common::logger->info("N: {}\tr: {}", n, it->second);
-    //             }
-    //         }
-    //         progress_bar += (vector_counts.size() - 1) % 1000;
-
-    //         auto get_r = std::make_shared<std::function<double(int64_t, const PairContainer&)>>(
-    //             [vc=std::move(vector_counts)](int64_t n, const auto &row) {
-    //                 auto it = vc[n].begin();
-    //                 if (vc[n].size() > 1) {
-    //                     std::vector<int64_t> counts;
-    //                     counts.reserve(row.size());
-    //                     for (const auto &[j, c] : row) {
-    //                         counts.emplace_back(c);
-    //                     }
-    //                     std::sort(counts.begin(), counts.end());
-    //                     it = vc[n].find(counts);
-    //                 }
-    //                 return it->second;
-    //             }
-    //         );
-
-    //         compute_min_pval = [compute_min_pval_r,get_r,num_labels_in,num_labels_out](int64_t n, const PairContainer &row) {
-    //             if (row.empty())
-    //                 return 1.0;
-
-    //             double r_base = (*get_r)(n, row);
-    //             if (r_base == 0.0)
-    //                 return 1.0;
-
-    //             double r_in = r_base * num_labels_in;
-    //             double r_out = r_base * num_labels_out;
-    //             double r = r_in + r_out;
-    //             double lscaling_base = lgamma(r) / log(2);
-
-    //             return compute_min_pval_r(r, r_in, r_out, lscaling_base, n);
-    //         };
-
-    //         compute_pval = [compute_pval_r,get_r,num_labels_in,num_labels_out](int64_t in_sum, int64_t out_sum, const PairContainer &row) {
-    //             if (row.empty())
-    //                 return 1.0;
-
-    //             if (num_labels_in == num_labels_out && in_sum == out_sum)
-    //                 return 1.0;
-
-    //             int64_t n = in_sum + out_sum;
-    //             double r_base = (*get_r)(n, row);
-
-    //             if (r_base == 0.0)
-    //                 return 1.0;
-
-    //             double r_in = r_base * num_labels_in;
-    //             double r_out = r_base * num_labels_out;
-    //             double r = r_in + r_out;
-    //             double lscaling_base = lgamma(r) / log(2);
-
-    //             return compute_pval_r(r, r_in, r_out, lscaling_base, in_sum, out_sum, row);
-    //         };
-    //     }
-    // } else if (config.test_type == "lnb_exact") {
-    //     double max_sum = *std::max_element(sums.begin(), sums.end());
-    //     std::vector<double> scale_factor;
-    //     scale_factor.reserve(sums.size());
-    //     std::transform(sums.begin(), sums.end(), std::back_inserter(scale_factor),
-    //                    [&](uint64_t sum) -> double { return max_sum / sum; });
-
-    //     common::logger->trace("Enumerating row count distributions");
-    //     std::vector<tsl::hopscotch_map<std::vector<uint64_t>, double, utils::VectorHash>> vector_counts;
-    //     {
-    //         std::vector<std::vector<tsl::hopscotch_map<std::vector<uint64_t>, double, utils::VectorHash>>> vector_counts_ms(num_threads + 1);
-
-    //         generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
-    //             if (!kept[row_i])
-    //                 return;
-
-    //             uint64_t in_sum = 0;
-    //             uint64_t out_sum = 0;
-    //             bool in_kmer = false;
-    //             bool out_kmer = false;
-
-    //             std::vector<uint64_t> vec;
-    //             vec.resize(total_labels);
-    //             size_t count_in = 0;
-    //             size_t count_out = 0;
-    //             size_t total_count = 0;
-    //             for (const auto &[j, c] : raw_row) {
-    //                 if (min_counts.size() && c < min_counts[j])
-    //                     continue;
-
-    //                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-    //                     out_sum += c;
-    //                     ++count_out;
-    //                 }
-
-    //                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-    //                     in_sum += c;
-    //                     ++count_in;
-    //                 }
-
-    //                 total_count += groups[j] != Group::OTHER;
-
-    //                 vec[j] = c;
-    //             }
-
-    //             if (total_count >= config.min_recurrence) {
-    //                 in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence;
-    //                 out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence;
-    //             }
-
-    //             if (!in_kmer && !out_kmer)
-    //                 return;
-
-    //             // if floating point error puts the sum slightly above the integer value, then round down
-    //             size_t n = in_sum + out_sum;
-    //             if (n >= vector_counts_ms[bucket_idx].size())
-    //                 vector_counts_ms[bucket_idx].resize(n + 1);
-
-    //             std::sort(vec.begin(), vec.end());
-    //             ++vector_counts_ms[bucket_idx][n][vec];
-    //         });
-
-    //         vector_counts = std::move(vector_counts_ms[0]);
-    //         size_t max_size = vector_counts.size();
-    //         for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-    //             max_size = std::max(max_size, vector_counts_ms[j].size());
-    //         }
-    //         vector_counts.resize(max_size);
-
-    //         ProgressBar progress_bar(vector_counts.size() - 1, "Merging row", std::cerr, !common::get_verbose());
-    //         #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
-    //         for (size_t n = vector_counts.size(); n > 0; --n) {
-    //             for (size_t j = 1; j < vector_counts_ms.size(); ++j) {
-    //                 if (n <= vector_counts_ms[j].size()) {
-    //                     for (const auto &[v, c] : vector_counts_ms[j][n - 1]) {
-    //                         vector_counts[n - 1][v] += c;
-    //                     }
-    //                 }
-    //             }
-    //             ++progress_bar;
-    //         }
-    //     }
-
-    //     auto optim = [](const auto &f, auto x_min, auto x_max) {
-    //         // common::logger->info("start");
-    //         while (x_max - x_min > 1e-3) {
-    //             double x_mid = (x_min + x_max) / 2;
-    //             // common::logger->info("({},{}) -> {}", x_min, x_max, f(x_mid));
-    //             double x_low_mid = (x_min + x_mid) / 2;
-    //             double x_high_mid = (x_mid + x_max) / 2;
-    //             if (f(x_low_mid) > f(x_high_mid)) {
-    //                 x_max = x_mid;
-    //             } else {
-    //                 x_min = x_mid;
-    //             }
-    //         }
-    //         return (x_min + x_max) / 2;
-    //     };
-
-    //     common::logger->trace("Fitting LN prior distribution for dispersions");
-    //     double ln_mu = 0.0;
-    //     double ln_var = 0.0;
-
-    //     size_t skipped = 0;
-    //     for (size_t n = 0; n < vector_counts.size(); ++n) {
-    //         for (const auto &[v, c] : vector_counts[n]) {
-    //             double total = 0;
-    //             double total2 = 0;
-    //             for (size_t j = 0; j < v.size(); ++j) {
-    //                 total += v[j] * scale_factor[j];
-    //                 total2 += v[j] * scale_factor[j] * v[j] * scale_factor[j];
-    //             }
-    //             double mu = total / total_labels;
-    //             double mu2 = mu * mu;
-    //             double var = total2 / total_labels - mu2;
-    //             if (var <= mu) {
-    //                 ++skipped;
-    //                 continue;
-    //             }
-
-    //             double r = mu2 / (var - mu);
-    //             ln_mu += -log(r) * c;
-    //             ln_var += log(r) * log(r) * c;
-    //         }
-    //     }
-
-    //     if (skipped)
-    //         common::logger->warn("Skipped {} / {} rows", skipped, nelem);
-
-    //     ln_mu /= (nelem - skipped);
-    //     ln_var = ln_var / (nelem - skipped) - ln_mu * ln_mu;
-
-    //     common::logger->trace("LN fit: mu: {}\tvar: {}\tE[X]: {}\tVar(X): {}\t1.0/E[X]: {}",
-    //                           ln_mu, ln_var,
-    //                           exp(ln_mu + ln_var/2), exp(ln_mu*2+ln_var)*(exp(ln_var)-1),
-    //                           exp(-ln_mu - ln_var/2));
-
-    //     double expr = exp(-ln_mu + ln_var / 2);
-    //     double target_p = expr / (max_sum / nelem + expr);
-
-    //     {
-    //         auto calc_r = [optim,ln_mu,ln_var,gs=total_labels,p=target_p](const auto &counts) {
-    //             return optim([&](double r) {
-    //                 double val = log(p) * gs * r - lgamma(r) * counts.size() + log(r) - pow(-log(r)-ln_mu, 2.0)/2/ln_var;
-    //                 for (double c : counts) {
-    //                     val += lgamma(c + r);
-    //                 }
-    //                 return val;
-    //             }, 0.0, 100.0);
-    //         };
-
-    //         ProgressBar progress_bar(vector_counts.size(), "Precomputing r's", std::cerr, !common::get_verbose());
-    //         #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
-    //         for (size_t n = 0; n < vector_counts.size(); ++n) {
-    //             if (n > 0 && n % 1000 == 0)
-    //                 progress_bar += 1000;
-
-    //             for (auto it = vector_counts[n].begin(); it != vector_counts[n].end(); ++it) {
-    //                 std::vector<double> scale_counts;
-    //                 scale_counts.reserve(it->first.size());
-    //                 for (size_t j = 0; j < it->first.size(); ++j) {
-    //                     if (it->first[j] > 0)
-    //                         scale_counts.emplace_back(it->first[j] * scale_factor[j]);
-    //                 }
-    //                 it.value() = calc_r(scale_counts);
-    //             }
-    //         }
-    //         progress_bar += vector_counts.size() % 1000;
-    //     }
-
-    //     auto get_r = std::make_shared<std::function<double(int64_t, std::vector<uint64_t>&)>>(
-    //         [vc=std::move(vector_counts),scale_factor](int64_t n, const auto &counts) {
-    //             auto it = vc[n].begin();
-    //             if (vc[n].size() > 1) {
-    //                 it = vc[n].find(counts);
-    //                 assert(it != vc[n].end());
-    //             }
-    //             return it->second;
-    //         }
-    //     );
-
-    //     compute_min_pval = [get_r,num_labels_in,num_labels_out,scale_factor,total_labels](int64_t, const PairContainer &row) {
-    //         if (row.empty())
-    //             return 1.0;
-
-    //         int64_t sum = 0;
-    //         double ssum = 0;
-    //         std::vector<double> counts;
-    //         std::vector<uint64_t> row_int(total_labels);
-    //         counts.reserve(row.size());
-    //         for (const auto &[j, c] : row) {
-    //             row_int[j] = c;
-    //             sum += c;
-    //             ssum += counts.emplace_back(c * scale_factor[j]);
-    //         }
-    //         // if floating point error puts the sum slightly above the integer value, then round down
-    //         int64_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
-
-    //         double r_base = (*get_r)(sum, row_int);
-    //         if (r_base == 0.0)
-    //             return 1.0;
-
-    //         double r_in = r_base * num_labels_in;
-    //         double r_out = r_base * num_labels_out;
-    //         double r = r_in + r_out;
-    //         double lscaling_base = lgamma(r) / log(2);
-
-    //         double half_div0 = -r_in * log2(r_in) - (n + r_out) * log2(n + r_out);
-    //         double half_divn = -r_out * log2(r_out) - (n + r_in) * log2(n + r_in);
-
-    //         double pval = 0.0;
-    //         double lscaling = lscaling_base - lgamma(r + n) / log(2);
-    //         if (half_div0 >= half_divn)
-    //             pval += exp2(lscaling + (lgamma(r_out + n) - lgamma(r_out)) / log(2));
-
-    //         if (half_divn >= half_div0)
-    //             pval += exp2(lscaling + (lgamma(r_in + n) - lgamma(r_in)) / log(2));
-
-    //         return std::min(1.0, pval);
-    //     };
-
-    //     compute_pval = [get_r,num_labels_in,num_labels_out,scale_factor,groups,total_labels](int64_t, int64_t, const PairContainer &row) {
-    //         if (row.empty())
-    //             return 1.0;
-
-    //         int64_t sum = 0;
-    //         double ssum = 0;
-    //         double in_sum = 0;
-    //         double out_sum = 0;
-    //         std::vector<double> counts;
-    //         std::vector<uint64_t> row_int(total_labels);
-    //         counts.reserve(row.size());
-    //         for (const auto &[j, c] : row) {
-    //             row_int[j] = c;
-    //             sum += c;
-    //             ssum += counts.emplace_back(c * scale_factor[j]);
-    //             if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-    //                 out_sum += counts.back();
-    //             }
-
-    //             if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-    //                 in_sum += counts.back();
-    //             }
-    //         }
-    //         // if floating point error puts the sum slightly above the integer value, then round down
-    //         int64_t n = sum - int(sum) < 1e-5 ? floor(sum) : ceil(sum);
-
-    //         double r_base = (*get_r)(sum, row_int);
-
-    //         if (r_base == 0.0)
-    //             return 1.0;
-
-    //         double r_in = r_base * num_labels_in;
-    //         double r_out = r_base * num_labels_out;
-    //         double r = r_in + r_out;
-    //         double lscaling_base = lgamma(r) / log(2);
-
-    //         double argmin_d = r_in / r * n;
-    //         if (in_sum == argmin_d)
-    //             return 1.0;
-
-    //         double lscaling = lscaling_base - lgamma(r + n) / log(2);
-
-    //         auto get_pval = [&](const auto &get_stat) {
-    //             double base_stat = get_stat(in_sum, out_sum,
-    //                                         r_in != r_out && in_sum > 0 ? log2(in_sum) : 0.0,
-    //                                         r_in != r_out && out_sum > 0 ? log2(out_sum) : 0.0);
-    //             double pval = 0.0;
-    //             int64_t s = 0;
-    //             int64_t t = n;
-    //             double ls = 0;
-    //             double lt = log2(t);
-    //             if (get_stat(s, t, ls, lt) >= base_stat) {
-    //                 double base = lscaling + (lgamma(n + r_out) - lgamma(r_out)) / log(2);
-    //                 pval += exp2(base);
-    //                 for (++s,--t; s <= n; ++s,--t) {
-    //                     ls = log2(s);
-    //                     lt = t > 0 ? log2(t) : 0.0;
-    //                     if (get_stat(s, t, ls, lt) < base_stat)
-    //                         break;
-
-    //                     base += log2(t + 1) + log2(s - 1 + r_in) - ls - log2(t + r_out);
-    //                     pval += exp2(base);
-    //                 }
-    //             }
-
-    //             int64_t sp = n;
-    //             t = 0;
-    //             ls = log2(sp);
-    //             lt = 0;
-    //             if (get_stat(sp, t, ls, lt) >= base_stat) {
-    //                 double base = lscaling + (lgamma(n + r_in) - lgamma(r_in)) / log(2);
-    //                 pval += exp2(base);
-    //                 for (--sp,++t; sp >= s; --sp,++t) {
-    //                     ls = sp > 0 ? log2(sp) : 0.0;
-    //                     lt = log2(t);
-    //                     if (get_stat(sp, t, ls, lt) < base_stat)
-    //                         break;
-
-    //                     base += log2(sp + 1) + log2(t - 1 + r_out) - lt - log2(sp + r_in);
-    //                     pval += exp2(base);
-    //                 }
-    //             }
-
-    //             return std::min(1.0, pval);
-    //         };
-
-    //         if (r_in == r_out) {
-    //             return get_pval([&](int64_t s, int64_t, double, double) { return abs(argmin_d - s); });
-    //         } else {
-    //             return get_pval([&](int64_t s, int64_t t, double ls, double lt) {
-    //                 return ls * s - (r_in + s)*log2(r_in + s) + lt * t - (t + r_out)*log2(t + r_out);
-    //             });
-    //         }
-    //     };
-
-    // } else if (config.test_type == "poisson_binom") {
-    //     // fit distribution
-    //     std::vector<double> p;
-    //     p.reserve(groups.size());
-    //     for (size_t i = 0; i < groups.size(); ++i) {
-    //         p.emplace_back(static_cast<double>(n_kmers[i]) / nelem);
-    //     }
-
-    //     common::logger->trace("p: {}", fmt::join(p, ","));
-
-    //     common::logger->trace("Precomputing PMFs");
-    //     std::vector<double> pmf_in { 1.0 };
-    //     std::vector<double> pmf_out { 1.0 };
-    //     std::vector<double> pmf_null { 1.0 };
-
-    //     for (size_t i = 1; i <= groups.size(); ++i) {
-    //         if (groups[i - 1] == Group::OUT || groups[i - 1] == Group::BOTH) {
-    //             std::vector<double> pmf_out_cur(pmf_out.size() + 1);
-    //             pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
-    //             pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
-    //             for (size_t k = 1; k < pmf_out.size(); ++k) {
-    //                 pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1] + (1.0 - p[i - 1]) * pmf_out[k];
-    //             }
-    //             std::swap(pmf_out_cur, pmf_out);
-    //         }
-
-    //         if (groups[i - 1] == Group::IN || groups[i - 1] == Group::BOTH) {
-    //             std::vector<double> pmf_in_cur(pmf_in.size() + 1);
-    //             pmf_in_cur[0] = (1.0 - p[i - 1]) * pmf_in[0];
-    //             pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
-    //             for (size_t k = 1; k < pmf_in.size(); ++k) {
-    //                 pmf_in_cur[k] = p[i - 1] * pmf_in[k - 1] + (1.0 - p[i - 1]) * pmf_in[k];
-    //             }
-    //             std::swap(pmf_in_cur, pmf_in);
-    //         }
-    //         std::vector<double> pmf_null_cur(i + 1);
-    //         pmf_null_cur[0] = (1.0 - p[i - 1]) * pmf_null[0];
-    //         pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
-    //         for (size_t k = 1; k < pmf_null.size(); ++k) {
-    //             pmf_null_cur[k] = p[i - 1] * pmf_null[k - 1] + (1.0 - p[i - 1]) * pmf_null[k];
-    //         }
-    //         std::swap(pmf_null_cur, pmf_null);
-    //     }
-
-    //     if (pmf_in.size() != num_labels_in + 1) {
-    //         common::logger->error("PMF in wrong: {} != {}", pmf_in.size(), num_labels_in + 1);
-    //         throw std::domain_error("");
-    //     }
-
-    //     if (pmf_out.size() != num_labels_out + 1) {
-    //         common::logger->error("PMF out wrong: {} != {}", pmf_out.size(), num_labels_out + 1);
-    //         throw std::domain_error("");
-    //     }
-
-    //     if (pmf_null.size() != num_labels_in + num_labels_out + 1) {
-    //         common::logger->error("PMF null wrong: {} != {}", pmf_null.size(), num_labels_in + num_labels_out + 1);
-    //         throw std::domain_error("");
-    //     }
-
-    //     common::logger->trace("Precomputing p-values");
-    //     std::vector<std::vector<double>> pvals(num_labels_in + num_labels_out + 1);
-    //     std::vector<double> mid_points(num_labels_in + num_labels_out + 1);
-    //     mid_points.emplace_back(0);
-    //     pvals[0].emplace_back(1.0);
-
-    //     double min_pval = 1.0;
-
-    //     for (size_t n = 1; n < pvals.size(); ++n) {
-    //         std::vector<double> probs;
-    //         size_t front = n - std::min(n, num_labels_out);
-    //         for (uint64_t s = 0; s < pmf_in.size(); ++s) {
-    //             if (s > n)
-    //                 break;
-    //             uint64_t t = n - s;
-    //             if (s < pmf_in.size() && t < pmf_out.size()) {
-    //                 if (s < front) {
-    //                     common::logger->error("Attempting non-zero p-value in impossible configuration: {},{}", n, s);
-    //                     throw std::domain_error("");
-    //                 }
-    //                 probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t]) - log2(pmf_null[n])));
-    //             } else {
-    //                 probs.emplace_back(0.0);
-    //             }
-    //         }
-
-    //         for (uint64_t s = 0; s < probs.size(); ++s) {
-    //             pvals[n].emplace_back(0.0);
-    //             if (s > n)
-    //                 break;
-    //             uint64_t t = n - s;
-    //             if (s >= pmf_in.size() || t >= pmf_out.size())
-    //                 continue;
-    //             for (uint64_t sp = 0; sp < probs.size(); ++sp) {
-    //                 if (sp > n)
-    //                     break;
-    //                 uint64_t tp = n - sp;
-    //                 if (sp >= pmf_in.size() || tp >= pmf_out.size())
-    //                     continue;
-    //                 if (probs[sp] <= probs[s])
-    //                     pvals[n][s] += probs[sp];
-    //             }
-    //         }
-
-    //         for (uint64_t s = 0; s < front; ++s) {
-    //             if (pvals[n][s] > 0) {
-    //                 common::logger->error("Non-zero p-value in impossible configuration: {},{}", n, s);
-    //                 throw std::domain_error("");
-    //             }
-    //         }
-    //         double local_min_pval = std::min(pvals[n][front],
-    //                                          pvals[n].back());
-    //         min_pval = std::min(min_pval, local_min_pval);
-
-    //         size_t max_prob_counts = 0;
-    //         double max_prob_pos = 0;
-    //         // double max_prob = -1;
-
-    //         for (uint64_t s = front; s < pvals[n].size(); ++s) {
-    //             if (pvals[n][s] < local_min_pval) {
-    //                 common::logger->error("Min p-value not at boundary: {},{}: {} < {}", n, s, pvals[n][s], local_min_pval);
-    //                 throw std::domain_error("");
-    //             }
-    //         }
-    //         if (front + 1 == pvals[n].size()) {
-    //             max_prob_counts = 1;
-    //             max_prob_pos = front;
-    //         } else if (front + 2 == pvals[n].size()) {
-    //             max_prob_counts = 2;
-    //             max_prob_pos = front + front + 1;
-    //         } else {
-    //             for (uint64_t s = front + 1; s < pvals[n].size(); ++s) {
-    //                 if (probs[s] == probs[s-1]) {
-    //                     max_prob_counts = 2;
-    //                     max_prob_pos = s + s;
-    //                     break;
-    //                 }
-
-    //                 if (s + 1 < pvals[n].size()) {
-    //                     if (probs[s-1] < probs[s] && probs[s] < probs[s+1])
-    //                         continue;
-
-    //                     if (probs[s-1] > probs[s] && probs[s] > probs[s+1])
-    //                         continue;
-
-    //                     if (probs[s] > probs[s-1] && probs[s] > probs[s+1]) {
-    //                         if (probs[s] - probs[s-1] == probs[s]-probs[s+1]) {
-    //                             max_prob_counts = 1;
-    //                             max_prob_pos = s;
-    //                         } else if (probs[s] - probs[s-1] > probs[s]-probs[s+1]) {
-    //                             max_prob_counts = 2;
-    //                             max_prob_pos = s + s + 1;
-    //                         } else {
-    //                             max_prob_counts = 2;
-    //                             max_prob_pos = s + s - 1;
-    //                         }
-    //                     }
-    //                 }
-    //             }
-    //         }
-
-    //         if (max_prob_counts == 0) {
-    //             max_prob_counts = 2;
-    //             max_prob_pos = front - 1 + front;
-    //         }
-
-    //         mid_points[n] = static_cast<double>(max_prob_pos) / max_prob_counts;
-    //         common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}",
-    //                               n, mid_points[n],
-    //                               fmt::join(probs,","),
-    //                               fmt::join(pvals[n],","));
-    //     }
-
-    //     common::logger->trace("Min. p-value: {}", min_pval);
-    //     if (min_pval >= config.family_wise_error_rate) {
-    //         common::logger->warn("No significant p-values achievable");
-    //         auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
-    //             graph_ptr, [](node_index) { return false; }, true,
-    //             is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    //         );
+    if (config.test_by_unitig) {
+        common::logger->trace("Combining p-values for unitigs");
+        std::mutex m_vec_mu;
+        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+            double pval_min = 1.0;
+            {
+                size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), path[0]) - offsets.begin() - 1;
+                std::lock_guard<std::mutex> lock(m_vec_mu);
+                pval_min = get<PValStorage>(pvals_min_buckets[bucket_idx][path[0] - offsets[bucket_idx]]);
+            }
 
-    //         auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
-    //             graph_ptr, [](node_index) { return false; }, true,
-    //             is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    //         );
+            if (pval_min * k_min >= config.family_wise_error_rate)
+                return;
 
-    //         return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage{}, nullptr);
-    //     }
+            std::vector<double> pvals;
+            pvals.reserve(path.size());
+            double comb_eff_size = 0.0;
+            {
+                std::lock_guard<std::mutex> lock(m_vec_mu);
+                for (node_index node : path) {
+                    size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), node) - offsets.begin() - 1;
+                    pvals.emplace_back(get<PValStorage>(pvals_buckets[bucket_idx][node - offsets[bucket_idx]]));
+                    comb_eff_size += get<PValStorage>(eff_size_buckets[bucket_idx][node - offsets[bucket_idx]]);
+                }
+            }
+            double comp_pval = combine_pvals(pvals);
+            if (comp_pval * k >= config.family_wise_error_rate || comb_eff_size == 0.0)
+                return;
 
-    //     compute_min_pval = [pvals,num_labels_out,&groups](int64_t, const PairContainer &row) {
-    //         size_t n = 0;
-    //         for (const auto &[j, c] : row) {
-    //             n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
-    //         }
-    //         size_t front = n - std::min(n, num_labels_out);
-    //         return std::min(pvals[n][front],
-    //                         pvals[n].back());
-    //     };
-
-    //     compute_pval = [pvals,mid_points,&groups](int64_t, int64_t, const auto &row) {
-    //         int64_t n = 0;
-    //         uint64_t s = 0;
-    //         for (const auto &[j, c] : row) {
-    //             n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
-    //             s += (groups[j] == Group::IN);
-    //         }
+            auto &indicator = comb_eff_size > 0 ? indicator_in : indicator_out;
+            for (node_index node : path) {
+                set_bit(indicator.data(), node, true, std::memory_order_relaxed);
+            }
+        });
 
-    //         return pvals[n][s];
-    //     };
+        pvals_min_buckets.resize(0);
+        tmp_min_buckets.resize(0);
+        pvals_buckets.resize(0);
+        tmp_buckets.resize(0);
+        eff_size_buckets.resize(0);
+        tmp_eff_buckets.resize(0);
+    }
 
-    // } else if (config.test_type == "notest") {
-    //     compute_min_pval = [&](int64_t n, const PairContainer&) { return n > 0 ? 0.0 : 1.0; };
-    //     compute_pval = [&](int64_t, int64_t, const auto &row) { return row.size() ? 0.0 : 1.0; };
-    // } else {
-    //     throw std::runtime_error("Test not implemented");
-    // }
+    std::atomic_thread_fence(std::memory_order_acquire);
 
     // common::logger->trace("Computing minimum p-values");
     // std::vector<std::pair<double, size_t>> m;

From 4d87c98289e085ff34e10c5242934e79c8e551c0 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 20 Feb 2025 21:17:52 +0100
Subject: [PATCH 372/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 121 ++++++++++--------
 1 file changed, 65 insertions(+), 56 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b6c19f4c77..c0f2f7a01c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -112,7 +112,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Computing histogram from uncleaned counts");
     auto hists_map = get_hist_map(std::vector<size_t>(groups.size(), 1), nullptr);
 
-    sdsl::bit_vector kept(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), true);
+    sdsl::bit_vector kept_bv(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), true);
 
     std::vector<size_t> min_counts(groups.size(), config.min_count);
     std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
@@ -174,9 +174,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Updating histogram and marking discarded k-mers");
 
     if (groups.size())
-        hists_map = get_hist_map(min_counts, &kept);
+        hists_map = get_hist_map(min_counts, &kept_bv);
 
-    size_t nelem = sdsl::util::cnt_one_bits(kept);
+    bit_vector_stat kept(std::move(kept_bv));
+    size_t nelem = kept.num_set_bits();
 
     std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
     if (config.test_type != "notest" && config.test_by_unitig) {
@@ -274,13 +275,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     if (config.test_type != "notest" && config.test_by_unitig) {
         if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
             auto &pvals = pvals_buckets.emplace_back();
-            pvals.resize(graph_ptr->max_index() + 1);
+            pvals.reserve(graph_ptr->max_index());
 
             auto &pvals_min = pvals_min_buckets.emplace_back();
-            pvals_min.resize(graph_ptr->max_index() + 1);
+            pvals_min.reserve(graph_ptr->max_index());
 
             auto &eff_size = eff_size_buckets.emplace_back();
-            eff_size.resize(graph_ptr->max_index() + 1);
+            eff_size.reserve(graph_ptr->max_index());
         }
 
         if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
@@ -288,19 +289,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
                 pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
             }
-            pvals_buckets[0].push_back(0);
 
             for (size_t i = 0; i < get_num_threads() + 1; ++i) {
                 auto &tmp_file = tmp_min_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
                 pvals_min_buckets.emplace_back(tmp_file->name(), std::ios::out);
             }
-            pvals_min_buckets[0].push_back(0);
 
             for (size_t i = 0; i < get_num_threads() + 1; ++i) {
                 auto &tmp_file_eff = tmp_eff_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
                 eff_size_buckets.emplace_back(tmp_file_eff->name(), std::ios::out);
             }
-            eff_size_buckets[0].push_back(0);
         }
     }
 
@@ -686,7 +684,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 if (n > 0) {
                     double p = mu1 / (mu1 + mu2);
-                    auto bdist = boost::math::binomial(n, p);
+                    boost::math::binomial bdist(n, p);
                     double pval0 = boost::math::pdf(bdist, 0);
                     double pvaln = boost::math::pdf(bdist, n);
                     pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
@@ -714,15 +712,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 {
                     std::lock_guard<std::mutex> lock(m_vec_mu);
                     for (node_index node : path) {
-                        size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), node) - offsets.begin() - 1;
-                        pvals.emplace_back(get<PValStorage>(pvals_min_buckets[bucket_idx][node - offsets[bucket_idx]]));
+                        size_t row = AnnotatedDBG::graph_to_anno_index(node);
+                        size_t row_rank = kept.rank1(row);
+                        size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
+                        size_t offset = row_rank - offsets[bucket_idx];
+                        pvals.emplace_back(get<PValStorage>(pvals_min_buckets[bucket_idx][offset]));
                     }
                 }
                 double comp_min_pval = combine_pvals(pvals);
-                size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), path[0]) - offsets.begin() - 1;
+                size_t row = AnnotatedDBG::graph_to_anno_index(path[0]);
+                size_t row_rank = kept.rank1(row);
+                size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
+                size_t offset = row_rank - offsets[bucket_idx];
                 ++ms[bucket_idx][comp_min_pval];
                 std::lock_guard<std::mutex> lock(m_vec_mu);
-                set<PValStorage>(pvals_min_buckets[bucket_idx][path[0] - offsets[bucket_idx]], comp_min_pval);
+                set<PValStorage>(pvals_min_buckets[bucket_idx][offset], comp_min_pval);
             });
         }
 
@@ -830,51 +834,50 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         }
 
-        if (n == 0)
-            return;
-
+        double pval = 1.0;
         double p = mu1 / (mu1 + mu2);
-        auto bdist = boost::math::binomial(n, p);
-
-        double pval0 = boost::math::pdf(bdist, 0);
-        double pvaln = boost::math::pdf(bdist, n);
-        double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-        if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
-            return;
+        boost::math::binomial bdist(n, p);
+        if (n != 0) {
+            double pval0 = boost::math::pdf(bdist, 0);
+            double pvaln = boost::math::pdf(bdist, n);
+            double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+            if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
+                return;
 
-        auto get_deviance = [&](double y, double mu) {
-            y += 1e-8;
-            mu += 1e-8;
-            return 2 * (y * log(y/mu) - y + mu);
-        };
+            auto get_deviance = [&](double y, double mu) {
+                y += 1e-8;
+                mu += 1e-8;
+                return 2 * (y * log(y/mu) - y + mu);
+            };
 
-        std::vector<double> devs;
-        devs.reserve(n + 1);
-        for (int64_t s = 0; s <= n; ++s) {
-            devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-        }
+            std::vector<double> devs;
+            devs.reserve(n + 1);
+            for (int64_t s = 0; s <= n; ++s) {
+                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+            }
 
-        double pval = 0.0;
+            pval = 0.0;
 
-        int64_t s = 0;
-        for ( ; s <= n; ++s) {
-            if (devs[s] < devs[in_sum])
-                break;
-        }
-        if (s > 0)
-            pval += boost::math::cdf(bdist, s - 1);
+            int64_t s = 0;
+            for ( ; s <= n; ++s) {
+                if (devs[s] < devs[in_sum])
+                    break;
+            }
+            if (s > 0)
+                pval += boost::math::cdf(bdist, s - 1);
 
-        int64_t sp = n;
-        for ( ; sp >= s; --sp) {
-            if (devs[sp] < devs[in_sum])
-                break;
-        }
+            int64_t sp = n;
+            for ( ; sp >= s; --sp) {
+                if (devs[sp] < devs[in_sum])
+                    break;
+            }
 
-        if (sp < n)
-            pval += boost::math::cdf(boost::math::complement(bdist, sp));
+            if (sp < n)
+                pval += boost::math::cdf(boost::math::complement(bdist, sp));
 
-        if (!config.test_by_unitig && pval * k >= config.family_wise_error_rate)
-            return;
+            if (!config.test_by_unitig && pval * k >= config.family_wise_error_rate)
+                return;
+        }
 
         double eff_size = static_cast<double>(in_sum) - boost::math::mode(bdist);
         set_pval(pval, eff_size);
@@ -886,9 +889,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
             double pval_min = 1.0;
             {
-                size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), path[0]) - offsets.begin() - 1;
+                size_t row = AnnotatedDBG::graph_to_anno_index(path[0]);
+                size_t row_rank = kept.rank1(row);
+                size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
+                size_t offset = row_rank - offsets[bucket_idx];
                 std::lock_guard<std::mutex> lock(m_vec_mu);
-                pval_min = get<PValStorage>(pvals_min_buckets[bucket_idx][path[0] - offsets[bucket_idx]]);
+                pval_min = get<PValStorage>(pvals_min_buckets[bucket_idx][offset]);
             }
 
             if (pval_min * k_min >= config.family_wise_error_rate)
@@ -900,9 +906,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             {
                 std::lock_guard<std::mutex> lock(m_vec_mu);
                 for (node_index node : path) {
-                    size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), node) - offsets.begin() - 1;
-                    pvals.emplace_back(get<PValStorage>(pvals_buckets[bucket_idx][node - offsets[bucket_idx]]));
-                    comb_eff_size += get<PValStorage>(eff_size_buckets[bucket_idx][node - offsets[bucket_idx]]);
+                    size_t row = AnnotatedDBG::graph_to_anno_index(node);
+                    size_t row_rank = kept.rank1(row);
+                    size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
+                    size_t offset = row_rank - offsets[bucket_idx];
+                    pvals.emplace_back(get<PValStorage>(pvals_buckets[bucket_idx][offset]));
+                    comb_eff_size += get<PValStorage>(eff_size_buckets[bucket_idx][offset]);
                 }
             }
             double comp_pval = combine_pvals(pvals);

From 6bd351a1c577b1965be852722b34092d6594d3a3 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 24 Feb 2025 18:46:06 +0100
Subject: [PATCH 373/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 275 +++++++++++++-----
 1 file changed, 209 insertions(+), 66 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c0f2f7a01c..c1a9f16992 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -363,9 +363,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         });
     };
 
-    // precompute negative binomial fits for poisson_bayes test
+    // precompute negative binomial fits for poisson_bayes and nbinom_exact tests
     std::vector<std::pair<double, double>> nb_params(groups.size());
-    if (config.test_type == "poisson_bayes") {
+    std::pair<double, double> nb_params_a;
+    std::pair<double, double> nb_params_b;
+    std::pair<double, double> nb_params_null;
+    double nb_base = 0.0;
+    if (config.test_type == "poisson_bayes" || config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
         auto get_rp = [&](const auto &generate) {
             double mu = 0;
@@ -445,6 +449,44 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                     j, sums[j], (hist.end() - 1)->first, r * (1-p)/p, r*(1-p)/p/p, r, p);
             }
         }
+
+        if (config.test_type == "nbinom_exact") {
+            double mu_a = 0.0;
+            double mu_b = 0.0;
+            double var_a = 0.0;
+            double var_b = 0.0;
+            for (size_t j = 0; j < groups.size(); ++j) {
+                const auto &[r, p] = nb_params[j];
+                double mu = r * (1.0 - p) / p;
+                double var = mu / p;
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                    mu_a += mu;
+                    var_a += var;
+                }
+
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                    mu_b += mu;
+                    var_b += var;
+                }
+            }
+
+            nb_params_a.first = mu_a * mu_a / (var_a - mu_a);
+            nb_params_a.second = mu_a / var_a;
+            common::logger->trace("In: r: {}\tp: {}", nb_params_a.first, nb_params_a.second);
+
+            nb_params_b.first = mu_b * mu_b / (var_b - mu_b);
+            nb_params_b.second = mu_b / var_b;
+            common::logger->trace("Out: r: {}\tp: {}", nb_params_b.first, nb_params_b.second);
+
+            double mu_null = mu_a + mu_b;
+            double var_null = var_a + var_b; // the two are independent, so their covariance is 0
+            nb_params_null.first = mu_null * mu_null / (var_null - mu_null);
+            nb_params_null.second = mu_null / var_null;
+            common::logger->trace("Null: r: {}\tp: {}", nb_params_null.first, nb_params_null.second);
+
+            nb_base = (lgamma(nb_params_null.first) - lgamma(nb_params_a.first) - lgamma(nb_params_b.first)) / log(2.0);
+            nb_base += nb_params_a.first*log2(nb_params_a.second) + nb_params_b.first*log2(nb_params_b.second)-nb_params_null.first*log2(nb_params_null.second);
+        }
     }
 
     // precompute p-values for poisson_binom test
@@ -661,7 +703,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
                 size_t front = n - std::min(n, num_labels_out);
                 pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
-            } else {
+            } else if (config.test_type == "poisson_exact" || config.test_type == "poisson_bayes") {
                 double mu1 = 0.0;
                 double mu2 = 0.0;
                 if (config.test_type == "poisson_exact") {
@@ -689,6 +731,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     double pvaln = boost::math::pdf(bdist, n);
                     pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
                 }
+            } else if (config.test_type == "nbinom_exact") {
+                int64_t n = 0;
+                for (const auto &[j, c] : row) {
+                    n += c;
+                }
+
+                if (n > 0) {
+                    auto [r_n, p_n] = nb_params_null;
+                    auto [r_a, p_a] = nb_params_a;
+                    auto [r_b, p_b] = nb_params_b;
+
+                    double base = nb_base + (lgamma(n + 1) - lgamma(r_n + n)) / log(2.0) - n*log2(1.0-p_n);
+                    auto get_pval = [&](int64_t s) {
+                        int64_t t = n - s;
+                        double sbase = (lgamma(r_a+s)+lgamma(r_b+t)-lgamma(s+1)-lgamma(t+1)) / log(2.0);
+                        return exp2(base + sbase +s*log2(1.0-p_a) + t*log2(1.0-p_b));
+                    };
+
+                    double pval0 = get_pval(0);
+                    double pvaln = get_pval(n);
+                    pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                }
             }
 
             if (!config.test_by_unitig) {
@@ -789,6 +853,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         };
 
+        double eff_size = 0.0;
+        double pval = 1.0;
         if (config.test_type == "poisson_binom") {
             size_t n = 0;
             uint64_t s = 0;
@@ -802,84 +868,161 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
                 return;
 
-            double pval = pb_pvals[n][s];
-            double eff_size = static_cast<double>(s) - mid_points[n];
-            set_pval(pval, eff_size);
-            return;
-        }
-
-        double mu1 = 0.0;
-        double mu2 = 0.0;
-        if (config.test_type == "poisson_exact") {
-            mu1 = static_cast<double>(in_kmers) / nelem;
-            mu2 = static_cast<double>(out_kmers) / nelem;
-        }
-        int64_t n = 0;
-        int64_t in_sum = 0;
-        int64_t out_sum = 0;
-        for (const auto &[j, c] : row) {
-            n += c;
-            const auto &[r, p] = nb_params[j];
-            double lambda_j = config.test_type == "poisson_bayes"
-                ? sqrt((1.0 - p) * (r - 1.0 + c))
-                : 0.0;
-            if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                mu2 += lambda_j;
-                out_sum += c;
+            pval = pb_pvals[n][s];
+            eff_size = static_cast<double>(s) - mid_points[n];
+        } else if (config.test_type == "poisson_exact" || config.test_type == "poisson_bayes") {
+            double mu1 = 0.0;
+            double mu2 = 0.0;
+            if (config.test_type == "poisson_exact") {
+                mu1 = static_cast<double>(in_kmers) / nelem;
+                mu2 = static_cast<double>(out_kmers) / nelem;
             }
+            int64_t n = 0;
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+            // sdsl::bit_vector found(num_labels_in + num_labels_out);
+            for (const auto &[j, c] : row) {
+                n += c;
+                // double lambda_j = 0.0;
+                // if (config.test_type == "poisson_bayes") {
+                //     const auto &[r, p] = nb_params[j];
+                //     lambda_j = (1.0 - p) * (r - 1.0 + c);
+                //     found[j] = true;
+                // }
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                    // mu2 += lambda_j;
+                    out_sum += c;
+                }
 
-            if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                mu1 += lambda_j;
-                in_sum += c;
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                    // mu1 += lambda_j;
+                    in_sum += c;
+                }
+            }
+            if (config.test_type == "poisson_bayes") {
+                size_t unitig_id = kmer_to_unitig[AnnotatedDBG::anno_to_graph_index(row_i)];
+                auto &[unitig_size, unitig] = unitig_sums[unitig_id];
+                for (size_t j = 0; j < num_labels_in + num_labels_out; ++j) {
+                    int64_t unitig_sum = unitig[j];
+                    const auto &[r, p] = nb_params[j];
+                    double lambda_j = unitig_sum > 0 || r > 1
+                        ? (r - 1 + unitig_sum) / (unitig_size + p / (1.0 - p))
+                        : 0.0;
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                        mu2 += lambda_j;
+
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                        mu1 += lambda_j;
+                }
+                // for (size_t j = 0; j < found.size(); ++j) {
+                //     if (!found[j]) {
+                //         const auto &[r, p] = nb_params[j];
+                //         if (r > 1.0) {
+                //             double lambda_j = (1.0 - p) * (r - 1);
+                //             if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                //                 mu2 += lambda_j;
+
+                //             if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                //                 mu1 += lambda_j;
+                //         }
+                //     }
+                // }
             }
-        }
 
-        double pval = 1.0;
-        double p = mu1 / (mu1 + mu2);
-        boost::math::binomial bdist(n, p);
-        if (n != 0) {
-            double pval0 = boost::math::pdf(bdist, 0);
-            double pvaln = boost::math::pdf(bdist, n);
-            double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-            if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
-                return;
+            double p = mu1 / (mu1 + mu2);
+            boost::math::binomial bdist(n, p);
+            if (n != 0) {
+                double pval0 = boost::math::pdf(bdist, 0);
+                double pvaln = boost::math::pdf(bdist, n);
+                double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
+                    return;
+
+                auto get_deviance = [&](double y, double mu) {
+                    y += 1e-8;
+                    mu += 1e-8;
+                    return 2 * (y * log(y/mu) - y + mu);
+                };
+
+                std::vector<double> devs;
+                devs.reserve(n + 1);
+                for (int64_t s = 0; s <= n; ++s) {
+                    devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+                }
 
-            auto get_deviance = [&](double y, double mu) {
-                y += 1e-8;
-                mu += 1e-8;
-                return 2 * (y * log(y/mu) - y + mu);
-            };
+                pval = 0.0;
 
-            std::vector<double> devs;
-            devs.reserve(n + 1);
-            for (int64_t s = 0; s <= n; ++s) {
-                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-            }
+                int64_t s = 0;
+                for ( ; s <= n; ++s) {
+                    if (devs[s] < devs[in_sum])
+                        break;
+                }
+                if (s > 0)
+                    pval += boost::math::cdf(bdist, s - 1);
 
-            pval = 0.0;
+                int64_t sp = n;
+                for ( ; sp >= s; --sp) {
+                    if (devs[sp] < devs[in_sum])
+                        break;
+                }
 
-            int64_t s = 0;
-            for ( ; s <= n; ++s) {
-                if (devs[s] < devs[in_sum])
-                    break;
+                if (sp < n)
+                    pval += boost::math::cdf(boost::math::complement(bdist, sp));
+
+                if (!config.test_by_unitig && pval * k >= config.family_wise_error_rate)
+                    return;
             }
-            if (s > 0)
-                pval += boost::math::cdf(bdist, s - 1);
 
-            int64_t sp = n;
-            for ( ; sp >= s; --sp) {
-                if (devs[sp] < devs[in_sum])
-                    break;
+            eff_size = static_cast<double>(in_sum) - boost::math::mode(bdist);
+        } else if (config.test_type == "nbinom_exact") {
+            int64_t n = 0;
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+            for (const auto &[j, c] : row) {
+                n += c;
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                    out_sum += c;
+                }
+
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                    in_sum += c;
+                }
             }
 
-            if (sp < n)
-                pval += boost::math::cdf(boost::math::complement(bdist, sp));
+            if (n > 0) {
+                auto [r_n, p_n] = nb_params_null;
+                auto [r_a, p_a] = nb_params_a;
+                auto [r_b, p_b] = nb_params_b;
 
-            if (!config.test_by_unitig && pval * k >= config.family_wise_error_rate)
-                return;
+                double midpoint = r_a * n / (r_a + r_b);
+
+                eff_size = static_cast<double>(in_sum) - midpoint;
+                double in_dist = abs(eff_size);
+
+                double base = nb_base + (lgamma(n + 1) - lgamma(r_n + n)) / log(2.0) - n*log2(1.0-p_n);
+                auto get_pval = [&](int64_t s) {
+                    int64_t t = n - s;
+                    double sbase = (lgamma(r_a+s)+lgamma(r_b+t)-lgamma(s+1)-lgamma(t+1)) / log(2.0);
+                    return exp2(base + sbase +s*log2(1.0-p_a) + t*log2(1.0-p_b));
+                };
+
+                {
+                    double pval0 = get_pval(0);
+                    double pvaln = get_pval(n);
+                    double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                    if (!config.test_by_unitig && min_pval * k >= config.family_wise_error_rate)
+                        return;
+                }
+
+                pval = 0.0;
+                for (int64_t s = 0; s <= n; ++s) {
+                    double dist = abs(midpoint - s);
+                    if (dist >= in_dist)
+                        pval += get_pval(s);
+                }
+            }
         }
 
-        double eff_size = static_cast<double>(in_sum) - boost::math::mode(bdist);
         set_pval(pval, eff_size);
     });
 

From 47b05ede79b23dfdd51bb899f6cc3ac3be40327c Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Feb 2025 16:52:02 +0100
Subject: [PATCH 374/410] optim

---
 .../src/graph/annotated_graph_algorithm.cpp   | 530 ++++++++++--------
 1 file changed, 306 insertions(+), 224 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c1a9f16992..541e47fffe 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -101,6 +101,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     num_threads = get_num_threads();
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
 
+    auto combine_pvals = [dist=boost::math::cauchy()](const std::vector<long double> &pvals) {
+        long double stat = 0.0;
+        for (long double pval : pvals) {
+            stat += tan((0.5 - pval) * M_PI);
+        }
+        stat /= pvals.size();
+        return boost::math::cdf(boost::math::complement(dist, stat));
+    };
+
     size_t num_labels_both = std::count(groups.begin(), groups.end(), Group::BOTH);
     size_t num_labels_out = std::count(groups.begin(), groups.end(), Group::OUT);
     size_t num_labels_in = std::count(groups.begin(), groups.end(), Group::IN);
@@ -109,9 +118,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
 
-    common::logger->trace("Computing histogram from uncleaned counts");
-    auto hists_map = get_hist_map(std::vector<size_t>(groups.size(), 1), nullptr);
-
     sdsl::bit_vector kept_bv(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), true);
 
     std::vector<size_t> min_counts(groups.size(), config.min_count);
@@ -121,6 +127,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     // using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
 
     if (config.clean) {
+        common::logger->trace("Computing histogram from uncleaned counts");
+        auto hists_map = get_hist_map(std::vector<size_t>(groups.size(), 1), nullptr);
         common::logger->trace("Cleaning count columns");
 
         #pragma omp parallel for num_threads(num_parallel_files)
@@ -149,12 +157,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
-    auto correct_pvals = [&config](const std::vector<std::pair<double, size_t>> &m) {
+    auto correct_pvals = [&config](const std::vector<std::pair<long double, size_t>> &m) {
         size_t total = std::accumulate(m.begin(), m.end(), size_t(0),
                                        [](size_t sum, const auto &a) { return sum + a.second; });
         size_t acc = 0;
 
-        for (const auto &[pval_min, s] : m) {
+        for (size_t n = 0; n < m.size(); ++n) {
+            const auto &[pval_min, s] = m[n];
             if (s == 0)
                 continue;
 
@@ -162,19 +171,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             size_t nsig = total - acc;
             // there are nsig k-mers s.t. pval_min < alpha / c
             if (nsig > 0 && c >= nsig)
-                return std::make_pair(c, nsig);
+                return std::make_tuple(c, nsig, n);
 
             acc += s;
         }
 
-        return std::make_pair<size_t, size_t>(std::numeric_limits<size_t>::max(),
-                                              std::numeric_limits<size_t>::max());
+        return std::make_tuple(std::numeric_limits<size_t>::max(),
+                               std::numeric_limits<size_t>::max(),
+                               std::numeric_limits<size_t>::max());
     };
 
-    common::logger->trace("Updating histogram and marking discarded k-mers");
-
-    if (groups.size())
-        hists_map = get_hist_map(min_counts, &kept_bv);
+    common::logger->trace("Marking discarded k-mers");
+    auto hists_map = get_hist_map(min_counts, &kept_bv);
 
     bit_vector_stat kept(std::move(kept_bv));
     size_t nelem = kept.num_set_bits();
@@ -198,7 +206,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         std::sort(hists[j].begin(), hists[j].end(), utils::LessFirst());
         hists_map[j].clear();
     }
-    hists_map.clear();
+    hists_map.resize(0);
 
     for (size_t j = 0; j < groups.size(); ++j) {
         size_t total_c = 0;
@@ -364,16 +372,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     };
 
     // precompute negative binomial fits for poisson_bayes and nbinom_exact tests
-    std::vector<std::pair<double, double>> nb_params(groups.size());
-    std::pair<double, double> nb_params_a;
-    std::pair<double, double> nb_params_b;
-    std::pair<double, double> nb_params_null;
-    double nb_base = 0.0;
+    std::vector<std::pair<long double, long double>> nb_params(groups.size());
+    std::pair<long double, long double> nb_params_a;
+    std::pair<long double, long double> nb_params_b;
+    std::pair<long double, long double> nb_params_null;
+    long double nb_base = 0.0;
     if (config.test_type == "poisson_bayes" || config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
         auto get_rp = [&](const auto &generate) {
-            double mu = 0;
-            double var = 0;
+            long double mu = 0;
+            long double var = 0;
             size_t total = 0;
             generate([&](auto k, auto c) {
                 mu += k * c;
@@ -381,30 +389,30 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 total += c;
             });
             mu /= total;
-            double mu2 = mu * mu;
+            long double mu2 = mu * mu;
             var = (var - mu2 * total) / (total - 1);
 
-            double r_guess = mu * mu / (var - mu);
+            long double r_guess = mu * mu / (var - mu);
 
             if (mu >= var) {
                 common::logger->warn("Fit failed, falling back to Poisson: mu: {} >= var: {}", mu, var);
-                return std::make_pair(0.0, 1.0);
+                return std::make_pair<long double, long double>(0.0, 1.0);
             }
 
-            auto get_dl = [&](double r) {
-                double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
+            auto get_dl = [&](long double r) {
+                long double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
                 generate([&](auto k, auto c) {
                     dl += boost::math::digamma(k + r) * c;
                 });
                 return dl;
             };
 
-            double r_min = r_guess;
-            double r_max = r_guess;
+            long double r_min = r_guess;
+            long double r_max = r_guess;
 
             while (true) {
-                double dl_min = get_dl(r_min);
-                double dl_max = get_dl(r_max);
+                long double dl_min = get_dl(r_min);
+                long double dl_max = get_dl(r_max);
 
                 if (dl_min == 0) {
                     r_max = r_min;
@@ -427,11 +435,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             std::tie(r_min, r_max) = boost::math::tools::bisect(
-                get_dl, r_min, r_max, boost::math::tools::eps_tolerance<double>(5)
+                get_dl, r_min, r_max, boost::math::tools::eps_tolerance<long double>(5)
             );
 
-            double r = r_max;
-            double p = r / (r + mu);
+            long double r = r_max;
+            long double p = r / (r + mu);
             return std::make_pair(r, p);
         };
 
@@ -451,14 +459,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         if (config.test_type == "nbinom_exact") {
-            double mu_a = 0.0;
-            double mu_b = 0.0;
-            double var_a = 0.0;
-            double var_b = 0.0;
+            long double mu_a = 0.0;
+            long double mu_b = 0.0;
+            long double var_a = 0.0;
+            long double var_b = 0.0;
             for (size_t j = 0; j < groups.size(); ++j) {
                 const auto &[r, p] = nb_params[j];
-                double mu = r * (1.0 - p) / p;
-                double var = mu / p;
+                long double mu = r * (1.0 - p) / p;
+                long double var = mu / p;
                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                     mu_a += mu;
                     var_a += var;
@@ -478,38 +486,38 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             nb_params_b.second = mu_b / var_b;
             common::logger->trace("Out: r: {}\tp: {}", nb_params_b.first, nb_params_b.second);
 
-            double mu_null = mu_a + mu_b;
-            double var_null = var_a + var_b; // the two are independent, so their covariance is 0
+            long double mu_null = mu_a + mu_b;
+            long double var_null = var_a + var_b; // the two are independent, so their covariance is 0
             nb_params_null.first = mu_null * mu_null / (var_null - mu_null);
             nb_params_null.second = mu_null / var_null;
             common::logger->trace("Null: r: {}\tp: {}", nb_params_null.first, nb_params_null.second);
 
-            nb_base = (lgamma(nb_params_null.first) - lgamma(nb_params_a.first) - lgamma(nb_params_b.first)) / log(2.0);
-            nb_base += nb_params_a.first*log2(nb_params_a.second) + nb_params_b.first*log2(nb_params_b.second)-nb_params_null.first*log2(nb_params_null.second);
+            nb_base = (lgammal(nb_params_null.first) - lgammal(nb_params_a.first) - lgammal(nb_params_b.first)) / logl(2.0);
+            nb_base += nb_params_a.first*log2l(nb_params_a.second) + nb_params_b.first*log2l(nb_params_b.second)-nb_params_null.first*log2l(nb_params_null.second);
         }
     }
 
     // precompute p-values for poisson_binom test
-    std::vector<std::vector<double>> pb_pvals(num_labels_in + num_labels_out + 1);
-    std::vector<double> mid_points(num_labels_in + num_labels_out + 1);
+    std::vector<std::vector<long double>> pb_pvals(num_labels_in + num_labels_out + 1);
+    std::vector<long double> mid_points(num_labels_in + num_labels_out + 1);
     if (config.test_type == "poisson_binom") {
         // fit distribution
-        std::vector<double> p;
+        std::vector<long double> p;
         p.reserve(groups.size());
         for (size_t i = 0; i < groups.size(); ++i) {
-            p.emplace_back(static_cast<double>(n_kmers[i]) / nelem);
+            p.emplace_back(static_cast<long double>(n_kmers[i]) / nelem);
         }
 
         common::logger->trace("p: {}", fmt::join(p, ","));
 
         common::logger->trace("Precomputing PMFs");
-        std::vector<double> pmf_in { 1.0 };
-        std::vector<double> pmf_out { 1.0 };
-        std::vector<double> pmf_null { 1.0 };
+        std::vector<long double> pmf_in { 1.0 };
+        std::vector<long double> pmf_out { 1.0 };
+        std::vector<long double> pmf_null { 1.0 };
 
         for (size_t i = 1; i <= groups.size(); ++i) {
             if (groups[i - 1] == Group::OUT || groups[i - 1] == Group::BOTH) {
-                std::vector<double> pmf_out_cur(pmf_out.size() + 1);
+                std::vector<long double> pmf_out_cur(pmf_out.size() + 1);
                 pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
                 pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
                 for (size_t k = 1; k < pmf_out.size(); ++k) {
@@ -519,7 +527,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
 
             if (groups[i - 1] == Group::IN || groups[i - 1] == Group::BOTH) {
-                std::vector<double> pmf_in_cur(pmf_in.size() + 1);
+                std::vector<long double> pmf_in_cur(pmf_in.size() + 1);
                 pmf_in_cur[0] = (1.0 - p[i - 1]) * pmf_in[0];
                 pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
                 for (size_t k = 1; k < pmf_in.size(); ++k) {
@@ -527,7 +535,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
                 std::swap(pmf_in_cur, pmf_in);
             }
-            std::vector<double> pmf_null_cur(i + 1);
+            std::vector<long double> pmf_null_cur(i + 1);
             pmf_null_cur[0] = (1.0 - p[i - 1]) * pmf_null[0];
             pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
             for (size_t k = 1; k < pmf_null.size(); ++k) {
@@ -554,10 +562,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Precomputing p-values");
         pb_pvals[0].emplace_back(1.0);
 
-        double min_pval = 1.0;
+        long double min_pval = 1.0;
 
         for (size_t n = 1; n < pb_pvals.size(); ++n) {
-            std::vector<double> probs;
+            std::vector<long double> probs;
             size_t front = n - std::min(n, num_labels_out);
             for (uint64_t s = 0; s < pmf_in.size(); ++s) {
                 if (s > n)
@@ -598,12 +606,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     throw std::domain_error("");
                 }
             }
-            double local_min_pval = std::min(pb_pvals[n][front],
-                                            pb_pvals[n].back());
+            long double local_min_pval = std::min(pb_pvals[n][front],
+                                                  pb_pvals[n].back());
             min_pval = std::min(min_pval, local_min_pval);
 
             size_t max_prob_counts = 0;
-            double max_prob_pos = 0;
+            long double max_prob_pos = 0;
             // double max_prob = -1;
 
             for (uint64_t s = front; s < pb_pvals[n].size(); ++s) {
@@ -654,7 +662,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 max_prob_pos = front - 1 + front;
             }
 
-            mid_points[n] = static_cast<double>(max_prob_pos) / max_prob_counts;
+            mid_points[n] = static_cast<long double>(max_prob_pos) / max_prob_counts;
             common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}",
                                     n, mid_points[n],
                                     fmt::join(probs,","),
@@ -678,44 +686,35 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
     }
 
-    auto combine_pvals = [dist=boost::math::cauchy()](const std::vector<double> &pvals) {
-        double stat = 0.0;
-        for (double pval : pvals) {
-            stat += tan((0.5 - pval) * M_PI);
-        }
-        stat /= pvals.size();
-        return boost::math::cdf(boost::math::complement(dist, stat));
-    };
-
     // precompute minimal attainable p-values
-    std::vector<std::pair<double, size_t>> m;
+    std::vector<std::pair<long double, size_t>> m;
     std::vector<size_t> offsets;
     if (config.test_type != "notest") {
         common::logger->trace("Computing minimum p-values");
-        VectorMap<double, size_t> m_vec;
-        std::vector<VectorMap<double, size_t>> ms(num_threads + 1);
+        std::vector<std::vector<std::pair<long double, size_t>>> ms(num_threads + 1);
         generate_clean_rows([&](uint64_t row_i, const auto &row, uint64_t bucket_idx) {
-            double pval = 1.0;
+            long double pval = 1.0;
+            size_t n = 0;
             if (config.test_type == "poisson_binom") {
-                size_t n = 0;
                 for (const auto &[j, c] : row) {
                     n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
                 }
                 size_t front = n - std::min(n, num_labels_out);
                 pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
             } else if (config.test_type == "poisson_exact" || config.test_type == "poisson_bayes") {
-                double mu1 = 0.0;
-                double mu2 = 0.0;
+                long double mu1 = 0.0;
+                long double mu2 = 0.0;
                 if (config.test_type == "poisson_exact") {
-                    mu1 = static_cast<double>(in_kmers) / nelem;
-                    mu2 = static_cast<double>(out_kmers) / nelem;
+                    mu1 = static_cast<long double>(in_kmers) / nelem;
+                    mu2 = static_cast<long double>(out_kmers) / nelem;
                 }
-                int64_t n = 0;
+                sdsl::bit_vector found(num_labels_in + num_labels_out);
                 for (const auto &[j, c] : row) {
                     n += c;
                     if (config.test_type == "poisson_bayes") {
+                        found[j] = true;
                         const auto &[r, p] = nb_params[j];
-                        double lambda_j = sqrt((1.0 - p) * (c + r - 1));
+                        long double lambda_j = (1.0 - p) * (r + c - 1);
                         if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
                             mu2 += lambda_j;
 
@@ -725,90 +724,146 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 if (n > 0) {
-                    double p = mu1 / (mu1 + mu2);
-                    boost::math::binomial bdist(n, p);
-                    double pval0 = boost::math::pdf(bdist, 0);
-                    double pvaln = boost::math::pdf(bdist, n);
-                    pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                    if (ms[bucket_idx].size() <= n)
+                        ms[bucket_idx].resize(n + 1);
+
+                    if (ms[bucket_idx][n].second == 0) {
+                        if (config.test_type == "poisson_bayes") {
+                            for (size_t j = 0; j < found.size(); ++j) {
+                                if (!found[j]) {
+                                    const auto &[r, p] = nb_params[j];
+                                    if (r > 1.0) {
+                                        long double lambda_j = (1.0 - p) * (r - 1);
+                                        if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                                            mu2 += lambda_j;
+
+                                        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                                            mu1 += lambda_j;
+                                    }
+                                }
+                            }
+                        }
+
+                        long double p = mu1 / (mu1 + mu2);
+                        boost::math::binomial bdist(n, p);
+                        long double pval0 = boost::math::pdf(bdist, 0);
+                        long double pvaln = boost::math::pdf(bdist, n);
+                        pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                        ms[bucket_idx][n].first = pval;
+                        ms[bucket_idx][n].second = 1;
+                    } else {
+                       ++ms[bucket_idx][n].second;
+                    }
                 }
             } else if (config.test_type == "nbinom_exact") {
-                int64_t n = 0;
                 for (const auto &[j, c] : row) {
                     n += c;
                 }
 
                 if (n > 0) {
-                    auto [r_n, p_n] = nb_params_null;
-                    auto [r_a, p_a] = nb_params_a;
-                    auto [r_b, p_b] = nb_params_b;
-
-                    double base = nb_base + (lgamma(n + 1) - lgamma(r_n + n)) / log(2.0) - n*log2(1.0-p_n);
-                    auto get_pval = [&](int64_t s) {
-                        int64_t t = n - s;
-                        double sbase = (lgamma(r_a+s)+lgamma(r_b+t)-lgamma(s+1)-lgamma(t+1)) / log(2.0);
-                        return exp2(base + sbase +s*log2(1.0-p_a) + t*log2(1.0-p_b));
-                    };
-
-                    double pval0 = get_pval(0);
-                    double pvaln = get_pval(n);
-                    pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                    if (ms[bucket_idx].size() <= n)
+                        ms[bucket_idx].resize(n + 1);
+
+                    if (ms[bucket_idx][n].second == 0) {
+                        auto [r_n, p_n] = nb_params_null;
+                        auto [r_a, p_a] = nb_params_a;
+                        auto [r_b, p_b] = nb_params_b;
+
+                        long double l21p = log1p(-p_n) / log2l(2.0);
+                        long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0) - n*l21p;
+                        auto get_pval = [&](int64_t s) {
+                            int64_t t = n - s;
+                            long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1) + s * log1pl(-p_a) + t*log1pl(-p_b)) / logl(2.0);
+                            return exp2l(base + sbase);
+                        };
+
+                        long double pval0 = get_pval(0);
+                        long double pvaln = get_pval(n);
+                        pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                        ms[bucket_idx][n].first = pval;
+                        ms[bucket_idx][n].second = 1;
+                    } else {
+                        ++ms[bucket_idx][n].second;
+                    }
                 }
             }
-
-            if (!config.test_by_unitig) {
-                ++ms[bucket_idx][pval];
-            } else {
-                push_back(pvals_min_buckets[bucket_idx], pval);
-            }
         });
 
-        if (config.test_by_unitig) {
-            common::logger->trace("Combining minimal attainable p-values for unitigs");
-            offsets.reserve(num_threads + 1);
-            offsets.emplace_back(0);
-            for (const auto &bucket : pvals_min_buckets) {
-                offsets.emplace_back(offsets.back() + bucket.size());
-            }
-            std::mutex m_vec_mu;
-            clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-                std::vector<double> pvals;
-                pvals.reserve(path.size());
-                {
-                    std::lock_guard<std::mutex> lock(m_vec_mu);
-                    for (node_index node : path) {
-                        size_t row = AnnotatedDBG::graph_to_anno_index(node);
-                        size_t row_rank = kept.rank1(row);
-                        size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
-                        size_t offset = row_rank - offsets[bucket_idx];
-                        pvals.emplace_back(get<PValStorage>(pvals_min_buckets[bucket_idx][offset]));
-                    }
-                }
-                double comp_min_pval = combine_pvals(pvals);
-                size_t row = AnnotatedDBG::graph_to_anno_index(path[0]);
-                size_t row_rank = kept.rank1(row);
-                size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
-                size_t offset = row_rank - offsets[bucket_idx];
-                ++ms[bucket_idx][comp_min_pval];
-                std::lock_guard<std::mutex> lock(m_vec_mu);
-                set<PValStorage>(pvals_min_buckets[bucket_idx][offset], comp_min_pval);
-            });
+        // if (config.test_by_unitig) {
+        //     common::logger->trace("Combining minimal attainable p-values for unitigs");
+        //     offsets.reserve(num_threads + 1);
+        //     offsets.emplace_back(0);
+        //     for (const auto &bucket : pvals_min_buckets) {
+        //         offsets.emplace_back(offsets.back() + bucket.size());
+        //     }
+        //     std::mutex m_vec_mu;
+        //     clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+        //         std::vector<double> pvals;
+        //         pvals.reserve(path.size());
+        //         {
+        //             std::lock_guard<std::mutex> lock(m_vec_mu);
+        //             for (node_index node : path) {
+        //                 size_t row = AnnotatedDBG::graph_to_anno_index(node);
+        //                 size_t row_rank = kept.rank1(row);
+        //                 size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
+        //                 size_t offset = row_rank - offsets[bucket_idx];
+        //                 pvals.emplace_back(get<PValStorage>(pvals_min_buckets[bucket_idx][offset]));
+        //             }
+        //         }
+        //         double comp_min_pval = combine_pvals(pvals);
+        //         size_t row = AnnotatedDBG::graph_to_anno_index(path[0]);
+        //         size_t row_rank = kept.rank1(row);
+        //         size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
+        //         size_t offset = row_rank - offsets[bucket_idx];
+        //         ++ms[bucket_idx][comp_min_pval];
+        //         std::lock_guard<std::mutex> lock(m_vec_mu);
+        //         set<PValStorage>(pvals_min_buckets[bucket_idx][offset], comp_min_pval);
+        //     });
+        // }
+
+        std::swap(m, ms[0]);
+        size_t max_size = m.size();
+        for (size_t i = 1; i < ms.size(); ++i) {
+            max_size = std::max(max_size, ms[i].size());
         }
-
-        std::swap(m_vec, ms[0]);
+        m.resize(max_size);
         for (size_t i = 1; i < ms.size(); ++i) {
-            for (const auto &[pval, c] : ms[i]) {
-                m_vec[pval] += c;
+            for (size_t n = 0; n < ms[i].size(); ++i) {
+                const auto &[pval, c] = ms[i][n];
+                m[n].first = pval;
+                m[n].second += c;
             }
         }
+    }
+
+    if (m.size() > 1) {
+        auto it = m.begin();
+        auto jt = it + 1;
+        while (jt != m.end()) {
+            while (it->second == 0 && jt != m.end()) {
+                ++it;
+                ++jt;
+            }
+            while (jt != m.end() && jt->second == 0) {
+                ++jt;
+            }
+            if (jt == m.end())
+                break;
 
-        m = const_cast<std::vector<std::pair<double, size_t>>&&>(m_vec.values_container());
-        std::sort(m.rbegin(), m.rend());
+            if (jt->first - it->first > 1e-5) {
+                common::logger->error("min p-vals not sorted: {} !>= {}", it->first, jt->first);
+                throw std::runtime_error("p-val fail");
+            }
+            ++it;
+            ++jt;
+        }
     }
 
     // determine cutoffs for multiple testing correction
-    auto [k_min, k] = correct_pvals(m);
+    auto [k_min, k, n_cutoff] = correct_pvals(m);
 
-    common::logger->trace("Picked: k: {}\tn: {} / {}\tpval_min: {}", k_min, k, nelem, config.family_wise_error_rate / k);
+    common::logger->trace("Picked: k: {}\tn: {} / {}\tpval_min: {}\tn_cutoff: {}",
+                          k_min, k, nelem, config.family_wise_error_rate / k, n_cutoff);
 
     common::logger->trace("Running differential tests");
     std::atomic_thread_fence(std::memory_order_release);
@@ -837,7 +892,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             return;
         }
 
-        auto set_pval = [&](double pval, double eff_size) {
+        auto set_pval = [&](long double pval, long double eff_size) {
             if (!config.test_by_unitig) {
                 if (pval * k < config.family_wise_error_rate) {
                     node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
@@ -853,8 +908,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             }
         };
 
-        double eff_size = 0.0;
-        double pval = 1.0;
+        long double eff_size = 0.0;
+        long double pval = 1.0;
         if (config.test_type == "poisson_binom") {
             size_t n = 0;
             uint64_t s = 0;
@@ -862,97 +917,91 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
                 s += (groups[j] == Group::IN);
             }
-            size_t front = n - std::min(n, num_labels_out);
-            double min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
 
-            if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
+            if (n < n_cutoff)
                 return;
 
+            // size_t front = n - std::min(n, num_labels_out);
+            // double min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+
+            // if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
+            //     return;
+
             pval = pb_pvals[n][s];
             eff_size = static_cast<double>(s) - mid_points[n];
         } else if (config.test_type == "poisson_exact" || config.test_type == "poisson_bayes") {
-            double mu1 = 0.0;
-            double mu2 = 0.0;
+            long double mu1 = 0.0;
+            long double mu2 = 0.0;
             if (config.test_type == "poisson_exact") {
                 mu1 = static_cast<double>(in_kmers) / nelem;
                 mu2 = static_cast<double>(out_kmers) / nelem;
             }
-            int64_t n = 0;
+            size_t n = 0;
             int64_t in_sum = 0;
             int64_t out_sum = 0;
-            // sdsl::bit_vector found(num_labels_in + num_labels_out);
+            sdsl::bit_vector found(num_labels_in + num_labels_out);
             for (const auto &[j, c] : row) {
                 n += c;
-                // double lambda_j = 0.0;
-                // if (config.test_type == "poisson_bayes") {
-                //     const auto &[r, p] = nb_params[j];
-                //     lambda_j = (1.0 - p) * (r - 1.0 + c);
-                //     found[j] = true;
-                // }
+                long double lambda_j = 0.0;
+                if (config.test_type == "poisson_bayes") {
+                    const auto &[r, p] = nb_params[j];
+                    lambda_j = (1.0 - p) * (r - 1.0 + c);
+                    found[j] = true;
+                }
                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                    // mu2 += lambda_j;
+                    mu2 += lambda_j;
                     out_sum += c;
                 }
 
                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                    // mu1 += lambda_j;
+                    mu1 += lambda_j;
                     in_sum += c;
                 }
             }
+
+            if (n < n_cutoff)
+                return;
+
             if (config.test_type == "poisson_bayes") {
-                size_t unitig_id = kmer_to_unitig[AnnotatedDBG::anno_to_graph_index(row_i)];
-                auto &[unitig_size, unitig] = unitig_sums[unitig_id];
-                for (size_t j = 0; j < num_labels_in + num_labels_out; ++j) {
-                    int64_t unitig_sum = unitig[j];
-                    const auto &[r, p] = nb_params[j];
-                    double lambda_j = unitig_sum > 0 || r > 1
-                        ? (r - 1 + unitig_sum) / (unitig_size + p / (1.0 - p))
-                        : 0.0;
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                        mu2 += lambda_j;
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                        mu1 += lambda_j;
+                for (size_t j = 0; j < found.size(); ++j) {
+                    if (!found[j]) {
+                        const auto &[r, p] = nb_params[j];
+                        if (r > 1.0) {
+                            long double lambda_j = (1.0 - p) * (r - 1);
+                            if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                                mu2 += lambda_j;
+
+                            if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                                mu1 += lambda_j;
+                        }
+                    }
                 }
-                // for (size_t j = 0; j < found.size(); ++j) {
-                //     if (!found[j]) {
-                //         const auto &[r, p] = nb_params[j];
-                //         if (r > 1.0) {
-                //             double lambda_j = (1.0 - p) * (r - 1);
-                //             if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                //                 mu2 += lambda_j;
-
-                //             if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                //                 mu1 += lambda_j;
-                //         }
-                //     }
-                // }
             }
 
-            double p = mu1 / (mu1 + mu2);
+            long double p = mu1 / (mu1 + mu2);
             boost::math::binomial bdist(n, p);
             if (n != 0) {
-                double pval0 = boost::math::pdf(bdist, 0);
-                double pvaln = boost::math::pdf(bdist, n);
-                double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
-                    return;
+                // double pval0 = boost::math::pdf(bdist, 0);
+                // double pvaln = boost::math::pdf(bdist, n);
+                // double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                // if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
+                //     return;
 
-                auto get_deviance = [&](double y, double mu) {
+                auto get_deviance = [&](long double y, long double mu) {
                     y += 1e-8;
                     mu += 1e-8;
                     return 2 * (y * log(y/mu) - y + mu);
                 };
 
-                std::vector<double> devs;
+                std::vector<long double> devs;
                 devs.reserve(n + 1);
-                for (int64_t s = 0; s <= n; ++s) {
+                for (size_t s = 0; s <= n; ++s) {
                     devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
                 }
 
                 pval = 0.0;
 
-                int64_t s = 0;
+                size_t s = 0;
                 for ( ; s <= n; ++s) {
                     if (devs[s] < devs[in_sum])
                         break;
@@ -960,7 +1009,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (s > 0)
                     pval += boost::math::cdf(bdist, s - 1);
 
-                int64_t sp = n;
+                size_t sp = n;
                 for ( ; sp >= s; --sp) {
                     if (devs[sp] < devs[in_sum])
                         break;
@@ -971,11 +1020,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 if (!config.test_by_unitig && pval * k >= config.family_wise_error_rate)
                     return;
+
+                eff_size = static_cast<long double>(in_sum) - boost::math::mode(bdist);
             }
 
-            eff_size = static_cast<double>(in_sum) - boost::math::mode(bdist);
         } else if (config.test_type == "nbinom_exact") {
-            int64_t n = 0;
+            size_t n = 0;
             int64_t in_sum = 0;
             int64_t out_sum = 0;
             for (const auto &[j, c] : row) {
@@ -989,36 +1039,68 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
+            if (n < n_cutoff)
+                return;
+
             if (n > 0) {
-                auto [r_n, p_n] = nb_params_null;
                 auto [r_a, p_a] = nb_params_a;
                 auto [r_b, p_b] = nb_params_b;
+                long double midpoint = r_a * n / (r_a + r_b);
 
-                double midpoint = r_a * n / (r_a + r_b);
+                pval = 0.0;
 
-                eff_size = static_cast<double>(in_sum) - midpoint;
-                double in_dist = abs(eff_size);
+                auto [r_n, p_n] = nb_params_null;
+
+                auto get_deviance = [&](size_t s, size_t t) {
+                    if (s == 0)
+                        return (r_a*logl(r_a) + (n+r_b)*logl(n+r_b)) * -2.0;
+
+                    if (t == 0)
+                        return (r_b*logl(r_b) + (n+r_a)*logl(n+r_a)) * -2.0;
 
-                double base = nb_base + (lgamma(n + 1) - lgamma(r_n + n)) / log(2.0) - n*log2(1.0-p_n);
-                auto get_pval = [&](int64_t s) {
-                    int64_t t = n - s;
-                    double sbase = (lgamma(r_a+s)+lgamma(r_b+t)-lgamma(s+1)-lgamma(t+1)) / log(2.0);
-                    return exp2(base + sbase +s*log2(1.0-p_a) + t*log2(1.0-p_b));
+                    return (logl(s)*s - (r_a+s)*logl(r_a+s) + logl(t)*t - (r_b+t)*logl(r_b+t)) * 2.0;
                 };
 
-                {
-                    double pval0 = get_pval(0);
-                    double pvaln = get_pval(n);
-                    double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                    if (!config.test_by_unitig && min_pval * k >= config.family_wise_error_rate)
-                        return;
-                }
+                long double in_dev = get_deviance(in_sum, out_sum);
+                eff_size = static_cast<double>(in_sum) - midpoint;
 
-                pval = 0.0;
-                for (int64_t s = 0; s <= n; ++s) {
-                    double dist = abs(midpoint - s);
-                    if (dist >= in_dist)
-                        pval += get_pval(s);
+                if (in_dev > 0.0) {
+                    long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n) - n*log1pl(-p_n)) / logl(2.0);
+                    double l1pa = log1pl(-p_a) / logl(2.0);
+                    double l1pb = log1pl(-p_b) / logl(2.0);
+
+                    {
+                        size_t s = 0;
+                        size_t t = n;
+                        long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
+                        pval += exp2(base + sbase);
+                        for (++s; s <= n; ++s) {
+                            long double dev = get_deviance(s, t - 1);
+                            if (dev >= in_dev) {
+                                sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                                --t;
+                                pval += exp2(base + sbase);
+                            } else {
+                                break;
+                            }
+                        }
+                    }
+                    if (get_deviance(n, 0) >= in_dev) {
+                        size_t s = n;
+                        size_t t = 0;
+                        long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
+                        pval += exp2(base + sbase);
+                        for (++t; t <= n; ++t) {
+                            long double dev = get_deviance(s - 1, t);
+                            if (dev >= in_dev) {
+                                sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                                --s;
+                                pval += exp2(base + sbase);
+                            } else {
+                                break;
+                            }
+                        }
+                    }
                 }
             }
         }
@@ -1030,7 +1112,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Combining p-values for unitigs");
         std::mutex m_vec_mu;
         clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-            double pval_min = 1.0;
+            long double pval_min = 1.0;
             {
                 size_t row = AnnotatedDBG::graph_to_anno_index(path[0]);
                 size_t row_rank = kept.rank1(row);
@@ -1043,9 +1125,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (pval_min * k_min >= config.family_wise_error_rate)
                 return;
 
-            std::vector<double> pvals;
+            std::vector<long double> pvals;
             pvals.reserve(path.size());
-            double comb_eff_size = 0.0;
+            long double comb_eff_size = 0.0;
             {
                 std::lock_guard<std::mutex> lock(m_vec_mu);
                 for (node_index node : path) {
@@ -1057,7 +1139,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     comb_eff_size += get<PValStorage>(eff_size_buckets[bucket_idx][offset]);
                 }
             }
-            double comp_pval = combine_pvals(pvals);
+            long double comp_pval = combine_pvals(pvals);
             if (comp_pval * k >= config.family_wise_error_rate || comb_eff_size == 0.0)
                 return;
 

From e437355658c859f884ac142d2a2a0e42b515e0bc Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Feb 2025 17:34:32 +0100
Subject: [PATCH 375/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 51 ++++++++++---------
 1 file changed, 28 insertions(+), 23 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 541e47fffe..673244f76e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -399,7 +399,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return std::make_pair<long double, long double>(0.0, 1.0);
             }
 
-            auto get_dl = [&](long double r) {
+            auto get_dl_r = [&](long double r) {
                 long double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
                 generate([&](auto k, auto c) {
                     dl += boost::math::digamma(k + r) * c;
@@ -407,35 +407,40 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 return dl;
             };
 
-            long double r_min = r_guess;
-            long double r_max = r_guess;
+            // auto get_dl_a = [&](long double a) {
+            //     long double dl = (log())
+            //     return dl;
+            // }
 
-            while (true) {
-                long double dl_min = get_dl(r_min);
-                long double dl_max = get_dl(r_max);
+            // long double r_min = r_guess;
+            // long double r_max = r_guess;
 
-                if (dl_min == 0) {
-                    r_max = r_min;
-                    break;
-                }
+            // while (true) {
+            //     long double dl_min = get_dl(r_min);
+            //     long double dl_max = get_dl(r_max);
 
-                if (dl_max == 0) {
-                    r_min = r_max;
-                    break;
-                }
+            //     if (dl_min == 0) {
+            //         r_max = r_min;
+            //         break;
+            //     }
 
-                if (dl_min < 0)
-                    r_min /= 2;
+            //     if (dl_max == 0) {
+            //         r_min = r_max;
+            //         break;
+            //     }
 
-                if (dl_max > 0)
-                    r_max *= 2;
+            //     if (dl_min < 0)
+            //         r_min /= 2;
 
-                if (dl_min > 0 && dl_max < 0)
-                    break;
-            }
+            //     if (dl_max > 0)
+            //         r_max *= 2;
+
+            //     if (dl_min > 0 && dl_max < 0)
+            //         break;
+            // }
 
-            std::tie(r_min, r_max) = boost::math::tools::bisect(
-                get_dl, r_min, r_max, boost::math::tools::eps_tolerance<long double>(5)
+            auto [r_min, r_max] = boost::math::tools::bisect(
+                get_dl_r, 0.0, 1.0, boost::math::tools::eps_tolerance<long double>(5)
             );
 
             long double r = r_max;

From ed1b8668a0b4b43cc40f687a4d3b77308210b860 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 10 Mar 2025 11:48:34 +0100
Subject: [PATCH 376/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 2844 +++++++++++++----
 1 file changed, 2160 insertions(+), 684 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 673244f76e..039f37fafe 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -13,10 +13,12 @@
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
 #include <boost/math/distributions/cauchy.hpp>
+#include <boost/math/distributions/hypergeometric.hpp>
 
 #include "common/logger.hpp"
 #include "common/vectors/bitmap.hpp"
 #include "common/vector_map.hpp"
+#include "common/vector_set.hpp"
 #include "common/vectors/transpose.hpp"
 #include "common/hashers/hash.hpp"
 #include "graph/representation/masked_graph.hpp"
@@ -82,6 +84,81 @@ void push_back(PValStorage &v, double val) {
     v.push_back(bit_cast<typename PValStorage::value_type>(val));
 }
 
+template <typename Generator>
+std::tuple<long double, long double, long double, long double> get_rp(const Generator &generate) {
+    long double mu = 0;
+    long double var = 0;
+    size_t total = 0;
+    generate([&](auto k, auto c) {
+        mu += k * c;
+        var += k * k * c;
+        total += c;
+    });
+    mu /= total;
+    long double mu2 = mu * mu;
+    var = (var - mu2 * total) / (total - 1);
+
+    if (mu >= var) {
+        common::logger->warn("Fit failed, falling back to Poisson: mu: {} >= var: {}", mu, var);
+        return { 0.0, 1.0, mu, var };
+    }
+
+    auto get_l = [&](long double r) {
+        long double p = r / (r + mu);
+        long double l = -lgammal(r) * total + mu * total * log1pl(-p) + total * r * logl(p);
+        generate([&](auto k, auto c) {
+            l += lgammal(k + r) * c;
+        });
+        return l;
+    };
+
+    auto get_dl_r = [&](long double r) {
+        long double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
+        generate([&](auto k, auto c) {
+            dl += boost::math::digamma(k + r) * c;
+        });
+        return dl;
+    };
+
+    auto get_dl_a = [&](long double a) {
+        long double dl = mu * mu * total / (1.0 + a * mu) + total * logl(1 + a * mu) / a / a + total / a / a * boost::math::digamma(1.0 / a);
+        generate([&](auto k, auto c) {
+            dl -= boost::math::digamma(k + 1.0 / a) / a / a * c;
+        });
+        return dl;
+    };
+
+    long double r1 = 0.0;
+    long double r2 = 0.0;
+    try {
+        auto [r_min, r_max] = boost::math::tools::bisect(
+            get_dl_r, std::numeric_limits<double>::min(), 1.0, boost::math::tools::eps_tolerance<long double>(5)
+        );
+        r1 = (r_min + r_max) / 2.0;
+    } catch (boost::wrapexcept<boost::math::evaluation_error> &e) {}
+
+    try {
+        auto [a_min, a_max] = boost::math::tools::bisect(
+            get_dl_a, std::numeric_limits<double>::min(), 1.0, boost::math::tools::eps_tolerance<long double>(5)
+        );
+        r2 = (1.0 / a_min + 1.0 / a_max) / 2.0;
+    } catch (boost::wrapexcept<boost::math::evaluation_error> &e) {}
+
+    long double r = 0.0;
+    if (r1 == 0 && r2 == 0) {
+        throw std::runtime_error("Failed to fit");
+    } else if (r1 == 0) {
+        r = r2;
+    } else if (r2 == 0) {
+        r = r1;
+    } else {
+        r = get_l(r1) > get_l(r2) ? r1 : r2;
+    }
+
+    long double p = r / (r + mu);
+    return { r, p, mu, var };
+};
+
 template <typename value_type, class PValStorage, typename HistGetter, typename Generator>
 std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
@@ -187,19 +264,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     bit_vector_stat kept(std::move(kept_bv));
     size_t nelem = kept.num_set_bits();
 
-    std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
-    if (config.test_type != "notest" && config.test_by_unitig) {
-        clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
-            graph_ptr,
-            [&](node_index node) {
-                return node != DeBruijnGraph::npos
-                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
-            },
-            true,
-            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-        );
-    }
-
     std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
     for (size_t j = 0; j < hists.size(); ++j) {
         hists[j] = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(hists_map[j].values_container());
@@ -225,27 +289,41 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Computing aggregate statistics");
     int64_t in_kmers = 0;
     int64_t out_kmers = 0;
+    int64_t in_sq_kmers = 0;
+    int64_t out_sq_kmers = 0;
+    int64_t total_sq_kmers = 0;
     std::vector<uint64_t> max_obs_vals(groups.size());
     std::vector<size_t> n_kmers(groups.size());
     std::vector<uint64_t> sums(groups.size());
+    std::vector<uint64_t> sq_sums(groups.size());
+    uint64_t max_in_obs_val = 0.0;
+    uint64_t max_out_obs_val = 0.0;
 
     int64_t total_kmers = 0;
     for (size_t j = 0; j < groups.size(); ++j) {
         for (const auto &[k, c] : hists[j]) {
             sums[j] += k * c;
+            sq_sums[j] += k * k * c;
             if (k != 0)
                 n_kmers[j] += c;
 
             max_obs_vals[j] = std::max(max_obs_vals[j], k);
         }
 
-        if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+        if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
             out_kmers += sums[j];
+            out_sq_kmers += sq_sums[j];
+            max_out_obs_val += max_obs_vals[j];
+        }
 
-        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+        if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
             in_kmers += sums[j];
+            in_sq_kmers += sq_sums[j];
+            max_in_obs_val += max_obs_vals[j];
+        }
 
         total_kmers += sums[j];
+        total_sq_kmers += sq_sums[j];
 
         common::logger->trace("{}: n_unique: {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
                               j, n_kmers[j], sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
@@ -272,43 +350,43 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     //     mean_buckets[0].push_back(0);
     // }
 
-    std::vector<PValStorage> pvals_buckets;
-    std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
-    std::vector<PValStorage> pvals_min_buckets;
-    std::vector<std::unique_ptr<utils::TempFile>> tmp_min_buckets;
-    std::vector<PValStorage> eff_size_buckets;
-    std::vector<std::unique_ptr<utils::TempFile>> tmp_eff_buckets;
-    // constexpr bool preallocated = std::is_same_v<PValStorage, std::vector<uint64_t>>;
-
-    if (config.test_type != "notest" && config.test_by_unitig) {
-        if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-            auto &pvals = pvals_buckets.emplace_back();
-            pvals.reserve(graph_ptr->max_index());
-
-            auto &pvals_min = pvals_min_buckets.emplace_back();
-            pvals_min.reserve(graph_ptr->max_index());
-
-            auto &eff_size = eff_size_buckets.emplace_back();
-            eff_size.reserve(graph_ptr->max_index());
-        }
+    // std::vector<PValStorage> pvals_buckets;
+    // std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
+    // std::vector<PValStorage> pvals_min_buckets;
+    // std::vector<std::unique_ptr<utils::TempFile>> tmp_min_buckets;
+    // std::vector<PValStorage> eff_size_buckets;
+    // std::vector<std::unique_ptr<utils::TempFile>> tmp_eff_buckets;
+    // // constexpr bool preallocated = std::is_same_v<PValStorage, std::vector<uint64_t>>;
+
+    // if (config.test_type != "notest" && config.test_by_unitig) {
+    //     if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
+    //         auto &pvals = pvals_buckets.emplace_back();
+    //         pvals.reserve(graph_ptr->max_index());
+
+    //         auto &pvals_min = pvals_min_buckets.emplace_back();
+    //         pvals_min.reserve(graph_ptr->max_index());
+
+    //         auto &eff_size = eff_size_buckets.emplace_back();
+    //         eff_size.reserve(graph_ptr->max_index());
+    //     }
 
-        if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-            for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-                auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-                pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
-            }
+    //     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
+    //         for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+    //             auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+    //             pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
+    //         }
 
-            for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-                auto &tmp_file = tmp_min_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-                pvals_min_buckets.emplace_back(tmp_file->name(), std::ios::out);
-            }
+    //         for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+    //             auto &tmp_file = tmp_min_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+    //             pvals_min_buckets.emplace_back(tmp_file->name(), std::ios::out);
+    //         }
 
-            for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-                auto &tmp_file_eff = tmp_eff_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-                eff_size_buckets.emplace_back(tmp_file_eff->name(), std::ios::out);
-            }
-        }
-    }
+    //         for (size_t i = 0; i < get_num_threads() + 1; ++i) {
+    //             auto &tmp_file_eff = tmp_eff_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
+    //             eff_size_buckets.emplace_back(tmp_file_eff->name(), std::ios::out);
+    //         }
+    //     }
+    // }
 
     // if ((config.test_type == "gnb_exact" || config.test_type == "lnb_exact") && config.test_by_unitig) {
 
@@ -325,16 +403,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     sdsl::bit_vector indicator_in;
     sdsl::bit_vector indicator_out;
 
-    // if (!config.test_by_unitig) {
-        common::logger->trace("Allocating k-mer bitmasks");
-        indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-        indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-    // }
-
-    // std::function<double(int64_t, int64_t, const PairContainer&)> compute_pval;
-    // std::function<double(int64_t, const PairContainer&)> compute_min_pval;
-
-    // std::vector<VectorMap<uint64_t, std::pair<size_t, uint64_t>>> count_maps;
+    common::logger->trace("Allocating k-mer bitmasks");
+    indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
+    indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
 
     common::logger->trace("Test: {}\tby unitig: {}", config.test_type, config.test_by_unitig);
 
@@ -371,798 +442,2203 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         });
     };
 
-    // precompute negative binomial fits for poisson_bayes and nbinom_exact tests
-    std::vector<std::pair<long double, long double>> nb_params(groups.size());
-    std::pair<long double, long double> nb_params_a;
-    std::pair<long double, long double> nb_params_b;
-    std::pair<long double, long double> nb_params_null;
-    long double nb_base = 0.0;
-    if (config.test_type == "poisson_bayes" || config.test_type == "nbinom_exact") {
-        common::logger->trace("Fitting per-sample negative binomial distributions");
-        auto get_rp = [&](const auto &generate) {
-            long double mu = 0;
-            long double var = 0;
-            size_t total = 0;
-            generate([&](auto k, auto c) {
-                mu += k * c;
-                var += k * k * c;
-                total += c;
-            });
-            mu /= total;
-            long double mu2 = mu * mu;
-            var = (var - mu2 * total) / (total - 1);
+    std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
+    std::vector<size_t> kmer_to_unitig;
+    std::vector<std::tuple<size_t, size_t, size_t>> counts;
+    // std::vector<std::pair<size_t, std::vector<uint64_t>>> agg_counts;
+    if (config.test_by_unitig && config.test_type != "notest") {
+        clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
+            graph_ptr,
+            [&](node_index node) {
+                return node != DeBruijnGraph::npos
+                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
+            },
+            true,
+            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+        );
+
+        common::logger->trace("Associating k-mers to unitigs");
+        kmer_to_unitig.resize(graph_ptr->max_index() + 1);
+        // std::atomic_thread_fence(std::memory_order_release);
+        // std::atomic<size_t> num_unitigs{0};
+        std::mutex mu;
+        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+            size_t unitig_id = 0;
+            {
+                unitig_id = counts.size();
+                std::lock_guard<std::mutex> lock(mu);
+                counts.emplace_back(path.size(), 0, 0);
+            }
+            // size_t unitig_id = num_unitigs.fetch_add(1, std::memory_order_relaxed);
+            for (node_index node : path) {
+                kmer_to_unitig[node] = unitig_id;
+            }
+        }, num_threads);
+        // std::atomic_thread_fence(std::memory_order_acquire);
+        common::logger->trace("Assembled {} unitigs", counts.size());
 
-            long double r_guess = mu * mu / (var - mu);
+        common::logger->trace("Aggregating counts for unitigs");
+        generate_clean_rows([&](uint64_t row_i, const auto &row, size_t) {
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+            size_t unitig_id = kmer_to_unitig[node];
+            auto &[len, in_count, out_count] = counts[unitig_id];
+            for (const auto &[j, c] : row) {
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                    out_count += c;
 
-            if (mu >= var) {
-                common::logger->warn("Fit failed, falling back to Poisson: mu: {} >= var: {}", mu, var);
-                return std::make_pair<long double, long double>(0.0, 1.0);
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                    in_count += c;
             }
+        });
 
-            auto get_dl_r = [&](long double r) {
-                long double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
-                generate([&](auto k, auto c) {
-                    dl += boost::math::digamma(k + r) * c;
-                });
-                return dl;
-            };
+        common::logger->trace("Computing unitig count histogram");
+        std::vector<VectorMap<size_t, size_t>> unitig_hists_map(2);
+        for (const auto &[len, in_count, out_count] : counts) {
+            size_t avg_in_count = std::ceil(static_cast<double>(in_count) / len);
+            size_t avg_out_count = std::ceil(static_cast<double>(out_count) / len);
+            ++unitig_hists_map[0][avg_in_count];
+            ++unitig_hists_map[1][avg_out_count];
+        }
 
-            // auto get_dl_a = [&](long double a) {
-            //     long double dl = (log())
-            //     return dl;
-            // }
+        common::logger->trace("Fitting negative binomials");
+        auto [r_a, p_a, mu_a, var_a] = get_rp([&](const auto &callback) {
+            for (const auto &[k, c] : unitig_hists_map[0]) {
+                callback(k, c);
+            }
+        });
+        auto [r_b, p_b, mu_b, var_b] = get_rp([&](const auto &callback) {
+            for (const auto &[k, c] : unitig_hists_map[1]) {
+                callback(k, c);
+            }
+        });
+        auto [r_n, p_n, mu_n, var_n] = get_rp([&](const auto &callback) {
+            for (const auto &[k, c] : unitig_hists_map[0]) {
+                auto find = unitig_hists_map[1].find(k);
+                if (find != unitig_hists_map[1].end()) {
+                    callback(k, c + find->second);
+                } else {
+                    callback(k, c);
+                }
+            }
 
-            // long double r_min = r_guess;
-            // long double r_max = r_guess;
+            for (const auto &[k, c] : unitig_hists_map[1]) {
+                if (!unitig_hists_map[0].count(k))
+                    callback(k, c);
+            }
+        });
 
-            // while (true) {
-            //     long double dl_min = get_dl(r_min);
-            //     long double dl_max = get_dl(r_max);
+        common::logger->trace("In: r: {}\tp: {}\t mu: {}\tsample var: {}\tvar: {}", r_a, p_a, mu_a, var_a, mu_a / p_a);
+        common::logger->trace("Out: r: {}\tp: {}", r_b, p_b, mu_b, var_b, mu_b / p_b);
+        common::logger->trace("Null: r: {}\tp: {}", r_n, p_n, mu_n, var_n, mu_n / p_n);
 
-            //     if (dl_min == 0) {
-            //         r_max = r_min;
-            //         break;
-            //     }
+        long double nb_base = (lgammal(r_n) - lgammal(r_a) - lgammal(r_b)) / logl(2.0)
+                    + r_a*log2l(p_a) + r_b*log2l(p_b)-r_n*log2l(p_n);
 
-            //     if (dl_max == 0) {
-            //         r_min = r_max;
-            //         break;
-            //     }
+        common::logger->trace("Allocating p-value storage");
+        std::vector<std::tuple<long double, Group, size_t>> pvals(counts.size(), std::make_tuple(1.0, Group::OTHER, counts.size()));
 
-            //     if (dl_min < 0)
-            //         r_min /= 2;
+        common::logger->trace("Calculating p-values");
+        size_t rows_per_update = 10000;
+        ProgressBar progress_bar(pvals.size(), "Streaming unitigs", std::cerr, !common::get_verbose());
+        #pragma omp parallel for num_threads(num_threads) schedule(static)
+        for (size_t i = 0; i < counts.size(); ++i) {
+            if (i > 0 && (i % rows_per_update) == 0)
+                progress_bar += rows_per_update;
 
-            //     if (dl_max > 0)
-            //         r_max *= 2;
+            const auto &[len, in_sum, out_sum] = counts[i];
+            size_t n = in_sum + out_sum;
 
-            //     if (dl_min > 0 && dl_max < 0)
-            //         break;
-            // }
+            long double eff_size = 0.0;
+            auto &[pval, group, idx] = pvals[i];
+            idx = i;
 
-            auto [r_min, r_max] = boost::math::tools::bisect(
-                get_dl_r, 0.0, 1.0, boost::math::tools::eps_tolerance<long double>(5)
-            );
+            long double midpoint = r_a * n / (r_a + r_b);
 
-            long double r = r_max;
-            long double p = r / (r + mu);
-            return std::make_pair(r, p);
-        };
+            auto get_deviance_a = [&](long double s) {
+                if (s == 0)
+                    return r_a * logl(p_a) * -2.0;
 
-        #pragma omp parallel for num_threads(num_parallel_files)
-        for (size_t j = 0; j < groups.size(); ++j) {
-            const auto &hist = hists[j];
-            if (hist.size()) {
-                nb_params[j] = get_rp([&](const auto &callback) {
-                    for (const auto &[k, c] : hist) {
-                        callback(k, c);
-                    }
-                });
-                const auto &[r, p] = nb_params[j];
-                common::logger->trace("{}: size: {}\tmax_val: {}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
-                                    j, sums[j], (hist.end() - 1)->first, r * (1-p)/p, r*(1-p)/p/p, r, p);
-            }
-        }
+                return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s) + r_a * (logl(r_a) - logl(p_a))) * 2.0;
+            };
 
-        if (config.test_type == "nbinom_exact") {
-            long double mu_a = 0.0;
-            long double mu_b = 0.0;
-            long double var_a = 0.0;
-            long double var_b = 0.0;
-            for (size_t j = 0; j < groups.size(); ++j) {
-                const auto &[r, p] = nb_params[j];
-                long double mu = r * (1.0 - p) / p;
-                long double var = mu / p;
-                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                    mu_a += mu;
-                    var_a += var;
-                }
+            auto get_deviance_b = [&](long double t) {
+                if (t == 0)
+                    return r_b * logl(p_b) * -2.0;
+
+                return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t) + r_b * (logl(r_b) - logl(p_b))) * 2.0;
+            };
+
+            auto get_deviance = [&](long double s, long double t) {
+                return get_deviance_a(s) + get_deviance_b(t);
+            };
+
+            long double min_dev = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
+            long double in_dev = get_deviance(in_sum, out_sum);
 
-                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                    mu_b += mu;
-                    var_b += var;
+            if (in_dev > min_dev) {
+                eff_size = static_cast<long double>(in_sum) - midpoint;
+                if (eff_size > 0) {
+                    group = Group::IN;
+                } else if (eff_size < 0) {
+                    group = Group::OUT;
+                }
+                pval = 0.0;
+                idx = i;
+                long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n) - n*log1pl(-p_n)) / logl(2.0);
+                double l1pa = log1pl(-p_a) / logl(2.0);
+                double l1pb = log1pl(-p_b) / logl(2.0);
+
+                {
+                    size_t s = 0;
+                    size_t t = n;
+                    long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
+                    pval += exp2(base + sbase);
+                    for (++s; s <= n; ++s) {
+                        long double dev = get_deviance(s, t - 1);
+                        if (dev >= in_dev) {
+                            sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                            --t;
+                            pval += exp2(base + sbase);
+                            if (pval >= config.family_wise_error_rate)
+                                break;
+                        } else {
+                            break;
+                        }
+                    }
+                }
+                if (pval < config.family_wise_error_rate && get_deviance(n, 0) >= in_dev) {
+                    size_t s = n;
+                    size_t t = 0;
+                    long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
+                    pval += exp2(base + sbase);
+                    for (++t; t <= n; ++t) {
+                        long double dev = get_deviance(s - 1, t);
+                        if (dev >= in_dev) {
+                            sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                            --s;
+                            pval += exp2(base + sbase);
+                            if (pval >= config.family_wise_error_rate)
+                                break;
+                        } else {
+                            break;
+                        }
+                    }
                 }
             }
+        }
 
-            nb_params_a.first = mu_a * mu_a / (var_a - mu_a);
-            nb_params_a.second = mu_a / var_a;
-            common::logger->trace("In: r: {}\tp: {}", nb_params_a.first, nb_params_a.second);
+        progress_bar += (pvals.size() % rows_per_update);
 
-            nb_params_b.first = mu_b * mu_b / (var_b - mu_b);
-            nb_params_b.second = mu_b / var_b;
-            common::logger->trace("Out: r: {}\tp: {}", nb_params_b.first, nb_params_b.second);
+        common::logger->trace("Correcting p-values");
+        common::logger->trace("Sorting {} / {} p-vals", pvals.size(), counts.size());
+        std::sort(pvals.begin(), pvals.end());
+        long double harm = 1.0;
+        // long double harm = 0.0;
+        // for (size_t i = 1; i <= nelem; ++i) {
+        //     harm += 1.0 / i;
+        // }
 
-            long double mu_null = mu_a + mu_b;
-            long double var_null = var_a + var_b; // the two are independent, so their covariance is 0
-            nb_params_null.first = mu_null * mu_null / (var_null - mu_null);
-            nb_params_null.second = mu_null / var_null;
-            common::logger->trace("Null: r: {}\tp: {}", nb_params_null.first, nb_params_null.second);
+        common::logger->trace("Selecing cut-off");
+        size_t k = 0;
+        for (size_t i = 0; i < pvals.size(); ++i) {
+            const auto &[pval, group, idx] = pvals[i];
+            if (pval >= config.family_wise_error_rate)
+                break;
 
-            nb_base = (lgammal(nb_params_null.first) - lgammal(nb_params_a.first) - lgammal(nb_params_b.first)) / logl(2.0);
-            nb_base += nb_params_a.first*log2l(nb_params_a.second) + nb_params_b.first*log2l(nb_params_b.second)-nb_params_null.first*log2l(nb_params_null.second);
+            if (pval <= config.family_wise_error_rate * (i + 1) / pvals.size() / harm)
+                k = std::max(i + 1, k);
         }
-    }
 
-    // precompute p-values for poisson_binom test
-    std::vector<std::vector<long double>> pb_pvals(num_labels_in + num_labels_out + 1);
-    std::vector<long double> mid_points(num_labels_in + num_labels_out + 1);
-    if (config.test_type == "poisson_binom") {
-        // fit distribution
-        std::vector<long double> p;
-        p.reserve(groups.size());
-        for (size_t i = 0; i < groups.size(); ++i) {
-            p.emplace_back(static_cast<long double>(n_kmers[i]) / nelem);
+        common::logger->trace("Found {} / {} significant p-values. Minimum p-value is {}",
+                            k, pvals.size(), std::get<0>(pvals[0]));
+
+        for (size_t i = k; i < pvals.size(); ++i) {
+            std::get<1>(pvals[i]) = Group::BOTH;
         }
 
-        common::logger->trace("p: {}", fmt::join(p, ","));
+        common::logger->trace("Reordering p-values");
+        std::sort(pvals.begin(), pvals.end(), [&](const auto &a, const auto &b) {
+            return std::get<2>(a) < std::get<2>(b);
+        });
+
+        common::logger->trace("Marking significant p-values");
+        std::atomic_thread_fence(std::memory_order_release);
+        generate_clean_rows([&](uint64_t row_i, const auto &row, size_t) {
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+            size_t unitig_id = kmer_to_unitig[node];
+            const auto &[pval, group, idx] = pvals[unitig_id];
+            if (group == Group::IN)
+                set_bit(indicator_in.data(), node, std::memory_order_relaxed);
+
+            if (group == Group::OUT)
+                set_bit(indicator_out.data(), node, std::memory_order_relaxed);
+        });
+        std::atomic_thread_fence(std::memory_order_acquire);
+    } else {
+        // precompute negative binomial fits for poisson_bayes and nbinom_exact tests
+        std::vector<std::pair<long double, long double>> nb_params(groups.size());
+        std::pair<long double, long double> nb_params_a;
+        std::pair<long double, long double> nb_params_b;
+        std::pair<long double, long double> nb_params_null;
+        long double nb_base = 0.0;
+        // long double alpha = in_kmers;
+        // long double beta = out_kmers;
+        // long double hg_base = (lgammal(alpha + beta) - lgammal(alpha) - lgammal(beta)) / logl(2.0);
+        if (config.test_type == "nbinom_exact") {
+            common::logger->trace("Fitting per-sample negative binomial distributions");
 
-        common::logger->trace("Precomputing PMFs");
-        std::vector<long double> pmf_in { 1.0 };
-        std::vector<long double> pmf_out { 1.0 };
-        std::vector<long double> pmf_null { 1.0 };
 
-        for (size_t i = 1; i <= groups.size(); ++i) {
-            if (groups[i - 1] == Group::OUT || groups[i - 1] == Group::BOTH) {
-                std::vector<long double> pmf_out_cur(pmf_out.size() + 1);
-                pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
-                pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
-                for (size_t k = 1; k < pmf_out.size(); ++k) {
-                    pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1] + (1.0 - p[i - 1]) * pmf_out[k];
+            #pragma omp parallel for num_threads(num_parallel_files)
+            for (size_t j = 0; j < groups.size(); ++j) {
+                const auto &hist = hists[j];
+                if (hist.size()) {
+                    auto [r, p, mu, var] = get_rp([&](const auto &callback) {
+                        for (const auto &[k, c] : hist) {
+                            callback(k, c);
+                        }
+                    });
+                    nb_params[j] = std::make_pair(r, p);
+                    common::logger->trace("{}: size: {}\tmax_val: {}\tsample mean: {}\tsample var: {}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
+                                        j, sums[j], (hist.end() - 1)->first,
+                                        mu, var,
+                                        r * (1-p)/p, r*(1-p)/p/p,
+                                        r, p);
                 }
-                std::swap(pmf_out_cur, pmf_out);
             }
 
-            if (groups[i - 1] == Group::IN || groups[i - 1] == Group::BOTH) {
-                std::vector<long double> pmf_in_cur(pmf_in.size() + 1);
-                pmf_in_cur[0] = (1.0 - p[i - 1]) * pmf_in[0];
-                pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
-                for (size_t k = 1; k < pmf_in.size(); ++k) {
-                    pmf_in_cur[k] = p[i - 1] * pmf_in[k - 1] + (1.0 - p[i - 1]) * pmf_in[k];
+            if (config.test_type == "nbinom_exact") {
+                // based on this method for combining gamma distributions
+                // https://stats.stackexchange.com/questions/72479/generic-sum-of-gamma-random-variables
+                long double num_a = 0.0;
+                long double num_b = 0.0;
+
+                long double denom_a = 0.0;
+                long double denom_b = 0.0;
+
+                for (size_t j = 0; j < groups.size(); ++j) {
+                    const auto &[r, p] = nb_params[j];
+                    long double theta = (1.0 - p) / p;
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                        num_b += theta * r;
+                        denom_b += theta * theta * r;
+                    }
+
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                        num_a += theta * r;
+                        denom_a += theta * theta * r;
+                    }
                 }
-                std::swap(pmf_in_cur, pmf_in);
-            }
-            std::vector<long double> pmf_null_cur(i + 1);
-            pmf_null_cur[0] = (1.0 - p[i - 1]) * pmf_null[0];
-            pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
-            for (size_t k = 1; k < pmf_null.size(); ++k) {
-                pmf_null_cur[k] = p[i - 1] * pmf_null[k - 1] + (1.0 - p[i - 1]) * pmf_null[k];
+
+                long double r_a = num_a * num_a / denom_a;
+                long double theta_a = num_a / r_a;
+
+                long double r_b = num_b * num_b / denom_b;
+                long double theta_b = num_b / r_b;
+
+                long double r_null = pow(num_a + num_a, 2.0) / (denom_a + denom_b);
+                long double theta_null = (num_a + num_b) / r_null;
+
+                nb_params_a.first = r_a;
+                nb_params_b.first = r_b;
+                nb_params_null.first = r_null;
+
+                // theta = (1 - p) / p
+                // ptheta = 1 - p
+                // ptheta + p = 1
+                // p = 1/(1+theta)
+
+                nb_params_a.second = 1.0 / (theta_a + 1.0);
+                nb_params_b.second = 1.0 / (theta_b + 1.0);
+                nb_params_null.second = 1.0 / (theta_null + 1.0);
+                // long double mu_a = 0.0;
+                // long double mu_b = 0.0;
+                // long double var_a = 0.0;
+                // long double var_b = 0.0;
+                // for (size_t j = 0; j < groups.size(); ++j) {
+                //     const auto &[r, p] = nb_params[j];
+                //     long double mu = r * (1.0 - p) / p;
+                //     long double var = mu / p;
+                //     if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                //         mu_a += mu;
+                //         var_a += var;
+                //     }
+
+                //     if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                //         mu_b += mu;
+                //         var_b += var;
+                //     }
+                // }
+
+                // nb_params_a.first = mu_a * mu_a / (var_a - mu_a);
+                // nb_params_a.second = mu_a / var_a;
+                common::logger->trace("In: r: {}\tp: {}", nb_params_a.first, nb_params_a.second);
+
+                // nb_params_b.first = mu_b * mu_b / (var_b - mu_b);
+                // nb_params_b.second = mu_b / var_b;
+                common::logger->trace("Out: r: {}\tp: {}", nb_params_b.first, nb_params_b.second);
+
+                // long double mu_null = mu_a + mu_b;
+                // long double var_null = var_a + var_b; // the two are independent, so their covariance is 0
+                // nb_params_null.first = mu_null * mu_null / (var_null - mu_null);
+                // nb_params_null.second = mu_null / var_null;
+                common::logger->trace("Null: r: {}\tp: {}", nb_params_null.first, nb_params_null.second);
+
+                nb_base = (lgammal(nb_params_null.first) - lgammal(nb_params_a.first) - lgammal(nb_params_b.first)) / logl(2.0);
+                nb_base += nb_params_a.first*log2l(nb_params_a.second) + nb_params_b.first*log2l(nb_params_b.second)-nb_params_null.first*log2l(nb_params_null.second);
             }
-            std::swap(pmf_null_cur, pmf_null);
         }
 
-        if (pmf_in.size() != num_labels_in + 1) {
-            common::logger->error("PMF in wrong: {} != {}", pmf_in.size(), num_labels_in + 1);
-            throw std::domain_error("");
+        using RowGenerator = std::function<void(const std::function<void(size_t, uint64_t)>&)>;
+        using GetLambda = std::function<std::pair<long double, long double>(size_t, const RowGenerator&)>;
+        GetLambda get_lambda;
+
+        std::pair<long double, long double> pln_params_a;
+        std::pair<long double, long double> pln_params_b;
+        std::pair<long double, long double> pln_params_null;
+        if (config.test_type == "poisson_bayes") {
+            auto get_params = [&](long double kmers, long double sq_kmers) {
+                long double mu = static_cast<long double>(kmers) / nelem;
+                long double mu2 = mu * mu;
+                long double var = sq_kmers / nelem - mu2;
+                long double log_sig2 = log((var - mu) / mu2 + 1);
+                long double log_mu = log(mu) - log_sig2 / 2.0;
+                return std::make_pair(log_mu, log_sig2);
+            };
+
+            pln_params_a = get_params(in_kmers, in_sq_kmers);
+            pln_params_b = get_params(out_kmers, out_sq_kmers);
+            pln_params_null = get_params(total_kmers, total_sq_kmers);
+            common::logger->trace("Lognormal MoM\tin:\tmu: {}\tvar: {}", pln_params_a.first, pln_params_a.second);
+            common::logger->trace("Lognormal MoM\tout:\tmu: {}\tvar: {}", pln_params_b.first, pln_params_b.second);
+            common::logger->trace("Lognormal MoM\tnull:\tmu: {}\tvar: {}", pln_params_null.first, pln_params_null.second);
+
+            // common::logger->trace("Precomputing lambdas");
+
+            // #pragma omp parallel for num_threads(num_threads)
+            // for (size_t j = 0; j < groups.size(); ++j) {
+            //     auto [mu, sig2] = groups[j] == Group::IN ? pln_params_a : pln_params_b;
+            //     for (const auto &[k, c] : hists[j]) {
+            //         auto [lambda_j_min, lambda_j_max] = boost::math::tools::bisect(
+            //             [&](long double lambda) {
+            //                 return (mu - logl(lambda)) / sig2 - lambda + k - 1;
+            //             },
+            //             std::numeric_limits<long double>::min(),
+            //             static_cast<long double>(max_obs_vals[j]),
+            //             boost::math::tools::eps_tolerance<long double>(5)
+            //         );
+            //         lambdas[j][k] = (lambda_j_min + lambda_j_max) / 2;
+            //     }
+            // }
         }
 
-        if (pmf_out.size() != num_labels_out + 1) {
-            common::logger->error("PMF out wrong: {} != {}", pmf_out.size(), num_labels_out + 1);
-            throw std::domain_error("");
+        if (config.test_type == "poisson_exact") {
+            get_lambda = [&](size_t, const RowGenerator&) {
+                return std::make_pair(static_cast<long double>(in_kmers) / nelem,
+                                    static_cast<long double>(out_kmers) / nelem);
+            };
         }
 
-        if (pmf_null.size() != num_labels_in + num_labels_out + 1) {
-            common::logger->error("PMF null wrong: {} != {}", pmf_null.size(), num_labels_in + num_labels_out + 1);
-            throw std::domain_error("");
+        std::vector<std::pair<tsl::hopscotch_map<size_t, long double>,
+                            tsl::hopscotch_map<size_t, long double>>> lambdas(num_threads + 1);
+        if (config.test_type == "poisson_bayes") {
+            get_lambda = [&](size_t bucket_idx, const RowGenerator &generate) -> std::pair<long double, long double> {
+                size_t in_sum = 0;
+                size_t out_sum = 0;
+                generate([&](size_t j, uint64_t c) {
+                    Group gp = groups[j];
+
+                    if (gp == Group::OUT || gp == Group::BOTH)
+                        out_sum += c;
+
+                    if (gp == Group::IN || gp == Group::BOTH)
+                        in_sum += c;
+                });
+
+                auto &[in_lambdas, out_lambdas] = lambdas[bucket_idx];
+                auto in_found = in_lambdas.find(in_sum);
+                auto out_found = out_lambdas.find(out_sum);
+
+                if (in_found == in_lambdas.end()) {
+                    auto [mu, sig2] = pln_params_a;
+                    auto [lambda_j_min, lambda_j_max] = boost::math::tools::bisect(
+                        [&](long double lambda) {
+                            return (mu - logl(lambda)) / sig2 - lambda + in_sum - 1;
+                        },
+                        std::numeric_limits<long double>::min(),
+                        static_cast<long double>(max_in_obs_val),
+                        boost::math::tools::eps_tolerance<long double>(5)
+                    );
+                    long double lambda = (lambda_j_min + lambda_j_max) / 2;
+                    in_found = in_lambdas.try_emplace(in_sum, lambda).first;
+                }
+
+                if (out_found == out_lambdas.end()) {
+                    auto [mu, sig2] = pln_params_b;
+                    auto [lambda_j_min, lambda_j_max] = boost::math::tools::bisect(
+                        [&](long double lambda) {
+                            return (mu - logl(lambda)) / sig2 - lambda + out_sum - 1;
+                        },
+                        std::numeric_limits<long double>::min(),
+                        static_cast<long double>(max_out_obs_val),
+                        boost::math::tools::eps_tolerance<long double>(5)
+                    );
+                    long double lambda = (lambda_j_min + lambda_j_max) / 2;
+                    out_found = out_lambdas.try_emplace(out_sum, lambda).first;
+                }
+
+                return std::make_pair(in_found->second, out_found->second);
+            };
         }
 
-        common::logger->trace("Precomputing p-values");
-        pb_pvals[0].emplace_back(1.0);
+        // precompute p-values for poisson_binom test
+        std::vector<std::vector<long double>> pb_pvals(num_labels_in + num_labels_out + 1);
+        std::vector<long double> mid_points(num_labels_in + num_labels_out + 1);
+        if (config.test_type == "poisson_binom") {
+            // fit distribution
+            std::vector<long double> p;
+            p.reserve(groups.size());
+            for (size_t i = 0; i < groups.size(); ++i) {
+                p.emplace_back(static_cast<long double>(n_kmers[i]) / nelem);
+            }
 
-        long double min_pval = 1.0;
+            common::logger->trace("p: {}", fmt::join(p, ","));
 
-        for (size_t n = 1; n < pb_pvals.size(); ++n) {
-            std::vector<long double> probs;
-            size_t front = n - std::min(n, num_labels_out);
-            for (uint64_t s = 0; s < pmf_in.size(); ++s) {
-                if (s > n)
-                    break;
-                uint64_t t = n - s;
-                if (s < pmf_in.size() && t < pmf_out.size()) {
-                    if (s < front) {
-                        common::logger->error("Attempting non-zero p-value in impossible configuration: {},{}", n, s);
-                        throw std::domain_error("");
+            common::logger->trace("Precomputing PMFs");
+            std::vector<long double> pmf_in { 1.0 };
+            std::vector<long double> pmf_out { 1.0 };
+            std::vector<long double> pmf_null { 1.0 };
+
+            for (size_t i = 1; i <= groups.size(); ++i) {
+                if (groups[i - 1] == Group::OUT || groups[i - 1] == Group::BOTH) {
+                    std::vector<long double> pmf_out_cur(pmf_out.size() + 1);
+                    pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
+                    pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
+                    for (size_t k = 1; k < pmf_out.size(); ++k) {
+                        pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1] + (1.0 - p[i - 1]) * pmf_out[k];
                     }
-                    probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t]) - log2(pmf_null[n])));
-                } else {
-                    probs.emplace_back(0.0);
+                    std::swap(pmf_out_cur, pmf_out);
                 }
-            }
 
-            for (uint64_t s = 0; s < probs.size(); ++s) {
-                pb_pvals[n].emplace_back(0.0);
-                if (s > n)
-                    break;
-                uint64_t t = n - s;
-                if (s >= pmf_in.size() || t >= pmf_out.size())
-                    continue;
-                for (uint64_t sp = 0; sp < probs.size(); ++sp) {
-                    if (sp > n)
-                        break;
-                    uint64_t tp = n - sp;
-                    if (sp >= pmf_in.size() || tp >= pmf_out.size())
-                        continue;
-                    if (probs[sp] <= probs[s])
-                        pb_pvals[n][s] += probs[sp];
+                if (groups[i - 1] == Group::IN || groups[i - 1] == Group::BOTH) {
+                    std::vector<long double> pmf_in_cur(pmf_in.size() + 1);
+                    pmf_in_cur[0] = (1.0 - p[i - 1]) * pmf_in[0];
+                    pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
+                    for (size_t k = 1; k < pmf_in.size(); ++k) {
+                        pmf_in_cur[k] = p[i - 1] * pmf_in[k - 1] + (1.0 - p[i - 1]) * pmf_in[k];
+                    }
+                    std::swap(pmf_in_cur, pmf_in);
                 }
+                std::vector<long double> pmf_null_cur(i + 1);
+                pmf_null_cur[0] = (1.0 - p[i - 1]) * pmf_null[0];
+                pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
+                for (size_t k = 1; k < pmf_null.size(); ++k) {
+                    pmf_null_cur[k] = p[i - 1] * pmf_null[k - 1] + (1.0 - p[i - 1]) * pmf_null[k];
+                }
+                std::swap(pmf_null_cur, pmf_null);
             }
 
-            for (uint64_t s = 0; s < front; ++s) {
-                if (pb_pvals[n][s] > 0) {
-                    common::logger->error("Non-zero p-value in impossible configuration: {},{}", n, s);
-                    throw std::domain_error("");
-                }
+            if (pmf_in.size() != num_labels_in + 1) {
+                common::logger->error("PMF in wrong: {} != {}", pmf_in.size(), num_labels_in + 1);
+                throw std::domain_error("");
             }
-            long double local_min_pval = std::min(pb_pvals[n][front],
-                                                  pb_pvals[n].back());
-            min_pval = std::min(min_pval, local_min_pval);
-
-            size_t max_prob_counts = 0;
-            long double max_prob_pos = 0;
-            // double max_prob = -1;
-
-            for (uint64_t s = front; s < pb_pvals[n].size(); ++s) {
-                if (pb_pvals[n][s] < local_min_pval) {
-                    common::logger->error("Min p-value not at boundary: {},{}: {} < {}", n, s, pb_pvals[n][s], local_min_pval);
-                    throw std::domain_error("");
-                }
+
+            if (pmf_out.size() != num_labels_out + 1) {
+                common::logger->error("PMF out wrong: {} != {}", pmf_out.size(), num_labels_out + 1);
+                throw std::domain_error("");
             }
-            if (front + 1 == pb_pvals[n].size()) {
-                max_prob_counts = 1;
-                max_prob_pos = front;
-            } else if (front + 2 == pb_pvals[n].size()) {
-                max_prob_counts = 2;
-                max_prob_pos = front + front + 1;
-            } else {
-                for (uint64_t s = front + 1; s < pb_pvals[n].size(); ++s) {
-                    if (probs[s] == probs[s-1]) {
-                        max_prob_counts = 2;
-                        max_prob_pos = s + s;
+
+            if (pmf_null.size() != num_labels_in + num_labels_out + 1) {
+                common::logger->error("PMF null wrong: {} != {}", pmf_null.size(), num_labels_in + num_labels_out + 1);
+                throw std::domain_error("");
+            }
+
+            common::logger->trace("Precomputing p-values");
+            pb_pvals[0].emplace_back(1.0);
+
+            long double min_pval = 1.0;
+
+            for (size_t n = 1; n < pb_pvals.size(); ++n) {
+                std::vector<long double> probs;
+                size_t front = n - std::min(n, num_labels_out);
+                for (uint64_t s = 0; s < pmf_in.size(); ++s) {
+                    if (s > n)
                         break;
+                    uint64_t t = n - s;
+                    if (s < pmf_in.size() && t < pmf_out.size()) {
+                        if (s < front) {
+                            common::logger->error("Attempting non-zero p-value in impossible configuration: {},{}", n, s);
+                            throw std::domain_error("");
+                        }
+                        probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t]) - log2(pmf_null[n])));
+                    } else {
+                        probs.emplace_back(0.0);
                     }
+                }
 
-                    if (s + 1 < pb_pvals[n].size()) {
-                        if (probs[s-1] < probs[s] && probs[s] < probs[s+1])
+                for (uint64_t s = 0; s < probs.size(); ++s) {
+                    pb_pvals[n].emplace_back(0.0);
+                    if (s > n)
+                        break;
+                    uint64_t t = n - s;
+                    if (s >= pmf_in.size() || t >= pmf_out.size())
+                        continue;
+                    for (uint64_t sp = 0; sp < probs.size(); ++sp) {
+                        if (sp > n)
+                            break;
+                        uint64_t tp = n - sp;
+                        if (sp >= pmf_in.size() || tp >= pmf_out.size())
                             continue;
+                        if (probs[sp] <= probs[s])
+                            pb_pvals[n][s] += probs[sp];
+                    }
+                }
 
-                        if (probs[s-1] > probs[s] && probs[s] > probs[s+1])
-                            continue;
+                for (uint64_t s = 0; s < front; ++s) {
+                    if (pb_pvals[n][s] > 0) {
+                        common::logger->error("Non-zero p-value in impossible configuration: {},{}", n, s);
+                        throw std::domain_error("");
+                    }
+                }
+                long double local_min_pval = std::min(pb_pvals[n][front],
+                                                    pb_pvals[n].back());
+                min_pval = std::min(min_pval, local_min_pval);
+
+                size_t max_prob_counts = 0;
+                long double max_prob_pos = 0;
+                // double max_prob = -1;
+
+                for (uint64_t s = front; s < pb_pvals[n].size(); ++s) {
+                    if (pb_pvals[n][s] < local_min_pval) {
+                        common::logger->error("Min p-value not at boundary: {},{}: {} < {}", n, s, pb_pvals[n][s], local_min_pval);
+                        throw std::domain_error("");
+                    }
+                }
+                if (front + 1 == pb_pvals[n].size()) {
+                    max_prob_counts = 1;
+                    max_prob_pos = front;
+                } else if (front + 2 == pb_pvals[n].size()) {
+                    max_prob_counts = 2;
+                    max_prob_pos = front + front + 1;
+                } else {
+                    for (uint64_t s = front + 1; s < pb_pvals[n].size(); ++s) {
+                        if (probs[s] == probs[s-1]) {
+                            max_prob_counts = 2;
+                            max_prob_pos = s + s;
+                            break;
+                        }
 
-                        if (probs[s] > probs[s-1] && probs[s] > probs[s+1]) {
-                            if (probs[s] - probs[s-1] == probs[s]-probs[s+1]) {
-                                max_prob_counts = 1;
-                                max_prob_pos = s;
-                            } else if (probs[s] - probs[s-1] > probs[s]-probs[s+1]) {
-                                max_prob_counts = 2;
-                                max_prob_pos = s + s + 1;
-                            } else {
-                                max_prob_counts = 2;
-                                max_prob_pos = s + s - 1;
+                        if (s + 1 < pb_pvals[n].size()) {
+                            if (probs[s-1] < probs[s] && probs[s] < probs[s+1])
+                                continue;
+
+                            if (probs[s-1] > probs[s] && probs[s] > probs[s+1])
+                                continue;
+
+                            if (probs[s] > probs[s-1] && probs[s] > probs[s+1]) {
+                                if (probs[s] - probs[s-1] == probs[s]-probs[s+1]) {
+                                    max_prob_counts = 1;
+                                    max_prob_pos = s;
+                                } else if (probs[s] - probs[s-1] > probs[s]-probs[s+1]) {
+                                    max_prob_counts = 2;
+                                    max_prob_pos = s + s + 1;
+                                } else {
+                                    max_prob_counts = 2;
+                                    max_prob_pos = s + s - 1;
+                                }
                             }
                         }
                     }
                 }
-            }
 
-            if (max_prob_counts == 0) {
-                max_prob_counts = 2;
-                max_prob_pos = front - 1 + front;
+                if (max_prob_counts == 0) {
+                    max_prob_counts = 2;
+                    max_prob_pos = front - 1 + front;
+                }
+
+                mid_points[n] = static_cast<long double>(max_prob_pos) / max_prob_counts;
+                common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}",
+                                        n, mid_points[n],
+                                        fmt::join(probs,","),
+                                        fmt::join(pb_pvals[n],","));
             }
 
-            mid_points[n] = static_cast<long double>(max_prob_pos) / max_prob_counts;
-            common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}",
-                                    n, mid_points[n],
-                                    fmt::join(probs,","),
-                                    fmt::join(pb_pvals[n],","));
+            common::logger->trace("Min. p-value: {}", min_pval);
+            if (min_pval >= config.family_wise_error_rate) {
+                common::logger->warn("No significant p-values achievable");
+                auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
+                    graph_ptr, [](node_index) { return false; }, true,
+                    is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+                );
+
+                auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
+                    graph_ptr, [](node_index) { return false; }, true,
+                    is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+                );
+
+                return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage{}, nullptr);
+            }
         }
 
-        common::logger->trace("Min. p-value: {}", min_pval);
-        if (min_pval >= config.family_wise_error_rate) {
-            common::logger->warn("No significant p-values achievable");
-            auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
-                graph_ptr, [](node_index) { return false; }, true,
-                is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-            );
+        std::pair<long double, long double> bb_params_a;
+        std::pair<long double, long double> bb_params_b;
+        std::pair<long double, long double> bb_params_null;
+        long double bb_base = 0.0;
+        if (config.test_type == "bb_hypergeometric") {
+            auto get_ab = [&](long double n, long double n2) {
+                long double mu = n / nelem;
+                long double var = n2 / nelem - mu * mu;
+                long double a = mu * (n * mu - mu * mu - var) / n / (var - mu + mu * mu / n);
+                long double b = a * (n / mu - 1);
+                return std::make_pair(a, b);
+            };
 
-            auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
-                graph_ptr, [](node_index) { return false; }, true,
-                is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-            );
+            bb_params_a = get_ab(in_kmers, in_sq_kmers);
+            bb_params_b = get_ab(out_kmers, out_sq_kmers);
+            bb_params_null = get_ab(total_kmers, total_sq_kmers);
+
+            const auto &[a_a, b_a] = bb_params_a;
+            const auto &[a_b, b_b] = bb_params_b;
+            const auto &[a_n, b_n] = bb_params_null;
 
-            return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage{}, nullptr);
+            long double nx = static_cast<long double>(in_kmers);
+            long double ny = static_cast<long double>(out_kmers);
+
+            bb_base = (lgammal(nx + 1) + lgammal(ny + 1) - lgammal(nx + ny + 1)
+                        - lgammal(a_a + b_a + nx) - lgammal(a_b + b_b + ny)
+                        + lgammal(a_n + b_n + nx + ny) + lgammal(a_n) + lgammal(b_n) - lgammal(a_n + b_n)
+                        - lgammal(a_a) - lgammal(b_a) + lgammal(a_a + b_a)
+                        - lgammal(a_b) - lgammal(b_b) + lgammal(a_b + b_b)) / logl(2.0);
         }
-    }
 
-    // precompute minimal attainable p-values
-    std::vector<std::pair<long double, size_t>> m;
-    std::vector<size_t> offsets;
-    if (config.test_type != "notest") {
-        common::logger->trace("Computing minimum p-values");
-        std::vector<std::vector<std::pair<long double, size_t>>> ms(num_threads + 1);
-        generate_clean_rows([&](uint64_t row_i, const auto &row, uint64_t bucket_idx) {
-            long double pval = 1.0;
-            size_t n = 0;
-            if (config.test_type == "poisson_binom") {
-                for (const auto &[j, c] : row) {
-                    n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
-                }
-                size_t front = n - std::min(n, num_labels_out);
-                pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
-            } else if (config.test_type == "poisson_exact" || config.test_type == "poisson_bayes") {
-                long double mu1 = 0.0;
-                long double mu2 = 0.0;
-                if (config.test_type == "poisson_exact") {
-                    mu1 = static_cast<long double>(in_kmers) / nelem;
-                    mu2 = static_cast<long double>(out_kmers) / nelem;
-                }
-                sdsl::bit_vector found(num_labels_in + num_labels_out);
-                for (const auto &[j, c] : row) {
-                    n += c;
-                    if (config.test_type == "poisson_bayes") {
-                        found[j] = true;
-                        const auto &[r, p] = nb_params[j];
-                        long double lambda_j = (1.0 - p) * (r + c - 1);
+        std::pair<long double, long double> gp_params_a;
+        std::pair<long double, long double> gp_params_b;
+        std::vector<std::vector<long double>> gp_pvals;
+        // std::pair<long double, long double> gp_params_n;
+        // long double gp_base = 0.0;
+        if (config.test_type == "gpoisson") {
+            auto &[lambda_a, omega_a] = gp_params_a;
+            auto &[lambda_b, omega_b] = gp_params_b;
+            // auto &[lambda_n, omega_n] = gp_params_n;
+
+            lambda_a = static_cast<long double>(in_kmers) / nelem;
+            lambda_b = static_cast<long double>(out_kmers) / nelem;
+            // lambda_n = static_cast<long double>(total_kmers) / nelem;
+
+            common::logger->trace("Calculating row sums");
+            tsl::hopscotch_map<size_t, size_t> row_sums_a;
+            tsl::hopscotch_map<size_t, size_t> row_sums_b;
+            VectorSet<size_t> row_sums_n;
+            // tsl::hopscotch_map<size_t, size_t> row_sums_n;
+            {
+                std::vector<tsl::hopscotch_map<size_t, size_t>> row_sums_a_m(num_threads + 1);
+                std::vector<tsl::hopscotch_map<size_t, size_t>> row_sums_b_m(num_threads + 1);
+                std::vector<VectorSet<size_t>> row_sums_n_m(num_threads + 1);
+                generate_clean_rows([&](auto, const auto &row, size_t bucket_idx) {
+                    size_t in_sum = 0;
+                    size_t out_sum = 0;
+                    for (const auto &[j, c] : row) {
+                        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                            in_sum += c;
+
                         if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                            mu2 += lambda_j;
+                            out_sum += c;
+                    }
+                    ++row_sums_a_m[bucket_idx][in_sum];
+                    ++row_sums_b_m[bucket_idx][out_sum];
+                    row_sums_n_m[bucket_idx].emplace(in_sum + out_sum);
+                    // ++row_sums_n_m[bucket_idx][in_sum + out_sum];
+                });
 
-                        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                            mu1 += lambda_j;
+                row_sums_a = std::move(row_sums_a_m[0]);
+                row_sums_b = std::move(row_sums_b_m[0]);
+                row_sums_n = std::move(row_sums_n_m[0]);
+
+                for (size_t i = 1; i <= num_threads; ++i) {
+                    for (const auto &[k, c] : row_sums_a_m[i]) {
+                        row_sums_a[k] += c;
+                    }
+                    for (const auto &[k, c] : row_sums_b_m[i]) {
+                        row_sums_b[k] += c;
                     }
+                    for (size_t k : row_sums_n_m[i]) {
+                        row_sums_n.emplace(k);
+                    }
+                    // for (const auto &[k, c] : row_sums_n_m[i]) {
+                    //     row_sums_n[k] += c;
+                    // }
                 }
+            }
 
-                if (n > 0) {
-                    if (ms[bucket_idx].size() <= n)
-                        ms[bucket_idx].resize(n + 1);
-
-                    if (ms[bucket_idx][n].second == 0) {
-                        if (config.test_type == "poisson_bayes") {
-                            for (size_t j = 0; j < found.size(); ++j) {
-                                if (!found[j]) {
-                                    const auto &[r, p] = nb_params[j];
-                                    if (r > 1.0) {
-                                        long double lambda_j = (1.0 - p) * (r - 1);
-                                        if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                                            mu2 += lambda_j;
-
-                                        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                                            mu1 += lambda_j;
-                                    }
-                                }
-                            }
-                        }
+            long double var_a = static_cast<long double>(in_sq_kmers) / nelem - lambda_a * lambda_a;
+            long double var_b = static_cast<long double>(out_sq_kmers) / nelem - lambda_b * lambda_b;
+            // long double var_n = static_cast<long double>(total_sq_kmers) / nelem - lambda_n * lambda_n;
 
-                        long double p = mu1 / (mu1 + mu2);
-                        boost::math::binomial bdist(n, p);
-                        long double pval0 = boost::math::pdf(bdist, 0);
-                        long double pvaln = boost::math::pdf(bdist, n);
-                        pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                        ms[bucket_idx][n].first = pval;
-                        ms[bucket_idx][n].second = 1;
-                    } else {
-                       ++ms[bucket_idx][n].second;
-                    }
+            auto get_omega = [&](long double lambda, long double var, const auto &generate) {
+                long double val = sqrtl(lambda / var);
+                if (1.0 <= val) {
+                    common::logger->trace("Invalid omega: {}\tlambda: {}\tvar: {}", 1.0 - val, lambda, var);
+                    throw std::runtime_error("Fit fail");
                 }
-            } else if (config.test_type == "nbinom_exact") {
-                for (const auto &[j, c] : row) {
-                    n += c;
+
+                return 1.0 - val;
+                // auto [omega_min, omega_max] = boost::math::tools::bisect([&](long double omega) {
+                //     long double dl = nelem / (1.0 - omega);
+                //     generate([&](int64_t k, auto c) {
+                //         dl += (static_cast<long double>(k) - lambda) * (k - 1) / ((1.0 - omega) * lambda + k * omega) * c;
+                //     });
+                //     return dl;
+                // }, 0.0, 1.0 - std::numeric_limits<float>::min(), boost::math::tools::eps_tolerance<double>(5));
+                // return (omega_min + omega_max) / 2.0;
+            };
+
+            omega_a = get_omega(lambda_a, var_a, [&](const auto &callback) {
+                for (const auto &[k, c] : row_sums_a) {
+                    callback(k, c);
+                }
+            });
+            omega_b = get_omega(lambda_b, var_b, [&](const auto &callback) {
+                for (const auto &[k, c] : row_sums_b) {
+                    callback(k, c);
                 }
+            });
+            // omega_n = get_omega(lambda_n, var_n, [&](const auto &callback) {
+            //     for (const auto &[k, c] : row_sums_n) {
+            //         callback(k, c);
+            //     }
+            // });
 
-                if (n > 0) {
-                    if (ms[bucket_idx].size() <= n)
-                        ms[bucket_idx].resize(n + 1);
+            common::logger->trace("Generalized Poisson In:\tlambda: {}\tomega: {}\tsample variance: {}\tvar: {}",
+                                  lambda_a, omega_a, var_a, lambda_a / pow(1.0 - omega_a, 2.0));
+            common::logger->trace("Generalized Poisson Out:\tlambda: {}\tomega: {}\tsample variance: {}\tvar: {}",
+                                  lambda_b, omega_b, var_b, lambda_b / pow(1.0 - omega_b, 2.0));
+            // common::logger->trace("Generalized Poisson Null:\tlambda: {}\tomega: {}\tsample variance: {}\tvar: {}",
+            //                       lambda_n, omega_n, var_n, lambda_n / pow(1.0 - omega_n, 2.0));
 
-                    if (ms[bucket_idx][n].second == 0) {
-                        auto [r_n, p_n] = nb_params_null;
-                        auto [r_a, p_a] = nb_params_a;
-                        auto [r_b, p_b] = nb_params_b;
+            long double gp_base = (log1pl(-omega_a) + log1pl(-omega_b) - (1.0 - omega_a) * lambda_a - (1.0 - omega_b) * lambda_b) / logl(2.0)
+                        + log2l(lambda_a) + log2l(lambda_b);
 
-                        long double l21p = log1p(-p_n) / log2l(2.0);
-                        long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0) - n*l21p;
-                        auto get_pval = [&](int64_t s) {
-                            int64_t t = n - s;
-                            long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1) + s * log1pl(-p_a) + t*log1pl(-p_b)) / logl(2.0);
-                            return exp2l(base + sbase);
-                        };
+            gp_pvals.resize(*std::max_element(row_sums_n.begin(), row_sums_n.end()) + 1);
 
-                        long double pval0 = get_pval(0);
-                        long double pvaln = get_pval(n);
-                        pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                        ms[bucket_idx][n].first = pval;
-                        ms[bucket_idx][n].second = 1;
-                    } else {
-                        ++ms[bucket_idx][n].second;
+            size_t rows_per_update = 1000;
+            ProgressBar progress_bar(row_sums_n.size(), "Precomputing p-values", std::cerr, !common::get_verbose());
+            #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+            for (size_t i = 0; i < row_sums_n.size(); ++i) {
+                if (i > 0 && (i % rows_per_update) == 0)
+                    progress_bar += rows_per_update;
+
+                size_t n = row_sums_n.values_container()[i];
+                size_t s = 0;
+                size_t t = n;
+                long double lprob = gp_base + (-lgammal(s + 1) - lgamma(t + 1) - omega_a * s - omega_b * t) / logl(2.0);
+                // auto get_prob = [&](int64_t s) {
+                //     int64_t t = n - s;
+                //     long double lprob = gp_base + (-lgammal(s + 1) - lgamma(t + 1) - omega_a * s - omega_b * t) / logl(2.0);
+                //     if (s == 0) {
+                //         lprob -= log1pl(-omega_a) * lambda_a / logl(2.0);
+                //     } else if (s > 1) {
+                //         lprob += log2l((1.0 - omega_a) * lambda_a + omega_a * s) * (s - 1);
+                //     }
+                //     if (t == 0) {
+                //         lprob -= log1pl(-omega_b) * lambda_b / logl(2.0);
+                //     } else if (t > 1) {
+                //         lprob += log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
+                //     }
+                //     return exp2l(lprob);
+                // };
+                std::vector<long double> probs;
+                probs.reserve(n + 1);
+                long double cur_lprob = lprob - log1pl(-omega_a) * lambda_a / logl(2.0)
+                                            + log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
+                probs.emplace_back(exp2l(cur_lprob));
+                long double prob_sum = probs.back();
+                for (size_t s = 1; s <= n; ++s) {
+                    int64_t t = n - s;
+                    lprob += log2l(t + 1) - log2l(s) - omega_a + omega_b;
+                    long double cur_lprob = lprob;
+                    if (s == 0) {
+                        cur_lprob -= log1pl(-omega_a) * lambda_a / logl(2.0);
+                    } else if (s > 1) {
+                        cur_lprob += log2l((1.0 - omega_a) * lambda_a + omega_a * s) * (s - 1);
+                    }
+                    if (t == 0) {
+                        cur_lprob -= log1pl(-omega_b) * lambda_b / logl(2.0);
+                    } else if (t > 1) {
+                        cur_lprob += log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
                     }
+                    probs.emplace_back(exp2l(cur_lprob));
+                    prob_sum += probs.back();
                 }
-            }
-        });
 
-        // if (config.test_by_unitig) {
-        //     common::logger->trace("Combining minimal attainable p-values for unitigs");
-        //     offsets.reserve(num_threads + 1);
-        //     offsets.emplace_back(0);
-        //     for (const auto &bucket : pvals_min_buckets) {
-        //         offsets.emplace_back(offsets.back() + bucket.size());
-        //     }
-        //     std::mutex m_vec_mu;
-        //     clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-        //         std::vector<double> pvals;
-        //         pvals.reserve(path.size());
-        //         {
-        //             std::lock_guard<std::mutex> lock(m_vec_mu);
-        //             for (node_index node : path) {
-        //                 size_t row = AnnotatedDBG::graph_to_anno_index(node);
-        //                 size_t row_rank = kept.rank1(row);
-        //                 size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
-        //                 size_t offset = row_rank - offsets[bucket_idx];
-        //                 pvals.emplace_back(get<PValStorage>(pvals_min_buckets[bucket_idx][offset]));
-        //             }
-        //         }
-        //         double comp_min_pval = combine_pvals(pvals);
-        //         size_t row = AnnotatedDBG::graph_to_anno_index(path[0]);
-        //         size_t row_rank = kept.rank1(row);
-        //         size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
-        //         size_t offset = row_rank - offsets[bucket_idx];
-        //         ++ms[bucket_idx][comp_min_pval];
-        //         std::lock_guard<std::mutex> lock(m_vec_mu);
-        //         set<PValStorage>(pvals_min_buckets[bucket_idx][offset], comp_min_pval);
-        //     });
-        // }
-
-        std::swap(m, ms[0]);
-        size_t max_size = m.size();
-        for (size_t i = 1; i < ms.size(); ++i) {
-            max_size = std::max(max_size, ms[i].size());
-        }
-        m.resize(max_size);
-        for (size_t i = 1; i < ms.size(); ++i) {
-            for (size_t n = 0; n < ms[i].size(); ++i) {
-                const auto &[pval, c] = ms[i][n];
-                m[n].first = pval;
-                m[n].second += c;
+                auto &pvals_n = gp_pvals[n];
+                pvals_n.reserve(n + 1);
+                for (size_t s = 0; s <= n; ++s) {
+                    for (size_t sp = 0; sp <= n; ++sp) {
+                        if (probs[sp] <= probs[s])
+                            pvals_n[s] += probs[sp];
+                    }
+                    pvals_n[s] /= prob_sum;
+                }
             }
+            progress_bar += (row_sums_n.size() % rows_per_update);
         }
-    }
 
-    if (m.size() > 1) {
-        auto it = m.begin();
-        auto jt = it + 1;
-        while (jt != m.end()) {
-            while (it->second == 0 && jt != m.end()) {
-                ++it;
-                ++jt;
-            }
-            while (jt != m.end() && jt->second == 0) {
-                ++jt;
+        long double pig_base_a = 0.0;
+        long double pig_base_b = 0.0;
+        long double pig_base_n = 0.0;
+        long double lambda_a = 0.0;
+        long double lambda_b = 0.0;
+        long double lambda_n = 0.0;
+        if (config.test_type == "pig") {
+            // long double mu_a = static_cast<long double>(in_kmers) / nelem;
+            // long double mu_b = static_cast<long double>(out_kmers) / nelem;
+
+            // long double var_a = static_cast<long double>(in_sq_kmers) / nelem - mu_a * mu_a;
+            // long double var_b = static_cast<long double>(out_sq_kmers) / nelem - mu_b * mu_b;
+
+            // common::logger->trace("PIG\tIn:\tmu: {}\tvar: {}\tvar/mu: {}\tOut:\tmu: {}\tvar: {}\tvar/mu: {}",
+            //                       mu_a, var_a, var_a/mu_a,
+            //                       mu_b, var_b, var_b/mu_b);
+
+            auto get_lp = [&](long double x, long double mu, long double lambda) {
+                long double phi = 1.0L / lambda;
+                int64_t i;
+                long double p, pi1m, pi2m;
+                long double twophi = phi + phi;
+
+                long double A, B;
+                long double mu2 = mu * mu;
+                long double twophimu2 = twophi * mu2;
+
+                p = (1.0L - sqrtl(1.0L + twophimu2))/phi/mu; /* log p[0] */
+                if (x == 0.0) {
+                    if (p > 0) {
+                        common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu, lambda, x, p);
+                        throw std::runtime_error("Fail");
+                    }
+                    return p;
+                }
+
+                pi2m = expl(p);                /* p[i - 2] = p[0]*/
+                p = logl(mu) + p - log1pl(twophimu2)/2.0L;   /* log p[1] */
+                if (x == 1.0) {
+                    if (p > 0) {
+                        common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu, lambda, x, p);
+                        throw std::runtime_error("Fail");
+                    }
+                    return p;
+                }
+
+                pi1m = expl(p);                    /* p[i - 1] = p[1] */
+                A = 1.0L/(1.0L + 1.0L/twophimu2);        /* constant in first term */
+                B = mu2/(1.0L + twophimu2);            /* constant in second term */
+                for (i = 2; i <= x; i++) {
+                    p = A * (1.0L - 1.5L/i) * pi1m + (B * pi2m)/(i * (i - 1));
+                    pi2m = pi1m;
+                    pi1m = p;
+                }
+
+                if (p > 1.0) {
+                    common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu, lambda, x, logl(p));
+                    throw std::runtime_error("Fail");
+                }
+
+                return logl(p);
+            };
+
+            std::vector<long double> lambdas(groups.size());
+            #pragma omp parallel for num_threads(num_threads)
+            for (size_t i = 0; i < groups.size(); ++i) {
+                long double mu = static_cast<long double>(sums[i]) / nelem;
+                long double lambda = 0.0;
+                long double val = 0.0;
+                try {
+                    std::tie(lambda, val) = boost::math::tools::brent_find_minima([&](long double lambda) {
+                        long double val = 0.0;
+                        for (const auto &[k, c] : hists[i]) {
+                            val += get_lp(k, mu, lambda) * c;
+                        }
+                        return -val;
+                    }, std::numeric_limits<long double>::min(), 1.0L, 5);
+                } catch (boost::wrapexcept<boost::math::evaluation_error> &) {}
+                try {
+                    auto [phi, val2] = boost::math::tools::brent_find_minima([&](long double phi) {
+                        long double val = 0.0;
+                        for (const auto &[k, c] : hists[i]) {
+                            val += get_lp(k, mu, 1.0L / phi) * c;
+                        }
+                        return -val;
+                    }, std::numeric_limits<long double>::min(), 1.0L, 5);
+                    if (val == 0.0 || val2 < val)
+                        lambda = 1.0L / phi;
+                } catch (boost::wrapexcept<boost::math::evaluation_error> &) {}
+                if (val == 0.0 || lambda == 0.0) {
+                    throw std::runtime_error("FAILS");
+                }
+
+                long double var = static_cast<long double>(sq_sums[i]) / nelem - mu * mu;
+                common::logger->trace("i: {}\tmu: {}\tsample var: {}\tlambda: {}\tvar: {}",
+                                        i, mu, var, lambda, mu + mu * mu * mu / lambda);
+                lambdas[i] = lambda;
             }
-            if (jt == m.end())
-                break;
 
-            if (jt->first - it->first > 1e-5) {
-                common::logger->error("min p-vals not sorted: {} !>= {}", it->first, jt->first);
-                throw std::runtime_error("p-val fail");
+            size_t min_sum_a = std::numeric_limits<size_t>::max();
+            size_t min_sum_b = std::numeric_limits<size_t>::max();
+            size_t min_elem_a = 0;
+            size_t min_elem_b = 0;
+            for (size_t i = 0; i < lambdas.size(); ++i) {
+                if (groups[i] == Group::IN && sums[i] < min_sum_a) {
+                    min_sum_a = sums[i];
+                    min_elem_a = i;
+                }
+
+                if (groups[i] == Group::OUT && sums[i] < min_sum_b) {
+                    min_sum_b = sums[i];
+                    min_elem_b = i;
+                }
             }
-            ++it;
-            ++jt;
-        }
-    }
+            size_t min_sum_n = std::min(min_sum_a, min_sum_b);
+            size_t min_elem_n = min_sum_a < min_sum_b ? min_elem_a : min_elem_b;
 
-    // determine cutoffs for multiple testing correction
-    auto [k_min, k, n_cutoff] = correct_pvals(m);
+            long double min_lambda_a = lambdas[min_elem_a];
+            long double min_lambda_b = lambdas[min_elem_b];
+            long double min_lambda_n = lambdas[min_elem_n];
 
-    common::logger->trace("Picked: k: {}\tn: {} / {}\tpval_min: {}\tn_cutoff: {}",
-                          k_min, k, nelem, config.family_wise_error_rate / k, n_cutoff);
+            lambda_a = 0.0;
+            lambda_b = 0.0;
+            lambda_n = 0.0;
+            for (size_t i = 0; i < lambdas.size(); ++i) {
+                if (groups[i] == Group::IN)
+                    lambda_a += pow(static_cast<long double>(sums[i]) / min_sum_a, 2.0);
 
-    common::logger->trace("Running differential tests");
-    std::atomic_thread_fence(std::memory_order_release);
-    generate_clean_rows([&](uint64_t row_i, const auto &row, uint64_t bucket_idx) {
-        if (config.test_type == "notest") {
-            size_t count_in = 0;
-            size_t count_out = 0;
-            for (const auto &[j, c] : row) {
-                if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                    ++count_out;
+                if (groups[i] == Group::OUT)
+                    lambda_b += pow(static_cast<long double>(sums[i]) / min_sum_b, 2.0);
 
-                if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                    ++count_in;
+                lambda_n += pow(static_cast<long double>(sums[i]) / min_sum_n, 2.0);
             }
+            lambda_a *= min_lambda_a;
+            lambda_b *= min_lambda_b;
+            lambda_n *= min_lambda_n;
 
-            bool in_kmer = count_in >= config.min_in_recurrence && count_in <= config.max_in_recurrence;
-            bool out_kmer = count_out >= config.min_out_recurrence && count_out <= config.max_out_recurrence;
+            common::logger->trace("PIG:\tIN:\tmu: {}\tlambda: {}",
+                                       static_cast<long double>(in_kmers) / nelem, lambda_a);
+            common::logger->trace("PIG:\tOUT:\tmu: {}\tlambda: {}",
+                                        static_cast<long double>(out_kmers) / nelem, lambda_b);
+            common::logger->trace("PIG:\tNULL:\tmu: {}\tlambda: {}",
+                                            static_cast<long double>(total_kmers) / nelem, lambda_n);
 
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            if (in_kmer)
-                set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
+            // long double phi_a = (var_a - mu_a) / mu_a / mu_a / mu_a;
+            // long double phi_b = (var_b - mu_b) / mu_b / mu_b / mu_b;
+
+
+            // long double lambda_a = 1.0 / phi_a;
+            // long double lambda_b = 1.0 / phi_b;
+
+            // common::logger->trace("PIG\tIn:\tlambda: {}\tOut:\tlambda: {}", lambda_a, lambda_b);
+
+            // long double min_mu = std::min(mu_a, mu_b);
+            // long double w_a = mu_a / min_mu;
+            // long double w_b = mu_b / min_mu;
+            // long double min_lambda = min_mu == mu_a ? lambda_a : lambda_b;
+
+            // long double mu_n = mu_a + mu_b;
+            // long double lambda_n = min_lambda * pow(w_a + w_b, 2.0);
+            // // throw std::runtime_error("Check");
+
+            // common::logger->trace("PIG\tNull:\tmu: {}\tlambda: {}", mu_n, lambda_n);
 
-            if (out_kmer)
-                set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
+            // pig_base_a = 0.5 * (logl(2.0) - logl(M_PI) + logl(lambda_a)) + lambda_a / mu_a;
+            // pig_base_b = 0.5 * (logl(2.0) - logl(M_PI) + logl(lambda_b)) + lambda_b / mu_b;
+            // pig_base_n = 0.5 * (logl(2.0) - logl(M_PI) + logl(lambda_n)) + lambda_n / mu_n;
 
-            return;
+
+            // long double var_n = static_cast<long double>(total_sq_kmers) / nelem - mu_n * mu_n;
+            // long double lambda = mu_n * mu_n * mu_n / (var_n - mu_n);
+
+            // common::logger->trace("PIG: lambda: {}", lambda);
         }
 
-        auto set_pval = [&](long double pval, long double eff_size) {
-            if (!config.test_by_unitig) {
-                if (pval * k < config.family_wise_error_rate) {
-                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                    if (eff_size > 0) {
-                        set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
-                    } else if (eff_size < 0) {
-                        set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
+        // precompute minimal attainable p-values
+        std::vector<std::pair<long double, size_t>> m;
+        std::vector<size_t> offsets;
+        if (config.test_type != "notest" && config.test_type != "nbinom_exact" && config.test_type != "pig") {
+            common::logger->trace("Computing minimum p-values");
+            std::vector<std::vector<std::pair<long double, size_t>>> ms(num_threads + 1);
+            std::vector<VectorMap<long double, size_t>> ms_map(num_threads + 1);
+            std::vector<std::vector<std::vector<long double>>> gp_pvals_m(num_threads + 1);
+            std::mutex mu;
+            generate_clean_rows([&](uint64_t row_i, const auto &row, uint64_t bucket_idx) {
+                long double pval = 1.0;
+                size_t n = 0;
+                if (config.test_type == "poisson_binom") {
+                    for (const auto &[j, c] : row) {
+                        n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
+                    }
+                    size_t front = n - std::min(n, num_labels_out);
+                    pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+                } else if (config.test_type == "poisson_exact") {
+                    sdsl::bit_vector found(num_labels_in + num_labels_out);
+                    size_t n = 0;
+                    for (const auto &[j, c] : row) {
+                        n += c;
+                        found[j] = true;
+                    }
+
+                    auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto &callback) {
+                        for (const auto &[j, c] : row) {
+                            callback(j, c);
+                        }
+                        for (size_t j = 0; j < found.size(); ++j) {
+                            if (!found[j])
+                                callback(j, 0);
+                        }
+                    });
+
+                    if (n > 0) {
+                        if (ms[bucket_idx].size() <= n)
+                            ms[bucket_idx].resize(n + 1);
+
+                        if (ms[bucket_idx][n].second == 0) {
+                            long double p = mu1 / (mu1 + mu2);
+                            boost::math::binomial bdist(n, p);
+                            long double pval0 = boost::math::pdf(bdist, 0);
+                            long double pvaln = boost::math::pdf(bdist, n);
+                            pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                            ms[bucket_idx][n].first = pval;
+                            ms[bucket_idx][n].second = 1;
+                        } else {
+                            ++ms[bucket_idx][n].second;
+                        }
+                    }
+                } else if (config.test_type == "nbinom_exact") {
+                    for (const auto &[j, c] : row) {
+                        n += c;
+                    }
+
+                    if (n > 0) {
+                        if (ms[bucket_idx].size() <= n)
+                            ms[bucket_idx].resize(n + 1);
+
+                        if (ms[bucket_idx][n].second == 0) {
+                            auto [r_n, p_n] = nb_params_null;
+                            auto [r_a, p_a] = nb_params_a;
+                            auto [r_b, p_b] = nb_params_b;
+
+                            long double l21p = log1p(-p_n) / log2l(2.0);
+                            long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0) - n*l21p;
+                            auto get_pval = [&](int64_t s) {
+                                int64_t t = n - s;
+                                long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1) + s * log1pl(-p_a) + t*log1pl(-p_b)) / logl(2.0);
+                                return exp2l(base + sbase);
+                            };
+
+                            long double pval0 = get_pval(0);
+                            long double pvaln = get_pval(n);
+                            pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                            ms[bucket_idx][n].first = pval;
+                            ms[bucket_idx][n].second = 1;
+                        } else {
+                            ++ms[bucket_idx][n].second;
+                        }
+                    }
+                } else if (config.test_type == "hypergeometric" || config.test_type == "poisson_bayes") {
+                    size_t n = 0;
+                    for (const auto &[j, c] : row) {
+                        n += c;
+                    }
+                    if (n > 0) {
+                        // if (ms[bucket_idx].size() <= n)
+                        //     ms[bucket_idx].resize(n + 1);
+
+                        // if (ms[bucket_idx][n].second == 0) {
+                            sdsl::bit_vector found(groups.size());
+                            auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto &callback) {
+                                for (const auto &[j, c] : row) {
+                                    callback(j, c);
+                                }
+                                for (size_t j = 0; j < found.size(); ++j) {
+                                    if (!found[j])
+                                        callback(j, 0);
+                                }
+                            });
+                            boost::math::binomial_distribution dist(static_cast<long double>(n), mu1 / (mu1 + mu2));
+                            // boost::math::hypergeometric_distribution dist(mu1 * nelem, n, (mu1 + mu2) * nelem);
+                            long double pval0 = boost::math::pdf(dist, 0);
+                            long double pvaln = boost::math::pdf(dist, n);
+                            pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                            ++ms_map[bucket_idx][pval];
+                            // ms[bucket_idx][n].first = pval;
+                            // ms[bucket_idx][n].second = 1;
+                        // } else {
+                        //    ++ms[bucket_idx][n].second;
+                        // }
+                    }
+                } else if (config.test_type == "pig") {
+                    size_t n = 0;
+                    for (const auto &[j, c] : row) {
+                        n += c;
+                    }
+
+                    if (n > 0) {
+                        if (ms[bucket_idx].size() <= n)
+                            ms[bucket_idx].resize(n + 1);
+
+                        if (ms[bucket_idx][n].second == 0) {
+                            // https://search.r-project.org/CRAN/refmans/actuar/html/PoissonInverseGaussian.html
+                            long double mu_a = static_cast<long double>(in_kmers) / nelem;
+                            long double mu_b = static_cast<long double>(out_kmers) / nelem;
+
+                            // long double var_a = static_cast<long double>(in_sq_kmers) / nelem - mu_a * mu_a;
+                            // long double var_b = static_cast<long double>(out_sq_kmers) / nelem - mu_b * mu_b;
+
+                            // long double phi_a = (var_a - mu_a) / mu_a / mu_a / mu_a;
+                            // long double phi_b = (var_b - mu_b) / mu_b / mu_b / mu_b;
+                            // long double lambda_a = 1.0 / phi_a;
+                            // long double lambda_b = 1.0 / phi_b;
+
+                            auto get_lp = [&](long double x, long double mu, long double lambda, long double base) {
+                                long double phi = 1.0L / lambda;
+                                int64_t i;
+                                long double p, pi1m, pi2m;
+                                long double twophi = phi + phi;
+
+                                long double A, B;
+                                long double mu2 = mu * mu;
+                                long double twophimu2 = twophi * mu2;
+
+                                p = (1.0L - sqrtl(1.0L + twophimu2))/phi/mu; /* log p[0] */
+                                if (x == 0.0) {
+                                    if (p > 0) {
+                                        common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, p);
+                                        throw std::runtime_error("Fail");
+                                    }
+                                    return p;
+                                }
+
+                                pi2m = expl(p);                /* p[i - 2] = p[0]*/
+                                p = logl(mu) + p - log1pl(twophimu2)/2.0L;   /* log p[1] */
+                                if (x == 1.0) {
+                                    if (p > 0) {
+                                        common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, p);
+                                        throw std::runtime_error("Fail");
+                                    }
+                                    return p;
+                                }
+
+                                pi1m = expl(p);                    /* p[i - 1] = p[1] */
+                                A = 1.0L/(1.0L + 1.0L/twophimu2);        /* constant in first term */
+                                B = mu2/(1.0L + twophimu2);            /* constant in second term */
+                                for (i = 2; i <= x; i++) {
+                                    p = A * (1.0L - 1.5L/i) * pi1m + (B * pi2m)/(i * (i - 1));
+                                    pi2m = pi1m;
+                                    pi1m = p;
+                                }
+
+                                if (p > 1.0) {
+                                    common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, logl(p));
+                                    throw std::runtime_error("Fail");
+                                }
+
+                                return logl(p);
+                                // long double lprob = 0.0;
+                                // if (x == 0) {
+                                //     // https://github.com/cran/actuar/blob/acf6c71ebef1374230b1c24789ce4a925134142f/src/poisinvgauss.c#L66
+                                //     long double mu2 = mu * mu;
+                                //     long double phi = 1.0 / lambda;
+                                //     long double twophi = phi + phi;
+                                //     long double twophimu2 = twophi * mu2;
+
+                                //     lprob = (1.0 - sqrtl(1.0 + twophimu2))/phi/mu;
+                                // } else {
+                                //     lprob = base - lgamma(x + 1)
+                                //             - 0.5*(x - 0.5) * (logl(2.0) - logl(lambda) + logl(1.0 + lambda / 2 / mu / mu))
+                                //             + logl(cyl_bessel_kl(x - 0.5, sqrtl(lambda * 2 * (1.0 + lambda / 2 / mu / mu))));
+                                // }
+                                // if (lprob > 0) {
+                                //     common::logger->error("mu: {}\tlambda: {}\tx: {}\tn: {}\tlog(pval): {}\tpval: {}",
+                                //         mu, lambda, x, n, lprob, expl(lprob));
+                                //     throw std::runtime_error("pval > 1");
+                                // }
+                                // return lprob;
+                            };
+
+                            // long double min_mu = std::min(mu_a, mu_b);
+                            // long double w_a = mu_a / min_mu;
+                            // long double w_b = mu_b / min_mu;
+                            // long double min_lambda = min_mu == mu_a ? lambda_a : lambda_b;
+
+                            long double mu_n = mu_a + mu_b;
+                            // long double lambda_n = min_lambda * pow(w_a + w_b, 2.0);
+
+                            long double base_lprob = get_lp(n, mu_n, lambda_n, pig_base_n);
+                            auto get_lprob = [&](int64_t s) {
+                                return get_lp(s, mu_a, lambda_a, pig_base_a) + get_lp(n - s, mu_b, lambda_b, pig_base_b) - base_lprob;
+                            };
+
+                            long double lpval0 = get_lprob(0);
+                            long double lpvaln = get_lprob(n);
+
+                            pval = expl(std::min(lpval0, lpvaln)) * (1 + (lpval0 == lpvaln));
+                            ms[bucket_idx][n].first = pval;
+                            ms[bucket_idx][n].second = 1;
+                        } else {
+                            ++ms[bucket_idx][n].second;
+                        }
+                    }
+                } else if (config.test_type == "gpoisson") {
+                    size_t n = 0;
+                    for (const auto &[j, c] : row) {
+                        n += c;
+                    }
+
+                    if (n > 0) {
+                        if (ms[bucket_idx].size() <= n) {
+                            gp_pvals_m[bucket_idx].resize(n + 1);
+                            ms[bucket_idx].resize(n + 1);
+                        }
+
+                        if (ms[bucket_idx][n].second == 0) {
+                            // const auto &[lambda_a, omega_a] = gp_params_a;
+                            // const auto &[lambda_b, omega_b] = gp_params_b;
+                            // size_t s = 0;
+                            // size_t t = n;
+                            // long double lprob = gp_base + (-lgammal(s + 1) - lgamma(t + 1) - omega_a * s - omega_b * t) / logl(2.0);
+                            // // auto get_prob = [&](int64_t s) {
+                            // //     int64_t t = n - s;
+                            // //     long double lprob = gp_base + (-lgammal(s + 1) - lgamma(t + 1) - omega_a * s - omega_b * t) / logl(2.0);
+                            // //     if (s == 0) {
+                            // //         lprob -= log1pl(-omega_a) * lambda_a / logl(2.0);
+                            // //     } else if (s > 1) {
+                            // //         lprob += log2l((1.0 - omega_a) * lambda_a + omega_a * s) * (s - 1);
+                            // //     }
+                            // //     if (t == 0) {
+                            // //         lprob -= log1pl(-omega_b) * lambda_b / logl(2.0);
+                            // //     } else if (t > 1) {
+                            // //         lprob += log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
+                            // //     }
+                            // //     return exp2l(lprob);
+                            // // };
+                            // std::vector<long double> probs;
+                            // probs.reserve(n + 1);
+                            // long double cur_lprob = lprob - log1pl(-omega_a) * lambda_a / logl(2.0)
+                            //                             + log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
+                            // probs.emplace_back(exp2l(cur_lprob));
+                            // long double prob_sum = probs.back();
+                            // for (size_t s = 1; s <= n; ++s) {
+                            //     int64_t t = n - s;
+                            //     lprob += log2l(t + 1) - log2l(s) - omega_a + omega_b;
+                            //     long double cur_lprob = lprob;
+                            //     if (s == 0) {
+                            //         cur_lprob -= log1pl(-omega_a) * lambda_a / logl(2.0);
+                            //     } else if (s > 1) {
+                            //         cur_lprob += log2l((1.0 - omega_a) * lambda_a + omega_a * s) * (s - 1);
+                            //     }
+                            //     if (t == 0) {
+                            //         cur_lprob -= log1pl(-omega_b) * lambda_b / logl(2.0);
+                            //     } else if (t > 1) {
+                            //         cur_lprob += log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
+                            //     }
+                            //     probs.emplace_back(exp2l(cur_lprob));
+                            //     prob_sum += probs.back();
+                            // }
+
+                            const auto &pvals_n = gp_pvals[n];
+                            // pvals_n.reserve(n + 1);
+                            // for (size_t s = 0; s <= n; ++s) {
+                            //     for (size_t sp = 0; sp <= n; ++sp) {
+                            //         if (probs[sp] <= probs[s])
+                            //             pvals_n[s] += probs[sp];
+                            //     }
+                            //     pvals_n[s] /= prob_sum;
+                            // }
+
+                            pval = std::min(pvals_n[0], pvals_n[n]);
+                            ms[bucket_idx][n].first = pval;
+                            ms[bucket_idx][n].second = 1;
+                        } else {
+                            ++ms[bucket_idx][n].second;
+                        }
+                    }
+                } else if (config.test_type == "bb_hypergeometric") {
+                    size_t n = 0;
+                    for (const auto &[j, c] : row) {
+                        n += c;
+                    }
+                    if (n > 0) {
+                        if (ms[bucket_idx].size() <= n)
+                            ms[bucket_idx].resize(n + 1);
+
+                        if (ms[bucket_idx][n].second == 0) {
+                            const auto &[a_a, b_a] = bb_params_a;
+                            const auto &[a_b, b_b] = bb_params_b;
+                            const auto &[a_n, b_n] = bb_params_null;
+                            long double nx = static_cast<long double>(in_kmers);
+                            long double ny = static_cast<long double>(out_kmers);
+                            long double base = (lgammal(n + 1) + lgammal(nx + ny - n + 1)
+                                                - lgammal(a_n + n) - lgammal(b_n + nx + ny - n)) / logl(2.0) + bb_base;
+                            int64_t s = 0;
+                            long double pval0 = (-lgammal(s + 1) - lgammal(nx - s + 1)
+                                                - lgammal(n - s + 1) - lgammal(ny - n + s + 1)
+                                                + lgammal(a_a + s) + lgammal(b_a + nx - s) + lgammal(a_b + n - s) + lgammal(b_b + ny - n + s)) / logl(2.0) + base;
+                            s = n;
+                            long double pvaln = (-lgammal(s + 1) - lgammal(nx - s + 1)
+                                                - lgammal(n - s + 1) - lgammal(ny - n + s + 1)
+                                                + lgammal(a_a + s) + lgammal(b_a + nx - s) + lgammal(a_b + n - s) + lgammal(b_b + ny - n + s)) / logl(2.0) + base;
+                            pval = exp2l(std::min(pval0, pvaln)) * (1 + (pval0 == pvaln));
+                            ms[bucket_idx][n].first = pval;
+                            ms[bucket_idx][n].second = 1;
+                        } else {
+                        ++ms[bucket_idx][n].second;
+                        }
                     }
                 }
+
+                // if (config.test_by_unitig) {
+                //     node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                //     size_t unitig_id = kmer_to_unitig[node];
+                //     std::lock_guard<std::mutex> lock(mu);
+                //     ++counts_map[unitig_id][n];
+                // }
+            });
+
+            // if (config.test_by_unitig) {
+            //     common::logger->trace("Combining minimal attainable p-values for unitigs");
+            //     offsets.reserve(num_threads + 1);
+            //     offsets.emplace_back(0);
+            //     for (const auto &bucket : pvals_min_buckets) {
+            //         offsets.emplace_back(offsets.back() + bucket.size());
+            //     }
+            //     std::mutex m_vec_mu;
+            //     clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+            //         std::vector<long double> pvals;
+            //         pvals.reserve(path.size());
+            //         {
+            //             std::lock_guard<std::mutex> lock(m_vec_mu);
+            //             for (node_index node : path) {
+            //                 size_t row = AnnotatedDBG::graph_to_anno_index(node);
+            //                 size_t row_rank = kept.rank1(row);
+            //                 size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
+            //                 size_t offset = row_rank - offsets[bucket_idx];
+            //                 pvals.emplace_back(get<PValStorage>(pvals_min_buckets[bucket_idx][offset]));
+            //             }
+            //         }
+            //         double comp_min_pval = combine_pvals(pvals);
+            //         size_t row = AnnotatedDBG::graph_to_anno_index(path[0]);
+            //         size_t row_rank = kept.rank1(row);
+            //         size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
+            //         size_t offset = row_rank - offsets[bucket_idx];
+            //         ++ms[bucket_idx][comp_min_pval];
+            //         std::lock_guard<std::mutex> lock(m_vec_mu);
+            //         set<PValStorage>(pvals_min_buckets[bucket_idx][offset], comp_min_pval);
+            //     });
+            // }
+
+            if (config.test_type == "poisson_bayes") {
+                VectorMap<long double, size_t> m_map = std::move(ms_map[0]);
+                for (size_t i = 1; i < ms_map.size(); ++i) {
+                    for (const auto &[pval, c] : ms_map[i]) {
+                        m_map[pval] += c;
+                    }
+                }
+                m = const_cast<std::vector<std::pair<long double, size_t>>&&>(m_map.values_container());
+                std::sort(m.rbegin(), m.rend());
             } else {
-                push_back(pvals_buckets[bucket_idx], pval);
-                push_back(eff_size_buckets[bucket_idx], eff_size);
-            }
-        };
+                std::swap(m, ms[0]);
+                // std::swap(gp_pvals, gp_pvals_m[0]);
+                size_t max_size = m.size();
+                for (size_t i = 1; i < ms.size(); ++i) {
+                    max_size = std::max(max_size, ms[i].size());
+                }
+                m.resize(max_size);
+                // gp_pvals.resize(max_size);
+                for (size_t i = 1; i < ms.size(); ++i) {
+                    for (size_t n = 0; n < ms[i].size(); ++i) {
+                        const auto &[pval, c] = ms[i][n];
+                        m[n].first = pval;
+                        m[n].second += c;
+                        // if (gp_pvals[n].empty() && gp_pvals_m[i][n].size())
+                        //     std::swap(gp_pvals[n], gp_pvals_m[i][n]);
+                    }
+                }
 
-        long double eff_size = 0.0;
-        long double pval = 1.0;
-        if (config.test_type == "poisson_binom") {
-            size_t n = 0;
-            uint64_t s = 0;
-            for (const auto &[j, c] : row) {
-                n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
-                s += (groups[j] == Group::IN);
+                // if (config.test_type == "gpoisson") {
+                //     m.erase(std::remove_if(m.begin(), m.end(), [&](const auto &a) { return !a.second; }), m.end());
+                //     std::sort(m.rbegin(), m.rend());
+                // }
             }
 
-            if (n < n_cutoff)
-                return;
+            if (m.size() > 1) {
+                auto it = m.begin();
+                auto jt = it + 1;
+                while (jt != m.end()) {
+                    while (it->second == 0 && jt != m.end()) {
+                        ++it;
+                        ++jt;
+                    }
+                    while (jt != m.end() && jt->second == 0) {
+                        ++jt;
+                    }
+                    if (jt == m.end())
+                        break;
 
-            // size_t front = n - std::min(n, num_labels_out);
-            // double min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+                    if (it->first > 1.0) {
+                        common::logger->error("pval > 1.0: {}", it->first);
+                        throw std::runtime_error("p-val fail");
+                    }
 
-            // if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
-            //     return;
+                    if (jt->first > 1.0) {
+                        common::logger->error("pval > 1.0: {}", jt->first);
+                        throw std::runtime_error("p-val fail");
+                    }
 
-            pval = pb_pvals[n][s];
-            eff_size = static_cast<double>(s) - mid_points[n];
-        } else if (config.test_type == "poisson_exact" || config.test_type == "poisson_bayes") {
-            long double mu1 = 0.0;
-            long double mu2 = 0.0;
-            if (config.test_type == "poisson_exact") {
-                mu1 = static_cast<double>(in_kmers) / nelem;
-                mu2 = static_cast<double>(out_kmers) / nelem;
-            }
-            size_t n = 0;
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-            sdsl::bit_vector found(num_labels_in + num_labels_out);
-            for (const auto &[j, c] : row) {
-                n += c;
-                long double lambda_j = 0.0;
-                if (config.test_type == "poisson_bayes") {
-                    const auto &[r, p] = nb_params[j];
-                    lambda_j = (1.0 - p) * (r - 1.0 + c);
-                    found[j] = true;
-                }
-                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                    mu2 += lambda_j;
-                    out_sum += c;
+                    if (jt->first - it->first > 1e-5) {
+                        common::logger->error("min p-vals not sorted: {} vs. {}\t{} !>= {}",
+                                              it - m.begin(), jt - m.begin(),
+                                              it->first, jt->first);
+                        throw std::runtime_error("p-val fail");
+                    }
+                    ++it;
+                    ++jt;
                 }
+            }
+
+            // if (config.test_by_unitig) {
+            //     common::logger->trace("Correcting for monotigs");
+            //     for (const auto &count_map : counts_map) {
+            //         for (const auto &[n, c] : count_map) {
+            //             if (c > 1)
+            //                 m[n].second -= c - 1;
+            //         }
+            //     }
+            // }
+        }
+
+        // determine cutoffs for multiple testing correction
+        auto [k_min, k, n_cutoff] = correct_pvals(m);
+        if (config.test_type == "nbinom_exact" || config.test_type == "pig") {
+            k_min = 1;
+            k = 1;
+            n_cutoff = 1;
+        }
+        // n_cutoff = 1;
+        // k = 1;
+
+        common::logger->trace("Picked: k: {}\tn: {} / {}\tpval_min: {}\tn_cutoff: {}",
+                            k_min, k, nelem, config.family_wise_error_rate / k, n_cutoff);
+
+        common::logger->trace("Running differential tests");
+        std::vector<std::pair<long double, size_t>> nb_pvals;
+        if (config.test_type == "nbinom_exact" || config.test_type == "pig")
+            nb_pvals.resize(kept.num_set_bits(), std::make_pair(1.0, kept.size()));
+
+        std::atomic_thread_fence(std::memory_order_release);
+        generate_clean_rows([&](uint64_t row_i, const auto &row, uint64_t bucket_idx) {
+            if (config.test_type == "notest") {
+                size_t count_in = 0;
+                size_t count_out = 0;
+                for (const auto &[j, c] : row) {
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                        ++count_out;
 
-                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                    mu1 += lambda_j;
-                    in_sum += c;
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                        ++count_in;
                 }
-            }
 
-            if (n < n_cutoff)
+                bool in_kmer = count_in >= config.min_in_recurrence && count_in <= config.max_in_recurrence;
+                bool out_kmer = count_out >= config.min_out_recurrence && count_out <= config.max_out_recurrence;
+
+                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                if (in_kmer)
+                    set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
+
+                if (out_kmer)
+                    set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
+
                 return;
+            }
 
-            if (config.test_type == "poisson_bayes") {
-                for (size_t j = 0; j < found.size(); ++j) {
-                    if (!found[j]) {
-                        const auto &[r, p] = nb_params[j];
-                        if (r > 1.0) {
-                            long double lambda_j = (1.0 - p) * (r - 1);
-                            if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                                mu2 += lambda_j;
-
-                            if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                                mu1 += lambda_j;
+            auto set_pval = [&](long double pval, long double eff_size) {
+                // if (!config.test_by_unitig) {
+                    if (pval * k < config.family_wise_error_rate) {
+                        if (eff_size == 0) {
+                            common::logger->error("Effect size 0 when p-value is {}", pval);
+                            throw std::runtime_error("Pval failure");
+                        }
+
+                        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                        if (eff_size > 0) {
+                            set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
+                        } else if (eff_size < 0) {
+                            set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
                         }
                     }
+                // } else {
+                //     push_back(pvals_buckets[bucket_idx], pval);
+                //     push_back(eff_size_buckets[bucket_idx], eff_size);
+                // }
+            };
+
+            long double eff_size = 0.0;
+            long double pval = 1.0;
+            if (config.test_type == "poisson_binom") {
+                size_t n = 0;
+                uint64_t s = 0;
+                for (const auto &[j, c] : row) {
+                    n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
+                    s += (groups[j] == Group::IN);
                 }
-            }
 
-            long double p = mu1 / (mu1 + mu2);
-            boost::math::binomial bdist(n, p);
-            if (n != 0) {
-                // double pval0 = boost::math::pdf(bdist, 0);
-                // double pvaln = boost::math::pdf(bdist, n);
-                // double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                if (n < n_cutoff)
+                    return;
+
+                // size_t front = n - std::min(n, num_labels_out);
+                // double min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+
                 // if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
                 //     return;
 
-                auto get_deviance = [&](long double y, long double mu) {
-                    y += 1e-8;
-                    mu += 1e-8;
-                    return 2 * (y * log(y/mu) - y + mu);
-                };
+                pval = pb_pvals[n][s];
+                eff_size = static_cast<double>(s) - mid_points[n];
+            } else if (config.test_type == "poisson_exact") {
+                size_t n = 0;
+                sdsl::bit_vector found(num_labels_in + num_labels_out);
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                for (const auto &[j, c] : row) {
+                    n += c;
+                    found[j] = true;
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                        out_sum += c;
 
-                std::vector<long double> devs;
-                devs.reserve(n + 1);
-                for (size_t s = 0; s <= n; ++s) {
-                    devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                        in_sum += c;
                 }
 
-                pval = 0.0;
+                auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto &callback) {
+                    for (const auto &[j, c] : row) {
+                        callback(j, c);
+                    }
+                    for (size_t j = 0; j < found.size(); ++j) {
+                        if (!found[j])
+                            callback(j, 0);
+                    }
+                });
 
-                size_t s = 0;
-                for ( ; s <= n; ++s) {
-                    if (devs[s] < devs[in_sum])
-                        break;
-                }
-                if (s > 0)
-                    pval += boost::math::cdf(bdist, s - 1);
+                if (n < n_cutoff)
+                    return;
 
-                size_t sp = n;
-                for ( ; sp >= s; --sp) {
-                    if (devs[sp] < devs[in_sum])
-                        break;
+                long double p = mu1 / (mu1 + mu2);
+                boost::math::binomial bdist(n, p);
+                if (n != 0) {
+                    // double pval0 = boost::math::pdf(bdist, 0);
+                    // double pvaln = boost::math::pdf(bdist, n);
+                    // double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                    // if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
+                    //     return;
+
+                    auto get_deviance = [&](long double y, long double mu) {
+                        y += 1e-8;
+                        mu += 1e-8;
+                        return 2 * (y * log(y/mu) - y + mu);
+                    };
+
+                    std::vector<long double> devs;
+                    devs.reserve(n + 1);
+                    for (size_t s = 0; s <= n; ++s) {
+                        devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
+                    }
+
+                    pval = 0.0;
+
+                    size_t s = 0;
+                    for ( ; s <= n; ++s) {
+                        if (devs[s] < devs[in_sum])
+                            break;
+                    }
+                    if (s > 0)
+                        pval += boost::math::cdf(bdist, s - 1);
+
+                    size_t sp = n;
+                    for ( ; sp >= s; --sp) {
+                        if (devs[sp] < devs[in_sum])
+                            break;
+                    }
+
+                    if (sp < n)
+                        pval += boost::math::cdf(boost::math::complement(bdist, sp));
+
+                    if (pval * k >= config.family_wise_error_rate)
+                        return;
+
+                    eff_size = static_cast<long double>(in_sum) - boost::math::mode(bdist);
                 }
 
-                if (sp < n)
-                    pval += boost::math::cdf(boost::math::complement(bdist, sp));
+            } else if (config.test_type == "pig") {
+                size_t n = 0;
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                for (const auto &[j, c] : row) {
+                    n += c;
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                        out_sum += c;
+                    }
+
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                        in_sum += c;
+                    }
+                }
 
-                if (!config.test_by_unitig && pval * k >= config.family_wise_error_rate)
+                if (n < n_cutoff)
                     return;
 
-                eff_size = static_cast<long double>(in_sum) - boost::math::mode(bdist);
-            }
+                if (n > 0) {
+                    long double mu_a = static_cast<long double>(in_kmers) / nelem;
+                    long double mu_b = static_cast<long double>(out_kmers) / nelem;
+
+                    // long double var_a = static_cast<long double>(in_sq_kmers) / nelem - mu_a * mu_a;
+                    // long double var_b = static_cast<long double>(out_sq_kmers) / nelem - mu_b * mu_b;
+
+                    // long double phi_a = (var_a - mu_a) / mu_a / mu_a / mu_a;
+                    // long double phi_b = (var_b - mu_b) / mu_b / mu_b / mu_b;
+                    // long double lambda_a = 1.0 / phi_a;
+                    // long double lambda_b = 1.0 / phi_b;
+
+                    auto get_lprobs = [&](long double mu, long double lambda) {
+                        // https://github.com/cran/actuar/blob/acf6c71ebef1374230b1c24789ce4a925134142f/src/poisinvgauss.c#L66
+                        long double phi = 1.0L / lambda;
+                        uint64_t i;
+                        long double p, pi1m, pi2m;
+                        long double twophi = phi + phi;
+
+                        long double A, B;
+                        long double mu2 = mu * mu;
+                        long double twophimu2 = twophi * mu2;
+
+                        p = (1.0L - sqrtl(1.0L + twophimu2))/phi/mu; /* log p[0] */
+                        std::vector<long double> lprobs;
+                        lprobs.reserve(n);
+                        lprobs.emplace_back(p);
+                        // if (x == 0.0) {
+                        //     if (p > 0) {
+                        //         common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, p);
+                        //         throw std::runtime_error("Fail");
+                        //     }
+                        //     return p;
+                        // }
+
+                        pi2m = expl(p);                /* p[i - 2] = p[0]*/
+                        p = logl(mu) + p - log1pl(twophimu2)/2.0L;   /* log p[1] */
+                        lprobs.emplace_back(p);
+                        // if (x == 1.0) {
+                        //     if (p > 0) {
+                        //         common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, p);
+                        //         throw std::runtime_error("Fail");
+                        //     }
+                        //     return p;
+                        // }
+
+                        pi1m = expl(p);                    /* p[i - 1] = p[1] */
+                        A = 1.0L/(1.0L + 1.0L/twophimu2);        /* constant in first term */
+                        B = mu2/(1.0L + twophimu2);            /* constant in second term */
+                        for (i = 2; i <= n; i++) {
+                            p = A * (1.0L - 1.5L/i) * pi1m + (B * pi2m)/(i * (i - 1));
+                            pi2m = pi1m;
+                            pi1m = p;
+                            lprobs.emplace_back(logl(p));
+                        }
 
-        } else if (config.test_type == "nbinom_exact") {
-            size_t n = 0;
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-            for (const auto &[j, c] : row) {
-                n += c;
-                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                    out_sum += c;
+                        return lprobs;
+
+                        // if (p > 1.0) {
+                        //     common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, logl(p));
+                        //     throw std::runtime_error("Fail");
+                        // }
+
+                        // return logl(p);
+                    };
+
+                    // long double min_mu = std::min(mu_a, mu_b);
+                    // long double w_a = mu_a / min_mu;
+                    // long double w_b = mu_b / min_mu;
+                    // long double min_lambda = min_mu == mu_a ? lambda_a : lambda_b;
+
+                    long double mu_n = mu_a + mu_b;
+                    // long double lambda_n = min_lambda * pow(w_a + w_b, 2.0);
+
+                    auto get_deviance = [&](long double x, long double mu, long double lambda) {
+                        return lambda * lambda * pow(x - mu, 2.0) / x / mu / mu;
+                    };
+
+                    auto get_total_deviance = [&](size_t s) {
+                        if (s == 0 || s == n)
+                            return std::numeric_limits<long double>::max();
+
+                        return get_deviance(s, mu_a, lambda_a) + get_deviance(n - s, mu_b, lambda_b);
+                    };
+
+                    long double base_lprob = get_lprobs(mu_n, lambda_n).back();
+                    auto lprobs_a = get_lprobs(mu_a, lambda_a);
+                    auto lprobs_b = get_lprobs(mu_b, lambda_b);
+                    auto get_lprob = [&](int64_t s) {
+                        return lprobs_a[s] + lprobs_b[n - s] - base_lprob;
+                        // return get_lp(s, mu_a, lambda_a) + get_lp(n - s, mu_b, lambda_b) - base_lprob;
+                    };
+
+                    long double in_lprob = get_lprob(in_sum);
+                    long double in_deviance = get_total_deviance(in_sum);
+                    pval = 0.0;
+                    eff_size = static_cast<long double>(in_sum) - mu_a / mu_n * n;
+                    for (size_t s = 0; s <= n; ++s) {
+                        long double dev = get_total_deviance(s);
+                        if (dev >= in_deviance) {
+                            long double lprob = get_lprob(s);
+                            pval += expl(lprob);
+                            if (pval >= config.family_wise_error_rate)
+                                break;
+                        }
+                    }
+                    // size_t s = 0;
+                    // for ( ; s <= n; ++s) {
+                    //     long double lprob = get_lprob(s);
+                    //     if (lprob <= in_lprob) {
+                    //         pval += expl(lprob);
+                    //     } else {
+                    //         break;
+                    //     }
+                    // }
+                    // for (size_t t = 0; t <= n; ++t) {
+                    //     size_t sp = n - t;
+                    //     if (sp == s)
+                    //         break;
+                    //     long double lprob = get_lprob(sp);
+                    //     if (lprob <= in_lprob) {
+                    //         pval += expl(lprob);
+                    //     } else {
+                    //         break;
+                    //     }
+                    // }
+
+                    if (pval == 0.0) {
+                        common::logger->trace("In: ({},{})\tOut: ({},{})\tNull: ({},{})\tn: {}\tin_sum: {}\tin_lprob: {} = {} + {} - {}",
+                            mu_a,lambda_a,
+                            mu_b,lambda_b,
+                            mu_n,lambda_n,
+                            n, in_sum, in_lprob,
+                            lprobs_a[in_sum],
+                            lprobs_b[out_sum],
+                            // get_lp(in_sum, mu_a, lambda_a),
+                            // get_lp(out_sum, mu_b, lambda_b),
+                            base_lprob
+                        );
+                        throw std::runtime_error("Fail calc p-val");
+                    }
+
+                    size_t nb_idx = kept.rank1(row_i) - 1;
+                    nb_pvals[nb_idx].first = pval;
+                    nb_pvals[nb_idx].second = row_i;
                 }
+            } else if (config.test_type == "gpoisson") {
+                size_t n = 0;
+                size_t in_sum = 0;
+                size_t out_sum = 0;
+                for (const auto &[j, c] : row) {
+                    n += c;
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                        out_sum += c;
 
-                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                    in_sum += c;
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                        in_sum += c;
                 }
-            }
 
-            if (n < n_cutoff)
-                return;
+                if (n < n_cutoff)
+                    return;
+
+                if (n != 0) {
+                    size_t mode = 0;
+                    size_t nmode = 0;
+                    const auto &pvals = gp_pvals[n];
+                    for (size_t s = 0; s <= n; ++s) {
+                        if (pvals[s] >= 1.0) {
+                            mode += s;
+                            ++nmode;
+                        }
+                    }
+                    long double midpoint = static_cast<long double>(mode) / nmode;
+                    eff_size = static_cast<long double>(in_sum) - midpoint;
+                    pval = pvals[in_sum];
+                    // const auto &[lambda_a, omega_a] = gp_params_a;
+                    // const auto &[lambda_b, omega_b] = gp_params_b;
+                    // const auto &cprobs = prob_totals[n];
+                    // size_t mode = 0;
+                    // long double last_prob = probs[0];
+                    // long double in_prob = probs[0];
+                    // for (size_t i = 1; i < probs.size(); ++i) {
+                    //     long double cur_prob = probs[i] - probs[i - 1];
+                    //     if (cur_prob > last_prob) {
+                    //         last_prob = cur_prob;
+                    //         mode = i;
+                    //     }
+                    //     if (i == in_sum)
+                    //         in_prob = cur_prob;
+                    // }
+                    // pval = 0.0;
+                    // eff_size = static_cast<ssize_t>(in_sum) - mode;
+                    // long double in_prob = probs[in_sum];
+                    // for (size_t s = 0; s <= mode; ++s) {
+                    //     if (probs[s] <= in_prob)
+                    //         pval +=
+                    // }
+                    // const auto &[lambda_n, omega_n] = gp_params_n;
+                    // long double base = gp_base + lgammal(n + 1) + omega_n * n - logl((1.0 - omega_n)*lambda_n + omega_n*n) * (n - 1);
+                    // auto get_lprob = [&](int64_t s) {
+                    //     int64_t t = n - s;
+                    //     long double lprob = base - lgammal(s + 1) - lgammal(t + 1) - omega_a * s - omega_b * t;
+                    //     if (s == 0) {
+                    //         lprob -= logl(lambda_a) + log1pl(-omega_a);
+                    //     } else if (s > 1) {
+                    //         lprob += logl((1.0 - omega_a) * lambda_a + omega_a*s) * (s - 1);
+                    //     }
+                    //     if (t == 0) {
+                    //         lprob -= logl(lambda_b) + log1pl(-omega_b);
+                    //     } else if (t > 1) {
+                    //         lprob += logl((1.0 - omega_b) * lambda_b + omega_b*t) * (t - 1);
+                    //     }
+                    //     return lprob;
+                    // };
+                    // // {
+                    // //     long double lpval0 = get_lprob(0);
+                    // //     long double lpvaln = get_lprob(n);
+                    // //     long double min_pval = expl(std::min(lpval0, lpvaln)) * (1 + (lpval0 == lpvaln));
+                    // //     if (min_pval * k_min >= config.family_wise_error_rate)
+                    // //         return;
+                    // // }
+                    // pval = 0.0;
+                    // eff_size = static_cast<long double>(in_sum) - lambda_a / lambda_n;
+                    // long double inlp = get_lprob(in_sum);
+                    // for (size_t s = 0; s <= n; ++s) {
+                    //     long double lp = get_lprob(s);
+                    //     if (lp <= inlp)
+                    //         pval += expl(lp);
+
+                    //     if (pval >= k * config.family_wise_error_rate)
+                    //         break;
+                    // }
+                }
+            } else if (config.test_type == "nbinom_exact") {
+                size_t nb_idx = kept.rank1(row_i) - 1;
+                size_t n = 0;
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                for (const auto &[j, c] : row) {
+                    n += c;
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                        out_sum += c;
+                    }
 
-            if (n > 0) {
-                auto [r_a, p_a] = nb_params_a;
-                auto [r_b, p_b] = nb_params_b;
-                long double midpoint = r_a * n / (r_a + r_b);
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                        in_sum += c;
+                    }
+                }
 
-                pval = 0.0;
+                // if (n < n_cutoff)
+                //     return;
 
-                auto [r_n, p_n] = nb_params_null;
+                if (n > 0) {
+                    // long double min_pval = 1.0;
+                    auto [r_a, p_a] = nb_params_a;
+                    auto [r_b, p_b] = nb_params_b;
+                    auto [r_n, p_n] = nb_params_null;
+                    long double min_pval = 1.0;
+                    {
+                        long double l21p = log1p(-p_n) / log2l(2.0);
+                        long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0) - n*l21p;
+                        auto get_pval = [&](int64_t s) {
+                            int64_t t = n - s;
+                            long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1) + s * log1pl(-p_a) + t*log1pl(-p_b)) / logl(2.0);
+                            return exp2l(base + sbase);
+                        };
 
-                auto get_deviance = [&](size_t s, size_t t) {
-                    if (s == 0)
-                        return (r_a*logl(r_a) + (n+r_b)*logl(n+r_b)) * -2.0;
+                        long double pval0 = get_pval(0);
+                        long double pvaln = get_pval(n);
+                        min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                    }
 
-                    if (t == 0)
-                        return (r_b*logl(r_b) + (n+r_a)*logl(n+r_a)) * -2.0;
+                    if (min_pval < config.family_wise_error_rate) {
+                        nb_pvals[nb_idx].second = row_i;
+                        long double midpoint = r_a * n / (r_a + r_b);
 
-                    return (logl(s)*s - (r_a+s)*logl(r_a+s) + logl(t)*t - (r_b+t)*logl(r_b+t)) * 2.0;
-                };
+                        auto get_deviance_a = [&](long double s) {
+                            if (s == 0)
+                                return r_a * logl(p_a) * -2.0;
 
-                long double in_dev = get_deviance(in_sum, out_sum);
-                eff_size = static_cast<double>(in_sum) - midpoint;
+                            return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s) + r_a * (logl(r_a) - logl(p_a))) * 2.0;
+                        };
 
-                if (in_dev > 0.0) {
-                    long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n) - n*log1pl(-p_n)) / logl(2.0);
-                    double l1pa = log1pl(-p_a) / logl(2.0);
-                    double l1pb = log1pl(-p_b) / logl(2.0);
+                        auto get_deviance_b = [&](long double t) {
+                            if (t == 0)
+                                return r_b * logl(p_b) * -2.0;
 
-                    {
-                        size_t s = 0;
-                        size_t t = n;
-                        long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
-                        pval += exp2(base + sbase);
-                        for (++s; s <= n; ++s) {
-                            long double dev = get_deviance(s, t - 1);
-                            if (dev >= in_dev) {
-                                sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
-                                --t;
+                            return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t) + r_b * (logl(r_b) - logl(p_b))) * 2.0;
+                        };
+
+                        auto get_deviance = [&](long double s, long double t) {
+                            return get_deviance_a(s) + get_deviance_b(t);
+                        };
+
+                        long double min_dev = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
+                        long double in_dev = get_deviance(in_sum, out_sum);
+
+                        if (in_dev > min_dev) {
+                            eff_size = static_cast<long double>(in_sum) - midpoint;
+                            pval = 0.0;
+                            long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n) - n*log1pl(-p_n)) / logl(2.0);
+                            double l1pa = log1pl(-p_a) / logl(2.0);
+                            double l1pb = log1pl(-p_b) / logl(2.0);
+
+                            {
+                                size_t s = 0;
+                                size_t t = n;
+                                long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
                                 pval += exp2(base + sbase);
-                            } else {
-                                break;
+                                for (++s; s <= n; ++s) {
+                                    long double dev = get_deviance(s, t - 1);
+                                    if (dev >= in_dev) {
+                                        sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                                        --t;
+                                        pval += exp2(base + sbase);
+                                        if (pval >= config.family_wise_error_rate)
+                                            break;
+                                    } else {
+                                        break;
+                                    }
+                                }
                             }
-                        }
-                    }
-                    if (get_deviance(n, 0) >= in_dev) {
-                        size_t s = n;
-                        size_t t = 0;
-                        long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
-                        pval += exp2(base + sbase);
-                        for (++t; t <= n; ++t) {
-                            long double dev = get_deviance(s - 1, t);
-                            if (dev >= in_dev) {
-                                sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
-                                --s;
+                            if (pval < config.family_wise_error_rate && get_deviance(n, 0) >= in_dev) {
+                                size_t s = n;
+                                size_t t = 0;
+                                long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
                                 pval += exp2(base + sbase);
-                            } else {
-                                break;
+                                for (++t; t <= n; ++t) {
+                                    long double dev = get_deviance(s - 1, t);
+                                    if (dev >= in_dev) {
+                                        sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                                        --s;
+                                        pval += exp2(base + sbase);
+                                        if (pval >= config.family_wise_error_rate)
+                                            break;
+                                    } else {
+                                        break;
+                                    }
+                                }
                             }
+                            nb_pvals[nb_idx].first = pval;
                         }
                     }
                 }
-            }
-        }
+            } else if (config.test_type == "hypergeometric" || config.test_type == "poisson_bayes") {
+                size_t n = 0;
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                for (const auto &[j, c] : row) {
+                    n += c;
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                        out_sum += c;
 
-        set_pval(pval, eff_size);
-    });
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                        in_sum += c;
+                }
+                if (n != 0) {
+                    sdsl::bit_vector found(groups.size());
+                    auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto &callback) {
+                        for (const auto &[j, c] : row) {
+                            callback(j, c);
+                        }
+                        for (size_t j = 0; j < found.size(); ++j) {
+                            if (!found[j])
+                                callback(j, 0);
+                        }
+                    });
+                    boost::math::binomial_distribution dist(static_cast<long double>(n), mu1 / (mu1 + mu2));
+                    // boost::math::hypergeometric_distribution dist(mu1 * nelem, n, (mu1 + mu2) * nelem);
+                    eff_size = static_cast<double>(in_sum) - boost::math::mode(dist);
+
+                    long double pval0 = boost::math::pdf(dist, 0);
+                    long double pvaln = boost::math::pdf(dist, n);
+                    long double pval_min = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                    if (pval_min * k_min >= config.family_wise_error_rate)
+                        return;
 
-    if (config.test_by_unitig) {
-        common::logger->trace("Combining p-values for unitigs");
-        std::mutex m_vec_mu;
-        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-            long double pval_min = 1.0;
-            {
-                size_t row = AnnotatedDBG::graph_to_anno_index(path[0]);
-                size_t row_rank = kept.rank1(row);
-                size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
-                size_t offset = row_rank - offsets[bucket_idx];
-                std::lock_guard<std::mutex> lock(m_vec_mu);
-                pval_min = get<PValStorage>(pvals_min_buckets[bucket_idx][offset]);
+                    long double in_prob = boost::math::pdf(dist, in_sum);
+                    if (eff_size > 0) {
+                        pval = boost::math::cdf(boost::math::complement(dist, in_sum - 1))
+                                + boost::math::cdf(dist, floor(boost::math::quantile(boost::math::complement(dist, in_prob))));
+                    } else if (eff_size < 0) {
+                        pval = boost::math::cdf(dist, in_sum)
+                                + boost::math::cdf(dist, ceil(boost::math::quantile(dist, in_prob)));
+                    }
+
+                    // double in_sz = abs(eff_size);
+
+                    // pval = 0.0;
+
+                    // size_t s = 0;
+                    // for ( ; s <= n; ++s) {
+                    //     double sz = abs(static_cast<double>(s) - mean);
+                    //     if (sz < in_sz)
+                    //         break;
+                    // }
+                    // if (s > 0)
+                    //     pval += boost::math::cdf(dist, s - 1);
+
+                    // size_t sp = n;
+                    // for ( ; sp >= s; --sp) {
+                    //     double sz = abs(static_cast<double>(sp) - mean);
+                    //     if (sz < in_sz)
+                    //         break;
+                    // }
+
+                    // if (sp < n)
+                    //     pval += boost::math::cdf(boost::math::complement(dist, sp));
+
+                    if (pval * k >= config.family_wise_error_rate)
+                        return;
+                }
+            } else if (config.test_type == "bb_hypergeometric") {
+                size_t n = 0;
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                for (const auto &[j, c] : row) {
+                    n += c;
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                        out_sum += c;
+
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                        in_sum += c;
+                }
+                if (n != 0) {
+                    std::vector<long double> lprobs;
+                    lprobs.reserve(n + 1);
+                    const auto &[a_a, b_a] = bb_params_a;
+                    const auto &[a_b, b_b] = bb_params_b;
+                    const auto &[a_n, b_n] = bb_params_null;
+                    long double nx = static_cast<long double>(in_kmers);
+                    long double ny = static_cast<long double>(out_kmers);
+                    long double base = (lgammal(n + 1) + lgammal(nx + ny - n + 1)
+                                        - lgammal(a_n + n) - lgammal(b_n + nx + ny - n)) / logl(2.0) + bb_base;
+                    uint64_t s = 0;
+                    lprobs.emplace_back((-lgammal(s + 1) - lgammal(nx - s + 1)
+                                        - lgammal(n - s + 1) - lgammal(ny - n + s + 1)
+                                        + lgammal(a_a + s) + lgammal(b_a + nx - s) + lgammal(a_b + n - s) + lgammal(b_b + ny - n + s)) / logl(2.0) + base);
+
+                    for (++s; s <= n; ++s) {
+                        lprobs.emplace_back(lprobs.back()
+                            - log2l(s + 1) + log2l(nx - (s - 1) + 1)
+                            + log2l(n - (s - 1) + 1) - log2l(ny - n + s + 1)
+                            + log2l(a_a + s) - log2l(b_a + nx - (s - 1)) - log2l(a_b + n - (s - 1)) + log2l(b_b + ny - n + s)
+                        );
+                    }
+                    if (pval * k >= config.family_wise_error_rate)
+                        return;
+                }
             }
 
-            if (pval_min * k_min >= config.family_wise_error_rate)
-                return;
+            set_pval(pval, eff_size);
+        });
 
-            std::vector<long double> pvals;
-            pvals.reserve(path.size());
-            long double comb_eff_size = 0.0;
-            {
-                std::lock_guard<std::mutex> lock(m_vec_mu);
-                for (node_index node : path) {
-                    size_t row = AnnotatedDBG::graph_to_anno_index(node);
-                    size_t row_rank = kept.rank1(row);
-                    size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
-                    size_t offset = row_rank - offsets[bucket_idx];
-                    pvals.emplace_back(get<PValStorage>(pvals_buckets[bucket_idx][offset]));
-                    comb_eff_size += get<PValStorage>(eff_size_buckets[bucket_idx][offset]);
+        if (config.test_type == "nbinom_exact" || config.test_type == "pig") {
+            common::logger->trace("Correcting p-vals");
+            nb_pvals.erase(std::remove_if(nb_pvals.begin(), nb_pvals.end(),
+                                        [&](const auto &a) { return a.second == kept.size(); }),
+                        nb_pvals.end());
+            common::logger->trace("Sorting {} / {} p-vals", nb_pvals.size(), kept.num_set_bits());
+            std::sort(nb_pvals.begin(), nb_pvals.end());
+            long double harm = 1.0;
+            // long double harm = 0.0;
+            // for (size_t i = 1; i <= nelem; ++i) {
+            //     harm += 1.0 / i;
+            // }
+
+            common::logger->trace("Selecing cut-off");
+            size_t k = 0;
+            for (size_t i = 0; i < nb_pvals.size(); ++i) {
+                if (nb_pvals[i].first >= config.family_wise_error_rate)
+                    break;
+
+                if (nb_pvals[i].first <= config.family_wise_error_rate * (i + 1) / nb_pvals.size() / harm) {
+                    k = std::max(i + 1, k);
+                    node_index node = AnnotatedDBG::anno_to_graph_index(nb_pvals[i].second);
+                    if (!indicator_in[node] && !indicator_out[node]) {
+                        common::logger->error("Node {} not marked, but p-value {} significant", node, nb_pvals[i].first);
+                        throw std::runtime_error("Index fail");
+                    }
                 }
             }
-            long double comp_pval = combine_pvals(pvals);
-            if (comp_pval * k >= config.family_wise_error_rate || comb_eff_size == 0.0)
-                return;
 
-            auto &indicator = comb_eff_size > 0 ? indicator_in : indicator_out;
-            for (node_index node : path) {
-                set_bit(indicator.data(), node, true, std::memory_order_relaxed);
+            common::logger->trace("Found {} / {} significant p-values. Minimum p-value is {}",
+                                k, nb_pvals.size(), nb_pvals[0].first);
+            for (size_t i = k; i < nb_pvals.size(); ++i) {
+                const auto &[pval, row_i] = nb_pvals[i];
+                if (nb_pvals[i].first >= config.family_wise_error_rate)
+                    break;
+
+                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                if (!indicator_in[node] && !indicator_out[node]) {
+                    common::logger->error("Node {} not marked, but original p-value {} significant", node, nb_pvals[i].first);
+                    throw std::runtime_error("Index fail");
+                }
+                indicator_in[node] = false;
+                indicator_out[node] = false;
             }
-        });
+        }
+        // if (config.test_by_unitig) {
+        //     common::logger->trace("Combining p-values for unitigs");
+        //     std::mutex m_vec_mu;
+        //     clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
+        //         long double pval_min = 1.0;
+        //         {
+        //             size_t row = AnnotatedDBG::graph_to_anno_index(path[0]);
+        //             size_t row_rank = kept.rank1(row);
+        //             size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
+        //             size_t offset = row_rank - offsets[bucket_idx];
+        //             std::lock_guard<std::mutex> lock(m_vec_mu);
+        //             pval_min = get<PValStorage>(pvals_min_buckets[bucket_idx][offset]);
+        //         }
 
-        pvals_min_buckets.resize(0);
-        tmp_min_buckets.resize(0);
-        pvals_buckets.resize(0);
-        tmp_buckets.resize(0);
-        eff_size_buckets.resize(0);
-        tmp_eff_buckets.resize(0);
-    }
+        //         if (pval_min * k_min >= config.family_wise_error_rate)
+        //             return;
+
+        //         std::vector<long double> pvals;
+        //         pvals.reserve(path.size());
+        //         long double comb_eff_size = 0.0;
+        //         {
+        //             std::lock_guard<std::mutex> lock(m_vec_mu);
+        //             for (node_index node : path) {
+        //                 size_t row = AnnotatedDBG::graph_to_anno_index(node);
+        //                 size_t row_rank = kept.rank1(row);
+        //                 size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
+        //                 size_t offset = row_rank - offsets[bucket_idx];
+        //                 pvals.emplace_back(get<PValStorage>(pvals_buckets[bucket_idx][offset]));
+        //                 comb_eff_size += get<PValStorage>(eff_size_buckets[bucket_idx][offset]);
+        //             }
+        //         }
+        //         long double comp_pval = combine_pvals(pvals);
+        //         if (comp_pval * k >= config.family_wise_error_rate || comb_eff_size == 0.0)
+        //             return;
 
-    std::atomic_thread_fence(std::memory_order_acquire);
+        //         auto &indicator = comb_eff_size > 0 ? indicator_in : indicator_out;
+        //         for (node_index node : path) {
+        //             set_bit(indicator.data(), node, true, std::memory_order_relaxed);
+        //         }
+        //     });
+
+        //     pvals_min_buckets.resize(0);
+        //     tmp_min_buckets.resize(0);
+        //     pvals_buckets.resize(0);
+        //     tmp_buckets.resize(0);
+        //     eff_size_buckets.resize(0);
+        //     tmp_eff_buckets.resize(0);
+        // }
+        std::atomic_thread_fence(std::memory_order_acquire);
+    }
 
     // common::logger->trace("Computing minimum p-values");
     // std::vector<std::pair<double, size_t>> m;

From 29d6fa2ef6c5e021aa6eca736fbe500787d26a16 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 11 Mar 2025 10:27:15 +0100
Subject: [PATCH 377/410] ttt

---
 .../src/graph/annotated_graph_algorithm.cpp   | 268 +++++++++++++-----
 1 file changed, 197 insertions(+), 71 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 039f37fafe..0352922a15 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -15,6 +15,8 @@
 #include <boost/math/distributions/cauchy.hpp>
 #include <boost/math/distributions/hypergeometric.hpp>
 
+#include <sdust.h>
+
 #include "common/logger.hpp"
 #include "common/vectors/bitmap.hpp"
 #include "common/vector_map.hpp"
@@ -69,6 +71,22 @@ bit_cast(const From& src) noexcept
     return dst;
 }
 
+#if ! _PROTEIN_GRAPH
+inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
+    int n;
+    std::unique_ptr<uint64_t, decltype(std::free)*> r {
+        sdust(0, (const uint8_t*)s.data(), s.size(), T, W, &n),
+        std::free
+    };
+    return n > 0;
+}
+#else
+inline bool is_low_complexity(std::string_view, int = 20, int = 64) {
+    // TODO: implement a checker here
+    return false;
+}
+#endif
+
 template <class PValStorage>
 double get(typename PValStorage::reference&& ref) {
     return bit_cast<double>(static_cast<typename PValStorage::value_type>(ref));
@@ -260,77 +278,32 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Marking discarded k-mers");
     auto hists_map = get_hist_map(min_counts, &kept_bv);
+    std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
+    // bit_vector_stat kept(std::move(kept_bv));
+    bit_vector_stat kept(kept_bv);
+
+    clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
+        graph_ptr,
+        [&](node_index node) {
+            return node != DeBruijnGraph::npos
+                    && kept[AnnotatedDBG::graph_to_anno_index(node)];
+        },
+        true,
+        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
+    );
 
-    bit_vector_stat kept(std::move(kept_bv));
-    size_t nelem = kept.num_set_bits();
-
-    std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
-    for (size_t j = 0; j < hists.size(); ++j) {
-        hists[j] = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(hists_map[j].values_container());
-        std::sort(hists[j].begin(), hists[j].end(), utils::LessFirst());
-        hists_map[j].clear();
-    }
-    hists_map.resize(0);
-
-    for (size_t j = 0; j < groups.size(); ++j) {
-        size_t total_c = 0;
-        for (const auto &[k, c] : hists[j]) {
-            total_c += c;
-        }
-
-        assert(total_c == nelem);
-
-        if (total_c != nelem) {
-            common::logger->error("FAIL {}: {} != {}", j, total_c, nelem);
-            throw std::runtime_error("GGG");
-        }
-    }
-
-    common::logger->trace("Computing aggregate statistics");
-    int64_t in_kmers = 0;
-    int64_t out_kmers = 0;
-    int64_t in_sq_kmers = 0;
-    int64_t out_sq_kmers = 0;
-    int64_t total_sq_kmers = 0;
-    std::vector<uint64_t> max_obs_vals(groups.size());
-    std::vector<size_t> n_kmers(groups.size());
-    std::vector<uint64_t> sums(groups.size());
-    std::vector<uint64_t> sq_sums(groups.size());
-    uint64_t max_in_obs_val = 0.0;
-    uint64_t max_out_obs_val = 0.0;
-
-    int64_t total_kmers = 0;
-    for (size_t j = 0; j < groups.size(); ++j) {
-        for (const auto &[k, c] : hists[j]) {
-            sums[j] += k * c;
-            sq_sums[j] += k * k * c;
-            if (k != 0)
-                n_kmers[j] += c;
-
-            max_obs_vals[j] = std::max(max_obs_vals[j], k);
-        }
-
-        if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-            out_kmers += sums[j];
-            out_sq_kmers += sq_sums[j];
-            max_out_obs_val += max_obs_vals[j];
-        }
-
-        if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-            in_kmers += sums[j];
-            in_sq_kmers += sq_sums[j];
-            max_in_obs_val += max_obs_vals[j];
-        }
-
-        total_kmers += sums[j];
-        total_sq_kmers += sq_sums[j];
+    // common::logger->trace("Filtering low-complexity k-mers");
+    // std::atomic_thread_fence(std::memory_order_release);
+    // clean_masked_graph->call_sequences([&](const std::string &contig, const auto &path) {
+    //     for (size_t i = 0; i < path.size(); ++i) {
+    //         if (is_low_complexity(std::string_view(contig.c_str() + i, graph_ptr->get_k())))
+    //             unset_bit(kept_bv.data(), AnnotatedDBG::graph_to_anno_index(path[i]), true, std::memory_order_relaxed);
+    //     }
+    // }, num_threads);
+    // std::atomic_thread_fence(std::memory_order_acquire);
+    // kept = bit_vector_stat(std::move(kept_bv));
 
-        common::logger->trace("{}: n_unique: {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
-                              j, n_kmers[j], sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
-    }
 
-    common::logger->trace("Number of kept unique k-mers: {}\tNumber of kept k-mers: {}",
-                          nelem, total_kmers);
 
     // common::logger->trace("Allocating p-value storage");
     // auto nullpval = bit_cast<uint64_t>(double(1.1));
@@ -437,12 +410,164 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             bool in_kmer = count_in >= config.min_in_recurrence && count_in <= config.max_in_recurrence;
             bool out_kmer = count_out >= config.min_out_recurrence && count_out <= config.max_out_recurrence;
 
-            if (in_kmer || out_kmer)
+            if (in_kmer || out_kmer) {
                 callback(row_i, row, bucket_idx);
+                // std::vector<std::pair<size_t, size_t>> new_row;
+                // for (const auto &[j, c] : row) {
+                //     if (c < 1024)
+                //         new_row.emplace_back(j, c);
+                // }
+                // callback(row_i, new_row, bucket_idx);
+            }
         });
     };
 
-    std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
+    // common::logger->trace("Marking low-complexity k-mers");
+    // std::atomic_thread_fence(std::memory_order_release);
+    // clean_masked_graph->call_sequences([&](const std::string &contig, const auto &path) {
+    //     for (size_t i = 0; i < path.size(); ++i) {
+    //         std::string_view kmer(contig.c_str() + i, clean_masked_graph->get_k());
+    //         if (is_low_complexity(kmer)) {
+    //             auto row = AnnotatedDBG::graph_to_anno_index(path[i]);
+    //             unset_bit(kept_bv.data(), row, true, std::memory_order_relaxed);
+    //         }
+    //     }
+    // }, num_threads);
+    // std::atomic_thread_fence(std::memory_order_acquire);
+
+    // common::logger->trace("Discarding counts from low-complexity k-mers");
+    // {
+    //     std::vector<std::vector<VectorMap<uint64_t, size_t>>> hist_maps_m(num_threads + 1);
+    //     for (auto &hist_maps : hist_maps_m) {
+    //         hist_maps.resize(groups.size());
+    //     }
+    //     std::atomic_thread_fence(std::memory_order_release);
+    //     generate_clean_rows([&](auto row_i, const auto &row, size_t bucket_idx) {
+    //         if (fetch_bit(kept_bv.data(), row_i, true, std::memory_order_acquire)) {
+    //             if (row.empty()) {
+    //                 unset_bit(kept_bv.data(), row_i, true, std::memory_order_release);
+    //             } else {
+    //                 sdsl::bit_vector found(groups.size());
+    //                 for (const auto &[j, c] : row) {
+    //                     found[j] = true;
+    //                     ++hist_maps_m[bucket_idx][j][c];
+    //                 }
+    //                 for (size_t j = 0; j < found.size(); ++j) {
+    //                     if (!found[j])
+    //                         ++hist_maps_m[bucket_idx][j][0];
+    //                 }
+    //             }
+    //         }
+    //     });
+    //     std::atomic_thread_fence(std::memory_order_acquire);
+    //     std::swap(hists_map, hist_maps_m[0]);
+    //     for (size_t i = 1; i < hist_maps_m.size(); ++i) {
+    //         for (size_t j = 0; j < hist_maps_m[i].size(); ++j) {
+    //             for (const auto &[k, c] : hist_maps_m[i][j]) {
+    //                 hists_map[j][k] += c;
+    //             }
+    //         }
+    //     }
+    // }
+
+    // std::atomic_thread_fence(std::memory_order_release);
+    // generate_rows([&](uint64_t row_i, const auto &row, size_t) {
+    //     if (kept[row_i] && !kept_bv[row_i]) {
+    //         sdsl::bit_vector found(groups.size());
+    //         for (const auto &[j, c] : row) {
+    //             found[j] = true;
+    //             auto find = hists_map[j].find(c);
+    //             assert(find != hists_map[j].end());
+    //             uint64_t prev = __atomic_fetch_sub(&find.value(), 1, std::memory_order_relaxed);
+    //             std::ignore = prev;
+    //             assert(prev);
+    //         }
+    //         for (size_t j = 0; j < found.size(); ++j) {
+    //             if (!found[j]) {
+    //                 auto find = hists_map[j].find(0);
+    //                 assert(find != hists_map[j].end());
+    //                 uint64_t prev = __atomic_fetch_sub(&find.value(), 1, std::memory_order_relaxed);
+    //                 std::ignore = prev;
+    //                 assert(prev);
+    //             }
+    //         }
+    //     }
+    // });
+    // std::atomic_thread_fence(std::memory_order_acquire);
+    // size_t old_nelem = kept.num_set_bits();
+    // kept = bit_vector_stat(std::move(kept_bv));
+
+    size_t nelem = kept.num_set_bits();
+    // common::logger->trace("Kept {} / {} high-complexity k-mers", nelem, old_nelem);
+
+    for (size_t j = 0; j < groups.size(); ++j) {
+        size_t total_c = 0;
+        for (const auto &[k, c] : hists_map[j]) {
+            total_c += c;
+        }
+
+        assert(total_c == nelem);
+
+        if (total_c != nelem) {
+            common::logger->error("{}: {} != {}", j, total_c, nelem);
+            throw std::runtime_error("FAIL");
+        }
+    }
+
+    common::logger->trace("Computing aggregate statistics");
+    int64_t in_kmers = 0;
+    int64_t out_kmers = 0;
+    int64_t in_sq_kmers = 0;
+    int64_t out_sq_kmers = 0;
+    int64_t total_sq_kmers = 0;
+    std::vector<uint64_t> max_obs_vals(groups.size());
+    std::vector<size_t> n_kmers(groups.size());
+    std::vector<uint64_t> sums(groups.size());
+    std::vector<uint64_t> sq_sums(groups.size());
+    uint64_t max_in_obs_val = 0.0;
+    uint64_t max_out_obs_val = 0.0;
+
+    int64_t total_kmers = 0;
+    for (size_t j = 0; j < groups.size(); ++j) {
+        for (const auto &[k, c] : hists_map[j]) {
+            sums[j] += k * c;
+            sq_sums[j] += k * k * c;
+            if (k != 0)
+                n_kmers[j] += c;
+
+            max_obs_vals[j] = std::max(max_obs_vals[j], k);
+        }
+
+        if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+            out_kmers += sums[j];
+            out_sq_kmers += sq_sums[j];
+            max_out_obs_val += max_obs_vals[j];
+        }
+
+        if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+            in_kmers += sums[j];
+            in_sq_kmers += sq_sums[j];
+            max_in_obs_val += max_obs_vals[j];
+        }
+
+        total_kmers += sums[j];
+        total_sq_kmers += sq_sums[j];
+
+        common::logger->trace("{}: n_unique: {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
+                              j, n_kmers[j], sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
+    }
+
+    common::logger->trace("Number of kept unique k-mers: {}\tNumber of kept k-mers: {}",
+                          nelem, total_kmers);
+
+    std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
+    for (size_t j = 0; j < hists.size(); ++j) {
+        hists[j] = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(hists_map[j].values_container());
+        std::sort(hists[j].begin(), hists[j].end(), utils::LessFirst());
+        hists_map[j].clear();
+    }
+    hists_map.resize(0);
+
     std::vector<size_t> kmer_to_unitig;
     std::vector<std::tuple<size_t, size_t, size_t>> counts;
     // std::vector<std::pair<size_t, std::vector<uint64_t>>> agg_counts;
@@ -2544,7 +2669,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             set_pval(pval, eff_size);
         });
 
-        if (config.test_type == "nbinom_exact" || config.test_type == "pig") {
+        // if (config.test_type == "nbinom_exact" || config.test_type == "pig") {
+        if (false) {
             common::logger->trace("Correcting p-vals");
             nb_pvals.erase(std::remove_if(nb_pvals.begin(), nb_pvals.end(),
                                         [&](const auto &a) { return a.second == kept.size(); }),

From 2343d83ffe1f7f39faed796f55efbe4c7921afeb Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 12 Mar 2025 18:22:08 +0100
Subject: [PATCH 378/410] bm

---
 metagraph/.clang-format => .clang-format      |    0
 .../src/graph/annotated_graph_algorithm.cpp   | 2172 +++++++++++------
 2 files changed, 1415 insertions(+), 757 deletions(-)
 rename metagraph/.clang-format => .clang-format (100%)

diff --git a/metagraph/.clang-format b/.clang-format
similarity index 100%
rename from metagraph/.clang-format
rename to .clang-format
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0352922a15..88e49bd11e 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -8,6 +8,8 @@
 #include <boost/math/distributions/poisson.hpp>
 #include <boost/math/distributions/binomial.hpp>
 #include <boost/math/distributions/negative_binomial.hpp>
+#include <boost/math/distributions/normal.hpp>
+#include <boost/math/distributions/students_t.hpp>
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/tools/minima.hpp>
 #include <boost/math/special_functions/digamma.hpp>
@@ -44,26 +46,18 @@ double CDF_CUTOFF = 0.95;
 double HIST_CUTOFF = 1.00;
 uint64_t N_BUCKETS_FOR_ESTIMATION = 3;
 
-enum Group {
-    IN,
-    OUT,
-    OTHER,
-    BOTH
-};
+enum Group { IN, OUT, OTHER, BOTH };
 
 
-template<class To, class From>
-std::enable_if_t<
-    sizeof(To) == sizeof(From) &&
-    std::is_trivially_copyable_v<From> &&
-    std::is_trivially_copyable_v<To>,
-    To>
+template <class To, class From>
+std::enable_if_t<sizeof(To) == sizeof(From) && std::is_trivially_copyable_v<From>
+                         && std::is_trivially_copyable_v<To>,
+                 To>
 // constexpr support needs compiler magic
-bit_cast(const From& src) noexcept
-{
+bit_cast(const From& src) noexcept {
     static_assert(std::is_trivially_constructible_v<To>,
-        "This implementation additionally requires "
-        "destination type to be trivially constructible");
+                  "This implementation additionally requires "
+                  "destination type to be trivially constructible");
 
     static_assert(sizeof(To) == sizeof(From));
     To dst;
@@ -71,12 +65,11 @@ bit_cast(const From& src) noexcept
     return dst;
 }
 
-#if ! _PROTEIN_GRAPH
+#if !_PROTEIN_GRAPH
 inline bool is_low_complexity(std::string_view s, int T = 20, int W = 64) {
     int n;
     std::unique_ptr<uint64_t, decltype(std::free)*> r {
-        sdust(0, (const uint8_t*)s.data(), s.size(), T, W, &n),
-        std::free
+        sdust(0, (const uint8_t*)s.data(), s.size(), T, W, &n), std::free
     };
     return n > 0;
 }
@@ -98,12 +91,13 @@ void set(typename PValStorage::reference&& ref, double val) {
 }
 
 template <class PValStorage>
-void push_back(PValStorage &v, double val) {
+void push_back(PValStorage& v, double val) {
     v.push_back(bit_cast<typename PValStorage::value_type>(val));
 }
 
 template <typename Generator>
-std::tuple<long double, long double, long double, long double> get_rp(const Generator &generate) {
+std::tuple<long double, long double, long double, long double>
+get_rp(const Generator& generate) {
     long double mu = 0;
     long double var = 0;
     size_t total = 0;
@@ -117,29 +111,27 @@ std::tuple<long double, long double, long double, long double> get_rp(const Gene
     var = (var - mu2 * total) / (total - 1);
 
     if (mu >= var) {
-        common::logger->warn("Fit failed, falling back to Poisson: mu: {} >= var: {}", mu, var);
+        common::logger->warn("Fit failed, falling back to Poisson: mu: {} >= var: {}", mu,
+                             var);
         return { 0.0, 1.0, mu, var };
     }
 
     auto get_l = [&](long double r) {
         long double p = r / (r + mu);
         long double l = -lgammal(r) * total + mu * total * log1pl(-p) + total * r * logl(p);
-        generate([&](auto k, auto c) {
-            l += lgammal(k + r) * c;
-        });
+        generate([&](auto k, auto c) { l += lgammal(k + r) * c; });
         return l;
     };
 
     auto get_dl_r = [&](long double r) {
         long double dl = (log(r) - log(r + mu) - boost::math::digamma(r)) * total;
-        generate([&](auto k, auto c) {
-            dl += boost::math::digamma(k + r) * c;
-        });
+        generate([&](auto k, auto c) { dl += boost::math::digamma(k + r) * c; });
         return dl;
     };
 
     auto get_dl_a = [&](long double a) {
-        long double dl = mu * mu * total / (1.0 + a * mu) + total * logl(1 + a * mu) / a / a + total / a / a * boost::math::digamma(1.0 / a);
+        long double dl = mu * mu * total / (1.0 + a * mu) + total * logl(1 + a * mu) / a / a
+                + total / a / a * boost::math::digamma(1.0 / a);
         generate([&](auto k, auto c) {
             dl -= boost::math::digamma(k + 1.0 / a) / a / a * c;
         });
@@ -150,17 +142,19 @@ std::tuple<long double, long double, long double, long double> get_rp(const Gene
     long double r2 = 0.0;
     try {
         auto [r_min, r_max] = boost::math::tools::bisect(
-            get_dl_r, std::numeric_limits<double>::min(), 1.0, boost::math::tools::eps_tolerance<long double>(5)
-        );
+                get_dl_r, std::numeric_limits<double>::min(), 1.0,
+                boost::math::tools::eps_tolerance<long double>(5));
         r1 = (r_min + r_max) / 2.0;
-    } catch (boost::wrapexcept<boost::math::evaluation_error> &e) {}
+    } catch (boost::wrapexcept<boost::math::evaluation_error>& e) {
+    }
 
     try {
         auto [a_min, a_max] = boost::math::tools::bisect(
-            get_dl_a, std::numeric_limits<double>::min(), 1.0, boost::math::tools::eps_tolerance<long double>(5)
-        );
+                get_dl_a, std::numeric_limits<double>::min(), 1.0,
+                boost::math::tools::eps_tolerance<long double>(5));
         r2 = (1.0 / a_min + 1.0 / a_max) / 2.0;
-    } catch (boost::wrapexcept<boost::math::evaluation_error> &e) {}
+    } catch (boost::wrapexcept<boost::math::evaluation_error>& e) {
+    }
 
     long double r = 0.0;
     if (r1 == 0 && r2 == 0) {
@@ -174,21 +168,252 @@ std::tuple<long double, long double, long double, long double> get_rp(const Gene
     }
 
     long double p = r / (r + mu);
+    // long double p = 0.1;
+    // r = p * mu / (1.0 - p);
     return { r, p, mu, var };
 };
 
+template <class G1, class G2>
+std::pair<long double, long double> mann_whitneyu(const G1& generate_a, const G2& generate_b) {
+    std::vector<long double> x;
+    std::vector<long double> y;
+    std::vector<long double> xy;
+    generate_a([&](auto c) {
+        x.emplace_back(c);
+        xy.emplace_back(c);
+    });
+    generate_b([&](auto c) {
+        y.emplace_back(c);
+        xy.emplace_back(c);
+    });
+
+    long double n1 = x.size();
+    long double n2 = y.size();
+    VectorMap<long double, size_t> counts;
+    for (long double c : xy) {
+        ++counts[c];
+    }
+    if (counts.size() <= 1)
+        return std::make_pair(1.0, 0.0);
+
+    std::vector<std::pair<long double, size_t>> ranked_counts = counts.values_container();
+    std::sort(ranked_counts.begin(), ranked_counts.end());
+    tsl::hopscotch_map<long double, long double> count_to_rank;
+    size_t cur_rank = 0;
+    for (const auto& [v, c] : ranked_counts) {
+        size_t last_rank = cur_rank;
+        cur_rank += c;
+        long double r = 0.0;
+        for (size_t i = last_rank + 1; i <= cur_rank; ++i) {
+            r += i;
+        }
+        r /= c;
+        count_to_rank[v] = r;
+    }
+
+    long double R1 = 0.0;
+    for (long double v : x) {
+        R1 += count_to_rank[v];
+    }
+    long double R2 = 0.0;
+    for (long double v : y) {
+        R2 += count_to_rank[v];
+    }
+
+    long double U1 = R1 - n1 * (n1 + 1) / 2.0;
+    long double U2 = n1 * n2 - U1;
+    long double U = std::max(U1, U2);
+
+    long double mu = n1 * n2 / 2.0;
+    long double n = n1 + n2;
+    long double tie = 0.0;
+    for (const auto& [v, c] : ranked_counts) {
+        tie += c * c * c - c;
+    }
+    long double s = sqrtl(n1 * n2 / 12 * ((n + 1) - tie / (n * (n - 1))));
+    long double numerator = U - mu - 0.5;
+    long double z = numerator / s;
+    boost::math::normal dist;
+    long double p = boost::math::cdf(boost::math::complement(dist, z)) * 2;
+    p = std::min(p, 1.0L);
+
+    long double eff_size = R1 / n1 - R2 / n2;
+    return std::make_pair(p, eff_size);
+}
+
+template <class G1, class G2>
+std::pair<long double, long double> brunner_munzel(const G1& generate_a,
+                                                   const G2& generate_b) {
+    std::vector<long double> x;
+    std::vector<long double> y;
+    std::vector<long double> xy;
+    generate_a([&](auto c) {
+        x.emplace_back(c);
+        xy.emplace_back(c);
+    });
+    generate_b([&](auto c) {
+        y.emplace_back(c);
+        xy.emplace_back(c);
+    });
+
+    long double nx = x.size();
+    long double ny = y.size();
+    VectorMap<long double, size_t> counts;
+    for (long double c : xy) {
+        ++counts[c];
+    }
+    if (counts.size() <= 1)
+        return std::make_pair(1.0, 0.0);
+
+    std::vector<std::pair<long double, size_t>> ranked_counts = counts.values_container();
+    std::sort(ranked_counts.begin(), ranked_counts.end());
+    tsl::hopscotch_map<long double, long double> count_to_rank;
+    size_t cur_rank = 0;
+    for (const auto& [v, c] : ranked_counts) {
+        size_t last_rank = cur_rank;
+        cur_rank += c;
+        long double r = 0.0;
+        for (size_t i = last_rank + 1; i <= cur_rank; ++i) {
+            r += i;
+        }
+        r /= c;
+        count_to_rank[v] = r;
+    }
+
+    std::vector<long double> rankc;
+    rankc.reserve(xy.size());
+    for (long double v : xy) {
+        rankc.emplace_back(count_to_rank[v]);
+    }
+
+    std::vector<long double> rankcx;
+    rankcx.reserve(x.size());
+    for (long double v : x) {
+        rankcx.emplace_back(count_to_rank[v]);
+    }
+    std::vector<long double> rankcy;
+    rankcy.reserve(y.size());
+    for (long double v : y) {
+        rankcy.emplace_back(count_to_rank[v]);
+    }
+
+    long double rankcx_mean = std::accumulate(rankcx.begin(), rankcx.end(), 0.0L) / nx;
+    long double rankcy_mean = std::accumulate(rankcy.begin(), rankcy.end(), 0.0L) / ny;
+
+    counts.clear();
+    for (long double c : x) {
+        ++counts[c];
+    }
+    ranked_counts = counts.values_container();
+    std::sort(ranked_counts.begin(), ranked_counts.end());
+    count_to_rank.clear();
+    cur_rank = 0;
+    for (const auto& [v, c] : ranked_counts) {
+        size_t last_rank = cur_rank;
+        cur_rank += c;
+        long double r = 0.0;
+        for (size_t i = last_rank + 1; i <= cur_rank; ++i) {
+            r += i;
+        }
+        r /= c;
+        count_to_rank[v] = r;
+    }
+    std::vector<long double> rankx;
+    rankx.reserve(x.size());
+    for (long double v : x) {
+        rankx.emplace_back(count_to_rank[v]);
+    }
+
+
+    counts.clear();
+    for (long double c : y) {
+        ++counts[c];
+    }
+    ranked_counts = counts.values_container();
+    std::sort(ranked_counts.begin(), ranked_counts.end());
+    count_to_rank.clear();
+    cur_rank = 0;
+    for (const auto& [v, c] : ranked_counts) {
+        size_t last_rank = cur_rank;
+        cur_rank += c;
+        long double r = 0.0;
+        for (size_t i = last_rank + 1; i <= cur_rank; ++i) {
+            r += i;
+        }
+        r /= c;
+        count_to_rank[v] = r;
+    }
+    std::vector<long double> ranky;
+    ranky.reserve(y.size());
+    for (long double v : x) {
+        ranky.emplace_back(count_to_rank[v]);
+    }
+
+    long double rankx_mean = std::accumulate(rankx.begin(), rankx.end(), 0.0L) / nx;
+    long double ranky_mean = std::accumulate(ranky.begin(), ranky.end(), 0.0L) / ny;
+
+    long double Sx = 0.0;
+    for (size_t i = 0; i < x.size(); ++i) {
+        Sx += pow(rankcx[i] - rankx[i] - rankcx_mean + rankx_mean, 2.0);
+    }
+    Sx /= nx - 1;
+    long double Sy = 0.0;
+    for (size_t i = 0; i < y.size(); ++i) {
+        Sy += pow(rankcy[i] - ranky[i] - rankcy_mean + ranky_mean, 2.0);
+    }
+    Sy /= ny - 1;
+
+    long double wbfn = nx * ny * (rankcy_mean - rankcx_mean);
+
+    if (wbfn == 0.0)
+        return std::make_pair(1.0, 0.0);
+
+    long double wbfn_denom = (nx + ny) * sqrtl(nx * Sx + ny * Sy);
+    if (wbfn_denom == 0.0)
+        return std::make_pair(0.0, -wbfn);
+
+    wbfn /= wbfn_denom;
+    long double p = 1.0;
+    long double eff_size = -wbfn;
+
+    if (nx + ny >= 50) {
+        p = boost::math::cdf(boost::math::complement(boost::math::normal(), abs(wbfn))) * 2.0;
+    } else if (nx + ny >= 20) {
+        long double df_denom = pow(nx * Sx, 2.0) / (nx - 1);
+        df_denom += pow(ny * Sy, 2.0) / (ny - 1);
+        if (df_denom == 0.0) {
+            p = boost::math::cdf(boost::math::complement(boost::math::normal(), abs(wbfn)))
+                    * 2.0;
+        } else {
+            long double df_numer = pow(nx * Sx + ny * Sy, 2.0);
+            long double df = df_numer / df_denom;
+            p = boost::math::cdf(
+                        boost::math::complement(boost::math::students_t(df), abs(wbfn)))
+                    * 2.0;
+        }
+    } else {
+        throw std::runtime_error("Too few samples");
+    }
+
+    return std::make_pair(p, eff_size);
+}
+
 template <typename value_type, class PValStorage, typename HistGetter, typename Generator>
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         const HistGetter &get_hist_map,
-                         const Generator &generate_rows,
-                         const std::vector<Group> &groups,
-                         const DifferentialAssemblyConfig &config,
-                         size_t num_threads = 1,
-                         std::filesystem::path tmp_dir = "",
-                         size_t num_parallel_files = std::numeric_limits<size_t>::max(),
-                         const std::function<void()> &deallocate = []() {},
-                         uint8_t max_width = 64) {
+std::tuple<std::shared_ptr<DeBruijnGraph>,
+           std::shared_ptr<DeBruijnGraph>,
+           PValStorage,
+           std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual(
+        std::shared_ptr<const DeBruijnGraph> graph_ptr,
+        const HistGetter& get_hist_map,
+        const Generator& generate_rows,
+        const std::vector<Group>& groups,
+        const DifferentialAssemblyConfig& config,
+        size_t num_threads = 1,
+        std::filesystem::path tmp_dir = "",
+        size_t num_parallel_files = std::numeric_limits<size_t>::max(),
+        const std::function<void()>& deallocate = []() {},
+        uint8_t max_width = 64) {
     if (auto canonical = std::dynamic_pointer_cast<const CanonicalDBG>(graph_ptr))
         graph_ptr = canonical->get_graph_ptr();
 
@@ -196,14 +421,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     num_threads = get_num_threads();
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
 
-    auto combine_pvals = [dist=boost::math::cauchy()](const std::vector<long double> &pvals) {
-        long double stat = 0.0;
-        for (long double pval : pvals) {
-            stat += tan((0.5 - pval) * M_PI);
-        }
-        stat /= pvals.size();
-        return boost::math::cdf(boost::math::complement(dist, stat));
-    };
+    auto combine_pvals
+            = [dist = boost::math::cauchy()](const std::vector<long double>& pvals) {
+                  long double stat = 0.0;
+                  for (long double pval : pvals) {
+                      stat += tan((0.5 - pval) * M_PI);
+                  }
+                  stat /= pvals.size();
+                  return boost::math::cdf(boost::math::complement(dist, stat));
+              };
 
     size_t num_labels_both = std::count(groups.begin(), groups.end(), Group::BOTH);
     size_t num_labels_out = std::count(groups.begin(), groups.end(), Group::OUT);
@@ -213,7 +439,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     common::logger->trace("Graph mode: {}", is_primary ? "PRIMARY" : "other");
 
-    sdsl::bit_vector kept_bv(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1), true);
+    sdsl::bit_vector kept_bv(AnnotatedDBG::graph_to_anno_index(graph_ptr->max_index() + 1),
+                             true);
 
     std::vector<size_t> min_counts(groups.size(), config.min_count);
     std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
@@ -226,7 +453,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         auto hists_map = get_hist_map(std::vector<size_t>(groups.size(), 1), nullptr);
         common::logger->trace("Cleaning count columns");
 
-        #pragma omp parallel for num_threads(num_parallel_files)
+#pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
             if (groups[j] == Group::OTHER) {
                 min_counts[j] = std::numeric_limits<size_t>::max();
@@ -235,30 +462,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             // set cutoff for lower end of distribution
             auto [mean_est, nzeros_est] = estimate_ztp_mean(
-                [&](const auto &callback) {
-                    for (const auto &[k, c] : hists_map[j]) {
-                        if (k > 0 && k <= N_BUCKETS_FOR_ESTIMATION)
-                            callback(k, c);
-                    }
-                },
-                max_width,
-                0,
-                N_BUCKETS_FOR_ESTIMATION
-            );
-
-            min_counts[j] = boost::math::quantile(
-                boost::math::poisson(mean_est), exp(-mean_est) - CDF_CUTOFF * expm1(-mean_est)
-            );
+                    [&](const auto& callback) {
+                        for (const auto& [k, c] : hists_map[j]) {
+                            if (k > 0 && k <= N_BUCKETS_FOR_ESTIMATION)
+                                callback(k, c);
+                        }
+                    },
+                    max_width, 0, N_BUCKETS_FOR_ESTIMATION);
+
+            min_counts[j]
+                    = boost::math::quantile(boost::math::poisson(mean_est),
+                                            exp(-mean_est) - CDF_CUTOFF * expm1(-mean_est));
         }
     }
 
-    auto correct_pvals = [&config](const std::vector<std::pair<long double, size_t>> &m) {
-        size_t total = std::accumulate(m.begin(), m.end(), size_t(0),
-                                       [](size_t sum, const auto &a) { return sum + a.second; });
+    auto correct_pvals = [&config](const std::vector<std::pair<long double, size_t>>& m) {
+        size_t total
+                = std::accumulate(m.begin(), m.end(), size_t(0),
+                                  [](size_t sum, const auto& a) { return sum + a.second; });
         size_t acc = 0;
 
         for (size_t n = 0; n < m.size(); ++n) {
-            const auto &[pval_min, s] = m[n];
+            const auto& [pval_min, s] = m[n];
             if (s == 0)
                 continue;
 
@@ -283,14 +508,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     bit_vector_stat kept(kept_bv);
 
     clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
-        graph_ptr,
-        [&](node_index node) {
-            return node != DeBruijnGraph::npos
-                    && kept[AnnotatedDBG::graph_to_anno_index(node)];
-        },
-        true,
-        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    );
+            graph_ptr,
+            [&](node_index node) {
+                return node != DeBruijnGraph::npos
+                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
+            },
+            true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
     // common::logger->trace("Filtering low-complexity k-mers");
     // std::atomic_thread_fence(std::memory_order_release);
@@ -304,7 +527,6 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     // kept = bit_vector_stat(std::move(kept_bv));
 
 
-
     // common::logger->trace("Allocating p-value storage");
     // auto nullpval = bit_cast<uint64_t>(double(1.1));
 
@@ -383,15 +605,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Test: {}\tby unitig: {}", config.test_type, config.test_by_unitig);
 
     // prefilter rows
-    auto generate_clean_rows = [&](const auto &callback) {
-        generate_rows([&](uint64_t row_i, const auto &row, size_t bucket_idx) {
+    auto generate_clean_rows = [&](const auto& callback) {
+        generate_rows([&](uint64_t row_i, const auto& row, size_t bucket_idx) {
             if (!kept[row_i])
                 return;
 
             size_t count_in = 0;
             size_t count_out = 0;
             size_t total_count = 0;
-            for (const auto &[j, c] : row) {
+            for (const auto& [j, c] : row) {
                 if (min_counts.size() && c < min_counts[j])
                     continue;
 
@@ -407,8 +629,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (total_count < config.min_recurrence)
                 return;
 
-            bool in_kmer = count_in >= config.min_in_recurrence && count_in <= config.max_in_recurrence;
-            bool out_kmer = count_out >= config.min_out_recurrence && count_out <= config.max_out_recurrence;
+            bool in_kmer = count_in >= config.min_in_recurrence
+                    && count_in <= config.max_in_recurrence;
+            bool out_kmer = count_out >= config.min_out_recurrence
+                    && count_out <= config.max_out_recurrence;
 
             if (in_kmer || out_kmer) {
                 callback(row_i, row, bucket_idx);
@@ -437,8 +661,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     // common::logger->trace("Discarding counts from low-complexity k-mers");
     // {
-    //     std::vector<std::vector<VectorMap<uint64_t, size_t>>> hist_maps_m(num_threads + 1);
-    //     for (auto &hist_maps : hist_maps_m) {
+    //     std::vector<std::vector<VectorMap<uint64_t, size_t>>> hist_maps_m(num_threads +
+    //     1); for (auto &hist_maps : hist_maps_m) {
     //         hist_maps.resize(groups.size());
     //     }
     //     std::atomic_thread_fence(std::memory_order_release);
@@ -478,17 +702,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     //             found[j] = true;
     //             auto find = hists_map[j].find(c);
     //             assert(find != hists_map[j].end());
-    //             uint64_t prev = __atomic_fetch_sub(&find.value(), 1, std::memory_order_relaxed);
-    //             std::ignore = prev;
-    //             assert(prev);
+    //             uint64_t prev = __atomic_fetch_sub(&find.value(), 1,
+    //             std::memory_order_relaxed); std::ignore = prev; assert(prev);
     //         }
     //         for (size_t j = 0; j < found.size(); ++j) {
     //             if (!found[j]) {
     //                 auto find = hists_map[j].find(0);
     //                 assert(find != hists_map[j].end());
-    //                 uint64_t prev = __atomic_fetch_sub(&find.value(), 1, std::memory_order_relaxed);
-    //                 std::ignore = prev;
-    //                 assert(prev);
+    //                 uint64_t prev = __atomic_fetch_sub(&find.value(), 1,
+    //                 std::memory_order_relaxed); std::ignore = prev; assert(prev);
     //             }
     //         }
     //     }
@@ -502,7 +724,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     for (size_t j = 0; j < groups.size(); ++j) {
         size_t total_c = 0;
-        for (const auto &[k, c] : hists_map[j]) {
+        for (const auto& [k, c] : hists_map[j]) {
             total_c += c;
         }
 
@@ -529,7 +751,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     int64_t total_kmers = 0;
     for (size_t j = 0; j < groups.size(); ++j) {
-        for (const auto &[k, c] : hists_map[j]) {
+        for (const auto& [k, c] : hists_map[j]) {
             sums[j] += k * c;
             sq_sums[j] += k * k * c;
             if (k != 0)
@@ -553,8 +775,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         total_kmers += sums[j];
         total_sq_kmers += sq_sums[j];
 
-        common::logger->trace("{}: n_unique: {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
-                              j, n_kmers[j], sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
+        common::logger->trace(
+                "{}: n_unique: {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
+                j, n_kmers[j], sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
     }
 
     common::logger->trace("Number of kept unique k-mers: {}\tNumber of kept k-mers: {}",
@@ -562,7 +785,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
     std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
     for (size_t j = 0; j < hists.size(); ++j) {
-        hists[j] = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(hists_map[j].values_container());
+        hists[j] = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(
+                hists_map[j].values_container());
         std::sort(hists[j].begin(), hists[j].end(), utils::LessFirst());
         hists_map[j].clear();
     }
@@ -573,41 +797,41 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     // std::vector<std::pair<size_t, std::vector<uint64_t>>> agg_counts;
     if (config.test_by_unitig && config.test_type != "notest") {
         clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
-            graph_ptr,
-            [&](node_index node) {
-                return node != DeBruijnGraph::npos
-                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
-            },
-            true,
-            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-        );
+                graph_ptr,
+                [&](node_index node) {
+                    return node != DeBruijnGraph::npos
+                            && kept[AnnotatedDBG::graph_to_anno_index(node)];
+                },
+                true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
         common::logger->trace("Associating k-mers to unitigs");
         kmer_to_unitig.resize(graph_ptr->max_index() + 1);
         // std::atomic_thread_fence(std::memory_order_release);
         // std::atomic<size_t> num_unitigs{0};
         std::mutex mu;
-        clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-            size_t unitig_id = 0;
-            {
-                unitig_id = counts.size();
-                std::lock_guard<std::mutex> lock(mu);
-                counts.emplace_back(path.size(), 0, 0);
-            }
-            // size_t unitig_id = num_unitigs.fetch_add(1, std::memory_order_relaxed);
-            for (node_index node : path) {
-                kmer_to_unitig[node] = unitig_id;
-            }
-        }, num_threads);
+        clean_masked_graph->call_unitigs(
+                [&](const std::string&, const auto& path) {
+                    size_t unitig_id = 0;
+                    {
+                        unitig_id = counts.size();
+                        std::lock_guard<std::mutex> lock(mu);
+                        counts.emplace_back(path.size(), 0, 0);
+                    }
+                    // size_t unitig_id = num_unitigs.fetch_add(1, std::memory_order_relaxed);
+                    for (node_index node : path) {
+                        kmer_to_unitig[node] = unitig_id;
+                    }
+                },
+                num_threads);
         // std::atomic_thread_fence(std::memory_order_acquire);
         common::logger->trace("Assembled {} unitigs", counts.size());
 
         common::logger->trace("Aggregating counts for unitigs");
-        generate_clean_rows([&](uint64_t row_i, const auto &row, size_t) {
+        generate_clean_rows([&](uint64_t row_i, const auto& row, size_t) {
             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
             size_t unitig_id = kmer_to_unitig[node];
-            auto &[len, in_count, out_count] = counts[unitig_id];
-            for (const auto &[j, c] : row) {
+            auto& [len, in_count, out_count] = counts[unitig_id];
+            for (const auto& [j, c] : row) {
                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
                     out_count += c;
 
@@ -618,7 +842,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         common::logger->trace("Computing unitig count histogram");
         std::vector<VectorMap<size_t, size_t>> unitig_hists_map(2);
-        for (const auto &[len, in_count, out_count] : counts) {
+        for (const auto& [len, in_count, out_count] : counts) {
             size_t avg_in_count = std::ceil(static_cast<double>(in_count) / len);
             size_t avg_out_count = std::ceil(static_cast<double>(out_count) / len);
             ++unitig_hists_map[0][avg_in_count];
@@ -626,18 +850,18 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         common::logger->trace("Fitting negative binomials");
-        auto [r_a, p_a, mu_a, var_a] = get_rp([&](const auto &callback) {
-            for (const auto &[k, c] : unitig_hists_map[0]) {
+        auto [r_a, p_a, mu_a, var_a] = get_rp([&](const auto& callback) {
+            for (const auto& [k, c] : unitig_hists_map[0]) {
                 callback(k, c);
             }
         });
-        auto [r_b, p_b, mu_b, var_b] = get_rp([&](const auto &callback) {
-            for (const auto &[k, c] : unitig_hists_map[1]) {
+        auto [r_b, p_b, mu_b, var_b] = get_rp([&](const auto& callback) {
+            for (const auto& [k, c] : unitig_hists_map[1]) {
                 callback(k, c);
             }
         });
-        auto [r_n, p_n, mu_n, var_n] = get_rp([&](const auto &callback) {
-            for (const auto &[k, c] : unitig_hists_map[0]) {
+        auto [r_n, p_n, mu_n, var_n] = get_rp([&](const auto& callback) {
+            for (const auto& [k, c] : unitig_hists_map[0]) {
                 auto find = unitig_hists_map[1].find(k);
                 if (find != unitig_hists_map[1].end()) {
                     callback(k, c + find->second);
@@ -646,35 +870,38 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            for (const auto &[k, c] : unitig_hists_map[1]) {
+            for (const auto& [k, c] : unitig_hists_map[1]) {
                 if (!unitig_hists_map[0].count(k))
                     callback(k, c);
             }
         });
 
-        common::logger->trace("In: r: {}\tp: {}\t mu: {}\tsample var: {}\tvar: {}", r_a, p_a, mu_a, var_a, mu_a / p_a);
+        common::logger->trace("In: r: {}\tp: {}\t mu: {}\tsample var: {}\tvar: {}", r_a,
+                              p_a, mu_a, var_a, mu_a / p_a);
         common::logger->trace("Out: r: {}\tp: {}", r_b, p_b, mu_b, var_b, mu_b / p_b);
         common::logger->trace("Null: r: {}\tp: {}", r_n, p_n, mu_n, var_n, mu_n / p_n);
 
         long double nb_base = (lgammal(r_n) - lgammal(r_a) - lgammal(r_b)) / logl(2.0)
-                    + r_a*log2l(p_a) + r_b*log2l(p_b)-r_n*log2l(p_n);
+                + r_a * log2l(p_a) + r_b * log2l(p_b) - r_n * log2l(p_n);
 
         common::logger->trace("Allocating p-value storage");
-        std::vector<std::tuple<long double, Group, size_t>> pvals(counts.size(), std::make_tuple(1.0, Group::OTHER, counts.size()));
+        std::vector<std::tuple<long double, Group, size_t>> pvals(
+                counts.size(), std::make_tuple(1.0, Group::OTHER, counts.size()));
 
         common::logger->trace("Calculating p-values");
         size_t rows_per_update = 10000;
-        ProgressBar progress_bar(pvals.size(), "Streaming unitigs", std::cerr, !common::get_verbose());
-        #pragma omp parallel for num_threads(num_threads) schedule(static)
+        ProgressBar progress_bar(pvals.size(), "Streaming unitigs", std::cerr,
+                                 !common::get_verbose());
+#pragma omp parallel for num_threads(num_threads) schedule(static)
         for (size_t i = 0; i < counts.size(); ++i) {
             if (i > 0 && (i % rows_per_update) == 0)
                 progress_bar += rows_per_update;
 
-            const auto &[len, in_sum, out_sum] = counts[i];
+            const auto& [len, in_sum, out_sum] = counts[i];
             size_t n = in_sum + out_sum;
 
             long double eff_size = 0.0;
-            auto &[pval, group, idx] = pvals[i];
+            auto& [pval, group, idx] = pvals[i];
             idx = i;
 
             long double midpoint = r_a * n / (r_a + r_b);
@@ -683,21 +910,26 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (s == 0)
                     return r_a * logl(p_a) * -2.0;
 
-                return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s) + r_a * (logl(r_a) - logl(p_a))) * 2.0;
+                return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
+                        + r_a * (logl(r_a) - logl(p_a)))
+                        * 2.0;
             };
 
             auto get_deviance_b = [&](long double t) {
                 if (t == 0)
                     return r_b * logl(p_b) * -2.0;
 
-                return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t) + r_b * (logl(r_b) - logl(p_b))) * 2.0;
+                return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
+                        + r_b * (logl(r_b) - logl(p_b)))
+                        * 2.0;
             };
 
             auto get_deviance = [&](long double s, long double t) {
                 return get_deviance_a(s) + get_deviance_b(t);
             };
 
-            long double min_dev = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
+            long double min_dev
+                    = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
             long double in_dev = get_deviance(in_sum, out_sum);
 
             if (in_dev > min_dev) {
@@ -709,19 +941,24 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
                 pval = 0.0;
                 idx = i;
-                long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n) - n*log1pl(-p_n)) / logl(2.0);
+                long double base = nb_base
+                        + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n)) / logl(2.0);
                 double l1pa = log1pl(-p_a) / logl(2.0);
                 double l1pb = log1pl(-p_b) / logl(2.0);
 
                 {
                     size_t s = 0;
                     size_t t = n;
-                    long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
+                    long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                         - lgammal(s + 1) - lgammal(t + 1))
+                                    / logl(2.0)
+                            + s * l1pa + t * l1pb;
                     pval += exp2(base + sbase);
                     for (++s; s <= n; ++s) {
                         long double dev = get_deviance(s, t - 1);
                         if (dev >= in_dev) {
-                            sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                            sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
+                                    + log2l(t + 1) + l1pa - l1pb;
                             --t;
                             pval += exp2(base + sbase);
                             if (pval >= config.family_wise_error_rate)
@@ -734,12 +971,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (pval < config.family_wise_error_rate && get_deviance(n, 0) >= in_dev) {
                     size_t s = n;
                     size_t t = 0;
-                    long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
+                    long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                         - lgammal(s + 1) - lgammal(t + 1))
+                                    / logl(2.0)
+                            + s * l1pa + t * l1pb;
                     pval += exp2(base + sbase);
                     for (++t; t <= n; ++t) {
                         long double dev = get_deviance(s - 1, t);
                         if (dev >= in_dev) {
-                            sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                            sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
+                                    + log2l(t + 1) + l1pa - l1pb;
                             --s;
                             pval += exp2(base + sbase);
                             if (pval >= config.family_wise_error_rate)
@@ -766,7 +1007,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         common::logger->trace("Selecing cut-off");
         size_t k = 0;
         for (size_t i = 0; i < pvals.size(); ++i) {
-            const auto &[pval, group, idx] = pvals[i];
+            const auto& [pval, group, idx] = pvals[i];
             if (pval >= config.family_wise_error_rate)
                 break;
 
@@ -775,23 +1016,23 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         }
 
         common::logger->trace("Found {} / {} significant p-values. Minimum p-value is {}",
-                            k, pvals.size(), std::get<0>(pvals[0]));
+                              k, pvals.size(), std::get<0>(pvals[0]));
 
         for (size_t i = k; i < pvals.size(); ++i) {
             std::get<1>(pvals[i]) = Group::BOTH;
         }
 
         common::logger->trace("Reordering p-values");
-        std::sort(pvals.begin(), pvals.end(), [&](const auto &a, const auto &b) {
+        std::sort(pvals.begin(), pvals.end(), [&](const auto& a, const auto& b) {
             return std::get<2>(a) < std::get<2>(b);
         });
 
         common::logger->trace("Marking significant p-values");
         std::atomic_thread_fence(std::memory_order_release);
-        generate_clean_rows([&](uint64_t row_i, const auto &row, size_t) {
+        generate_clean_rows([&](uint64_t row_i, const auto& row, size_t) {
             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
             size_t unitig_id = kmer_to_unitig[node];
-            const auto &[pval, group, idx] = pvals[unitig_id];
+            const auto& [pval, group, idx] = pvals[unitig_id];
             if (group == Group::IN)
                 set_bit(indicator_in.data(), node, std::memory_order_relaxed);
 
@@ -813,21 +1054,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             common::logger->trace("Fitting per-sample negative binomial distributions");
 
 
-            #pragma omp parallel for num_threads(num_parallel_files)
+#pragma omp parallel for num_threads(num_parallel_files)
             for (size_t j = 0; j < groups.size(); ++j) {
-                const auto &hist = hists[j];
+                const auto& hist = hists[j];
                 if (hist.size()) {
-                    auto [r, p, mu, var] = get_rp([&](const auto &callback) {
-                        for (const auto &[k, c] : hist) {
+                    auto [r, p, mu, var] = get_rp([&](const auto& callback) {
+                        for (const auto& [k, c] : hist) {
                             callback(k, c);
                         }
                     });
                     nb_params[j] = std::make_pair(r, p);
-                    common::logger->trace("{}: size: {}\tmax_val: {}\tsample mean: {}\tsample var: {}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
-                                        j, sums[j], (hist.end() - 1)->first,
-                                        mu, var,
-                                        r * (1-p)/p, r*(1-p)/p/p,
-                                        r, p);
+                    common::logger->trace(
+                            "{}: size: {}\tmax_val: {}\tsample mean: {}\tsample var: "
+                            "{}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
+                            j, sums[j], (hist.end() - 1)->first, mu, var, r * (1 - p) / p,
+                            r * (1 - p) / p / p, r, p);
                 }
             }
 
@@ -841,7 +1082,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 long double denom_b = 0.0;
 
                 for (size_t j = 0; j < groups.size(); ++j) {
-                    const auto &[r, p] = nb_params[j];
+                    const auto& [r, p] = nb_params[j];
                     long double theta = (1.0 - p) / p;
                     if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                         num_b += theta * r;
@@ -896,25 +1137,34 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 // nb_params_a.first = mu_a * mu_a / (var_a - mu_a);
                 // nb_params_a.second = mu_a / var_a;
-                common::logger->trace("In: r: {}\tp: {}", nb_params_a.first, nb_params_a.second);
+                common::logger->trace("In: r: {}\tp: {}", nb_params_a.first,
+                                      nb_params_a.second);
 
                 // nb_params_b.first = mu_b * mu_b / (var_b - mu_b);
                 // nb_params_b.second = mu_b / var_b;
-                common::logger->trace("Out: r: {}\tp: {}", nb_params_b.first, nb_params_b.second);
+                common::logger->trace("Out: r: {}\tp: {}", nb_params_b.first,
+                                      nb_params_b.second);
 
                 // long double mu_null = mu_a + mu_b;
-                // long double var_null = var_a + var_b; // the two are independent, so their covariance is 0
-                // nb_params_null.first = mu_null * mu_null / (var_null - mu_null);
-                // nb_params_null.second = mu_null / var_null;
-                common::logger->trace("Null: r: {}\tp: {}", nb_params_null.first, nb_params_null.second);
-
-                nb_base = (lgammal(nb_params_null.first) - lgammal(nb_params_a.first) - lgammal(nb_params_b.first)) / logl(2.0);
-                nb_base += nb_params_a.first*log2l(nb_params_a.second) + nb_params_b.first*log2l(nb_params_b.second)-nb_params_null.first*log2l(nb_params_null.second);
+                // long double var_null = var_a + var_b; // the two are independent, so
+                // their covariance is 0 nb_params_null.first = mu_null * mu_null /
+                // (var_null - mu_null); nb_params_null.second = mu_null / var_null;
+                common::logger->trace("Null: r: {}\tp: {}", nb_params_null.first,
+                                      nb_params_null.second);
+
+                nb_base = (lgammal(nb_params_null.first) - lgammal(nb_params_a.first)
+                           - lgammal(nb_params_b.first))
+                        / logl(2.0);
+                nb_base += nb_params_a.first * log2l(nb_params_a.second)
+                        + nb_params_b.first * log2l(nb_params_b.second)
+                        - nb_params_null.first * log2l(nb_params_null.second);
             }
         }
 
-        using RowGenerator = std::function<void(const std::function<void(size_t, uint64_t)>&)>;
-        using GetLambda = std::function<std::pair<long double, long double>(size_t, const RowGenerator&)>;
+        using RowGenerator
+                = std::function<void(const std::function<void(size_t, uint64_t)>&)>;
+        using GetLambda
+                = std::function<std::pair<long double, long double>(size_t, const RowGenerator&)>;
         GetLambda get_lambda;
 
         std::pair<long double, long double> pln_params_a;
@@ -933,9 +1183,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             pln_params_a = get_params(in_kmers, in_sq_kmers);
             pln_params_b = get_params(out_kmers, out_sq_kmers);
             pln_params_null = get_params(total_kmers, total_sq_kmers);
-            common::logger->trace("Lognormal MoM\tin:\tmu: {}\tvar: {}", pln_params_a.first, pln_params_a.second);
-            common::logger->trace("Lognormal MoM\tout:\tmu: {}\tvar: {}", pln_params_b.first, pln_params_b.second);
-            common::logger->trace("Lognormal MoM\tnull:\tmu: {}\tvar: {}", pln_params_null.first, pln_params_null.second);
+            common::logger->trace("Lognormal MoM\tin:\tmu: {}\tvar: {}",
+                                  pln_params_a.first, pln_params_a.second);
+            common::logger->trace("Lognormal MoM\tout:\tmu: {}\tvar: {}",
+                                  pln_params_b.first, pln_params_b.second);
+            common::logger->trace("Lognormal MoM\tnull:\tmu: {}\tvar: {}",
+                                  pln_params_null.first, pln_params_null.second);
 
             // common::logger->trace("Precomputing lambdas");
 
@@ -959,14 +1212,16 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         if (config.test_type == "poisson_exact") {
             get_lambda = [&](size_t, const RowGenerator&) {
                 return std::make_pair(static_cast<long double>(in_kmers) / nelem,
-                                    static_cast<long double>(out_kmers) / nelem);
+                                      static_cast<long double>(out_kmers) / nelem);
             };
         }
 
         std::vector<std::pair<tsl::hopscotch_map<size_t, long double>,
-                            tsl::hopscotch_map<size_t, long double>>> lambdas(num_threads + 1);
+                              tsl::hopscotch_map<size_t, long double>>>
+                lambdas(num_threads + 1);
         if (config.test_type == "poisson_bayes") {
-            get_lambda = [&](size_t bucket_idx, const RowGenerator &generate) -> std::pair<long double, long double> {
+            get_lambda = [&](size_t bucket_idx, const RowGenerator& generate)
+                    -> std::pair<long double, long double> {
                 size_t in_sum = 0;
                 size_t out_sum = 0;
                 generate([&](size_t j, uint64_t c) {
@@ -979,20 +1234,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         in_sum += c;
                 });
 
-                auto &[in_lambdas, out_lambdas] = lambdas[bucket_idx];
+                auto& [in_lambdas, out_lambdas] = lambdas[bucket_idx];
                 auto in_found = in_lambdas.find(in_sum);
                 auto out_found = out_lambdas.find(out_sum);
 
                 if (in_found == in_lambdas.end()) {
                     auto [mu, sig2] = pln_params_a;
                     auto [lambda_j_min, lambda_j_max] = boost::math::tools::bisect(
-                        [&](long double lambda) {
-                            return (mu - logl(lambda)) / sig2 - lambda + in_sum - 1;
-                        },
-                        std::numeric_limits<long double>::min(),
-                        static_cast<long double>(max_in_obs_val),
-                        boost::math::tools::eps_tolerance<long double>(5)
-                    );
+                            [&](long double lambda) {
+                                return (mu - logl(lambda)) / sig2 - lambda + in_sum - 1;
+                            },
+                            std::numeric_limits<long double>::min(),
+                            static_cast<long double>(max_in_obs_val),
+                            boost::math::tools::eps_tolerance<long double>(5));
                     long double lambda = (lambda_j_min + lambda_j_max) / 2;
                     in_found = in_lambdas.try_emplace(in_sum, lambda).first;
                 }
@@ -1000,13 +1254,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (out_found == out_lambdas.end()) {
                     auto [mu, sig2] = pln_params_b;
                     auto [lambda_j_min, lambda_j_max] = boost::math::tools::bisect(
-                        [&](long double lambda) {
-                            return (mu - logl(lambda)) / sig2 - lambda + out_sum - 1;
-                        },
-                        std::numeric_limits<long double>::min(),
-                        static_cast<long double>(max_out_obs_val),
-                        boost::math::tools::eps_tolerance<long double>(5)
-                    );
+                            [&](long double lambda) {
+                                return (mu - logl(lambda)) / sig2 - lambda + out_sum - 1;
+                            },
+                            std::numeric_limits<long double>::min(),
+                            static_cast<long double>(max_out_obs_val),
+                            boost::math::tools::eps_tolerance<long double>(5));
                     long double lambda = (lambda_j_min + lambda_j_max) / 2;
                     out_found = out_lambdas.try_emplace(out_sum, lambda).first;
                 }
@@ -1039,7 +1292,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
                     pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
                     for (size_t k = 1; k < pmf_out.size(); ++k) {
-                        pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1] + (1.0 - p[i - 1]) * pmf_out[k];
+                        pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1]
+                                + (1.0 - p[i - 1]) * pmf_out[k];
                     }
                     std::swap(pmf_out_cur, pmf_out);
                 }
@@ -1049,7 +1303,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     pmf_in_cur[0] = (1.0 - p[i - 1]) * pmf_in[0];
                     pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
                     for (size_t k = 1; k < pmf_in.size(); ++k) {
-                        pmf_in_cur[k] = p[i - 1] * pmf_in[k - 1] + (1.0 - p[i - 1]) * pmf_in[k];
+                        pmf_in_cur[k]
+                                = p[i - 1] * pmf_in[k - 1] + (1.0 - p[i - 1]) * pmf_in[k];
                     }
                     std::swap(pmf_in_cur, pmf_in);
                 }
@@ -1057,23 +1312,27 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 pmf_null_cur[0] = (1.0 - p[i - 1]) * pmf_null[0];
                 pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
                 for (size_t k = 1; k < pmf_null.size(); ++k) {
-                    pmf_null_cur[k] = p[i - 1] * pmf_null[k - 1] + (1.0 - p[i - 1]) * pmf_null[k];
+                    pmf_null_cur[k]
+                            = p[i - 1] * pmf_null[k - 1] + (1.0 - p[i - 1]) * pmf_null[k];
                 }
                 std::swap(pmf_null_cur, pmf_null);
             }
 
             if (pmf_in.size() != num_labels_in + 1) {
-                common::logger->error("PMF in wrong: {} != {}", pmf_in.size(), num_labels_in + 1);
+                common::logger->error("PMF in wrong: {} != {}", pmf_in.size(),
+                                      num_labels_in + 1);
                 throw std::domain_error("");
             }
 
             if (pmf_out.size() != num_labels_out + 1) {
-                common::logger->error("PMF out wrong: {} != {}", pmf_out.size(), num_labels_out + 1);
+                common::logger->error("PMF out wrong: {} != {}", pmf_out.size(),
+                                      num_labels_out + 1);
                 throw std::domain_error("");
             }
 
             if (pmf_null.size() != num_labels_in + num_labels_out + 1) {
-                common::logger->error("PMF null wrong: {} != {}", pmf_null.size(), num_labels_in + num_labels_out + 1);
+                common::logger->error("PMF null wrong: {} != {}", pmf_null.size(),
+                                      num_labels_in + num_labels_out + 1);
                 throw std::domain_error("");
             }
 
@@ -1091,10 +1350,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     uint64_t t = n - s;
                     if (s < pmf_in.size() && t < pmf_out.size()) {
                         if (s < front) {
-                            common::logger->error("Attempting non-zero p-value in impossible configuration: {},{}", n, s);
+                            common::logger->error(
+                                    "Attempting non-zero p-value in impossible "
+                                    "configuration: {},{}",
+                                    n, s);
                             throw std::domain_error("");
                         }
-                        probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t]) - log2(pmf_null[n])));
+                        probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t])
+                                                - log2(pmf_null[n])));
                     } else {
                         probs.emplace_back(0.0);
                     }
@@ -1120,12 +1383,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 for (uint64_t s = 0; s < front; ++s) {
                     if (pb_pvals[n][s] > 0) {
-                        common::logger->error("Non-zero p-value in impossible configuration: {},{}", n, s);
+                        common::logger->error(
+                                "Non-zero p-value in impossible configuration: {},{}", n, s);
                         throw std::domain_error("");
                     }
                 }
-                long double local_min_pval = std::min(pb_pvals[n][front],
-                                                    pb_pvals[n].back());
+                long double local_min_pval
+                        = std::min(pb_pvals[n][front], pb_pvals[n].back());
                 min_pval = std::min(min_pval, local_min_pval);
 
                 size_t max_prob_counts = 0;
@@ -1134,7 +1398,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                 for (uint64_t s = front; s < pb_pvals[n].size(); ++s) {
                     if (pb_pvals[n][s] < local_min_pval) {
-                        common::logger->error("Min p-value not at boundary: {},{}: {} < {}", n, s, pb_pvals[n][s], local_min_pval);
+                        common::logger->error(
+                                "Min p-value not at boundary: {},{}: {} < {}", n, s,
+                                pb_pvals[n][s], local_min_pval);
                         throw std::domain_error("");
                     }
                 }
@@ -1146,24 +1412,24 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     max_prob_pos = front + front + 1;
                 } else {
                     for (uint64_t s = front + 1; s < pb_pvals[n].size(); ++s) {
-                        if (probs[s] == probs[s-1]) {
+                        if (probs[s] == probs[s - 1]) {
                             max_prob_counts = 2;
                             max_prob_pos = s + s;
                             break;
                         }
 
                         if (s + 1 < pb_pvals[n].size()) {
-                            if (probs[s-1] < probs[s] && probs[s] < probs[s+1])
+                            if (probs[s - 1] < probs[s] && probs[s] < probs[s + 1])
                                 continue;
 
-                            if (probs[s-1] > probs[s] && probs[s] > probs[s+1])
+                            if (probs[s - 1] > probs[s] && probs[s] > probs[s + 1])
                                 continue;
 
-                            if (probs[s] > probs[s-1] && probs[s] > probs[s+1]) {
-                                if (probs[s] - probs[s-1] == probs[s]-probs[s+1]) {
+                            if (probs[s] > probs[s - 1] && probs[s] > probs[s + 1]) {
+                                if (probs[s] - probs[s - 1] == probs[s] - probs[s + 1]) {
                                     max_prob_counts = 1;
                                     max_prob_pos = s;
-                                } else if (probs[s] - probs[s-1] > probs[s]-probs[s+1]) {
+                                } else if (probs[s] - probs[s - 1] > probs[s] - probs[s + 1]) {
                                     max_prob_counts = 2;
                                     max_prob_pos = s + s + 1;
                                 } else {
@@ -1181,58 +1447,125 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 mid_points[n] = static_cast<long double>(max_prob_pos) / max_prob_counts;
-                common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}",
-                                        n, mid_points[n],
-                                        fmt::join(probs,","),
-                                        fmt::join(pb_pvals[n],","));
+                common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}", n, mid_points[n],
+                                      fmt::join(probs, ","), fmt::join(pb_pvals[n], ","));
             }
 
             common::logger->trace("Min. p-value: {}", min_pval);
             if (min_pval >= config.family_wise_error_rate) {
                 common::logger->warn("No significant p-values achievable");
                 auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
-                    graph_ptr, [](node_index) { return false; }, true,
-                    is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-                );
+                        graph_ptr, [](node_index) { return false; }, true,
+                        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
                 auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
-                    graph_ptr, [](node_index) { return false; }, true,
-                    is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-                );
+                        graph_ptr, [](node_index) { return false; }, true,
+                        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
-                return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage{}, nullptr);
+                return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage {},
+                                       nullptr);
             }
         }
 
-        std::pair<long double, long double> bb_params_a;
-        std::pair<long double, long double> bb_params_b;
-        std::pair<long double, long double> bb_params_null;
-        long double bb_base = 0.0;
+        // std::pair<long double, long double> bb_params_a;
+        // std::pair<long double, long double> bb_params_b;
+        // std::pair<long double, long double> bb_params_null;
+        std::vector<std::tuple<long double, long double, long double>> bb_bases;
+        // long double bb_alpha = 0.0;
+        // long double bb_beta = 0.0;
         if (config.test_type == "bb_hypergeometric") {
-            auto get_ab = [&](long double n, long double n2) {
-                long double mu = n / nelem;
-                long double var = n2 / nelem - mu * mu;
-                long double a = mu * (n * mu - mu * mu - var) / n / (var - mu + mu * mu / n);
-                long double b = a * (n / mu - 1);
-                return std::make_pair(a, b);
-            };
+            std::vector<std::vector<std::tuple<size_t, long double, long double>>> probs_m(
+                    num_threads + 1);
+            generate_clean_rows([&](auto row_i, const auto& row, size_t bucket_idx) {
+                size_t n = 0;
+                size_t in_sum = 0;
+                for (const auto& [j, c] : row) {
+                    if (groups[j] == Group::IN)
+                        in_sum += c;
 
-            bb_params_a = get_ab(in_kmers, in_sq_kmers);
-            bb_params_b = get_ab(out_kmers, out_sq_kmers);
-            bb_params_null = get_ab(total_kmers, total_sq_kmers);
+                    n += c;
+                }
+                long double p = static_cast<long double>(in_sum) / n;
+                if (probs_m[bucket_idx].size() <= n)
+                    probs_m[bucket_idx].resize(n + 1);
+
+                auto& [c, prob_sum, sq_prob_sum] = probs_m[bucket_idx][n];
+                prob_sum += p;
+                sq_prob_sum += p * p;
+                ++c;
+            });
 
-            const auto &[a_a, b_a] = bb_params_a;
-            const auto &[a_b, b_b] = bb_params_b;
-            const auto &[a_n, b_n] = bb_params_null;
+            size_t max_size = 0;
+            for (const auto& probs : probs_m) {
+                max_size = std::max(max_size, probs.size());
+            }
+            std::vector<std::tuple<size_t, long double, long double>> probs(max_size);
+            for (const auto& prob : probs_m) {
+                for (size_t n = 0; n < prob.size(); ++n) {
+                    auto& [total_c, total_prob_sum, total_sq_prob_sum] = probs[n];
+                    const auto& [c, prob_sum, sq_prob_sum] = prob[n];
+                    total_c += c;
+                    total_prob_sum += prob_sum;
+                    total_sq_prob_sum += sq_prob_sum;
+                }
+            }
+            bb_bases.resize(max_size);
+
+            // convert prob sums and sq prob sums to mean and variance
+            common::logger->trace("Fitting beta distributions");
+            ProgressBar progress_bar(probs.size(), "Fitting", std::cerr,
+                                     !common::get_verbose());
+#pragma omp parallel for num_threads(num_threads) schedule(static)
+            for (size_t n = 0; n < probs.size(); ++n) {
+                ++progress_bar;
+                const auto& [c, prob_sum, sq_prob_sum] = probs[n];
+
+                if (c == 0)
+                    continue;
 
-            long double nx = static_cast<long double>(in_kmers);
-            long double ny = static_cast<long double>(out_kmers);
+                auto& [bb_alpha, bb_beta, bb_base] = bb_bases[n];
+                long double mn = prob_sum / c;
+                long double nu = n; // using the mean-sample size parametrization
+                // long double var = sq_prob_sum / c - mn * mn;
+                // long double nu = mn * (1.0 - mn) / var - 1.0; // MoM estimate
+                bb_alpha = nu * mn;
+                bb_beta = nu * (1.0 - mn);
+                long double bb_null = bb_alpha + bb_beta;
 
-            bb_base = (lgammal(nx + 1) + lgammal(ny + 1) - lgammal(nx + ny + 1)
-                        - lgammal(a_a + b_a + nx) - lgammal(a_b + b_b + ny)
-                        + lgammal(a_n + b_n + nx + ny) + lgammal(a_n) + lgammal(b_n) - lgammal(a_n + b_n)
-                        - lgammal(a_a) - lgammal(b_a) + lgammal(a_a + b_a)
-                        - lgammal(a_b) - lgammal(b_b) + lgammal(a_b + b_b)) / logl(2.0);
+                bb_base = (lgammal(bb_null) - lgammal(bb_alpha) - lgammal(bb_beta))
+                        / logl(2.0);
+
+                common::logger->trace("n: {}\talpha: {}\tbeta: {}", n, bb_alpha, bb_beta);
+            }
+
+
+            // common::logger->trace("Beta:\tmean: {}\tvar: {}\talpha: {}\tbeta: {}", mn,
+            //                       var, bb_alpha, bb_beta);
+            // nb_base += bb_alpha*log2l(0.1) + bb_beta*log2l(0.1)-bb_null*log2l(0.1);
+
+            // auto get_ab = [&](long double n, long double n2) {
+            //     long double mu = n / nelem;
+            //     long double var = n2 / nelem - mu * mu;
+            //     long double a = mu * (n * mu - mu * mu - var) / n / (var - mu + mu * mu
+            //     / n); long double b = a * (n / mu - 1); return std::make_pair(a, b);
+            // };
+
+            // bb_params_a = get_ab(in_kmers, in_sq_kmers);
+            // bb_params_b = get_ab(out_kmers, out_sq_kmers);
+            // bb_params_null = get_ab(total_kmers, total_sq_kmers);
+
+            // const auto &[a_a, b_a] = bb_params_a;
+            // const auto &[a_b, b_b] = bb_params_b;
+            // const auto &[a_n, b_n] = bb_params_null;
+
+            // long double nx = static_cast<long double>(in_kmers);
+            // long double ny = static_cast<long double>(out_kmers);
+
+            // bb_base = (lgammal(nx + 1) + lgammal(ny + 1) - lgammal(nx + ny + 1)
+            //             - lgammal(a_a + b_a + nx) - lgammal(a_b + b_b + ny)
+            //             + lgammal(a_n + b_n + nx + ny) + lgammal(a_n) + lgammal(b_n) - lgammal(a_n + b_n)
+            //             - lgammal(a_a) - lgammal(b_a) + lgammal(a_a + b_a)
+            //             - lgammal(a_b) - lgammal(b_b) + lgammal(a_b + b_b)) / logl(2.0);
         }
 
         std::pair<long double, long double> gp_params_a;
@@ -1241,8 +1574,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         // std::pair<long double, long double> gp_params_n;
         // long double gp_base = 0.0;
         if (config.test_type == "gpoisson") {
-            auto &[lambda_a, omega_a] = gp_params_a;
-            auto &[lambda_b, omega_b] = gp_params_b;
+            auto& [lambda_a, omega_a] = gp_params_a;
+            auto& [lambda_b, omega_b] = gp_params_b;
             // auto &[lambda_n, omega_n] = gp_params_n;
 
             lambda_a = static_cast<long double>(in_kmers) / nelem;
@@ -1258,10 +1591,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 std::vector<tsl::hopscotch_map<size_t, size_t>> row_sums_a_m(num_threads + 1);
                 std::vector<tsl::hopscotch_map<size_t, size_t>> row_sums_b_m(num_threads + 1);
                 std::vector<VectorSet<size_t>> row_sums_n_m(num_threads + 1);
-                generate_clean_rows([&](auto, const auto &row, size_t bucket_idx) {
+                generate_clean_rows([&](auto, const auto& row, size_t bucket_idx) {
                     size_t in_sum = 0;
                     size_t out_sum = 0;
-                    for (const auto &[j, c] : row) {
+                    for (const auto& [j, c] : row) {
                         if (groups[j] == Group::IN || groups[j] == Group::BOTH)
                             in_sum += c;
 
@@ -1279,10 +1612,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 row_sums_n = std::move(row_sums_n_m[0]);
 
                 for (size_t i = 1; i <= num_threads; ++i) {
-                    for (const auto &[k, c] : row_sums_a_m[i]) {
+                    for (const auto& [k, c] : row_sums_a_m[i]) {
                         row_sums_a[k] += c;
                     }
-                    for (const auto &[k, c] : row_sums_b_m[i]) {
+                    for (const auto& [k, c] : row_sums_b_m[i]) {
                         row_sums_b[k] += c;
                     }
                     for (size_t k : row_sums_n_m[i]) {
@@ -1294,14 +1627,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
             }
 
-            long double var_a = static_cast<long double>(in_sq_kmers) / nelem - lambda_a * lambda_a;
-            long double var_b = static_cast<long double>(out_sq_kmers) / nelem - lambda_b * lambda_b;
+            long double var_a
+                    = static_cast<long double>(in_sq_kmers) / nelem - lambda_a * lambda_a;
+            long double var_b
+                    = static_cast<long double>(out_sq_kmers) / nelem - lambda_b * lambda_b;
             // long double var_n = static_cast<long double>(total_sq_kmers) / nelem - lambda_n * lambda_n;
 
-            auto get_omega = [&](long double lambda, long double var, const auto &generate) {
+            auto get_omega = [&](long double lambda, long double var, const auto& generate) {
                 long double val = sqrtl(lambda / var);
                 if (1.0 <= val) {
-                    common::logger->trace("Invalid omega: {}\tlambda: {}\tvar: {}", 1.0 - val, lambda, var);
+                    common::logger->trace("Invalid omega: {}\tlambda: {}\tvar: {}",
+                                          1.0 - val, lambda, var);
                     throw std::runtime_error("Fit fail");
                 }
 
@@ -1316,13 +1652,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 // return (omega_min + omega_max) / 2.0;
             };
 
-            omega_a = get_omega(lambda_a, var_a, [&](const auto &callback) {
-                for (const auto &[k, c] : row_sums_a) {
+            omega_a = get_omega(lambda_a, var_a, [&](const auto& callback) {
+                for (const auto& [k, c] : row_sums_a) {
                     callback(k, c);
                 }
             });
-            omega_b = get_omega(lambda_b, var_b, [&](const auto &callback) {
-                for (const auto &[k, c] : row_sums_b) {
+            omega_b = get_omega(lambda_b, var_b, [&](const auto& callback) {
+                for (const auto& [k, c] : row_sums_b) {
                     callback(k, c);
                 }
             });
@@ -1332,21 +1668,28 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             //     }
             // });
 
-            common::logger->trace("Generalized Poisson In:\tlambda: {}\tomega: {}\tsample variance: {}\tvar: {}",
-                                  lambda_a, omega_a, var_a, lambda_a / pow(1.0 - omega_a, 2.0));
-            common::logger->trace("Generalized Poisson Out:\tlambda: {}\tomega: {}\tsample variance: {}\tvar: {}",
-                                  lambda_b, omega_b, var_b, lambda_b / pow(1.0 - omega_b, 2.0));
+            common::logger->trace(
+                    "Generalized Poisson In:\tlambda: {}\tomega: {}\tsample variance: "
+                    "{}\tvar: {}",
+                    lambda_a, omega_a, var_a, lambda_a / pow(1.0 - omega_a, 2.0));
+            common::logger->trace(
+                    "Generalized Poisson Out:\tlambda: {}\tomega: {}\tsample variance: "
+                    "{}\tvar: {}",
+                    lambda_b, omega_b, var_b, lambda_b / pow(1.0 - omega_b, 2.0));
             // common::logger->trace("Generalized Poisson Null:\tlambda: {}\tomega: {}\tsample variance: {}\tvar: {}",
             //                       lambda_n, omega_n, var_n, lambda_n / pow(1.0 - omega_n, 2.0));
 
-            long double gp_base = (log1pl(-omega_a) + log1pl(-omega_b) - (1.0 - omega_a) * lambda_a - (1.0 - omega_b) * lambda_b) / logl(2.0)
-                        + log2l(lambda_a) + log2l(lambda_b);
+            long double gp_base = (log1pl(-omega_a) + log1pl(-omega_b)
+                                   - (1.0 - omega_a) * lambda_a - (1.0 - omega_b) * lambda_b)
+                            / logl(2.0)
+                    + log2l(lambda_a) + log2l(lambda_b);
 
             gp_pvals.resize(*std::max_element(row_sums_n.begin(), row_sums_n.end()) + 1);
 
             size_t rows_per_update = 1000;
-            ProgressBar progress_bar(row_sums_n.size(), "Precomputing p-values", std::cerr, !common::get_verbose());
-            #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+            ProgressBar progress_bar(row_sums_n.size(), "Precomputing p-values",
+                                     std::cerr, !common::get_verbose());
+#pragma omp parallel for num_threads(num_threads) schedule(dynamic)
             for (size_t i = 0; i < row_sums_n.size(); ++i) {
                 if (i > 0 && (i % rows_per_update) == 0)
                     progress_bar += rows_per_update;
@@ -1354,7 +1697,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 size_t n = row_sums_n.values_container()[i];
                 size_t s = 0;
                 size_t t = n;
-                long double lprob = gp_base + (-lgammal(s + 1) - lgamma(t + 1) - omega_a * s - omega_b * t) / logl(2.0);
+                long double lprob = gp_base
+                        + (-lgammal(s + 1) - lgamma(t + 1) - omega_a * s - omega_b * t)
+                                / logl(2.0);
                 // auto get_prob = [&](int64_t s) {
                 //     int64_t t = n - s;
                 //     long double lprob = gp_base + (-lgammal(s + 1) - lgamma(t + 1) - omega_a * s - omega_b * t) / logl(2.0);
@@ -1373,7 +1718,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 std::vector<long double> probs;
                 probs.reserve(n + 1);
                 long double cur_lprob = lprob - log1pl(-omega_a) * lambda_a / logl(2.0)
-                                            + log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
+                        + log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
                 probs.emplace_back(exp2l(cur_lprob));
                 long double prob_sum = probs.back();
                 for (size_t s = 1; s <= n; ++s) {
@@ -1383,18 +1728,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     if (s == 0) {
                         cur_lprob -= log1pl(-omega_a) * lambda_a / logl(2.0);
                     } else if (s > 1) {
-                        cur_lprob += log2l((1.0 - omega_a) * lambda_a + omega_a * s) * (s - 1);
+                        cur_lprob += log2l((1.0 - omega_a) * lambda_a + omega_a * s)
+                                * (s - 1);
                     }
                     if (t == 0) {
                         cur_lprob -= log1pl(-omega_b) * lambda_b / logl(2.0);
                     } else if (t > 1) {
-                        cur_lprob += log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
+                        cur_lprob += log2l((1.0 - omega_b) * lambda_b + omega_b * t)
+                                * (t - 1);
                     }
                     probs.emplace_back(exp2l(cur_lprob));
                     prob_sum += probs.back();
                 }
 
-                auto &pvals_n = gp_pvals[n];
+                auto& pvals_n = gp_pvals[n];
                 pvals_n.reserve(n + 1);
                 for (size_t s = 0; s <= n; ++s) {
                     for (size_t sp = 0; sp <= n; ++sp) {
@@ -1434,36 +1781,39 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 long double mu2 = mu * mu;
                 long double twophimu2 = twophi * mu2;
 
-                p = (1.0L - sqrtl(1.0L + twophimu2))/phi/mu; /* log p[0] */
+                p = (1.0L - sqrtl(1.0L + twophimu2)) / phi / mu; /* log p[0] */
                 if (x == 0.0) {
                     if (p > 0) {
-                        common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu, lambda, x, p);
+                        common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu,
+                                              lambda, x, p);
                         throw std::runtime_error("Fail");
                     }
                     return p;
                 }
 
-                pi2m = expl(p);                /* p[i - 2] = p[0]*/
-                p = logl(mu) + p - log1pl(twophimu2)/2.0L;   /* log p[1] */
+                pi2m = expl(p); /* p[i - 2] = p[0]*/
+                p = logl(mu) + p - log1pl(twophimu2) / 2.0L; /* log p[1] */
                 if (x == 1.0) {
                     if (p > 0) {
-                        common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu, lambda, x, p);
+                        common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu,
+                                              lambda, x, p);
                         throw std::runtime_error("Fail");
                     }
                     return p;
                 }
 
-                pi1m = expl(p);                    /* p[i - 1] = p[1] */
-                A = 1.0L/(1.0L + 1.0L/twophimu2);        /* constant in first term */
-                B = mu2/(1.0L + twophimu2);            /* constant in second term */
+                pi1m = expl(p); /* p[i - 1] = p[1] */
+                A = 1.0L / (1.0L + 1.0L / twophimu2); /* constant in first term */
+                B = mu2 / (1.0L + twophimu2); /* constant in second term */
                 for (i = 2; i <= x; i++) {
-                    p = A * (1.0L - 1.5L/i) * pi1m + (B * pi2m)/(i * (i - 1));
+                    p = A * (1.0L - 1.5L / i) * pi1m + (B * pi2m) / (i * (i - 1));
                     pi2m = pi1m;
                     pi1m = p;
                 }
 
                 if (p > 1.0) {
-                    common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu, lambda, x, logl(p));
+                    common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu,
+                                          lambda, x, logl(p));
                     throw std::runtime_error("Fail");
                 }
 
@@ -1471,38 +1821,45 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             };
 
             std::vector<long double> lambdas(groups.size());
-            #pragma omp parallel for num_threads(num_threads)
+#pragma omp parallel for num_threads(num_threads)
             for (size_t i = 0; i < groups.size(); ++i) {
                 long double mu = static_cast<long double>(sums[i]) / nelem;
                 long double lambda = 0.0;
                 long double val = 0.0;
                 try {
-                    std::tie(lambda, val) = boost::math::tools::brent_find_minima([&](long double lambda) {
-                        long double val = 0.0;
-                        for (const auto &[k, c] : hists[i]) {
-                            val += get_lp(k, mu, lambda) * c;
-                        }
-                        return -val;
-                    }, std::numeric_limits<long double>::min(), 1.0L, 5);
-                } catch (boost::wrapexcept<boost::math::evaluation_error> &) {}
+                    std::tie(lambda, val) = boost::math::tools::brent_find_minima(
+                            [&](long double lambda) {
+                                long double val = 0.0;
+                                for (const auto& [k, c] : hists[i]) {
+                                    val += get_lp(k, mu, lambda) * c;
+                                }
+                                return -val;
+                            },
+                            std::numeric_limits<long double>::min(), 1.0L, 5);
+                } catch (boost::wrapexcept<boost::math::evaluation_error>&) {
+                }
                 try {
-                    auto [phi, val2] = boost::math::tools::brent_find_minima([&](long double phi) {
-                        long double val = 0.0;
-                        for (const auto &[k, c] : hists[i]) {
-                            val += get_lp(k, mu, 1.0L / phi) * c;
-                        }
-                        return -val;
-                    }, std::numeric_limits<long double>::min(), 1.0L, 5);
+                    auto [phi, val2] = boost::math::tools::brent_find_minima(
+                            [&](long double phi) {
+                                long double val = 0.0;
+                                for (const auto& [k, c] : hists[i]) {
+                                    val += get_lp(k, mu, 1.0L / phi) * c;
+                                }
+                                return -val;
+                            },
+                            std::numeric_limits<long double>::min(), 1.0L, 5);
                     if (val == 0.0 || val2 < val)
                         lambda = 1.0L / phi;
-                } catch (boost::wrapexcept<boost::math::evaluation_error> &) {}
+                } catch (boost::wrapexcept<boost::math::evaluation_error>&) {
+                }
                 if (val == 0.0 || lambda == 0.0) {
                     throw std::runtime_error("FAILS");
                 }
 
                 long double var = static_cast<long double>(sq_sums[i]) / nelem - mu * mu;
-                common::logger->trace("i: {}\tmu: {}\tsample var: {}\tlambda: {}\tvar: {}",
-                                        i, mu, var, lambda, mu + mu * mu * mu / lambda);
+                common::logger->trace(
+                        "i: {}\tmu: {}\tsample var: {}\tlambda: {}\tvar: {}", i, mu, var,
+                        lambda, mu + mu * mu * mu / lambda);
                 lambdas[i] = lambda;
             }
 
@@ -1545,11 +1902,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             lambda_n *= min_lambda_n;
 
             common::logger->trace("PIG:\tIN:\tmu: {}\tlambda: {}",
-                                       static_cast<long double>(in_kmers) / nelem, lambda_a);
+                                  static_cast<long double>(in_kmers) / nelem, lambda_a);
             common::logger->trace("PIG:\tOUT:\tmu: {}\tlambda: {}",
-                                        static_cast<long double>(out_kmers) / nelem, lambda_b);
+                                  static_cast<long double>(out_kmers) / nelem, lambda_b);
             common::logger->trace("PIG:\tNULL:\tmu: {}\tlambda: {}",
-                                            static_cast<long double>(total_kmers) / nelem, lambda_n);
+                                  static_cast<long double>(total_kmers) / nelem, lambda_n);
 
             // long double phi_a = (var_a - mu_a) / mu_a / mu_a / mu_a;
             // long double phi_b = (var_b - mu_b) / mu_b / mu_b / mu_b;
@@ -1576,8 +1933,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             // pig_base_n = 0.5 * (logl(2.0) - logl(M_PI) + logl(lambda_n)) + lambda_n / mu_n;
 
 
-            // long double var_n = static_cast<long double>(total_sq_kmers) / nelem - mu_n * mu_n;
-            // long double lambda = mu_n * mu_n * mu_n / (var_n - mu_n);
+            // long double var_n = static_cast<long double>(total_sq_kmers) / nelem - mu_n
+            // * mu_n; long double lambda = mu_n * mu_n * mu_n / (var_n - mu_n);
 
             // common::logger->trace("PIG: lambda: {}", lambda);
         }
@@ -1585,31 +1942,34 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         // precompute minimal attainable p-values
         std::vector<std::pair<long double, size_t>> m;
         std::vector<size_t> offsets;
-        if (config.test_type != "notest" && config.test_type != "nbinom_exact" && config.test_type != "pig") {
+        if (config.test_type != "notest" && config.test_type != "nbinom_exact"
+            && config.test_type != "pig" && config.test_type != "mwu"
+            && config.test_type != "bm" && config.test_type != "bb_hypergeometric") {
             common::logger->trace("Computing minimum p-values");
             std::vector<std::vector<std::pair<long double, size_t>>> ms(num_threads + 1);
             std::vector<VectorMap<long double, size_t>> ms_map(num_threads + 1);
             std::vector<std::vector<std::vector<long double>>> gp_pvals_m(num_threads + 1);
             std::mutex mu;
-            generate_clean_rows([&](uint64_t row_i, const auto &row, uint64_t bucket_idx) {
+            generate_clean_rows([&](uint64_t row_i, const auto& row, uint64_t bucket_idx) {
                 long double pval = 1.0;
                 size_t n = 0;
                 if (config.test_type == "poisson_binom") {
-                    for (const auto &[j, c] : row) {
-                        n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
+                    for (const auto& [j, c] : row) {
+                        n += static_cast<int64_t>(groups[j] != Group::OTHER)
+                                + (groups[j] == Group::BOTH);
                     }
                     size_t front = n - std::min(n, num_labels_out);
                     pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
                 } else if (config.test_type == "poisson_exact") {
                     sdsl::bit_vector found(num_labels_in + num_labels_out);
                     size_t n = 0;
-                    for (const auto &[j, c] : row) {
+                    for (const auto& [j, c] : row) {
                         n += c;
                         found[j] = true;
                     }
 
-                    auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto &callback) {
-                        for (const auto &[j, c] : row) {
+                    auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto& callback) {
+                        for (const auto& [j, c] : row) {
                             callback(j, c);
                         }
                         for (size_t j = 0; j < found.size(); ++j) {
@@ -1635,7 +1995,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         }
                     }
                 } else if (config.test_type == "nbinom_exact") {
-                    for (const auto &[j, c] : row) {
+                    for (const auto& [j, c] : row) {
                         n += c;
                     }
 
@@ -1649,10 +2009,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             auto [r_b, p_b] = nb_params_b;
 
                             long double l21p = log1p(-p_n) / log2l(2.0);
-                            long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0) - n*l21p;
+                            long double base = nb_base
+                                    + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0)
+                                    - n * l21p;
                             auto get_pval = [&](int64_t s) {
                                 int64_t t = n - s;
-                                long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1) + s * log1pl(-p_a) + t*log1pl(-p_b)) / logl(2.0);
+                                long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                                     - lgammal(s + 1) - lgammal(t + 1)
+                                                     + s * log1pl(-p_a) + t * log1pl(-p_b))
+                                        / logl(2.0);
                                 return exp2l(base + sbase);
                             };
 
@@ -1665,9 +2030,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             ++ms[bucket_idx][n].second;
                         }
                     }
-                } else if (config.test_type == "hypergeometric" || config.test_type == "poisson_bayes") {
+                } else if (config.test_type == "hypergeometric"
+                           || config.test_type == "poisson_bayes") {
                     size_t n = 0;
-                    for (const auto &[j, c] : row) {
+                    for (const auto& [j, c] : row) {
                         n += c;
                     }
                     if (n > 0) {
@@ -1675,31 +2041,32 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         //     ms[bucket_idx].resize(n + 1);
 
                         // if (ms[bucket_idx][n].second == 0) {
-                            sdsl::bit_vector found(groups.size());
-                            auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto &callback) {
-                                for (const auto &[j, c] : row) {
-                                    callback(j, c);
-                                }
-                                for (size_t j = 0; j < found.size(); ++j) {
-                                    if (!found[j])
-                                        callback(j, 0);
-                                }
-                            });
-                            boost::math::binomial_distribution dist(static_cast<long double>(n), mu1 / (mu1 + mu2));
-                            // boost::math::hypergeometric_distribution dist(mu1 * nelem, n, (mu1 + mu2) * nelem);
-                            long double pval0 = boost::math::pdf(dist, 0);
-                            long double pvaln = boost::math::pdf(dist, n);
-                            pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                            ++ms_map[bucket_idx][pval];
-                            // ms[bucket_idx][n].first = pval;
-                            // ms[bucket_idx][n].second = 1;
+                        sdsl::bit_vector found(groups.size());
+                        auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto& callback) {
+                            for (const auto& [j, c] : row) {
+                                callback(j, c);
+                            }
+                            for (size_t j = 0; j < found.size(); ++j) {
+                                if (!found[j])
+                                    callback(j, 0);
+                            }
+                        });
+                        boost::math::binomial_distribution dist(static_cast<long double>(n),
+                                                                mu1 / (mu1 + mu2));
+                        // boost::math::hypergeometric_distribution dist(mu1 * nelem, n, (mu1 + mu2) * nelem);
+                        long double pval0 = boost::math::pdf(dist, 0);
+                        long double pvaln = boost::math::pdf(dist, n);
+                        pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                        ++ms_map[bucket_idx][pval];
+                        // ms[bucket_idx][n].first = pval;
+                        // ms[bucket_idx][n].second = 1;
                         // } else {
                         //    ++ms[bucket_idx][n].second;
                         // }
                     }
                 } else if (config.test_type == "pig") {
                     size_t n = 0;
-                    for (const auto &[j, c] : row) {
+                    for (const auto& [j, c] : row) {
                         n += c;
                     }
 
@@ -1712,15 +2079,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             long double mu_a = static_cast<long double>(in_kmers) / nelem;
                             long double mu_b = static_cast<long double>(out_kmers) / nelem;
 
-                            // long double var_a = static_cast<long double>(in_sq_kmers) / nelem - mu_a * mu_a;
-                            // long double var_b = static_cast<long double>(out_sq_kmers) / nelem - mu_b * mu_b;
+                            // long double var_a = static_cast<long double>(in_sq_kmers) /
+                            // nelem - mu_a * mu_a; long double var_b = static_cast<long
+                            // double>(out_sq_kmers) / nelem - mu_b * mu_b;
 
                             // long double phi_a = (var_a - mu_a) / mu_a / mu_a / mu_a;
                             // long double phi_b = (var_b - mu_b) / mu_b / mu_b / mu_b;
                             // long double lambda_a = 1.0 / phi_a;
                             // long double lambda_b = 1.0 / phi_b;
 
-                            auto get_lp = [&](long double x, long double mu, long double lambda, long double base) {
+                            auto get_lp = [&](long double x, long double mu,
+                                              long double lambda, long double base) {
                                 long double phi = 1.0L / lambda;
                                 int64_t i;
                                 long double p, pi1m, pi2m;
@@ -1730,36 +2099,46 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                 long double mu2 = mu * mu;
                                 long double twophimu2 = twophi * mu2;
 
-                                p = (1.0L - sqrtl(1.0L + twophimu2))/phi/mu; /* log p[0] */
+                                p = (1.0L - sqrtl(1.0L + twophimu2)) / phi
+                                        / mu; /* log p[0] */
                                 if (x == 0.0) {
                                     if (p > 0) {
-                                        common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, p);
+                                        common::logger->error(
+                                                "mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: "
+                                                "{}",
+                                                mu, lambda, n, x, p);
                                         throw std::runtime_error("Fail");
                                     }
                                     return p;
                                 }
 
-                                pi2m = expl(p);                /* p[i - 2] = p[0]*/
-                                p = logl(mu) + p - log1pl(twophimu2)/2.0L;   /* log p[1] */
+                                pi2m = expl(p); /* p[i - 2] = p[0]*/
+                                p = logl(mu) + p - log1pl(twophimu2) / 2.0L; /* log p[1] */
                                 if (x == 1.0) {
                                     if (p > 0) {
-                                        common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, p);
+                                        common::logger->error(
+                                                "mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: "
+                                                "{}",
+                                                mu, lambda, n, x, p);
                                         throw std::runtime_error("Fail");
                                     }
                                     return p;
                                 }
 
-                                pi1m = expl(p);                    /* p[i - 1] = p[1] */
-                                A = 1.0L/(1.0L + 1.0L/twophimu2);        /* constant in first term */
-                                B = mu2/(1.0L + twophimu2);            /* constant in second term */
+                                pi1m = expl(p); /* p[i - 1] = p[1] */
+                                A = 1.0L / (1.0L + 1.0L / twophimu2); /* constant in first term */
+                                B = mu2 / (1.0L + twophimu2); /* constant in second term */
                                 for (i = 2; i <= x; i++) {
-                                    p = A * (1.0L - 1.5L/i) * pi1m + (B * pi2m)/(i * (i - 1));
+                                    p = A * (1.0L - 1.5L / i) * pi1m
+                                            + (B * pi2m) / (i * (i - 1));
                                     pi2m = pi1m;
                                     pi1m = p;
                                 }
 
                                 if (p > 1.0) {
-                                    common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, logl(p));
+                                    common::logger->error(
+                                            "mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}",
+                                            mu, lambda, n, x, logl(p));
                                     throw std::runtime_error("Fail");
                                 }
 
@@ -1775,11 +2154,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                 //     lprob = (1.0 - sqrtl(1.0 + twophimu2))/phi/mu;
                                 // } else {
                                 //     lprob = base - lgamma(x + 1)
-                                //             - 0.5*(x - 0.5) * (logl(2.0) - logl(lambda) + logl(1.0 + lambda / 2 / mu / mu))
-                                //             + logl(cyl_bessel_kl(x - 0.5, sqrtl(lambda * 2 * (1.0 + lambda / 2 / mu / mu))));
+                                //             - 0.5*(x - 0.5) * (logl(2.0) - logl(lambda)
+                                //             + logl(1.0 + lambda / 2 / mu / mu))
+                                //             + logl(cyl_bessel_kl(x - 0.5, sqrtl(lambda
+                                //             * 2 * (1.0 + lambda / 2 / mu / mu))));
                                 // }
                                 // if (lprob > 0) {
-                                //     common::logger->error("mu: {}\tlambda: {}\tx: {}\tn: {}\tlog(pval): {}\tpval: {}",
+                                //     common::logger->error("mu: {}\tlambda: {}\tx:
+                                //     {}\tn: {}\tlog(pval): {}\tpval: {}",
                                 //         mu, lambda, x, n, lprob, expl(lprob));
                                 //     throw std::runtime_error("pval > 1");
                                 // }
@@ -1796,7 +2178,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                             long double base_lprob = get_lp(n, mu_n, lambda_n, pig_base_n);
                             auto get_lprob = [&](int64_t s) {
-                                return get_lp(s, mu_a, lambda_a, pig_base_a) + get_lp(n - s, mu_b, lambda_b, pig_base_b) - base_lprob;
+                                return get_lp(s, mu_a, lambda_a, pig_base_a)
+                                        + get_lp(n - s, mu_b, lambda_b, pig_base_b)
+                                        - base_lprob;
                             };
 
                             long double lpval0 = get_lprob(0);
@@ -1811,7 +2195,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
                 } else if (config.test_type == "gpoisson") {
                     size_t n = 0;
-                    for (const auto &[j, c] : row) {
+                    for (const auto& [j, c] : row) {
                         n += c;
                     }
 
@@ -1866,7 +2250,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             //     prob_sum += probs.back();
                             // }
 
-                            const auto &pvals_n = gp_pvals[n];
+                            const auto& pvals_n = gp_pvals[n];
                             // pvals_n.reserve(n + 1);
                             // for (size_t s = 0; s <= n; ++s) {
                             //     for (size_t sp = 0; sp <= n; ++sp) {
@@ -1883,38 +2267,49 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             ++ms[bucket_idx][n].second;
                         }
                     }
-                } else if (config.test_type == "bb_hypergeometric") {
-                    size_t n = 0;
-                    for (const auto &[j, c] : row) {
-                        n += c;
-                    }
-                    if (n > 0) {
-                        if (ms[bucket_idx].size() <= n)
-                            ms[bucket_idx].resize(n + 1);
-
-                        if (ms[bucket_idx][n].second == 0) {
-                            const auto &[a_a, b_a] = bb_params_a;
-                            const auto &[a_b, b_b] = bb_params_b;
-                            const auto &[a_n, b_n] = bb_params_null;
-                            long double nx = static_cast<long double>(in_kmers);
-                            long double ny = static_cast<long double>(out_kmers);
-                            long double base = (lgammal(n + 1) + lgammal(nx + ny - n + 1)
-                                                - lgammal(a_n + n) - lgammal(b_n + nx + ny - n)) / logl(2.0) + bb_base;
-                            int64_t s = 0;
-                            long double pval0 = (-lgammal(s + 1) - lgammal(nx - s + 1)
-                                                - lgammal(n - s + 1) - lgammal(ny - n + s + 1)
-                                                + lgammal(a_a + s) + lgammal(b_a + nx - s) + lgammal(a_b + n - s) + lgammal(b_b + ny - n + s)) / logl(2.0) + base;
-                            s = n;
-                            long double pvaln = (-lgammal(s + 1) - lgammal(nx - s + 1)
-                                                - lgammal(n - s + 1) - lgammal(ny - n + s + 1)
-                                                + lgammal(a_a + s) + lgammal(b_a + nx - s) + lgammal(a_b + n - s) + lgammal(b_b + ny - n + s)) / logl(2.0) + base;
-                            pval = exp2l(std::min(pval0, pvaln)) * (1 + (pval0 == pvaln));
-                            ms[bucket_idx][n].first = pval;
-                            ms[bucket_idx][n].second = 1;
-                        } else {
-                        ++ms[bucket_idx][n].second;
-                        }
-                    }
+                    // } else if (config.test_type == "bb_hypergeometric") {
+                    //     size_t n = 0;
+                    //     for (const auto& [j, c] : row) {
+                    //         n += c;
+                    //     }
+                    //     if (n > 0) {
+                    //         if (ms[bucket_idx].size() <= n)
+                    //             ms[bucket_idx].resize(n + 1);
+
+                    //         if (ms[bucket_idx][n].second == 0) {
+                    //             const auto& [a_a, b_a] = bb_params_a;
+                    //             const auto& [a_b, b_b] = bb_params_b;
+                    //             const auto& [a_n, b_n] = bb_params_null;
+                    //             long double nx = static_cast<long double>(in_kmers);
+                    //             long double ny = static_cast<long double>(out_kmers);
+                    //             long double base
+                    //                     = (lgammal(n + 1) + lgammal(nx + ny - n + 1)
+                    //                        - lgammal(a_n + n) - lgammal(b_n + nx + ny - n))
+                    //                             / logl(2.0)
+                    //                     + bb_base;
+                    //             int64_t s = 0;
+                    //             long double pval0
+                    //                     = (-lgammal(s + 1) - lgammal(nx - s + 1)
+                    //                        - lgammal(n - s + 1) - lgammal(ny - n + s + 1)
+                    //                        + lgammal(a_a + s) + lgammal(b_a + nx - s)
+                    //                        + lgammal(a_b + n - s) + lgammal(b_b + ny - n + s))
+                    //                             / logl(2.0)
+                    //                     + base;
+                    //             s = n;
+                    //             long double pvaln
+                    //                     = (-lgammal(s + 1) - lgammal(nx - s + 1)
+                    //                        - lgammal(n - s + 1) - lgammal(ny - n + s + 1)
+                    //                        + lgammal(a_a + s) + lgammal(b_a + nx - s)
+                    //                        + lgammal(a_b + n - s) + lgammal(b_b + ny - n + s))
+                    //                             / logl(2.0)
+                    //                     + base;
+                    //             pval = exp2l(std::min(pval0, pvaln)) * (1 + (pval0 == pvaln));
+                    //             ms[bucket_idx][n].first = pval;
+                    //             ms[bucket_idx][n].second = 1;
+                    //         } else {
+                    //             ++ms[bucket_idx][n].second;
+                    //         }
+                    //     }
                 }
 
                 // if (config.test_by_unitig) {
@@ -1960,11 +2355,12 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (config.test_type == "poisson_bayes") {
                 VectorMap<long double, size_t> m_map = std::move(ms_map[0]);
                 for (size_t i = 1; i < ms_map.size(); ++i) {
-                    for (const auto &[pval, c] : ms_map[i]) {
+                    for (const auto& [pval, c] : ms_map[i]) {
                         m_map[pval] += c;
                     }
                 }
-                m = const_cast<std::vector<std::pair<long double, size_t>>&&>(m_map.values_container());
+                m = const_cast<std::vector<std::pair<long double, size_t>>&&>(
+                        m_map.values_container());
                 std::sort(m.rbegin(), m.rend());
             } else {
                 std::swap(m, ms[0]);
@@ -1977,7 +2373,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 // gp_pvals.resize(max_size);
                 for (size_t i = 1; i < ms.size(); ++i) {
                     for (size_t n = 0; n < ms[i].size(); ++i) {
-                        const auto &[pval, c] = ms[i][n];
+                        const auto& [pval, c] = ms[i][n];
                         m[n].first = pval;
                         m[n].second += c;
                         // if (gp_pvals[n].empty() && gp_pvals_m[i][n].size())
@@ -1986,8 +2382,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
 
                 // if (config.test_type == "gpoisson") {
-                //     m.erase(std::remove_if(m.begin(), m.end(), [&](const auto &a) { return !a.second; }), m.end());
-                //     std::sort(m.rbegin(), m.rend());
+                //     m.erase(std::remove_if(m.begin(), m.end(), [&](const auto &a) {
+                //     return !a.second; }), m.end()); std::sort(m.rbegin(), m.rend());
                 // }
             }
 
@@ -2016,9 +2412,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     }
 
                     if (jt->first - it->first > 1e-5) {
-                        common::logger->error("min p-vals not sorted: {} vs. {}\t{} !>= {}",
-                                              it - m.begin(), jt - m.begin(),
-                                              it->first, jt->first);
+                        common::logger->error(
+                                "min p-vals not sorted: {} vs. {}\t{} !>= {}",
+                                it - m.begin(), jt - m.begin(), it->first, jt->first);
                         throw std::runtime_error("p-val fail");
                     }
                     ++it;
@@ -2039,7 +2435,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
         // determine cutoffs for multiple testing correction
         auto [k_min, k, n_cutoff] = correct_pvals(m);
-        if (config.test_type == "nbinom_exact" || config.test_type == "pig") {
+        if (config.test_type == "nbinom_exact" || config.test_type == "pig"
+            || config.test_type == "mwu" || config.test_type == "bm"
+            || config.test_type == "bb_hypergeometric") {
             k_min = 1;
             k = 1;
             n_cutoff = 1;
@@ -2048,19 +2446,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         // k = 1;
 
         common::logger->trace("Picked: k: {}\tn: {} / {}\tpval_min: {}\tn_cutoff: {}",
-                            k_min, k, nelem, config.family_wise_error_rate / k, n_cutoff);
+                              k_min, k, nelem, config.family_wise_error_rate / k, n_cutoff);
 
         common::logger->trace("Running differential tests");
         std::vector<std::pair<long double, size_t>> nb_pvals;
-        if (config.test_type == "nbinom_exact" || config.test_type == "pig")
+        if (config.test_type == "nbinom_exact" || config.test_type == "pig"
+            || config.test_type == "mwu" || config.test_type == "bm"
+            || config.test_type == "bb_hypergeometric")
             nb_pvals.resize(kept.num_set_bits(), std::make_pair(1.0, kept.size()));
 
         std::atomic_thread_fence(std::memory_order_release);
-        generate_clean_rows([&](uint64_t row_i, const auto &row, uint64_t bucket_idx) {
+        generate_clean_rows([&](uint64_t row_i, const auto& row, uint64_t bucket_idx) {
             if (config.test_type == "notest") {
                 size_t count_in = 0;
                 size_t count_out = 0;
-                for (const auto &[j, c] : row) {
+                for (const auto& [j, c] : row) {
                     if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
                         ++count_out;
 
@@ -2068,8 +2468,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         ++count_in;
                 }
 
-                bool in_kmer = count_in >= config.min_in_recurrence && count_in <= config.max_in_recurrence;
-                bool out_kmer = count_out >= config.min_out_recurrence && count_out <= config.max_out_recurrence;
+                bool in_kmer = count_in >= config.min_in_recurrence
+                        && count_in <= config.max_in_recurrence;
+                bool out_kmer = count_out >= config.min_out_recurrence
+                        && count_out <= config.max_out_recurrence;
 
                 node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
                 if (in_kmer)
@@ -2083,19 +2485,19 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
             auto set_pval = [&](long double pval, long double eff_size) {
                 // if (!config.test_by_unitig) {
-                    if (pval * k < config.family_wise_error_rate) {
-                        if (eff_size == 0) {
-                            common::logger->error("Effect size 0 when p-value is {}", pval);
-                            throw std::runtime_error("Pval failure");
-                        }
+                if (pval * k < config.family_wise_error_rate) {
+                    if (eff_size == 0) {
+                        common::logger->error("Effect size 0 when p-value is {}", pval);
+                        throw std::runtime_error("Pval failure");
+                    }
 
-                        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                        if (eff_size > 0) {
-                            set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
-                        } else if (eff_size < 0) {
-                            set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
-                        }
+                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                    if (eff_size > 0) {
+                        set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
+                    } else if (eff_size < 0) {
+                        set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
                     }
+                }
                 // } else {
                 //     push_back(pvals_buckets[bucket_idx], pval);
                 //     push_back(eff_size_buckets[bucket_idx], eff_size);
@@ -2107,8 +2509,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             if (config.test_type == "poisson_binom") {
                 size_t n = 0;
                 uint64_t s = 0;
-                for (const auto &[j, c] : row) {
-                    n += static_cast<int64_t>(groups[j] != Group::OTHER) + (groups[j] == Group::BOTH);
+                for (const auto& [j, c] : row) {
+                    n += static_cast<int64_t>(groups[j] != Group::OTHER)
+                            + (groups[j] == Group::BOTH);
                     s += (groups[j] == Group::IN);
                 }
 
@@ -2128,7 +2531,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 sdsl::bit_vector found(num_labels_in + num_labels_out);
                 int64_t in_sum = 0;
                 int64_t out_sum = 0;
-                for (const auto &[j, c] : row) {
+                for (const auto& [j, c] : row) {
                     n += c;
                     found[j] = true;
                     if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
@@ -2138,8 +2541,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         in_sum += c;
                 }
 
-                auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto &callback) {
-                    for (const auto &[j, c] : row) {
+                auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto& callback) {
+                    for (const auto& [j, c] : row) {
                         callback(j, c);
                     }
                     for (size_t j = 0; j < found.size(); ++j) {
@@ -2163,7 +2566,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     auto get_deviance = [&](long double y, long double mu) {
                         y += 1e-8;
                         mu += 1e-8;
-                        return 2 * (y * log(y/mu) - y + mu);
+                        return 2 * (y * log(y / mu) - y + mu);
                     };
 
                     std::vector<long double> devs;
@@ -2175,7 +2578,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     pval = 0.0;
 
                     size_t s = 0;
-                    for ( ; s <= n; ++s) {
+                    for (; s <= n; ++s) {
                         if (devs[s] < devs[in_sum])
                             break;
                     }
@@ -2183,7 +2586,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         pval += boost::math::cdf(bdist, s - 1);
 
                     size_t sp = n;
-                    for ( ; sp >= s; --sp) {
+                    for (; sp >= s; --sp) {
                         if (devs[sp] < devs[in_sum])
                             break;
                     }
@@ -2201,7 +2604,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 size_t n = 0;
                 int64_t in_sum = 0;
                 int64_t out_sum = 0;
-                for (const auto &[j, c] : row) {
+                for (const auto& [j, c] : row) {
                     n += c;
                     if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                         out_sum += c;
@@ -2238,34 +2641,36 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         long double mu2 = mu * mu;
                         long double twophimu2 = twophi * mu2;
 
-                        p = (1.0L - sqrtl(1.0L + twophimu2))/phi/mu; /* log p[0] */
+                        p = (1.0L - sqrtl(1.0L + twophimu2)) / phi / mu; /* log p[0] */
                         std::vector<long double> lprobs;
                         lprobs.reserve(n);
                         lprobs.emplace_back(p);
                         // if (x == 0.0) {
                         //     if (p > 0) {
-                        //         common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, p);
-                        //         throw std::runtime_error("Fail");
+                        //         common::logger->error("mu: {}\tlambda: {}\tn: {}\tx:
+                        //         {}\tlogp: {}", mu, lambda, n, x, p); throw
+                        //         std::runtime_error("Fail");
                         //     }
                         //     return p;
                         // }
 
-                        pi2m = expl(p);                /* p[i - 2] = p[0]*/
-                        p = logl(mu) + p - log1pl(twophimu2)/2.0L;   /* log p[1] */
+                        pi2m = expl(p); /* p[i - 2] = p[0]*/
+                        p = logl(mu) + p - log1pl(twophimu2) / 2.0L; /* log p[1] */
                         lprobs.emplace_back(p);
                         // if (x == 1.0) {
                         //     if (p > 0) {
-                        //         common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, p);
-                        //         throw std::runtime_error("Fail");
+                        //         common::logger->error("mu: {}\tlambda: {}\tn: {}\tx:
+                        //         {}\tlogp: {}", mu, lambda, n, x, p); throw
+                        //         std::runtime_error("Fail");
                         //     }
                         //     return p;
                         // }
 
-                        pi1m = expl(p);                    /* p[i - 1] = p[1] */
-                        A = 1.0L/(1.0L + 1.0L/twophimu2);        /* constant in first term */
-                        B = mu2/(1.0L + twophimu2);            /* constant in second term */
+                        pi1m = expl(p); /* p[i - 1] = p[1] */
+                        A = 1.0L / (1.0L + 1.0L / twophimu2); /* constant in first term */
+                        B = mu2 / (1.0L + twophimu2); /* constant in second term */
                         for (i = 2; i <= n; i++) {
-                            p = A * (1.0L - 1.5L/i) * pi1m + (B * pi2m)/(i * (i - 1));
+                            p = A * (1.0L - 1.5L / i) * pi1m + (B * pi2m) / (i * (i - 1));
                             pi2m = pi1m;
                             pi1m = p;
                             lprobs.emplace_back(logl(p));
@@ -2274,8 +2679,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                         return lprobs;
 
                         // if (p > 1.0) {
-                        //     common::logger->error("mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}", mu, lambda, n, x, logl(p));
-                        //     throw std::runtime_error("Fail");
+                        //     common::logger->error("mu: {}\tlambda: {}\tn: {}\tx:
+                        //     {}\tlogp: {}", mu, lambda, n, x, logl(p)); throw
+                        //     std::runtime_error("Fail");
                         // }
 
                         // return logl(p);
@@ -2289,15 +2695,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     long double mu_n = mu_a + mu_b;
                     // long double lambda_n = min_lambda * pow(w_a + w_b, 2.0);
 
-                    auto get_deviance = [&](long double x, long double mu, long double lambda) {
-                        return lambda * lambda * pow(x - mu, 2.0) / x / mu / mu;
-                    };
+                    auto get_deviance
+                            = [&](long double x, long double mu, long double lambda) {
+                                  return lambda * lambda * pow(x - mu, 2.0) / x / mu / mu;
+                              };
 
                     auto get_total_deviance = [&](size_t s) {
                         if (s == 0 || s == n)
                             return std::numeric_limits<long double>::max();
 
-                        return get_deviance(s, mu_a, lambda_a) + get_deviance(n - s, mu_b, lambda_b);
+                        return get_deviance(s, mu_a, lambda_a)
+                                + get_deviance(n - s, mu_b, lambda_b);
                     };
 
                     long double base_lprob = get_lprobs(mu_n, lambda_n).back();
@@ -2343,17 +2751,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     // }
 
                     if (pval == 0.0) {
-                        common::logger->trace("In: ({},{})\tOut: ({},{})\tNull: ({},{})\tn: {}\tin_sum: {}\tin_lprob: {} = {} + {} - {}",
-                            mu_a,lambda_a,
-                            mu_b,lambda_b,
-                            mu_n,lambda_n,
-                            n, in_sum, in_lprob,
-                            lprobs_a[in_sum],
-                            lprobs_b[out_sum],
-                            // get_lp(in_sum, mu_a, lambda_a),
-                            // get_lp(out_sum, mu_b, lambda_b),
-                            base_lprob
-                        );
+                        common::logger->trace(
+                                "In: ({},{})\tOut: ({},{})\tNull: ({},{})\tn: "
+                                "{}\tin_sum: {}\tin_lprob: {} = {} + {} - {}",
+                                mu_a, lambda_a, mu_b, lambda_b, mu_n, lambda_n, n, in_sum,
+                                in_lprob, lprobs_a[in_sum], lprobs_b[out_sum],
+                                // get_lp(in_sum, mu_a, lambda_a),
+                                // get_lp(out_sum, mu_b, lambda_b),
+                                base_lprob);
                         throw std::runtime_error("Fail calc p-val");
                     }
 
@@ -2365,7 +2770,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 size_t n = 0;
                 size_t in_sum = 0;
                 size_t out_sum = 0;
-                for (const auto &[j, c] : row) {
+                for (const auto& [j, c] : row) {
                     n += c;
                     if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
                         out_sum += c;
@@ -2380,7 +2785,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 if (n != 0) {
                     size_t mode = 0;
                     size_t nmode = 0;
-                    const auto &pvals = gp_pvals[n];
+                    const auto& pvals = gp_pvals[n];
                     for (size_t s = 0; s <= n; ++s) {
                         if (pvals[s] >= 1.0) {
                             mode += s;
@@ -2453,7 +2858,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 size_t n = 0;
                 int64_t in_sum = 0;
                 int64_t out_sum = 0;
-                for (const auto &[j, c] : row) {
+                for (const auto& [j, c] : row) {
                     n += c;
                     if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                         out_sum += c;
@@ -2475,10 +2880,15 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     long double min_pval = 1.0;
                     {
                         long double l21p = log1p(-p_n) / log2l(2.0);
-                        long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0) - n*l21p;
+                        long double base = nb_base
+                                + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0)
+                                - n * l21p;
                         auto get_pval = [&](int64_t s) {
                             int64_t t = n - s;
-                            long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1) + s * log1pl(-p_a) + t*log1pl(-p_b)) / logl(2.0);
+                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                                 - lgammal(s + 1) - lgammal(t + 1)
+                                                 + s * log1pl(-p_a) + t * log1pl(-p_b))
+                                    / logl(2.0);
                             return exp2l(base + sbase);
                         };
 
@@ -2495,39 +2905,51 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             if (s == 0)
                                 return r_a * logl(p_a) * -2.0;
 
-                            return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s) + r_a * (logl(r_a) - logl(p_a))) * 2.0;
+                            return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
+                                    + r_a * (logl(r_a) - logl(p_a)))
+                                    * 2.0;
                         };
 
                         auto get_deviance_b = [&](long double t) {
                             if (t == 0)
                                 return r_b * logl(p_b) * -2.0;
 
-                            return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t) + r_b * (logl(r_b) - logl(p_b))) * 2.0;
+                            return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
+                                    + r_b * (logl(r_b) - logl(p_b)))
+                                    * 2.0;
                         };
 
                         auto get_deviance = [&](long double s, long double t) {
                             return get_deviance_a(s) + get_deviance_b(t);
                         };
 
-                        long double min_dev = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
+                        long double min_dev
+                                = get_deviance(midpoint,
+                                               static_cast<long double>(n) - midpoint);
                         long double in_dev = get_deviance(in_sum, out_sum);
 
                         if (in_dev > min_dev) {
                             eff_size = static_cast<long double>(in_sum) - midpoint;
                             pval = 0.0;
-                            long double base = nb_base + (lgammal(n + 1) - lgammal(r_n + n) - n*log1pl(-p_n)) / logl(2.0);
+                            long double base = nb_base
+                                    + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n))
+                                            / logl(2.0);
                             double l1pa = log1pl(-p_a) / logl(2.0);
                             double l1pb = log1pl(-p_b) / logl(2.0);
 
                             {
                                 size_t s = 0;
                                 size_t t = n;
-                                long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
+                                long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                                     - lgammal(s + 1) - lgammal(t + 1))
+                                                / logl(2.0)
+                                        + s * l1pa + t * l1pb;
                                 pval += exp2(base + sbase);
                                 for (++s; s <= n; ++s) {
                                     long double dev = get_deviance(s, t - 1);
                                     if (dev >= in_dev) {
-                                        sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                                        sbase += log2l(r_a + s) - log2l(r_b + t)
+                                                - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
                                         --t;
                                         pval += exp2(base + sbase);
                                         if (pval >= config.family_wise_error_rate)
@@ -2537,15 +2959,20 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                     }
                                 }
                             }
-                            if (pval < config.family_wise_error_rate && get_deviance(n, 0) >= in_dev) {
+                            if (pval < config.family_wise_error_rate
+                                && get_deviance(n, 0) >= in_dev) {
                                 size_t s = n;
                                 size_t t = 0;
-                                long double sbase = (lgammal(r_a+s)+lgammal(r_b+t)-lgammal(s+1)-lgammal(t+1)) / logl(2.0) + s*l1pa + t*l1pb;
+                                long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                                     - lgammal(s + 1) - lgammal(t + 1))
+                                                / logl(2.0)
+                                        + s * l1pa + t * l1pb;
                                 pval += exp2(base + sbase);
                                 for (++t; t <= n; ++t) {
                                     long double dev = get_deviance(s - 1, t);
                                     if (dev >= in_dev) {
-                                        sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                                        sbase -= log2l(r_a + s) - log2l(r_b + t)
+                                                - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
                                         --s;
                                         pval += exp2(base + sbase);
                                         if (pval >= config.family_wise_error_rate)
@@ -2557,13 +2984,17 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             }
                             nb_pvals[nb_idx].first = pval;
                         }
+                    } else {
+                        nb_pvals[nb_idx].second = row_i;
+                        nb_pvals[nb_idx].first = 1.1;
                     }
                 }
-            } else if (config.test_type == "hypergeometric" || config.test_type == "poisson_bayes") {
+            } else if (config.test_type == "hypergeometric"
+                       || config.test_type == "poisson_bayes") {
                 size_t n = 0;
                 int64_t in_sum = 0;
                 int64_t out_sum = 0;
-                for (const auto &[j, c] : row) {
+                for (const auto& [j, c] : row) {
                     n += c;
                     if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
                         out_sum += c;
@@ -2573,8 +3004,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 }
                 if (n != 0) {
                     sdsl::bit_vector found(groups.size());
-                    auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto &callback) {
-                        for (const auto &[j, c] : row) {
+                    auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto& callback) {
+                        for (const auto& [j, c] : row) {
                             callback(j, c);
                         }
                         for (size_t j = 0; j < found.size(); ++j) {
@@ -2582,7 +3013,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                                 callback(j, 0);
                         }
                     });
-                    boost::math::binomial_distribution dist(static_cast<long double>(n), mu1 / (mu1 + mu2));
+                    boost::math::binomial_distribution dist(static_cast<long double>(n),
+                                                            mu1 / (mu1 + mu2));
                     // boost::math::hypergeometric_distribution dist(mu1 * nelem, n, (mu1 + mu2) * nelem);
                     eff_size = static_cast<double>(in_sum) - boost::math::mode(dist);
 
@@ -2595,10 +3027,14 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     long double in_prob = boost::math::pdf(dist, in_sum);
                     if (eff_size > 0) {
                         pval = boost::math::cdf(boost::math::complement(dist, in_sum - 1))
-                                + boost::math::cdf(dist, floor(boost::math::quantile(boost::math::complement(dist, in_prob))));
+                                + boost::math::cdf(
+                                        dist,
+                                        floor(boost::math::quantile(
+                                                boost::math::complement(dist, in_prob))));
                     } else if (eff_size < 0) {
                         pval = boost::math::cdf(dist, in_sum)
-                                + boost::math::cdf(dist, ceil(boost::math::quantile(dist, in_prob)));
+                                + boost::math::cdf(
+                                        dist, ceil(boost::math::quantile(dist, in_prob)));
                     }
 
                     // double in_sz = abs(eff_size);
@@ -2627,55 +3063,234 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                     if (pval * k >= config.family_wise_error_rate)
                         return;
                 }
+            } else if (config.test_type == "mwu") {
+                auto generate_a = [&](const auto& callback) {
+                    sdsl::bit_vector found(groups.size());
+                    for (const auto& [j, c] : row) {
+                        found[j] = true;
+                        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                            callback(static_cast<long double>(c) / sums[j]);
+                    }
+                    for (size_t j = 0; j < found.size(); ++j) {
+                        if (!found[j] && (groups[j] == Group::IN || groups[j] == Group::BOTH))
+                            callback(0.0L);
+                    }
+                };
+                auto generate_b = [&](const auto& callback) {
+                    sdsl::bit_vector found(groups.size());
+                    for (const auto& [j, c] : row) {
+                        found[j] = true;
+                        if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                            callback(static_cast<long double>(c) / sums[j]);
+                    }
+                    for (size_t j = 0; j < found.size(); ++j) {
+                        if (!found[j] && (groups[j] == Group::OUT || groups[j] == Group::BOTH))
+                            callback(0.0L);
+                    }
+                };
+
+                std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
+
+                if (pval < config.family_wise_error_rate) {
+                    size_t nb_idx = kept.rank1(row_i) - 1;
+                    nb_pvals[nb_idx].first = pval;
+                    nb_pvals[nb_idx].second = row_i;
+                }
+            } else if (config.test_type == "mwu") {
+            } else if (config.test_type == "bm") {
+                auto generate_a = [&](const auto& callback) {
+                    sdsl::bit_vector found(groups.size());
+                    for (const auto& [j, c] : row) {
+                        found[j] = true;
+                        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                            callback(static_cast<long double>(c) / sums[j]);
+                    }
+                    for (size_t j = 0; j < found.size(); ++j) {
+                        if (!found[j] && (groups[j] == Group::IN || groups[j] == Group::BOTH))
+                            callback(0.0L);
+                    }
+                };
+                auto generate_b = [&](const auto& callback) {
+                    sdsl::bit_vector found(groups.size());
+                    for (const auto& [j, c] : row) {
+                        found[j] = true;
+                        if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                            callback(static_cast<long double>(c) / sums[j]);
+                    }
+                    for (size_t j = 0; j < found.size(); ++j) {
+                        if (!found[j] && (groups[j] == Group::OUT || groups[j] == Group::BOTH))
+                            callback(0.0L);
+                    }
+                };
+
+                std::tie(pval, eff_size) = brunner_munzel(generate_a, generate_b);
+
+                if (pval < config.family_wise_error_rate) {
+                    size_t nb_idx = kept.rank1(row_i) - 1;
+                    nb_pvals[nb_idx].first = pval;
+                    nb_pvals[nb_idx].second = row_i;
+                }
+
             } else if (config.test_type == "bb_hypergeometric") {
+                size_t nb_idx = kept.rank1(row_i) - 1;
                 size_t n = 0;
                 int64_t in_sum = 0;
                 int64_t out_sum = 0;
-                for (const auto &[j, c] : row) {
+                for (const auto& [j, c] : row) {
                     n += c;
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                         out_sum += c;
+                    }
 
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
                         in_sum += c;
+                    }
                 }
-                if (n != 0) {
-                    std::vector<long double> lprobs;
-                    lprobs.reserve(n + 1);
-                    const auto &[a_a, b_a] = bb_params_a;
-                    const auto &[a_b, b_b] = bb_params_b;
-                    const auto &[a_n, b_n] = bb_params_null;
-                    long double nx = static_cast<long double>(in_kmers);
-                    long double ny = static_cast<long double>(out_kmers);
-                    long double base = (lgammal(n + 1) + lgammal(nx + ny - n + 1)
-                                        - lgammal(a_n + n) - lgammal(b_n + nx + ny - n)) / logl(2.0) + bb_base;
-                    uint64_t s = 0;
-                    lprobs.emplace_back((-lgammal(s + 1) - lgammal(nx - s + 1)
-                                        - lgammal(n - s + 1) - lgammal(ny - n + s + 1)
-                                        + lgammal(a_a + s) + lgammal(b_a + nx - s) + lgammal(a_b + n - s) + lgammal(b_b + ny - n + s)) / logl(2.0) + base);
 
-                    for (++s; s <= n; ++s) {
-                        lprobs.emplace_back(lprobs.back()
-                            - log2l(s + 1) + log2l(nx - (s - 1) + 1)
-                            + log2l(n - (s - 1) + 1) - log2l(ny - n + s + 1)
-                            + log2l(a_a + s) - log2l(b_a + nx - (s - 1)) - log2l(a_b + n - (s - 1)) + log2l(b_b + ny - n + s)
-                        );
+                // if (n < n_cutoff)
+                //     return;
+
+                if (n > 0) {
+                    // long double min_pval = 1.0;
+                    const auto& [r_a, r_b, bb_base] = bb_bases[n];
+                    long double r_n = r_a + r_b;
+                    long double p_a = 0.1;
+                    long double p_b = 0.1;
+                    long double p_n = 0.1;
+                    long double min_pval = 1.0;
+                    {
+                        long double l21p = log1p(-p_n) / log2l(2.0);
+                        long double base = bb_base
+                                + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0)
+                                - n * l21p;
+                        auto get_pval = [&](int64_t s) {
+                            int64_t t = n - s;
+                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                                 - lgammal(s + 1) - lgammal(t + 1)
+                                                 + s * log1pl(-p_a) + t * log1pl(-p_b))
+                                    / logl(2.0);
+                            return exp2l(base + sbase);
+                        };
+
+                        long double pval0 = get_pval(0);
+                        long double pvaln = get_pval(n);
+                        min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
+                    }
+
+                    if (min_pval < config.family_wise_error_rate) {
+                        nb_pvals[nb_idx].second = row_i;
+                        long double midpoint = r_a * n / (r_a + r_b);
+
+                        auto get_deviance_a = [&](long double s) {
+                            if (s == 0)
+                                return r_a * logl(p_a) * -2.0;
+
+                            return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
+                                    + r_a * (logl(r_a) - logl(p_a)))
+                                    * 2.0;
+                        };
+
+                        auto get_deviance_b = [&](long double t) {
+                            if (t == 0)
+                                return r_b * logl(p_b) * -2.0;
+
+                            return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
+                                    + r_b * (logl(r_b) - logl(p_b)))
+                                    * 2.0;
+                        };
+
+                        auto get_deviance = [&](long double s, long double t) {
+                            return get_deviance_a(s) + get_deviance_b(t);
+                        };
+
+                        long double min_dev
+                                = get_deviance(midpoint,
+                                               static_cast<long double>(n) - midpoint);
+                        long double in_dev = get_deviance(in_sum, out_sum);
+
+                        if (in_dev > min_dev) {
+                            eff_size = static_cast<long double>(in_sum) - midpoint;
+                            pval = 0.0;
+                            long double base = bb_base
+                                    + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n))
+                                            / logl(2.0);
+                            double l1pa = log1pl(-p_a) / logl(2.0);
+                            double l1pb = log1pl(-p_b) / logl(2.0);
+
+                            {
+                                size_t s = 0;
+                                size_t t = n;
+                                long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                                     - lgammal(s + 1) - lgammal(t + 1))
+                                                / logl(2.0)
+                                        + s * l1pa + t * l1pb;
+                                pval += exp2(base + sbase);
+                                for (++s; s <= n; ++s) {
+                                    long double dev = get_deviance(s, t - 1);
+                                    if (dev >= in_dev) {
+                                        sbase += log2l(r_a + s) - log2l(r_b + t)
+                                                - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                                        --t;
+                                        pval += exp2(base + sbase);
+                                        if (pval >= config.family_wise_error_rate)
+                                            break;
+                                    } else {
+                                        break;
+                                    }
+                                }
+                            }
+                            if (pval < config.family_wise_error_rate
+                                && get_deviance(n, 0) >= in_dev) {
+                                size_t s = n;
+                                size_t t = 0;
+                                long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                                     - lgammal(s + 1) - lgammal(t + 1))
+                                                / logl(2.0)
+                                        + s * l1pa + t * l1pb;
+                                pval += exp2(base + sbase);
+                                for (++t; t <= n; ++t) {
+                                    long double dev = get_deviance(s - 1, t);
+                                    if (dev >= in_dev) {
+                                        sbase -= log2l(r_a + s) - log2l(r_b + t)
+                                                - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                                        --s;
+                                        pval += exp2(base + sbase);
+                                        if (pval >= config.family_wise_error_rate)
+                                            break;
+                                    } else {
+                                        break;
+                                    }
+                                }
+                            }
+                            nb_pvals[nb_idx].first = pval;
+                        }
+                    } else {
+                        nb_pvals[nb_idx].second = row_i;
+                        nb_pvals[nb_idx].first = 1.1;
                     }
-                    if (pval * k >= config.family_wise_error_rate)
-                        return;
                 }
             }
 
             set_pval(pval, eff_size);
         });
 
-        // if (config.test_type == "nbinom_exact" || config.test_type == "pig") {
-        if (false) {
+        if (config.test_type == "nbinom_exact" || config.test_type == "pig"
+            || config.test_type == "mwu" || config.test_type == "bm"
+            || config.test_type == "bb_hypergeometric") {
+            // if (false) {
             common::logger->trace("Correcting p-vals");
             nb_pvals.erase(std::remove_if(nb_pvals.begin(), nb_pvals.end(),
-                                        [&](const auto &a) { return a.second == kept.size(); }),
-                        nb_pvals.end());
-            common::logger->trace("Sorting {} / {} p-vals", nb_pvals.size(), kept.num_set_bits());
+                                          [&](const auto& a) { return a.first > 1.0; }),
+                           nb_pvals.end());
+            size_t num_tests = nb_pvals.size();
+            common::logger->trace("Discarded {} / {} untestable hypotheses",
+                                  nelem - num_tests, nelem);
+            nb_pvals.erase(std::remove_if(nb_pvals.begin(), nb_pvals.end(),
+                                          [&](const auto& a) {
+                                              return a.second == kept.size();
+                                          }),
+                           nb_pvals.end());
+            common::logger->trace("Sorting {} / {} p-vals", nb_pvals.size(), num_tests);
             std::sort(nb_pvals.begin(), nb_pvals.end());
             long double harm = 1.0;
             // long double harm = 0.0;
@@ -2683,32 +3298,38 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
             //     harm += 1.0 / i;
             // }
 
-            common::logger->trace("Selecing cut-off");
+            common::logger->trace("Selecting cut-off");
             size_t k = 0;
             for (size_t i = 0; i < nb_pvals.size(); ++i) {
                 if (nb_pvals[i].first >= config.family_wise_error_rate)
                     break;
 
-                if (nb_pvals[i].first <= config.family_wise_error_rate * (i + 1) / nb_pvals.size() / harm) {
+                if (nb_pvals[i].first
+                    <= config.family_wise_error_rate * (i + 1) / num_tests / harm) {
                     k = std::max(i + 1, k);
                     node_index node = AnnotatedDBG::anno_to_graph_index(nb_pvals[i].second);
                     if (!indicator_in[node] && !indicator_out[node]) {
-                        common::logger->error("Node {} not marked, but p-value {} significant", node, nb_pvals[i].first);
+                        common::logger->error(
+                                "Node {} not marked, but p-value {} significant", node,
+                                nb_pvals[i].first);
                         throw std::runtime_error("Index fail");
                     }
                 }
             }
 
-            common::logger->trace("Found {} / {} significant p-values. Minimum p-value is {}",
-                                k, nb_pvals.size(), nb_pvals[0].first);
+            common::logger->trace(
+                    "Found {} / {} significant p-values. Minimum p-value is {}", k,
+                    nb_pvals.size(), nb_pvals[0].first);
             for (size_t i = k; i < nb_pvals.size(); ++i) {
-                const auto &[pval, row_i] = nb_pvals[i];
+                const auto& [pval, row_i] = nb_pvals[i];
                 if (nb_pvals[i].first >= config.family_wise_error_rate)
                     break;
 
                 node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
                 if (!indicator_in[node] && !indicator_out[node]) {
-                    common::logger->error("Node {} not marked, but original p-value {} significant", node, nb_pvals[i].first);
+                    common::logger->error(
+                            "Node {} not marked, but original p-value {} significant",
+                            node, nb_pvals[i].first);
                     throw std::runtime_error("Index fail");
                 }
                 indicator_in[node] = false;
@@ -2771,7 +3392,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     // std::vector<int64_t> m_sums;
     // {
     //     std::vector<std::vector<std::pair<double, size_t>>> ms(num_threads + 1);
-    //     std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>, std::pair<double, size_t>, utils::VectorHash>>> ms_vecs(num_threads + 1);
+    //     std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>,
+    //     std::pair<double, size_t>, utils::VectorHash>>> ms_vecs(num_threads + 1);
     //     generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
     //         int64_t in_sum = 0;
     //         int64_t out_sum = 0;
@@ -2808,19 +3430,21 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     //             }
 
     //             if (total_count >= config.min_recurrence) {
-    //                 double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-    //                 if (out_stat != 0)
+    //                 double out_stat = static_cast<double>(out_sum) / out_kmers *
+    //                 in_kmers; if (out_stat != 0)
     //                     out_stat_int = out_stat > 0 ? ceil(out_stat) : floor(out_stat);
 
-    //                 in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-    //                 out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+    //                 in_kmer = count_in >= config.min_in_recurrence && count_out <=
+    //                 config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat :
+    //                 0.0); out_kmer = count_out >= config.min_out_recurrence && count_in
+    //                 <= config.max_in_recurrence && in_sum < out_stat;
     //             }
     //         } else {
     //             return;
     //         }
 
-    //         size_t n = config.test_type != "poisson_binom" ? in_sum + out_sum : row.size();
-    //         if (!in_kmer && !out_kmer) {
+    //         size_t n = config.test_type != "poisson_binom" ? in_sum + out_sum :
+    //         row.size(); if (!in_kmer && !out_kmer) {
     //             row.clear();
     //             vals.clear();
     //             n = 0;
@@ -2878,9 +3502,9 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     //         for (size_t i = 1; i < ms.size(); ++i) {
     //             size_t end = std::min(ms[0].size(), ms[i].size());
     //             for (size_t j = 0; j < end; ++j) {
-    //                 assert(ms[0][j].first == 1.1 || ms[i][j].first == 1.1 || ms[0][j] == ms[i][j]);
-    //                 ms[0][j].first = std::min(ms[0][j].first, ms[i][j].first);
-    //                 ms[0][j].second += ms[i][j].second;
+    //                 assert(ms[0][j].first == 1.1 || ms[i][j].first == 1.1 || ms[0][j]
+    //                 == ms[i][j]); ms[0][j].first = std::min(ms[0][j].first,
+    //                 ms[i][j].first); ms[0][j].second += ms[i][j].second;
     //             }
 
     //             if (ms[i].size() > ms[0].size()) {
@@ -2938,7 +3562,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     // sdsl::bit_vector unitig_start;
     // if (!config.test_by_unitig) {
     //     std::tie(k, n_cutoff) = correct_pvals(m, nelem, m_sums);
-    //     common::logger->trace("Picked: k: {}\tn: {}\tpval_min: {}", k, n_cutoff, config.family_wise_error_rate / k);
+    //     common::logger->trace("Picked: k: {}\tn: {}\tpval_min: {}", k, n_cutoff,
+    //     config.family_wise_error_rate / k);
     // } else {
     //     clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
     //         graph_ptr,
@@ -3064,9 +3689,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     //             }
 
     //             if (total_count >= config.min_recurrence) {
-    //                 double out_stat = static_cast<double>(out_sum) / out_kmers * in_kmers;
-    //                 in_kmer = count_in >= config.min_in_recurrence && count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat : 0.0);
-    //                 out_kmer = count_out >= config.min_out_recurrence && count_in <= config.max_in_recurrence && in_sum < out_stat;
+    //                 double out_stat = static_cast<double>(out_sum) / out_kmers *
+    //                 in_kmers; in_kmer = count_in >= config.min_in_recurrence &&
+    //                 count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ?
+    //                 out_stat : 0.0); out_kmer = count_out >= config.min_out_recurrence
+    //                 && count_in <= config.max_in_recurrence && in_sum < out_stat;
     //             }
     //         } else {
     //             if (keep_all_pvals) {
@@ -3112,9 +3739,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     //                 return;
     //             }
 
-    //             if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact" && pval < 1.1 && m[n].first - pval > 1e-10) {
-    //                 common::logger->error("Min p-val estimate too high: min {} > cur {}\tn: {}\ttest: {}", m[n].first, pval, n, config.test_type);
-    //                 throw std::runtime_error("Test failed");
+    //             if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact"
+    //             && pval < 1.1 && m[n].first - pval > 1e-10) {
+    //                 common::logger->error("Min p-val estimate too high: min {} > cur
+    //                 {}\tn: {}\ttest: {}", m[n].first, pval, n, config.test_type); throw
+    //                 std::runtime_error("Test failed");
     //             }
 
     //             if (config.test_type != "notest" && keep_all_pvals) {
@@ -3126,9 +3755,11 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     //                 }
     //             }
 
-    //             if (!config.test_by_unitig && in_kmer != out_kmer && pval * k < config.family_wise_error_rate) {
+    //             if (!config.test_by_unitig && in_kmer != out_kmer && pval * k <
+    //             config.family_wise_error_rate) {
     //                 bool use_atomic = parallel && (node % 64 == 0);
-    //                 set_bit((in_kmer ? indicator_in : indicator_out).data(), node, use_atomic, MO_RELAXED);
+    //                 set_bit((in_kmer ? indicator_in : indicator_out).data(), node,
+    //                 use_atomic, MO_RELAXED);
     //             }
     //         }
     //     });
@@ -3241,25 +3872,27 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Done! Assembling contigs.");
 
     auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
-        graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
-        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    );
+            graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
+            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
     auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
-        graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_out)), true,
-        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    );
+            graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_out)), true,
+            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
-    return std::make_tuple(masked_graph_in, masked_graph_out, std::move(pvals), std::move(tmp_file));
+    return std::make_tuple(masked_graph_in, masked_graph_out, std::move(pvals),
+                           std::move(tmp_file));
 }
 
 template <class PValStorage>
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
+std::tuple<std::shared_ptr<DeBruijnGraph>,
+           std::shared_ptr<DeBruijnGraph>,
+           PValStorage,
+           std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
-                         const std::vector<std::string> &files,
-                         const tsl::hopscotch_set<Label> &labels_in,
-                         const tsl::hopscotch_set<Label> &labels_out,
-                         const DifferentialAssemblyConfig &config,
+                         const std::vector<std::string>& files,
+                         const tsl::hopscotch_set<Label>& labels_in,
+                         const tsl::hopscotch_set<Label>& labels_out,
+                         const DifferentialAssemblyConfig& config,
                          size_t num_threads,
                          std::filesystem::path tmp_dir,
                          size_t num_parallel_files) {
@@ -3268,7 +3901,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
 
     size_t total_labels = 0;
-    for (const auto &file : files) {
+    for (const auto& file : files) {
         total_labels += annot::ColumnCompressed<>::read_num_labels(file);
     }
 
@@ -3283,168 +3916,186 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
         column_values_all = ColumnValuesDisk();
     }
 
-    return std::visit([&](auto&& column_values_all) {
-        using ColumnValues = typename std::decay<decltype(column_values_all)>::type;
-        using ValuesContainerPtr = typename ColumnValues::value_type;
-        using ValuesContainer = typename std::decay<typename ValuesContainerPtr::element_type>::type;
-        using value_type = typename ValuesContainer::value_type;
-        using PairContainer = Vector<std::pair<uint64_t, value_type>>;
-
-        std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
-        std::vector<Group> groups(total_labels);
-        if (files.empty()) {
-            throw std::runtime_error("No files provided");
-        }
-
-        if (std::filesystem::exists(files[0] + ".counts")) {
-            column_values_all.resize(total_labels);
-            annot::ColumnCompressed<>::load_columns_and_values(
-                files,
-                [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector>&& column, ValuesContainer&& column_values) {
-                    bool is_in = labels_in.count(label);
-                    bool is_out = labels_out.count(label);
-                    if (is_in) {
-                        groups[offset] = !is_out ? Group::IN : Group::BOTH;
-                    } else {
-                        groups[offset] = is_out ? Group::OUT : Group::OTHER;
-                    }
-                    columns_all[offset].reset(column.release());
-                    column_values_all[offset] = std::make_unique<ValuesContainer>(std::move(column_values));
-                },
-                num_parallel_files
-            );
-        } else {
-            annot::ColumnCompressed<>::merge_load(
-                files,
-                [&](uint64_t offset, const Label &label, std::unique_ptr<bit_vector>&& column) {
-                    bool is_in = labels_in.count(label);
-                    bool is_out = labels_out.count(label);
-                    if (is_in) {
-                        groups[offset] = !is_out ? Group::IN : Group::BOTH;
-                    } else {
-                        groups[offset] = is_out ? Group::OUT : Group::OTHER;
-                    }
-                    columns_all[offset].reset(column.release());
-                },
-                num_parallel_files
-            );
-        }
-
-        uint8_t max_width = 0;
-        for (const auto &col_vals : column_values_all) {
-            max_width = std::max(max_width, col_vals->width());
-        }
-
-        auto generate_rows = [&](const auto &callback) {
-            utils::call_rows<std::unique_ptr<const bit_vector>,
-                             std::unique_ptr<const ValuesContainer>,
-                             PairContainer, false>(columns_all, column_values_all, callback);
-        };
+    return std::visit(
+            [&](auto&& column_values_all) {
+                using ColumnValues = typename std::decay<decltype(column_values_all)>::type;
+                using ValuesContainerPtr = typename ColumnValues::value_type;
+                using ValuesContainer =
+                        typename std::decay<typename ValuesContainerPtr::element_type>::type;
+                using value_type = typename ValuesContainer::value_type;
+                using PairContainer = Vector<std::pair<uint64_t, value_type>>;
+
+                std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
+                std::vector<Group> groups(total_labels);
+                if (files.empty()) {
+                    throw std::runtime_error("No files provided");
+                }
 
-        bool parallel = get_num_threads() > 1;
+                if (std::filesystem::exists(files[0] + ".counts")) {
+                    column_values_all.resize(total_labels);
+                    annot::ColumnCompressed<>::load_columns_and_values(
+                            files,
+                            [&](uint64_t offset, const Label& label,
+                                std::unique_ptr<bit_vector>&& column,
+                                ValuesContainer&& column_values) {
+                                bool is_in = labels_in.count(label);
+                                bool is_out = labels_out.count(label);
+                                if (is_in) {
+                                    groups[offset] = !is_out ? Group::IN : Group::BOTH;
+                                } else {
+                                    groups[offset] = is_out ? Group::OUT : Group::OTHER;
+                                }
+                                columns_all[offset].reset(column.release());
+                                column_values_all[offset]
+                                        = std::make_unique<ValuesContainer>(
+                                                std::move(column_values));
+                            },
+                            num_parallel_files);
+                } else {
+                    annot::ColumnCompressed<>::merge_load(
+                            files,
+                            [&](uint64_t offset, const Label& label,
+                                std::unique_ptr<bit_vector>&& column) {
+                                bool is_in = labels_in.count(label);
+                                bool is_out = labels_out.count(label);
+                                if (is_in) {
+                                    groups[offset] = !is_out ? Group::IN : Group::BOTH;
+                                } else {
+                                    groups[offset] = is_out ? Group::OUT : Group::OTHER;
+                                }
+                                columns_all[offset].reset(column.release());
+                            },
+                            num_parallel_files);
+                }
 
-        return mask_nodes_by_label_dual<value_type, PValStorage>(
-            graph_ptr,
-            [&](const std::vector<size_t> &min_counts, sdsl::bit_vector *kept) -> std::vector<VectorMap<uint64_t, size_t>> {
-                common::logger->trace("Calculating count histograms");
-                std::vector<std::vector<VectorMap<uint64_t, size_t>>> hists_map_p(num_parallel_files);
-                for (auto &hists_map : hists_map_p) {
-                    hists_map.resize(groups.size());
+                uint8_t max_width = 0;
+                for (const auto& col_vals : column_values_all) {
+                    max_width = std::max(max_width, col_vals->width());
                 }
 
-                std::atomic_thread_fence(std::memory_order_release);
-                generate_rows([&](uint64_t row_i, const auto &row, size_t thread_id) {
-                    if (row.empty()) {
-                        if (kept)
-                            unset_bit(kept->data(), row_i, parallel, std::memory_order_relaxed);
+                auto generate_rows = [&](const auto& callback) {
+                    utils::call_rows<std::unique_ptr<const bit_vector>,
+                                     std::unique_ptr<const ValuesContainer>, PairContainer, false>(
+                            columns_all, column_values_all, callback);
+                };
+
+                bool parallel = get_num_threads() > 1;
+
+                return mask_nodes_by_label_dual<value_type, PValStorage>(
+                        graph_ptr,
+                        [&](const std::vector<size_t>& min_counts, sdsl::bit_vector* kept)
+                                -> std::vector<VectorMap<uint64_t, size_t>> {
+                            common::logger->trace("Calculating count histograms");
+                            std::vector<std::vector<VectorMap<uint64_t, size_t>>> hists_map_p(
+                                    num_parallel_files);
+                            for (auto& hists_map : hists_map_p) {
+                                hists_map.resize(groups.size());
+                            }
 
-                        return;
-                    }
+                            std::atomic_thread_fence(std::memory_order_release);
+                            generate_rows([&](uint64_t row_i, const auto& row,
+                                              size_t thread_id) {
+                                if (row.empty()) {
+                                    if (kept)
+                                        unset_bit(kept->data(), row_i, parallel,
+                                                  std::memory_order_relaxed);
 
-                    bool found = false;
-                    Vector<uint64_t> counts(groups.size());
-                    for (const auto &[j, raw_c] : row) {
-                        if (min_counts.empty() || raw_c >= min_counts[j]) {
-                            counts[j] = raw_c;
-                            found = true;
-                        }
-                    }
+                                    return;
+                                }
 
-                    if (!found) {
-                        if (kept)
-                            unset_bit(kept->data(), row_i, parallel, std::memory_order_relaxed);
+                                bool found = false;
+                                Vector<uint64_t> counts(groups.size());
+                                for (const auto& [j, raw_c] : row) {
+                                    if (min_counts.empty() || raw_c >= min_counts[j]) {
+                                        counts[j] = raw_c;
+                                        found = true;
+                                    }
+                                }
 
-                        return;
-                    }
+                                if (!found) {
+                                    if (kept)
+                                        unset_bit(kept->data(), row_i, parallel,
+                                                  std::memory_order_relaxed);
 
-                    for (size_t j = 0; j < counts.size(); ++j) {
-                        ++hists_map_p[thread_id][j][counts[j]];
-                    }
-                });
-                std::atomic_thread_fence(std::memory_order_acquire);
+                                    return;
+                                }
 
-                common::logger->trace("Merging histograms");
-                for (size_t thread_id = 1; thread_id < hists_map_p.size(); ++thread_id) {
-                    for (size_t j = 0; j < hists_map_p[thread_id].size(); ++j) {
-                        for (const auto &[k, c] : hists_map_p[thread_id][j]) {
-                            hists_map_p[0][j][k] += c;
-                        }
-                    }
-                }
+                                for (size_t j = 0; j < counts.size(); ++j) {
+                                    ++hists_map_p[thread_id][j][counts[j]];
+                                }
+                            });
+                            std::atomic_thread_fence(std::memory_order_acquire);
+
+                            common::logger->trace("Merging histograms");
+                            for (size_t thread_id = 1; thread_id < hists_map_p.size();
+                                 ++thread_id) {
+                                for (size_t j = 0; j < hists_map_p[thread_id].size(); ++j) {
+                                    for (const auto& [k, c] : hists_map_p[thread_id][j]) {
+                                        hists_map_p[0][j][k] += c;
+                                    }
+                                }
+                            }
 
-                return hists_map_p[0];
-            },
-            generate_rows,
-            groups,
-            config, num_threads, tmp_dir, num_parallel_files,
-            [&]() {
-                columns_all.clear();
-                column_values_all.clear();
+                            return hists_map_p[0];
+                        },
+                        generate_rows, groups, config, num_threads, tmp_dir,
+                        num_parallel_files,
+                        [&]() {
+                            columns_all.clear();
+                            column_values_all.clear();
+                        },
+                        max_width);
             },
-            max_width
-        );
-    }, column_values_all);
+            column_values_all);
 }
 
-template
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
+template std::tuple<std::shared_ptr<DeBruijnGraph>,
+                    std::shared_ptr<DeBruijnGraph>,
+                    std::vector<uint64_t>,
+                    std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual<std::vector<uint64_t>>(std::shared_ptr<const DeBruijnGraph>,
-                         const std::vector<std::string> &,
-                         const tsl::hopscotch_set<Label> &,
-                         const tsl::hopscotch_set<Label> &,
-                         const DifferentialAssemblyConfig &,
-                         size_t,
-                         std::filesystem::path,
-                         size_t);
-template
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
+                                                const std::vector<std::string>&,
+                                                const tsl::hopscotch_set<Label>&,
+                                                const tsl::hopscotch_set<Label>&,
+                                                const DifferentialAssemblyConfig&,
+                                                size_t,
+                                                std::filesystem::path,
+                                                size_t);
+template std::tuple<std::shared_ptr<DeBruijnGraph>,
+                    std::shared_ptr<DeBruijnGraph>,
+                    sdsl::int_vector_buffer<64>,
+                    std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(std::shared_ptr<const DeBruijnGraph>,
-                         const std::vector<std::string> &,
-                         const tsl::hopscotch_set<Label> &,
-                         const tsl::hopscotch_set<Label> &,
-                         const DifferentialAssemblyConfig &,
-                         size_t,
-                         std::filesystem::path,
-                         size_t);
+                                                      const std::vector<std::string>&,
+                                                      const tsl::hopscotch_set<Label>&,
+                                                      const tsl::hopscotch_set<Label>&,
+                                                      const DifferentialAssemblyConfig&,
+                                                      size_t,
+                                                      std::filesystem::path,
+                                                      size_t);
 
 template <class PValStorage>
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, PValStorage, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_in,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &labels_out,
-                         const DifferentialAssemblyConfig &config,
-                         size_t num_threads,
-                         std::filesystem::path tmp_dir) {
+std::tuple<std::shared_ptr<DeBruijnGraph>,
+           std::shared_ptr<DeBruijnGraph>,
+           PValStorage,
+           std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual(
+        const AnnotatedDBG& anno_graph,
+        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>& labels_in,
+        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>& labels_out,
+        const DifferentialAssemblyConfig& config,
+        size_t num_threads,
+        std::filesystem::path tmp_dir) {
     num_threads = get_num_threads();
     common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
     common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
 
     size_t total_labels = labels_in.size() + labels_out.size();
-    const auto &annotation = anno_graph.get_annotator();
+    const auto& annotation = anno_graph.get_annotator();
 
     std::vector<Group> groups;
     groups.reserve(total_labels);
-    for (const auto &label : annotation.get_label_encoder().get_labels()) {
+    for (const auto& label : annotation.get_label_encoder().get_labels()) {
         bool is_in = labels_in.count(label);
         bool is_out = labels_out.count(label);
         if (is_in) {
@@ -3454,37 +4105,37 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         }
     }
 
-    auto graph_ptr = std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr());
+    auto graph_ptr
+            = std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr());
 
-    const auto &matrix = annotation.get_matrix();
+    const auto& matrix = annotation.get_matrix();
     using Row = annot::matrix::BinaryMatrix::Row;
 
-    if (const auto *int_matrix = dynamic_cast<const annot::matrix::IntMatrix*>(&matrix)) {
+    if (const auto* int_matrix = dynamic_cast<const annot::matrix::IntMatrix*>(&matrix)) {
         using value_type = annot::matrix::IntMatrix::Value;
         return mask_nodes_by_label_dual<value_type, PValStorage>(
-            graph_ptr,
-            [&](const std::vector<size_t> &min_counts, sdsl::bit_vector *unmark_discarded) {
-                return int_matrix->get_histograms(min_counts, unmark_discarded);
-            },
-            [&](const auto &callback) {
-                int_matrix->call_row_values(callback, false /* ordered */);
-            },
-            groups,
-            config, num_threads, tmp_dir, num_threads
-        );
+                graph_ptr,
+                [&](const std::vector<size_t>& min_counts, sdsl::bit_vector* unmark_discarded) {
+                    return int_matrix->get_histograms(min_counts, unmark_discarded);
+                },
+                [&](const auto& callback) {
+                    int_matrix->call_row_values(callback, false /* ordered */);
+                },
+                groups, config, num_threads, tmp_dir, num_threads);
     } else {
-        auto generate_bit_rows = [&](const auto &callback) {
+        auto generate_bit_rows = [&](const auto& callback) {
             size_t n = matrix.num_rows();
             size_t batch_size = (n + num_threads - 1) / num_threads;
             size_t rows_per_update = 10000;
             ProgressBar progress_bar(n, "Streaming rows", std::cerr, !common::get_verbose());
-            #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+#pragma omp parallel for num_threads(num_threads) schedule(dynamic)
             for (size_t k = 0; k < n; k += batch_size) {
                 size_t begin = k;
                 size_t end = std::min(begin + batch_size, n);
                 size_t thread_id = k / batch_size;
                 std::vector<Row> row_ids;
-                for (size_t row_batch_i = begin; row_batch_i < end; row_batch_i += rows_per_update) {
+                for (size_t row_batch_i = begin; row_batch_i < end;
+                     row_batch_i += rows_per_update) {
                     size_t row_batch_end = std::min(row_batch_i + rows_per_update, end);
                     row_ids.resize(row_batch_end - row_batch_i);
                     std::iota(row_ids.begin(), row_ids.end(), row_batch_i);
@@ -3500,79 +4151,86 @@ mask_nodes_by_label_dual(const AnnotatedDBG &anno_graph,
         bool parallel = get_num_threads() > 1;
 
         return mask_nodes_by_label_dual<uint64_t, PValStorage>(
-            graph_ptr,
-            [&](const std::vector<size_t> &, sdsl::bit_vector *kept) -> std::vector<VectorMap<uint64_t, size_t>> {
-                common::logger->trace("Calculating count histograms");
-                std::vector<std::vector<VectorMap<uint64_t, size_t>>> hists_map_p(num_threads);
-                for (auto &hists_map : hists_map_p) {
-                    hists_map.resize(groups.size());
-                }
-                std::atomic_thread_fence(std::memory_order_release);
-                generate_bit_rows([&](uint64_t row_i, const auto &set_bits, size_t thread_id) {
-                    if (set_bits.empty()) {
-                        if (kept)
-                            unset_bit(kept->data(), row_i, parallel, std::memory_order_relaxed);
-
-                        return;
+                graph_ptr,
+                [&](const std::vector<size_t>&,
+                    sdsl::bit_vector* kept) -> std::vector<VectorMap<uint64_t, size_t>> {
+                    common::logger->trace("Calculating count histograms");
+                    std::vector<std::vector<VectorMap<uint64_t, size_t>>> hists_map_p(
+                            num_threads);
+                    for (auto& hists_map : hists_map_p) {
+                        hists_map.resize(groups.size());
                     }
+                    std::atomic_thread_fence(std::memory_order_release);
+                    generate_bit_rows(
+                            [&](uint64_t row_i, const auto& set_bits, size_t thread_id) {
+                                if (set_bits.empty()) {
+                                    if (kept)
+                                        unset_bit(kept->data(), row_i, parallel,
+                                                  std::memory_order_relaxed);
+
+                                    return;
+                                }
 
-                    std::vector<bool> container(groups.size());
-                    for (auto j : set_bits) {
-                        container[j] = true;
-                    }
+                                std::vector<bool> container(groups.size());
+                                for (auto j : set_bits) {
+                                    container[j] = true;
+                                }
 
-                    for (size_t j = 0; j < container.size(); ++j) {
-                        ++hists_map_p[thread_id][j][container[j]];
-                    }
-                });
-                std::atomic_thread_fence(std::memory_order_acquire);
+                                for (size_t j = 0; j < container.size(); ++j) {
+                                    ++hists_map_p[thread_id][j][container[j]];
+                                }
+                            });
+                    std::atomic_thread_fence(std::memory_order_acquire);
 
-                common::logger->trace("Merging histograms");
-                for (size_t thread_id = 1; thread_id < hists_map_p.size(); ++thread_id) {
-                    for (size_t j = 0; j < hists_map_p[thread_id].size(); ++j) {
-                        for (const auto &[k, c] : hists_map_p[thread_id][j]) {
-                            hists_map_p[0][j][k] += c;
+                    common::logger->trace("Merging histograms");
+                    for (size_t thread_id = 1; thread_id < hists_map_p.size(); ++thread_id) {
+                        for (size_t j = 0; j < hists_map_p[thread_id].size(); ++j) {
+                            for (const auto& [k, c] : hists_map_p[thread_id][j]) {
+                                hists_map_p[0][j][k] += c;
+                            }
                         }
                     }
-                }
 
-                return hists_map_p[0];
-            },
-            [&](const auto &callback) {
-                generate_bit_rows([&](uint64_t row_i, const auto &set_bits, size_t thread_id) {
-                    Vector<std::pair<uint64_t, uint64_t>> row;
-                    row.reserve(set_bits.size());
-                    for (auto j : set_bits) {
-                        row.emplace_back(j, 1);
-                    }
+                    return hists_map_p[0];
+                },
+                [&](const auto& callback) {
+                    generate_bit_rows(
+                            [&](uint64_t row_i, const auto& set_bits, size_t thread_id) {
+                                Vector<std::pair<uint64_t, uint64_t>> row;
+                                row.reserve(set_bits.size());
+                                for (auto j : set_bits) {
+                                    row.emplace_back(j, 1);
+                                }
 
-                    callback(row_i, row, thread_id);
-                });
-            },
-            groups,
-            config, num_threads, tmp_dir, num_threads
-        );
+                                callback(row_i, row, thread_id);
+                            });
+                },
+                groups, config, num_threads, tmp_dir, num_threads);
     }
 }
 
-template
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, std::vector<uint64_t>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<std::vector<uint64_t>>(const AnnotatedDBG &,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
-                         const DifferentialAssemblyConfig &,
-                         size_t,
-                         std::filesystem::path);
-template
-std::tuple<std::shared_ptr<DeBruijnGraph>, std::shared_ptr<DeBruijnGraph>, sdsl::int_vector_buffer<64>, std::unique_ptr<utils::TempFile>>
-mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(const AnnotatedDBG &,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
-                         const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label> &,
-                         const DifferentialAssemblyConfig &,
-                         size_t,
-                         std::filesystem::path);
+template std::tuple<std::shared_ptr<DeBruijnGraph>,
+                    std::shared_ptr<DeBruijnGraph>,
+                    std::vector<uint64_t>,
+                    std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<std::vector<uint64_t>>(
+        const AnnotatedDBG&,
+        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
+        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
+        const DifferentialAssemblyConfig&,
+        size_t,
+        std::filesystem::path);
+template std::tuple<std::shared_ptr<DeBruijnGraph>,
+                    std::shared_ptr<DeBruijnGraph>,
+                    sdsl::int_vector_buffer<64>,
+                    std::unique_ptr<utils::TempFile>>
+mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(
+        const AnnotatedDBG&,
+        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
+        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
+        const DifferentialAssemblyConfig&,
+        size_t,
+        std::filesystem::path);
 
 } // namespace graph
 } // namespace mtg
-
-

From fe751b5131b127cedecb1d8303aee5d7f5f9866d Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 12 Mar 2025 21:14:03 +0100
Subject: [PATCH 379/410] tt

---
 .../src/graph/annotated_graph_algorithm.cpp   | 30 +++++++++++--------
 1 file changed, 18 insertions(+), 12 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 88e49bd11e..30f2f24f1f 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1944,7 +1944,8 @@ mask_nodes_by_label_dual(
         std::vector<size_t> offsets;
         if (config.test_type != "notest" && config.test_type != "nbinom_exact"
             && config.test_type != "pig" && config.test_type != "mwu"
-            && config.test_type != "bm" && config.test_type != "bb_hypergeometric") {
+            && config.test_type != "bm" && config.test_type != "bb_hypergeometric"
+            && config.test_type != "poisson_binom") {
             common::logger->trace("Computing minimum p-values");
             std::vector<std::vector<std::pair<long double, size_t>>> ms(num_threads + 1);
             std::vector<VectorMap<long double, size_t>> ms_map(num_threads + 1);
@@ -2436,8 +2437,8 @@ mask_nodes_by_label_dual(
         // determine cutoffs for multiple testing correction
         auto [k_min, k, n_cutoff] = correct_pvals(m);
         if (config.test_type == "nbinom_exact" || config.test_type == "pig"
-            || config.test_type == "mwu" || config.test_type == "bm"
-            || config.test_type == "bb_hypergeometric") {
+            || config.test_type == "poisson_binom" || config.test_type == "mwu"
+            || config.test_type == "bm" || config.test_type == "bb_hypergeometric") {
             k_min = 1;
             k = 1;
             n_cutoff = 1;
@@ -2451,8 +2452,8 @@ mask_nodes_by_label_dual(
         common::logger->trace("Running differential tests");
         std::vector<std::pair<long double, size_t>> nb_pvals;
         if (config.test_type == "nbinom_exact" || config.test_type == "pig"
-            || config.test_type == "mwu" || config.test_type == "bm"
-            || config.test_type == "bb_hypergeometric")
+            || config.test_type == "poisson_binom" || config.test_type == "mwu"
+            || config.test_type == "bm" || config.test_type == "bb_hypergeometric")
             nb_pvals.resize(kept.num_set_bits(), std::make_pair(1.0, kept.size()));
 
         std::atomic_thread_fence(std::memory_order_release);
@@ -2526,6 +2527,11 @@ mask_nodes_by_label_dual(
 
                 pval = pb_pvals[n][s];
                 eff_size = static_cast<double>(s) - mid_points[n];
+                size_t nb_idx = kept.rank1(row_i) - 1;
+                nb_pvals[nb_idx].second = row_i;
+                if (pval < config.family_wise_error_rate) {
+                    nb_pvals[nb_idx].first = pval;
+                }
             } else if (config.test_type == "poisson_exact") {
                 size_t n = 0;
                 sdsl::bit_vector found(num_labels_in + num_labels_out);
@@ -3275,8 +3281,8 @@ mask_nodes_by_label_dual(
         });
 
         if (config.test_type == "nbinom_exact" || config.test_type == "pig"
-            || config.test_type == "mwu" || config.test_type == "bm"
-            || config.test_type == "bb_hypergeometric") {
+            || config.test_type == "poisson_binom" || config.test_type == "mwu"
+            || config.test_type == "bm" || config.test_type == "bb_hypergeometric") {
             // if (false) {
             common::logger->trace("Correcting p-vals");
             nb_pvals.erase(std::remove_if(nb_pvals.begin(), nb_pvals.end(),
@@ -3292,11 +3298,11 @@ mask_nodes_by_label_dual(
                            nb_pvals.end());
             common::logger->trace("Sorting {} / {} p-vals", nb_pvals.size(), num_tests);
             std::sort(nb_pvals.begin(), nb_pvals.end());
-            long double harm = 1.0;
-            // long double harm = 0.0;
-            // for (size_t i = 1; i <= nelem; ++i) {
-            //     harm += 1.0 / i;
-            // }
+            // long double harm = 1.0;
+            long double harm = 0.0;
+            for (size_t i = 1; i <= num_tests; ++i) {
+                harm += 1.0 / i;
+            }
 
             common::logger->trace("Selecting cut-off");
             size_t k = 0;

From ea8e76c4283168a02c386f85ae853f758a14aaf0 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 13 Mar 2025 18:18:59 +0100
Subject: [PATCH 380/410] fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 222 ++++++++++++++----
 1 file changed, 180 insertions(+), 42 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 30f2f24f1f..0485b273b9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1282,38 +1282,41 @@ mask_nodes_by_label_dual(
             common::logger->trace("p: {}", fmt::join(p, ","));
 
             common::logger->trace("Precomputing PMFs");
-            std::vector<long double> pmf_in { 1.0 };
-            std::vector<long double> pmf_out { 1.0 };
-            std::vector<long double> pmf_null { 1.0 };
+            std::vector<long double> pmf_in { 1.0L };
+            std::vector<long double> pmf_out { 1.0L };
+            std::vector<long double> pmf_null { 1.0L };
 
             for (size_t i = 1; i <= groups.size(); ++i) {
                 if (groups[i - 1] == Group::OUT || groups[i - 1] == Group::BOTH) {
                     std::vector<long double> pmf_out_cur(pmf_out.size() + 1);
-                    pmf_out_cur[0] = (1.0 - p[i - 1]) * pmf_out[0];
+                    // pmf_out_cur[0] = (1.0L - p[i - 1]) * pmf_out[0];
+                    pmf_out_cur[0] = expl(log1pl(-p[i - 1]) + logl(pmf_out[0]));
                     pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
                     for (size_t k = 1; k < pmf_out.size(); ++k) {
                         pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1]
-                                + (1.0 - p[i - 1]) * pmf_out[k];
+                                + (1.0L - p[i - 1]) * pmf_out[k];
                     }
                     std::swap(pmf_out_cur, pmf_out);
                 }
 
                 if (groups[i - 1] == Group::IN || groups[i - 1] == Group::BOTH) {
                     std::vector<long double> pmf_in_cur(pmf_in.size() + 1);
-                    pmf_in_cur[0] = (1.0 - p[i - 1]) * pmf_in[0];
+                    // pmf_in_cur[0] = (1.0L - p[i - 1]) * pmf_in[0];
+                    pmf_in_cur[0] = expl(log1pl(-p[i - 1]) + logl(pmf_in[0]));
                     pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
                     for (size_t k = 1; k < pmf_in.size(); ++k) {
                         pmf_in_cur[k]
-                                = p[i - 1] * pmf_in[k - 1] + (1.0 - p[i - 1]) * pmf_in[k];
+                                = p[i - 1] * pmf_in[k - 1] + (1.0L - p[i - 1]) * pmf_in[k];
                     }
                     std::swap(pmf_in_cur, pmf_in);
                 }
                 std::vector<long double> pmf_null_cur(i + 1);
-                pmf_null_cur[0] = (1.0 - p[i - 1]) * pmf_null[0];
+                // pmf_null_cur[0] = (1.0L - p[i - 1]) * pmf_null[0];
+                pmf_null_cur[0] = expl(log1pl(-p[i - 1]) + logl(pmf_null[0]));
                 pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
                 for (size_t k = 1; k < pmf_null.size(); ++k) {
                     pmf_null_cur[k]
-                            = p[i - 1] * pmf_null[k - 1] + (1.0 - p[i - 1]) * pmf_null[k];
+                            = p[i - 1] * pmf_null[k - 1] + (1.0L - p[i - 1]) * pmf_null[k];
                 }
                 std::swap(pmf_null_cur, pmf_null);
             }
@@ -1340,6 +1343,7 @@ mask_nodes_by_label_dual(
             pb_pvals[0].emplace_back(1.0);
 
             long double min_pval = 1.0;
+            // long double last_local_min_pval = 1.0;
 
             for (size_t n = 1; n < pb_pvals.size(); ++n) {
                 std::vector<long double> probs;
@@ -1356,13 +1360,19 @@ mask_nodes_by_label_dual(
                                     n, s);
                             throw std::domain_error("");
                         }
-                        probs.emplace_back(exp2(log2(pmf_in[s]) + log2(pmf_out[t])
-                                                - log2(pmf_null[n])));
+                        probs.emplace_back(exp2l(log2l(pmf_in[s]) + log2l(pmf_out[t])
+                                                 - log2l(pmf_null[n])));
                     } else {
                         probs.emplace_back(0.0);
                     }
                 }
 
+                long double sum_probs = std::accumulate(probs.begin(), probs.end(), 0.0L);
+                if (abs(sum_probs - 1.0L) > 1e-5) {
+                    common::logger->error("Sum of probs for n={} = {}", n, sum_probs);
+                    throw std::runtime_error("Fail");
+                }
+
                 for (uint64_t s = 0; s < probs.size(); ++s) {
                     pb_pvals[n].emplace_back(0.0);
                     if (s > n)
@@ -1449,6 +1459,13 @@ mask_nodes_by_label_dual(
                 mid_points[n] = static_cast<long double>(max_prob_pos) / max_prob_counts;
                 common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}", n, mid_points[n],
                                       fmt::join(probs, ","), fmt::join(pb_pvals[n], ","));
+
+                // if (local_min_pval > last_local_min_pval) {
+                //     common::logger->error("Min p-vals failed: n-1: {} -> {}\tn: {} -> {}",
+                //                           n - 1, last_local_min_pval, n, local_min_pval);
+                //     throw std::runtime_error("Min p-val failed");
+                // }
+                // last_local_min_pval = local_min_pval;
             }
 
             common::logger->trace("Min. p-value: {}", min_pval);
@@ -1939,13 +1956,14 @@ mask_nodes_by_label_dual(
             // common::logger->trace("PIG: lambda: {}", lambda);
         }
 
+        bool fdr = config.test_type == "nbinom_exact" || config.test_type == "pig"
+                || config.test_type == "mwu" || config.test_type == "bm"
+                || config.test_type == "bb_hypergeometric" || config.test_type == "cmh";
+
         // precompute minimal attainable p-values
         std::vector<std::pair<long double, size_t>> m;
         std::vector<size_t> offsets;
-        if (config.test_type != "notest" && config.test_type != "nbinom_exact"
-            && config.test_type != "pig" && config.test_type != "mwu"
-            && config.test_type != "bm" && config.test_type != "bb_hypergeometric"
-            && config.test_type != "poisson_binom") {
+        if (config.test_type != "notest" && !fdr) {
             common::logger->trace("Computing minimum p-values");
             std::vector<std::vector<std::pair<long double, size_t>>> ms(num_threads + 1);
             std::vector<VectorMap<long double, size_t>> ms_map(num_threads + 1);
@@ -1959,8 +1977,18 @@ mask_nodes_by_label_dual(
                         n += static_cast<int64_t>(groups[j] != Group::OTHER)
                                 + (groups[j] == Group::BOTH);
                     }
-                    size_t front = n - std::min(n, num_labels_out);
-                    pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+                    if (ms[bucket_idx].size() <= n)
+                        ms[bucket_idx].resize(n + 1);
+
+                    if (ms[bucket_idx][n].second == 0) {
+                        size_t front = n - std::min(n, num_labels_out);
+                        pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+                        ms[bucket_idx][n].first = pval;
+                        ms[bucket_idx][n].second = 1;
+                    } else {
+                        ++ms[bucket_idx][n].second;
+                    }
+
                 } else if (config.test_type == "poisson_exact") {
                     sdsl::bit_vector found(num_labels_in + num_labels_out);
                     size_t n = 0;
@@ -2382,6 +2410,9 @@ mask_nodes_by_label_dual(
                     }
                 }
 
+                if (config.test_type == "poisson_binom")
+                    std::sort(m.rbegin(), m.rend());
+
                 // if (config.test_type == "gpoisson") {
                 //     m.erase(std::remove_if(m.begin(), m.end(), [&](const auto &a) {
                 //     return !a.second; }), m.end()); std::sort(m.rbegin(), m.rend());
@@ -2402,13 +2433,15 @@ mask_nodes_by_label_dual(
                     if (jt == m.end())
                         break;
 
-                    if (it->first > 1.0) {
-                        common::logger->error("pval > 1.0: {}", it->first);
+                    if (it->first - 1.0L > 1e-5L) {
+                        common::logger->error("pval it > 1.0: {} ({})", it->first,
+                                              it->first - 1.0L);
                         throw std::runtime_error("p-val fail");
                     }
 
-                    if (jt->first > 1.0) {
-                        common::logger->error("pval > 1.0: {}", jt->first);
+                    if (jt->first - 1.0L > 1e-5L) {
+                        common::logger->error("pval jt > 1.0: {} ({})", jt->first,
+                                              jt->first - 1.0L);
                         throw std::runtime_error("p-val fail");
                     }
 
@@ -2436,9 +2469,7 @@ mask_nodes_by_label_dual(
 
         // determine cutoffs for multiple testing correction
         auto [k_min, k, n_cutoff] = correct_pvals(m);
-        if (config.test_type == "nbinom_exact" || config.test_type == "pig"
-            || config.test_type == "poisson_binom" || config.test_type == "mwu"
-            || config.test_type == "bm" || config.test_type == "bb_hypergeometric") {
+        if (fdr) {
             k_min = 1;
             k = 1;
             n_cutoff = 1;
@@ -2451,9 +2482,7 @@ mask_nodes_by_label_dual(
 
         common::logger->trace("Running differential tests");
         std::vector<std::pair<long double, size_t>> nb_pvals;
-        if (config.test_type == "nbinom_exact" || config.test_type == "pig"
-            || config.test_type == "poisson_binom" || config.test_type == "mwu"
-            || config.test_type == "bm" || config.test_type == "bb_hypergeometric")
+        if (fdr)
             nb_pvals.resize(kept.num_set_bits(), std::make_pair(1.0, kept.size()));
 
         std::atomic_thread_fence(std::memory_order_release);
@@ -2516,21 +2545,23 @@ mask_nodes_by_label_dual(
                     s += (groups[j] == Group::IN);
                 }
 
-                if (n < n_cutoff)
-                    return;
+                // if (n < n_cutoff)
+                //     return;
 
-                // size_t front = n - std::min(n, num_labels_out);
-                // double min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+                size_t front = n - std::min(n, num_labels_out);
+                double min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
 
-                // if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
-                //     return;
+                if (min_pval * k_min >= config.family_wise_error_rate)
+                    return;
 
                 pval = pb_pvals[n][s];
                 eff_size = static_cast<double>(s) - mid_points[n];
-                size_t nb_idx = kept.rank1(row_i) - 1;
-                nb_pvals[nb_idx].second = row_i;
-                if (pval < config.family_wise_error_rate) {
-                    nb_pvals[nb_idx].first = pval;
+                if (fdr) {
+                    size_t nb_idx = kept.rank1(row_i) - 1;
+                    nb_pvals[nb_idx].second = row_i;
+                    if (pval < config.family_wise_error_rate) {
+                        nb_pvals[nb_idx].first = pval;
+                    }
                 }
             } else if (config.test_type == "poisson_exact") {
                 size_t n = 0;
@@ -3102,7 +3133,6 @@ mask_nodes_by_label_dual(
                     nb_pvals[nb_idx].first = pval;
                     nb_pvals[nb_idx].second = row_i;
                 }
-            } else if (config.test_type == "mwu") {
             } else if (config.test_type == "bm") {
                 auto generate_a = [&](const auto& callback) {
                     sdsl::bit_vector found(groups.size());
@@ -3137,6 +3167,98 @@ mask_nodes_by_label_dual(
                     nb_pvals[nb_idx].second = row_i;
                 }
 
+            } else if (config.test_type == "cmh") {
+                // size_t n = 0;
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                for (const auto& [j, c] : row) {
+                    // n += c;
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                        out_sum += c;
+                    }
+
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                        in_sum += c;
+                    }
+                }
+                eff_size = static_cast<long double>(in_sum) / in_kmers
+                        - static_cast<long double>(out_sum) / out_kmers;
+                // // int64_t a = in_sum;
+                // int64_t b = in_kmers - in_sum;
+                // int64_t c = out_sum;
+                // // int64_t d = out_kmers - out_sum;
+                // // int64_t N = total_kmers;
+                // if (b + c >= 25) {
+                //     long double stat = static_cast<long double>(pow(b - c, 2.0)) / (b + c);
+                //     pval = boost::math::cdf(
+                //             boost::math::complement(boost::math::chi_squared(1), stat));
+                // } else if (b >= c) {
+                //     pval = boost::math::cdf(
+                //             boost::math::complement(boost::math::binomial(b + c, 0.5), b - 1));
+                // } else {
+                //     throw std::runtime_error("CMH not implemented for this case");
+                // }
+                int64_t t = total_kmers;
+                int64_t n1 = in_kmers;
+                int64_t n2 = out_kmers;
+                int64_t m1 = in_sum + out_sum;
+                int64_t m2 = t - m1;
+                assert(m2 >= 0);
+
+                if (m2 == 0) {
+                    common::logger->error(
+                            "This row has all counts. Too few data points. Use fisher "
+                            "instead");
+                    throw std::domain_error("Test failed");
+                }
+
+                double lbase
+                        = log(n1) + log(n2) + log(m1) + log(m2) - log(t) * 2 - log(t - 1);
+                double factor = static_cast<double>(n1 * m1) / t;
+
+                auto get_chi_stat = [&](int64_t a) {
+                    int64_t b = n1 - a;
+                    int64_t c = m1 - a;
+                    int64_t d = m2 - b;
+                    assert(d == n2 - c);
+
+                    if (b < 0 || c < 0 || d < 0)
+                        return 0.0;
+
+                    double a_shift = static_cast<double>(a) - factor;
+
+                    if (a_shift > 0)
+                        return exp(log(a_shift) * 2.0 - lbase);
+
+                    return a_shift * a_shift / exp(lbase);
+                };
+
+                double chi_stat = get_chi_stat(in_sum);
+                if (chi_stat <= 0) {
+                    common::logger->error(
+                            "Test statistic {} <= 0, too few data points. Use fisher "
+                            "instead.\t{}\t{},{},{},{}\t{}\t{}",
+                            chi_stat, in_sum, n1, n2, m1, m2, t, factor);
+                    throw std::domain_error("Test failed");
+                }
+
+                double max_chi_stat
+                        = std::max(get_chi_stat(0), get_chi_stat(std::min(n1, m1)));
+                if (max_chi_stat <= 0) {
+                    common::logger->error(
+                            "Best test statistic {} <= 0, too few data points. Use "
+                            "fisher instead.\t{},{},{},{}\t{}\t{}",
+                            max_chi_stat, n1, n2, m1, m2, t, factor);
+                    throw std::domain_error("Test failed");
+                }
+
+                pval = boost::math::cdf(
+                        boost::math::complement(boost::math::chi_squared(1), chi_stat));
+                if (pval < config.family_wise_error_rate) {
+                    size_t nb_idx = kept.rank1(row_i) - 1;
+                    nb_pvals[nb_idx].first = pval;
+                    nb_pvals[nb_idx].second = row_i;
+                }
             } else if (config.test_type == "bb_hypergeometric") {
                 size_t nb_idx = kept.rank1(row_i) - 1;
                 size_t n = 0;
@@ -3280,9 +3402,7 @@ mask_nodes_by_label_dual(
             set_pval(pval, eff_size);
         });
 
-        if (config.test_type == "nbinom_exact" || config.test_type == "pig"
-            || config.test_type == "poisson_binom" || config.test_type == "mwu"
-            || config.test_type == "bm" || config.test_type == "bb_hypergeometric") {
+        if (fdr) {
             // if (false) {
             common::logger->trace("Correcting p-vals");
             nb_pvals.erase(std::remove_if(nb_pvals.begin(), nb_pvals.end(),
@@ -3298,7 +3418,25 @@ mask_nodes_by_label_dual(
                            nb_pvals.end());
             common::logger->trace("Sorting {} / {} p-vals", nb_pvals.size(), num_tests);
             std::sort(nb_pvals.begin(), nb_pvals.end());
-            // long double harm = 1.0;
+
+            // size_t k = 0;
+            // for (size_t i = 0; i < nb_pvals.size(); ++i) {
+            //     const auto& [pval, row_i] = nb_pvals[i];
+            //     if (pval >= config.family_wise_error_rate)
+            //         break;
+
+            //     if (pval * (num_tests - i) >= config.family_wise_error_rate) {
+            //         node_index node =
+            //         AnnotatedDBG::anno_to_graph_index(nb_pvals[i].second);
+            //         indicator_in[node] = false; indicator_out[node] = false;
+            //     } else {
+            //         ++k;
+            //     }
+            // }
+            // common::logger->trace(
+            //         "Found {} / {} significant p-values. Minimum p-value is {}", k,
+            //         nb_pvals.size(), nb_pvals[0].first);
+
             long double harm = 0.0;
             for (size_t i = 1; i <= num_tests; ++i) {
                 harm += 1.0 / i;

From 52dc145e1293c2ac79e9f32298db011942a918aa Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 14 Mar 2025 00:08:42 +0100
Subject: [PATCH 381/410] unitig fixes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 316 +++++++++++-------
 1 file changed, 190 insertions(+), 126 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 0485b273b9..07a5239c93 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -795,15 +795,14 @@ mask_nodes_by_label_dual(
     std::vector<size_t> kmer_to_unitig;
     std::vector<std::tuple<size_t, size_t, size_t>> counts;
     // std::vector<std::pair<size_t, std::vector<uint64_t>>> agg_counts;
-    if (config.test_by_unitig && config.test_type != "notest") {
-        clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
-                graph_ptr,
-                [&](node_index node) {
-                    return node != DeBruijnGraph::npos
-                            && kept[AnnotatedDBG::graph_to_anno_index(node)];
-                },
-                true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
-
+    clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
+            graph_ptr,
+            [&](node_index node) {
+                return node != DeBruijnGraph::npos
+                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
+            },
+            true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+    if (false && config.test_by_unitig && config.test_type != "notest") {
         common::logger->trace("Associating k-mers to unitigs");
         kmer_to_unitig.resize(graph_ptr->max_index() + 1);
         // std::atomic_thread_fence(std::memory_order_release);
@@ -1956,9 +1955,10 @@ mask_nodes_by_label_dual(
             // common::logger->trace("PIG: lambda: {}", lambda);
         }
 
-        bool fdr = config.test_type == "nbinom_exact" || config.test_type == "pig"
-                || config.test_type == "mwu" || config.test_type == "bm"
-                || config.test_type == "bb_hypergeometric" || config.test_type == "cmh";
+        bool fdr = config.test_by_unitig || config.test_type == "nbinom_exact"
+                || config.test_type == "pig" || config.test_type == "mwu"
+                || config.test_type == "bm" || config.test_type == "bb_hypergeometric"
+                || config.test_type == "cmh";
 
         // precompute minimal attainable p-values
         std::vector<std::pair<long double, size_t>> m;
@@ -2482,8 +2482,11 @@ mask_nodes_by_label_dual(
 
         common::logger->trace("Running differential tests");
         std::vector<std::pair<long double, size_t>> nb_pvals;
-        if (fdr)
+        std::vector<long double> eff_sizes;
+        if (fdr) {
             nb_pvals.resize(kept.num_set_bits(), std::make_pair(1.0, kept.size()));
+            eff_sizes.resize(kept.num_set_bits());
+        }
 
         std::atomic_thread_fence(std::memory_order_release);
         generate_clean_rows([&](uint64_t row_i, const auto& row, uint64_t bucket_idx) {
@@ -2514,6 +2517,12 @@ mask_nodes_by_label_dual(
             }
 
             auto set_pval = [&](long double pval, long double eff_size) {
+                if (fdr) {
+                    size_t nb_idx = kept.rank1(row_i) - 1;
+                    nb_pvals[nb_idx].first = pval;
+                    nb_pvals[nb_idx].second = row_i;
+                    eff_sizes[nb_idx] = eff_size;
+                }
                 // if (!config.test_by_unitig) {
                 if (pval * k < config.family_wise_error_rate) {
                     if (eff_size == 0) {
@@ -2521,11 +2530,15 @@ mask_nodes_by_label_dual(
                         throw std::runtime_error("Pval failure");
                     }
 
-                    node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                    if (eff_size > 0) {
-                        set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
-                    } else if (eff_size < 0) {
-                        set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
+                    if (!config.test_by_unitig) {
+                        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                        if (eff_size > 0) {
+                            set_bit(indicator_in.data(), node, true,
+                                    std::memory_order_relaxed);
+                        } else if (eff_size < 0) {
+                            set_bit(indicator_out.data(), node, true,
+                                    std::memory_order_relaxed);
+                        }
                     }
                 }
                 // } else {
@@ -2548,21 +2561,16 @@ mask_nodes_by_label_dual(
                 // if (n < n_cutoff)
                 //     return;
 
-                size_t front = n - std::min(n, num_labels_out);
-                double min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+                if (!fdr) {
+                    size_t front = n - std::min(n, num_labels_out);
+                    double min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
 
-                if (min_pval * k_min >= config.family_wise_error_rate)
-                    return;
+                    if (min_pval * k_min >= config.family_wise_error_rate)
+                        return;
+                }
 
                 pval = pb_pvals[n][s];
                 eff_size = static_cast<double>(s) - mid_points[n];
-                if (fdr) {
-                    size_t nb_idx = kept.rank1(row_i) - 1;
-                    nb_pvals[nb_idx].second = row_i;
-                    if (pval < config.family_wise_error_rate) {
-                        nb_pvals[nb_idx].first = pval;
-                    }
-                }
             } else if (config.test_type == "poisson_exact") {
                 size_t n = 0;
                 sdsl::bit_vector found(num_labels_in + num_labels_out);
@@ -2891,7 +2899,7 @@ mask_nodes_by_label_dual(
                     // }
                 }
             } else if (config.test_type == "nbinom_exact") {
-                size_t nb_idx = kept.rank1(row_i) - 1;
+                // size_t nb_idx = kept.rank1(row_i) - 1;
                 size_t n = 0;
                 int64_t in_sum = 0;
                 int64_t out_sum = 0;
@@ -2934,97 +2942,96 @@ mask_nodes_by_label_dual(
                         min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
                     }
 
-                    if (min_pval < config.family_wise_error_rate) {
-                        nb_pvals[nb_idx].second = row_i;
-                        long double midpoint = r_a * n / (r_a + r_b);
+                    // if (min_pval < config.family_wise_error_rate) {
+                    // nb_pvals[nb_idx].second = row_i;
+                    long double midpoint = r_a * n / (r_a + r_b);
 
-                        auto get_deviance_a = [&](long double s) {
-                            if (s == 0)
-                                return r_a * logl(p_a) * -2.0;
+                    auto get_deviance_a = [&](long double s) {
+                        if (s == 0)
+                            return r_a * logl(p_a) * -2.0;
 
-                            return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
-                                    + r_a * (logl(r_a) - logl(p_a)))
-                                    * 2.0;
-                        };
+                        return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
+                                + r_a * (logl(r_a) - logl(p_a)))
+                                * 2.0;
+                    };
 
-                        auto get_deviance_b = [&](long double t) {
-                            if (t == 0)
-                                return r_b * logl(p_b) * -2.0;
+                    auto get_deviance_b = [&](long double t) {
+                        if (t == 0)
+                            return r_b * logl(p_b) * -2.0;
 
-                            return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
-                                    + r_b * (logl(r_b) - logl(p_b)))
-                                    * 2.0;
-                        };
+                        return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
+                                + r_b * (logl(r_b) - logl(p_b)))
+                                * 2.0;
+                    };
 
-                        auto get_deviance = [&](long double s, long double t) {
-                            return get_deviance_a(s) + get_deviance_b(t);
-                        };
+                    auto get_deviance = [&](long double s, long double t) {
+                        return get_deviance_a(s) + get_deviance_b(t);
+                    };
 
-                        long double min_dev
-                                = get_deviance(midpoint,
-                                               static_cast<long double>(n) - midpoint);
-                        long double in_dev = get_deviance(in_sum, out_sum);
+                    long double min_dev
+                            = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
+                    long double in_dev = get_deviance(in_sum, out_sum);
 
-                        if (in_dev > min_dev) {
-                            eff_size = static_cast<long double>(in_sum) - midpoint;
-                            pval = 0.0;
-                            long double base = nb_base
-                                    + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n))
-                                            / logl(2.0);
-                            double l1pa = log1pl(-p_a) / logl(2.0);
-                            double l1pb = log1pl(-p_b) / logl(2.0);
+                    if (in_dev > min_dev) {
+                        eff_size = static_cast<long double>(in_sum) - midpoint;
+                        pval = 0.0;
+                        long double base = nb_base
+                                + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n))
+                                        / logl(2.0);
+                        double l1pa = log1pl(-p_a) / logl(2.0);
+                        double l1pb = log1pl(-p_b) / logl(2.0);
 
-                            {
-                                size_t s = 0;
-                                size_t t = n;
-                                long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                                     - lgammal(s + 1) - lgammal(t + 1))
-                                                / logl(2.0)
-                                        + s * l1pa + t * l1pb;
-                                pval += exp2(base + sbase);
-                                for (++s; s <= n; ++s) {
-                                    long double dev = get_deviance(s, t - 1);
-                                    if (dev >= in_dev) {
-                                        sbase += log2l(r_a + s) - log2l(r_b + t)
-                                                - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
-                                        --t;
-                                        pval += exp2(base + sbase);
-                                        if (pval >= config.family_wise_error_rate)
-                                            break;
-                                    } else {
+                        {
+                            size_t s = 0;
+                            size_t t = n;
+                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                                 - lgammal(s + 1) - lgammal(t + 1))
+                                            / logl(2.0)
+                                    + s * l1pa + t * l1pb;
+                            pval += exp2(base + sbase);
+                            for (++s; s <= n; ++s) {
+                                long double dev = get_deviance(s, t - 1);
+                                if (dev >= in_dev) {
+                                    sbase += log2l(r_a + s) - log2l(r_b + t)
+                                            - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                                    --t;
+                                    pval += exp2(base + sbase);
+                                    if (pval >= config.family_wise_error_rate)
                                         break;
-                                    }
+                                } else {
+                                    break;
                                 }
                             }
-                            if (pval < config.family_wise_error_rate
-                                && get_deviance(n, 0) >= in_dev) {
-                                size_t s = n;
-                                size_t t = 0;
-                                long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                                     - lgammal(s + 1) - lgammal(t + 1))
-                                                / logl(2.0)
-                                        + s * l1pa + t * l1pb;
-                                pval += exp2(base + sbase);
-                                for (++t; t <= n; ++t) {
-                                    long double dev = get_deviance(s - 1, t);
-                                    if (dev >= in_dev) {
-                                        sbase -= log2l(r_a + s) - log2l(r_b + t)
-                                                - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
-                                        --s;
-                                        pval += exp2(base + sbase);
-                                        if (pval >= config.family_wise_error_rate)
-                                            break;
-                                    } else {
+                        }
+                        if (pval < config.family_wise_error_rate
+                            && get_deviance(n, 0) >= in_dev) {
+                            size_t s = n;
+                            size_t t = 0;
+                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                                 - lgammal(s + 1) - lgammal(t + 1))
+                                            / logl(2.0)
+                                    + s * l1pa + t * l1pb;
+                            pval += exp2(base + sbase);
+                            for (++t; t <= n; ++t) {
+                                long double dev = get_deviance(s - 1, t);
+                                if (dev >= in_dev) {
+                                    sbase -= log2l(r_a + s) - log2l(r_b + t)
+                                            - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
+                                    --s;
+                                    pval += exp2(base + sbase);
+                                    if (pval >= config.family_wise_error_rate)
                                         break;
-                                    }
+                                } else {
+                                    break;
                                 }
                             }
-                            nb_pvals[nb_idx].first = pval;
                         }
-                    } else {
-                        nb_pvals[nb_idx].second = row_i;
-                        nb_pvals[nb_idx].first = 1.1;
+                        // nb_pvals[nb_idx].first = pval;
                     }
+                    // } else {
+                    //     nb_pvals[nb_idx].second = row_i;
+                    //     nb_pvals[nb_idx].first = 1.1;
+                    // }
                 }
             } else if (config.test_type == "hypergeometric"
                        || config.test_type == "poisson_bayes") {
@@ -3128,11 +3135,11 @@ mask_nodes_by_label_dual(
 
                 std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
 
-                if (pval < config.family_wise_error_rate) {
-                    size_t nb_idx = kept.rank1(row_i) - 1;
-                    nb_pvals[nb_idx].first = pval;
-                    nb_pvals[nb_idx].second = row_i;
-                }
+                // if (pval < config.family_wise_error_rate) {
+                //     size_t nb_idx = kept.rank1(row_i) - 1;
+                //     nb_pvals[nb_idx].first = pval;
+                //     nb_pvals[nb_idx].second = row_i;
+                // }
             } else if (config.test_type == "bm") {
                 auto generate_a = [&](const auto& callback) {
                     sdsl::bit_vector found(groups.size());
@@ -3161,11 +3168,11 @@ mask_nodes_by_label_dual(
 
                 std::tie(pval, eff_size) = brunner_munzel(generate_a, generate_b);
 
-                if (pval < config.family_wise_error_rate) {
-                    size_t nb_idx = kept.rank1(row_i) - 1;
-                    nb_pvals[nb_idx].first = pval;
-                    nb_pvals[nb_idx].second = row_i;
-                }
+                // if (pval < config.family_wise_error_rate) {
+                //     size_t nb_idx = kept.rank1(row_i) - 1;
+                //     nb_pvals[nb_idx].first = pval;
+                //     nb_pvals[nb_idx].second = row_i;
+                // }
 
             } else if (config.test_type == "cmh") {
                 // size_t n = 0;
@@ -3254,11 +3261,11 @@ mask_nodes_by_label_dual(
 
                 pval = boost::math::cdf(
                         boost::math::complement(boost::math::chi_squared(1), chi_stat));
-                if (pval < config.family_wise_error_rate) {
-                    size_t nb_idx = kept.rank1(row_i) - 1;
-                    nb_pvals[nb_idx].first = pval;
-                    nb_pvals[nb_idx].second = row_i;
-                }
+                // if (pval < config.family_wise_error_rate) {
+                //     size_t nb_idx = kept.rank1(row_i) - 1;
+                //     nb_pvals[nb_idx].first = pval;
+                //     nb_pvals[nb_idx].second = row_i;
+                // }
             } else if (config.test_type == "bb_hypergeometric") {
                 size_t nb_idx = kept.rank1(row_i) - 1;
                 size_t n = 0;
@@ -3402,13 +3409,63 @@ mask_nodes_by_label_dual(
             set_pval(pval, eff_size);
         });
 
+        std::atomic_thread_fence(std::memory_order_acquire);
+
         if (fdr) {
+            size_t num_tests = nb_pvals.size();
+            std::vector<std::pair<long double, size_t>> sorted_unitig_counts;
+            if (config.test_by_unitig) {
+                VectorMap<long double, size_t> pval_to_count;
+                std::mutex mu;
+                common::logger->trace("Combining p-values within unitigs");
+                num_tests = 0;
+                std::atomic_thread_fence(std::memory_order_release);
+                clean_masked_graph->call_unitigs(
+                        [&](const std::string&, const auto& path) {
+                            __atomic_fetch_add(&num_tests, 1, std::memory_order_relaxed);
+                            std::vector<long double> pvals;
+                            pvals.reserve(path.size());
+                            long double agg_eff_size = 0.0;
+                            for (node_index node : path) {
+                                auto row_i = AnnotatedDBG::graph_to_anno_index(node);
+                                size_t nb_idx = kept.rank1(row_i) - 1;
+                                long double pval = nb_pvals[nb_idx].first;
+                                long double eff_size = eff_sizes[nb_idx];
+                                pvals.emplace_back(pval);
+                                agg_eff_size += eff_size;
+                            }
+                            long double agg_pval = combine_pvals(pvals);
+                            for (node_index node : path) {
+                                auto row_i = AnnotatedDBG::graph_to_anno_index(node);
+                                size_t nb_idx = kept.rank1(row_i) - 1;
+                                nb_pvals[nb_idx].first = agg_pval;
+                                if (agg_pval < config.family_wise_error_rate) {
+                                    if (agg_eff_size > 0) {
+                                        set_bit(indicator_in.data(), node, true,
+                                                std::memory_order_relaxed);
+                                    } else {
+                                        set_bit(indicator_out.data(), node, true,
+                                                std::memory_order_relaxed);
+                                    }
+                                }
+                            }
+                            std::lock_guard<std::mutex> lock(mu);
+                            pval_to_count[agg_pval] += path.size();
+                        },
+                        num_threads);
+                std::atomic_thread_fence(std::memory_order_acquire);
+                sorted_unitig_counts
+                        = const_cast<std::vector<std::pair<long double, size_t>>&&>(
+                                pval_to_count.values_container());
+                std::sort(sorted_unitig_counts.begin(), sorted_unitig_counts.end());
+                for (size_t i = 1; i < sorted_unitig_counts.size(); ++i) {
+                    sorted_unitig_counts[i].second += sorted_unitig_counts[i - 1].second;
+                }
+            }
+
             // if (false) {
             common::logger->trace("Correcting p-vals");
-            nb_pvals.erase(std::remove_if(nb_pvals.begin(), nb_pvals.end(),
-                                          [&](const auto& a) { return a.first > 1.0; }),
-                           nb_pvals.end());
-            size_t num_tests = nb_pvals.size();
+
             common::logger->trace("Discarded {} / {} untestable hypotheses",
                                   nelem - num_tests, nelem);
             nb_pvals.erase(std::remove_if(nb_pvals.begin(), nb_pvals.end(),
@@ -3444,12 +3501,19 @@ mask_nodes_by_label_dual(
 
             common::logger->trace("Selecting cut-off");
             size_t k = 0;
+            auto it = sorted_unitig_counts.begin();
             for (size_t i = 0; i < nb_pvals.size(); ++i) {
                 if (nb_pvals[i].first >= config.family_wise_error_rate)
                     break;
 
+                if (it != sorted_unitig_counts.end() && i == it->second)
+                    ++it;
+
+                size_t cur_count = it == sorted_unitig_counts.end()
+                        ? i
+                        : it - sorted_unitig_counts.begin();
                 if (nb_pvals[i].first
-                    <= config.family_wise_error_rate * (i + 1) / num_tests / harm) {
+                    <= config.family_wise_error_rate * (cur_count + 1) / num_tests / harm) {
                     k = std::max(i + 1, k);
                     node_index node = AnnotatedDBG::anno_to_graph_index(nb_pvals[i].second);
                     if (!indicator_in[node] && !indicator_out[node]) {
@@ -3464,6 +3528,7 @@ mask_nodes_by_label_dual(
             common::logger->trace(
                     "Found {} / {} significant p-values. Minimum p-value is {}", k,
                     nb_pvals.size(), nb_pvals[0].first);
+
             for (size_t i = k; i < nb_pvals.size(); ++i) {
                 const auto& [pval, row_i] = nb_pvals[i];
                 if (nb_pvals[i].first >= config.family_wise_error_rate)
@@ -3528,7 +3593,6 @@ mask_nodes_by_label_dual(
         //     eff_size_buckets.resize(0);
         //     tmp_eff_buckets.resize(0);
         // }
-        std::atomic_thread_fence(std::memory_order_acquire);
     }
 
     // common::logger->trace("Computing minimum p-values");

From b1abc44da9abfd8098bbffb9091a96d19f61a51f Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 19 Mar 2025 13:29:46 +0100
Subject: [PATCH 382/410] minor

---
 metagraph/external-libraries/sshash           |  2 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 20 +++++++++----------
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/metagraph/external-libraries/sshash b/metagraph/external-libraries/sshash
index c62d1ae0d2..c86c661a1f 160000
--- a/metagraph/external-libraries/sshash
+++ b/metagraph/external-libraries/sshash
@@ -1 +1 @@
-Subproject commit c62d1ae0d2c194d36638b565d2dc488532d28613
+Subproject commit c86c661a1f82320e15f349d12ce849ee4641bcbb
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 07a5239c93..c065dd8116 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -398,6 +398,16 @@ std::pair<long double, long double> brunner_munzel(const G1& generate_a,
     return std::make_pair(p, eff_size);
 }
 
+long double combine_pvals(const std::vector<long double>& pvals) {
+    static const boost::math::cauchy dist;
+    long double stat = 0.0;
+    for (long double pval : pvals) {
+        stat += tan((0.5 - pval) * M_PI);
+    }
+    stat /= pvals.size();
+    return boost::math::cdf(boost::math::complement(dist, stat));
+};
+
 template <typename value_type, class PValStorage, typename HistGetter, typename Generator>
 std::tuple<std::shared_ptr<DeBruijnGraph>,
            std::shared_ptr<DeBruijnGraph>,
@@ -421,16 +431,6 @@ mask_nodes_by_label_dual(
     num_threads = get_num_threads();
     bool is_primary = graph_ptr->get_mode() == DeBruijnGraph::PRIMARY;
 
-    auto combine_pvals
-            = [dist = boost::math::cauchy()](const std::vector<long double>& pvals) {
-                  long double stat = 0.0;
-                  for (long double pval : pvals) {
-                      stat += tan((0.5 - pval) * M_PI);
-                  }
-                  stat /= pvals.size();
-                  return boost::math::cdf(boost::math::complement(dist, stat));
-              };
-
     size_t num_labels_both = std::count(groups.begin(), groups.end(), Group::BOTH);
     size_t num_labels_out = std::count(groups.begin(), groups.end(), Group::OUT);
     size_t num_labels_in = std::count(groups.begin(), groups.end(), Group::IN);

From 4b4e60fbe25a9f580f3d139062a284ab46c22243 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 20 Mar 2025 12:02:48 +0100
Subject: [PATCH 383/410] unitig

---
 .../int_matrix/csr_matrix/csr_matrix_flat.cpp |  27 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 595 +++++++++++-------
 2 files changed, 404 insertions(+), 218 deletions(-)

diff --git a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
index 74eb4b1ca5..d67876d368 100644
--- a/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
+++ b/metagraph/src/annotation/int_matrix/csr_matrix/csr_matrix_flat.cpp
@@ -284,13 +284,26 @@ auto CSRMatrixFlat::get_row_values(const std::vector<BinaryMatrix::Row> &rows) c
     const auto &flat = matrix_.data();
 
     std::visit([&](const auto &v) {
-        for (size_t i = 0; i < rows.size(); ++i) {
-            uint64_t idx_begin = rows[i] * num_columns_;
-            uint64_t idx_end = idx_begin + num_columns_;
-            uint64_t r = flat.rank1(idx_begin) - flat[idx_begin];
-            flat.call_ones_in_range(idx_begin, idx_end, [&](uint64_t j) {
-                row_values[i].emplace_back(j % num_columns_, v[r++]);
-            });
+        using S = std::decay_t<decltype(v)>;
+        if constexpr(std::is_same_v<S, sdsl::int_vector_buffer<>>) {
+            sdsl::int_vector_buffer<> vcopy(v.filename(), std::ios::in, v.buffersize(), 0, false, offset_);
+            for (size_t i = 0; i < rows.size(); ++i) {
+                uint64_t idx_begin = rows[i] * num_columns_;
+                uint64_t idx_end = idx_begin + num_columns_;
+                uint64_t r = flat.rank1(idx_begin) - flat[idx_begin];
+                flat.call_ones_in_range(idx_begin, idx_end, [&](uint64_t j) {
+                    row_values[i].emplace_back(j % num_columns_, vcopy[r++]);
+                });
+            }
+        } else {
+            for (size_t i = 0; i < rows.size(); ++i) {
+                uint64_t idx_begin = rows[i] * num_columns_;
+                uint64_t idx_end = idx_begin + num_columns_;
+                uint64_t r = flat.rank1(idx_begin) - flat[idx_begin];
+                flat.call_ones_in_range(idx_begin, idx_end, [&](uint64_t j) {
+                    row_values[i].emplace_back(j % num_columns_, v[r++]);
+                });
+            }
         }
     }, values_);
 
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index c065dd8116..17028b1379 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -408,7 +408,7 @@ long double combine_pvals(const std::vector<long double>& pvals) {
     return boost::math::cdf(boost::math::complement(dist, stat));
 };
 
-template <typename value_type, class PValStorage, typename HistGetter, typename Generator>
+template <typename value_type, class PValStorage, typename HistGetter, typename Generator, typename UnitigGenerator>
 std::tuple<std::shared_ptr<DeBruijnGraph>,
            std::shared_ptr<DeBruijnGraph>,
            PValStorage,
@@ -417,6 +417,7 @@ mask_nodes_by_label_dual(
         std::shared_ptr<const DeBruijnGraph> graph_ptr,
         const HistGetter& get_hist_map,
         const Generator& generate_rows,
+        const UnitigGenerator& generate_unitigs,
         const std::vector<Group>& groups,
         const DifferentialAssemblyConfig& config,
         size_t num_threads = 1,
@@ -802,243 +803,391 @@ mask_nodes_by_label_dual(
                         && kept[AnnotatedDBG::graph_to_anno_index(node)];
             },
             true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
-    if (false && config.test_by_unitig && config.test_type != "notest") {
-        common::logger->trace("Associating k-mers to unitigs");
-        kmer_to_unitig.resize(graph_ptr->max_index() + 1);
-        // std::atomic_thread_fence(std::memory_order_release);
-        // std::atomic<size_t> num_unitigs{0};
-        std::mutex mu;
-        clean_masked_graph->call_unitigs(
-                [&](const std::string&, const auto& path) {
-                    size_t unitig_id = 0;
-                    {
-                        unitig_id = counts.size();
-                        std::lock_guard<std::mutex> lock(mu);
-                        counts.emplace_back(path.size(), 0, 0);
+    if (config.test_by_unitig && config.test_type != "notest") {
+        uint64_t num_tests = 0;
+        std::vector<std::tuple<long double, uint64_t, node_index>> nb_pvals;
+        nb_pvals.resize(nelem, std::make_tuple(1.0L, graph_ptr->max_index() + 1, graph_ptr->max_index() + 1));
+        std::atomic_thread_fence(std::memory_order_release);
+        generate_unitigs(*clean_masked_graph, [&](const auto &path, const auto &row_values) {
+            uint64_t unitig_id = __atomic_fetch_add(&num_tests, 1, std::memory_order_relaxed);
+            long double pval = 1.0;
+            long double eff_size = 0.0;
+            if (config.test_type == "mwu") {
+                auto generate_a = [&](const auto &callback) {
+                    size_t total = num_labels_in * path.size();
+                    size_t observed = 0;
+                    for (const auto &values : row_values) {
+                        for (const auto &[j, c] : values) {
+                            if (groups[j] == Group::IN) {
+                                callback(static_cast<long double>(c) / sums[j]);
+                                ++observed;
+                            }
+                        }
                     }
-                    // size_t unitig_id = num_unitigs.fetch_add(1, std::memory_order_relaxed);
-                    for (node_index node : path) {
-                        kmer_to_unitig[node] = unitig_id;
+                    for (size_t n = observed; n < total; ++n) {
+                        callback(0.0L);
                     }
-                },
-                num_threads);
-        // std::atomic_thread_fence(std::memory_order_acquire);
-        common::logger->trace("Assembled {} unitigs", counts.size());
-
-        common::logger->trace("Aggregating counts for unitigs");
-        generate_clean_rows([&](uint64_t row_i, const auto& row, size_t) {
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            size_t unitig_id = kmer_to_unitig[node];
-            auto& [len, in_count, out_count] = counts[unitig_id];
-            for (const auto& [j, c] : row) {
-                if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                    out_count += c;
+                };
+                auto generate_b = [&](const auto &callback) {
+                    size_t total = num_labels_out * path.size();
+                    size_t observed = 0;
+                    for (const auto &values : row_values) {
+                        for (const auto &[j, c] : values) {
+                            if (groups[j] == Group::OUT) {
+                                callback(static_cast<long double>(c) / sums[j]);
+                                ++observed;
+                            }
+                        }
+                    }
+                    for (size_t n = observed; n < total; ++n) {
+                        callback(0.0L);
+                    }
+                };
 
-                if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                    in_count += c;
-            }
-        });
+                std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
 
-        common::logger->trace("Computing unitig count histogram");
-        std::vector<VectorMap<size_t, size_t>> unitig_hists_map(2);
-        for (const auto& [len, in_count, out_count] : counts) {
-            size_t avg_in_count = std::ceil(static_cast<double>(in_count) / len);
-            size_t avg_out_count = std::ceil(static_cast<double>(out_count) / len);
-            ++unitig_hists_map[0][avg_in_count];
-            ++unitig_hists_map[1][avg_out_count];
-        }
+            } else if (config.test_type == "cmh") {
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                for (const auto &values : row_values) {
+                    for (const auto& [j, c] : values) {
+                        if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                            out_sum += c;
+                        }
 
-        common::logger->trace("Fitting negative binomials");
-        auto [r_a, p_a, mu_a, var_a] = get_rp([&](const auto& callback) {
-            for (const auto& [k, c] : unitig_hists_map[0]) {
-                callback(k, c);
-            }
-        });
-        auto [r_b, p_b, mu_b, var_b] = get_rp([&](const auto& callback) {
-            for (const auto& [k, c] : unitig_hists_map[1]) {
-                callback(k, c);
-            }
-        });
-        auto [r_n, p_n, mu_n, var_n] = get_rp([&](const auto& callback) {
-            for (const auto& [k, c] : unitig_hists_map[0]) {
-                auto find = unitig_hists_map[1].find(k);
-                if (find != unitig_hists_map[1].end()) {
-                    callback(k, c + find->second);
-                } else {
-                    callback(k, c);
+                        if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                            in_sum += c;
+                        }
+                    }
                 }
-            }
-
-            for (const auto& [k, c] : unitig_hists_map[1]) {
-                if (!unitig_hists_map[0].count(k))
-                    callback(k, c);
-            }
-        });
-
-        common::logger->trace("In: r: {}\tp: {}\t mu: {}\tsample var: {}\tvar: {}", r_a,
-                              p_a, mu_a, var_a, mu_a / p_a);
-        common::logger->trace("Out: r: {}\tp: {}", r_b, p_b, mu_b, var_b, mu_b / p_b);
-        common::logger->trace("Null: r: {}\tp: {}", r_n, p_n, mu_n, var_n, mu_n / p_n);
-
-        long double nb_base = (lgammal(r_n) - lgammal(r_a) - lgammal(r_b)) / logl(2.0)
-                + r_a * log2l(p_a) + r_b * log2l(p_b) - r_n * log2l(p_n);
-
-        common::logger->trace("Allocating p-value storage");
-        std::vector<std::tuple<long double, Group, size_t>> pvals(
-                counts.size(), std::make_tuple(1.0, Group::OTHER, counts.size()));
-
-        common::logger->trace("Calculating p-values");
-        size_t rows_per_update = 10000;
-        ProgressBar progress_bar(pvals.size(), "Streaming unitigs", std::cerr,
-                                 !common::get_verbose());
-#pragma omp parallel for num_threads(num_threads) schedule(static)
-        for (size_t i = 0; i < counts.size(); ++i) {
-            if (i > 0 && (i % rows_per_update) == 0)
-                progress_bar += rows_per_update;
-
-            const auto& [len, in_sum, out_sum] = counts[i];
-            size_t n = in_sum + out_sum;
-
-            long double eff_size = 0.0;
-            auto& [pval, group, idx] = pvals[i];
-            idx = i;
+                eff_size = static_cast<long double>(in_sum) / in_kmers
+                        - static_cast<long double>(out_sum) / out_kmers;
+                int64_t t = total_kmers;
+                int64_t n1 = in_kmers;
+                int64_t n2 = out_kmers;
+                int64_t m1 = in_sum + out_sum;
+                int64_t m2 = t - m1;
+                assert(m2 >= 0);
 
-            long double midpoint = r_a * n / (r_a + r_b);
+                double lbase
+                        = log(n1) + log(n2) + log(m1) + log(m2) - log(t) * 2 - log(t - 1);
+                double factor = static_cast<double>(n1 * m1) / t;
 
-            auto get_deviance_a = [&](long double s) {
-                if (s == 0)
-                    return r_a * logl(p_a) * -2.0;
+                auto get_chi_stat = [&](int64_t a) {
+                    int64_t b = n1 - a;
+                    int64_t c = m1 - a;
+                    int64_t d = m2 - b;
+                    assert(d == n2 - c);
 
-                return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
-                        + r_a * (logl(r_a) - logl(p_a)))
-                        * 2.0;
-            };
+                    if (b < 0 || c < 0 || d < 0)
+                        return 0.0;
 
-            auto get_deviance_b = [&](long double t) {
-                if (t == 0)
-                    return r_b * logl(p_b) * -2.0;
+                    double a_shift = static_cast<double>(a) - factor;
 
-                return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
-                        + r_b * (logl(r_b) - logl(p_b)))
-                        * 2.0;
-            };
+                    if (a_shift > 0)
+                        return exp(log(a_shift) * 2.0 - lbase);
 
-            auto get_deviance = [&](long double s, long double t) {
-                return get_deviance_a(s) + get_deviance_b(t);
-            };
+                    return a_shift * a_shift / exp(lbase);
+                };
 
-            long double min_dev
-                    = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
-            long double in_dev = get_deviance(in_sum, out_sum);
+                double chi_stat = get_chi_stat(in_sum);
+                pval = boost::math::cdf(
+                        boost::math::complement(boost::math::chi_squared(1), chi_stat));
+            }
 
-            if (in_dev > min_dev) {
-                eff_size = static_cast<long double>(in_sum) - midpoint;
-                if (eff_size > 0) {
-                    group = Group::IN;
-                } else if (eff_size < 0) {
-                    group = Group::OUT;
-                }
-                pval = 0.0;
-                idx = i;
-                long double base = nb_base
-                        + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n)) / logl(2.0);
-                double l1pa = log1pl(-p_a) / logl(2.0);
-                double l1pb = log1pl(-p_b) / logl(2.0);
-
-                {
-                    size_t s = 0;
-                    size_t t = n;
-                    long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                         - lgammal(s + 1) - lgammal(t + 1))
-                                    / logl(2.0)
-                            + s * l1pa + t * l1pb;
-                    pval += exp2(base + sbase);
-                    for (++s; s <= n; ++s) {
-                        long double dev = get_deviance(s, t - 1);
-                        if (dev >= in_dev) {
-                            sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
-                                    + log2l(t + 1) + l1pa - l1pb;
-                            --t;
-                            pval += exp2(base + sbase);
-                            if (pval >= config.family_wise_error_rate)
-                                break;
-                        } else {
-                            break;
-                        }
-                    }
-                }
-                if (pval < config.family_wise_error_rate && get_deviance(n, 0) >= in_dev) {
-                    size_t s = n;
-                    size_t t = 0;
-                    long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                         - lgammal(s + 1) - lgammal(t + 1))
-                                    / logl(2.0)
-                            + s * l1pa + t * l1pb;
-                    pval += exp2(base + sbase);
-                    for (++t; t <= n; ++t) {
-                        long double dev = get_deviance(s - 1, t);
-                        if (dev >= in_dev) {
-                            sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
-                                    + log2l(t + 1) + l1pa - l1pb;
-                            --s;
-                            pval += exp2(base + sbase);
-                            if (pval >= config.family_wise_error_rate)
-                                break;
-                        } else {
-                            break;
-                        }
-                    }
+            if (eff_size != 0 && pval < config.family_wise_error_rate) {
+                auto *data = eff_size > 0 ? indicator_in.data() : indicator_out.data();
+                for (node_index node : path) {
+                    auto row_i = AnnotatedDBG::graph_to_anno_index(node);
+                    size_t nb_idx = kept.rank1(row_i) - 1;
+                    nb_pvals[nb_idx] = std::make_tuple(pval, unitig_id, node);
+                    set_bit(data, node, true, std::memory_order_relaxed);
                 }
             }
-        }
-
-        progress_bar += (pvals.size() % rows_per_update);
+        });
+        std::atomic_thread_fence(std::memory_order_acquire);
+        std::sort(nb_pvals.begin(), nb_pvals.end());
 
-        common::logger->trace("Correcting p-values");
-        common::logger->trace("Sorting {} / {} p-vals", pvals.size(), counts.size());
-        std::sort(pvals.begin(), pvals.end());
-        long double harm = 1.0;
-        // long double harm = 0.0;
-        // for (size_t i = 1; i <= nelem; ++i) {
-        //     harm += 1.0 / i;
-        // }
+        long double harm = 0.0;
+        for (size_t i = 1; i <= num_tests; ++i) {
+            harm += 1.0 / i;
+        }
 
-        common::logger->trace("Selecing cut-off");
+        common::logger->trace("Selecting cut-off");
         size_t k = 0;
-        for (size_t i = 0; i < pvals.size(); ++i) {
-            const auto& [pval, group, idx] = pvals[i];
+        uint64_t last_unitig_id = graph_ptr->max_index() + 1;
+        size_t cur_count = 0;
+        for (size_t i = 0; i < nb_pvals.size(); ++i) {
+            const auto &[pval, unitig_id, node] = nb_pvals[i];
             if (pval >= config.family_wise_error_rate)
                 break;
 
-            if (pval <= config.family_wise_error_rate * (i + 1) / pvals.size() / harm)
+            if (unitig_id != last_unitig_id) {
+                last_unitig_id = unitig_id;
+                ++cur_count;
+            }
+
+            if (pval
+                <= config.family_wise_error_rate * cur_count / num_tests / harm) {
                 k = std::max(i + 1, k);
+            }
         }
 
-        common::logger->trace("Found {} / {} significant p-values. Minimum p-value is {}",
-                              k, pvals.size(), std::get<0>(pvals[0]));
+        common::logger->trace(
+                "Found {} / {} significant p-values. Minimum p-value is {}", k,
+                nb_pvals.size(), std::get<0>(nb_pvals[0]));
+
+        for (size_t i = k; i < nb_pvals.size(); ++i) {
+            const auto& [pval, unitig_id, node] = nb_pvals[i];
+            if (pval >= config.family_wise_error_rate)
+                break;
 
-        for (size_t i = k; i < pvals.size(); ++i) {
-            std::get<1>(pvals[i]) = Group::BOTH;
+            if (!indicator_in[node] && !indicator_out[node]) {
+                common::logger->error(
+                        "Node {} not marked, but original p-value {} significant",
+                        node, pval);
+                throw std::runtime_error("Index fail");
+            }
+            indicator_in[node] = false;
+            indicator_out[node] = false;
         }
 
-        common::logger->trace("Reordering p-values");
-        std::sort(pvals.begin(), pvals.end(), [&](const auto& a, const auto& b) {
-            return std::get<2>(a) < std::get<2>(b);
-        });
-
-        common::logger->trace("Marking significant p-values");
-        std::atomic_thread_fence(std::memory_order_release);
-        generate_clean_rows([&](uint64_t row_i, const auto& row, size_t) {
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            size_t unitig_id = kmer_to_unitig[node];
-            const auto& [pval, group, idx] = pvals[unitig_id];
-            if (group == Group::IN)
-                set_bit(indicator_in.data(), node, std::memory_order_relaxed);
-
-            if (group == Group::OUT)
-                set_bit(indicator_out.data(), node, std::memory_order_relaxed);
-        });
-        std::atomic_thread_fence(std::memory_order_acquire);
+//         common::logger->trace("Associating k-mers to unitigs");
+//         kmer_to_unitig.resize(graph_ptr->max_index() + 1);
+//         // std::atomic_thread_fence(std::memory_order_release);
+//         // std::atomic<size_t> num_unitigs{0};
+//         std::mutex mu;
+//         clean_masked_graph->call_unitigs(
+//                 [&](const std::string&, const auto& path) {
+//                     size_t unitig_id = 0;
+//                     {
+//                         unitig_id = counts.size();
+//                         std::lock_guard<std::mutex> lock(mu);
+//                         counts.emplace_back(path.size(), 0, 0);
+//                     }
+//                     // size_t unitig_id = num_unitigs.fetch_add(1, std::memory_order_relaxed);
+//                     for (node_index node : path) {
+//                         kmer_to_unitig[node] = unitig_id;
+//                     }
+//                 },
+//                 num_threads);
+//         // std::atomic_thread_fence(std::memory_order_acquire);
+//         common::logger->trace("Assembled {} unitigs", counts.size());
+
+//         common::logger->trace("Aggregating counts for unitigs");
+//         generate_clean_rows([&](uint64_t row_i, const auto& row, size_t) {
+//             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+//             size_t unitig_id = kmer_to_unitig[node];
+//             auto& [len, in_count, out_count] = counts[unitig_id];
+//             for (const auto& [j, c] : row) {
+//                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+//                     out_count += c;
+
+//                 if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+//                     in_count += c;
+//             }
+//         });
+
+//         common::logger->trace("Computing unitig count histogram");
+//         std::vector<VectorMap<size_t, size_t>> unitig_hists_map(2);
+//         for (const auto& [len, in_count, out_count] : counts) {
+//             size_t avg_in_count = std::ceil(static_cast<double>(in_count) / len);
+//             size_t avg_out_count = std::ceil(static_cast<double>(out_count) / len);
+//             ++unitig_hists_map[0][avg_in_count];
+//             ++unitig_hists_map[1][avg_out_count];
+//         }
+
+//         common::logger->trace("Fitting negative binomials");
+//         auto [r_a, p_a, mu_a, var_a] = get_rp([&](const auto& callback) {
+//             for (const auto& [k, c] : unitig_hists_map[0]) {
+//                 callback(k, c);
+//             }
+//         });
+//         auto [r_b, p_b, mu_b, var_b] = get_rp([&](const auto& callback) {
+//             for (const auto& [k, c] : unitig_hists_map[1]) {
+//                 callback(k, c);
+//             }
+//         });
+//         auto [r_n, p_n, mu_n, var_n] = get_rp([&](const auto& callback) {
+//             for (const auto& [k, c] : unitig_hists_map[0]) {
+//                 auto find = unitig_hists_map[1].find(k);
+//                 if (find != unitig_hists_map[1].end()) {
+//                     callback(k, c + find->second);
+//                 } else {
+//                     callback(k, c);
+//                 }
+//             }
+
+//             for (const auto& [k, c] : unitig_hists_map[1]) {
+//                 if (!unitig_hists_map[0].count(k))
+//                     callback(k, c);
+//             }
+//         });
+
+//         common::logger->trace("In: r: {}\tp: {}\t mu: {}\tsample var: {}\tvar: {}", r_a,
+//                               p_a, mu_a, var_a, mu_a / p_a);
+//         common::logger->trace("Out: r: {}\tp: {}", r_b, p_b, mu_b, var_b, mu_b / p_b);
+//         common::logger->trace("Null: r: {}\tp: {}", r_n, p_n, mu_n, var_n, mu_n / p_n);
+
+//         long double nb_base = (lgammal(r_n) - lgammal(r_a) - lgammal(r_b)) / logl(2.0)
+//                 + r_a * log2l(p_a) + r_b * log2l(p_b) - r_n * log2l(p_n);
+
+//         common::logger->trace("Allocating p-value storage");
+//         std::vector<std::tuple<long double, Group, size_t>> pvals(
+//                 counts.size(), std::make_tuple(1.0, Group::OTHER, counts.size()));
+
+//         common::logger->trace("Calculating p-values");
+//         size_t rows_per_update = 10000;
+//         ProgressBar progress_bar(pvals.size(), "Streaming unitigs", std::cerr,
+//                                  !common::get_verbose());
+// #pragma omp parallel for num_threads(num_threads) schedule(static)
+//         for (size_t i = 0; i < counts.size(); ++i) {
+//             if (i > 0 && (i % rows_per_update) == 0)
+//                 progress_bar += rows_per_update;
+
+//             const auto& [len, in_sum, out_sum] = counts[i];
+//             size_t n = in_sum + out_sum;
+
+//             long double eff_size = 0.0;
+//             auto& [pval, group, idx] = pvals[i];
+//             idx = i;
+
+//             long double midpoint = r_a * n / (r_a + r_b);
+
+//             auto get_deviance_a = [&](long double s) {
+//                 if (s == 0)
+//                     return r_a * logl(p_a) * -2.0;
+
+//                 return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
+//                         + r_a * (logl(r_a) - logl(p_a)))
+//                         * 2.0;
+//             };
+
+//             auto get_deviance_b = [&](long double t) {
+//                 if (t == 0)
+//                     return r_b * logl(p_b) * -2.0;
+
+//                 return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
+//                         + r_b * (logl(r_b) - logl(p_b)))
+//                         * 2.0;
+//             };
+
+//             auto get_deviance = [&](long double s, long double t) {
+//                 return get_deviance_a(s) + get_deviance_b(t);
+//             };
+
+//             long double min_dev
+//                     = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
+//             long double in_dev = get_deviance(in_sum, out_sum);
+
+//             if (in_dev > min_dev) {
+//                 eff_size = static_cast<long double>(in_sum) - midpoint;
+//                 if (eff_size > 0) {
+//                     group = Group::IN;
+//                 } else if (eff_size < 0) {
+//                     group = Group::OUT;
+//                 }
+//                 pval = 0.0;
+//                 idx = i;
+//                 long double base = nb_base
+//                         + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n)) / logl(2.0);
+//                 double l1pa = log1pl(-p_a) / logl(2.0);
+//                 double l1pb = log1pl(-p_b) / logl(2.0);
+
+//                 {
+//                     size_t s = 0;
+//                     size_t t = n;
+//                     long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+//                                          - lgammal(s + 1) - lgammal(t + 1))
+//                                     / logl(2.0)
+//                             + s * l1pa + t * l1pb;
+//                     pval += exp2(base + sbase);
+//                     for (++s; s <= n; ++s) {
+//                         long double dev = get_deviance(s, t - 1);
+//                         if (dev >= in_dev) {
+//                             sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
+//                                     + log2l(t + 1) + l1pa - l1pb;
+//                             --t;
+//                             pval += exp2(base + sbase);
+//                             if (pval >= config.family_wise_error_rate)
+//                                 break;
+//                         } else {
+//                             break;
+//                         }
+//                     }
+//                 }
+//                 if (pval < config.family_wise_error_rate && get_deviance(n, 0) >= in_dev) {
+//                     size_t s = n;
+//                     size_t t = 0;
+//                     long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+//                                          - lgammal(s + 1) - lgammal(t + 1))
+//                                     / logl(2.0)
+//                             + s * l1pa + t * l1pb;
+//                     pval += exp2(base + sbase);
+//                     for (++t; t <= n; ++t) {
+//                         long double dev = get_deviance(s - 1, t);
+//                         if (dev >= in_dev) {
+//                             sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
+//                                     + log2l(t + 1) + l1pa - l1pb;
+//                             --s;
+//                             pval += exp2(base + sbase);
+//                             if (pval >= config.family_wise_error_rate)
+//                                 break;
+//                         } else {
+//                             break;
+//                         }
+//                     }
+//                 }
+//             }
+//         }
+
+//         progress_bar += (pvals.size() % rows_per_update);
+
+//         common::logger->trace("Correcting p-values");
+//         common::logger->trace("Sorting {} / {} p-vals", pvals.size(), counts.size());
+//         std::sort(pvals.begin(), pvals.end());
+//         long double harm = 1.0;
+//         // long double harm = 0.0;
+//         // for (size_t i = 1; i <= nelem; ++i) {
+//         //     harm += 1.0 / i;
+//         // }
+
+//         common::logger->trace("Selecing cut-off");
+//         size_t k = 0;
+//         for (size_t i = 0; i < pvals.size(); ++i) {
+//             const auto& [pval, group, idx] = pvals[i];
+//             if (pval >= config.family_wise_error_rate)
+//                 break;
+
+//             if (pval <= config.family_wise_error_rate * (i + 1) / pvals.size() / harm)
+//                 k = std::max(i + 1, k);
+//         }
+
+//         common::logger->trace("Found {} / {} significant p-values. Minimum p-value is {}",
+//                               k, pvals.size(), std::get<0>(pvals[0]));
+
+//         for (size_t i = k; i < pvals.size(); ++i) {
+//             std::get<1>(pvals[i]) = Group::BOTH;
+//         }
+
+//         common::logger->trace("Reordering p-values");
+//         std::sort(pvals.begin(), pvals.end(), [&](const auto& a, const auto& b) {
+//             return std::get<2>(a) < std::get<2>(b);
+//         });
+
+//         common::logger->trace("Marking significant p-values");
+//         std::atomic_thread_fence(std::memory_order_release);
+//         generate_clean_rows([&](uint64_t row_i, const auto& row, size_t) {
+//             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+//             size_t unitig_id = kmer_to_unitig[node];
+//             const auto& [pval, group, idx] = pvals[unitig_id];
+//             if (group == Group::IN)
+//                 set_bit(indicator_in.data(), node, std::memory_order_relaxed);
+
+//             if (group == Group::OUT)
+//                 set_bit(indicator_out.data(), node, std::memory_order_relaxed);
+//         });
+//         std::atomic_thread_fence(std::memory_order_acquire);
     } else {
         // precompute negative binomial fits for poisson_bayes and nbinom_exact tests
         std::vector<std::pair<long double, long double>> nb_params(groups.size());
@@ -4187,6 +4336,10 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                             columns_all, column_values_all, callback);
                 };
 
+                auto generate_unitigs = [&](const DeBruijnGraph&, const auto&) {
+                    throw std::runtime_error("Unitigs not implemented for column annotations");
+                };
+
                 bool parallel = get_num_threads() > 1;
 
                 return mask_nodes_by_label_dual<value_type, PValStorage>(
@@ -4246,7 +4399,7 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                             return hists_map_p[0];
                         },
-                        generate_rows, groups, config, num_threads, tmp_dir,
+                        generate_rows, generate_unitigs, groups, config, num_threads, tmp_dir,
                         num_parallel_files,
                         [&]() {
                             columns_all.clear();
@@ -4321,6 +4474,7 @@ mask_nodes_by_label_dual(
 
     if (const auto* int_matrix = dynamic_cast<const annot::matrix::IntMatrix*>(&matrix)) {
         using value_type = annot::matrix::IntMatrix::Value;
+        // std::mutex mu;
         return mask_nodes_by_label_dual<value_type, PValStorage>(
                 graph_ptr,
                 [&](const std::vector<size_t>& min_counts, sdsl::bit_vector* unmark_discarded) {
@@ -4329,6 +4483,22 @@ mask_nodes_by_label_dual(
                 [&](const auto& callback) {
                     int_matrix->call_row_values(callback, false /* ordered */);
                 },
+                [&](const DeBruijnGraph &graph, const auto &callback) {
+                    graph.call_unitigs([&](const std::string&, const auto &path) {
+                        std::vector<annot::matrix::BinaryMatrix::Row> rows;
+                        rows.reserve(path.size());
+                        for (auto node : path) {
+                            rows.emplace_back(AnnotatedDBG::graph_to_anno_index(node));
+                        }
+                        auto row_values = int_matrix->get_row_values(rows);
+                        // std::vector<annot::matrix::IntMatrix::RowValues> row_values;
+                        // {
+                        //     std::lock_guard<std::mutex> lock(mu);
+                        //     row_values = int_matrix->get_row_values(rows);
+                        // }
+                        callback(path, row_values);
+                    }, num_threads);
+                },
                 groups, config, num_threads, tmp_dir, num_threads);
     } else {
         auto generate_bit_rows = [&](const auto& callback) {
@@ -4413,6 +4583,9 @@ mask_nodes_by_label_dual(
                                 callback(row_i, row, thread_id);
                             });
                 },
+                [&](const DeBruijnGraph &, const auto &) {
+                    throw std::runtime_error("Unitigs not implemented for binary annotations");
+                },
                 groups, config, num_threads, tmp_dir, num_threads);
     }
 }

From 2cdb25a6a0a682e20593f60718e175e79c0c9cc1 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 21 Mar 2025 10:20:29 +0100
Subject: [PATCH 384/410] switch to new eigen gitlab repo

---
 metagraph/external-libraries/eigen | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/external-libraries/eigen b/metagraph/external-libraries/eigen
index 36b9596275..a7c7d329d8 160000
--- a/metagraph/external-libraries/eigen
+++ b/metagraph/external-libraries/eigen
@@ -1 +1 @@
-Subproject commit 36b95962756c1fce8e29b1f8bc45967f30773c00
+Subproject commit a7c7d329d89e8484be58df6078a586c44523db37

From 3b35599cda5e2d5e6924c47538a0cfb8f23f0363 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Mar 2025 12:09:45 +0100
Subject: [PATCH 385/410] unitig test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 630 ++++++++----------
 .../graph/representation/hash/dbg_sshash.cpp  |  91 +++
 .../graph/representation/hash/dbg_sshash.hpp  |   9 +
 3 files changed, 369 insertions(+), 361 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 17028b1379..f7a09a3b4d 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -28,6 +28,7 @@
 #include "graph/representation/masked_graph.hpp"
 #include "graph/graph_cleaning.hpp"
 #include "graph/representation/canonical_dbg.hpp"
+#include "graph/representation/hash/dbg_sshash.hpp"
 #include "annotation/representation/column_compressed/annotate_column_compressed.hpp"
 #include "annotation/int_matrix/base/int_matrix.hpp"
 
@@ -408,7 +409,20 @@ long double combine_pvals(const std::vector<long double>& pvals) {
     return boost::math::cdf(boost::math::complement(dist, stat));
 };
 
-template <typename value_type, class PValStorage, typename HistGetter, typename Generator, typename UnitigGenerator>
+
+struct PairVectorHash {
+    template <class Vector>
+    std::size_t operator()(const Vector& vector) const {
+        uint64_t hash = 0;
+        for (const auto& [a, b] : vector) {
+            hash ^= a + 0x9e3779b9 + (hash << 6) + (hash >> 2);
+            hash ^= b + 0x9e3779b9 + (hash << 6) + (hash >> 2);
+        }
+        return static_cast<std::size_t>(hash);
+    }
+};
+
+template <typename value_type, class PValStorage, typename PairContainer, typename HistGetter, typename Generator, typename UnitigGenerator>
 std::tuple<std::shared_ptr<DeBruijnGraph>,
            std::shared_ptr<DeBruijnGraph>,
            PValStorage,
@@ -804,107 +818,236 @@ mask_nodes_by_label_dual(
             },
             true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
     if (config.test_by_unitig && config.test_type != "notest") {
-        uint64_t num_tests = 0;
-        std::vector<std::tuple<long double, uint64_t, node_index>> nb_pvals;
-        nb_pvals.resize(nelem, std::make_tuple(1.0L, graph_ptr->max_index() + 1, graph_ptr->max_index() + 1));
-        std::atomic_thread_fence(std::memory_order_release);
-        generate_unitigs(*clean_masked_graph, [&](const auto &path, const auto &row_values) {
-            uint64_t unitig_id = __atomic_fetch_add(&num_tests, 1, std::memory_order_relaxed);
-            long double pval = 1.0;
-            long double eff_size = 0.0;
-            if (config.test_type == "mwu") {
-                auto generate_a = [&](const auto &callback) {
-                    size_t total = num_labels_in * path.size();
-                    size_t observed = 0;
-                    for (const auto &values : row_values) {
-                        for (const auto &[j, c] : values) {
-                            if (groups[j] == Group::IN) {
-                                callback(static_cast<long double>(c) / sums[j]);
-                                ++observed;
-                            }
-                        }
-                    }
-                    for (size_t n = observed; n < total; ++n) {
-                        callback(0.0L);
-                    }
-                };
-                auto generate_b = [&](const auto &callback) {
-                    size_t total = num_labels_out * path.size();
-                    size_t observed = 0;
-                    for (const auto &values : row_values) {
-                        for (const auto &[j, c] : values) {
-                            if (groups[j] == Group::OUT) {
-                                callback(static_cast<long double>(c) / sums[j]);
-                                ++observed;
-                            }
-                        }
-                    }
-                    for (size_t n = observed; n < total; ++n) {
-                        callback(0.0L);
-                    }
-                };
+        // using RowStorage = tsl::hopscotch_map<
+        //         PairContainer, std::pair<long double, std::vector<size_t>>, PairVectorHash>;
+        // RowStorage row_storage;
+        // std::mutex mu;
+        // generate_clean_rows([&](auto, const auto& row, size_t) {
+        //     std::lock_guard<std::mutex> lock(mu);
 
-                std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
+        // });
 
-            } else if (config.test_type == "cmh") {
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
-                for (const auto &values : row_values) {
-                    for (const auto& [j, c] : values) {
-                        if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                            out_sum += c;
-                        }
+        using IDMap = tsl::hopscotch_map<size_t, size_t>;
+        std::vector<std::tuple<size_t, size_t, long double, long double, IDMap>> unitig_sizes;
+        std::vector<size_t> kmer_to_unitig(nelem, nelem);
+        {
+            std::mutex mu;
+            std::atomic_thread_fence(std::memory_order_release);
+            clean_masked_graph->call_unitigs([&](const std::string&, const auto& path) {
+                size_t unitig_id;
+                {
+                    std::lock_guard<std::mutex> lock(mu);
+                    unitig_id = unitig_sizes.size();
+                    auto& [size, sofar, pval, eff_size, bucket]
+                            = unitig_sizes.emplace_back();
+                    size = path.size();
+                    pval = 1.0;
+                }
+                for (node_index node : path) {
+                    auto row_i = AnnotatedDBG::graph_to_anno_index(node);
+                    size_t idx = kept.rank1(row_i) - 1;
+                    kmer_to_unitig[idx] = unitig_id;
+                }
+            });
+            std::atomic_thread_fence(std::memory_order_acquire);
+
+
+            VectorSet<PairContainer, PairVectorHash> unique_rows;
+
+            std::atomic_thread_fence(std::memory_order_release);
+            generate_clean_rows([&](auto row_i, const auto& row, size_t) {
+                size_t idx = kept.rank1(row_i) - 1;
+                size_t unitig_id = kmer_to_unitig[idx];
+                auto& [unitig_size, unitig_sofar, pval, eff_size, unitig_bucket]
+                        = unitig_sizes[unitig_id];
+                size_t added
+                        = __atomic_add_fetch(&unitig_sofar, 1, std::memory_order_acq_rel);
+                {
+                    std::lock_guard<std::mutex> lock(mu);
+                    auto it = unique_rows.emplace(row).first;
+                    size_t annot_id = it - unique_rows.begin();
+                    ++unitig_bucket[annot_id];
+                }
 
-                        if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                            in_sum += c;
+                if (added == unitig_size) {
+                    // we've observed everything, now do the test
+                    auto generate_a = [&](const auto& callback) {
+                        for (const auto& [annot_id, n] : unitig_bucket) {
+                            const auto& row = *(unique_rows.begin() + annot_id);
+                            sdsl::bit_vector found(groups.size());
+                            for (const auto& [j, c] : row) {
+                                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                                    found[j] = true;
+                                    for (size_t i = 0; i < n; ++i) {
+                                        callback(static_cast<long double>(c) / sums[j]);
+                                    }
+                                }
+                            }
+                            for (size_t j = 0; j < found.size(); ++j) {
+                                if (!found[j]
+                                    && (groups[j] == Group::IN || groups[j] == Group::BOTH)) {
+                                    for (size_t i = 0; i < n; ++i) {
+                                        callback(0.0L);
+                                    }
+                                }
+                            }
                         }
-                    }
+                    };
+                    auto generate_b = [&](const auto& callback) {
+                        for (const auto& [annot_id, n] : unitig_bucket) {
+                            const auto& row = *(unique_rows.begin() + annot_id);
+                            sdsl::bit_vector found(groups.size());
+                            for (const auto& [j, c] : row) {
+                                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                                    found[j] = true;
+                                    for (size_t i = 0; i < n; ++i) {
+                                        callback(static_cast<long double>(c) / sums[j]);
+                                    }
+                                }
+                            }
+                            for (size_t j = 0; j < found.size(); ++j) {
+                                if (!found[j]
+                                    && (groups[j] == Group::OUT || groups[j] == Group::BOTH)) {
+                                    for (size_t i = 0; i < n; ++i) {
+                                        callback(0.0L);
+                                    }
+                                }
+                            }
+                        }
+                    };
+                    std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
+                    unitig_bucket = IDMap();
                 }
-                eff_size = static_cast<long double>(in_sum) / in_kmers
-                        - static_cast<long double>(out_sum) / out_kmers;
-                int64_t t = total_kmers;
-                int64_t n1 = in_kmers;
-                int64_t n2 = out_kmers;
-                int64_t m1 = in_sum + out_sum;
-                int64_t m2 = t - m1;
-                assert(m2 >= 0);
-
-                double lbase
-                        = log(n1) + log(n2) + log(m1) + log(m2) - log(t) * 2 - log(t - 1);
-                double factor = static_cast<double>(n1 * m1) / t;
-
-                auto get_chi_stat = [&](int64_t a) {
-                    int64_t b = n1 - a;
-                    int64_t c = m1 - a;
-                    int64_t d = m2 - b;
-                    assert(d == n2 - c);
-
-                    if (b < 0 || c < 0 || d < 0)
-                        return 0.0;
-
-                    double a_shift = static_cast<double>(a) - factor;
-
-                    if (a_shift > 0)
-                        return exp(log(a_shift) * 2.0 - lbase);
-
-                    return a_shift * a_shift / exp(lbase);
-                };
+            });
+            std::atomic_thread_fence(std::memory_order_acquire);
+        }
+        uint64_t num_tests = unitig_sizes.size();
 
-                double chi_stat = get_chi_stat(in_sum);
-                pval = boost::math::cdf(
-                        boost::math::complement(boost::math::chi_squared(1), chi_stat));
+        std::vector<std::tuple<long double, uint64_t, node_index>> nb_pvals;
+        nb_pvals.resize(nelem,
+                        std::make_tuple(1.0L, graph_ptr->max_index() + 1,
+                                        graph_ptr->max_index() + 1));
+
+        size_t idx = 0;
+        kept.call_ones([&](auto row_i) {
+            size_t unitig_id = kmer_to_unitig[idx];
+            const auto& [unitig_size, unitig_sofar, pval, eff_size, unitig_bucket]
+                    = unitig_sizes[unitig_id];
+            if (unitig_bucket.size()) {
+                throw std::runtime_error("Found non-empty bucket");
             }
 
             if (eff_size != 0 && pval < config.family_wise_error_rate) {
-                auto *data = eff_size > 0 ? indicator_in.data() : indicator_out.data();
-                for (node_index node : path) {
-                    auto row_i = AnnotatedDBG::graph_to_anno_index(node);
-                    size_t nb_idx = kept.rank1(row_i) - 1;
-                    nb_pvals[nb_idx] = std::make_tuple(pval, unitig_id, node);
-                    set_bit(data, node, true, std::memory_order_relaxed);
+                auto& [kmer_pval, kmer_unitig_id, node] = nb_pvals[idx];
+                kmer_pval = pval;
+                kmer_unitig_id = unitig_id;
+                node = AnnotatedDBG::anno_to_graph_index(row_i);
+                if (eff_size > 0) {
+                    indicator_in[node] = true;
+                } else {
+                    indicator_out[node] = true;
                 }
             }
+            ++idx;
         });
+        // generate_unitigs(*clean_masked_graph, [&](const auto& path, const auto& row_values) {
+        //     uint64_t unitig_id
+        //             = __atomic_fetch_add(&num_tests, 1, std::memory_order_relaxed);
+        //     long double pval = 1.0;
+        //     long double eff_size = 0.0;
+        //     if (config.test_type == "mwu") {
+        //         auto generate_a = [&](const auto& callback) {
+        //             size_t total = num_labels_in * path.size();
+        //             size_t observed = 0;
+        //             for (const auto& values : row_values) {
+        //                 for (const auto& [j, c] : values) {
+        //                     if (groups[j] == Group::IN) {
+        //                         callback(static_cast<long double>(c) / sums[j]);
+        //                         ++observed;
+        //                     }
+        //                 }
+        //             }
+        //             for (size_t n = observed; n < total; ++n) {
+        //                 callback(0.0L);
+        //             }
+        //         };
+        //         auto generate_b = [&](const auto& callback) {
+        //             size_t total = num_labels_out * path.size();
+        //             size_t observed = 0;
+        //             for (const auto& values : row_values) {
+        //                 for (const auto& [j, c] : values) {
+        //                     if (groups[j] == Group::OUT) {
+        //                         callback(static_cast<long double>(c) / sums[j]);
+        //                         ++observed;
+        //                     }
+        //                 }
+        //             }
+        //             for (size_t n = observed; n < total; ++n) {
+        //                 callback(0.0L);
+        //             }
+        //         };
+
+        //         std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
+
+        //     } else if (config.test_type == "cmh") {
+        //         int64_t in_sum = 0;
+        //         int64_t out_sum = 0;
+        //         for (const auto& values : row_values) {
+        //             for (const auto& [j, c] : values) {
+        //                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+        //                     out_sum += c;
+        //                 }
+
+        //                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+        //                     in_sum += c;
+        //                 }
+        //             }
+        //         }
+        //         eff_size = static_cast<long double>(in_sum) / in_kmers
+        //                 - static_cast<long double>(out_sum) / out_kmers;
+        //         int64_t t = total_kmers;
+        //         int64_t n1 = in_kmers;
+        //         int64_t n2 = out_kmers;
+        //         int64_t m1 = in_sum + out_sum;
+        //         int64_t m2 = t - m1;
+        //         assert(m2 >= 0);
+
+        //         double lbase
+        //                 = log(n1) + log(n2) + log(m1) + log(m2) - log(t) * 2 - log(t - 1);
+        //         double factor = static_cast<double>(n1 * m1) / t;
+
+        //         auto get_chi_stat = [&](int64_t a) {
+        //             int64_t b = n1 - a;
+        //             int64_t c = m1 - a;
+        //             int64_t d = m2 - b;
+        //             assert(d == n2 - c);
+
+        //             if (b < 0 || c < 0 || d < 0)
+        //                 return 0.0;
+
+        //             double a_shift = static_cast<double>(a) - factor;
+
+        //             if (a_shift > 0)
+        //                 return exp(log(a_shift) * 2.0 - lbase);
+
+        //             return a_shift * a_shift / exp(lbase);
+        //         };
+
+        //         double chi_stat = get_chi_stat(in_sum);
+        //         pval = boost::math::cdf(
+        //                 boost::math::complement(boost::math::chi_squared(1), chi_stat));
+        //     }
+
+        //     if (eff_size != 0 && pval < config.family_wise_error_rate) {
+        //         auto* data = eff_size > 0 ? indicator_in.data() : indicator_out.data();
+        //         for (node_index node : path) {
+        //             auto row_i = AnnotatedDBG::graph_to_anno_index(node);
+        //             size_t nb_idx = kept.rank1(row_i) - 1;
+        //             nb_pvals[nb_idx] = std::make_tuple(pval, unitig_id, node);
+        //             set_bit(data, node, true, std::memory_order_relaxed);
+        //         }
+        //     }
+        // });
         std::atomic_thread_fence(std::memory_order_acquire);
         std::sort(nb_pvals.begin(), nb_pvals.end());
 
@@ -918,7 +1061,7 @@ mask_nodes_by_label_dual(
         uint64_t last_unitig_id = graph_ptr->max_index() + 1;
         size_t cur_count = 0;
         for (size_t i = 0; i < nb_pvals.size(); ++i) {
-            const auto &[pval, unitig_id, node] = nb_pvals[i];
+            const auto& [pval, unitig_id, node] = nb_pvals[i];
             if (pval >= config.family_wise_error_rate)
                 break;
 
@@ -927,15 +1070,13 @@ mask_nodes_by_label_dual(
                 ++cur_count;
             }
 
-            if (pval
-                <= config.family_wise_error_rate * cur_count / num_tests / harm) {
+            if (pval <= config.family_wise_error_rate * cur_count / num_tests / harm) {
                 k = std::max(i + 1, k);
             }
         }
 
-        common::logger->trace(
-                "Found {} / {} significant p-values. Minimum p-value is {}", k,
-                nb_pvals.size(), std::get<0>(nb_pvals[0]));
+        common::logger->trace("Found {} / {} significant p-values. Minimum p-value is {}",
+                              k, nb_pvals.size(), std::get<0>(nb_pvals[0]));
 
         for (size_t i = k; i < nb_pvals.size(); ++i) {
             const auto& [pval, unitig_id, node] = nb_pvals[i];
@@ -944,250 +1085,13 @@ mask_nodes_by_label_dual(
 
             if (!indicator_in[node] && !indicator_out[node]) {
                 common::logger->error(
-                        "Node {} not marked, but original p-value {} significant",
-                        node, pval);
+                        "Node {} not marked, but original p-value {} significant", node,
+                        pval);
                 throw std::runtime_error("Index fail");
             }
             indicator_in[node] = false;
             indicator_out[node] = false;
         }
-
-//         common::logger->trace("Associating k-mers to unitigs");
-//         kmer_to_unitig.resize(graph_ptr->max_index() + 1);
-//         // std::atomic_thread_fence(std::memory_order_release);
-//         // std::atomic<size_t> num_unitigs{0};
-//         std::mutex mu;
-//         clean_masked_graph->call_unitigs(
-//                 [&](const std::string&, const auto& path) {
-//                     size_t unitig_id = 0;
-//                     {
-//                         unitig_id = counts.size();
-//                         std::lock_guard<std::mutex> lock(mu);
-//                         counts.emplace_back(path.size(), 0, 0);
-//                     }
-//                     // size_t unitig_id = num_unitigs.fetch_add(1, std::memory_order_relaxed);
-//                     for (node_index node : path) {
-//                         kmer_to_unitig[node] = unitig_id;
-//                     }
-//                 },
-//                 num_threads);
-//         // std::atomic_thread_fence(std::memory_order_acquire);
-//         common::logger->trace("Assembled {} unitigs", counts.size());
-
-//         common::logger->trace("Aggregating counts for unitigs");
-//         generate_clean_rows([&](uint64_t row_i, const auto& row, size_t) {
-//             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-//             size_t unitig_id = kmer_to_unitig[node];
-//             auto& [len, in_count, out_count] = counts[unitig_id];
-//             for (const auto& [j, c] : row) {
-//                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-//                     out_count += c;
-
-//                 if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-//                     in_count += c;
-//             }
-//         });
-
-//         common::logger->trace("Computing unitig count histogram");
-//         std::vector<VectorMap<size_t, size_t>> unitig_hists_map(2);
-//         for (const auto& [len, in_count, out_count] : counts) {
-//             size_t avg_in_count = std::ceil(static_cast<double>(in_count) / len);
-//             size_t avg_out_count = std::ceil(static_cast<double>(out_count) / len);
-//             ++unitig_hists_map[0][avg_in_count];
-//             ++unitig_hists_map[1][avg_out_count];
-//         }
-
-//         common::logger->trace("Fitting negative binomials");
-//         auto [r_a, p_a, mu_a, var_a] = get_rp([&](const auto& callback) {
-//             for (const auto& [k, c] : unitig_hists_map[0]) {
-//                 callback(k, c);
-//             }
-//         });
-//         auto [r_b, p_b, mu_b, var_b] = get_rp([&](const auto& callback) {
-//             for (const auto& [k, c] : unitig_hists_map[1]) {
-//                 callback(k, c);
-//             }
-//         });
-//         auto [r_n, p_n, mu_n, var_n] = get_rp([&](const auto& callback) {
-//             for (const auto& [k, c] : unitig_hists_map[0]) {
-//                 auto find = unitig_hists_map[1].find(k);
-//                 if (find != unitig_hists_map[1].end()) {
-//                     callback(k, c + find->second);
-//                 } else {
-//                     callback(k, c);
-//                 }
-//             }
-
-//             for (const auto& [k, c] : unitig_hists_map[1]) {
-//                 if (!unitig_hists_map[0].count(k))
-//                     callback(k, c);
-//             }
-//         });
-
-//         common::logger->trace("In: r: {}\tp: {}\t mu: {}\tsample var: {}\tvar: {}", r_a,
-//                               p_a, mu_a, var_a, mu_a / p_a);
-//         common::logger->trace("Out: r: {}\tp: {}", r_b, p_b, mu_b, var_b, mu_b / p_b);
-//         common::logger->trace("Null: r: {}\tp: {}", r_n, p_n, mu_n, var_n, mu_n / p_n);
-
-//         long double nb_base = (lgammal(r_n) - lgammal(r_a) - lgammal(r_b)) / logl(2.0)
-//                 + r_a * log2l(p_a) + r_b * log2l(p_b) - r_n * log2l(p_n);
-
-//         common::logger->trace("Allocating p-value storage");
-//         std::vector<std::tuple<long double, Group, size_t>> pvals(
-//                 counts.size(), std::make_tuple(1.0, Group::OTHER, counts.size()));
-
-//         common::logger->trace("Calculating p-values");
-//         size_t rows_per_update = 10000;
-//         ProgressBar progress_bar(pvals.size(), "Streaming unitigs", std::cerr,
-//                                  !common::get_verbose());
-// #pragma omp parallel for num_threads(num_threads) schedule(static)
-//         for (size_t i = 0; i < counts.size(); ++i) {
-//             if (i > 0 && (i % rows_per_update) == 0)
-//                 progress_bar += rows_per_update;
-
-//             const auto& [len, in_sum, out_sum] = counts[i];
-//             size_t n = in_sum + out_sum;
-
-//             long double eff_size = 0.0;
-//             auto& [pval, group, idx] = pvals[i];
-//             idx = i;
-
-//             long double midpoint = r_a * n / (r_a + r_b);
-
-//             auto get_deviance_a = [&](long double s) {
-//                 if (s == 0)
-//                     return r_a * logl(p_a) * -2.0;
-
-//                 return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
-//                         + r_a * (logl(r_a) - logl(p_a)))
-//                         * 2.0;
-//             };
-
-//             auto get_deviance_b = [&](long double t) {
-//                 if (t == 0)
-//                     return r_b * logl(p_b) * -2.0;
-
-//                 return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
-//                         + r_b * (logl(r_b) - logl(p_b)))
-//                         * 2.0;
-//             };
-
-//             auto get_deviance = [&](long double s, long double t) {
-//                 return get_deviance_a(s) + get_deviance_b(t);
-//             };
-
-//             long double min_dev
-//                     = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
-//             long double in_dev = get_deviance(in_sum, out_sum);
-
-//             if (in_dev > min_dev) {
-//                 eff_size = static_cast<long double>(in_sum) - midpoint;
-//                 if (eff_size > 0) {
-//                     group = Group::IN;
-//                 } else if (eff_size < 0) {
-//                     group = Group::OUT;
-//                 }
-//                 pval = 0.0;
-//                 idx = i;
-//                 long double base = nb_base
-//                         + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n)) / logl(2.0);
-//                 double l1pa = log1pl(-p_a) / logl(2.0);
-//                 double l1pb = log1pl(-p_b) / logl(2.0);
-
-//                 {
-//                     size_t s = 0;
-//                     size_t t = n;
-//                     long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-//                                          - lgammal(s + 1) - lgammal(t + 1))
-//                                     / logl(2.0)
-//                             + s * l1pa + t * l1pb;
-//                     pval += exp2(base + sbase);
-//                     for (++s; s <= n; ++s) {
-//                         long double dev = get_deviance(s, t - 1);
-//                         if (dev >= in_dev) {
-//                             sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
-//                                     + log2l(t + 1) + l1pa - l1pb;
-//                             --t;
-//                             pval += exp2(base + sbase);
-//                             if (pval >= config.family_wise_error_rate)
-//                                 break;
-//                         } else {
-//                             break;
-//                         }
-//                     }
-//                 }
-//                 if (pval < config.family_wise_error_rate && get_deviance(n, 0) >= in_dev) {
-//                     size_t s = n;
-//                     size_t t = 0;
-//                     long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-//                                          - lgammal(s + 1) - lgammal(t + 1))
-//                                     / logl(2.0)
-//                             + s * l1pa + t * l1pb;
-//                     pval += exp2(base + sbase);
-//                     for (++t; t <= n; ++t) {
-//                         long double dev = get_deviance(s - 1, t);
-//                         if (dev >= in_dev) {
-//                             sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
-//                                     + log2l(t + 1) + l1pa - l1pb;
-//                             --s;
-//                             pval += exp2(base + sbase);
-//                             if (pval >= config.family_wise_error_rate)
-//                                 break;
-//                         } else {
-//                             break;
-//                         }
-//                     }
-//                 }
-//             }
-//         }
-
-//         progress_bar += (pvals.size() % rows_per_update);
-
-//         common::logger->trace("Correcting p-values");
-//         common::logger->trace("Sorting {} / {} p-vals", pvals.size(), counts.size());
-//         std::sort(pvals.begin(), pvals.end());
-//         long double harm = 1.0;
-//         // long double harm = 0.0;
-//         // for (size_t i = 1; i <= nelem; ++i) {
-//         //     harm += 1.0 / i;
-//         // }
-
-//         common::logger->trace("Selecing cut-off");
-//         size_t k = 0;
-//         for (size_t i = 0; i < pvals.size(); ++i) {
-//             const auto& [pval, group, idx] = pvals[i];
-//             if (pval >= config.family_wise_error_rate)
-//                 break;
-
-//             if (pval <= config.family_wise_error_rate * (i + 1) / pvals.size() / harm)
-//                 k = std::max(i + 1, k);
-//         }
-
-//         common::logger->trace("Found {} / {} significant p-values. Minimum p-value is {}",
-//                               k, pvals.size(), std::get<0>(pvals[0]));
-
-//         for (size_t i = k; i < pvals.size(); ++i) {
-//             std::get<1>(pvals[i]) = Group::BOTH;
-//         }
-
-//         common::logger->trace("Reordering p-values");
-//         std::sort(pvals.begin(), pvals.end(), [&](const auto& a, const auto& b) {
-//             return std::get<2>(a) < std::get<2>(b);
-//         });
-
-//         common::logger->trace("Marking significant p-values");
-//         std::atomic_thread_fence(std::memory_order_release);
-//         generate_clean_rows([&](uint64_t row_i, const auto& row, size_t) {
-//             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-//             size_t unitig_id = kmer_to_unitig[node];
-//             const auto& [pval, group, idx] = pvals[unitig_id];
-//             if (group == Group::IN)
-//                 set_bit(indicator_in.data(), node, std::memory_order_relaxed);
-
-//             if (group == Group::OUT)
-//                 set_bit(indicator_out.data(), node, std::memory_order_relaxed);
-//         });
-//         std::atomic_thread_fence(std::memory_order_acquire);
     } else {
         // precompute negative binomial fits for poisson_bayes and nbinom_exact tests
         std::vector<std::pair<long double, long double>> nb_params(groups.size());
@@ -3661,8 +3565,8 @@ mask_nodes_by_label_dual(
                 size_t cur_count = it == sorted_unitig_counts.end()
                         ? i
                         : it - sorted_unitig_counts.begin();
-                if (nb_pvals[i].first
-                    <= config.family_wise_error_rate * (cur_count + 1) / num_tests / harm) {
+                if (nb_pvals[i].first <= config.family_wise_error_rate * (cur_count + 1)
+                            / num_tests / harm) {
                     k = std::max(i + 1, k);
                     node_index node = AnnotatedDBG::anno_to_graph_index(nb_pvals[i].second);
                     if (!indicator_in[node] && !indicator_out[node]) {
@@ -4337,12 +4241,13 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
                 };
 
                 auto generate_unitigs = [&](const DeBruijnGraph&, const auto&) {
-                    throw std::runtime_error("Unitigs not implemented for column annotations");
+                    throw std::runtime_error(
+                            "Unitigs not implemented for column annotations");
                 };
 
                 bool parallel = get_num_threads() > 1;
 
-                return mask_nodes_by_label_dual<value_type, PValStorage>(
+                return mask_nodes_by_label_dual<value_type, PValStorage, PairContainer>(
                         graph_ptr,
                         [&](const std::vector<size_t>& min_counts, sdsl::bit_vector* kept)
                                 -> std::vector<VectorMap<uint64_t, size_t>> {
@@ -4399,8 +4304,8 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
 
                             return hists_map_p[0];
                         },
-                        generate_rows, generate_unitigs, groups, config, num_threads, tmp_dir,
-                        num_parallel_files,
+                        generate_rows, generate_unitigs, groups, config, num_threads,
+                        tmp_dir, num_parallel_files,
                         [&]() {
                             columns_all.clear();
                             column_values_all.clear();
@@ -4474,32 +4379,32 @@ mask_nodes_by_label_dual(
 
     if (const auto* int_matrix = dynamic_cast<const annot::matrix::IntMatrix*>(&matrix)) {
         using value_type = annot::matrix::IntMatrix::Value;
+        using PairContainer = Vector<std::pair<uint64_t, value_type>>;
         // std::mutex mu;
-        return mask_nodes_by_label_dual<value_type, PValStorage>(
-                graph_ptr,
-                [&](const std::vector<size_t>& min_counts, sdsl::bit_vector* unmark_discarded) {
-                    return int_matrix->get_histograms(min_counts, unmark_discarded);
-                },
-                [&](const auto& callback) {
-                    int_matrix->call_row_values(callback, false /* ordered */);
-                },
-                [&](const DeBruijnGraph &graph, const auto &callback) {
-                    graph.call_unitigs([&](const std::string&, const auto &path) {
+
+        auto generate_unitigs = [&](const DeBruijnGraph& graph, const auto& callback) {
+            graph.call_unitigs(
+                    [&](const std::string&, const auto& path) {
                         std::vector<annot::matrix::BinaryMatrix::Row> rows;
                         rows.reserve(path.size());
                         for (auto node : path) {
                             rows.emplace_back(AnnotatedDBG::graph_to_anno_index(node));
                         }
                         auto row_values = int_matrix->get_row_values(rows);
-                        // std::vector<annot::matrix::IntMatrix::RowValues> row_values;
-                        // {
-                        //     std::lock_guard<std::mutex> lock(mu);
-                        //     row_values = int_matrix->get_row_values(rows);
-                        // }
                         callback(path, row_values);
-                    }, num_threads);
+                    },
+                    num_threads);
+        };
+
+        return mask_nodes_by_label_dual<value_type, PValStorage, PairContainer>(
+                graph_ptr,
+                [&](const std::vector<size_t>& min_counts, sdsl::bit_vector* unmark_discarded) {
+                    return int_matrix->get_histograms(min_counts, unmark_discarded);
                 },
-                groups, config, num_threads, tmp_dir, num_threads);
+                [&](const auto& callback) {
+                    int_matrix->call_row_values(callback, false /* ordered */);
+                },
+                generate_unitigs, groups, config, num_threads, tmp_dir, num_threads);
     } else {
         auto generate_bit_rows = [&](const auto& callback) {
             size_t n = matrix.num_rows();
@@ -4528,7 +4433,9 @@ mask_nodes_by_label_dual(
 
         bool parallel = get_num_threads() > 1;
 
-        return mask_nodes_by_label_dual<uint64_t, PValStorage>(
+        using value_type = annot::matrix::IntMatrix::Value;
+        using PairContainer = Vector<std::pair<uint64_t, value_type>>;
+        return mask_nodes_by_label_dual<uint64_t, PValStorage, PairContainer>(
                 graph_ptr,
                 [&](const std::vector<size_t>&,
                     sdsl::bit_vector* kept) -> std::vector<VectorMap<uint64_t, size_t>> {
@@ -4583,8 +4490,9 @@ mask_nodes_by_label_dual(
                                 callback(row_i, row, thread_id);
                             });
                 },
-                [&](const DeBruijnGraph &, const auto &) {
-                    throw std::runtime_error("Unitigs not implemented for binary annotations");
+                [&](const DeBruijnGraph&, const auto&) {
+                    throw std::runtime_error(
+                            "Unitigs not implemented for binary annotations");
                 },
                 groups, config, num_threads, tmp_dir, num_threads);
     }
diff --git a/metagraph/src/graph/representation/hash/dbg_sshash.cpp b/metagraph/src/graph/representation/hash/dbg_sshash.cpp
index 744dd89754..c952776175 100644
--- a/metagraph/src/graph/representation/hash/dbg_sshash.cpp
+++ b/metagraph/src/graph/representation/hash/dbg_sshash.cpp
@@ -104,6 +104,25 @@ std::pair<bit_vector_smart, bit_vector_smart> generate_succ_pred(const DBGSSHash
 
                         last_single_outdeg = false;
 
+                        /*
+                        auto suffix = sshash::get_suffix(kmer);
+                        bool calc_minimizer = false;
+                        uint64_t last_minimizer;
+                        for (size_t i = 0; i < kmer_t::alphabet_size; i++) {
+                            kmer_t new_kmer = suffix;
+                            new_kmer.kth_char_or(dict.k() - 1,
+                                                 kmer_t::char_to_uint(kmer_t::alphabet[i]));
+                            kmer_t new_kmer_rc = new_kmer;
+                            new_kmer_rc.reverse_complement_inplace(dict.k());
+                            if (!calc_minimizer) {
+                                calc_minimizer = true;
+                                last_minimizer = sshash::util::compute_minimizer(
+                                        new_kmer, dict.k(), dict.m(), dict.seed());
+                            } else {
+                            }
+                        }
+                        */
+
                         auto nb = dict.kmer_neighbours(kmer, with_rc);
                         bool found_fw = false;
                         size_t found_i = 0;
@@ -685,6 +704,77 @@ std::string DBGSSHash::get_node_sequence(node_index node) const {
     return str_kmer;
 }
 
+bool DBGSSHash::has_no_outgoing(node_index node) const {
+    if (node < succ_is_next_.size()) {
+        if (succ_is_next_[node])
+            return false;
+    } else if (pred_is_prev_[reverse_complement(node)]) {
+        return false;
+    }
+
+    return DeBruijnGraph::has_no_outgoing(node);
+}
+
+bool DBGSSHash::has_single_outgoing(node_index node) const {
+    if (node < succ_is_next_.size()) {
+        if (succ_is_next_[node])
+            return true;
+    } else if (pred_is_prev_[reverse_complement(node)]) {
+        return true;
+    }
+
+    return DeBruijnGraph::has_single_outgoing(node);
+}
+
+bool DBGSSHash::has_multiple_outgoing(node_index node) const {
+    if (node < succ_is_next_.size()) {
+        if (succ_is_next_[node])
+            return false;
+    } else if (pred_is_prev_[reverse_complement(node)]) {
+        return false;
+    }
+
+    return DeBruijnGraph::has_multiple_outgoing(node);
+}
+
+bool DBGSSHash::has_no_incoming(node_index node) const {
+    if (node < pred_is_prev_.size()) {
+        if (pred_is_prev_[node])
+            return false;
+    } else if (succ_is_next_[reverse_complement(node)]) {
+        return false;
+    }
+
+    return DeBruijnGraph::has_no_incoming(node);
+}
+
+bool DBGSSHash::has_single_incoming(node_index node) const {
+    if (node < pred_is_prev_.size()) {
+        if (pred_is_prev_[node])
+            return true;
+    } else if (succ_is_next_[reverse_complement(node)]) {
+        return true;
+    }
+    return DeBruijnGraph::has_single_incoming(node);
+}
+
+void DBGSSHash::call_source_nodes(const std::function<void(node_index)>& callback) const {
+    call_zeros(pred_is_prev_, [&](node_index n) {
+        if (n && DeBruijnGraph::has_no_incoming(n))
+            callback(n);
+    });
+
+    if (num_nodes() > dict_size()) {
+        call_zeros(succ_is_next_, [&](node_index n) {
+            if (n) {
+                n = reverse_complement(n);
+                if (DeBruijnGraph::has_no_outgoing(n))
+                    callback(n);
+            }
+        });
+    }
+}
+
 void DBGSSHash::serialize(std::ostream& out) const {
     essentials::generic_saver saver(out);
 
@@ -719,6 +809,7 @@ bool DBGSSHash::load(std::istream& in) {
 
     if (num_nodes_) {
         std::visit([&](auto& d) { d.visit(loader); }, dict_);
+        // std::tie(succ_is_next_, pred_is_prev_) = generate_succ_pred(*this, num_nodes_);
         succ_is_next_.load(in);
         pred_is_prev_.load(in);
     }
diff --git a/metagraph/src/graph/representation/hash/dbg_sshash.hpp b/metagraph/src/graph/representation/hash/dbg_sshash.hpp
index 72d93746a0..d1b82c30b5 100644
--- a/metagraph/src/graph/representation/hash/dbg_sshash.hpp
+++ b/metagraph/src/graph/representation/hash/dbg_sshash.hpp
@@ -88,6 +88,15 @@ class DBGSSHash : public DeBruijnGraph {
 
     size_t indegree(node_index) const override;
 
+    bool has_no_outgoing(node_index node) const override;
+    bool has_single_outgoing(node_index node) const override;
+    bool has_multiple_outgoing(node_index node) const override;
+
+    bool has_no_incoming(node_index node) const override;
+    bool has_single_incoming(node_index node) const override;
+
+    void call_source_nodes(const std::function<void(node_index)>& callback) const override;
+
     node_index kmer_to_node(std::string_view kmer) const override;
 
     template <bool with_rc = true>

From a163692426196cc8d098d6e6053ff643bb5b8d99 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Mar 2025 14:07:27 +0100
Subject: [PATCH 386/410] this works

---
 .../src/graph/annotated_graph_algorithm.cpp   | 345 ++++++++++++------
 1 file changed, 232 insertions(+), 113 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index f7a09a3b4d..a903546604 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -822,134 +822,253 @@ mask_nodes_by_label_dual(
         //         PairContainer, std::pair<long double, std::vector<size_t>>, PairVectorHash>;
         // RowStorage row_storage;
         // std::mutex mu;
-        // generate_clean_rows([&](auto, const auto& row, size_t) {
-        //     std::lock_guard<std::mutex> lock(mu);
+        // generate_clean_rows([&](auto row_i, const auto& row, size_t) {
+        //     // std::lock_guard<std::mutex> lock(mu);
 
         // });
 
-        using IDMap = tsl::hopscotch_map<size_t, size_t>;
-        std::vector<std::tuple<size_t, size_t, long double, long double, IDMap>> unitig_sizes;
-        std::vector<size_t> kmer_to_unitig(nelem, nelem);
-        {
-            std::mutex mu;
-            std::atomic_thread_fence(std::memory_order_release);
-            clean_masked_graph->call_unitigs([&](const std::string&, const auto& path) {
-                size_t unitig_id;
-                {
-                    std::lock_guard<std::mutex> lock(mu);
-                    unitig_id = unitig_sizes.size();
-                    auto& [size, sofar, pval, eff_size, bucket]
-                            = unitig_sizes.emplace_back();
-                    size = path.size();
-                    pval = 1.0;
+        // std::vector<size_t> kmer_to_unitig(nelem, nelem);
+        // using IDMap = tsl::hopscotch_map<size_t, size_t>;
+        // std::vector<std::tuple<size_t, size_t, long double, long double, IDMap>> unitig_sizes;
+        // {
+        //     std::mutex mu;
+        //     std::atomic_thread_fence(std::memory_order_release);
+        //     clean_masked_graph->call_unitigs([&](const std::string&, const auto& path) {
+        //         size_t unitig_id;
+        //         {
+        //             std::lock_guard<std::mutex> lock(mu);
+        //             unitig_id = unitig_sizes.size();
+        //             auto& [size, sofar, pval, eff_size, bucket]
+        //                     = unitig_sizes.emplace_back();
+        //             size = path.size();
+        //             pval = 1.0;
+        //         }
+        //         for (node_index node : path) {
+        //             auto row_i = AnnotatedDBG::graph_to_anno_index(node);
+        //             size_t idx = kept.rank1(row_i) - 1;
+        //             kmer_to_unitig[idx] = unitig_id;
+        //         }
+        //     });
+        //     std::atomic_thread_fence(std::memory_order_acquire);
+
+
+        //     VectorSet<PairContainer, PairVectorHash> unique_rows;
+
+        //     std::atomic_thread_fence(std::memory_order_release);
+        //     generate_clean_rows([&](auto row_i, const auto& row, size_t) {
+        //         size_t idx = kept.rank1(row_i) - 1;
+        //         size_t unitig_id = kmer_to_unitig[idx];
+        //         auto& [unitig_size, unitig_sofar, pval, eff_size, unitig_bucket]
+        //                 = unitig_sizes[unitig_id];
+        //         size_t added
+        //                 = __atomic_add_fetch(&unitig_sofar, 1, std::memory_order_acq_rel);
+        //         {
+        //             std::lock_guard<std::mutex> lock(mu);
+        //             auto it = unique_rows.emplace(row).first;
+        //             size_t annot_id = it - unique_rows.begin();
+        //             ++unitig_bucket[annot_id];
+        //         }
+
+        //         if (added == unitig_size) {
+        //             // we've observed everything, now do the test
+        //             auto generate_a = [&](const auto& callback) {
+        //                 for (const auto& [annot_id, n] : unitig_bucket) {
+        //                     const auto& row = *(unique_rows.begin() + annot_id);
+        //                     sdsl::bit_vector found(groups.size());
+        //                     for (const auto& [j, c] : row) {
+        //                         if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+        //                             found[j] = true;
+        //                             for (size_t i = 0; i < n; ++i) {
+        //                                 callback(static_cast<long double>(c) / sums[j]);
+        //                             }
+        //                         }
+        //                     }
+        //                     for (size_t j = 0; j < found.size(); ++j) {
+        //                         if (!found[j]
+        //                             && (groups[j] == Group::IN || groups[j] == Group::BOTH)) {
+        //                             for (size_t i = 0; i < n; ++i) {
+        //                                 callback(0.0L);
+        //                             }
+        //                         }
+        //                     }
+        //                 }
+        //             };
+        //             auto generate_b = [&](const auto& callback) {
+        //                 for (const auto& [annot_id, n] : unitig_bucket) {
+        //                     const auto& row = *(unique_rows.begin() + annot_id);
+        //                     sdsl::bit_vector found(groups.size());
+        //                     for (const auto& [j, c] : row) {
+        //                         if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+        //                             found[j] = true;
+        //                             for (size_t i = 0; i < n; ++i) {
+        //                                 callback(static_cast<long double>(c) / sums[j]);
+        //                             }
+        //                         }
+        //                     }
+        //                     for (size_t j = 0; j < found.size(); ++j) {
+        //                         if (!found[j]
+        //                             && (groups[j] == Group::OUT || groups[j] == Group::BOTH)) {
+        //                             for (size_t i = 0; i < n; ++i) {
+        //                                 callback(0.0L);
+        //                             }
+        //                         }
+        //                     }
+        //                 }
+        //             };
+        //             std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
+        //             unitig_bucket = IDMap();
+        //         }
+        //     });
+        //     std::atomic_thread_fence(std::memory_order_acquire);
+        // }
+        // uint64_t num_tests = unitig_sizes.size();
+
+        // std::vector<std::tuple<long double, uint64_t, node_index>> nb_pvals;
+        // nb_pvals.resize(nelem,
+        //                 std::make_tuple(1.0L, graph_ptr->max_index() + 1,
+        //                                 graph_ptr->max_index() + 1));
+        std::vector<std::tuple<long double, size_t, Group, node_index>> nb_pvals;
+        nb_pvals.resize(nelem,
+                        std::make_tuple(1.0L, graph_ptr->max_index() + 1, Group::OTHER,
+                                        graph_ptr->max_index() + 1));
+
+        generate_clean_rows([&](auto row_i, const auto& row, size_t) {
+            auto generate_a = [&](const auto& callback) {
+                sdsl::bit_vector found(groups.size());
+                for (const auto& [j, c] : row) {
+                    found[j] = true;
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                        callback(static_cast<long double>(c) / sums[j]);
                 }
-                for (node_index node : path) {
-                    auto row_i = AnnotatedDBG::graph_to_anno_index(node);
-                    size_t idx = kept.rank1(row_i) - 1;
-                    kmer_to_unitig[idx] = unitig_id;
+                for (size_t j = 0; j < found.size(); ++j) {
+                    if (!found[j] && (groups[j] == Group::IN || groups[j] == Group::BOTH))
+                        callback(0.0L);
                 }
-            });
-            std::atomic_thread_fence(std::memory_order_acquire);
-
-
-            VectorSet<PairContainer, PairVectorHash> unique_rows;
-
-            std::atomic_thread_fence(std::memory_order_release);
-            generate_clean_rows([&](auto row_i, const auto& row, size_t) {
-                size_t idx = kept.rank1(row_i) - 1;
-                size_t unitig_id = kmer_to_unitig[idx];
-                auto& [unitig_size, unitig_sofar, pval, eff_size, unitig_bucket]
-                        = unitig_sizes[unitig_id];
-                size_t added
-                        = __atomic_add_fetch(&unitig_sofar, 1, std::memory_order_acq_rel);
-                {
-                    std::lock_guard<std::mutex> lock(mu);
-                    auto it = unique_rows.emplace(row).first;
-                    size_t annot_id = it - unique_rows.begin();
-                    ++unitig_bucket[annot_id];
+            };
+            auto generate_b = [&](const auto& callback) {
+                sdsl::bit_vector found(groups.size());
+                for (const auto& [j, c] : row) {
+                    found[j] = true;
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                        callback(static_cast<long double>(c) / sums[j]);
                 }
+                for (size_t j = 0; j < found.size(); ++j) {
+                    if (!found[j] && (groups[j] == Group::OUT || groups[j] == Group::BOTH))
+                        callback(0.0L);
+                }
+            };
 
-                if (added == unitig_size) {
-                    // we've observed everything, now do the test
-                    auto generate_a = [&](const auto& callback) {
-                        for (const auto& [annot_id, n] : unitig_bucket) {
-                            const auto& row = *(unique_rows.begin() + annot_id);
-                            sdsl::bit_vector found(groups.size());
-                            for (const auto& [j, c] : row) {
-                                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                                    found[j] = true;
-                                    for (size_t i = 0; i < n; ++i) {
-                                        callback(static_cast<long double>(c) / sums[j]);
-                                    }
-                                }
-                            }
-                            for (size_t j = 0; j < found.size(); ++j) {
-                                if (!found[j]
-                                    && (groups[j] == Group::IN || groups[j] == Group::BOTH)) {
-                                    for (size_t i = 0; i < n; ++i) {
-                                        callback(0.0L);
+            size_t idx = kept.rank1(row_i) - 1;
+            auto& [pval, monotig_id, group, node] = nb_pvals[idx];
+
+            long double eff_size;
+
+            std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
+            if (eff_size != 0.0) {
+                node = AnnotatedDBG::anno_to_graph_index(row_i);
+                if (eff_size > 0) {
+                    group = Group::IN;
+                } else if (eff_size < 0) {
+                    group = Group::OUT;
+                }
+            }
+        });
+
+        uint64_t num_tests = 0;
+        std::atomic_thread_fence(std::memory_order_release);
+        clean_masked_graph->call_unitigs(
+                [&](const std::string&, const auto& path) {
+                    std::vector<long double> pvals;
+                    pvals.reserve(path.size());
+
+                    Group last_group = Group::BOTH;
+                    size_t last_i = 0;
+
+                    std::vector<size_t> ranks;
+                    ranks.reserve(path.size());
+                    for (node_index node : path) {
+                        size_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                        ranks.emplace_back(kept.rank1(row_i) - 1);
+                    }
+
+                    for (size_t i = 0; i < path.size(); ++i) {
+                        size_t idx = ranks[i];
+                        const auto& [pval, monotig_id, group, stored_node] = nb_pvals[idx];
+                        if (group != last_group) {
+                            if (pvals.size()) {
+                                uint64_t monotig_id
+                                        = __atomic_fetch_add(&num_tests, 1,
+                                                             std::memory_order_relaxed);
+                                long double comb_pval = combine_pvals(pvals);
+                                for (size_t j = last_i; j < i; ++j) {
+                                    size_t jdx = ranks[j];
+                                    std::get<0>(nb_pvals[jdx]) = comb_pval;
+                                    std::get<1>(nb_pvals[jdx]) = monotig_id;
+                                    if (comb_pval < config.family_wise_error_rate) {
+                                        if (last_group == Group::IN) {
+                                            set_bit(indicator_in.data(), path[j], true,
+                                                    std::memory_order_relaxed);
+                                        } else if (last_group == Group::OUT) {
+                                            set_bit(indicator_out.data(), path[j], true,
+                                                    std::memory_order_relaxed);
+                                        }
                                     }
                                 }
                             }
+                            pvals.clear();
+                            last_group = group;
+                            last_i = i;
                         }
-                    };
-                    auto generate_b = [&](const auto& callback) {
-                        for (const auto& [annot_id, n] : unitig_bucket) {
-                            const auto& row = *(unique_rows.begin() + annot_id);
-                            sdsl::bit_vector found(groups.size());
-                            for (const auto& [j, c] : row) {
-                                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                                    found[j] = true;
-                                    for (size_t i = 0; i < n; ++i) {
-                                        callback(static_cast<long double>(c) / sums[j]);
-                                    }
-                                }
-                            }
-                            for (size_t j = 0; j < found.size(); ++j) {
-                                if (!found[j]
-                                    && (groups[j] == Group::OUT || groups[j] == Group::BOTH)) {
-                                    for (size_t i = 0; i < n; ++i) {
-                                        callback(0.0L);
-                                    }
+                        pvals.emplace_back(pval);
+                    }
+                    if (pvals.size()) {
+                        uint64_t monotig_id = __atomic_fetch_add(&num_tests, 1,
+                                                                 std::memory_order_relaxed);
+                        long double comb_pval = combine_pvals(pvals);
+                        for (size_t j = last_i; j < path.size(); ++j) {
+                            size_t jdx = ranks[j];
+                            std::get<0>(nb_pvals[jdx]) = comb_pval;
+                            std::get<1>(nb_pvals[jdx]) = monotig_id;
+                            if (comb_pval < config.family_wise_error_rate) {
+                                if (last_group == Group::IN) {
+                                    set_bit(indicator_in.data(), path[j], true,
+                                            std::memory_order_relaxed);
+                                } else if (last_group == Group::OUT) {
+                                    set_bit(indicator_out.data(), path[j], true,
+                                            std::memory_order_relaxed);
                                 }
                             }
                         }
-                    };
-                    std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
-                    unitig_bucket = IDMap();
-                }
-            });
-            std::atomic_thread_fence(std::memory_order_acquire);
-        }
-        uint64_t num_tests = unitig_sizes.size();
+                    }
+                },
+                num_threads);
+        std::atomic_thread_fence(std::memory_order_acquire);
+        common::logger->trace("Found {} monotigs", num_tests);
+
+        // size_t idx = 0;
+        // kept.call_ones([&](auto row_i) {
+        //     size_t unitig_id = kmer_to_unitig[idx];
+        //     const auto& [unitig_size, unitig_sofar, pval, eff_size, unitig_bucket]
+        //             = unitig_sizes[unitig_id];
+        //     if (unitig_bucket.size()) {
+        //         throw std::runtime_error("Found non-empty bucket");
+        //     }
 
-        std::vector<std::tuple<long double, uint64_t, node_index>> nb_pvals;
-        nb_pvals.resize(nelem,
-                        std::make_tuple(1.0L, graph_ptr->max_index() + 1,
-                                        graph_ptr->max_index() + 1));
+        //     if (eff_size != 0 && pval < config.family_wise_error_rate) {
+        //         auto& [kmer_pval, kmer_unitig_id, node] = nb_pvals[idx];
+        //         kmer_pval = pval;
+        //         kmer_unitig_id = unitig_id;
+        //         node = AnnotatedDBG::anno_to_graph_index(row_i);
+        //         if (eff_size > 0) {
+        //             indicator_in[node] = true;
+        //         } else {
+        //             indicator_out[node] = true;
+        //         }
+        //     }
+        //     ++idx;
+        // });
 
-        size_t idx = 0;
-        kept.call_ones([&](auto row_i) {
-            size_t unitig_id = kmer_to_unitig[idx];
-            const auto& [unitig_size, unitig_sofar, pval, eff_size, unitig_bucket]
-                    = unitig_sizes[unitig_id];
-            if (unitig_bucket.size()) {
-                throw std::runtime_error("Found non-empty bucket");
-            }
 
-            if (eff_size != 0 && pval < config.family_wise_error_rate) {
-                auto& [kmer_pval, kmer_unitig_id, node] = nb_pvals[idx];
-                kmer_pval = pval;
-                kmer_unitig_id = unitig_id;
-                node = AnnotatedDBG::anno_to_graph_index(row_i);
-                if (eff_size > 0) {
-                    indicator_in[node] = true;
-                } else {
-                    indicator_out[node] = true;
-                }
-            }
-            ++idx;
-        });
         // generate_unitigs(*clean_masked_graph, [&](const auto& path, const auto& row_values) {
         //     uint64_t unitig_id
         //             = __atomic_fetch_add(&num_tests, 1, std::memory_order_relaxed);
@@ -1061,7 +1180,7 @@ mask_nodes_by_label_dual(
         uint64_t last_unitig_id = graph_ptr->max_index() + 1;
         size_t cur_count = 0;
         for (size_t i = 0; i < nb_pvals.size(); ++i) {
-            const auto& [pval, unitig_id, node] = nb_pvals[i];
+            const auto& [pval, unitig_id, group, node] = nb_pvals[i];
             if (pval >= config.family_wise_error_rate)
                 break;
 
@@ -1079,7 +1198,7 @@ mask_nodes_by_label_dual(
                               k, nb_pvals.size(), std::get<0>(nb_pvals[0]));
 
         for (size_t i = k; i < nb_pvals.size(); ++i) {
-            const auto& [pval, unitig_id, node] = nb_pvals[i];
+            const auto& [pval, unitig_id, group, node] = nb_pvals[i];
             if (pval >= config.family_wise_error_rate)
                 break;
 

From 7db58558e7b89dcac7f449e8c36430175f16e52f Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Mar 2025 15:38:23 +0100
Subject: [PATCH 387/410] cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 2111 +----------------
 1 file changed, 3 insertions(+), 2108 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a903546604..6433b81210 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -242,163 +242,6 @@ std::pair<long double, long double> mann_whitneyu(const G1& generate_a, const G2
     return std::make_pair(p, eff_size);
 }
 
-template <class G1, class G2>
-std::pair<long double, long double> brunner_munzel(const G1& generate_a,
-                                                   const G2& generate_b) {
-    std::vector<long double> x;
-    std::vector<long double> y;
-    std::vector<long double> xy;
-    generate_a([&](auto c) {
-        x.emplace_back(c);
-        xy.emplace_back(c);
-    });
-    generate_b([&](auto c) {
-        y.emplace_back(c);
-        xy.emplace_back(c);
-    });
-
-    long double nx = x.size();
-    long double ny = y.size();
-    VectorMap<long double, size_t> counts;
-    for (long double c : xy) {
-        ++counts[c];
-    }
-    if (counts.size() <= 1)
-        return std::make_pair(1.0, 0.0);
-
-    std::vector<std::pair<long double, size_t>> ranked_counts = counts.values_container();
-    std::sort(ranked_counts.begin(), ranked_counts.end());
-    tsl::hopscotch_map<long double, long double> count_to_rank;
-    size_t cur_rank = 0;
-    for (const auto& [v, c] : ranked_counts) {
-        size_t last_rank = cur_rank;
-        cur_rank += c;
-        long double r = 0.0;
-        for (size_t i = last_rank + 1; i <= cur_rank; ++i) {
-            r += i;
-        }
-        r /= c;
-        count_to_rank[v] = r;
-    }
-
-    std::vector<long double> rankc;
-    rankc.reserve(xy.size());
-    for (long double v : xy) {
-        rankc.emplace_back(count_to_rank[v]);
-    }
-
-    std::vector<long double> rankcx;
-    rankcx.reserve(x.size());
-    for (long double v : x) {
-        rankcx.emplace_back(count_to_rank[v]);
-    }
-    std::vector<long double> rankcy;
-    rankcy.reserve(y.size());
-    for (long double v : y) {
-        rankcy.emplace_back(count_to_rank[v]);
-    }
-
-    long double rankcx_mean = std::accumulate(rankcx.begin(), rankcx.end(), 0.0L) / nx;
-    long double rankcy_mean = std::accumulate(rankcy.begin(), rankcy.end(), 0.0L) / ny;
-
-    counts.clear();
-    for (long double c : x) {
-        ++counts[c];
-    }
-    ranked_counts = counts.values_container();
-    std::sort(ranked_counts.begin(), ranked_counts.end());
-    count_to_rank.clear();
-    cur_rank = 0;
-    for (const auto& [v, c] : ranked_counts) {
-        size_t last_rank = cur_rank;
-        cur_rank += c;
-        long double r = 0.0;
-        for (size_t i = last_rank + 1; i <= cur_rank; ++i) {
-            r += i;
-        }
-        r /= c;
-        count_to_rank[v] = r;
-    }
-    std::vector<long double> rankx;
-    rankx.reserve(x.size());
-    for (long double v : x) {
-        rankx.emplace_back(count_to_rank[v]);
-    }
-
-
-    counts.clear();
-    for (long double c : y) {
-        ++counts[c];
-    }
-    ranked_counts = counts.values_container();
-    std::sort(ranked_counts.begin(), ranked_counts.end());
-    count_to_rank.clear();
-    cur_rank = 0;
-    for (const auto& [v, c] : ranked_counts) {
-        size_t last_rank = cur_rank;
-        cur_rank += c;
-        long double r = 0.0;
-        for (size_t i = last_rank + 1; i <= cur_rank; ++i) {
-            r += i;
-        }
-        r /= c;
-        count_to_rank[v] = r;
-    }
-    std::vector<long double> ranky;
-    ranky.reserve(y.size());
-    for (long double v : x) {
-        ranky.emplace_back(count_to_rank[v]);
-    }
-
-    long double rankx_mean = std::accumulate(rankx.begin(), rankx.end(), 0.0L) / nx;
-    long double ranky_mean = std::accumulate(ranky.begin(), ranky.end(), 0.0L) / ny;
-
-    long double Sx = 0.0;
-    for (size_t i = 0; i < x.size(); ++i) {
-        Sx += pow(rankcx[i] - rankx[i] - rankcx_mean + rankx_mean, 2.0);
-    }
-    Sx /= nx - 1;
-    long double Sy = 0.0;
-    for (size_t i = 0; i < y.size(); ++i) {
-        Sy += pow(rankcy[i] - ranky[i] - rankcy_mean + ranky_mean, 2.0);
-    }
-    Sy /= ny - 1;
-
-    long double wbfn = nx * ny * (rankcy_mean - rankcx_mean);
-
-    if (wbfn == 0.0)
-        return std::make_pair(1.0, 0.0);
-
-    long double wbfn_denom = (nx + ny) * sqrtl(nx * Sx + ny * Sy);
-    if (wbfn_denom == 0.0)
-        return std::make_pair(0.0, -wbfn);
-
-    wbfn /= wbfn_denom;
-    long double p = 1.0;
-    long double eff_size = -wbfn;
-
-    if (nx + ny >= 50) {
-        p = boost::math::cdf(boost::math::complement(boost::math::normal(), abs(wbfn))) * 2.0;
-    } else if (nx + ny >= 20) {
-        long double df_denom = pow(nx * Sx, 2.0) / (nx - 1);
-        df_denom += pow(ny * Sy, 2.0) / (ny - 1);
-        if (df_denom == 0.0) {
-            p = boost::math::cdf(boost::math::complement(boost::math::normal(), abs(wbfn)))
-                    * 2.0;
-        } else {
-            long double df_numer = pow(nx * Sx + ny * Sy, 2.0);
-            long double df = df_numer / df_denom;
-            p = boost::math::cdf(
-                        boost::math::complement(boost::math::students_t(df), abs(wbfn)))
-                    * 2.0;
-        }
-    } else {
-        throw std::runtime_error("Too few samples");
-    }
-
-    return std::make_pair(p, eff_size);
-}
-
 long double combine_pvals(const std::vector<long double>& pvals) {
     static const boost::math::cauchy dist;
     long double stat = 0.0;
@@ -1338,48 +1181,6 @@ mask_nodes_by_label_dual(
                 = std::function<std::pair<long double, long double>(size_t, const RowGenerator&)>;
         GetLambda get_lambda;
 
-        std::pair<long double, long double> pln_params_a;
-        std::pair<long double, long double> pln_params_b;
-        std::pair<long double, long double> pln_params_null;
-        if (config.test_type == "poisson_bayes") {
-            auto get_params = [&](long double kmers, long double sq_kmers) {
-                long double mu = static_cast<long double>(kmers) / nelem;
-                long double mu2 = mu * mu;
-                long double var = sq_kmers / nelem - mu2;
-                long double log_sig2 = log((var - mu) / mu2 + 1);
-                long double log_mu = log(mu) - log_sig2 / 2.0;
-                return std::make_pair(log_mu, log_sig2);
-            };
-
-            pln_params_a = get_params(in_kmers, in_sq_kmers);
-            pln_params_b = get_params(out_kmers, out_sq_kmers);
-            pln_params_null = get_params(total_kmers, total_sq_kmers);
-            common::logger->trace("Lognormal MoM\tin:\tmu: {}\tvar: {}",
-                                  pln_params_a.first, pln_params_a.second);
-            common::logger->trace("Lognormal MoM\tout:\tmu: {}\tvar: {}",
-                                  pln_params_b.first, pln_params_b.second);
-            common::logger->trace("Lognormal MoM\tnull:\tmu: {}\tvar: {}",
-                                  pln_params_null.first, pln_params_null.second);
-
-            // common::logger->trace("Precomputing lambdas");
-
-            // #pragma omp parallel for num_threads(num_threads)
-            // for (size_t j = 0; j < groups.size(); ++j) {
-            //     auto [mu, sig2] = groups[j] == Group::IN ? pln_params_a : pln_params_b;
-            //     for (const auto &[k, c] : hists[j]) {
-            //         auto [lambda_j_min, lambda_j_max] = boost::math::tools::bisect(
-            //             [&](long double lambda) {
-            //                 return (mu - logl(lambda)) / sig2 - lambda + k - 1;
-            //             },
-            //             std::numeric_limits<long double>::min(),
-            //             static_cast<long double>(max_obs_vals[j]),
-            //             boost::math::tools::eps_tolerance<long double>(5)
-            //         );
-            //         lambdas[j][k] = (lambda_j_min + lambda_j_max) / 2;
-            //     }
-            // }
-        }
-
         if (config.test_type == "poisson_exact") {
             get_lambda = [&](size_t, const RowGenerator&) {
                 return std::make_pair(static_cast<long double>(in_kmers) / nelem,
@@ -1387,58 +1188,6 @@ mask_nodes_by_label_dual(
             };
         }
 
-        std::vector<std::pair<tsl::hopscotch_map<size_t, long double>,
-                              tsl::hopscotch_map<size_t, long double>>>
-                lambdas(num_threads + 1);
-        if (config.test_type == "poisson_bayes") {
-            get_lambda = [&](size_t bucket_idx, const RowGenerator& generate)
-                    -> std::pair<long double, long double> {
-                size_t in_sum = 0;
-                size_t out_sum = 0;
-                generate([&](size_t j, uint64_t c) {
-                    Group gp = groups[j];
-
-                    if (gp == Group::OUT || gp == Group::BOTH)
-                        out_sum += c;
-
-                    if (gp == Group::IN || gp == Group::BOTH)
-                        in_sum += c;
-                });
-
-                auto& [in_lambdas, out_lambdas] = lambdas[bucket_idx];
-                auto in_found = in_lambdas.find(in_sum);
-                auto out_found = out_lambdas.find(out_sum);
-
-                if (in_found == in_lambdas.end()) {
-                    auto [mu, sig2] = pln_params_a;
-                    auto [lambda_j_min, lambda_j_max] = boost::math::tools::bisect(
-                            [&](long double lambda) {
-                                return (mu - logl(lambda)) / sig2 - lambda + in_sum - 1;
-                            },
-                            std::numeric_limits<long double>::min(),
-                            static_cast<long double>(max_in_obs_val),
-                            boost::math::tools::eps_tolerance<long double>(5));
-                    long double lambda = (lambda_j_min + lambda_j_max) / 2;
-                    in_found = in_lambdas.try_emplace(in_sum, lambda).first;
-                }
-
-                if (out_found == out_lambdas.end()) {
-                    auto [mu, sig2] = pln_params_b;
-                    auto [lambda_j_min, lambda_j_max] = boost::math::tools::bisect(
-                            [&](long double lambda) {
-                                return (mu - logl(lambda)) / sig2 - lambda + out_sum - 1;
-                            },
-                            std::numeric_limits<long double>::min(),
-                            static_cast<long double>(max_out_obs_val),
-                            boost::math::tools::eps_tolerance<long double>(5));
-                    long double lambda = (lambda_j_min + lambda_j_max) / 2;
-                    out_found = out_lambdas.try_emplace(out_sum, lambda).first;
-                }
-
-                return std::make_pair(in_found->second, out_found->second);
-            };
-        }
-
         // precompute p-values for poisson_binom test
         std::vector<std::vector<long double>> pb_pvals(num_labels_in + num_labels_out + 1);
         std::vector<long double> mid_points(num_labels_in + num_labels_out + 1);
@@ -1655,482 +1404,8 @@ mask_nodes_by_label_dual(
             }
         }
 
-        // std::pair<long double, long double> bb_params_a;
-        // std::pair<long double, long double> bb_params_b;
-        // std::pair<long double, long double> bb_params_null;
-        std::vector<std::tuple<long double, long double, long double>> bb_bases;
-        // long double bb_alpha = 0.0;
-        // long double bb_beta = 0.0;
-        if (config.test_type == "bb_hypergeometric") {
-            std::vector<std::vector<std::tuple<size_t, long double, long double>>> probs_m(
-                    num_threads + 1);
-            generate_clean_rows([&](auto row_i, const auto& row, size_t bucket_idx) {
-                size_t n = 0;
-                size_t in_sum = 0;
-                for (const auto& [j, c] : row) {
-                    if (groups[j] == Group::IN)
-                        in_sum += c;
-
-                    n += c;
-                }
-                long double p = static_cast<long double>(in_sum) / n;
-                if (probs_m[bucket_idx].size() <= n)
-                    probs_m[bucket_idx].resize(n + 1);
-
-                auto& [c, prob_sum, sq_prob_sum] = probs_m[bucket_idx][n];
-                prob_sum += p;
-                sq_prob_sum += p * p;
-                ++c;
-            });
-
-            size_t max_size = 0;
-            for (const auto& probs : probs_m) {
-                max_size = std::max(max_size, probs.size());
-            }
-            std::vector<std::tuple<size_t, long double, long double>> probs(max_size);
-            for (const auto& prob : probs_m) {
-                for (size_t n = 0; n < prob.size(); ++n) {
-                    auto& [total_c, total_prob_sum, total_sq_prob_sum] = probs[n];
-                    const auto& [c, prob_sum, sq_prob_sum] = prob[n];
-                    total_c += c;
-                    total_prob_sum += prob_sum;
-                    total_sq_prob_sum += sq_prob_sum;
-                }
-            }
-            bb_bases.resize(max_size);
-
-            // convert prob sums and sq prob sums to mean and variance
-            common::logger->trace("Fitting beta distributions");
-            ProgressBar progress_bar(probs.size(), "Fitting", std::cerr,
-                                     !common::get_verbose());
-#pragma omp parallel for num_threads(num_threads) schedule(static)
-            for (size_t n = 0; n < probs.size(); ++n) {
-                ++progress_bar;
-                const auto& [c, prob_sum, sq_prob_sum] = probs[n];
-
-                if (c == 0)
-                    continue;
-
-                auto& [bb_alpha, bb_beta, bb_base] = bb_bases[n];
-                long double mn = prob_sum / c;
-                long double nu = n; // using the mean-sample size parametrization
-                // long double var = sq_prob_sum / c - mn * mn;
-                // long double nu = mn * (1.0 - mn) / var - 1.0; // MoM estimate
-                bb_alpha = nu * mn;
-                bb_beta = nu * (1.0 - mn);
-                long double bb_null = bb_alpha + bb_beta;
-
-                bb_base = (lgammal(bb_null) - lgammal(bb_alpha) - lgammal(bb_beta))
-                        / logl(2.0);
-
-                common::logger->trace("n: {}\talpha: {}\tbeta: {}", n, bb_alpha, bb_beta);
-            }
-
-
-            // common::logger->trace("Beta:\tmean: {}\tvar: {}\talpha: {}\tbeta: {}", mn,
-            //                       var, bb_alpha, bb_beta);
-            // nb_base += bb_alpha*log2l(0.1) + bb_beta*log2l(0.1)-bb_null*log2l(0.1);
-
-            // auto get_ab = [&](long double n, long double n2) {
-            //     long double mu = n / nelem;
-            //     long double var = n2 / nelem - mu * mu;
-            //     long double a = mu * (n * mu - mu * mu - var) / n / (var - mu + mu * mu
-            //     / n); long double b = a * (n / mu - 1); return std::make_pair(a, b);
-            // };
-
-            // bb_params_a = get_ab(in_kmers, in_sq_kmers);
-            // bb_params_b = get_ab(out_kmers, out_sq_kmers);
-            // bb_params_null = get_ab(total_kmers, total_sq_kmers);
-
-            // const auto &[a_a, b_a] = bb_params_a;
-            // const auto &[a_b, b_b] = bb_params_b;
-            // const auto &[a_n, b_n] = bb_params_null;
-
-            // long double nx = static_cast<long double>(in_kmers);
-            // long double ny = static_cast<long double>(out_kmers);
-
-            // bb_base = (lgammal(nx + 1) + lgammal(ny + 1) - lgammal(nx + ny + 1)
-            //             - lgammal(a_a + b_a + nx) - lgammal(a_b + b_b + ny)
-            //             + lgammal(a_n + b_n + nx + ny) + lgammal(a_n) + lgammal(b_n) - lgammal(a_n + b_n)
-            //             - lgammal(a_a) - lgammal(b_a) + lgammal(a_a + b_a)
-            //             - lgammal(a_b) - lgammal(b_b) + lgammal(a_b + b_b)) / logl(2.0);
-        }
-
-        std::pair<long double, long double> gp_params_a;
-        std::pair<long double, long double> gp_params_b;
-        std::vector<std::vector<long double>> gp_pvals;
-        // std::pair<long double, long double> gp_params_n;
-        // long double gp_base = 0.0;
-        if (config.test_type == "gpoisson") {
-            auto& [lambda_a, omega_a] = gp_params_a;
-            auto& [lambda_b, omega_b] = gp_params_b;
-            // auto &[lambda_n, omega_n] = gp_params_n;
-
-            lambda_a = static_cast<long double>(in_kmers) / nelem;
-            lambda_b = static_cast<long double>(out_kmers) / nelem;
-            // lambda_n = static_cast<long double>(total_kmers) / nelem;
-
-            common::logger->trace("Calculating row sums");
-            tsl::hopscotch_map<size_t, size_t> row_sums_a;
-            tsl::hopscotch_map<size_t, size_t> row_sums_b;
-            VectorSet<size_t> row_sums_n;
-            // tsl::hopscotch_map<size_t, size_t> row_sums_n;
-            {
-                std::vector<tsl::hopscotch_map<size_t, size_t>> row_sums_a_m(num_threads + 1);
-                std::vector<tsl::hopscotch_map<size_t, size_t>> row_sums_b_m(num_threads + 1);
-                std::vector<VectorSet<size_t>> row_sums_n_m(num_threads + 1);
-                generate_clean_rows([&](auto, const auto& row, size_t bucket_idx) {
-                    size_t in_sum = 0;
-                    size_t out_sum = 0;
-                    for (const auto& [j, c] : row) {
-                        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                            in_sum += c;
-
-                        if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                            out_sum += c;
-                    }
-                    ++row_sums_a_m[bucket_idx][in_sum];
-                    ++row_sums_b_m[bucket_idx][out_sum];
-                    row_sums_n_m[bucket_idx].emplace(in_sum + out_sum);
-                    // ++row_sums_n_m[bucket_idx][in_sum + out_sum];
-                });
-
-                row_sums_a = std::move(row_sums_a_m[0]);
-                row_sums_b = std::move(row_sums_b_m[0]);
-                row_sums_n = std::move(row_sums_n_m[0]);
-
-                for (size_t i = 1; i <= num_threads; ++i) {
-                    for (const auto& [k, c] : row_sums_a_m[i]) {
-                        row_sums_a[k] += c;
-                    }
-                    for (const auto& [k, c] : row_sums_b_m[i]) {
-                        row_sums_b[k] += c;
-                    }
-                    for (size_t k : row_sums_n_m[i]) {
-                        row_sums_n.emplace(k);
-                    }
-                    // for (const auto &[k, c] : row_sums_n_m[i]) {
-                    //     row_sums_n[k] += c;
-                    // }
-                }
-            }
-
-            long double var_a
-                    = static_cast<long double>(in_sq_kmers) / nelem - lambda_a * lambda_a;
-            long double var_b
-                    = static_cast<long double>(out_sq_kmers) / nelem - lambda_b * lambda_b;
-            // long double var_n = static_cast<long double>(total_sq_kmers) / nelem - lambda_n * lambda_n;
-
-            auto get_omega = [&](long double lambda, long double var, const auto& generate) {
-                long double val = sqrtl(lambda / var);
-                if (1.0 <= val) {
-                    common::logger->trace("Invalid omega: {}\tlambda: {}\tvar: {}",
-                                          1.0 - val, lambda, var);
-                    throw std::runtime_error("Fit fail");
-                }
-
-                return 1.0 - val;
-                // auto [omega_min, omega_max] = boost::math::tools::bisect([&](long double omega) {
-                //     long double dl = nelem / (1.0 - omega);
-                //     generate([&](int64_t k, auto c) {
-                //         dl += (static_cast<long double>(k) - lambda) * (k - 1) / ((1.0 - omega) * lambda + k * omega) * c;
-                //     });
-                //     return dl;
-                // }, 0.0, 1.0 - std::numeric_limits<float>::min(), boost::math::tools::eps_tolerance<double>(5));
-                // return (omega_min + omega_max) / 2.0;
-            };
-
-            omega_a = get_omega(lambda_a, var_a, [&](const auto& callback) {
-                for (const auto& [k, c] : row_sums_a) {
-                    callback(k, c);
-                }
-            });
-            omega_b = get_omega(lambda_b, var_b, [&](const auto& callback) {
-                for (const auto& [k, c] : row_sums_b) {
-                    callback(k, c);
-                }
-            });
-            // omega_n = get_omega(lambda_n, var_n, [&](const auto &callback) {
-            //     for (const auto &[k, c] : row_sums_n) {
-            //         callback(k, c);
-            //     }
-            // });
-
-            common::logger->trace(
-                    "Generalized Poisson In:\tlambda: {}\tomega: {}\tsample variance: "
-                    "{}\tvar: {}",
-                    lambda_a, omega_a, var_a, lambda_a / pow(1.0 - omega_a, 2.0));
-            common::logger->trace(
-                    "Generalized Poisson Out:\tlambda: {}\tomega: {}\tsample variance: "
-                    "{}\tvar: {}",
-                    lambda_b, omega_b, var_b, lambda_b / pow(1.0 - omega_b, 2.0));
-            // common::logger->trace("Generalized Poisson Null:\tlambda: {}\tomega: {}\tsample variance: {}\tvar: {}",
-            //                       lambda_n, omega_n, var_n, lambda_n / pow(1.0 - omega_n, 2.0));
-
-            long double gp_base = (log1pl(-omega_a) + log1pl(-omega_b)
-                                   - (1.0 - omega_a) * lambda_a - (1.0 - omega_b) * lambda_b)
-                            / logl(2.0)
-                    + log2l(lambda_a) + log2l(lambda_b);
-
-            gp_pvals.resize(*std::max_element(row_sums_n.begin(), row_sums_n.end()) + 1);
-
-            size_t rows_per_update = 1000;
-            ProgressBar progress_bar(row_sums_n.size(), "Precomputing p-values",
-                                     std::cerr, !common::get_verbose());
-#pragma omp parallel for num_threads(num_threads) schedule(dynamic)
-            for (size_t i = 0; i < row_sums_n.size(); ++i) {
-                if (i > 0 && (i % rows_per_update) == 0)
-                    progress_bar += rows_per_update;
-
-                size_t n = row_sums_n.values_container()[i];
-                size_t s = 0;
-                size_t t = n;
-                long double lprob = gp_base
-                        + (-lgammal(s + 1) - lgamma(t + 1) - omega_a * s - omega_b * t)
-                                / logl(2.0);
-                // auto get_prob = [&](int64_t s) {
-                //     int64_t t = n - s;
-                //     long double lprob = gp_base + (-lgammal(s + 1) - lgamma(t + 1) - omega_a * s - omega_b * t) / logl(2.0);
-                //     if (s == 0) {
-                //         lprob -= log1pl(-omega_a) * lambda_a / logl(2.0);
-                //     } else if (s > 1) {
-                //         lprob += log2l((1.0 - omega_a) * lambda_a + omega_a * s) * (s - 1);
-                //     }
-                //     if (t == 0) {
-                //         lprob -= log1pl(-omega_b) * lambda_b / logl(2.0);
-                //     } else if (t > 1) {
-                //         lprob += log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
-                //     }
-                //     return exp2l(lprob);
-                // };
-                std::vector<long double> probs;
-                probs.reserve(n + 1);
-                long double cur_lprob = lprob - log1pl(-omega_a) * lambda_a / logl(2.0)
-                        + log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
-                probs.emplace_back(exp2l(cur_lprob));
-                long double prob_sum = probs.back();
-                for (size_t s = 1; s <= n; ++s) {
-                    int64_t t = n - s;
-                    lprob += log2l(t + 1) - log2l(s) - omega_a + omega_b;
-                    long double cur_lprob = lprob;
-                    if (s == 0) {
-                        cur_lprob -= log1pl(-omega_a) * lambda_a / logl(2.0);
-                    } else if (s > 1) {
-                        cur_lprob += log2l((1.0 - omega_a) * lambda_a + omega_a * s)
-                                * (s - 1);
-                    }
-                    if (t == 0) {
-                        cur_lprob -= log1pl(-omega_b) * lambda_b / logl(2.0);
-                    } else if (t > 1) {
-                        cur_lprob += log2l((1.0 - omega_b) * lambda_b + omega_b * t)
-                                * (t - 1);
-                    }
-                    probs.emplace_back(exp2l(cur_lprob));
-                    prob_sum += probs.back();
-                }
-
-                auto& pvals_n = gp_pvals[n];
-                pvals_n.reserve(n + 1);
-                for (size_t s = 0; s <= n; ++s) {
-                    for (size_t sp = 0; sp <= n; ++sp) {
-                        if (probs[sp] <= probs[s])
-                            pvals_n[s] += probs[sp];
-                    }
-                    pvals_n[s] /= prob_sum;
-                }
-            }
-            progress_bar += (row_sums_n.size() % rows_per_update);
-        }
-
-        long double pig_base_a = 0.0;
-        long double pig_base_b = 0.0;
-        long double pig_base_n = 0.0;
-        long double lambda_a = 0.0;
-        long double lambda_b = 0.0;
-        long double lambda_n = 0.0;
-        if (config.test_type == "pig") {
-            // long double mu_a = static_cast<long double>(in_kmers) / nelem;
-            // long double mu_b = static_cast<long double>(out_kmers) / nelem;
-
-            // long double var_a = static_cast<long double>(in_sq_kmers) / nelem - mu_a * mu_a;
-            // long double var_b = static_cast<long double>(out_sq_kmers) / nelem - mu_b * mu_b;
-
-            // common::logger->trace("PIG\tIn:\tmu: {}\tvar: {}\tvar/mu: {}\tOut:\tmu: {}\tvar: {}\tvar/mu: {}",
-            //                       mu_a, var_a, var_a/mu_a,
-            //                       mu_b, var_b, var_b/mu_b);
-
-            auto get_lp = [&](long double x, long double mu, long double lambda) {
-                long double phi = 1.0L / lambda;
-                int64_t i;
-                long double p, pi1m, pi2m;
-                long double twophi = phi + phi;
-
-                long double A, B;
-                long double mu2 = mu * mu;
-                long double twophimu2 = twophi * mu2;
-
-                p = (1.0L - sqrtl(1.0L + twophimu2)) / phi / mu; /* log p[0] */
-                if (x == 0.0) {
-                    if (p > 0) {
-                        common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu,
-                                              lambda, x, p);
-                        throw std::runtime_error("Fail");
-                    }
-                    return p;
-                }
-
-                pi2m = expl(p); /* p[i - 2] = p[0]*/
-                p = logl(mu) + p - log1pl(twophimu2) / 2.0L; /* log p[1] */
-                if (x == 1.0) {
-                    if (p > 0) {
-                        common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu,
-                                              lambda, x, p);
-                        throw std::runtime_error("Fail");
-                    }
-                    return p;
-                }
-
-                pi1m = expl(p); /* p[i - 1] = p[1] */
-                A = 1.0L / (1.0L + 1.0L / twophimu2); /* constant in first term */
-                B = mu2 / (1.0L + twophimu2); /* constant in second term */
-                for (i = 2; i <= x; i++) {
-                    p = A * (1.0L - 1.5L / i) * pi1m + (B * pi2m) / (i * (i - 1));
-                    pi2m = pi1m;
-                    pi1m = p;
-                }
-
-                if (p > 1.0) {
-                    common::logger->error("mu: {}\tlambda: {}\tx: {}\tlogp: {}", mu,
-                                          lambda, x, logl(p));
-                    throw std::runtime_error("Fail");
-                }
-
-                return logl(p);
-            };
-
-            std::vector<long double> lambdas(groups.size());
-#pragma omp parallel for num_threads(num_threads)
-            for (size_t i = 0; i < groups.size(); ++i) {
-                long double mu = static_cast<long double>(sums[i]) / nelem;
-                long double lambda = 0.0;
-                long double val = 0.0;
-                try {
-                    std::tie(lambda, val) = boost::math::tools::brent_find_minima(
-                            [&](long double lambda) {
-                                long double val = 0.0;
-                                for (const auto& [k, c] : hists[i]) {
-                                    val += get_lp(k, mu, lambda) * c;
-                                }
-                                return -val;
-                            },
-                            std::numeric_limits<long double>::min(), 1.0L, 5);
-                } catch (boost::wrapexcept<boost::math::evaluation_error>&) {
-                }
-                try {
-                    auto [phi, val2] = boost::math::tools::brent_find_minima(
-                            [&](long double phi) {
-                                long double val = 0.0;
-                                for (const auto& [k, c] : hists[i]) {
-                                    val += get_lp(k, mu, 1.0L / phi) * c;
-                                }
-                                return -val;
-                            },
-                            std::numeric_limits<long double>::min(), 1.0L, 5);
-                    if (val == 0.0 || val2 < val)
-                        lambda = 1.0L / phi;
-                } catch (boost::wrapexcept<boost::math::evaluation_error>&) {
-                }
-                if (val == 0.0 || lambda == 0.0) {
-                    throw std::runtime_error("FAILS");
-                }
-
-                long double var = static_cast<long double>(sq_sums[i]) / nelem - mu * mu;
-                common::logger->trace(
-                        "i: {}\tmu: {}\tsample var: {}\tlambda: {}\tvar: {}", i, mu, var,
-                        lambda, mu + mu * mu * mu / lambda);
-                lambdas[i] = lambda;
-            }
-
-            size_t min_sum_a = std::numeric_limits<size_t>::max();
-            size_t min_sum_b = std::numeric_limits<size_t>::max();
-            size_t min_elem_a = 0;
-            size_t min_elem_b = 0;
-            for (size_t i = 0; i < lambdas.size(); ++i) {
-                if (groups[i] == Group::IN && sums[i] < min_sum_a) {
-                    min_sum_a = sums[i];
-                    min_elem_a = i;
-                }
-
-                if (groups[i] == Group::OUT && sums[i] < min_sum_b) {
-                    min_sum_b = sums[i];
-                    min_elem_b = i;
-                }
-            }
-            size_t min_sum_n = std::min(min_sum_a, min_sum_b);
-            size_t min_elem_n = min_sum_a < min_sum_b ? min_elem_a : min_elem_b;
-
-            long double min_lambda_a = lambdas[min_elem_a];
-            long double min_lambda_b = lambdas[min_elem_b];
-            long double min_lambda_n = lambdas[min_elem_n];
-
-            lambda_a = 0.0;
-            lambda_b = 0.0;
-            lambda_n = 0.0;
-            for (size_t i = 0; i < lambdas.size(); ++i) {
-                if (groups[i] == Group::IN)
-                    lambda_a += pow(static_cast<long double>(sums[i]) / min_sum_a, 2.0);
-
-                if (groups[i] == Group::OUT)
-                    lambda_b += pow(static_cast<long double>(sums[i]) / min_sum_b, 2.0);
-
-                lambda_n += pow(static_cast<long double>(sums[i]) / min_sum_n, 2.0);
-            }
-            lambda_a *= min_lambda_a;
-            lambda_b *= min_lambda_b;
-            lambda_n *= min_lambda_n;
-
-            common::logger->trace("PIG:\tIN:\tmu: {}\tlambda: {}",
-                                  static_cast<long double>(in_kmers) / nelem, lambda_a);
-            common::logger->trace("PIG:\tOUT:\tmu: {}\tlambda: {}",
-                                  static_cast<long double>(out_kmers) / nelem, lambda_b);
-            common::logger->trace("PIG:\tNULL:\tmu: {}\tlambda: {}",
-                                  static_cast<long double>(total_kmers) / nelem, lambda_n);
-
-            // long double phi_a = (var_a - mu_a) / mu_a / mu_a / mu_a;
-            // long double phi_b = (var_b - mu_b) / mu_b / mu_b / mu_b;
-
-
-            // long double lambda_a = 1.0 / phi_a;
-            // long double lambda_b = 1.0 / phi_b;
-
-            // common::logger->trace("PIG\tIn:\tlambda: {}\tOut:\tlambda: {}", lambda_a, lambda_b);
-
-            // long double min_mu = std::min(mu_a, mu_b);
-            // long double w_a = mu_a / min_mu;
-            // long double w_b = mu_b / min_mu;
-            // long double min_lambda = min_mu == mu_a ? lambda_a : lambda_b;
-
-            // long double mu_n = mu_a + mu_b;
-            // long double lambda_n = min_lambda * pow(w_a + w_b, 2.0);
-            // // throw std::runtime_error("Check");
-
-            // common::logger->trace("PIG\tNull:\tmu: {}\tlambda: {}", mu_n, lambda_n);
-
-            // pig_base_a = 0.5 * (logl(2.0) - logl(M_PI) + logl(lambda_a)) + lambda_a / mu_a;
-            // pig_base_b = 0.5 * (logl(2.0) - logl(M_PI) + logl(lambda_b)) + lambda_b / mu_b;
-            // pig_base_n = 0.5 * (logl(2.0) - logl(M_PI) + logl(lambda_n)) + lambda_n / mu_n;
-
-
-            // long double var_n = static_cast<long double>(total_sq_kmers) / nelem - mu_n
-            // * mu_n; long double lambda = mu_n * mu_n * mu_n / (var_n - mu_n);
-
-            // common::logger->trace("PIG: lambda: {}", lambda);
-        }
-
         bool fdr = config.test_by_unitig || config.test_type == "nbinom_exact"
-                || config.test_type == "pig" || config.test_type == "mwu"
-                || config.test_type == "bm" || config.test_type == "bb_hypergeometric"
-                || config.test_type == "cmh";
+                || config.test_type == "mwu" || config.test_type == "cmh";
 
         // precompute minimal attainable p-values
         std::vector<std::pair<long double, size_t>> m;
@@ -2231,327 +1506,8 @@ mask_nodes_by_label_dual(
                             ++ms[bucket_idx][n].second;
                         }
                     }
-                } else if (config.test_type == "hypergeometric"
-                           || config.test_type == "poisson_bayes") {
-                    size_t n = 0;
-                    for (const auto& [j, c] : row) {
-                        n += c;
-                    }
-                    if (n > 0) {
-                        // if (ms[bucket_idx].size() <= n)
-                        //     ms[bucket_idx].resize(n + 1);
-
-                        // if (ms[bucket_idx][n].second == 0) {
-                        sdsl::bit_vector found(groups.size());
-                        auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto& callback) {
-                            for (const auto& [j, c] : row) {
-                                callback(j, c);
-                            }
-                            for (size_t j = 0; j < found.size(); ++j) {
-                                if (!found[j])
-                                    callback(j, 0);
-                            }
-                        });
-                        boost::math::binomial_distribution dist(static_cast<long double>(n),
-                                                                mu1 / (mu1 + mu2));
-                        // boost::math::hypergeometric_distribution dist(mu1 * nelem, n, (mu1 + mu2) * nelem);
-                        long double pval0 = boost::math::pdf(dist, 0);
-                        long double pvaln = boost::math::pdf(dist, n);
-                        pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                        ++ms_map[bucket_idx][pval];
-                        // ms[bucket_idx][n].first = pval;
-                        // ms[bucket_idx][n].second = 1;
-                        // } else {
-                        //    ++ms[bucket_idx][n].second;
-                        // }
-                    }
-                } else if (config.test_type == "pig") {
-                    size_t n = 0;
-                    for (const auto& [j, c] : row) {
-                        n += c;
-                    }
-
-                    if (n > 0) {
-                        if (ms[bucket_idx].size() <= n)
-                            ms[bucket_idx].resize(n + 1);
-
-                        if (ms[bucket_idx][n].second == 0) {
-                            // https://search.r-project.org/CRAN/refmans/actuar/html/PoissonInverseGaussian.html
-                            long double mu_a = static_cast<long double>(in_kmers) / nelem;
-                            long double mu_b = static_cast<long double>(out_kmers) / nelem;
-
-                            // long double var_a = static_cast<long double>(in_sq_kmers) /
-                            // nelem - mu_a * mu_a; long double var_b = static_cast<long
-                            // double>(out_sq_kmers) / nelem - mu_b * mu_b;
-
-                            // long double phi_a = (var_a - mu_a) / mu_a / mu_a / mu_a;
-                            // long double phi_b = (var_b - mu_b) / mu_b / mu_b / mu_b;
-                            // long double lambda_a = 1.0 / phi_a;
-                            // long double lambda_b = 1.0 / phi_b;
-
-                            auto get_lp = [&](long double x, long double mu,
-                                              long double lambda, long double base) {
-                                long double phi = 1.0L / lambda;
-                                int64_t i;
-                                long double p, pi1m, pi2m;
-                                long double twophi = phi + phi;
-
-                                long double A, B;
-                                long double mu2 = mu * mu;
-                                long double twophimu2 = twophi * mu2;
-
-                                p = (1.0L - sqrtl(1.0L + twophimu2)) / phi
-                                        / mu; /* log p[0] */
-                                if (x == 0.0) {
-                                    if (p > 0) {
-                                        common::logger->error(
-                                                "mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: "
-                                                "{}",
-                                                mu, lambda, n, x, p);
-                                        throw std::runtime_error("Fail");
-                                    }
-                                    return p;
-                                }
-
-                                pi2m = expl(p); /* p[i - 2] = p[0]*/
-                                p = logl(mu) + p - log1pl(twophimu2) / 2.0L; /* log p[1] */
-                                if (x == 1.0) {
-                                    if (p > 0) {
-                                        common::logger->error(
-                                                "mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: "
-                                                "{}",
-                                                mu, lambda, n, x, p);
-                                        throw std::runtime_error("Fail");
-                                    }
-                                    return p;
-                                }
-
-                                pi1m = expl(p); /* p[i - 1] = p[1] */
-                                A = 1.0L / (1.0L + 1.0L / twophimu2); /* constant in first term */
-                                B = mu2 / (1.0L + twophimu2); /* constant in second term */
-                                for (i = 2; i <= x; i++) {
-                                    p = A * (1.0L - 1.5L / i) * pi1m
-                                            + (B * pi2m) / (i * (i - 1));
-                                    pi2m = pi1m;
-                                    pi1m = p;
-                                }
-
-                                if (p > 1.0) {
-                                    common::logger->error(
-                                            "mu: {}\tlambda: {}\tn: {}\tx: {}\tlogp: {}",
-                                            mu, lambda, n, x, logl(p));
-                                    throw std::runtime_error("Fail");
-                                }
-
-                                return logl(p);
-                                // long double lprob = 0.0;
-                                // if (x == 0) {
-                                //     // https://github.com/cran/actuar/blob/acf6c71ebef1374230b1c24789ce4a925134142f/src/poisinvgauss.c#L66
-                                //     long double mu2 = mu * mu;
-                                //     long double phi = 1.0 / lambda;
-                                //     long double twophi = phi + phi;
-                                //     long double twophimu2 = twophi * mu2;
-
-                                //     lprob = (1.0 - sqrtl(1.0 + twophimu2))/phi/mu;
-                                // } else {
-                                //     lprob = base - lgamma(x + 1)
-                                //             - 0.5*(x - 0.5) * (logl(2.0) - logl(lambda)
-                                //             + logl(1.0 + lambda / 2 / mu / mu))
-                                //             + logl(cyl_bessel_kl(x - 0.5, sqrtl(lambda
-                                //             * 2 * (1.0 + lambda / 2 / mu / mu))));
-                                // }
-                                // if (lprob > 0) {
-                                //     common::logger->error("mu: {}\tlambda: {}\tx:
-                                //     {}\tn: {}\tlog(pval): {}\tpval: {}",
-                                //         mu, lambda, x, n, lprob, expl(lprob));
-                                //     throw std::runtime_error("pval > 1");
-                                // }
-                                // return lprob;
-                            };
-
-                            // long double min_mu = std::min(mu_a, mu_b);
-                            // long double w_a = mu_a / min_mu;
-                            // long double w_b = mu_b / min_mu;
-                            // long double min_lambda = min_mu == mu_a ? lambda_a : lambda_b;
-
-                            long double mu_n = mu_a + mu_b;
-                            // long double lambda_n = min_lambda * pow(w_a + w_b, 2.0);
-
-                            long double base_lprob = get_lp(n, mu_n, lambda_n, pig_base_n);
-                            auto get_lprob = [&](int64_t s) {
-                                return get_lp(s, mu_a, lambda_a, pig_base_a)
-                                        + get_lp(n - s, mu_b, lambda_b, pig_base_b)
-                                        - base_lprob;
-                            };
-
-                            long double lpval0 = get_lprob(0);
-                            long double lpvaln = get_lprob(n);
-
-                            pval = expl(std::min(lpval0, lpvaln)) * (1 + (lpval0 == lpvaln));
-                            ms[bucket_idx][n].first = pval;
-                            ms[bucket_idx][n].second = 1;
-                        } else {
-                            ++ms[bucket_idx][n].second;
-                        }
-                    }
-                } else if (config.test_type == "gpoisson") {
-                    size_t n = 0;
-                    for (const auto& [j, c] : row) {
-                        n += c;
-                    }
-
-                    if (n > 0) {
-                        if (ms[bucket_idx].size() <= n) {
-                            gp_pvals_m[bucket_idx].resize(n + 1);
-                            ms[bucket_idx].resize(n + 1);
-                        }
-
-                        if (ms[bucket_idx][n].second == 0) {
-                            // const auto &[lambda_a, omega_a] = gp_params_a;
-                            // const auto &[lambda_b, omega_b] = gp_params_b;
-                            // size_t s = 0;
-                            // size_t t = n;
-                            // long double lprob = gp_base + (-lgammal(s + 1) - lgamma(t + 1) - omega_a * s - omega_b * t) / logl(2.0);
-                            // // auto get_prob = [&](int64_t s) {
-                            // //     int64_t t = n - s;
-                            // //     long double lprob = gp_base + (-lgammal(s + 1) - lgamma(t + 1) - omega_a * s - omega_b * t) / logl(2.0);
-                            // //     if (s == 0) {
-                            // //         lprob -= log1pl(-omega_a) * lambda_a / logl(2.0);
-                            // //     } else if (s > 1) {
-                            // //         lprob += log2l((1.0 - omega_a) * lambda_a + omega_a * s) * (s - 1);
-                            // //     }
-                            // //     if (t == 0) {
-                            // //         lprob -= log1pl(-omega_b) * lambda_b / logl(2.0);
-                            // //     } else if (t > 1) {
-                            // //         lprob += log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
-                            // //     }
-                            // //     return exp2l(lprob);
-                            // // };
-                            // std::vector<long double> probs;
-                            // probs.reserve(n + 1);
-                            // long double cur_lprob = lprob - log1pl(-omega_a) * lambda_a / logl(2.0)
-                            //                             + log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
-                            // probs.emplace_back(exp2l(cur_lprob));
-                            // long double prob_sum = probs.back();
-                            // for (size_t s = 1; s <= n; ++s) {
-                            //     int64_t t = n - s;
-                            //     lprob += log2l(t + 1) - log2l(s) - omega_a + omega_b;
-                            //     long double cur_lprob = lprob;
-                            //     if (s == 0) {
-                            //         cur_lprob -= log1pl(-omega_a) * lambda_a / logl(2.0);
-                            //     } else if (s > 1) {
-                            //         cur_lprob += log2l((1.0 - omega_a) * lambda_a + omega_a * s) * (s - 1);
-                            //     }
-                            //     if (t == 0) {
-                            //         cur_lprob -= log1pl(-omega_b) * lambda_b / logl(2.0);
-                            //     } else if (t > 1) {
-                            //         cur_lprob += log2l((1.0 - omega_b) * lambda_b + omega_b * t) * (t - 1);
-                            //     }
-                            //     probs.emplace_back(exp2l(cur_lprob));
-                            //     prob_sum += probs.back();
-                            // }
-
-                            const auto& pvals_n = gp_pvals[n];
-                            // pvals_n.reserve(n + 1);
-                            // for (size_t s = 0; s <= n; ++s) {
-                            //     for (size_t sp = 0; sp <= n; ++sp) {
-                            //         if (probs[sp] <= probs[s])
-                            //             pvals_n[s] += probs[sp];
-                            //     }
-                            //     pvals_n[s] /= prob_sum;
-                            // }
-
-                            pval = std::min(pvals_n[0], pvals_n[n]);
-                            ms[bucket_idx][n].first = pval;
-                            ms[bucket_idx][n].second = 1;
-                        } else {
-                            ++ms[bucket_idx][n].second;
-                        }
-                    }
-                    // } else if (config.test_type == "bb_hypergeometric") {
-                    //     size_t n = 0;
-                    //     for (const auto& [j, c] : row) {
-                    //         n += c;
-                    //     }
-                    //     if (n > 0) {
-                    //         if (ms[bucket_idx].size() <= n)
-                    //             ms[bucket_idx].resize(n + 1);
-
-                    //         if (ms[bucket_idx][n].second == 0) {
-                    //             const auto& [a_a, b_a] = bb_params_a;
-                    //             const auto& [a_b, b_b] = bb_params_b;
-                    //             const auto& [a_n, b_n] = bb_params_null;
-                    //             long double nx = static_cast<long double>(in_kmers);
-                    //             long double ny = static_cast<long double>(out_kmers);
-                    //             long double base
-                    //                     = (lgammal(n + 1) + lgammal(nx + ny - n + 1)
-                    //                        - lgammal(a_n + n) - lgammal(b_n + nx + ny - n))
-                    //                             / logl(2.0)
-                    //                     + bb_base;
-                    //             int64_t s = 0;
-                    //             long double pval0
-                    //                     = (-lgammal(s + 1) - lgammal(nx - s + 1)
-                    //                        - lgammal(n - s + 1) - lgammal(ny - n + s + 1)
-                    //                        + lgammal(a_a + s) + lgammal(b_a + nx - s)
-                    //                        + lgammal(a_b + n - s) + lgammal(b_b + ny - n + s))
-                    //                             / logl(2.0)
-                    //                     + base;
-                    //             s = n;
-                    //             long double pvaln
-                    //                     = (-lgammal(s + 1) - lgammal(nx - s + 1)
-                    //                        - lgammal(n - s + 1) - lgammal(ny - n + s + 1)
-                    //                        + lgammal(a_a + s) + lgammal(b_a + nx - s)
-                    //                        + lgammal(a_b + n - s) + lgammal(b_b + ny - n + s))
-                    //                             / logl(2.0)
-                    //                     + base;
-                    //             pval = exp2l(std::min(pval0, pvaln)) * (1 + (pval0 == pvaln));
-                    //             ms[bucket_idx][n].first = pval;
-                    //             ms[bucket_idx][n].second = 1;
-                    //         } else {
-                    //             ++ms[bucket_idx][n].second;
-                    //         }
-                    //     }
-                }
-
-                // if (config.test_by_unitig) {
-                //     node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                //     size_t unitig_id = kmer_to_unitig[node];
-                //     std::lock_guard<std::mutex> lock(mu);
-                //     ++counts_map[unitig_id][n];
-                // }
-            });
-
-            // if (config.test_by_unitig) {
-            //     common::logger->trace("Combining minimal attainable p-values for unitigs");
-            //     offsets.reserve(num_threads + 1);
-            //     offsets.emplace_back(0);
-            //     for (const auto &bucket : pvals_min_buckets) {
-            //         offsets.emplace_back(offsets.back() + bucket.size());
-            //     }
-            //     std::mutex m_vec_mu;
-            //     clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-            //         std::vector<long double> pvals;
-            //         pvals.reserve(path.size());
-            //         {
-            //             std::lock_guard<std::mutex> lock(m_vec_mu);
-            //             for (node_index node : path) {
-            //                 size_t row = AnnotatedDBG::graph_to_anno_index(node);
-            //                 size_t row_rank = kept.rank1(row);
-            //                 size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
-            //                 size_t offset = row_rank - offsets[bucket_idx];
-            //                 pvals.emplace_back(get<PValStorage>(pvals_min_buckets[bucket_idx][offset]));
-            //             }
-            //         }
-            //         double comp_min_pval = combine_pvals(pvals);
-            //         size_t row = AnnotatedDBG::graph_to_anno_index(path[0]);
-            //         size_t row_rank = kept.rank1(row);
-            //         size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
-            //         size_t offset = row_rank - offsets[bucket_idx];
-            //         ++ms[bucket_idx][comp_min_pval];
-            //         std::lock_guard<std::mutex> lock(m_vec_mu);
-            //         set<PValStorage>(pvals_min_buckets[bucket_idx][offset], comp_min_pval);
-            //     });
-            // }
+                }
+            });
 
             if (config.test_type == "poisson_bayes") {
                 VectorMap<long double, size_t> m_map = std::move(ms_map[0]);
@@ -2584,11 +1540,6 @@ mask_nodes_by_label_dual(
 
                 if (config.test_type == "poisson_binom")
                     std::sort(m.rbegin(), m.rend());
-
-                // if (config.test_type == "gpoisson") {
-                //     m.erase(std::remove_if(m.begin(), m.end(), [&](const auto &a) {
-                //     return !a.second; }), m.end()); std::sort(m.rbegin(), m.rend());
-                // }
             }
 
             if (m.size() > 1) {
@@ -2627,16 +1578,6 @@ mask_nodes_by_label_dual(
                     ++jt;
                 }
             }
-
-            // if (config.test_by_unitig) {
-            //     common::logger->trace("Correcting for monotigs");
-            //     for (const auto &count_map : counts_map) {
-            //         for (const auto &[n, c] : count_map) {
-            //             if (c > 1)
-            //                 m[n].second -= c - 1;
-            //         }
-            //     }
-            // }
         }
 
         // determine cutoffs for multiple testing correction
@@ -2646,8 +1587,6 @@ mask_nodes_by_label_dual(
             k = 1;
             n_cutoff = 1;
         }
-        // n_cutoff = 1;
-        // k = 1;
 
         common::logger->trace("Picked: k: {}\tn: {} / {}\tpval_min: {}\tn_cutoff: {}",
                               k_min, k, nelem, config.family_wise_error_rate / k, n_cutoff);
@@ -2817,259 +1756,6 @@ mask_nodes_by_label_dual(
                     eff_size = static_cast<long double>(in_sum) - boost::math::mode(bdist);
                 }
 
-            } else if (config.test_type == "pig") {
-                size_t n = 0;
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
-                for (const auto& [j, c] : row) {
-                    n += c;
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                        out_sum += c;
-                    }
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                        in_sum += c;
-                    }
-                }
-
-                if (n < n_cutoff)
-                    return;
-
-                if (n > 0) {
-                    long double mu_a = static_cast<long double>(in_kmers) / nelem;
-                    long double mu_b = static_cast<long double>(out_kmers) / nelem;
-
-                    // long double var_a = static_cast<long double>(in_sq_kmers) / nelem - mu_a * mu_a;
-                    // long double var_b = static_cast<long double>(out_sq_kmers) / nelem - mu_b * mu_b;
-
-                    // long double phi_a = (var_a - mu_a) / mu_a / mu_a / mu_a;
-                    // long double phi_b = (var_b - mu_b) / mu_b / mu_b / mu_b;
-                    // long double lambda_a = 1.0 / phi_a;
-                    // long double lambda_b = 1.0 / phi_b;
-
-                    auto get_lprobs = [&](long double mu, long double lambda) {
-                        // https://github.com/cran/actuar/blob/acf6c71ebef1374230b1c24789ce4a925134142f/src/poisinvgauss.c#L66
-                        long double phi = 1.0L / lambda;
-                        uint64_t i;
-                        long double p, pi1m, pi2m;
-                        long double twophi = phi + phi;
-
-                        long double A, B;
-                        long double mu2 = mu * mu;
-                        long double twophimu2 = twophi * mu2;
-
-                        p = (1.0L - sqrtl(1.0L + twophimu2)) / phi / mu; /* log p[0] */
-                        std::vector<long double> lprobs;
-                        lprobs.reserve(n);
-                        lprobs.emplace_back(p);
-                        // if (x == 0.0) {
-                        //     if (p > 0) {
-                        //         common::logger->error("mu: {}\tlambda: {}\tn: {}\tx:
-                        //         {}\tlogp: {}", mu, lambda, n, x, p); throw
-                        //         std::runtime_error("Fail");
-                        //     }
-                        //     return p;
-                        // }
-
-                        pi2m = expl(p); /* p[i - 2] = p[0]*/
-                        p = logl(mu) + p - log1pl(twophimu2) / 2.0L; /* log p[1] */
-                        lprobs.emplace_back(p);
-                        // if (x == 1.0) {
-                        //     if (p > 0) {
-                        //         common::logger->error("mu: {}\tlambda: {}\tn: {}\tx:
-                        //         {}\tlogp: {}", mu, lambda, n, x, p); throw
-                        //         std::runtime_error("Fail");
-                        //     }
-                        //     return p;
-                        // }
-
-                        pi1m = expl(p); /* p[i - 1] = p[1] */
-                        A = 1.0L / (1.0L + 1.0L / twophimu2); /* constant in first term */
-                        B = mu2 / (1.0L + twophimu2); /* constant in second term */
-                        for (i = 2; i <= n; i++) {
-                            p = A * (1.0L - 1.5L / i) * pi1m + (B * pi2m) / (i * (i - 1));
-                            pi2m = pi1m;
-                            pi1m = p;
-                            lprobs.emplace_back(logl(p));
-                        }
-
-                        return lprobs;
-
-                        // if (p > 1.0) {
-                        //     common::logger->error("mu: {}\tlambda: {}\tn: {}\tx:
-                        //     {}\tlogp: {}", mu, lambda, n, x, logl(p)); throw
-                        //     std::runtime_error("Fail");
-                        // }
-
-                        // return logl(p);
-                    };
-
-                    // long double min_mu = std::min(mu_a, mu_b);
-                    // long double w_a = mu_a / min_mu;
-                    // long double w_b = mu_b / min_mu;
-                    // long double min_lambda = min_mu == mu_a ? lambda_a : lambda_b;
-
-                    long double mu_n = mu_a + mu_b;
-                    // long double lambda_n = min_lambda * pow(w_a + w_b, 2.0);
-
-                    auto get_deviance
-                            = [&](long double x, long double mu, long double lambda) {
-                                  return lambda * lambda * pow(x - mu, 2.0) / x / mu / mu;
-                              };
-
-                    auto get_total_deviance = [&](size_t s) {
-                        if (s == 0 || s == n)
-                            return std::numeric_limits<long double>::max();
-
-                        return get_deviance(s, mu_a, lambda_a)
-                                + get_deviance(n - s, mu_b, lambda_b);
-                    };
-
-                    long double base_lprob = get_lprobs(mu_n, lambda_n).back();
-                    auto lprobs_a = get_lprobs(mu_a, lambda_a);
-                    auto lprobs_b = get_lprobs(mu_b, lambda_b);
-                    auto get_lprob = [&](int64_t s) {
-                        return lprobs_a[s] + lprobs_b[n - s] - base_lprob;
-                        // return get_lp(s, mu_a, lambda_a) + get_lp(n - s, mu_b, lambda_b) - base_lprob;
-                    };
-
-                    long double in_lprob = get_lprob(in_sum);
-                    long double in_deviance = get_total_deviance(in_sum);
-                    pval = 0.0;
-                    eff_size = static_cast<long double>(in_sum) - mu_a / mu_n * n;
-                    for (size_t s = 0; s <= n; ++s) {
-                        long double dev = get_total_deviance(s);
-                        if (dev >= in_deviance) {
-                            long double lprob = get_lprob(s);
-                            pval += expl(lprob);
-                            if (pval >= config.family_wise_error_rate)
-                                break;
-                        }
-                    }
-                    // size_t s = 0;
-                    // for ( ; s <= n; ++s) {
-                    //     long double lprob = get_lprob(s);
-                    //     if (lprob <= in_lprob) {
-                    //         pval += expl(lprob);
-                    //     } else {
-                    //         break;
-                    //     }
-                    // }
-                    // for (size_t t = 0; t <= n; ++t) {
-                    //     size_t sp = n - t;
-                    //     if (sp == s)
-                    //         break;
-                    //     long double lprob = get_lprob(sp);
-                    //     if (lprob <= in_lprob) {
-                    //         pval += expl(lprob);
-                    //     } else {
-                    //         break;
-                    //     }
-                    // }
-
-                    if (pval == 0.0) {
-                        common::logger->trace(
-                                "In: ({},{})\tOut: ({},{})\tNull: ({},{})\tn: "
-                                "{}\tin_sum: {}\tin_lprob: {} = {} + {} - {}",
-                                mu_a, lambda_a, mu_b, lambda_b, mu_n, lambda_n, n, in_sum,
-                                in_lprob, lprobs_a[in_sum], lprobs_b[out_sum],
-                                // get_lp(in_sum, mu_a, lambda_a),
-                                // get_lp(out_sum, mu_b, lambda_b),
-                                base_lprob);
-                        throw std::runtime_error("Fail calc p-val");
-                    }
-
-                    size_t nb_idx = kept.rank1(row_i) - 1;
-                    nb_pvals[nb_idx].first = pval;
-                    nb_pvals[nb_idx].second = row_i;
-                }
-            } else if (config.test_type == "gpoisson") {
-                size_t n = 0;
-                size_t in_sum = 0;
-                size_t out_sum = 0;
-                for (const auto& [j, c] : row) {
-                    n += c;
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                        out_sum += c;
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                        in_sum += c;
-                }
-
-                if (n < n_cutoff)
-                    return;
-
-                if (n != 0) {
-                    size_t mode = 0;
-                    size_t nmode = 0;
-                    const auto& pvals = gp_pvals[n];
-                    for (size_t s = 0; s <= n; ++s) {
-                        if (pvals[s] >= 1.0) {
-                            mode += s;
-                            ++nmode;
-                        }
-                    }
-                    long double midpoint = static_cast<long double>(mode) / nmode;
-                    eff_size = static_cast<long double>(in_sum) - midpoint;
-                    pval = pvals[in_sum];
-                    // const auto &[lambda_a, omega_a] = gp_params_a;
-                    // const auto &[lambda_b, omega_b] = gp_params_b;
-                    // const auto &cprobs = prob_totals[n];
-                    // size_t mode = 0;
-                    // long double last_prob = probs[0];
-                    // long double in_prob = probs[0];
-                    // for (size_t i = 1; i < probs.size(); ++i) {
-                    //     long double cur_prob = probs[i] - probs[i - 1];
-                    //     if (cur_prob > last_prob) {
-                    //         last_prob = cur_prob;
-                    //         mode = i;
-                    //     }
-                    //     if (i == in_sum)
-                    //         in_prob = cur_prob;
-                    // }
-                    // pval = 0.0;
-                    // eff_size = static_cast<ssize_t>(in_sum) - mode;
-                    // long double in_prob = probs[in_sum];
-                    // for (size_t s = 0; s <= mode; ++s) {
-                    //     if (probs[s] <= in_prob)
-                    //         pval +=
-                    // }
-                    // const auto &[lambda_n, omega_n] = gp_params_n;
-                    // long double base = gp_base + lgammal(n + 1) + omega_n * n - logl((1.0 - omega_n)*lambda_n + omega_n*n) * (n - 1);
-                    // auto get_lprob = [&](int64_t s) {
-                    //     int64_t t = n - s;
-                    //     long double lprob = base - lgammal(s + 1) - lgammal(t + 1) - omega_a * s - omega_b * t;
-                    //     if (s == 0) {
-                    //         lprob -= logl(lambda_a) + log1pl(-omega_a);
-                    //     } else if (s > 1) {
-                    //         lprob += logl((1.0 - omega_a) * lambda_a + omega_a*s) * (s - 1);
-                    //     }
-                    //     if (t == 0) {
-                    //         lprob -= logl(lambda_b) + log1pl(-omega_b);
-                    //     } else if (t > 1) {
-                    //         lprob += logl((1.0 - omega_b) * lambda_b + omega_b*t) * (t - 1);
-                    //     }
-                    //     return lprob;
-                    // };
-                    // // {
-                    // //     long double lpval0 = get_lprob(0);
-                    // //     long double lpvaln = get_lprob(n);
-                    // //     long double min_pval = expl(std::min(lpval0, lpvaln)) * (1 + (lpval0 == lpvaln));
-                    // //     if (min_pval * k_min >= config.family_wise_error_rate)
-                    // //         return;
-                    // // }
-                    // pval = 0.0;
-                    // eff_size = static_cast<long double>(in_sum) - lambda_a / lambda_n;
-                    // long double inlp = get_lprob(in_sum);
-                    // for (size_t s = 0; s <= n; ++s) {
-                    //     long double lp = get_lprob(s);
-                    //     if (lp <= inlp)
-                    //         pval += expl(lp);
-
-                    //     if (pval >= k * config.family_wise_error_rate)
-                    //         break;
-                    // }
-                }
             } else if (config.test_type == "nbinom_exact") {
                 // size_t nb_idx = kept.rank1(row_i) - 1;
                 size_t n = 0;
@@ -3205,80 +1891,6 @@ mask_nodes_by_label_dual(
                     //     nb_pvals[nb_idx].first = 1.1;
                     // }
                 }
-            } else if (config.test_type == "hypergeometric"
-                       || config.test_type == "poisson_bayes") {
-                size_t n = 0;
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
-                for (const auto& [j, c] : row) {
-                    n += c;
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                        out_sum += c;
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                        in_sum += c;
-                }
-                if (n != 0) {
-                    sdsl::bit_vector found(groups.size());
-                    auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto& callback) {
-                        for (const auto& [j, c] : row) {
-                            callback(j, c);
-                        }
-                        for (size_t j = 0; j < found.size(); ++j) {
-                            if (!found[j])
-                                callback(j, 0);
-                        }
-                    });
-                    boost::math::binomial_distribution dist(static_cast<long double>(n),
-                                                            mu1 / (mu1 + mu2));
-                    // boost::math::hypergeometric_distribution dist(mu1 * nelem, n, (mu1 + mu2) * nelem);
-                    eff_size = static_cast<double>(in_sum) - boost::math::mode(dist);
-
-                    long double pval0 = boost::math::pdf(dist, 0);
-                    long double pvaln = boost::math::pdf(dist, n);
-                    long double pval_min = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                    if (pval_min * k_min >= config.family_wise_error_rate)
-                        return;
-
-                    long double in_prob = boost::math::pdf(dist, in_sum);
-                    if (eff_size > 0) {
-                        pval = boost::math::cdf(boost::math::complement(dist, in_sum - 1))
-                                + boost::math::cdf(
-                                        dist,
-                                        floor(boost::math::quantile(
-                                                boost::math::complement(dist, in_prob))));
-                    } else if (eff_size < 0) {
-                        pval = boost::math::cdf(dist, in_sum)
-                                + boost::math::cdf(
-                                        dist, ceil(boost::math::quantile(dist, in_prob)));
-                    }
-
-                    // double in_sz = abs(eff_size);
-
-                    // pval = 0.0;
-
-                    // size_t s = 0;
-                    // for ( ; s <= n; ++s) {
-                    //     double sz = abs(static_cast<double>(s) - mean);
-                    //     if (sz < in_sz)
-                    //         break;
-                    // }
-                    // if (s > 0)
-                    //     pval += boost::math::cdf(dist, s - 1);
-
-                    // size_t sp = n;
-                    // for ( ; sp >= s; --sp) {
-                    //     double sz = abs(static_cast<double>(sp) - mean);
-                    //     if (sz < in_sz)
-                    //         break;
-                    // }
-
-                    // if (sp < n)
-                    //     pval += boost::math::cdf(boost::math::complement(dist, sp));
-
-                    if (pval * k >= config.family_wise_error_rate)
-                        return;
-                }
             } else if (config.test_type == "mwu") {
                 auto generate_a = [&](const auto& callback) {
                     sdsl::bit_vector found(groups.size());
@@ -3312,40 +1924,6 @@ mask_nodes_by_label_dual(
                 //     nb_pvals[nb_idx].first = pval;
                 //     nb_pvals[nb_idx].second = row_i;
                 // }
-            } else if (config.test_type == "bm") {
-                auto generate_a = [&](const auto& callback) {
-                    sdsl::bit_vector found(groups.size());
-                    for (const auto& [j, c] : row) {
-                        found[j] = true;
-                        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                            callback(static_cast<long double>(c) / sums[j]);
-                    }
-                    for (size_t j = 0; j < found.size(); ++j) {
-                        if (!found[j] && (groups[j] == Group::IN || groups[j] == Group::BOTH))
-                            callback(0.0L);
-                    }
-                };
-                auto generate_b = [&](const auto& callback) {
-                    sdsl::bit_vector found(groups.size());
-                    for (const auto& [j, c] : row) {
-                        found[j] = true;
-                        if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                            callback(static_cast<long double>(c) / sums[j]);
-                    }
-                    for (size_t j = 0; j < found.size(); ++j) {
-                        if (!found[j] && (groups[j] == Group::OUT || groups[j] == Group::BOTH))
-                            callback(0.0L);
-                    }
-                };
-
-                std::tie(pval, eff_size) = brunner_munzel(generate_a, generate_b);
-
-                // if (pval < config.family_wise_error_rate) {
-                //     size_t nb_idx = kept.rank1(row_i) - 1;
-                //     nb_pvals[nb_idx].first = pval;
-                //     nb_pvals[nb_idx].second = row_i;
-                // }
-
             } else if (config.test_type == "cmh") {
                 // size_t n = 0;
                 int64_t in_sum = 0;
@@ -3438,144 +2016,6 @@ mask_nodes_by_label_dual(
                 //     nb_pvals[nb_idx].first = pval;
                 //     nb_pvals[nb_idx].second = row_i;
                 // }
-            } else if (config.test_type == "bb_hypergeometric") {
-                size_t nb_idx = kept.rank1(row_i) - 1;
-                size_t n = 0;
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
-                for (const auto& [j, c] : row) {
-                    n += c;
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                        out_sum += c;
-                    }
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                        in_sum += c;
-                    }
-                }
-
-                // if (n < n_cutoff)
-                //     return;
-
-                if (n > 0) {
-                    // long double min_pval = 1.0;
-                    const auto& [r_a, r_b, bb_base] = bb_bases[n];
-                    long double r_n = r_a + r_b;
-                    long double p_a = 0.1;
-                    long double p_b = 0.1;
-                    long double p_n = 0.1;
-                    long double min_pval = 1.0;
-                    {
-                        long double l21p = log1p(-p_n) / log2l(2.0);
-                        long double base = bb_base
-                                + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0)
-                                - n * l21p;
-                        auto get_pval = [&](int64_t s) {
-                            int64_t t = n - s;
-                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                                 - lgammal(s + 1) - lgammal(t + 1)
-                                                 + s * log1pl(-p_a) + t * log1pl(-p_b))
-                                    / logl(2.0);
-                            return exp2l(base + sbase);
-                        };
-
-                        long double pval0 = get_pval(0);
-                        long double pvaln = get_pval(n);
-                        min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                    }
-
-                    if (min_pval < config.family_wise_error_rate) {
-                        nb_pvals[nb_idx].second = row_i;
-                        long double midpoint = r_a * n / (r_a + r_b);
-
-                        auto get_deviance_a = [&](long double s) {
-                            if (s == 0)
-                                return r_a * logl(p_a) * -2.0;
-
-                            return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
-                                    + r_a * (logl(r_a) - logl(p_a)))
-                                    * 2.0;
-                        };
-
-                        auto get_deviance_b = [&](long double t) {
-                            if (t == 0)
-                                return r_b * logl(p_b) * -2.0;
-
-                            return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
-                                    + r_b * (logl(r_b) - logl(p_b)))
-                                    * 2.0;
-                        };
-
-                        auto get_deviance = [&](long double s, long double t) {
-                            return get_deviance_a(s) + get_deviance_b(t);
-                        };
-
-                        long double min_dev
-                                = get_deviance(midpoint,
-                                               static_cast<long double>(n) - midpoint);
-                        long double in_dev = get_deviance(in_sum, out_sum);
-
-                        if (in_dev > min_dev) {
-                            eff_size = static_cast<long double>(in_sum) - midpoint;
-                            pval = 0.0;
-                            long double base = bb_base
-                                    + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n))
-                                            / logl(2.0);
-                            double l1pa = log1pl(-p_a) / logl(2.0);
-                            double l1pb = log1pl(-p_b) / logl(2.0);
-
-                            {
-                                size_t s = 0;
-                                size_t t = n;
-                                long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                                     - lgammal(s + 1) - lgammal(t + 1))
-                                                / logl(2.0)
-                                        + s * l1pa + t * l1pb;
-                                pval += exp2(base + sbase);
-                                for (++s; s <= n; ++s) {
-                                    long double dev = get_deviance(s, t - 1);
-                                    if (dev >= in_dev) {
-                                        sbase += log2l(r_a + s) - log2l(r_b + t)
-                                                - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
-                                        --t;
-                                        pval += exp2(base + sbase);
-                                        if (pval >= config.family_wise_error_rate)
-                                            break;
-                                    } else {
-                                        break;
-                                    }
-                                }
-                            }
-                            if (pval < config.family_wise_error_rate
-                                && get_deviance(n, 0) >= in_dev) {
-                                size_t s = n;
-                                size_t t = 0;
-                                long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                                     - lgammal(s + 1) - lgammal(t + 1))
-                                                / logl(2.0)
-                                        + s * l1pa + t * l1pb;
-                                pval += exp2(base + sbase);
-                                for (++t; t <= n; ++t) {
-                                    long double dev = get_deviance(s - 1, t);
-                                    if (dev >= in_dev) {
-                                        sbase -= log2l(r_a + s) - log2l(r_b + t)
-                                                - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
-                                        --s;
-                                        pval += exp2(base + sbase);
-                                        if (pval >= config.family_wise_error_rate)
-                                            break;
-                                    } else {
-                                        break;
-                                    }
-                                }
-                            }
-                            nb_pvals[nb_idx].first = pval;
-                        }
-                    } else {
-                        nb_pvals[nb_idx].second = row_i;
-                        nb_pvals[nb_idx].first = 1.1;
-                    }
-                }
             }
 
             set_pval(pval, eff_size);
@@ -3648,24 +2088,6 @@ mask_nodes_by_label_dual(
             common::logger->trace("Sorting {} / {} p-vals", nb_pvals.size(), num_tests);
             std::sort(nb_pvals.begin(), nb_pvals.end());
 
-            // size_t k = 0;
-            // for (size_t i = 0; i < nb_pvals.size(); ++i) {
-            //     const auto& [pval, row_i] = nb_pvals[i];
-            //     if (pval >= config.family_wise_error_rate)
-            //         break;
-
-            //     if (pval * (num_tests - i) >= config.family_wise_error_rate) {
-            //         node_index node =
-            //         AnnotatedDBG::anno_to_graph_index(nb_pvals[i].second);
-            //         indicator_in[node] = false; indicator_out[node] = false;
-            //     } else {
-            //         ++k;
-            //     }
-            // }
-            // common::logger->trace(
-            //         "Found {} / {} significant p-values. Minimum p-value is {}", k,
-            //         nb_pvals.size(), nb_pvals[0].first);
-
             long double harm = 0.0;
             for (size_t i = 1; i <= num_tests; ++i) {
                 harm += 1.0 / i;
@@ -3717,538 +2139,11 @@ mask_nodes_by_label_dual(
                 indicator_out[node] = false;
             }
         }
-        // if (config.test_by_unitig) {
-        //     common::logger->trace("Combining p-values for unitigs");
-        //     std::mutex m_vec_mu;
-        //     clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-        //         long double pval_min = 1.0;
-        //         {
-        //             size_t row = AnnotatedDBG::graph_to_anno_index(path[0]);
-        //             size_t row_rank = kept.rank1(row);
-        //             size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
-        //             size_t offset = row_rank - offsets[bucket_idx];
-        //             std::lock_guard<std::mutex> lock(m_vec_mu);
-        //             pval_min = get<PValStorage>(pvals_min_buckets[bucket_idx][offset]);
-        //         }
-
-        //         if (pval_min * k_min >= config.family_wise_error_rate)
-        //             return;
-
-        //         std::vector<long double> pvals;
-        //         pvals.reserve(path.size());
-        //         long double comb_eff_size = 0.0;
-        //         {
-        //             std::lock_guard<std::mutex> lock(m_vec_mu);
-        //             for (node_index node : path) {
-        //                 size_t row = AnnotatedDBG::graph_to_anno_index(node);
-        //                 size_t row_rank = kept.rank1(row);
-        //                 size_t bucket_idx = std::lower_bound(offsets.begin(), offsets.end(), row_rank) - offsets.begin() - 1;
-        //                 size_t offset = row_rank - offsets[bucket_idx];
-        //                 pvals.emplace_back(get<PValStorage>(pvals_buckets[bucket_idx][offset]));
-        //                 comb_eff_size += get<PValStorage>(eff_size_buckets[bucket_idx][offset]);
-        //             }
-        //         }
-        //         long double comp_pval = combine_pvals(pvals);
-        //         if (comp_pval * k >= config.family_wise_error_rate || comb_eff_size == 0.0)
-        //             return;
-
-        //         auto &indicator = comb_eff_size > 0 ? indicator_in : indicator_out;
-        //         for (node_index node : path) {
-        //             set_bit(indicator.data(), node, true, std::memory_order_relaxed);
-        //         }
-        //     });
-
-        //     pvals_min_buckets.resize(0);
-        //     tmp_min_buckets.resize(0);
-        //     pvals_buckets.resize(0);
-        //     tmp_buckets.resize(0);
-        //     eff_size_buckets.resize(0);
-        //     tmp_eff_buckets.resize(0);
-        // }
     }
 
-    // common::logger->trace("Computing minimum p-values");
-    // std::vector<std::pair<double, size_t>> m;
-    // std::vector<int64_t> m_sums;
-    // {
-    //     std::vector<std::vector<std::pair<double, size_t>>> ms(num_threads + 1);
-    //     std::vector<std::vector<tsl::hopscotch_map<std::vector<int64_t>,
-    //     std::pair<double, size_t>, utils::VectorHash>>> ms_vecs(num_threads + 1);
-    //     generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
-    //         int64_t in_sum = 0;
-    //         int64_t out_sum = 0;
-    //         int64_t out_stat_int = 0;
-    //         bool in_kmer = false;
-    //         bool out_kmer = false;
-    //         PairContainer row;
-    //         std::vector<int64_t> vals;
-    //         if (kept[row_i]) {
-    //             size_t count_in = 0;
-    //             size_t count_out = 0;
-    //             size_t total_count = 0;
-    //             for (const auto &[j, raw_c] : raw_row) {
-    //                 if (min_counts.size() && raw_c < min_counts[j])
-    //                     continue;
-
-    //                 uint64_t c = raw_c;
-    //                 if (count_maps.size())
-    //                     c = count_maps[j].find(c)->second.first;
-
-    //                 row.emplace_back(j, c);
-    //                 vals.emplace_back(c);
-    //                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-    //                     out_sum += c;
-    //                     ++count_out;
-    //                 }
-
-    //                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-    //                     in_sum += c;
-    //                     ++count_in;
-    //                 }
-
-    //                 total_count += groups[j] != Group::OTHER;
-    //             }
-
-    //             if (total_count >= config.min_recurrence) {
-    //                 double out_stat = static_cast<double>(out_sum) / out_kmers *
-    //                 in_kmers; if (out_stat != 0)
-    //                     out_stat_int = out_stat > 0 ? ceil(out_stat) : floor(out_stat);
-
-    //                 in_kmer = count_in >= config.min_in_recurrence && count_out <=
-    //                 config.max_out_recurrence && in_sum > (out_kmers > 0 ? out_stat :
-    //                 0.0); out_kmer = count_out >= config.min_out_recurrence && count_in
-    //                 <= config.max_in_recurrence && in_sum < out_stat;
-    //             }
-    //         } else {
-    //             return;
-    //         }
-
-    //         size_t n = config.test_type != "poisson_binom" ? in_sum + out_sum :
-    //         row.size(); if (!in_kmer && !out_kmer) {
-    //             row.clear();
-    //             vals.clear();
-    //             n = 0;
-    //         }
-
-    //         double pval_min = 0;
-    //         if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact") {
-    //             assert(bucket_idx < ms.size());
-    //             if (n >= ms[bucket_idx].size())
-    //                 ms[bucket_idx].resize(n + 1, std::make_pair(1.1, 0));
-
-    //             if (ms[bucket_idx][n].first == 1.1)
-    //                 ms[bucket_idx][n].first = compute_min_pval(n, row);
-
-    //             pval_min = ms[bucket_idx][n].first;
-    //             ++ms[bucket_idx][n].second;
-    //         } else {
-    //             std::sort(vals.begin(), vals.end());
-    //             if (n >= ms_vecs[bucket_idx].size())
-    //                 ms_vecs[bucket_idx].resize(n + 1);
-
-    //             auto find = ms_vecs[bucket_idx][n].find(vals);
-    //             if (find == ms_vecs[bucket_idx][n].end()) {
-    //                 find = ms_vecs[bucket_idx][n].try_emplace(
-    //                     vals,
-    //                     std::make_pair(compute_min_pval(n, row), 1)
-    //                 ).first;
-    //             } else {
-    //                 ++find.value().second;
-    //             }
-
-    //             pval_min = find->second.first;
-    //         }
-
-    //         if (config.test_by_unitig) {
-    //             bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
-    //             uint64_t eff_size = bit_cast<uint64_t, int64_t>(in_sum - out_stat_int);
-    //             if constexpr(preallocated) {
-    //                 node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-    //                 eff_size_buckets[bucket_idx][node] = eff_size;
-    //                 sum_buckets[bucket_idx][node] = n;
-    //                 if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact")
-    //                     pvals_min_buckets[bucket_idx][node] = bit_cast<uint64_t>(pval_min);
-    //             } else {
-    //                 eff_size_buckets[bucket_idx].push_back(eff_size);
-    //                 sum_buckets[bucket_idx].push_back(n);
-    //                 if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact")
-    //                     pvals_min_buckets[bucket_idx].push_back(bit_cast<uint64_t>(pval_min));
-    //             }
-    //         }
-    //     });
-
-    //     common::logger->trace("Merging min p-value tables");
-    //     if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact") {
-    //         for (size_t i = 1; i < ms.size(); ++i) {
-    //             size_t end = std::min(ms[0].size(), ms[i].size());
-    //             for (size_t j = 0; j < end; ++j) {
-    //                 assert(ms[0][j].first == 1.1 || ms[i][j].first == 1.1 || ms[0][j]
-    //                 == ms[i][j]); ms[0][j].first = std::min(ms[0][j].first,
-    //                 ms[i][j].first); ms[0][j].second += ms[i][j].second;
-    //             }
-
-    //             if (ms[i].size() > ms[0].size()) {
-    //                 std::copy(std::make_move_iterator(ms[i].begin() + ms[0].size()),
-    //                         std::make_move_iterator(ms[i].end()),
-    //                         std::back_inserter(ms[0]));
-    //             }
-    //         }
-    //         m = std::move(ms[0]);
-    //         ms.resize(0);
-    //     } else {
-    //         std::vector<std::tuple<double, int64_t, size_t>> mn;
-    //         for (size_t i = 0; i < ms_vecs.size(); ++i) {
-    //             for (size_t n = 0; n < ms_vecs[i].size(); ++n) {
-    //                 for (const auto &[vec, v] : ms_vecs[i][n]) {
-    //                     mn.emplace_back(v.first, n, v.second);
-    //                 }
-    //             }
-    //         }
-    //         ms_vecs.resize(0);
-    //         std::sort(mn.begin(), mn.end(), utils::GreaterFirst());
-
-    //         m.reserve(mn.size());
-    //         m_sums.reserve(mn.size());
-    //         for (const auto &[p, s, c] : mn) {
-    //             m.emplace_back(p, c);
-    //             m_sums.emplace_back(s);
-    //         }
-    //     }
-    // }
-
-    // size_t k = 0;
-    // size_t n_cutoff = 1;
-
-    // auto combine_pvals = [dist=boost::math::cauchy()](const std::vector<double> &pvals) {
-    //     double stat = 0.0;
-    //     for (double pval : pvals) {
-    //         stat += tan((0.5 - pval) * M_PI);
-    //     }
-    //     stat /= pvals.size();
-    //     return boost::math::cdf(boost::math::complement(dist, stat));
-    // };
-
-    // std::vector<size_t> boundaries;
-    // if (config.test_by_unitig) {
-    //     boundaries.reserve(eff_size_buckets.size() + 1);
-    //     boundaries.emplace_back(0);
-    //     for (size_t i = 0; i < eff_size_buckets.size(); ++i) {
-    //         boundaries.emplace_back(boundaries.back() + eff_size_buckets[i].size());
-    //     }
-    // }
-
-    // std::mutex pval_mu;
-    // std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
-    // sdsl::bit_vector unitig_start;
-    // if (!config.test_by_unitig) {
-    //     std::tie(k, n_cutoff) = correct_pvals(m, nelem, m_sums);
-    //     common::logger->trace("Picked: k: {}\tn: {}\tpval_min: {}", k, n_cutoff,
-    //     config.family_wise_error_rate / k);
-    // } else {
-    //     clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
-    //         graph_ptr,
-    //         [&](node_index node) {
-    //             return node != DeBruijnGraph::npos
-    //                     && kept[AnnotatedDBG::graph_to_anno_index(node)];
-    //         },
-    //         true,
-    //         is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC
-    //     );
-
-    //     std::vector<std::pair<double, int64_t>> comb_msums;
-    //     common::logger->trace("Allocating updated indicator");
-    //     unitig_start = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-
-    //     common::logger->trace("Combining min p-vals");
-    //     clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-    //         std::vector<size_t> bucket_idxs;
-    //         bucket_idxs.reserve(path.size());
-    //         for (node_index node : path) {
-    //             bucket_idxs.emplace_back(0);
-    //             for (size_t i = 1; i < boundaries.size(); ++i) {
-    //                 if (boundaries[i] > node)
-    //                     break;
-
-    //                 ++bucket_idxs.back();
-    //             }
-    //         }
-
-    //         std::vector<double> pvals_min;
-    //         pvals_min.reserve(path.size());
-
-    //         int64_t n_sum = 0;
-    //         {
-    //             std::lock_guard<std::mutex> lock(pval_mu);
-    //             for (size_t i = 0; i < path.size(); ++i) {
-    //                 size_t bucket_idx = bucket_idxs[i];
-    //                 size_t node_shift = path[i] - boundaries[bucket_idx];
-    //                 size_t n = sum_buckets[bucket_idx][node_shift];
-    //                 n_sum += n;
-
-    //                 if (config.test_type == "gnb_exact" || config.test_type == "lnb_exact") {
-    //                     pvals_min.emplace_back(bit_cast<double, uint64_t>(pvals_min_buckets[bucket_idx][node_shift]));
-    //                 } else {
-    //                     pvals_min.emplace_back(m[n].first);
-    //                 }
-    //             }
-    //         }
-
-    //         double comb_pval = *std::min_element(pvals_min.begin(), pvals_min.end());
-    //         if (comb_pval < config.family_wise_error_rate) {
-    //             for (node_index node : path) {
-    //                 set_bit(unitig_start.data(), node, parallel, MO_RELAXED);
-    //             }
-    //         }
-
-    //         std::lock_guard<std::mutex> lock(pval_mu);
-    //         comb_msums.emplace_back(comb_pval, n_sum);
-    //     }, num_threads);
-
-    //     std::sort(comb_msums.begin(), comb_msums.end(), utils::GreaterFirst());
-    //     std::vector<std::pair<double, size_t>> comb_m;
-    //     std::vector<int64_t> comb_m_sums;
-    //     comb_m.reserve(comb_msums.size());
-    //     comb_m_sums.reserve(comb_msums.size());
-    //     for (const auto &[p, s] : comb_msums) {
-    //         comb_m.emplace_back(p, 1);
-    //         comb_m_sums.emplace_back(s);
-    //     }
-
-    //     std::tie(k, n_cutoff) = correct_pvals(comb_m, nelem, comb_m_sums);
-    //     common::logger->trace("Picked: k: {}\tpval_min: {}", k, config.family_wise_error_rate / k);
-
-    //     sum_buckets.resize(0);
-    //     tmp_sum_buckets.resize(0);
-    //     pvals_min_buckets.resize(0);
-    //     tmp_min_buckets.resize(0);
-    // }
-
-    // if (k > 0) {
-    //     common::logger->trace("Running differential tests");
-    //     std::exception_ptr ex = nullptr;
-    //     std::mutex ex_mu;
-    //     bool keep_all_pvals = config.output_pvals || config.test_by_unitig;
-    //     std::atomic_thread_fence(std::memory_order_release);
-
-    //     generate_rows([&](uint64_t row_i, const auto &raw_row, size_t bucket_idx) {
-    //         if (ex)
-    //             return;
-
-    //         int64_t in_sum = 0;
-    //         int64_t out_sum = 0;
-    //         bool in_kmer = false;
-    //         bool out_kmer = false;
-    //         PairContainer row;
-    //         bucket_idx = std::min(bucket_idx, pvals_buckets.size() - 1);
-    //         node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-    //         double pval = 1.1;
-    //         if (kept[row_i]) {
-    //             size_t count_in = 0;
-    //             size_t count_out = 0;
-    //             size_t total_count = 0;
-    //             for (const auto &[j, raw_c] : raw_row) {
-    //                 if (min_counts.size() && raw_c < min_counts[j])
-    //                     continue;
-
-    //                 uint64_t c = raw_c;
-    //                 if (count_maps.size())
-    //                     c = count_maps[j].find(c)->second.first;
-
-    //                 row.emplace_back(j, c);
-    //                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-    //                     out_sum += c;
-    //                     ++count_out;
-    //                 }
-
-    //                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-    //                     in_sum += c;
-    //                     ++count_in;
-    //                 }
-
-    //                 total_count += groups[j] != Group::OTHER;
-    //             }
-
-    //             if (total_count >= config.min_recurrence) {
-    //                 double out_stat = static_cast<double>(out_sum) / out_kmers *
-    //                 in_kmers; in_kmer = count_in >= config.min_in_recurrence &&
-    //                 count_out <= config.max_out_recurrence && in_sum > (out_kmers > 0 ?
-    //                 out_stat : 0.0); out_kmer = count_out >= config.min_out_recurrence
-    //                 && count_in <= config.max_in_recurrence && in_sum < out_stat;
-    //             }
-    //         } else {
-    //             if (keep_all_pvals) {
-    //                 auto &pvals = pvals_buckets[bucket_idx];
-    //                 if constexpr(preallocated) {
-    //                     pvals[node] = bit_cast<uint64_t>(pval);
-    //                 } else {
-    //                     pvals.push_back(bit_cast<uint64_t>(pval));
-    //                 }
-    //             }
-    //             return;
-    //         }
-
-    //         size_t n = config.test_type != "poisson_binom" ? in_sum + out_sum : row.size();
-
-    //         if (!in_kmer && !out_kmer) {
-    //             row.clear();
-    //             n = 0;
-    //         }
-
-    //         if (!config.output_pvals && config.test_by_unitig && !unitig_start[node]) {
-    //             auto &pvals = pvals_buckets[bucket_idx];
-    //             if constexpr(preallocated) {
-    //                 pvals[node] = bit_cast<uint64_t>(pval);
-    //             } else {
-    //                 pvals.push_back(bit_cast<uint64_t>(pval));
-    //             }
-    //         }
-
-    //         if (config.output_pvals
-    //                 || (!config.test_by_unitig && n >= n_cutoff)
-    //                 || (config.test_by_unitig && unitig_start[node])) {
-    //             if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact" && m[n].first == 1.1) {
-    //                 common::logger->error("in: {}\tout: {}\tn: {}", in_sum, out_sum, n);
-    //                 throw std::runtime_error("Indexing invalid");
-    //             }
-
-    //             try {
-    //                 pval = compute_pval(in_sum, out_sum, row);
-    //             } catch (...) {
-    //                 std::lock_guard<std::mutex> lock(ex_mu);
-    //                 ex = std::current_exception();
-    //                 return;
-    //             }
-
-    //             if (config.test_type != "gnb_exact" && config.test_type != "lnb_exact"
-    //             && pval < 1.1 && m[n].first - pval > 1e-10) {
-    //                 common::logger->error("Min p-val estimate too high: min {} > cur
-    //                 {}\tn: {}\ttest: {}", m[n].first, pval, n, config.test_type); throw
-    //                 std::runtime_error("Test failed");
-    //             }
-
-    //             if (config.test_type != "notest" && keep_all_pvals) {
-    //                 auto &pvals = pvals_buckets[bucket_idx];
-    //                 if constexpr(preallocated) {
-    //                     pvals[node] = bit_cast<uint64_t>(pval);
-    //                 } else {
-    //                     pvals.push_back(bit_cast<uint64_t>(pval));
-    //                 }
-    //             }
-
-    //             if (!config.test_by_unitig && in_kmer != out_kmer && pval * k <
-    //             config.family_wise_error_rate) {
-    //                 bool use_atomic = parallel && (node % 64 == 0);
-    //                 set_bit((in_kmer ? indicator_in : indicator_out).data(), node,
-    //                 use_atomic, MO_RELAXED);
-    //             }
-    //         }
-    //     });
-
-    //     std::atomic_thread_fence(std::memory_order_acquire);
-
-    //     if (ex)
-    //         std::rethrow_exception(ex);
-
-    //     unitig_start = sdsl::bit_vector();
-    //     deallocate();
-
-    //     if (!config.test_by_unitig) {
-    //         boundaries.reserve(pvals_buckets.size() + 1);
-    //         boundaries.emplace_back(0);
-    //         for (size_t i = 0; i < pvals_buckets.size(); ++i) {
-    //             boundaries.emplace_back(boundaries.back() + pvals_buckets[i].size());
-    //         }
-    //     }
-
-    //     if (config.test_by_unitig) {
-    //         common::logger->trace("Allocating k-mer bitmasks");
-    //         indicator_in = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-    //         indicator_out = sdsl::bit_vector(graph_ptr->max_index() + 1, false);
-
-    //         common::logger->trace("Combining p-values within unitigs");
-    //         std::atomic<uint64_t> num_sig_unitigs{0};
-
-    //         std::atomic_thread_fence(std::memory_order_release);
-    //         clean_masked_graph->call_unitigs([&](const std::string&, const auto &path) {
-    //             int64_t comb_eff_size = 0;
-    //             std::vector<size_t> bucket_idxs;
-    //             bucket_idxs.reserve(path.size());
-    //             for (node_index node : path) {
-    //                 bucket_idxs.emplace_back(0);
-    //                 for (size_t i = 1; i < boundaries.size(); ++i) {
-    //                     if (boundaries[i] > node)
-    //                         break;
-
-    //                     ++bucket_idxs.back();
-    //                 }
-    //             }
-
-    //             // TODO: don't bother combining if the row sum is too small
-    //             std::vector<double> pvals;
-    //             pvals.reserve(path.size());
-    //             {
-    //                 std::lock_guard<std::mutex> lock(pval_mu);
-    //                 for (size_t i = 0; i < path.size(); ++i) {
-    //                     node_index node = path[i];
-    //                     size_t bucket_idx = bucket_idxs[i];
-    //                     size_t node_shift = node - boundaries[bucket_idx];
-
-    //                     comb_eff_size += bit_cast<int64_t, uint64_t>(eff_size_buckets[bucket_idx][node_shift]);
-
-    //                     double pval = bit_cast<double, uint64_t>(pvals_buckets[bucket_idx][node_shift]);
-    //                     pvals.emplace_back(pval);
-    //                 }
-    //             }
-    //             double comb_pval = combine_pvals(pvals);
-    //             if (comb_pval * k < config.family_wise_error_rate && comb_eff_size != 0) {
-    //                 ++num_sig_unitigs;
-    //                 auto *data = comb_eff_size > 0 ? indicator_in.data() : indicator_out.data();
-    //                 for (node_index node : path) {
-    //                     set_bit(data, node, parallel, MO_RELAXED);
-    //                 }
-    //             }
-
-    //             uint64_t comb_pval_enc = bit_cast<uint64_t, double>(comb_pval);
-
-    //             std::lock_guard<std::mutex> lock(pval_mu);
-    //             for (size_t i = 0; i < path.size(); ++i) {
-    //                 size_t bucket_idx = bucket_idxs[i];
-    //                 pvals_buckets[bucket_idx][path[i] - boundaries[bucket_idx]] = comb_pval_enc;
-    //             }
-    //         }, num_threads);
-    //         std::atomic_thread_fence(std::memory_order_acquire);
-    //         common::logger->trace("Found {} significant unitigs before correction", num_sig_unitigs);
-
-    //         eff_size_buckets.resize(0);
-    //         tmp_eff_buckets.resize(0);
-    //     }
-    // } else {
-    //     deallocate();
-    // }
-
     std::unique_ptr<utils::TempFile> tmp_file;
     PValStorage pvals;
 
-    // if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-    //     pvals = std::move(pvals_buckets[0]);
-    // }
-
-    // if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-    //     if (config.output_pvals) {
-    //         common::logger->trace("Merging buckets");
-    //         for (size_t i = 1; i < pvals_buckets.size(); ++i) {
-    //             for (size_t j = 0; j < pvals_buckets[i].size(); ++j) {
-    //                 pvals_buckets[0].push_back(pvals_buckets[i][j]);
-    //             }
-    //             pvals_buckets[i].close();
-    //         }
-    //     }
-    //     pvals = std::move(pvals_buckets[0]);
-    //     pvals_buckets.resize(0);
-    //     tmp_file = std::move(tmp_buckets[0]);
-    //     tmp_buckets.resize(0);
-    // }
-
     common::logger->trace("Done! Assembling contigs.");
 
     auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(

From 678db49b2e04390c026ca7d0a0c90f603598ef30 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Mar 2025 15:41:56 +0100
Subject: [PATCH 388/410] more cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 403 ------------------
 1 file changed, 403 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6433b81210..d2ed2a9b66 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -303,9 +303,6 @@ mask_nodes_by_label_dual(
     std::vector<size_t> min_counts(groups.size(), config.min_count);
     std::vector<uint64_t> check_cutoff(groups.size(), std::numeric_limits<uint64_t>::max());
 
-    // std::mutex agg_mu;
-    // using PairContainer = std::vector<std::pair<uint64_t, value_type>>;
-
     if (config.clean) {
         common::logger->trace("Computing histogram from uncleaned counts");
         auto hists_map = get_hist_map(std::vector<size_t>(groups.size(), 1), nullptr);
@@ -362,7 +359,6 @@ mask_nodes_by_label_dual(
     common::logger->trace("Marking discarded k-mers");
     auto hists_map = get_hist_map(min_counts, &kept_bv);
     std::unique_ptr<MaskedDeBruijnGraph> clean_masked_graph;
-    // bit_vector_stat kept(std::move(kept_bv));
     bit_vector_stat kept(kept_bv);
 
     clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
@@ -373,86 +369,6 @@ mask_nodes_by_label_dual(
             },
             true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
-    // common::logger->trace("Filtering low-complexity k-mers");
-    // std::atomic_thread_fence(std::memory_order_release);
-    // clean_masked_graph->call_sequences([&](const std::string &contig, const auto &path) {
-    //     for (size_t i = 0; i < path.size(); ++i) {
-    //         if (is_low_complexity(std::string_view(contig.c_str() + i, graph_ptr->get_k())))
-    //             unset_bit(kept_bv.data(), AnnotatedDBG::graph_to_anno_index(path[i]), true, std::memory_order_relaxed);
-    //     }
-    // }, num_threads);
-    // std::atomic_thread_fence(std::memory_order_acquire);
-    // kept = bit_vector_stat(std::move(kept_bv));
-
-
-    // common::logger->trace("Allocating p-value storage");
-    // auto nullpval = bit_cast<uint64_t>(double(1.1));
-
-    // std::vector<PValStorage> mean_buckets;
-    // std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
-    // if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-    //     auto &means = mean_buckets.emplace_back();
-    //     means.resize(graph_ptr->max_index() + 1, 0);
-    // }
-
-    // if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-    //     for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-    //         auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-    //         mean_buckets.emplace_back(tmp_file->name(), std::ios::out);
-    //     }
-    //     mean_buckets[0].push_back(0);
-    // }
-
-    // std::vector<PValStorage> pvals_buckets;
-    // std::vector<std::unique_ptr<utils::TempFile>> tmp_buckets;
-    // std::vector<PValStorage> pvals_min_buckets;
-    // std::vector<std::unique_ptr<utils::TempFile>> tmp_min_buckets;
-    // std::vector<PValStorage> eff_size_buckets;
-    // std::vector<std::unique_ptr<utils::TempFile>> tmp_eff_buckets;
-    // // constexpr bool preallocated = std::is_same_v<PValStorage, std::vector<uint64_t>>;
-
-    // if (config.test_type != "notest" && config.test_by_unitig) {
-    //     if constexpr(std::is_same_v<PValStorage, std::vector<uint64_t>>) {
-    //         auto &pvals = pvals_buckets.emplace_back();
-    //         pvals.reserve(graph_ptr->max_index());
-
-    //         auto &pvals_min = pvals_min_buckets.emplace_back();
-    //         pvals_min.reserve(graph_ptr->max_index());
-
-    //         auto &eff_size = eff_size_buckets.emplace_back();
-    //         eff_size.reserve(graph_ptr->max_index());
-    //     }
-
-    //     if constexpr(std::is_same_v<PValStorage, sdsl::int_vector_buffer<64>>) {
-    //         for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-    //             auto &tmp_file = tmp_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-    //             pvals_buckets.emplace_back(tmp_file->name(), std::ios::out);
-    //         }
-
-    //         for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-    //             auto &tmp_file = tmp_min_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-    //             pvals_min_buckets.emplace_back(tmp_file->name(), std::ios::out);
-    //         }
-
-    //         for (size_t i = 0; i < get_num_threads() + 1; ++i) {
-    //             auto &tmp_file_eff = tmp_eff_buckets.emplace_back(std::make_unique<utils::TempFile>(tmp_dir));
-    //             eff_size_buckets.emplace_back(tmp_file_eff->name(), std::ios::out);
-    //         }
-    //     }
-    // }
-
-    // if ((config.test_type == "gnb_exact" || config.test_type == "lnb_exact") && config.test_by_unitig) {
-
-    // }
-
-
-    // std::vector<PValStorage> sum_buckets;
-    // std::vector<std::unique_ptr<utils::TempFile>> tmp_sum_buckets;
-    // if (config.test_by_unitig) {
-    //     common::logger->trace("Allocating effect size and row sum storage");
-
-    // }
-
     sdsl::bit_vector indicator_in;
     sdsl::bit_vector indicator_out;
 
@@ -494,91 +410,11 @@ mask_nodes_by_label_dual(
 
             if (in_kmer || out_kmer) {
                 callback(row_i, row, bucket_idx);
-                // std::vector<std::pair<size_t, size_t>> new_row;
-                // for (const auto &[j, c] : row) {
-                //     if (c < 1024)
-                //         new_row.emplace_back(j, c);
-                // }
-                // callback(row_i, new_row, bucket_idx);
             }
         });
     };
 
-    // common::logger->trace("Marking low-complexity k-mers");
-    // std::atomic_thread_fence(std::memory_order_release);
-    // clean_masked_graph->call_sequences([&](const std::string &contig, const auto &path) {
-    //     for (size_t i = 0; i < path.size(); ++i) {
-    //         std::string_view kmer(contig.c_str() + i, clean_masked_graph->get_k());
-    //         if (is_low_complexity(kmer)) {
-    //             auto row = AnnotatedDBG::graph_to_anno_index(path[i]);
-    //             unset_bit(kept_bv.data(), row, true, std::memory_order_relaxed);
-    //         }
-    //     }
-    // }, num_threads);
-    // std::atomic_thread_fence(std::memory_order_acquire);
-
-    // common::logger->trace("Discarding counts from low-complexity k-mers");
-    // {
-    //     std::vector<std::vector<VectorMap<uint64_t, size_t>>> hist_maps_m(num_threads +
-    //     1); for (auto &hist_maps : hist_maps_m) {
-    //         hist_maps.resize(groups.size());
-    //     }
-    //     std::atomic_thread_fence(std::memory_order_release);
-    //     generate_clean_rows([&](auto row_i, const auto &row, size_t bucket_idx) {
-    //         if (fetch_bit(kept_bv.data(), row_i, true, std::memory_order_acquire)) {
-    //             if (row.empty()) {
-    //                 unset_bit(kept_bv.data(), row_i, true, std::memory_order_release);
-    //             } else {
-    //                 sdsl::bit_vector found(groups.size());
-    //                 for (const auto &[j, c] : row) {
-    //                     found[j] = true;
-    //                     ++hist_maps_m[bucket_idx][j][c];
-    //                 }
-    //                 for (size_t j = 0; j < found.size(); ++j) {
-    //                     if (!found[j])
-    //                         ++hist_maps_m[bucket_idx][j][0];
-    //                 }
-    //             }
-    //         }
-    //     });
-    //     std::atomic_thread_fence(std::memory_order_acquire);
-    //     std::swap(hists_map, hist_maps_m[0]);
-    //     for (size_t i = 1; i < hist_maps_m.size(); ++i) {
-    //         for (size_t j = 0; j < hist_maps_m[i].size(); ++j) {
-    //             for (const auto &[k, c] : hist_maps_m[i][j]) {
-    //                 hists_map[j][k] += c;
-    //             }
-    //         }
-    //     }
-    // }
-
-    // std::atomic_thread_fence(std::memory_order_release);
-    // generate_rows([&](uint64_t row_i, const auto &row, size_t) {
-    //     if (kept[row_i] && !kept_bv[row_i]) {
-    //         sdsl::bit_vector found(groups.size());
-    //         for (const auto &[j, c] : row) {
-    //             found[j] = true;
-    //             auto find = hists_map[j].find(c);
-    //             assert(find != hists_map[j].end());
-    //             uint64_t prev = __atomic_fetch_sub(&find.value(), 1,
-    //             std::memory_order_relaxed); std::ignore = prev; assert(prev);
-    //         }
-    //         for (size_t j = 0; j < found.size(); ++j) {
-    //             if (!found[j]) {
-    //                 auto find = hists_map[j].find(0);
-    //                 assert(find != hists_map[j].end());
-    //                 uint64_t prev = __atomic_fetch_sub(&find.value(), 1,
-    //                 std::memory_order_relaxed); std::ignore = prev; assert(prev);
-    //             }
-    //         }
-    //     }
-    // });
-    // std::atomic_thread_fence(std::memory_order_acquire);
-    // size_t old_nelem = kept.num_set_bits();
-    // kept = bit_vector_stat(std::move(kept_bv));
-
     size_t nelem = kept.num_set_bits();
-    // common::logger->trace("Kept {} / {} high-complexity k-mers", nelem, old_nelem);
 
     for (size_t j = 0; j < groups.size(); ++j) {
         size_t total_c = 0;
@@ -652,7 +488,6 @@ mask_nodes_by_label_dual(
 
     std::vector<size_t> kmer_to_unitig;
     std::vector<std::tuple<size_t, size_t, size_t>> counts;
-    // std::vector<std::pair<size_t, std::vector<uint64_t>>> agg_counts;
     clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
             graph_ptr,
             [&](node_index node) {
@@ -661,115 +496,6 @@ mask_nodes_by_label_dual(
             },
             true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
     if (config.test_by_unitig && config.test_type != "notest") {
-        // using RowStorage = tsl::hopscotch_map<
-        //         PairContainer, std::pair<long double, std::vector<size_t>>, PairVectorHash>;
-        // RowStorage row_storage;
-        // std::mutex mu;
-        // generate_clean_rows([&](auto row_i, const auto& row, size_t) {
-        //     // std::lock_guard<std::mutex> lock(mu);
-
-        // });
-
-        // std::vector<size_t> kmer_to_unitig(nelem, nelem);
-        // using IDMap = tsl::hopscotch_map<size_t, size_t>;
-        // std::vector<std::tuple<size_t, size_t, long double, long double, IDMap>> unitig_sizes;
-        // {
-        //     std::mutex mu;
-        //     std::atomic_thread_fence(std::memory_order_release);
-        //     clean_masked_graph->call_unitigs([&](const std::string&, const auto& path) {
-        //         size_t unitig_id;
-        //         {
-        //             std::lock_guard<std::mutex> lock(mu);
-        //             unitig_id = unitig_sizes.size();
-        //             auto& [size, sofar, pval, eff_size, bucket]
-        //                     = unitig_sizes.emplace_back();
-        //             size = path.size();
-        //             pval = 1.0;
-        //         }
-        //         for (node_index node : path) {
-        //             auto row_i = AnnotatedDBG::graph_to_anno_index(node);
-        //             size_t idx = kept.rank1(row_i) - 1;
-        //             kmer_to_unitig[idx] = unitig_id;
-        //         }
-        //     });
-        //     std::atomic_thread_fence(std::memory_order_acquire);
-
-
-        //     VectorSet<PairContainer, PairVectorHash> unique_rows;
-
-        //     std::atomic_thread_fence(std::memory_order_release);
-        //     generate_clean_rows([&](auto row_i, const auto& row, size_t) {
-        //         size_t idx = kept.rank1(row_i) - 1;
-        //         size_t unitig_id = kmer_to_unitig[idx];
-        //         auto& [unitig_size, unitig_sofar, pval, eff_size, unitig_bucket]
-        //                 = unitig_sizes[unitig_id];
-        //         size_t added
-        //                 = __atomic_add_fetch(&unitig_sofar, 1, std::memory_order_acq_rel);
-        //         {
-        //             std::lock_guard<std::mutex> lock(mu);
-        //             auto it = unique_rows.emplace(row).first;
-        //             size_t annot_id = it - unique_rows.begin();
-        //             ++unitig_bucket[annot_id];
-        //         }
-
-        //         if (added == unitig_size) {
-        //             // we've observed everything, now do the test
-        //             auto generate_a = [&](const auto& callback) {
-        //                 for (const auto& [annot_id, n] : unitig_bucket) {
-        //                     const auto& row = *(unique_rows.begin() + annot_id);
-        //                     sdsl::bit_vector found(groups.size());
-        //                     for (const auto& [j, c] : row) {
-        //                         if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-        //                             found[j] = true;
-        //                             for (size_t i = 0; i < n; ++i) {
-        //                                 callback(static_cast<long double>(c) / sums[j]);
-        //                             }
-        //                         }
-        //                     }
-        //                     for (size_t j = 0; j < found.size(); ++j) {
-        //                         if (!found[j]
-        //                             && (groups[j] == Group::IN || groups[j] == Group::BOTH)) {
-        //                             for (size_t i = 0; i < n; ++i) {
-        //                                 callback(0.0L);
-        //                             }
-        //                         }
-        //                     }
-        //                 }
-        //             };
-        //             auto generate_b = [&](const auto& callback) {
-        //                 for (const auto& [annot_id, n] : unitig_bucket) {
-        //                     const auto& row = *(unique_rows.begin() + annot_id);
-        //                     sdsl::bit_vector found(groups.size());
-        //                     for (const auto& [j, c] : row) {
-        //                         if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-        //                             found[j] = true;
-        //                             for (size_t i = 0; i < n; ++i) {
-        //                                 callback(static_cast<long double>(c) / sums[j]);
-        //                             }
-        //                         }
-        //                     }
-        //                     for (size_t j = 0; j < found.size(); ++j) {
-        //                         if (!found[j]
-        //                             && (groups[j] == Group::OUT || groups[j] == Group::BOTH)) {
-        //                             for (size_t i = 0; i < n; ++i) {
-        //                                 callback(0.0L);
-        //                             }
-        //                         }
-        //                     }
-        //                 }
-        //             };
-        //             std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
-        //             unitig_bucket = IDMap();
-        //         }
-        //     });
-        //     std::atomic_thread_fence(std::memory_order_acquire);
-        // }
-        // uint64_t num_tests = unitig_sizes.size();
-
-        // std::vector<std::tuple<long double, uint64_t, node_index>> nb_pvals;
-        // nb_pvals.resize(nelem,
-        //                 std::make_tuple(1.0L, graph_ptr->max_index() + 1,
-        //                                 graph_ptr->max_index() + 1));
         std::vector<std::tuple<long double, size_t, Group, node_index>> nb_pvals;
         nb_pvals.resize(nelem,
                         std::make_tuple(1.0L, graph_ptr->max_index() + 1, Group::OTHER,
@@ -887,129 +613,6 @@ mask_nodes_by_label_dual(
                 num_threads);
         std::atomic_thread_fence(std::memory_order_acquire);
         common::logger->trace("Found {} monotigs", num_tests);
-
-        // size_t idx = 0;
-        // kept.call_ones([&](auto row_i) {
-        //     size_t unitig_id = kmer_to_unitig[idx];
-        //     const auto& [unitig_size, unitig_sofar, pval, eff_size, unitig_bucket]
-        //             = unitig_sizes[unitig_id];
-        //     if (unitig_bucket.size()) {
-        //         throw std::runtime_error("Found non-empty bucket");
-        //     }
-
-        //     if (eff_size != 0 && pval < config.family_wise_error_rate) {
-        //         auto& [kmer_pval, kmer_unitig_id, node] = nb_pvals[idx];
-        //         kmer_pval = pval;
-        //         kmer_unitig_id = unitig_id;
-        //         node = AnnotatedDBG::anno_to_graph_index(row_i);
-        //         if (eff_size > 0) {
-        //             indicator_in[node] = true;
-        //         } else {
-        //             indicator_out[node] = true;
-        //         }
-        //     }
-        //     ++idx;
-        // });
-
-
-        // generate_unitigs(*clean_masked_graph, [&](const auto& path, const auto& row_values) {
-        //     uint64_t unitig_id
-        //             = __atomic_fetch_add(&num_tests, 1, std::memory_order_relaxed);
-        //     long double pval = 1.0;
-        //     long double eff_size = 0.0;
-        //     if (config.test_type == "mwu") {
-        //         auto generate_a = [&](const auto& callback) {
-        //             size_t total = num_labels_in * path.size();
-        //             size_t observed = 0;
-        //             for (const auto& values : row_values) {
-        //                 for (const auto& [j, c] : values) {
-        //                     if (groups[j] == Group::IN) {
-        //                         callback(static_cast<long double>(c) / sums[j]);
-        //                         ++observed;
-        //                     }
-        //                 }
-        //             }
-        //             for (size_t n = observed; n < total; ++n) {
-        //                 callback(0.0L);
-        //             }
-        //         };
-        //         auto generate_b = [&](const auto& callback) {
-        //             size_t total = num_labels_out * path.size();
-        //             size_t observed = 0;
-        //             for (const auto& values : row_values) {
-        //                 for (const auto& [j, c] : values) {
-        //                     if (groups[j] == Group::OUT) {
-        //                         callback(static_cast<long double>(c) / sums[j]);
-        //                         ++observed;
-        //                     }
-        //                 }
-        //             }
-        //             for (size_t n = observed; n < total; ++n) {
-        //                 callback(0.0L);
-        //             }
-        //         };
-
-        //         std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
-
-        //     } else if (config.test_type == "cmh") {
-        //         int64_t in_sum = 0;
-        //         int64_t out_sum = 0;
-        //         for (const auto& values : row_values) {
-        //             for (const auto& [j, c] : values) {
-        //                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-        //                     out_sum += c;
-        //                 }
-
-        //                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-        //                     in_sum += c;
-        //                 }
-        //             }
-        //         }
-        //         eff_size = static_cast<long double>(in_sum) / in_kmers
-        //                 - static_cast<long double>(out_sum) / out_kmers;
-        //         int64_t t = total_kmers;
-        //         int64_t n1 = in_kmers;
-        //         int64_t n2 = out_kmers;
-        //         int64_t m1 = in_sum + out_sum;
-        //         int64_t m2 = t - m1;
-        //         assert(m2 >= 0);
-
-        //         double lbase
-        //                 = log(n1) + log(n2) + log(m1) + log(m2) - log(t) * 2 - log(t - 1);
-        //         double factor = static_cast<double>(n1 * m1) / t;
-
-        //         auto get_chi_stat = [&](int64_t a) {
-        //             int64_t b = n1 - a;
-        //             int64_t c = m1 - a;
-        //             int64_t d = m2 - b;
-        //             assert(d == n2 - c);
-
-        //             if (b < 0 || c < 0 || d < 0)
-        //                 return 0.0;
-
-        //             double a_shift = static_cast<double>(a) - factor;
-
-        //             if (a_shift > 0)
-        //                 return exp(log(a_shift) * 2.0 - lbase);
-
-        //             return a_shift * a_shift / exp(lbase);
-        //         };
-
-        //         double chi_stat = get_chi_stat(in_sum);
-        //         pval = boost::math::cdf(
-        //                 boost::math::complement(boost::math::chi_squared(1), chi_stat));
-        //     }
-
-        //     if (eff_size != 0 && pval < config.family_wise_error_rate) {
-        //         auto* data = eff_size > 0 ? indicator_in.data() : indicator_out.data();
-        //         for (node_index node : path) {
-        //             auto row_i = AnnotatedDBG::graph_to_anno_index(node);
-        //             size_t nb_idx = kept.rank1(row_i) - 1;
-        //             nb_pvals[nb_idx] = std::make_tuple(pval, unitig_id, node);
-        //             set_bit(data, node, true, std::memory_order_relaxed);
-        //         }
-        //     }
-        // });
         std::atomic_thread_fence(std::memory_order_acquire);
         std::sort(nb_pvals.begin(), nb_pvals.end());
 
@@ -1061,9 +664,6 @@ mask_nodes_by_label_dual(
         std::pair<long double, long double> nb_params_b;
         std::pair<long double, long double> nb_params_null;
         long double nb_base = 0.0;
-        // long double alpha = in_kmers;
-        // long double beta = out_kmers;
-        // long double hg_base = (lgammal(alpha + beta) - lgammal(alpha) - lgammal(beta)) / logl(2.0);
         if (config.test_type == "nbinom_exact") {
             common::logger->trace("Fitting per-sample negative binomial distributions");
 
@@ -1521,7 +1121,6 @@ mask_nodes_by_label_dual(
                 std::sort(m.rbegin(), m.rend());
             } else {
                 std::swap(m, ms[0]);
-                // std::swap(gp_pvals, gp_pvals_m[0]);
                 size_t max_size = m.size();
                 for (size_t i = 1; i < ms.size(); ++i) {
                     max_size = std::max(max_size, ms[i].size());
@@ -1533,8 +1132,6 @@ mask_nodes_by_label_dual(
                         const auto& [pval, c] = ms[i][n];
                         m[n].first = pval;
                         m[n].second += c;
-                        // if (gp_pvals[n].empty() && gp_pvals_m[i][n].size())
-                        //     std::swap(gp_pvals[n], gp_pvals_m[i][n]);
                     }
                 }
 

From a027455590bddcc67949571185da3d997b2c5355 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Mar 2025 16:04:39 +0100
Subject: [PATCH 389/410] more cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 661 ++++++++----------
 1 file changed, 289 insertions(+), 372 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index d2ed2a9b66..30b02224b3 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -495,6 +495,295 @@ mask_nodes_by_label_dual(
                         && kept[AnnotatedDBG::graph_to_anno_index(node)];
             },
             true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+
+    // precompute negative binomial fits for poisson_bayes and nbinom_exact tests
+    std::vector<std::pair<long double, long double>> nb_params(groups.size());
+    std::pair<long double, long double> nb_params_a;
+    std::pair<long double, long double> nb_params_b;
+    std::pair<long double, long double> nb_params_null;
+    long double nb_base = 0.0;
+    if (config.test_type == "nbinom_exact") {
+        common::logger->trace("Fitting per-sample negative binomial distributions");
+
+
+#pragma omp parallel for num_threads(num_parallel_files)
+        for (size_t j = 0; j < groups.size(); ++j) {
+            const auto& hist = hists[j];
+            if (hist.size()) {
+                auto [r, p, mu, var] = get_rp([&](const auto& callback) {
+                    for (const auto& [k, c] : hist) {
+                        callback(k, c);
+                    }
+                });
+                nb_params[j] = std::make_pair(r, p);
+                common::logger->trace(
+                        "{}: size: {}\tmax_val: {}\tsample mean: {}\tsample var: "
+                        "{}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
+                        j, sums[j], (hist.end() - 1)->first, mu, var, r * (1 - p) / p,
+                        r * (1 - p) / p / p, r, p);
+            }
+        }
+        // based on this method for combining gamma distributions
+        // https://stats.stackexchange.com/questions/72479/generic-sum-of-gamma-random-variables
+        long double num_a = 0.0;
+        long double num_b = 0.0;
+
+        long double denom_a = 0.0;
+        long double denom_b = 0.0;
+
+        for (size_t j = 0; j < groups.size(); ++j) {
+            const auto& [r, p] = nb_params[j];
+            long double theta = (1.0 - p) / p;
+            if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                num_b += theta * r;
+                denom_b += theta * theta * r;
+            }
+
+            if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                num_a += theta * r;
+                denom_a += theta * theta * r;
+            }
+        }
+
+        long double r_a = num_a * num_a / denom_a;
+        long double theta_a = num_a / r_a;
+
+        long double r_b = num_b * num_b / denom_b;
+        long double theta_b = num_b / r_b;
+
+        long double r_null = pow(num_a + num_a, 2.0) / (denom_a + denom_b);
+        long double theta_null = (num_a + num_b) / r_null;
+
+        nb_params_a.first = r_a;
+        nb_params_b.first = r_b;
+        nb_params_null.first = r_null;
+
+        nb_params_a.second = 1.0 / (theta_a + 1.0);
+        nb_params_b.second = 1.0 / (theta_b + 1.0);
+        nb_params_null.second = 1.0 / (theta_null + 1.0);
+
+        common::logger->trace("In: r: {}\tp: {}", nb_params_a.first, nb_params_a.second);
+
+        common::logger->trace("Out: r: {}\tp: {}", nb_params_b.first, nb_params_b.second);
+
+        common::logger->trace("Null: r: {}\tp: {}", nb_params_null.first,
+                              nb_params_null.second);
+
+        nb_base = (lgammal(nb_params_null.first) - lgammal(nb_params_a.first)
+                   - lgammal(nb_params_b.first))
+                / logl(2.0);
+        nb_base += nb_params_a.first * log2l(nb_params_a.second)
+                + nb_params_b.first * log2l(nb_params_b.second)
+                - nb_params_null.first * log2l(nb_params_null.second);
+    }
+
+    long double mu1 = static_cast<long double>(in_kmers) / nelem;
+    long double mu2 = static_cast<long double>(out_kmers) / nelem;
+
+    // precompute p-values for poisson_binom test
+    std::vector<std::vector<long double>> pb_pvals(num_labels_in + num_labels_out + 1);
+    std::vector<long double> mid_points(num_labels_in + num_labels_out + 1);
+    if (config.test_type == "poisson_binom") {
+        // fit distribution
+        std::vector<long double> p;
+        p.reserve(groups.size());
+        for (size_t i = 0; i < groups.size(); ++i) {
+            p.emplace_back(static_cast<long double>(n_kmers[i]) / nelem);
+        }
+
+        common::logger->trace("p: {}", fmt::join(p, ","));
+
+        common::logger->trace("Precomputing PMFs");
+        std::vector<long double> pmf_in { 1.0L };
+        std::vector<long double> pmf_out { 1.0L };
+        std::vector<long double> pmf_null { 1.0L };
+
+        for (size_t i = 1; i <= groups.size(); ++i) {
+            if (groups[i - 1] == Group::OUT || groups[i - 1] == Group::BOTH) {
+                std::vector<long double> pmf_out_cur(pmf_out.size() + 1);
+                // pmf_out_cur[0] = (1.0L - p[i - 1]) * pmf_out[0];
+                pmf_out_cur[0] = expl(log1pl(-p[i - 1]) + logl(pmf_out[0]));
+                pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
+                for (size_t k = 1; k < pmf_out.size(); ++k) {
+                    pmf_out_cur[k]
+                            = p[i - 1] * pmf_out[k - 1] + (1.0L - p[i - 1]) * pmf_out[k];
+                }
+                std::swap(pmf_out_cur, pmf_out);
+            }
+
+            if (groups[i - 1] == Group::IN || groups[i - 1] == Group::BOTH) {
+                std::vector<long double> pmf_in_cur(pmf_in.size() + 1);
+                // pmf_in_cur[0] = (1.0L - p[i - 1]) * pmf_in[0];
+                pmf_in_cur[0] = expl(log1pl(-p[i - 1]) + logl(pmf_in[0]));
+                pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
+                for (size_t k = 1; k < pmf_in.size(); ++k) {
+                    pmf_in_cur[k] = p[i - 1] * pmf_in[k - 1] + (1.0L - p[i - 1]) * pmf_in[k];
+                }
+                std::swap(pmf_in_cur, pmf_in);
+            }
+            std::vector<long double> pmf_null_cur(i + 1);
+            // pmf_null_cur[0] = (1.0L - p[i - 1]) * pmf_null[0];
+            pmf_null_cur[0] = expl(log1pl(-p[i - 1]) + logl(pmf_null[0]));
+            pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
+            for (size_t k = 1; k < pmf_null.size(); ++k) {
+                pmf_null_cur[k]
+                        = p[i - 1] * pmf_null[k - 1] + (1.0L - p[i - 1]) * pmf_null[k];
+            }
+            std::swap(pmf_null_cur, pmf_null);
+        }
+
+        if (pmf_in.size() != num_labels_in + 1) {
+            common::logger->error("PMF in wrong: {} != {}", pmf_in.size(), num_labels_in + 1);
+            throw std::domain_error("");
+        }
+
+        if (pmf_out.size() != num_labels_out + 1) {
+            common::logger->error("PMF out wrong: {} != {}", pmf_out.size(),
+                                  num_labels_out + 1);
+            throw std::domain_error("");
+        }
+
+        if (pmf_null.size() != num_labels_in + num_labels_out + 1) {
+            common::logger->error("PMF null wrong: {} != {}", pmf_null.size(),
+                                  num_labels_in + num_labels_out + 1);
+            throw std::domain_error("");
+        }
+
+        common::logger->trace("Precomputing p-values");
+        pb_pvals[0].emplace_back(1.0);
+
+        long double min_pval = 1.0;
+        // long double last_local_min_pval = 1.0;
+
+        for (size_t n = 1; n < pb_pvals.size(); ++n) {
+            std::vector<long double> probs;
+            size_t front = n - std::min(n, num_labels_out);
+            for (uint64_t s = 0; s < pmf_in.size(); ++s) {
+                if (s > n)
+                    break;
+                uint64_t t = n - s;
+                if (s < pmf_in.size() && t < pmf_out.size()) {
+                    if (s < front) {
+                        common::logger->error(
+                                "Attempting non-zero p-value in impossible "
+                                "configuration: {},{}",
+                                n, s);
+                        throw std::domain_error("");
+                    }
+                    probs.emplace_back(exp2l(log2l(pmf_in[s]) + log2l(pmf_out[t])
+                                             - log2l(pmf_null[n])));
+                } else {
+                    probs.emplace_back(0.0);
+                }
+            }
+
+            long double sum_probs = std::accumulate(probs.begin(), probs.end(), 0.0L);
+            if (abs(sum_probs - 1.0L) > 1e-5) {
+                common::logger->error("Sum of probs for n={} = {}", n, sum_probs);
+                throw std::runtime_error("Fail");
+            }
+
+            for (uint64_t s = 0; s < probs.size(); ++s) {
+                pb_pvals[n].emplace_back(0.0);
+                if (s > n)
+                    break;
+                uint64_t t = n - s;
+                if (s >= pmf_in.size() || t >= pmf_out.size())
+                    continue;
+                for (uint64_t sp = 0; sp < probs.size(); ++sp) {
+                    if (sp > n)
+                        break;
+                    uint64_t tp = n - sp;
+                    if (sp >= pmf_in.size() || tp >= pmf_out.size())
+                        continue;
+                    if (probs[sp] <= probs[s])
+                        pb_pvals[n][s] += probs[sp];
+                }
+            }
+
+            for (uint64_t s = 0; s < front; ++s) {
+                if (pb_pvals[n][s] > 0) {
+                    common::logger->error(
+                            "Non-zero p-value in impossible configuration: {},{}", n, s);
+                    throw std::domain_error("");
+                }
+            }
+            long double local_min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
+            min_pval = std::min(min_pval, local_min_pval);
+
+            size_t max_prob_counts = 0;
+            long double max_prob_pos = 0;
+
+            for (uint64_t s = front; s < pb_pvals[n].size(); ++s) {
+                if (pb_pvals[n][s] < local_min_pval) {
+                    common::logger->error("Min p-value not at boundary: {},{}: {} < {}",
+                                          n, s, pb_pvals[n][s], local_min_pval);
+                    throw std::domain_error("");
+                }
+            }
+            if (front + 1 == pb_pvals[n].size()) {
+                max_prob_counts = 1;
+                max_prob_pos = front;
+            } else if (front + 2 == pb_pvals[n].size()) {
+                max_prob_counts = 2;
+                max_prob_pos = front + front + 1;
+            } else {
+                for (uint64_t s = front + 1; s < pb_pvals[n].size(); ++s) {
+                    if (probs[s] == probs[s - 1]) {
+                        max_prob_counts = 2;
+                        max_prob_pos = s + s;
+                        break;
+                    }
+
+                    if (s + 1 < pb_pvals[n].size()) {
+                        if (probs[s - 1] < probs[s] && probs[s] < probs[s + 1])
+                            continue;
+
+                        if (probs[s - 1] > probs[s] && probs[s] > probs[s + 1])
+                            continue;
+
+                        if (probs[s] > probs[s - 1] && probs[s] > probs[s + 1]) {
+                            if (probs[s] - probs[s - 1] == probs[s] - probs[s + 1]) {
+                                max_prob_counts = 1;
+                                max_prob_pos = s;
+                            } else if (probs[s] - probs[s - 1] > probs[s] - probs[s + 1]) {
+                                max_prob_counts = 2;
+                                max_prob_pos = s + s + 1;
+                            } else {
+                                max_prob_counts = 2;
+                                max_prob_pos = s + s - 1;
+                            }
+                        }
+                    }
+                }
+            }
+
+            if (max_prob_counts == 0) {
+                max_prob_counts = 2;
+                max_prob_pos = front - 1 + front;
+            }
+
+            mid_points[n] = static_cast<long double>(max_prob_pos) / max_prob_counts;
+            common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}", n, mid_points[n],
+                                  fmt::join(probs, ","), fmt::join(pb_pvals[n], ","));
+        }
+
+        common::logger->trace("Min. p-value: {}", min_pval);
+        if (min_pval >= config.family_wise_error_rate) {
+            common::logger->warn("No significant p-values achievable");
+            auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
+                    graph_ptr, [](node_index) { return false; }, true,
+                    is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+
+            auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
+                    graph_ptr, [](node_index) { return false; }, true,
+                    is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+
+            return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage {},
+                                   nullptr);
+        }
+    }
+
     if (config.test_by_unitig && config.test_type != "notest") {
         std::vector<std::tuple<long double, size_t, Group, node_index>> nb_pvals;
         nb_pvals.resize(nelem,
@@ -658,352 +947,6 @@ mask_nodes_by_label_dual(
             indicator_out[node] = false;
         }
     } else {
-        // precompute negative binomial fits for poisson_bayes and nbinom_exact tests
-        std::vector<std::pair<long double, long double>> nb_params(groups.size());
-        std::pair<long double, long double> nb_params_a;
-        std::pair<long double, long double> nb_params_b;
-        std::pair<long double, long double> nb_params_null;
-        long double nb_base = 0.0;
-        if (config.test_type == "nbinom_exact") {
-            common::logger->trace("Fitting per-sample negative binomial distributions");
-
-
-#pragma omp parallel for num_threads(num_parallel_files)
-            for (size_t j = 0; j < groups.size(); ++j) {
-                const auto& hist = hists[j];
-                if (hist.size()) {
-                    auto [r, p, mu, var] = get_rp([&](const auto& callback) {
-                        for (const auto& [k, c] : hist) {
-                            callback(k, c);
-                        }
-                    });
-                    nb_params[j] = std::make_pair(r, p);
-                    common::logger->trace(
-                            "{}: size: {}\tmax_val: {}\tsample mean: {}\tsample var: "
-                            "{}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
-                            j, sums[j], (hist.end() - 1)->first, mu, var, r * (1 - p) / p,
-                            r * (1 - p) / p / p, r, p);
-                }
-            }
-
-            if (config.test_type == "nbinom_exact") {
-                // based on this method for combining gamma distributions
-                // https://stats.stackexchange.com/questions/72479/generic-sum-of-gamma-random-variables
-                long double num_a = 0.0;
-                long double num_b = 0.0;
-
-                long double denom_a = 0.0;
-                long double denom_b = 0.0;
-
-                for (size_t j = 0; j < groups.size(); ++j) {
-                    const auto& [r, p] = nb_params[j];
-                    long double theta = (1.0 - p) / p;
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                        num_b += theta * r;
-                        denom_b += theta * theta * r;
-                    }
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                        num_a += theta * r;
-                        denom_a += theta * theta * r;
-                    }
-                }
-
-                long double r_a = num_a * num_a / denom_a;
-                long double theta_a = num_a / r_a;
-
-                long double r_b = num_b * num_b / denom_b;
-                long double theta_b = num_b / r_b;
-
-                long double r_null = pow(num_a + num_a, 2.0) / (denom_a + denom_b);
-                long double theta_null = (num_a + num_b) / r_null;
-
-                nb_params_a.first = r_a;
-                nb_params_b.first = r_b;
-                nb_params_null.first = r_null;
-
-                // theta = (1 - p) / p
-                // ptheta = 1 - p
-                // ptheta + p = 1
-                // p = 1/(1+theta)
-
-                nb_params_a.second = 1.0 / (theta_a + 1.0);
-                nb_params_b.second = 1.0 / (theta_b + 1.0);
-                nb_params_null.second = 1.0 / (theta_null + 1.0);
-                // long double mu_a = 0.0;
-                // long double mu_b = 0.0;
-                // long double var_a = 0.0;
-                // long double var_b = 0.0;
-                // for (size_t j = 0; j < groups.size(); ++j) {
-                //     const auto &[r, p] = nb_params[j];
-                //     long double mu = r * (1.0 - p) / p;
-                //     long double var = mu / p;
-                //     if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                //         mu_a += mu;
-                //         var_a += var;
-                //     }
-
-                //     if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                //         mu_b += mu;
-                //         var_b += var;
-                //     }
-                // }
-
-                // nb_params_a.first = mu_a * mu_a / (var_a - mu_a);
-                // nb_params_a.second = mu_a / var_a;
-                common::logger->trace("In: r: {}\tp: {}", nb_params_a.first,
-                                      nb_params_a.second);
-
-                // nb_params_b.first = mu_b * mu_b / (var_b - mu_b);
-                // nb_params_b.second = mu_b / var_b;
-                common::logger->trace("Out: r: {}\tp: {}", nb_params_b.first,
-                                      nb_params_b.second);
-
-                // long double mu_null = mu_a + mu_b;
-                // long double var_null = var_a + var_b; // the two are independent, so
-                // their covariance is 0 nb_params_null.first = mu_null * mu_null /
-                // (var_null - mu_null); nb_params_null.second = mu_null / var_null;
-                common::logger->trace("Null: r: {}\tp: {}", nb_params_null.first,
-                                      nb_params_null.second);
-
-                nb_base = (lgammal(nb_params_null.first) - lgammal(nb_params_a.first)
-                           - lgammal(nb_params_b.first))
-                        / logl(2.0);
-                nb_base += nb_params_a.first * log2l(nb_params_a.second)
-                        + nb_params_b.first * log2l(nb_params_b.second)
-                        - nb_params_null.first * log2l(nb_params_null.second);
-            }
-        }
-
-        using RowGenerator
-                = std::function<void(const std::function<void(size_t, uint64_t)>&)>;
-        using GetLambda
-                = std::function<std::pair<long double, long double>(size_t, const RowGenerator&)>;
-        GetLambda get_lambda;
-
-        if (config.test_type == "poisson_exact") {
-            get_lambda = [&](size_t, const RowGenerator&) {
-                return std::make_pair(static_cast<long double>(in_kmers) / nelem,
-                                      static_cast<long double>(out_kmers) / nelem);
-            };
-        }
-
-        // precompute p-values for poisson_binom test
-        std::vector<std::vector<long double>> pb_pvals(num_labels_in + num_labels_out + 1);
-        std::vector<long double> mid_points(num_labels_in + num_labels_out + 1);
-        if (config.test_type == "poisson_binom") {
-            // fit distribution
-            std::vector<long double> p;
-            p.reserve(groups.size());
-            for (size_t i = 0; i < groups.size(); ++i) {
-                p.emplace_back(static_cast<long double>(n_kmers[i]) / nelem);
-            }
-
-            common::logger->trace("p: {}", fmt::join(p, ","));
-
-            common::logger->trace("Precomputing PMFs");
-            std::vector<long double> pmf_in { 1.0L };
-            std::vector<long double> pmf_out { 1.0L };
-            std::vector<long double> pmf_null { 1.0L };
-
-            for (size_t i = 1; i <= groups.size(); ++i) {
-                if (groups[i - 1] == Group::OUT || groups[i - 1] == Group::BOTH) {
-                    std::vector<long double> pmf_out_cur(pmf_out.size() + 1);
-                    // pmf_out_cur[0] = (1.0L - p[i - 1]) * pmf_out[0];
-                    pmf_out_cur[0] = expl(log1pl(-p[i - 1]) + logl(pmf_out[0]));
-                    pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
-                    for (size_t k = 1; k < pmf_out.size(); ++k) {
-                        pmf_out_cur[k] = p[i - 1] * pmf_out[k - 1]
-                                + (1.0L - p[i - 1]) * pmf_out[k];
-                    }
-                    std::swap(pmf_out_cur, pmf_out);
-                }
-
-                if (groups[i - 1] == Group::IN || groups[i - 1] == Group::BOTH) {
-                    std::vector<long double> pmf_in_cur(pmf_in.size() + 1);
-                    // pmf_in_cur[0] = (1.0L - p[i - 1]) * pmf_in[0];
-                    pmf_in_cur[0] = expl(log1pl(-p[i - 1]) + logl(pmf_in[0]));
-                    pmf_in_cur[pmf_in.size()] = p[i - 1] * pmf_in.back();
-                    for (size_t k = 1; k < pmf_in.size(); ++k) {
-                        pmf_in_cur[k]
-                                = p[i - 1] * pmf_in[k - 1] + (1.0L - p[i - 1]) * pmf_in[k];
-                    }
-                    std::swap(pmf_in_cur, pmf_in);
-                }
-                std::vector<long double> pmf_null_cur(i + 1);
-                // pmf_null_cur[0] = (1.0L - p[i - 1]) * pmf_null[0];
-                pmf_null_cur[0] = expl(log1pl(-p[i - 1]) + logl(pmf_null[0]));
-                pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
-                for (size_t k = 1; k < pmf_null.size(); ++k) {
-                    pmf_null_cur[k]
-                            = p[i - 1] * pmf_null[k - 1] + (1.0L - p[i - 1]) * pmf_null[k];
-                }
-                std::swap(pmf_null_cur, pmf_null);
-            }
-
-            if (pmf_in.size() != num_labels_in + 1) {
-                common::logger->error("PMF in wrong: {} != {}", pmf_in.size(),
-                                      num_labels_in + 1);
-                throw std::domain_error("");
-            }
-
-            if (pmf_out.size() != num_labels_out + 1) {
-                common::logger->error("PMF out wrong: {} != {}", pmf_out.size(),
-                                      num_labels_out + 1);
-                throw std::domain_error("");
-            }
-
-            if (pmf_null.size() != num_labels_in + num_labels_out + 1) {
-                common::logger->error("PMF null wrong: {} != {}", pmf_null.size(),
-                                      num_labels_in + num_labels_out + 1);
-                throw std::domain_error("");
-            }
-
-            common::logger->trace("Precomputing p-values");
-            pb_pvals[0].emplace_back(1.0);
-
-            long double min_pval = 1.0;
-            // long double last_local_min_pval = 1.0;
-
-            for (size_t n = 1; n < pb_pvals.size(); ++n) {
-                std::vector<long double> probs;
-                size_t front = n - std::min(n, num_labels_out);
-                for (uint64_t s = 0; s < pmf_in.size(); ++s) {
-                    if (s > n)
-                        break;
-                    uint64_t t = n - s;
-                    if (s < pmf_in.size() && t < pmf_out.size()) {
-                        if (s < front) {
-                            common::logger->error(
-                                    "Attempting non-zero p-value in impossible "
-                                    "configuration: {},{}",
-                                    n, s);
-                            throw std::domain_error("");
-                        }
-                        probs.emplace_back(exp2l(log2l(pmf_in[s]) + log2l(pmf_out[t])
-                                                 - log2l(pmf_null[n])));
-                    } else {
-                        probs.emplace_back(0.0);
-                    }
-                }
-
-                long double sum_probs = std::accumulate(probs.begin(), probs.end(), 0.0L);
-                if (abs(sum_probs - 1.0L) > 1e-5) {
-                    common::logger->error("Sum of probs for n={} = {}", n, sum_probs);
-                    throw std::runtime_error("Fail");
-                }
-
-                for (uint64_t s = 0; s < probs.size(); ++s) {
-                    pb_pvals[n].emplace_back(0.0);
-                    if (s > n)
-                        break;
-                    uint64_t t = n - s;
-                    if (s >= pmf_in.size() || t >= pmf_out.size())
-                        continue;
-                    for (uint64_t sp = 0; sp < probs.size(); ++sp) {
-                        if (sp > n)
-                            break;
-                        uint64_t tp = n - sp;
-                        if (sp >= pmf_in.size() || tp >= pmf_out.size())
-                            continue;
-                        if (probs[sp] <= probs[s])
-                            pb_pvals[n][s] += probs[sp];
-                    }
-                }
-
-                for (uint64_t s = 0; s < front; ++s) {
-                    if (pb_pvals[n][s] > 0) {
-                        common::logger->error(
-                                "Non-zero p-value in impossible configuration: {},{}", n, s);
-                        throw std::domain_error("");
-                    }
-                }
-                long double local_min_pval
-                        = std::min(pb_pvals[n][front], pb_pvals[n].back());
-                min_pval = std::min(min_pval, local_min_pval);
-
-                size_t max_prob_counts = 0;
-                long double max_prob_pos = 0;
-                // double max_prob = -1;
-
-                for (uint64_t s = front; s < pb_pvals[n].size(); ++s) {
-                    if (pb_pvals[n][s] < local_min_pval) {
-                        common::logger->error(
-                                "Min p-value not at boundary: {},{}: {} < {}", n, s,
-                                pb_pvals[n][s], local_min_pval);
-                        throw std::domain_error("");
-                    }
-                }
-                if (front + 1 == pb_pvals[n].size()) {
-                    max_prob_counts = 1;
-                    max_prob_pos = front;
-                } else if (front + 2 == pb_pvals[n].size()) {
-                    max_prob_counts = 2;
-                    max_prob_pos = front + front + 1;
-                } else {
-                    for (uint64_t s = front + 1; s < pb_pvals[n].size(); ++s) {
-                        if (probs[s] == probs[s - 1]) {
-                            max_prob_counts = 2;
-                            max_prob_pos = s + s;
-                            break;
-                        }
-
-                        if (s + 1 < pb_pvals[n].size()) {
-                            if (probs[s - 1] < probs[s] && probs[s] < probs[s + 1])
-                                continue;
-
-                            if (probs[s - 1] > probs[s] && probs[s] > probs[s + 1])
-                                continue;
-
-                            if (probs[s] > probs[s - 1] && probs[s] > probs[s + 1]) {
-                                if (probs[s] - probs[s - 1] == probs[s] - probs[s + 1]) {
-                                    max_prob_counts = 1;
-                                    max_prob_pos = s;
-                                } else if (probs[s] - probs[s - 1] > probs[s] - probs[s + 1]) {
-                                    max_prob_counts = 2;
-                                    max_prob_pos = s + s + 1;
-                                } else {
-                                    max_prob_counts = 2;
-                                    max_prob_pos = s + s - 1;
-                                }
-                            }
-                        }
-                    }
-                }
-
-                if (max_prob_counts == 0) {
-                    max_prob_counts = 2;
-                    max_prob_pos = front - 1 + front;
-                }
-
-                mid_points[n] = static_cast<long double>(max_prob_pos) / max_prob_counts;
-                common::logger->trace("Midpoint: n: {}\tmp: {}\t{}\t{}", n, mid_points[n],
-                                      fmt::join(probs, ","), fmt::join(pb_pvals[n], ","));
-
-                // if (local_min_pval > last_local_min_pval) {
-                //     common::logger->error("Min p-vals failed: n-1: {} -> {}\tn: {} -> {}",
-                //                           n - 1, last_local_min_pval, n, local_min_pval);
-                //     throw std::runtime_error("Min p-val failed");
-                // }
-                // last_local_min_pval = local_min_pval;
-            }
-
-            common::logger->trace("Min. p-value: {}", min_pval);
-            if (min_pval >= config.family_wise_error_rate) {
-                common::logger->warn("No significant p-values achievable");
-                auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
-                        graph_ptr, [](node_index) { return false; }, true,
-                        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
-
-                auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
-                        graph_ptr, [](node_index) { return false; }, true,
-                        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
-
-                return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage {},
-                                       nullptr);
-            }
-        }
-
         bool fdr = config.test_by_unitig || config.test_type == "nbinom_exact"
                 || config.test_type == "mwu" || config.test_type == "cmh";
 
@@ -1044,16 +987,6 @@ mask_nodes_by_label_dual(
                         found[j] = true;
                     }
 
-                    auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto& callback) {
-                        for (const auto& [j, c] : row) {
-                            callback(j, c);
-                        }
-                        for (size_t j = 0; j < found.size(); ++j) {
-                            if (!found[j])
-                                callback(j, 0);
-                        }
-                    });
-
                     if (n > 0) {
                         if (ms[bucket_idx].size() <= n)
                             ms[bucket_idx].resize(n + 1);
@@ -1294,28 +1227,12 @@ mask_nodes_by_label_dual(
                         in_sum += c;
                 }
 
-                auto [mu1, mu2] = get_lambda(bucket_idx, [&](const auto& callback) {
-                    for (const auto& [j, c] : row) {
-                        callback(j, c);
-                    }
-                    for (size_t j = 0; j < found.size(); ++j) {
-                        if (!found[j])
-                            callback(j, 0);
-                    }
-                });
-
                 if (n < n_cutoff)
                     return;
 
                 long double p = mu1 / (mu1 + mu2);
                 boost::math::binomial bdist(n, p);
                 if (n != 0) {
-                    // double pval0 = boost::math::pdf(bdist, 0);
-                    // double pvaln = boost::math::pdf(bdist, n);
-                    // double min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                    // if (!config.test_by_unitig && min_pval * k_min >= config.family_wise_error_rate)
-                    //     return;
-
                     auto get_deviance = [&](long double y, long double mu) {
                         y += 1e-8;
                         mu += 1e-8;

From 61e3687ec9ce411a21da09e310659bad9e2162cd Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 25 Mar 2025 16:38:00 +0100
Subject: [PATCH 390/410] lots of cleanup

---
 .../src/graph/annotated_graph_algorithm.cpp   | 1119 +++++------------
 1 file changed, 333 insertions(+), 786 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 30b02224b3..b1785de4c7 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -579,6 +579,7 @@ mask_nodes_by_label_dual(
 
     long double mu1 = static_cast<long double>(in_kmers) / nelem;
     long double mu2 = static_cast<long double>(out_kmers) / nelem;
+    long double p = mu1 / (mu1 + mu2);
 
     // precompute p-values for poisson_binom test
     std::vector<std::vector<long double>> pb_pvals(num_labels_in + num_labels_out + 1);
@@ -784,875 +785,421 @@ mask_nodes_by_label_dual(
         }
     }
 
-    if (config.test_by_unitig && config.test_type != "notest") {
-        std::vector<std::tuple<long double, size_t, Group, node_index>> nb_pvals;
-        nb_pvals.resize(nelem,
-                        std::make_tuple(1.0L, graph_ptr->max_index() + 1, Group::OTHER,
-                                        graph_ptr->max_index() + 1));
+    common::logger->trace("Allocating p-value storage");
+    std::vector<std::tuple<long double, size_t, Group, node_index>> all_pvals;
+    all_pvals.resize(nelem,
+                     std::make_tuple(1.0L, graph_ptr->max_index() + 1, Group::OTHER,
+                                     graph_ptr->max_index() + 1));
 
-        generate_clean_rows([&](auto row_i, const auto& row, size_t) {
-            auto generate_a = [&](const auto& callback) {
-                sdsl::bit_vector found(groups.size());
-                for (const auto& [j, c] : row) {
-                    found[j] = true;
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                        callback(static_cast<long double>(c) / sums[j]);
-                }
-                for (size_t j = 0; j < found.size(); ++j) {
-                    if (!found[j] && (groups[j] == Group::IN || groups[j] == Group::BOTH))
-                        callback(0.0L);
-                }
-            };
-            auto generate_b = [&](const auto& callback) {
-                sdsl::bit_vector found(groups.size());
-                for (const auto& [j, c] : row) {
-                    found[j] = true;
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                        callback(static_cast<long double>(c) / sums[j]);
-                }
-                for (size_t j = 0; j < found.size(); ++j) {
-                    if (!found[j] && (groups[j] == Group::OUT || groups[j] == Group::BOTH))
-                        callback(0.0L);
-                }
-            };
+    common::logger->trace("Starting tests");
+    std::atomic_thread_fence(std::memory_order_release);
+    generate_clean_rows([&](uint64_t row_i, const auto& row, uint64_t bucket_idx) {
+        if (config.test_type == "notest") {
+            size_t count_in = 0;
+            size_t count_out = 0;
+            for (const auto& [j, c] : row) {
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                    ++count_out;
 
-            size_t idx = kept.rank1(row_i) - 1;
-            auto& [pval, monotig_id, group, node] = nb_pvals[idx];
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                    ++count_in;
+            }
 
-            long double eff_size;
+            bool in_kmer = count_in >= config.min_in_recurrence
+                    && count_in <= config.max_in_recurrence;
+            bool out_kmer = count_out >= config.min_out_recurrence
+                    && count_out <= config.max_out_recurrence;
 
-            std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
-            if (eff_size != 0.0) {
-                node = AnnotatedDBG::anno_to_graph_index(row_i);
-                if (eff_size > 0) {
-                    group = Group::IN;
-                } else if (eff_size < 0) {
-                    group = Group::OUT;
-                }
-            }
-        });
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+            if (in_kmer)
+                set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
 
-        uint64_t num_tests = 0;
-        std::atomic_thread_fence(std::memory_order_release);
-        clean_masked_graph->call_unitigs(
-                [&](const std::string&, const auto& path) {
-                    std::vector<long double> pvals;
-                    pvals.reserve(path.size());
-
-                    Group last_group = Group::BOTH;
-                    size_t last_i = 0;
-
-                    std::vector<size_t> ranks;
-                    ranks.reserve(path.size());
-                    for (node_index node : path) {
-                        size_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-                        ranks.emplace_back(kept.rank1(row_i) - 1);
-                    }
+            if (out_kmer)
+                set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
 
-                    for (size_t i = 0; i < path.size(); ++i) {
-                        size_t idx = ranks[i];
-                        const auto& [pval, monotig_id, group, stored_node] = nb_pvals[idx];
-                        if (group != last_group) {
-                            if (pvals.size()) {
-                                uint64_t monotig_id
-                                        = __atomic_fetch_add(&num_tests, 1,
-                                                             std::memory_order_relaxed);
-                                long double comb_pval = combine_pvals(pvals);
-                                for (size_t j = last_i; j < i; ++j) {
-                                    size_t jdx = ranks[j];
-                                    std::get<0>(nb_pvals[jdx]) = comb_pval;
-                                    std::get<1>(nb_pvals[jdx]) = monotig_id;
-                                    if (comb_pval < config.family_wise_error_rate) {
-                                        if (last_group == Group::IN) {
-                                            set_bit(indicator_in.data(), path[j], true,
-                                                    std::memory_order_relaxed);
-                                        } else if (last_group == Group::OUT) {
-                                            set_bit(indicator_out.data(), path[j], true,
-                                                    std::memory_order_relaxed);
-                                        }
-                                    }
-                                }
-                            }
-                            pvals.clear();
-                            last_group = group;
-                            last_i = i;
-                        }
-                        pvals.emplace_back(pval);
-                    }
-                    if (pvals.size()) {
-                        uint64_t monotig_id = __atomic_fetch_add(&num_tests, 1,
-                                                                 std::memory_order_relaxed);
-                        long double comb_pval = combine_pvals(pvals);
-                        for (size_t j = last_i; j < path.size(); ++j) {
-                            size_t jdx = ranks[j];
-                            std::get<0>(nb_pvals[jdx]) = comb_pval;
-                            std::get<1>(nb_pvals[jdx]) = monotig_id;
-                            if (comb_pval < config.family_wise_error_rate) {
-                                if (last_group == Group::IN) {
-                                    set_bit(indicator_in.data(), path[j], true,
-                                            std::memory_order_relaxed);
-                                } else if (last_group == Group::OUT) {
-                                    set_bit(indicator_out.data(), path[j], true,
-                                            std::memory_order_relaxed);
-                                }
-                            }
-                        }
-                    }
-                },
-                num_threads);
-        std::atomic_thread_fence(std::memory_order_acquire);
-        common::logger->trace("Found {} monotigs", num_tests);
-        std::atomic_thread_fence(std::memory_order_acquire);
-        std::sort(nb_pvals.begin(), nb_pvals.end());
-
-        long double harm = 0.0;
-        for (size_t i = 1; i <= num_tests; ++i) {
-            harm += 1.0 / i;
+            return;
         }
 
-        common::logger->trace("Selecting cut-off");
-        size_t k = 0;
-        uint64_t last_unitig_id = graph_ptr->max_index() + 1;
-        size_t cur_count = 0;
-        for (size_t i = 0; i < nb_pvals.size(); ++i) {
-            const auto& [pval, unitig_id, group, node] = nb_pvals[i];
-            if (pval >= config.family_wise_error_rate)
-                break;
-
-            if (unitig_id != last_unitig_id) {
-                last_unitig_id = unitig_id;
-                ++cur_count;
+        auto set_pval = [&](long double pval, long double eff_size) {
+            size_t idx = kept.rank1(row_i) - 1;
+            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+            Group group;
+            if (eff_size > 0) {
+                group = Group::IN;
+            } else if (eff_size < 0) {
+                group = Group::OUT;
+            } else {
+                group = Group::BOTH;
             }
 
-            if (pval <= config.family_wise_error_rate * cur_count / num_tests / harm) {
-                k = std::max(i + 1, k);
+            all_pvals[idx] = std::tie(pval, node, group, node);
+        };
+
+        long double eff_size = 0.0;
+        long double pval = 1.0;
+        if (config.test_type == "poisson_binom") {
+            size_t n = 0;
+            uint64_t s = 0;
+            for (const auto& [j, c] : row) {
+                n += static_cast<int64_t>(groups[j] != Group::OTHER)
+                        + (groups[j] == Group::BOTH);
+                s += (groups[j] == Group::IN);
             }
-        }
 
-        common::logger->trace("Found {} / {} significant p-values. Minimum p-value is {}",
-                              k, nb_pvals.size(), std::get<0>(nb_pvals[0]));
+            pval = pb_pvals[n][s];
+            eff_size = static_cast<double>(s) - mid_points[n];
+        } else if (config.test_type == "poisson_exact") {
+            size_t n = 0;
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+            for (const auto& [j, c] : row) {
+                n += c;
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                    out_sum += c;
+
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                    in_sum += c;
+            }
 
-        for (size_t i = k; i < nb_pvals.size(); ++i) {
-            const auto& [pval, unitig_id, group, node] = nb_pvals[i];
-            if (pval >= config.family_wise_error_rate)
-                break;
+            boost::math::binomial bdist(n, p);
+            auto get_deviance = [&](long double y, long double mu) {
+                y += 1e-8;
+                mu += 1e-8;
+                return 2 * (y * log(y / mu) - y + mu);
+            };
 
-            if (!indicator_in[node] && !indicator_out[node]) {
-                common::logger->error(
-                        "Node {} not marked, but original p-value {} significant", node,
-                        pval);
-                throw std::runtime_error("Index fail");
+            std::vector<long double> devs;
+            devs.reserve(n + 1);
+            for (size_t s = 0; s <= n; ++s) {
+                devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
             }
-            indicator_in[node] = false;
-            indicator_out[node] = false;
-        }
-    } else {
-        bool fdr = config.test_by_unitig || config.test_type == "nbinom_exact"
-                || config.test_type == "mwu" || config.test_type == "cmh";
-
-        // precompute minimal attainable p-values
-        std::vector<std::pair<long double, size_t>> m;
-        std::vector<size_t> offsets;
-        if (config.test_type != "notest" && !fdr) {
-            common::logger->trace("Computing minimum p-values");
-            std::vector<std::vector<std::pair<long double, size_t>>> ms(num_threads + 1);
-            std::vector<VectorMap<long double, size_t>> ms_map(num_threads + 1);
-            std::vector<std::vector<std::vector<long double>>> gp_pvals_m(num_threads + 1);
-            std::mutex mu;
-            generate_clean_rows([&](uint64_t row_i, const auto& row, uint64_t bucket_idx) {
-                long double pval = 1.0;
-                size_t n = 0;
-                if (config.test_type == "poisson_binom") {
-                    for (const auto& [j, c] : row) {
-                        n += static_cast<int64_t>(groups[j] != Group::OTHER)
-                                + (groups[j] == Group::BOTH);
-                    }
-                    if (ms[bucket_idx].size() <= n)
-                        ms[bucket_idx].resize(n + 1);
-
-                    if (ms[bucket_idx][n].second == 0) {
-                        size_t front = n - std::min(n, num_labels_out);
-                        pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
-                        ms[bucket_idx][n].first = pval;
-                        ms[bucket_idx][n].second = 1;
-                    } else {
-                        ++ms[bucket_idx][n].second;
-                    }
 
-                } else if (config.test_type == "poisson_exact") {
-                    sdsl::bit_vector found(num_labels_in + num_labels_out);
-                    size_t n = 0;
-                    for (const auto& [j, c] : row) {
-                        n += c;
-                        found[j] = true;
-                    }
+            pval = 0.0;
 
-                    if (n > 0) {
-                        if (ms[bucket_idx].size() <= n)
-                            ms[bucket_idx].resize(n + 1);
-
-                        if (ms[bucket_idx][n].second == 0) {
-                            long double p = mu1 / (mu1 + mu2);
-                            boost::math::binomial bdist(n, p);
-                            long double pval0 = boost::math::pdf(bdist, 0);
-                            long double pvaln = boost::math::pdf(bdist, n);
-                            pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                            ms[bucket_idx][n].first = pval;
-                            ms[bucket_idx][n].second = 1;
-                        } else {
-                            ++ms[bucket_idx][n].second;
-                        }
-                    }
-                } else if (config.test_type == "nbinom_exact") {
-                    for (const auto& [j, c] : row) {
-                        n += c;
-                    }
+            size_t s = 0;
+            for (; s <= n; ++s) {
+                if (devs[s] < devs[in_sum])
+                    break;
+            }
 
-                    if (n > 0) {
-                        if (ms[bucket_idx].size() <= n)
-                            ms[bucket_idx].resize(n + 1);
-
-                        if (ms[bucket_idx][n].second == 0) {
-                            auto [r_n, p_n] = nb_params_null;
-                            auto [r_a, p_a] = nb_params_a;
-                            auto [r_b, p_b] = nb_params_b;
-
-                            long double l21p = log1p(-p_n) / log2l(2.0);
-                            long double base = nb_base
-                                    + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0)
-                                    - n * l21p;
-                            auto get_pval = [&](int64_t s) {
-                                int64_t t = n - s;
-                                long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                                     - lgammal(s + 1) - lgammal(t + 1)
-                                                     + s * log1pl(-p_a) + t * log1pl(-p_b))
-                                        / logl(2.0);
-                                return exp2l(base + sbase);
-                            };
-
-                            long double pval0 = get_pval(0);
-                            long double pvaln = get_pval(n);
-                            pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                            ms[bucket_idx][n].first = pval;
-                            ms[bucket_idx][n].second = 1;
-                        } else {
-                            ++ms[bucket_idx][n].second;
-                        }
-                    }
-                }
-            });
+            if (s > 0)
+                pval += boost::math::cdf(bdist, s - 1);
 
-            if (config.test_type == "poisson_bayes") {
-                VectorMap<long double, size_t> m_map = std::move(ms_map[0]);
-                for (size_t i = 1; i < ms_map.size(); ++i) {
-                    for (const auto& [pval, c] : ms_map[i]) {
-                        m_map[pval] += c;
-                    }
-                }
-                m = const_cast<std::vector<std::pair<long double, size_t>>&&>(
-                        m_map.values_container());
-                std::sort(m.rbegin(), m.rend());
-            } else {
-                std::swap(m, ms[0]);
-                size_t max_size = m.size();
-                for (size_t i = 1; i < ms.size(); ++i) {
-                    max_size = std::max(max_size, ms[i].size());
-                }
-                m.resize(max_size);
-                // gp_pvals.resize(max_size);
-                for (size_t i = 1; i < ms.size(); ++i) {
-                    for (size_t n = 0; n < ms[i].size(); ++i) {
-                        const auto& [pval, c] = ms[i][n];
-                        m[n].first = pval;
-                        m[n].second += c;
-                    }
+            if (pval < config.family_wise_error_rate) {
+                size_t sp = n;
+                for (; sp >= s; --sp) {
+                    if (devs[sp] < devs[in_sum])
+                        break;
                 }
 
-                if (config.test_type == "poisson_binom")
-                    std::sort(m.rbegin(), m.rend());
+                if (sp < n)
+                    pval += boost::math::cdf(boost::math::complement(bdist, sp));
             }
 
-            if (m.size() > 1) {
-                auto it = m.begin();
-                auto jt = it + 1;
-                while (jt != m.end()) {
-                    while (it->second == 0 && jt != m.end()) {
-                        ++it;
-                        ++jt;
-                    }
-                    while (jt != m.end() && jt->second == 0) {
-                        ++jt;
-                    }
-                    if (jt == m.end())
-                        break;
+            eff_size = static_cast<long double>(in_sum) - boost::math::mode(bdist);
 
-                    if (it->first - 1.0L > 1e-5L) {
-                        common::logger->error("pval it > 1.0: {} ({})", it->first,
-                                              it->first - 1.0L);
-                        throw std::runtime_error("p-val fail");
-                    }
-
-                    if (jt->first - 1.0L > 1e-5L) {
-                        common::logger->error("pval jt > 1.0: {} ({})", jt->first,
-                                              jt->first - 1.0L);
-                        throw std::runtime_error("p-val fail");
-                    }
+        } else if (config.test_type == "nbinom_exact") {
+            size_t n = 0;
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+            for (const auto& [j, c] : row) {
+                n += c;
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                    out_sum += c;
+                }
 
-                    if (jt->first - it->first > 1e-5) {
-                        common::logger->error(
-                                "min p-vals not sorted: {} vs. {}\t{} !>= {}",
-                                it - m.begin(), jt - m.begin(), it->first, jt->first);
-                        throw std::runtime_error("p-val fail");
-                    }
-                    ++it;
-                    ++jt;
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                    in_sum += c;
                 }
             }
-        }
 
-        // determine cutoffs for multiple testing correction
-        auto [k_min, k, n_cutoff] = correct_pvals(m);
-        if (fdr) {
-            k_min = 1;
-            k = 1;
-            n_cutoff = 1;
-        }
+            auto [r_a, p_a] = nb_params_a;
+            auto [r_b, p_b] = nb_params_b;
+            auto [r_n, p_n] = nb_params_null;
 
-        common::logger->trace("Picked: k: {}\tn: {} / {}\tpval_min: {}\tn_cutoff: {}",
-                              k_min, k, nelem, config.family_wise_error_rate / k, n_cutoff);
+            long double midpoint = r_a * n / (r_a + r_b);
 
-        common::logger->trace("Running differential tests");
-        std::vector<std::pair<long double, size_t>> nb_pvals;
-        std::vector<long double> eff_sizes;
-        if (fdr) {
-            nb_pvals.resize(kept.num_set_bits(), std::make_pair(1.0, kept.size()));
-            eff_sizes.resize(kept.num_set_bits());
-        }
+            auto get_deviance_a = [&](long double s) {
+                if (s == 0)
+                    return r_a * logl(p_a) * -2.0;
 
-        std::atomic_thread_fence(std::memory_order_release);
-        generate_clean_rows([&](uint64_t row_i, const auto& row, uint64_t bucket_idx) {
-            if (config.test_type == "notest") {
-                size_t count_in = 0;
-                size_t count_out = 0;
-                for (const auto& [j, c] : row) {
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                        ++count_out;
+                return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
+                        + r_a * (logl(r_a) - logl(p_a)))
+                        * 2.0;
+            };
 
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                        ++count_in;
-                }
+            auto get_deviance_b = [&](long double t) {
+                if (t == 0)
+                    return r_b * logl(p_b) * -2.0;
 
-                bool in_kmer = count_in >= config.min_in_recurrence
-                        && count_in <= config.max_in_recurrence;
-                bool out_kmer = count_out >= config.min_out_recurrence
-                        && count_out <= config.max_out_recurrence;
+                return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
+                        + r_b * (logl(r_b) - logl(p_b)))
+                        * 2.0;
+            };
 
-                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                if (in_kmer)
-                    set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
+            auto get_deviance = [&](long double s, long double t) {
+                return get_deviance_a(s) + get_deviance_b(t);
+            };
 
-                if (out_kmer)
-                    set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
+            long double min_dev
+                    = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
+            long double in_dev = get_deviance(in_sum, out_sum);
 
-                return;
-            }
+            if (in_dev > min_dev) {
+                eff_size = static_cast<long double>(in_sum) - midpoint;
+                pval = 0.0;
+                long double base = nb_base
+                        + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n)) / logl(2.0);
+                double l1pa = log1pl(-p_a) / logl(2.0);
+                double l1pb = log1pl(-p_b) / logl(2.0);
 
-            auto set_pval = [&](long double pval, long double eff_size) {
-                if (fdr) {
-                    size_t nb_idx = kept.rank1(row_i) - 1;
-                    nb_pvals[nb_idx].first = pval;
-                    nb_pvals[nb_idx].second = row_i;
-                    eff_sizes[nb_idx] = eff_size;
-                }
-                // if (!config.test_by_unitig) {
-                if (pval * k < config.family_wise_error_rate) {
-                    if (eff_size == 0) {
-                        common::logger->error("Effect size 0 when p-value is {}", pval);
-                        throw std::runtime_error("Pval failure");
+                {
+                    size_t s = 0;
+                    size_t t = n;
+                    long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                         - lgammal(s + 1) - lgammal(t + 1))
+                                    / logl(2.0)
+                            + s * l1pa + t * l1pb;
+                    pval += exp2(base + sbase);
+                    for (++s; s <= n; ++s) {
+                        long double dev = get_deviance(s, t - 1);
+                        if (dev >= in_dev) {
+                            sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
+                                    + log2l(t + 1) + l1pa - l1pb;
+                            --t;
+                            pval += exp2(base + sbase);
+                            if (pval >= config.family_wise_error_rate)
+                                break;
+                        } else {
+                            break;
+                        }
                     }
+                }
 
-                    if (!config.test_by_unitig) {
-                        node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                        if (eff_size > 0) {
-                            set_bit(indicator_in.data(), node, true,
-                                    std::memory_order_relaxed);
-                        } else if (eff_size < 0) {
-                            set_bit(indicator_out.data(), node, true,
-                                    std::memory_order_relaxed);
+                if (pval < config.family_wise_error_rate && get_deviance(n, 0) >= in_dev) {
+                    size_t s = n;
+                    size_t t = 0;
+                    long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                         - lgammal(s + 1) - lgammal(t + 1))
+                                    / logl(2.0)
+                            + s * l1pa + t * l1pb;
+                    pval += exp2(base + sbase);
+                    for (++t; t <= n; ++t) {
+                        long double dev = get_deviance(s - 1, t);
+                        if (dev >= in_dev) {
+                            sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
+                                    + log2l(t + 1) + l1pa - l1pb;
+                            --s;
+                            pval += exp2(base + sbase);
+                            if (pval >= config.family_wise_error_rate)
+                                break;
+                        } else {
+                            break;
                         }
                     }
                 }
-                // } else {
-                //     push_back(pvals_buckets[bucket_idx], pval);
-                //     push_back(eff_size_buckets[bucket_idx], eff_size);
-                // }
-            };
-
-            long double eff_size = 0.0;
-            long double pval = 1.0;
-            if (config.test_type == "poisson_binom") {
-                size_t n = 0;
-                uint64_t s = 0;
+            }
+        } else if (config.test_type == "mwu") {
+            auto generate_a = [&](const auto& callback) {
+                sdsl::bit_vector found(groups.size());
                 for (const auto& [j, c] : row) {
-                    n += static_cast<int64_t>(groups[j] != Group::OTHER)
-                            + (groups[j] == Group::BOTH);
-                    s += (groups[j] == Group::IN);
+                    found[j] = true;
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                        callback(static_cast<long double>(c) / sums[j]);
                 }
-
-                // if (n < n_cutoff)
-                //     return;
-
-                if (!fdr) {
-                    size_t front = n - std::min(n, num_labels_out);
-                    double min_pval = std::min(pb_pvals[n][front], pb_pvals[n].back());
-
-                    if (min_pval * k_min >= config.family_wise_error_rate)
-                        return;
+                for (size_t j = 0; j < found.size(); ++j) {
+                    if (!found[j] && (groups[j] == Group::IN || groups[j] == Group::BOTH))
+                        callback(0.0L);
                 }
-
-                pval = pb_pvals[n][s];
-                eff_size = static_cast<double>(s) - mid_points[n];
-            } else if (config.test_type == "poisson_exact") {
-                size_t n = 0;
-                sdsl::bit_vector found(num_labels_in + num_labels_out);
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
+            };
+            auto generate_b = [&](const auto& callback) {
+                sdsl::bit_vector found(groups.size());
                 for (const auto& [j, c] : row) {
-                    n += c;
                     found[j] = true;
                     if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                        out_sum += c;
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                        in_sum += c;
+                        callback(static_cast<long double>(c) / sums[j]);
                 }
+                for (size_t j = 0; j < found.size(); ++j) {
+                    if (!found[j] && (groups[j] == Group::OUT || groups[j] == Group::BOTH))
+                        callback(0.0L);
+                }
+            };
 
-                if (n < n_cutoff)
-                    return;
-
-                long double p = mu1 / (mu1 + mu2);
-                boost::math::binomial bdist(n, p);
-                if (n != 0) {
-                    auto get_deviance = [&](long double y, long double mu) {
-                        y += 1e-8;
-                        mu += 1e-8;
-                        return 2 * (y * log(y / mu) - y + mu);
-                    };
-
-                    std::vector<long double> devs;
-                    devs.reserve(n + 1);
-                    for (size_t s = 0; s <= n; ++s) {
-                        devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
-                    }
-
-                    pval = 0.0;
-
-                    size_t s = 0;
-                    for (; s <= n; ++s) {
-                        if (devs[s] < devs[in_sum])
-                            break;
-                    }
-                    if (s > 0)
-                        pval += boost::math::cdf(bdist, s - 1);
-
-                    size_t sp = n;
-                    for (; sp >= s; --sp) {
-                        if (devs[sp] < devs[in_sum])
-                            break;
-                    }
-
-                    if (sp < n)
-                        pval += boost::math::cdf(boost::math::complement(bdist, sp));
-
-                    if (pval * k >= config.family_wise_error_rate)
-                        return;
+            std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
 
-                    eff_size = static_cast<long double>(in_sum) - boost::math::mode(bdist);
+        } else if (config.test_type == "cmh") {
+            int64_t in_sum = 0;
+            int64_t out_sum = 0;
+            for (const auto& [j, c] : row) {
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                    out_sum += c;
                 }
 
-            } else if (config.test_type == "nbinom_exact") {
-                // size_t nb_idx = kept.rank1(row_i) - 1;
-                size_t n = 0;
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
-                for (const auto& [j, c] : row) {
-                    n += c;
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                        out_sum += c;
-                    }
-
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                        in_sum += c;
-                    }
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                    in_sum += c;
                 }
+            }
+            eff_size = static_cast<long double>(in_sum) / in_kmers
+                    - static_cast<long double>(out_sum) / out_kmers;
 
-                // if (n < n_cutoff)
-                //     return;
-
-                if (n > 0) {
-                    // long double min_pval = 1.0;
-                    auto [r_a, p_a] = nb_params_a;
-                    auto [r_b, p_b] = nb_params_b;
-                    auto [r_n, p_n] = nb_params_null;
-                    long double min_pval = 1.0;
-                    {
-                        long double l21p = log1p(-p_n) / log2l(2.0);
-                        long double base = nb_base
-                                + (lgammal(n + 1) - lgammal(r_n + n)) / log2l(2.0)
-                                - n * l21p;
-                        auto get_pval = [&](int64_t s) {
-                            int64_t t = n - s;
-                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                                 - lgammal(s + 1) - lgammal(t + 1)
-                                                 + s * log1pl(-p_a) + t * log1pl(-p_b))
-                                    / logl(2.0);
-                            return exp2l(base + sbase);
-                        };
-
-                        long double pval0 = get_pval(0);
-                        long double pvaln = get_pval(n);
-                        min_pval = std::min(pval0, pvaln) * (1 + (pval0 == pvaln));
-                    }
+            int64_t t = total_kmers;
+            int64_t n1 = in_kmers;
+            int64_t n2 = out_kmers;
+            int64_t m1 = in_sum + out_sum;
+            int64_t m2 = t - m1;
+            assert(m2 >= 0);
 
-                    // if (min_pval < config.family_wise_error_rate) {
-                    // nb_pvals[nb_idx].second = row_i;
-                    long double midpoint = r_a * n / (r_a + r_b);
-
-                    auto get_deviance_a = [&](long double s) {
-                        if (s == 0)
-                            return r_a * logl(p_a) * -2.0;
-
-                        return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
-                                + r_a * (logl(r_a) - logl(p_a)))
-                                * 2.0;
-                    };
-
-                    auto get_deviance_b = [&](long double t) {
-                        if (t == 0)
-                            return r_b * logl(p_b) * -2.0;
-
-                        return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
-                                + r_b * (logl(r_b) - logl(p_b)))
-                                * 2.0;
-                    };
-
-                    auto get_deviance = [&](long double s, long double t) {
-                        return get_deviance_a(s) + get_deviance_b(t);
-                    };
-
-                    long double min_dev
-                            = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
-                    long double in_dev = get_deviance(in_sum, out_sum);
-
-                    if (in_dev > min_dev) {
-                        eff_size = static_cast<long double>(in_sum) - midpoint;
-                        pval = 0.0;
-                        long double base = nb_base
-                                + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n))
-                                        / logl(2.0);
-                        double l1pa = log1pl(-p_a) / logl(2.0);
-                        double l1pb = log1pl(-p_b) / logl(2.0);
-
-                        {
-                            size_t s = 0;
-                            size_t t = n;
-                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                                 - lgammal(s + 1) - lgammal(t + 1))
-                                            / logl(2.0)
-                                    + s * l1pa + t * l1pb;
-                            pval += exp2(base + sbase);
-                            for (++s; s <= n; ++s) {
-                                long double dev = get_deviance(s, t - 1);
-                                if (dev >= in_dev) {
-                                    sbase += log2l(r_a + s) - log2l(r_b + t)
-                                            - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
-                                    --t;
-                                    pval += exp2(base + sbase);
-                                    if (pval >= config.family_wise_error_rate)
-                                        break;
-                                } else {
-                                    break;
-                                }
-                            }
-                        }
-                        if (pval < config.family_wise_error_rate
-                            && get_deviance(n, 0) >= in_dev) {
-                            size_t s = n;
-                            size_t t = 0;
-                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                                 - lgammal(s + 1) - lgammal(t + 1))
-                                            / logl(2.0)
-                                    + s * l1pa + t * l1pb;
-                            pval += exp2(base + sbase);
-                            for (++t; t <= n; ++t) {
-                                long double dev = get_deviance(s - 1, t);
-                                if (dev >= in_dev) {
-                                    sbase -= log2l(r_a + s) - log2l(r_b + t)
-                                            - log2l(s + 1) + log2l(t + 1) + l1pa - l1pb;
-                                    --s;
-                                    pval += exp2(base + sbase);
-                                    if (pval >= config.family_wise_error_rate)
-                                        break;
-                                } else {
-                                    break;
-                                }
-                            }
-                        }
-                        // nb_pvals[nb_idx].first = pval;
-                    }
-                    // } else {
-                    //     nb_pvals[nb_idx].second = row_i;
-                    //     nb_pvals[nb_idx].first = 1.1;
-                    // }
-                }
-            } else if (config.test_type == "mwu") {
-                auto generate_a = [&](const auto& callback) {
-                    sdsl::bit_vector found(groups.size());
-                    for (const auto& [j, c] : row) {
-                        found[j] = true;
-                        if (groups[j] == Group::IN || groups[j] == Group::BOTH)
-                            callback(static_cast<long double>(c) / sums[j]);
-                    }
-                    for (size_t j = 0; j < found.size(); ++j) {
-                        if (!found[j] && (groups[j] == Group::IN || groups[j] == Group::BOTH))
-                            callback(0.0L);
-                    }
-                };
-                auto generate_b = [&](const auto& callback) {
-                    sdsl::bit_vector found(groups.size());
-                    for (const auto& [j, c] : row) {
-                        found[j] = true;
-                        if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
-                            callback(static_cast<long double>(c) / sums[j]);
-                    }
-                    for (size_t j = 0; j < found.size(); ++j) {
-                        if (!found[j] && (groups[j] == Group::OUT || groups[j] == Group::BOTH))
-                            callback(0.0L);
-                    }
-                };
+            if (m2 == 0) {
+                common::logger->error(
+                        "This row has all counts. Too few data points. Use fisher "
+                        "instead");
+                throw std::domain_error("Test failed");
+            }
 
-                std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
-
-                // if (pval < config.family_wise_error_rate) {
-                //     size_t nb_idx = kept.rank1(row_i) - 1;
-                //     nb_pvals[nb_idx].first = pval;
-                //     nb_pvals[nb_idx].second = row_i;
-                // }
-            } else if (config.test_type == "cmh") {
-                // size_t n = 0;
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
-                for (const auto& [j, c] : row) {
-                    // n += c;
-                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                        out_sum += c;
-                    }
+            double lbase = log(n1) + log(n2) + log(m1) + log(m2) - log(t) * 2 - log(t - 1);
+            double factor = static_cast<double>(n1 * m1) / t;
 
-                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                        in_sum += c;
-                    }
-                }
-                eff_size = static_cast<long double>(in_sum) / in_kmers
-                        - static_cast<long double>(out_sum) / out_kmers;
-                // // int64_t a = in_sum;
-                // int64_t b = in_kmers - in_sum;
-                // int64_t c = out_sum;
-                // // int64_t d = out_kmers - out_sum;
-                // // int64_t N = total_kmers;
-                // if (b + c >= 25) {
-                //     long double stat = static_cast<long double>(pow(b - c, 2.0)) / (b + c);
-                //     pval = boost::math::cdf(
-                //             boost::math::complement(boost::math::chi_squared(1), stat));
-                // } else if (b >= c) {
-                //     pval = boost::math::cdf(
-                //             boost::math::complement(boost::math::binomial(b + c, 0.5), b - 1));
-                // } else {
-                //     throw std::runtime_error("CMH not implemented for this case");
-                // }
-                int64_t t = total_kmers;
-                int64_t n1 = in_kmers;
-                int64_t n2 = out_kmers;
-                int64_t m1 = in_sum + out_sum;
-                int64_t m2 = t - m1;
-                assert(m2 >= 0);
-
-                if (m2 == 0) {
-                    common::logger->error(
-                            "This row has all counts. Too few data points. Use fisher "
-                            "instead");
-                    throw std::domain_error("Test failed");
-                }
+            auto get_chi_stat = [&](int64_t a) {
+                int64_t b = n1 - a;
+                int64_t c = m1 - a;
+                int64_t d = m2 - b;
+                assert(d == n2 - c);
 
-                double lbase
-                        = log(n1) + log(n2) + log(m1) + log(m2) - log(t) * 2 - log(t - 1);
-                double factor = static_cast<double>(n1 * m1) / t;
+                if (b < 0 || c < 0 || d < 0)
+                    return 0.0;
 
-                auto get_chi_stat = [&](int64_t a) {
-                    int64_t b = n1 - a;
-                    int64_t c = m1 - a;
-                    int64_t d = m2 - b;
-                    assert(d == n2 - c);
+                double a_shift = static_cast<double>(a) - factor;
 
-                    if (b < 0 || c < 0 || d < 0)
-                        return 0.0;
+                if (a_shift > 0)
+                    return exp(log(a_shift) * 2.0 - lbase);
 
-                    double a_shift = static_cast<double>(a) - factor;
+                return a_shift * a_shift / exp(lbase);
+            };
 
-                    if (a_shift > 0)
-                        return exp(log(a_shift) * 2.0 - lbase);
+            double chi_stat = get_chi_stat(in_sum);
+            if (chi_stat <= 0) {
+                common::logger->error(
+                        "Test statistic {} <= 0, too few data points. Use fisher "
+                        "instead.\t{}\t{},{},{},{}\t{}\t{}",
+                        chi_stat, in_sum, n1, n2, m1, m2, t, factor);
+                throw std::domain_error("Test failed");
+            }
 
-                    return a_shift * a_shift / exp(lbase);
-                };
+            double max_chi_stat = std::max(get_chi_stat(0), get_chi_stat(std::min(n1, m1)));
+            if (max_chi_stat <= 0) {
+                common::logger->error(
+                        "Best test statistic {} <= 0, too few data points. Use "
+                        "fisher instead.\t{},{},{},{}\t{}\t{}",
+                        max_chi_stat, n1, n2, m1, m2, t, factor);
+                throw std::domain_error("Test failed");
+            }
 
-                double chi_stat = get_chi_stat(in_sum);
-                if (chi_stat <= 0) {
-                    common::logger->error(
-                            "Test statistic {} <= 0, too few data points. Use fisher "
-                            "instead.\t{}\t{},{},{},{}\t{}\t{}",
-                            chi_stat, in_sum, n1, n2, m1, m2, t, factor);
-                    throw std::domain_error("Test failed");
-                }
+            pval = boost::math::cdf(
+                    boost::math::complement(boost::math::chi_squared(1), chi_stat));
+        }
 
-                double max_chi_stat
-                        = std::max(get_chi_stat(0), get_chi_stat(std::min(n1, m1)));
-                if (max_chi_stat <= 0) {
-                    common::logger->error(
-                            "Best test statistic {} <= 0, too few data points. Use "
-                            "fisher instead.\t{},{},{},{}\t{}\t{}",
-                            max_chi_stat, n1, n2, m1, m2, t, factor);
-                    throw std::domain_error("Test failed");
-                }
+        set_pval(pval, eff_size);
+    });
+    std::atomic_thread_fence(std::memory_order_acquire);
+
+    if (config.test_type != "notest") {
+        uint64_t num_tests = nelem;
+        if (config.test_by_unitig) {
+            num_tests = 0;
+            std::atomic_thread_fence(std::memory_order_release);
+            clean_masked_graph->call_unitigs(
+                    [&](const std::string&, const auto& path) {
+                        std::vector<long double> pvals;
+                        pvals.reserve(path.size());
 
-                pval = boost::math::cdf(
-                        boost::math::complement(boost::math::chi_squared(1), chi_stat));
-                // if (pval < config.family_wise_error_rate) {
-                //     size_t nb_idx = kept.rank1(row_i) - 1;
-                //     nb_pvals[nb_idx].first = pval;
-                //     nb_pvals[nb_idx].second = row_i;
-                // }
-            }
+                        Group last_group = Group::BOTH;
+                        size_t last_i = 0;
 
-            set_pval(pval, eff_size);
-        });
+                        std::vector<size_t> ranks;
+                        ranks.reserve(path.size());
+                        for (node_index node : path) {
+                            size_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                            ranks.emplace_back(kept.rank1(row_i) - 1);
+                        }
 
-        std::atomic_thread_fence(std::memory_order_acquire);
-
-        if (fdr) {
-            size_t num_tests = nb_pvals.size();
-            std::vector<std::pair<long double, size_t>> sorted_unitig_counts;
-            if (config.test_by_unitig) {
-                VectorMap<long double, size_t> pval_to_count;
-                std::mutex mu;
-                common::logger->trace("Combining p-values within unitigs");
-                num_tests = 0;
-                std::atomic_thread_fence(std::memory_order_release);
-                clean_masked_graph->call_unitigs(
-                        [&](const std::string&, const auto& path) {
-                            __atomic_fetch_add(&num_tests, 1, std::memory_order_relaxed);
-                            std::vector<long double> pvals;
-                            pvals.reserve(path.size());
-                            long double agg_eff_size = 0.0;
-                            for (node_index node : path) {
-                                auto row_i = AnnotatedDBG::graph_to_anno_index(node);
-                                size_t nb_idx = kept.rank1(row_i) - 1;
-                                long double pval = nb_pvals[nb_idx].first;
-                                long double eff_size = eff_sizes[nb_idx];
-                                pvals.emplace_back(pval);
-                                agg_eff_size += eff_size;
-                            }
-                            long double agg_pval = combine_pvals(pvals);
-                            for (node_index node : path) {
-                                auto row_i = AnnotatedDBG::graph_to_anno_index(node);
-                                size_t nb_idx = kept.rank1(row_i) - 1;
-                                nb_pvals[nb_idx].first = agg_pval;
-                                if (agg_pval < config.family_wise_error_rate) {
-                                    if (agg_eff_size > 0) {
-                                        set_bit(indicator_in.data(), node, true,
-                                                std::memory_order_relaxed);
-                                    } else {
-                                        set_bit(indicator_out.data(), node, true,
-                                                std::memory_order_relaxed);
+                        for (size_t i = 0; i < path.size(); ++i) {
+                            size_t idx = ranks[i];
+                            const auto& [pval, monotig_id, group, stored_node]
+                                    = all_pvals[idx];
+                            if (group != last_group) {
+                                if (pvals.size()) {
+                                    uint64_t monotig_id
+                                            = __atomic_fetch_add(&num_tests, 1,
+                                                                 std::memory_order_relaxed);
+                                    long double comb_pval = combine_pvals(pvals);
+                                    for (size_t j = last_i; j < i; ++j) {
+                                        size_t jdx = ranks[j];
+                                        std::get<0>(all_pvals[jdx]) = comb_pval;
+                                        std::get<1>(all_pvals[jdx]) = monotig_id;
                                     }
                                 }
+                                pvals.clear();
+                                last_group = group;
+                                last_i = i;
                             }
-                            std::lock_guard<std::mutex> lock(mu);
-                            pval_to_count[agg_pval] += path.size();
-                        },
-                        num_threads);
-                std::atomic_thread_fence(std::memory_order_acquire);
-                sorted_unitig_counts
-                        = const_cast<std::vector<std::pair<long double, size_t>>&&>(
-                                pval_to_count.values_container());
-                std::sort(sorted_unitig_counts.begin(), sorted_unitig_counts.end());
-                for (size_t i = 1; i < sorted_unitig_counts.size(); ++i) {
-                    sorted_unitig_counts[i].second += sorted_unitig_counts[i - 1].second;
-                }
-            }
-
-            // if (false) {
-            common::logger->trace("Correcting p-vals");
-
-            common::logger->trace("Discarded {} / {} untestable hypotheses",
-                                  nelem - num_tests, nelem);
-            nb_pvals.erase(std::remove_if(nb_pvals.begin(), nb_pvals.end(),
-                                          [&](const auto& a) {
-                                              return a.second == kept.size();
-                                          }),
-                           nb_pvals.end());
-            common::logger->trace("Sorting {} / {} p-vals", nb_pvals.size(), num_tests);
-            std::sort(nb_pvals.begin(), nb_pvals.end());
+                            pvals.emplace_back(pval);
+                        }
+                        if (pvals.size()) {
+                            uint64_t monotig_id
+                                    = __atomic_fetch_add(&num_tests, 1,
+                                                         std::memory_order_relaxed);
+                            long double comb_pval = combine_pvals(pvals);
+                            for (size_t j = last_i; j < path.size(); ++j) {
+                                size_t jdx = ranks[j];
+                                std::get<0>(all_pvals[jdx]) = comb_pval;
+                                std::get<1>(all_pvals[jdx]) = monotig_id;
+                            }
+                        }
+                    },
+                    num_threads);
+            std::atomic_thread_fence(std::memory_order_acquire);
+            common::logger->trace("Found {} monotigs", num_tests);
+        }
 
+        common::logger->trace("Correcting p-vals");
+        all_pvals.erase(std::remove_if(all_pvals.begin(), all_pvals.end(),
+                                       [&](const auto& a) {
+                                           return std::get<0>(a)
+                                                   >= config.family_wise_error_rate;
+                                       }),
+                        all_pvals.end());
+        common::logger->trace("Sorting {} / {} p-vals", all_pvals.size(), num_tests);
+        std::sort(all_pvals.begin(), all_pvals.end());
+
+        size_t num_sig = 0;
+        if (all_pvals.size() && std::get<0>(all_pvals[0]) < config.family_wise_error_rate) {
             long double harm = 0.0;
             for (size_t i = 1; i <= num_tests; ++i) {
                 harm += 1.0 / i;
             }
 
-            common::logger->trace("Selecting cut-off");
-            size_t k = 0;
-            auto it = sorted_unitig_counts.begin();
-            for (size_t i = 0; i < nb_pvals.size(); ++i) {
-                if (nb_pvals[i].first >= config.family_wise_error_rate)
-                    break;
-
-                if (it != sorted_unitig_counts.end() && i == it->second)
-                    ++it;
-
-                size_t cur_count = it == sorted_unitig_counts.end()
-                        ? i
-                        : it - sorted_unitig_counts.begin();
-                if (nb_pvals[i].first <= config.family_wise_error_rate * (cur_count + 1)
-                            / num_tests / harm) {
-                    k = std::max(i + 1, k);
-                    node_index node = AnnotatedDBG::anno_to_graph_index(nb_pvals[i].second);
-                    if (!indicator_in[node] && !indicator_out[node]) {
-                        common::logger->error(
-                                "Node {} not marked, but p-value {} significant", node,
-                                nb_pvals[i].first);
-                        throw std::runtime_error("Index fail");
-                    }
+            common::logger->trace("Selecting significant k-mers");
+            size_t last_tig_id = std::numeric_limits<size_t>::max();
+            size_t cur_count = 0;
+            for (const auto& [pval, tig_id, group, node] : all_pvals) {
+                if (tig_id != last_tig_id) {
+                    ++cur_count;
+                    last_tig_id = tig_id;
                 }
-            }
-
-            common::logger->trace(
-                    "Found {} / {} significant p-values. Minimum p-value is {}", k,
-                    nb_pvals.size(), nb_pvals[0].first);
-
-            for (size_t i = k; i < nb_pvals.size(); ++i) {
-                const auto& [pval, row_i] = nb_pvals[i];
-                if (nb_pvals[i].first >= config.family_wise_error_rate)
-                    break;
 
-                node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-                if (!indicator_in[node] && !indicator_out[node]) {
-                    common::logger->error(
-                            "Node {} not marked, but original p-value {} significant",
-                            node, nb_pvals[i].first);
-                    throw std::runtime_error("Index fail");
+                if (pval <= config.family_wise_error_rate * cur_count / num_tests / harm) {
+                    if (group == Group::IN) {
+                        indicator_in[node] = true;
+                        ++num_sig;
+                    } else if (group == Group::OUT) {
+                        indicator_out[node] = true;
+                        ++num_sig;
+                    }
                 }
-                indicator_in[node] = false;
-                indicator_out[node] = false;
             }
         }
+
+        common::logger->trace("Found {} / {} significant p-values.", num_sig, nelem);
     }
 
     std::unique_ptr<utils::TempFile> tmp_file;

From 9fd1544bdb8a23d70194aa95aafe0ef5cf7eaec5 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 26 Mar 2025 20:04:54 +0100
Subject: [PATCH 391/410] fix tests

---
 .clang-format                                 |   4 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 787 +++++++++---------
 2 files changed, 384 insertions(+), 407 deletions(-)

diff --git a/.clang-format b/.clang-format
index 2821d180bb..274694104a 100644
--- a/.clang-format
+++ b/.clang-format
@@ -41,7 +41,7 @@ ColumnLimit: 90
 CompactNamespaces: false
 ConstructorInitializerAllOnOneLineOrOnePerLine: true
 ConstructorInitializerIndentWidth: 4
-ContinuationIndentWidth: 8
+ContinuationIndentWidth: 4
 Cpp11BracedListStyle: false
 DerivePointerAlignment: false
 DisableFormat: false
@@ -51,11 +51,13 @@ ForEachMacros:
   - foreach
   - Q_FOREACH
   - BOOST_FOREACH
+IncludeBlocks: Regroup
 IndentCaseLabels: true
 IndentPPDirectives: None
 IndentWidth: 4
 IndentWrappedFunctionNames: false
 KeepEmptyLinesAtTheStartOfBlocks: false
+LambdaBodyIndentation: Signature
 MaxEmptyLinesToKeep: 2
 NamespaceIndentation: None
 PenaltyBreakAssignment: 2
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index b1785de4c7..5b768ee3f9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -52,7 +52,7 @@ enum Group { IN, OUT, OTHER, BOTH };
 
 template <class To, class From>
 std::enable_if_t<sizeof(To) == sizeof(From) && std::is_trivially_copyable_v<From>
-                         && std::is_trivially_copyable_v<To>,
+                     && std::is_trivially_copyable_v<To>,
                  To>
 // constexpr support needs compiler magic
 bit_cast(const From& src) noexcept {
@@ -132,27 +132,26 @@ get_rp(const Generator& generate) {
 
     auto get_dl_a = [&](long double a) {
         long double dl = mu * mu * total / (1.0 + a * mu) + total * logl(1 + a * mu) / a / a
-                + total / a / a * boost::math::digamma(1.0 / a);
-        generate([&](auto k, auto c) {
-            dl -= boost::math::digamma(k + 1.0 / a) / a / a * c;
-        });
+            + total / a / a * boost::math::digamma(1.0 / a);
+        generate(
+            [&](auto k, auto c) { dl -= boost::math::digamma(k + 1.0 / a) / a / a * c; });
         return dl;
     };
 
     long double r1 = 0.0;
     long double r2 = 0.0;
     try {
-        auto [r_min, r_max] = boost::math::tools::bisect(
-                get_dl_r, std::numeric_limits<double>::min(), 1.0,
-                boost::math::tools::eps_tolerance<long double>(5));
+        auto [r_min, r_max]
+            = boost::math::tools::bisect(get_dl_r, std::numeric_limits<double>::min(), 1.0,
+                                         boost::math::tools::eps_tolerance<long double>(5));
         r1 = (r_min + r_max) / 2.0;
     } catch (boost::wrapexcept<boost::math::evaluation_error>& e) {
     }
 
     try {
-        auto [a_min, a_max] = boost::math::tools::bisect(
-                get_dl_a, std::numeric_limits<double>::min(), 1.0,
-                boost::math::tools::eps_tolerance<long double>(5));
+        auto [a_min, a_max]
+            = boost::math::tools::bisect(get_dl_a, std::numeric_limits<double>::min(), 1.0,
+                                         boost::math::tools::eps_tolerance<long double>(5));
         r2 = (1.0 / a_min + 1.0 / a_max) / 2.0;
     } catch (boost::wrapexcept<boost::math::evaluation_error>& e) {
     }
@@ -271,17 +270,17 @@ std::tuple<std::shared_ptr<DeBruijnGraph>,
            PValStorage,
            std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(
-        std::shared_ptr<const DeBruijnGraph> graph_ptr,
-        const HistGetter& get_hist_map,
-        const Generator& generate_rows,
-        const UnitigGenerator& generate_unitigs,
-        const std::vector<Group>& groups,
-        const DifferentialAssemblyConfig& config,
-        size_t num_threads = 1,
-        std::filesystem::path tmp_dir = "",
-        size_t num_parallel_files = std::numeric_limits<size_t>::max(),
-        const std::function<void()>& deallocate = []() {},
-        uint8_t max_width = 64) {
+    std::shared_ptr<const DeBruijnGraph> graph_ptr,
+    const HistGetter& get_hist_map,
+    const Generator& generate_rows,
+    const UnitigGenerator& generate_unitigs,
+    const std::vector<Group>& groups,
+    const DifferentialAssemblyConfig& config,
+    size_t num_threads = 1,
+    std::filesystem::path tmp_dir = "",
+    size_t num_parallel_files = std::numeric_limits<size_t>::max(),
+    const std::function<void()>& deallocate = []() {},
+    uint8_t max_width = 64) {
     if (auto canonical = std::dynamic_pointer_cast<const CanonicalDBG>(graph_ptr))
         graph_ptr = canonical->get_graph_ptr();
 
@@ -317,24 +316,24 @@ mask_nodes_by_label_dual(
 
             // set cutoff for lower end of distribution
             auto [mean_est, nzeros_est] = estimate_ztp_mean(
-                    [&](const auto& callback) {
-                        for (const auto& [k, c] : hists_map[j]) {
-                            if (k > 0 && k <= N_BUCKETS_FOR_ESTIMATION)
-                                callback(k, c);
-                        }
-                    },
-                    max_width, 0, N_BUCKETS_FOR_ESTIMATION);
+                [&](const auto& callback) {
+                    for (const auto& [k, c] : hists_map[j]) {
+                        if (k > 0 && k <= N_BUCKETS_FOR_ESTIMATION)
+                            callback(k, c);
+                    }
+                },
+                max_width, 0, N_BUCKETS_FOR_ESTIMATION);
 
             min_counts[j]
-                    = boost::math::quantile(boost::math::poisson(mean_est),
-                                            exp(-mean_est) - CDF_CUTOFF * expm1(-mean_est));
+                = boost::math::quantile(boost::math::poisson(mean_est),
+                                        exp(-mean_est) - CDF_CUTOFF * expm1(-mean_est));
         }
     }
 
     auto correct_pvals = [&config](const std::vector<std::pair<long double, size_t>>& m) {
         size_t total
-                = std::accumulate(m.begin(), m.end(), size_t(0),
-                                  [](size_t sum, const auto& a) { return sum + a.second; });
+            = std::accumulate(m.begin(), m.end(), size_t(0),
+                              [](size_t sum, const auto& a) { return sum + a.second; });
         size_t acc = 0;
 
         for (size_t n = 0; n < m.size(); ++n) {
@@ -362,12 +361,12 @@ mask_nodes_by_label_dual(
     bit_vector_stat kept(kept_bv);
 
     clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
-            graph_ptr,
-            [&](node_index node) {
-                return node != DeBruijnGraph::npos
-                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
-            },
-            true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+        graph_ptr,
+        [&](node_index node) {
+            return node != DeBruijnGraph::npos
+                && kept[AnnotatedDBG::graph_to_anno_index(node)];
+        },
+        true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
     sdsl::bit_vector indicator_in;
     sdsl::bit_vector indicator_out;
@@ -404,9 +403,9 @@ mask_nodes_by_label_dual(
                 return;
 
             bool in_kmer = count_in >= config.min_in_recurrence
-                    && count_in <= config.max_in_recurrence;
+                && count_in <= config.max_in_recurrence;
             bool out_kmer = count_out >= config.min_out_recurrence
-                    && count_out <= config.max_out_recurrence;
+                && count_out <= config.max_out_recurrence;
 
             if (in_kmer || out_kmer) {
                 callback(row_i, row, bucket_idx);
@@ -444,6 +443,8 @@ mask_nodes_by_label_dual(
     uint64_t max_out_obs_val = 0.0;
 
     int64_t total_kmers = 0;
+    size_t in_nkmers = 0;
+    size_t out_nkmers = 0;
     for (size_t j = 0; j < groups.size(); ++j) {
         for (const auto& [k, c] : hists_map[j]) {
             sums[j] += k * c;
@@ -458,20 +459,22 @@ mask_nodes_by_label_dual(
             out_kmers += sums[j];
             out_sq_kmers += sq_sums[j];
             max_out_obs_val += max_obs_vals[j];
+            out_nkmers += n_kmers[j];
         }
 
         if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
             in_kmers += sums[j];
             in_sq_kmers += sq_sums[j];
             max_in_obs_val += max_obs_vals[j];
+            in_nkmers += n_kmers[j];
         }
 
         total_kmers += sums[j];
         total_sq_kmers += sq_sums[j];
 
         common::logger->trace(
-                "{}: n_unique: {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}",
-                j, n_kmers[j], sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
+            "{}: n_unique: {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}", j,
+            n_kmers[j], sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
     }
 
     common::logger->trace("Number of kept unique k-mers: {}\tNumber of kept k-mers: {}",
@@ -480,7 +483,7 @@ mask_nodes_by_label_dual(
     std::vector<std::vector<std::pair<uint64_t, size_t>>> hists(groups.size());
     for (size_t j = 0; j < hists.size(); ++j) {
         hists[j] = const_cast<std::vector<std::pair<uint64_t, size_t>>&&>(
-                hists_map[j].values_container());
+            hists_map[j].values_container());
         std::sort(hists[j].begin(), hists[j].end(), utils::LessFirst());
         hists_map[j].clear();
     }
@@ -489,12 +492,12 @@ mask_nodes_by_label_dual(
     std::vector<size_t> kmer_to_unitig;
     std::vector<std::tuple<size_t, size_t, size_t>> counts;
     clean_masked_graph = std::make_unique<MaskedDeBruijnGraph>(
-            graph_ptr,
-            [&](node_index node) {
-                return node != DeBruijnGraph::npos
-                        && kept[AnnotatedDBG::graph_to_anno_index(node)];
-            },
-            true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+        graph_ptr,
+        [&](node_index node) {
+            return node != DeBruijnGraph::npos
+                && kept[AnnotatedDBG::graph_to_anno_index(node)];
+        },
+        true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
     // precompute negative binomial fits for poisson_bayes and nbinom_exact tests
     std::vector<std::pair<long double, long double>> nb_params(groups.size());
@@ -517,10 +520,10 @@ mask_nodes_by_label_dual(
                 });
                 nb_params[j] = std::make_pair(r, p);
                 common::logger->trace(
-                        "{}: size: {}\tmax_val: {}\tsample mean: {}\tsample var: "
-                        "{}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
-                        j, sums[j], (hist.end() - 1)->first, mu, var, r * (1 - p) / p,
-                        r * (1 - p) / p / p, r, p);
+                    "{}: size: {}\tmax_val: {}\tsample mean: {}\tsample var: "
+                    "{}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
+                    j, sums[j], (hist.end() - 1)->first, mu, var, r * (1 - p) / p,
+                    r * (1 - p) / p / p, r, p);
             }
         }
         // based on this method for combining gamma distributions
@@ -571,10 +574,10 @@ mask_nodes_by_label_dual(
 
         nb_base = (lgammal(nb_params_null.first) - lgammal(nb_params_a.first)
                    - lgammal(nb_params_b.first))
-                / logl(2.0);
+            / logl(2.0);
         nb_base += nb_params_a.first * log2l(nb_params_a.second)
-                + nb_params_b.first * log2l(nb_params_b.second)
-                - nb_params_null.first * log2l(nb_params_null.second);
+            + nb_params_b.first * log2l(nb_params_b.second)
+            - nb_params_null.first * log2l(nb_params_null.second);
     }
 
     long double mu1 = static_cast<long double>(in_kmers) / nelem;
@@ -607,7 +610,7 @@ mask_nodes_by_label_dual(
                 pmf_out_cur[pmf_out.size()] = p[i - 1] * pmf_out.back();
                 for (size_t k = 1; k < pmf_out.size(); ++k) {
                     pmf_out_cur[k]
-                            = p[i - 1] * pmf_out[k - 1] + (1.0L - p[i - 1]) * pmf_out[k];
+                        = p[i - 1] * pmf_out[k - 1] + (1.0L - p[i - 1]) * pmf_out[k];
                 }
                 std::swap(pmf_out_cur, pmf_out);
             }
@@ -628,7 +631,7 @@ mask_nodes_by_label_dual(
             pmf_null_cur[pmf_null.size()] = p[i - 1] * pmf_null.back();
             for (size_t k = 1; k < pmf_null.size(); ++k) {
                 pmf_null_cur[k]
-                        = p[i - 1] * pmf_null[k - 1] + (1.0L - p[i - 1]) * pmf_null[k];
+                    = p[i - 1] * pmf_null[k - 1] + (1.0L - p[i - 1]) * pmf_null[k];
             }
             std::swap(pmf_null_cur, pmf_null);
         }
@@ -666,13 +669,13 @@ mask_nodes_by_label_dual(
                 if (s < pmf_in.size() && t < pmf_out.size()) {
                     if (s < front) {
                         common::logger->error(
-                                "Attempting non-zero p-value in impossible "
-                                "configuration: {},{}",
-                                n, s);
+                            "Attempting non-zero p-value in impossible "
+                            "configuration: {},{}",
+                            n, s);
                         throw std::domain_error("");
                     }
-                    probs.emplace_back(exp2l(log2l(pmf_in[s]) + log2l(pmf_out[t])
-                                             - log2l(pmf_null[n])));
+                    probs.emplace_back(
+                        exp2l(log2l(pmf_in[s]) + log2l(pmf_out[t]) - log2l(pmf_null[n])));
                 } else {
                     probs.emplace_back(0.0);
                 }
@@ -705,7 +708,7 @@ mask_nodes_by_label_dual(
             for (uint64_t s = 0; s < front; ++s) {
                 if (pb_pvals[n][s] > 0) {
                     common::logger->error(
-                            "Non-zero p-value in impossible configuration: {},{}", n, s);
+                        "Non-zero p-value in impossible configuration: {},{}", n, s);
                     throw std::domain_error("");
                 }
             }
@@ -773,12 +776,12 @@ mask_nodes_by_label_dual(
         if (min_pval >= config.family_wise_error_rate) {
             common::logger->warn("No significant p-values achievable");
             auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
-                    graph_ptr, [](node_index) { return false; }, true,
-                    is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+                graph_ptr, [](node_index) { return false; }, true,
+                is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
             auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
-                    graph_ptr, [](node_index) { return false; }, true,
-                    is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+                graph_ptr, [](node_index) { return false; }, true,
+                is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
             return std::make_tuple(masked_graph_in, masked_graph_out, PValStorage {},
                                    nullptr);
@@ -806,9 +809,9 @@ mask_nodes_by_label_dual(
             }
 
             bool in_kmer = count_in >= config.min_in_recurrence
-                    && count_in <= config.max_in_recurrence;
+                && count_in <= config.max_in_recurrence;
             bool out_kmer = count_out >= config.min_out_recurrence
-                    && count_out <= config.max_out_recurrence;
+                && count_out <= config.max_out_recurrence;
 
             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
             if (in_kmer)
@@ -842,7 +845,7 @@ mask_nodes_by_label_dual(
             uint64_t s = 0;
             for (const auto& [j, c] : row) {
                 n += static_cast<int64_t>(groups[j] != Group::OTHER)
-                        + (groups[j] == Group::BOTH);
+                    + (groups[j] == Group::BOTH);
                 s += (groups[j] == Group::IN);
             }
 
@@ -885,7 +888,7 @@ mask_nodes_by_label_dual(
             if (s > 0)
                 pval += boost::math::cdf(bdist, s - 1);
 
-            if (pval < config.family_wise_error_rate) {
+            if (config.test_by_unitig || pval < config.family_wise_error_rate) {
                 size_t sp = n;
                 for (; sp >= s; --sp) {
                     if (devs[sp] < devs[in_sum])
@@ -925,7 +928,7 @@ mask_nodes_by_label_dual(
 
                 return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
                         + r_a * (logl(r_a) - logl(p_a)))
-                        * 2.0;
+                    * 2.0;
             };
 
             auto get_deviance_b = [&](long double t) {
@@ -934,7 +937,7 @@ mask_nodes_by_label_dual(
 
                 return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
                         + r_b * (logl(r_b) - logl(p_b)))
-                        * 2.0;
+                    * 2.0;
             };
 
             auto get_deviance = [&](long double s, long double t) {
@@ -942,14 +945,14 @@ mask_nodes_by_label_dual(
             };
 
             long double min_dev
-                    = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
+                = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
             long double in_dev = get_deviance(in_sum, out_sum);
 
             if (in_dev > min_dev) {
                 eff_size = static_cast<long double>(in_sum) - midpoint;
                 pval = 0.0;
                 long double base = nb_base
-                        + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n)) / logl(2.0);
+                    + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n)) / logl(2.0);
                 double l1pa = log1pl(-p_a) / logl(2.0);
                 double l1pb = log1pl(-p_b) / logl(2.0);
 
@@ -958,17 +961,17 @@ mask_nodes_by_label_dual(
                     size_t t = n;
                     long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
                                          - lgammal(s + 1) - lgammal(t + 1))
-                                    / logl(2.0)
-                            + s * l1pa + t * l1pb;
+                            / logl(2.0)
+                        + s * l1pa + t * l1pb;
                     pval += exp2(base + sbase);
                     for (++s; s <= n; ++s) {
                         long double dev = get_deviance(s, t - 1);
                         if (dev >= in_dev) {
                             sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
-                                    + log2l(t + 1) + l1pa - l1pb;
+                                + log2l(t + 1) + l1pa - l1pb;
                             --t;
                             pval += exp2(base + sbase);
-                            if (pval >= config.family_wise_error_rate)
+                            if (!config.test_by_unitig && pval >= config.family_wise_error_rate)
                                 break;
                         } else {
                             break;
@@ -976,22 +979,23 @@ mask_nodes_by_label_dual(
                     }
                 }
 
-                if (pval < config.family_wise_error_rate && get_deviance(n, 0) >= in_dev) {
+                if ((config.test_by_unitig || pval < config.family_wise_error_rate)
+                    && get_deviance(n, 0) >= in_dev) {
                     size_t s = n;
                     size_t t = 0;
                     long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
                                          - lgammal(s + 1) - lgammal(t + 1))
-                                    / logl(2.0)
-                            + s * l1pa + t * l1pb;
+                            / logl(2.0)
+                        + s * l1pa + t * l1pb;
                     pval += exp2(base + sbase);
                     for (++t; t <= n; ++t) {
                         long double dev = get_deviance(s - 1, t);
                         if (dev >= in_dev) {
                             sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
-                                    + log2l(t + 1) + l1pa - l1pb;
+                                + log2l(t + 1) + l1pa - l1pb;
                             --s;
                             pval += exp2(base + sbase);
-                            if (pval >= config.family_wise_error_rate)
+                            if (!config.test_by_unitig && pval >= config.family_wise_error_rate)
                                 break;
                         } else {
                             break;
@@ -1027,75 +1031,54 @@ mask_nodes_by_label_dual(
 
             std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
 
-        } else if (config.test_type == "cmh") {
+        } else if (config.test_type == "cmh" || config.test_type == "cmh_binary") {
             int64_t in_sum = 0;
             int64_t out_sum = 0;
-            for (const auto& [j, c] : row) {
-                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                    out_sum += c;
+            if (config.test_type == "cmh") {
+                for (const auto& [j, c] : row) {
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                        out_sum += c;
+                    }
+
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                        in_sum += c;
+                    }
                 }
+            } else {
+                for (const auto& [j, c] : row) {
+                    if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+                        ++out_sum;
+                    }
 
-                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                    in_sum += c;
+                    if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+                        ++in_sum;
+                    }
                 }
             }
-            eff_size = static_cast<long double>(in_sum) / in_kmers
-                    - static_cast<long double>(out_sum) / out_kmers;
-
-            int64_t t = total_kmers;
-            int64_t n1 = in_kmers;
-            int64_t n2 = out_kmers;
-            int64_t m1 = in_sum + out_sum;
-            int64_t m2 = t - m1;
-            assert(m2 >= 0);
-
-            if (m2 == 0) {
-                common::logger->error(
-                        "This row has all counts. Too few data points. Use fisher "
-                        "instead");
-                throw std::domain_error("Test failed");
-            }
-
-            double lbase = log(n1) + log(n2) + log(m1) + log(m2) - log(t) * 2 - log(t - 1);
-            double factor = static_cast<double>(n1 * m1) / t;
-
-            auto get_chi_stat = [&](int64_t a) {
-                int64_t b = n1 - a;
-                int64_t c = m1 - a;
-                int64_t d = m2 - b;
-                assert(d == n2 - c);
-
-                if (b < 0 || c < 0 || d < 0)
-                    return 0.0;
 
-                double a_shift = static_cast<double>(a) - factor;
-
-                if (a_shift > 0)
-                    return exp(log(a_shift) * 2.0 - lbase);
-
-                return a_shift * a_shift / exp(lbase);
-            };
-
-            double chi_stat = get_chi_stat(in_sum);
-            if (chi_stat <= 0) {
-                common::logger->error(
-                        "Test statistic {} <= 0, too few data points. Use fisher "
-                        "instead.\t{}\t{},{},{},{}\t{}\t{}",
-                        chi_stat, in_sum, n1, n2, m1, m2, t, factor);
-                throw std::domain_error("Test failed");
+            long double T = config.test_type == "cmh" ? total_kmers : nelem;
+            long double a = in_sum;
+            long double c = out_sum;
+            int64_t ab = config.test_type == "cmh" ? in_kmers : in_nkmers;
+            long double b = ab - in_sum;
+            int64_t cd = config.test_type == "cmh" ? out_kmers : out_nkmers;
+            long double d = cd - out_sum;
+            long double ac = a + c;
+            long double bd = b + d;
+
+            if (b <= 0 || d <= 0) {
+                common::logger->error("a: {}\tb: {}\tc: {}\td: {}", a, b, c, d);
+                throw std::runtime_error("Stat calc fail");
             }
 
-            double max_chi_stat = std::max(get_chi_stat(0), get_chi_stat(std::min(n1, m1)));
-            if (max_chi_stat <= 0) {
-                common::logger->error(
-                        "Best test statistic {} <= 0, too few data points. Use "
-                        "fisher instead.\t{},{},{},{}\t{}\t{}",
-                        max_chi_stat, n1, n2, m1, m2, t, factor);
-                throw std::domain_error("Test failed");
-            }
+            eff_size = log2l(a) + log2l(d) - log2l(b) - log2l(c);
+            long double chi_stat
+                = pow(a - ab * ac / T, 2.0) / ab / ac / cd / bd * T * T * (T - 1.0L);
 
-            pval = boost::math::cdf(
-                    boost::math::complement(boost::math::chi_squared(1), chi_stat));
+            pval = chi_stat > 0
+                ? boost::math::cdf(
+                      boost::math::complement(boost::math::chi_squared(1), chi_stat))
+                : 1.0;
         }
 
         set_pval(pval, eff_size);
@@ -1108,55 +1091,53 @@ mask_nodes_by_label_dual(
             num_tests = 0;
             std::atomic_thread_fence(std::memory_order_release);
             clean_masked_graph->call_unitigs(
-                    [&](const std::string&, const auto& path) {
-                        std::vector<long double> pvals;
-                        pvals.reserve(path.size());
-
-                        Group last_group = Group::BOTH;
-                        size_t last_i = 0;
-
-                        std::vector<size_t> ranks;
-                        ranks.reserve(path.size());
-                        for (node_index node : path) {
-                            size_t row_i = AnnotatedDBG::graph_to_anno_index(node);
-                            ranks.emplace_back(kept.rank1(row_i) - 1);
-                        }
+                [&](const std::string&, const auto& path) {
+                    std::vector<long double> pvals;
+                    pvals.reserve(path.size());
+
+                    Group last_group = Group::BOTH;
+                    size_t last_i = 0;
+
+                    std::vector<size_t> ranks;
+                    ranks.reserve(path.size());
+                    for (node_index node : path) {
+                        size_t row_i = AnnotatedDBG::graph_to_anno_index(node);
+                        ranks.emplace_back(kept.rank1(row_i) - 1);
+                    }
 
-                        for (size_t i = 0; i < path.size(); ++i) {
-                            size_t idx = ranks[i];
-                            const auto& [pval, monotig_id, group, stored_node]
-                                    = all_pvals[idx];
-                            if (group != last_group) {
-                                if (pvals.size()) {
-                                    uint64_t monotig_id
-                                            = __atomic_fetch_add(&num_tests, 1,
-                                                                 std::memory_order_relaxed);
-                                    long double comb_pval = combine_pvals(pvals);
-                                    for (size_t j = last_i; j < i; ++j) {
-                                        size_t jdx = ranks[j];
-                                        std::get<0>(all_pvals[jdx]) = comb_pval;
-                                        std::get<1>(all_pvals[jdx]) = monotig_id;
-                                    }
-                                }
-                                pvals.clear();
-                                last_group = group;
-                                last_i = i;
-                            }
-                            pvals.emplace_back(pval);
-                        }
-                        if (pvals.size()) {
-                            uint64_t monotig_id
+                    for (size_t i = 0; i < path.size(); ++i) {
+                        size_t idx = ranks[i];
+                        const auto& [pval, monotig_id, group, stored_node] = all_pvals[idx];
+                        if (group != last_group) {
+                            if (pvals.size()) {
+                                uint64_t monotig_id
                                     = __atomic_fetch_add(&num_tests, 1,
                                                          std::memory_order_relaxed);
-                            long double comb_pval = combine_pvals(pvals);
-                            for (size_t j = last_i; j < path.size(); ++j) {
-                                size_t jdx = ranks[j];
-                                std::get<0>(all_pvals[jdx]) = comb_pval;
-                                std::get<1>(all_pvals[jdx]) = monotig_id;
+                                long double comb_pval = combine_pvals(pvals);
+                                for (size_t j = last_i; j < i; ++j) {
+                                    size_t jdx = ranks[j];
+                                    std::get<0>(all_pvals[jdx]) = comb_pval;
+                                    std::get<1>(all_pvals[jdx]) = monotig_id;
+                                }
                             }
+                            pvals.clear();
+                            last_group = group;
+                            last_i = i;
                         }
-                    },
-                    num_threads);
+                        pvals.emplace_back(pval);
+                    }
+                    if (pvals.size()) {
+                        uint64_t monotig_id
+                            = __atomic_fetch_add(&num_tests, 1, std::memory_order_relaxed);
+                        long double comb_pval = combine_pvals(pvals);
+                        for (size_t j = last_i; j < path.size(); ++j) {
+                            size_t jdx = ranks[j];
+                            std::get<0>(all_pvals[jdx]) = comb_pval;
+                            std::get<1>(all_pvals[jdx]) = monotig_id;
+                        }
+                    }
+                },
+                num_threads);
             std::atomic_thread_fence(std::memory_order_acquire);
             common::logger->trace("Found {} monotigs", num_tests);
         }
@@ -1165,7 +1146,7 @@ mask_nodes_by_label_dual(
         all_pvals.erase(std::remove_if(all_pvals.begin(), all_pvals.end(),
                                        [&](const auto& a) {
                                            return std::get<0>(a)
-                                                   >= config.family_wise_error_rate;
+                                               >= config.family_wise_error_rate;
                                        }),
                         all_pvals.end());
         common::logger->trace("Sorting {} / {} p-vals", all_pvals.size(), num_tests);
@@ -1208,12 +1189,12 @@ mask_nodes_by_label_dual(
     common::logger->trace("Done! Assembling contigs.");
 
     auto masked_graph_in = std::make_shared<MaskedDeBruijnGraph>(
-            graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
-            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+        graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_in)), true,
+        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
     auto masked_graph_out = std::make_shared<MaskedDeBruijnGraph>(
-            graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_out)), true,
-            is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
+        graph_ptr, std::make_unique<bitmap_vector>(std::move(indicator_out)), true,
+        is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
     return std::make_tuple(masked_graph_in, masked_graph_out, std::move(pvals),
                            std::move(tmp_file));
@@ -1253,141 +1234,138 @@ mask_nodes_by_label_dual(std::shared_ptr<const DeBruijnGraph> graph_ptr,
     }
 
     return std::visit(
-            [&](auto&& column_values_all) {
-                using ColumnValues = typename std::decay<decltype(column_values_all)>::type;
-                using ValuesContainerPtr = typename ColumnValues::value_type;
-                using ValuesContainer =
-                        typename std::decay<typename ValuesContainerPtr::element_type>::type;
-                using value_type = typename ValuesContainer::value_type;
-                using PairContainer = Vector<std::pair<uint64_t, value_type>>;
-
-                std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
-                std::vector<Group> groups(total_labels);
-                if (files.empty()) {
-                    throw std::runtime_error("No files provided");
-                }
+        [&](auto&& column_values_all) {
+            using ColumnValues = typename std::decay<decltype(column_values_all)>::type;
+            using ValuesContainerPtr = typename ColumnValues::value_type;
+            using ValuesContainer =
+                typename std::decay<typename ValuesContainerPtr::element_type>::type;
+            using value_type = typename ValuesContainer::value_type;
+            using PairContainer = Vector<std::pair<uint64_t, value_type>>;
+
+            std::vector<std::unique_ptr<const bit_vector>> columns_all(total_labels);
+            std::vector<Group> groups(total_labels);
+            if (files.empty()) {
+                throw std::runtime_error("No files provided");
+            }
 
-                if (std::filesystem::exists(files[0] + ".counts")) {
-                    column_values_all.resize(total_labels);
-                    annot::ColumnCompressed<>::load_columns_and_values(
-                            files,
-                            [&](uint64_t offset, const Label& label,
-                                std::unique_ptr<bit_vector>&& column,
-                                ValuesContainer&& column_values) {
-                                bool is_in = labels_in.count(label);
-                                bool is_out = labels_out.count(label);
-                                if (is_in) {
-                                    groups[offset] = !is_out ? Group::IN : Group::BOTH;
-                                } else {
-                                    groups[offset] = is_out ? Group::OUT : Group::OTHER;
-                                }
-                                columns_all[offset].reset(column.release());
-                                column_values_all[offset]
-                                        = std::make_unique<ValuesContainer>(
-                                                std::move(column_values));
-                            },
-                            num_parallel_files);
-                } else {
-                    annot::ColumnCompressed<>::merge_load(
-                            files,
-                            [&](uint64_t offset, const Label& label,
-                                std::unique_ptr<bit_vector>&& column) {
-                                bool is_in = labels_in.count(label);
-                                bool is_out = labels_out.count(label);
-                                if (is_in) {
-                                    groups[offset] = !is_out ? Group::IN : Group::BOTH;
-                                } else {
-                                    groups[offset] = is_out ? Group::OUT : Group::OTHER;
-                                }
-                                columns_all[offset].reset(column.release());
-                            },
-                            num_parallel_files);
-                }
+            if (std::filesystem::exists(files[0] + ".counts")) {
+                column_values_all.resize(total_labels);
+                annot::ColumnCompressed<>::load_columns_and_values(
+                    files,
+                    [&](uint64_t offset, const Label& label,
+                        std::unique_ptr<bit_vector>&& column,
+                        ValuesContainer&& column_values) {
+                        bool is_in = labels_in.count(label);
+                        bool is_out = labels_out.count(label);
+                        if (is_in) {
+                            groups[offset] = !is_out ? Group::IN : Group::BOTH;
+                        } else {
+                            groups[offset] = is_out ? Group::OUT : Group::OTHER;
+                        }
+                        columns_all[offset].reset(column.release());
+                        column_values_all[offset]
+                            = std::make_unique<ValuesContainer>(std::move(column_values));
+                    },
+                    num_parallel_files);
+            } else {
+                annot::ColumnCompressed<>::merge_load(
+                    files,
+                    [&](uint64_t offset, const Label& label,
+                        std::unique_ptr<bit_vector>&& column) {
+                        bool is_in = labels_in.count(label);
+                        bool is_out = labels_out.count(label);
+                        if (is_in) {
+                            groups[offset] = !is_out ? Group::IN : Group::BOTH;
+                        } else {
+                            groups[offset] = is_out ? Group::OUT : Group::OTHER;
+                        }
+                        columns_all[offset].reset(column.release());
+                    },
+                    num_parallel_files);
+            }
 
-                uint8_t max_width = 0;
-                for (const auto& col_vals : column_values_all) {
-                    max_width = std::max(max_width, col_vals->width());
-                }
+            uint8_t max_width = 0;
+            for (const auto& col_vals : column_values_all) {
+                max_width = std::max(max_width, col_vals->width());
+            }
 
-                auto generate_rows = [&](const auto& callback) {
-                    utils::call_rows<std::unique_ptr<const bit_vector>,
-                                     std::unique_ptr<const ValuesContainer>, PairContainer, false>(
-                            columns_all, column_values_all, callback);
-                };
-
-                auto generate_unitigs = [&](const DeBruijnGraph&, const auto&) {
-                    throw std::runtime_error(
-                            "Unitigs not implemented for column annotations");
-                };
-
-                bool parallel = get_num_threads() > 1;
-
-                return mask_nodes_by_label_dual<value_type, PValStorage, PairContainer>(
-                        graph_ptr,
-                        [&](const std::vector<size_t>& min_counts, sdsl::bit_vector* kept)
-                                -> std::vector<VectorMap<uint64_t, size_t>> {
-                            common::logger->trace("Calculating count histograms");
-                            std::vector<std::vector<VectorMap<uint64_t, size_t>>> hists_map_p(
-                                    num_parallel_files);
-                            for (auto& hists_map : hists_map_p) {
-                                hists_map.resize(groups.size());
-                            }
+            auto generate_rows = [&](const auto& callback) {
+                utils::call_rows<std::unique_ptr<const bit_vector>,
+                                 std::unique_ptr<const ValuesContainer>, PairContainer, false>(
+                    columns_all, column_values_all, callback);
+            };
 
-                            std::atomic_thread_fence(std::memory_order_release);
-                            generate_rows([&](uint64_t row_i, const auto& row,
-                                              size_t thread_id) {
-                                if (row.empty()) {
-                                    if (kept)
-                                        unset_bit(kept->data(), row_i, parallel,
-                                                  std::memory_order_relaxed);
+            auto generate_unitigs = [&](const DeBruijnGraph&, const auto&) {
+                throw std::runtime_error(
+                    "Unitigs not implemented for column annotations");
+            };
 
-                                    return;
-                                }
+            bool parallel = get_num_threads() > 1;
 
-                                bool found = false;
-                                Vector<uint64_t> counts(groups.size());
-                                for (const auto& [j, raw_c] : row) {
-                                    if (min_counts.empty() || raw_c >= min_counts[j]) {
-                                        counts[j] = raw_c;
-                                        found = true;
-                                    }
-                                }
+            return mask_nodes_by_label_dual<value_type, PValStorage, PairContainer>(
+                graph_ptr,
+                [&](const std::vector<size_t>& min_counts,
+                    sdsl::bit_vector* kept) -> std::vector<VectorMap<uint64_t, size_t>> {
+                    common::logger->trace("Calculating count histograms");
+                    std::vector<std::vector<VectorMap<uint64_t, size_t>>> hists_map_p(
+                        num_parallel_files);
+                    for (auto& hists_map : hists_map_p) {
+                        hists_map.resize(groups.size());
+                    }
 
-                                if (!found) {
-                                    if (kept)
-                                        unset_bit(kept->data(), row_i, parallel,
-                                                  std::memory_order_relaxed);
+                    std::atomic_thread_fence(std::memory_order_release);
+                    generate_rows([&](uint64_t row_i, const auto& row, size_t thread_id) {
+                        if (row.empty()) {
+                            if (kept)
+                                unset_bit(kept->data(), row_i, parallel,
+                                          std::memory_order_relaxed);
 
-                                    return;
-                                }
+                            return;
+                        }
 
-                                for (size_t j = 0; j < counts.size(); ++j) {
-                                    ++hists_map_p[thread_id][j][counts[j]];
-                                }
-                            });
-                            std::atomic_thread_fence(std::memory_order_acquire);
-
-                            common::logger->trace("Merging histograms");
-                            for (size_t thread_id = 1; thread_id < hists_map_p.size();
-                                 ++thread_id) {
-                                for (size_t j = 0; j < hists_map_p[thread_id].size(); ++j) {
-                                    for (const auto& [k, c] : hists_map_p[thread_id][j]) {
-                                        hists_map_p[0][j][k] += c;
-                                    }
-                                }
+                        bool found = false;
+                        Vector<uint64_t> counts(groups.size());
+                        for (const auto& [j, raw_c] : row) {
+                            if (min_counts.empty() || raw_c >= min_counts[j]) {
+                                counts[j] = raw_c;
+                                found = true;
                             }
+                        }
 
-                            return hists_map_p[0];
-                        },
-                        generate_rows, generate_unitigs, groups, config, num_threads,
-                        tmp_dir, num_parallel_files,
-                        [&]() {
-                            columns_all.clear();
-                            column_values_all.clear();
-                        },
-                        max_width);
-            },
-            column_values_all);
+                        if (!found) {
+                            if (kept)
+                                unset_bit(kept->data(), row_i, parallel,
+                                          std::memory_order_relaxed);
+
+                            return;
+                        }
+
+                        for (size_t j = 0; j < counts.size(); ++j) {
+                            ++hists_map_p[thread_id][j][counts[j]];
+                        }
+                    });
+                    std::atomic_thread_fence(std::memory_order_acquire);
+
+                    common::logger->trace("Merging histograms");
+                    for (size_t thread_id = 1; thread_id < hists_map_p.size(); ++thread_id) {
+                        for (size_t j = 0; j < hists_map_p[thread_id].size(); ++j) {
+                            for (const auto& [k, c] : hists_map_p[thread_id][j]) {
+                                hists_map_p[0][j][k] += c;
+                            }
+                        }
+                    }
+
+                    return hists_map_p[0];
+                },
+                generate_rows, generate_unitigs, groups, config, num_threads, tmp_dir,
+                num_parallel_files,
+                [&]() {
+                    columns_all.clear();
+                    column_values_all.clear();
+                },
+                max_width);
+        },
+        column_values_all);
 }
 
 template std::tuple<std::shared_ptr<DeBruijnGraph>,
@@ -1421,12 +1399,12 @@ std::tuple<std::shared_ptr<DeBruijnGraph>,
            PValStorage,
            std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual(
-        const AnnotatedDBG& anno_graph,
-        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>& labels_in,
-        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>& labels_out,
-        const DifferentialAssemblyConfig& config,
-        size_t num_threads,
-        std::filesystem::path tmp_dir) {
+    const AnnotatedDBG& anno_graph,
+    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>& labels_in,
+    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>& labels_out,
+    const DifferentialAssemblyConfig& config,
+    size_t num_threads,
+    std::filesystem::path tmp_dir) {
     num_threads = get_num_threads();
     common::logger->trace("Labels in: {}", fmt::join(labels_in, ","));
     common::logger->trace("Labels out: {}", fmt::join(labels_out, ","));
@@ -1447,7 +1425,7 @@ mask_nodes_by_label_dual(
     }
 
     auto graph_ptr
-            = std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr());
+        = std::dynamic_pointer_cast<const DeBruijnGraph>(anno_graph.get_graph_ptr());
 
     const auto& matrix = annotation.get_matrix();
     using Row = annot::matrix::BinaryMatrix::Row;
@@ -1459,27 +1437,27 @@ mask_nodes_by_label_dual(
 
         auto generate_unitigs = [&](const DeBruijnGraph& graph, const auto& callback) {
             graph.call_unitigs(
-                    [&](const std::string&, const auto& path) {
-                        std::vector<annot::matrix::BinaryMatrix::Row> rows;
-                        rows.reserve(path.size());
-                        for (auto node : path) {
-                            rows.emplace_back(AnnotatedDBG::graph_to_anno_index(node));
-                        }
-                        auto row_values = int_matrix->get_row_values(rows);
-                        callback(path, row_values);
-                    },
-                    num_threads);
+                [&](const std::string&, const auto& path) {
+                    std::vector<annot::matrix::BinaryMatrix::Row> rows;
+                    rows.reserve(path.size());
+                    for (auto node : path) {
+                        rows.emplace_back(AnnotatedDBG::graph_to_anno_index(node));
+                    }
+                    auto row_values = int_matrix->get_row_values(rows);
+                    callback(path, row_values);
+                },
+                num_threads);
         };
 
         return mask_nodes_by_label_dual<value_type, PValStorage, PairContainer>(
-                graph_ptr,
-                [&](const std::vector<size_t>& min_counts, sdsl::bit_vector* unmark_discarded) {
-                    return int_matrix->get_histograms(min_counts, unmark_discarded);
-                },
-                [&](const auto& callback) {
-                    int_matrix->call_row_values(callback, false /* ordered */);
-                },
-                generate_unitigs, groups, config, num_threads, tmp_dir, num_threads);
+            graph_ptr,
+            [&](const std::vector<size_t>& min_counts, sdsl::bit_vector* unmark_discarded) {
+                return int_matrix->get_histograms(min_counts, unmark_discarded);
+            },
+            [&](const auto& callback) {
+                int_matrix->call_row_values(callback, false /* ordered */);
+            },
+            generate_unitigs, groups, config, num_threads, tmp_dir, num_threads);
     } else {
         auto generate_bit_rows = [&](const auto& callback) {
             size_t n = matrix.num_rows();
@@ -1511,65 +1489,62 @@ mask_nodes_by_label_dual(
         using value_type = annot::matrix::IntMatrix::Value;
         using PairContainer = Vector<std::pair<uint64_t, value_type>>;
         return mask_nodes_by_label_dual<uint64_t, PValStorage, PairContainer>(
-                graph_ptr,
-                [&](const std::vector<size_t>&,
-                    sdsl::bit_vector* kept) -> std::vector<VectorMap<uint64_t, size_t>> {
-                    common::logger->trace("Calculating count histograms");
-                    std::vector<std::vector<VectorMap<uint64_t, size_t>>> hists_map_p(
-                            num_threads);
-                    for (auto& hists_map : hists_map_p) {
-                        hists_map.resize(groups.size());
+            graph_ptr,
+            [&](const std::vector<size_t>&,
+                sdsl::bit_vector* kept) -> std::vector<VectorMap<uint64_t, size_t>> {
+                common::logger->trace("Calculating count histograms");
+                std::vector<std::vector<VectorMap<uint64_t, size_t>>> hists_map_p(num_threads);
+                for (auto& hists_map : hists_map_p) {
+                    hists_map.resize(groups.size());
+                }
+                std::atomic_thread_fence(std::memory_order_release);
+                generate_bit_rows([&](uint64_t row_i, const auto& set_bits, size_t thread_id) {
+                    if (set_bits.empty()) {
+                        if (kept)
+                            unset_bit(kept->data(), row_i, parallel,
+                                      std::memory_order_relaxed);
+
+                        return;
                     }
-                    std::atomic_thread_fence(std::memory_order_release);
-                    generate_bit_rows(
-                            [&](uint64_t row_i, const auto& set_bits, size_t thread_id) {
-                                if (set_bits.empty()) {
-                                    if (kept)
-                                        unset_bit(kept->data(), row_i, parallel,
-                                                  std::memory_order_relaxed);
-
-                                    return;
-                                }
 
-                                std::vector<bool> container(groups.size());
-                                for (auto j : set_bits) {
-                                    container[j] = true;
-                                }
+                    std::vector<bool> container(groups.size());
+                    for (auto j : set_bits) {
+                        container[j] = true;
+                    }
 
-                                for (size_t j = 0; j < container.size(); ++j) {
-                                    ++hists_map_p[thread_id][j][container[j]];
-                                }
-                            });
-                    std::atomic_thread_fence(std::memory_order_acquire);
+                    for (size_t j = 0; j < container.size(); ++j) {
+                        ++hists_map_p[thread_id][j][container[j]];
+                    }
+                });
+                std::atomic_thread_fence(std::memory_order_acquire);
 
-                    common::logger->trace("Merging histograms");
-                    for (size_t thread_id = 1; thread_id < hists_map_p.size(); ++thread_id) {
-                        for (size_t j = 0; j < hists_map_p[thread_id].size(); ++j) {
-                            for (const auto& [k, c] : hists_map_p[thread_id][j]) {
-                                hists_map_p[0][j][k] += c;
-                            }
+                common::logger->trace("Merging histograms");
+                for (size_t thread_id = 1; thread_id < hists_map_p.size(); ++thread_id) {
+                    for (size_t j = 0; j < hists_map_p[thread_id].size(); ++j) {
+                        for (const auto& [k, c] : hists_map_p[thread_id][j]) {
+                            hists_map_p[0][j][k] += c;
                         }
                     }
+                }
 
-                    return hists_map_p[0];
-                },
-                [&](const auto& callback) {
-                    generate_bit_rows(
-                            [&](uint64_t row_i, const auto& set_bits, size_t thread_id) {
-                                Vector<std::pair<uint64_t, uint64_t>> row;
-                                row.reserve(set_bits.size());
-                                for (auto j : set_bits) {
-                                    row.emplace_back(j, 1);
-                                }
+                return hists_map_p[0];
+            },
+            [&](const auto& callback) {
+                generate_bit_rows([&](uint64_t row_i, const auto& set_bits, size_t thread_id) {
+                    Vector<std::pair<uint64_t, uint64_t>> row;
+                    row.reserve(set_bits.size());
+                    for (auto j : set_bits) {
+                        row.emplace_back(j, 1);
+                    }
 
-                                callback(row_i, row, thread_id);
-                            });
-                },
-                [&](const DeBruijnGraph&, const auto&) {
-                    throw std::runtime_error(
-                            "Unitigs not implemented for binary annotations");
-                },
-                groups, config, num_threads, tmp_dir, num_threads);
+                    callback(row_i, row, thread_id);
+                });
+            },
+            [&](const DeBruijnGraph&, const auto&) {
+                throw std::runtime_error(
+                    "Unitigs not implemented for binary annotations");
+            },
+            groups, config, num_threads, tmp_dir, num_threads);
     }
 }
 
@@ -1578,23 +1553,23 @@ template std::tuple<std::shared_ptr<DeBruijnGraph>,
                     std::vector<uint64_t>,
                     std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual<std::vector<uint64_t>>(
-        const AnnotatedDBG&,
-        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
-        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
-        const DifferentialAssemblyConfig&,
-        size_t,
-        std::filesystem::path);
+    const AnnotatedDBG&,
+    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
+    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
+    const DifferentialAssemblyConfig&,
+    size_t,
+    std::filesystem::path);
 template std::tuple<std::shared_ptr<DeBruijnGraph>,
                     std::shared_ptr<DeBruijnGraph>,
                     sdsl::int_vector_buffer<64>,
                     std::unique_ptr<utils::TempFile>>
 mask_nodes_by_label_dual<sdsl::int_vector_buffer<64>>(
-        const AnnotatedDBG&,
-        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
-        const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
-        const DifferentialAssemblyConfig&,
-        size_t,
-        std::filesystem::path);
+    const AnnotatedDBG&,
+    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
+    const tsl::hopscotch_set<typename AnnotatedDBG::Annotator::Label>&,
+    const DifferentialAssemblyConfig&,
+    size_t,
+    std::filesystem::path);
 
 } // namespace graph
 } // namespace mtg

From cfcf098c93f5760782a713e8bacb4bd532ff6f0a Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 27 Mar 2025 22:22:15 +0100
Subject: [PATCH 392/410] minor

---
 .../src/graph/annotated_graph_algorithm.cpp   | 324 ++++++++++++++----
 1 file changed, 263 insertions(+), 61 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5b768ee3f9..d9903d22fa 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -657,7 +657,6 @@ mask_nodes_by_label_dual(
         pb_pvals[0].emplace_back(1.0);
 
         long double min_pval = 1.0;
-        // long double last_local_min_pval = 1.0;
 
         for (size_t n = 1; n < pb_pvals.size(); ++n) {
             std::vector<long double> probs;
@@ -789,11 +788,173 @@ mask_nodes_by_label_dual(
     }
 
     common::logger->trace("Allocating p-value storage");
-    std::vector<std::tuple<long double, size_t, Group, node_index>> all_pvals;
+    std::vector<std::tuple<long double, size_t, long double, node_index>> all_pvals;
     all_pvals.resize(nelem,
                      std::make_tuple(1.0L, graph_ptr->max_index() + 1, Group::OTHER,
                                      graph_ptr->max_index() + 1));
 
+    bool is_discrete = false && !config.test_by_unitig
+        && (config.test_type == "poisson_binom" || config.test_type == "poisson_exact"
+            || config.test_type == "nbinom_exact");
+
+    uint64_t num_tests = nelem;
+
+    std::vector<std::pair<long double, size_t>> min_pvals;
+    long double min_pval_cutoff = 1.0;
+    if (is_discrete) {
+        {
+            std::vector<std::vector<std::pair<long double, size_t>>> min_pvals_b(
+                num_threads + 1);
+            generate_clean_rows([&](uint64_t, const auto& row, uint64_t bucket_id) {
+                size_t n = 0;
+                int64_t in_sum = 0;
+                int64_t out_sum = 0;
+                if (config.test_type == "poisson_binom") {
+                    for (const auto& [j, c] : row) {
+                        n += static_cast<int64_t>(groups[j] != Group::OTHER)
+                            + (groups[j] == Group::BOTH);
+                    }
+                } else {
+                    for (const auto& [j, c] : row) {
+                        if (groups[j] == Group::IN || groups[j] == Group::OUT)
+                            in_sum += c;
+
+                        if (groups[j] == Group::OUT || groups[j] == Group::OUT)
+                            out_sum += c;
+                    }
+                    n = in_sum + out_sum;
+                }
+
+                auto& min_pval_b = min_pvals_b[bucket_id];
+
+                if (min_pval_b.size() <= n)
+                    min_pval_b.resize(n + 1, std::make_pair(1.0L, 0));
+
+                if (!min_pval_b[n].second) {
+                    // compute the min-attainable p-value
+                    if (config.test_type == "poisson_binom") {
+                        size_t front = n - std::min(n, num_labels_out);
+                        size_t back = std::min(n, num_labels_in);
+                        assert(s >= front);
+                        long double pval0 = pb_pvals[n][front];
+                        long double pvaln = pb_pvals[n][back];
+                        min_pval_b[n].first = std::min(pval0, pvaln)
+                            * (1 + (pval0 == pvaln && front != back));
+                    } else if (config.test_type == "poisson_exact") {
+                        boost::math::binomial bdist(n, p);
+                        auto get_deviance = [&](long double y, long double mu) {
+                            y += 1e-8;
+                            mu += 1e-8;
+                            return 2 * (y * log(y / mu) - y + mu);
+                        };
+
+                        long double dev0 = get_deviance(0, mu1) + get_deviance(n, mu2);
+                        long double devn = get_deviance(n, mu1) + get_deviance(0, mu2);
+
+                        min_pval_b[n].first = 0.0L;
+                        if (dev0 >= devn)
+                            min_pval_b[n].first += boost::math::pdf(bdist, 0);
+
+                        if (dev0 <= devn)
+                            min_pval_b[n].first += boost::math::pdf(bdist, n);
+
+                    } else if (config.test_type == "nbinom_exact") {
+                        auto [r_a, p_a] = nb_params_a;
+                        auto [r_b, p_b] = nb_params_b;
+                        auto [r_n, p_n] = nb_params_null;
+
+                        long double base = nb_base
+                            + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n))
+                                / logl(2.0);
+                        double l1pa = log1pl(-p_a) / logl(2.0);
+                        double l1pb = log1pl(-p_b) / logl(2.0);
+
+                        auto get_deviance_a = [&](long double s) {
+                            if (s == 0)
+                                return r_a * logl(p_a) * -2.0;
+
+                            return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
+                                    + r_a * (logl(r_a) - logl(p_a)))
+                                * 2.0;
+                        };
+
+                        auto get_deviance_b = [&](long double t) {
+                            if (t == 0)
+                                return r_b * logl(p_b) * -2.0;
+
+                            return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
+                                    + r_b * (logl(r_b) - logl(p_b)))
+                                * 2.0;
+                        };
+
+                        auto get_deviance = [&](long double s, long double t) {
+                            return get_deviance_a(s) + get_deviance_b(t);
+                        };
+
+                        long double dev0 = get_deviance(0, n);
+                        long double devn = get_deviance(n, 0);
+
+                        min_pval_b[n].first = 0.0;
+                        if (dev0 >= devn) {
+                            size_t s = 0;
+                            size_t t = n;
+                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                                 - lgammal(s + 1) - lgammal(t + 1))
+                                    / logl(2.0)
+                                + s * l1pa + t * l1pb;
+                            min_pval_b[n].first += exp2(base + sbase);
+                        }
+
+                        if (dev0 <= devn) {
+                            size_t s = n;
+                            size_t t = 0;
+                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                                 - lgammal(s + 1) - lgammal(t + 1))
+                                    / logl(2.0)
+                                + s * l1pa + t * l1pb;
+                            min_pval_b[n].first += exp2(base + sbase);
+                        }
+                    }
+                }
+                ++min_pval_b[n].second;
+            });
+            size_t max_size = 0;
+            for (const auto& min_pvals : min_pvals_b) {
+                max_size = std::max(max_size, min_pvals.size());
+            }
+            min_pvals.resize(max_size, std::make_pair(1.0L, 0));
+            for (const auto& min_pval_b : min_pvals_b) {
+                for (size_t n = 0; n < min_pval_b.size(); ++n) {
+                    min_pvals[n].first = std::min(min_pvals[n].first, min_pval_b[n].first);
+                    min_pvals[n].second += min_pval_b[n].second;
+                }
+            }
+        }
+
+        std::sort(min_pvals.rbegin(), min_pvals.rend());
+        long double harm = 0.0;
+        for (size_t i = 1; i <= num_tests; ++i) {
+            harm += 1.0L / i;
+        }
+        for (const auto& [pval, count] : min_pvals) {
+            if (!count)
+                continue;
+
+            // common::logger->trace("Trying:\tc: {}\tpval: {}\tcutoff: {}", num_tests, pval,
+            //                       config.family_wise_error_rate / harm);
+            if (pval >= config.family_wise_error_rate / harm) {
+                min_pval_cutoff = pval;
+                for (size_t i = num_tests; i > num_tests - count; --i) {
+                    harm -= 1.0L / i;
+                }
+                num_tests -= count;
+            }
+        }
+        common::logger->trace(
+            "Only running tests where min pval < {}. Keeping {} / {} tests",
+            min_pval_cutoff, num_tests, nelem);
+    }
+
     common::logger->trace("Starting tests");
     std::atomic_thread_fence(std::memory_order_release);
     generate_clean_rows([&](uint64_t row_i, const auto& row, uint64_t bucket_idx) {
@@ -826,16 +987,7 @@ mask_nodes_by_label_dual(
         auto set_pval = [&](long double pval, long double eff_size) {
             size_t idx = kept.rank1(row_i) - 1;
             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            Group group;
-            if (eff_size > 0) {
-                group = Group::IN;
-            } else if (eff_size < 0) {
-                group = Group::OUT;
-            } else {
-                group = Group::BOTH;
-            }
-
-            all_pvals[idx] = std::tie(pval, node, group, node);
+            all_pvals[idx] = std::tie(pval, node, eff_size, node);
         };
 
         long double eff_size = 0.0;
@@ -849,6 +1001,19 @@ mask_nodes_by_label_dual(
                 s += (groups[j] == Group::IN);
             }
 
+            if (is_discrete) {
+                size_t front = n - std::min(n, num_labels_out);
+                size_t back = std::min(n, num_labels_in);
+                assert(s >= front);
+                long double pval0 = pb_pvals[n][front];
+                long double pvaln = pb_pvals[n][back];
+                long double min_pval
+                    = std::min(pval0, pvaln) * (1 + (pval0 == pvaln && front != back));
+
+                if (min_pval >= min_pval_cutoff)
+                    return;
+            }
+
             pval = pb_pvals[n][s];
             eff_size = static_cast<double>(s) - mid_points[n];
         } else if (config.test_type == "poisson_exact") {
@@ -877,29 +1042,53 @@ mask_nodes_by_label_dual(
                 devs.emplace_back(get_deviance(s, mu1) + get_deviance(n - s, mu2));
             }
 
-            pval = 0.0;
+            if (is_discrete) {
+                long double dev0 = devs[0];
+                long double devn = devs[n];
 
-            size_t s = 0;
-            for (; s <= n; ++s) {
-                if (devs[s] < devs[in_sum])
-                    break;
+                long double min_pval = 0;
+
+                if (dev0 > devn) {
+                    min_pval = boost::math::pdf(bdist, 0);
+                } else if (dev0 < devn) {
+                    min_pval = boost::math::pdf(bdist, n);
+                } else {
+                    min_pval = boost::math::pdf(bdist, 0) + boost::math::pdf(bdist, n);
+                }
+
+                if (min_pval >= min_pval_cutoff)
+                    return;
             }
 
-            if (s > 0)
-                pval += boost::math::cdf(bdist, s - 1);
+            auto get_pval_eff_size = [&](int64_t in_sum, int64_t out_sum) {
+                long double pval = 0.0;
 
-            if (config.test_by_unitig || pval < config.family_wise_error_rate) {
-                size_t sp = n;
-                for (; sp >= s; --sp) {
-                    if (devs[sp] < devs[in_sum])
+                size_t s = 0;
+                for (; s <= n; ++s) {
+                    if (devs[s] < devs[in_sum])
                         break;
                 }
 
-                if (sp < n)
-                    pval += boost::math::cdf(boost::math::complement(bdist, sp));
-            }
+                if (s > 0)
+                    pval += boost::math::cdf(bdist, s - 1);
+
+                if (config.test_by_unitig || pval < config.family_wise_error_rate) {
+                    size_t sp = n;
+                    for (; sp >= s; --sp) {
+                        if (devs[sp] < devs[in_sum])
+                            break;
+                    }
+
+                    if (sp < n)
+                        pval += boost::math::cdf(boost::math::complement(bdist, sp));
+                }
+
+                long double eff_size
+                    = static_cast<long double>(in_sum) - boost::math::mode(bdist);
+                return std::make_pair(pval, eff_size);
+            };
 
-            eff_size = static_cast<long double>(in_sum) - boost::math::mode(bdist);
+            std::tie(pval, eff_size) = get_pval_eff_size(in_sum, out_sum);
 
         } else if (config.test_type == "nbinom_exact") {
             size_t n = 0;
@@ -956,7 +1145,36 @@ mask_nodes_by_label_dual(
                 double l1pa = log1pl(-p_a) / logl(2.0);
                 double l1pb = log1pl(-p_b) / logl(2.0);
 
-                {
+                if (is_discrete) {
+                    long double dev0 = get_deviance(0, n);
+                    long double devn = get_deviance(n, 0);
+
+                    long double min_pval = 0.0;
+                    if (dev0 >= devn) {
+                        size_t s = 0;
+                        size_t t = n;
+                        long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                             - lgammal(s + 1) - lgammal(t + 1))
+                                / logl(2.0)
+                            + s * l1pa + t * l1pb;
+                        min_pval += exp2(base + sbase);
+                    }
+
+                    if (dev0 <= devn) {
+                        size_t s = n;
+                        size_t t = 0;
+                        long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+                                             - lgammal(s + 1) - lgammal(t + 1))
+                                / logl(2.0)
+                            + s * l1pa + t * l1pb;
+                        min_pval += exp2(base + sbase);
+                    }
+
+                    if (min_pval >= min_pval_cutoff)
+                        return;
+                }
+
+                if (is_discrete) {
                     size_t s = 0;
                     size_t t = n;
                     long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
@@ -1086,18 +1304,16 @@ mask_nodes_by_label_dual(
     std::atomic_thread_fence(std::memory_order_acquire);
 
     if (config.test_type != "notest") {
-        uint64_t num_tests = nelem;
         if (config.test_by_unitig) {
             num_tests = 0;
             std::atomic_thread_fence(std::memory_order_release);
             clean_masked_graph->call_unitigs(
                 [&](const std::string&, const auto& path) {
+                    size_t cur_monotig_id
+                        = __atomic_fetch_add(&num_tests, 1, std::memory_order_relaxed);
                     std::vector<long double> pvals;
                     pvals.reserve(path.size());
 
-                    Group last_group = Group::BOTH;
-                    size_t last_i = 0;
-
                     std::vector<size_t> ranks;
                     ranks.reserve(path.size());
                     for (node_index node : path) {
@@ -1105,36 +1321,22 @@ mask_nodes_by_label_dual(
                         ranks.emplace_back(kept.rank1(row_i) - 1);
                     }
 
+                    long double comb_eff_size = 0.0;
                     for (size_t i = 0; i < path.size(); ++i) {
                         size_t idx = ranks[i];
-                        const auto& [pval, monotig_id, group, stored_node] = all_pvals[idx];
-                        if (group != last_group) {
-                            if (pvals.size()) {
-                                uint64_t monotig_id
-                                    = __atomic_fetch_add(&num_tests, 1,
-                                                         std::memory_order_relaxed);
-                                long double comb_pval = combine_pvals(pvals);
-                                for (size_t j = last_i; j < i; ++j) {
-                                    size_t jdx = ranks[j];
-                                    std::get<0>(all_pvals[jdx]) = comb_pval;
-                                    std::get<1>(all_pvals[jdx]) = monotig_id;
-                                }
-                            }
-                            pvals.clear();
-                            last_group = group;
-                            last_i = i;
-                        }
+                        const auto& [pval, monotig_id, eff_size, stored_node]
+                            = all_pvals[idx];
                         pvals.emplace_back(pval);
+                        comb_eff_size += eff_size;
                     }
-                    if (pvals.size()) {
-                        uint64_t monotig_id
-                            = __atomic_fetch_add(&num_tests, 1, std::memory_order_relaxed);
-                        long double comb_pval = combine_pvals(pvals);
-                        for (size_t j = last_i; j < path.size(); ++j) {
-                            size_t jdx = ranks[j];
-                            std::get<0>(all_pvals[jdx]) = comb_pval;
-                            std::get<1>(all_pvals[jdx]) = monotig_id;
-                        }
+
+                    long double comb_pval = combine_pvals(pvals);
+                    for (size_t i = 0; i < path.size(); ++i) {
+                        size_t idx = ranks[i];
+                        auto& [pval, monotig_id, eff_size, stored_node] = all_pvals[idx];
+                        pval = comb_pval;
+                        monotig_id = cur_monotig_id;
+                        eff_size = comb_eff_size;
                     }
                 },
                 num_threads);
@@ -1162,17 +1364,17 @@ mask_nodes_by_label_dual(
             common::logger->trace("Selecting significant k-mers");
             size_t last_tig_id = std::numeric_limits<size_t>::max();
             size_t cur_count = 0;
-            for (const auto& [pval, tig_id, group, node] : all_pvals) {
+            for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
                 if (tig_id != last_tig_id) {
                     ++cur_count;
                     last_tig_id = tig_id;
                 }
 
                 if (pval <= config.family_wise_error_rate * cur_count / num_tests / harm) {
-                    if (group == Group::IN) {
+                    if (eff_size > 0) {
                         indicator_in[node] = true;
                         ++num_sig;
-                    } else if (group == Group::OUT) {
+                    } else if (eff_size < 0) {
                         indicator_out[node] = true;
                         ++num_sig;
                     }

From be4dc72cea7f3eebc1f340f584fdac5f0e37e231 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 28 Mar 2025 19:41:33 +0100
Subject: [PATCH 393/410] fix

---
 .../src/graph/annotated_graph_algorithm.cpp   | 54 +++++++++++--------
 1 file changed, 33 insertions(+), 21 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index d9903d22fa..253f26d7b1 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1249,7 +1249,8 @@ mask_nodes_by_label_dual(
 
             std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
 
-        } else if (config.test_type == "cmh" || config.test_type == "cmh_binary") {
+        } else if (config.test_type == "cmh" || config.test_type == "cmh_binary"
+                   || config.test_type == "fisher_binary") {
             int64_t in_sum = 0;
             int64_t out_sum = 0;
             if (config.test_type == "cmh") {
@@ -1274,7 +1275,7 @@ mask_nodes_by_label_dual(
                 }
             }
 
-            long double T = config.test_type == "cmh" ? total_kmers : nelem;
+            long double T = config.test_type == "cmh" ? total_kmers : in_nkmers + out_nkmers;
             long double a = in_sum;
             long double c = out_sum;
             int64_t ab = config.test_type == "cmh" ? in_kmers : in_nkmers;
@@ -1290,13 +1291,19 @@ mask_nodes_by_label_dual(
             }
 
             eff_size = log2l(a) + log2l(d) - log2l(b) - log2l(c);
-            long double chi_stat
-                = pow(a - ab * ac / T, 2.0) / ab / ac / cd / bd * T * T * (T - 1.0L);
-
-            pval = chi_stat > 0
-                ? boost::math::cdf(
-                      boost::math::complement(boost::math::chi_squared(1), chi_stat))
-                : 1.0;
+            if (config.test_type == "cmh" || config.test_type == "cmh_binary") {
+                long double chi_stat
+                    = pow(a - ab * ac / T, 2.0) / ab / ac / cd / bd * T * T * (T - 1.0L);
+
+                pval = chi_stat > 0
+                    ? boost::math::cdf(
+                          boost::math::complement(boost::math::chi_squared(1), chi_stat))
+                    : 1.0;
+            } else {
+                pval = expl(lgammal(ab + 1) + lgammal(cd + 1) + lgammal(ac + 1)
+                            + lgammal(bd + 1) - lgammal(a + 1) - lgammal(b + 1)
+                            - lgammal(c + 1) - lgammal(d + 1) - lgammal(T + 1));
+            }
         }
 
         set_pval(pval, eff_size);
@@ -1363,23 +1370,28 @@ mask_nodes_by_label_dual(
 
             common::logger->trace("Selecting significant k-mers");
             size_t last_tig_id = std::numeric_limits<size_t>::max();
-            size_t cur_count = 0;
-            for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
+            size_t cur_count = num_tests + 1;
+            auto it = all_pvals.rbegin();
+            for (; it != all_pvals.rend(); ++it) {
+                const auto& [pval, tig_id, eff_size, node] = *it;
                 if (tig_id != last_tig_id) {
-                    ++cur_count;
+                    --cur_count;
                     last_tig_id = tig_id;
                 }
 
-                if (pval <= config.family_wise_error_rate * cur_count / num_tests / harm) {
-                    if (eff_size > 0) {
-                        indicator_in[node] = true;
-                        ++num_sig;
-                    } else if (eff_size < 0) {
-                        indicator_out[node] = true;
-                        ++num_sig;
-                    }
-                }
+                if (pval <= config.family_wise_error_rate * cur_count / num_tests / harm)
+                    break;
             }
+
+            std::for_each(it, all_pvals.rend(), [&](const auto& b) {
+                const auto& [pval, tig_id, eff_size, node] = b;
+                num_sig += (eff_size != 0);
+                if (eff_size > 0) {
+                    indicator_in[node] = true;
+                } else if (eff_size < 0) {
+                    indicator_out[node] = true;
+                }
+            });
         }
 
         common::logger->trace("Found {} / {} significant p-values.", num_sig, nelem);

From 95de5d30f682a2a2cabff8a814a5c258dc550bfe Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 1 Apr 2025 01:25:08 +0200
Subject: [PATCH 394/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 161 +++++++++++++++---
 1 file changed, 142 insertions(+), 19 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 253f26d7b1..36945d48dd 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -508,7 +508,6 @@ mask_nodes_by_label_dual(
     if (config.test_type == "nbinom_exact") {
         common::logger->trace("Fitting per-sample negative binomial distributions");
 
-
 #pragma omp parallel for num_threads(num_parallel_files)
         for (size_t j = 0; j < groups.size(); ++j) {
             const auto& hist = hists[j];
@@ -795,7 +794,7 @@ mask_nodes_by_label_dual(
 
     bool is_discrete = false && !config.test_by_unitig
         && (config.test_type == "poisson_binom" || config.test_type == "poisson_exact"
-            || config.test_type == "nbinom_exact");
+            || config.test_type == "nbinom_exact"); // || config.test_type == "mwu");
 
     uint64_t num_tests = nelem;
 
@@ -809,7 +808,7 @@ mask_nodes_by_label_dual(
                 size_t n = 0;
                 int64_t in_sum = 0;
                 int64_t out_sum = 0;
-                if (config.test_type == "poisson_binom") {
+                if (config.test_type == "mwu" || config.test_type == "poisson_binom") {
                     for (const auto& [j, c] : row) {
                         n += static_cast<int64_t>(groups[j] != Group::OTHER)
                             + (groups[j] == Group::BOTH);
@@ -914,6 +913,33 @@ mask_nodes_by_label_dual(
                                 + s * l1pa + t * l1pb;
                             min_pval_b[n].first += exp2(base + sbase);
                         }
+                    } else if (config.test_type == "mwu") {
+                        std::vector<size_t> vals(groups.size());
+                        auto it = vals.begin();
+                        for (size_t i = n; i > 0; --i, ++it) {
+                            *it = i;
+                        }
+                        auto generate_a_in = [&](const auto& callback) {
+                            std::for_each(vals.begin(), vals.begin() + n,
+                                          [&](size_t i) { callback(i); });
+                        };
+                        auto generate_b_in = [&](const auto& callback) {
+                            std::for_each(vals.begin() + n, vals.end(),
+                                          [&](size_t i) { callback(i); });
+                        };
+                        auto generate_a_out = [&](const auto& callback) {
+                            std::for_each(vals.begin() + n, vals.end(),
+                                          [&](size_t i) { callback(i); });
+                        };
+                        auto generate_b_out = [&](const auto& callback) {
+                            std::for_each(vals.begin(), vals.begin() + n,
+                                          [&](size_t i) { callback(i); });
+                        };
+
+                        long double pval0
+                            = mann_whitneyu(generate_a_out, generate_b_out).first;
+                        long double pvaln = mann_whitneyu(generate_a_in, generate_b_in).first;
+                        min_pval_b[n].first = std::min(pval0, pvaln);
                     }
                 }
                 ++min_pval_b[n].second;
@@ -932,21 +958,22 @@ mask_nodes_by_label_dual(
         }
 
         std::sort(min_pvals.rbegin(), min_pvals.rend());
-        long double harm = 0.0;
-        for (size_t i = 1; i <= num_tests; ++i) {
-            harm += 1.0L / i;
-        }
+        // long double harm = 0.0;
+        // for (size_t i = 1; i <= num_tests; ++i) {
+        //     harm += 1.0L / i;
+        // }
         for (const auto& [pval, count] : min_pvals) {
             if (!count)
                 continue;
 
             // common::logger->trace("Trying:\tc: {}\tpval: {}\tcutoff: {}", num_tests, pval,
             //                       config.family_wise_error_rate / harm);
-            if (pval >= config.family_wise_error_rate / harm) {
+            // if (pval >= config.family_wise_error_rate / harm) {
+            if (pval >= config.family_wise_error_rate / num_tests) {
                 min_pval_cutoff = pval;
-                for (size_t i = num_tests; i > num_tests - count; --i) {
-                    harm -= 1.0L / i;
-                }
+                // for (size_t i = num_tests; i > num_tests - count; --i) {
+                //     harm -= 1.0L / i;
+                // }
                 num_tests -= count;
             }
         }
@@ -961,12 +988,18 @@ mask_nodes_by_label_dual(
         if (config.test_type == "notest") {
             size_t count_in = 0;
             size_t count_out = 0;
+            long double rel_in_count = 0;
+            long double rel_out_count = 0;
             for (const auto& [j, c] : row) {
-                if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                     ++count_out;
+                    rel_out_count += static_cast<long double>(c) / out_kmers;
+                }
 
-                if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
                     ++count_in;
+                    rel_in_count += static_cast<long double>(c) / in_kmers;
+                }
             }
 
             bool in_kmer = count_in >= config.min_in_recurrence
@@ -975,10 +1008,10 @@ mask_nodes_by_label_dual(
                 && count_out <= config.max_out_recurrence;
 
             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            if (in_kmer)
+            if (in_kmer && rel_in_count > rel_out_count)
                 set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
 
-            if (out_kmer)
+            if (out_kmer && rel_in_count < rel_out_count)
                 set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
 
             return;
@@ -1174,7 +1207,7 @@ mask_nodes_by_label_dual(
                         return;
                 }
 
-                if (is_discrete) {
+                {
                     size_t s = 0;
                     size_t t = n;
                     long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
@@ -1222,6 +1255,40 @@ mask_nodes_by_label_dual(
                 }
             }
         } else if (config.test_type == "mwu") {
+            if (is_discrete) {
+                size_t n = 0;
+                for (const auto& [j, c] : row) {
+                    n += static_cast<int64_t>(groups[j] != Group::OTHER)
+                        + (groups[j] == Group::BOTH);
+                }
+                std::vector<size_t> vals(groups.size());
+                auto it = vals.begin();
+                for (size_t i = n; i > 0; --i, ++it) {
+                    *it = i;
+                }
+                auto generate_a_in = [&](const auto& callback) {
+                    std::for_each(vals.begin(), vals.begin() + n,
+                                  [&](size_t i) { callback(i); });
+                };
+                auto generate_b_in = [&](const auto& callback) {
+                    std::for_each(vals.begin() + n, vals.end(),
+                                  [&](size_t i) { callback(i); });
+                };
+                auto generate_a_out = [&](const auto& callback) {
+                    std::for_each(vals.begin() + n, vals.end(),
+                                  [&](size_t i) { callback(i); });
+                };
+                auto generate_b_out = [&](const auto& callback) {
+                    std::for_each(vals.begin(), vals.begin() + n,
+                                  [&](size_t i) { callback(i); });
+                };
+
+                long double pval0 = mann_whitneyu(generate_a_out, generate_b_out).first;
+                long double pvaln = mann_whitneyu(generate_a_in, generate_b_in).first;
+
+                if (std::min(pval0, pvaln) >= min_pval_cutoff)
+                    return;
+            }
             auto generate_a = [&](const auto& callback) {
                 sdsl::bit_vector found(groups.size());
                 for (const auto& [j, c] : row) {
@@ -1363,13 +1430,16 @@ mask_nodes_by_label_dual(
 
         size_t num_sig = 0;
         if (all_pvals.size() && std::get<0>(all_pvals[0]) < config.family_wise_error_rate) {
+            common::logger->trace("Selecting significant k-mers");
+            common::logger->trace("Min observed p-value: {}", std::get<0>(all_pvals[0]));
+            size_t last_tig_id = std::numeric_limits<size_t>::max();
+
+            // Benjamini - Yekutieli
             long double harm = 0.0;
             for (size_t i = 1; i <= num_tests; ++i) {
                 harm += 1.0 / i;
             }
 
-            common::logger->trace("Selecting significant k-mers");
-            size_t last_tig_id = std::numeric_limits<size_t>::max();
             size_t cur_count = num_tests + 1;
             auto it = all_pvals.rbegin();
             for (; it != all_pvals.rend(); ++it) {
@@ -1379,7 +1449,15 @@ mask_nodes_by_label_dual(
                     last_tig_id = tig_id;
                 }
 
-                if (pval <= config.family_wise_error_rate * cur_count / num_tests / harm)
+                // Benjamini-Yekutieli
+                long double cutoff
+                    = config.family_wise_error_rate * cur_count / num_tests / harm;
+
+                // // Hochberg
+                // long double cutoff
+                //     = config.family_wise_error_rate / (num_tests + 1 - cur_count);
+
+                if (pval <= cutoff)
                     break;
             }
 
@@ -1392,6 +1470,51 @@ mask_nodes_by_label_dual(
                     indicator_out[node] = true;
                 }
             });
+
+            // Holm-Bonferroni or Sidak
+            // size_t cur_count = 0;
+            // common::logger->trace(
+            //     "First p-value cutoff: {}",
+            //     1.0L - pow(1.0L - config.family_wise_error_rate, 1.0L / num_tests));
+            // for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
+            //     if (tig_id != last_tig_id) {
+            //         ++cur_count;
+            //         last_tig_id = tig_id;
+            //     }
+
+            //     // holm bonferroni
+            //     // long double cutoff = config.family_wise_error_rate / (num_tests + 1 - cur_count);
+
+            //     // Sidak
+            //     long double cutoff = 1.0L
+            //         - pow(1.0L - config.family_wise_error_rate,
+            //               1.0L / (num_tests + 1 - cur_count));
+
+            //     if (pval > cutoff)
+            //         break;
+
+            //     if (eff_size > 0) {
+            //         indicator_in[node] = true;
+            //         ++num_sig;
+            //     } else if (eff_size < 0) {
+            //         indicator_out[node] = true;
+            //         ++num_sig;
+            //     }
+            // }
+
+            // Bonferroni
+            // for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
+            //     if (pval >= config.family_wise_error_rate / num_tests)
+            //         break;
+
+            //     if (eff_size > 0) {
+            //         indicator_in[node] = true;
+            //         ++num_sig;
+            //     } else if (eff_size < 0) {
+            //         indicator_out[node] = true;
+            //         ++num_sig;
+            //     }
+            // }
         }
 
         common::logger->trace("Found {} / {} significant p-values.", num_sig, nelem);

From dc518bff3f4ebed8a2836e6dc2772d4c449924e0 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 2 Apr 2025 15:17:15 +0200
Subject: [PATCH 395/410] update

---
 .../src/graph/annotated_graph_algorithm.cpp   | 217 +++++++++++-------
 1 file changed, 130 insertions(+), 87 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 36945d48dd..8379595d4c 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1419,102 +1419,145 @@ mask_nodes_by_label_dual(
         }
 
         common::logger->trace("Correcting p-vals");
-        all_pvals.erase(std::remove_if(all_pvals.begin(), all_pvals.end(),
-                                       [&](const auto& a) {
-                                           return std::get<0>(a)
-                                               >= config.family_wise_error_rate;
-                                       }),
-                        all_pvals.end());
-        common::logger->trace("Sorting {} / {} p-vals", all_pvals.size(), num_tests);
-        std::sort(all_pvals.begin(), all_pvals.end());
-
         size_t num_sig = 0;
-        if (all_pvals.size() && std::get<0>(all_pvals[0]) < config.family_wise_error_rate) {
-            common::logger->trace("Selecting significant k-mers");
-            common::logger->trace("Min observed p-value: {}", std::get<0>(all_pvals[0]));
-            size_t last_tig_id = std::numeric_limits<size_t>::max();
-
-            // Benjamini - Yekutieli
-            long double harm = 0.0;
-            for (size_t i = 1; i <= num_tests; ++i) {
-                harm += 1.0 / i;
-            }
+        if (true) {
+            all_pvals.erase(std::remove_if(all_pvals.begin(), all_pvals.end(),
+                                           [&](const auto& a) {
+                                               return std::get<0>(a)
+                                                   >= config.family_wise_error_rate;
+                                           }),
+                            all_pvals.end());
+            common::logger->trace("Sorting {} / {} p-vals", all_pvals.size(), num_tests);
+            std::sort(all_pvals.begin(), all_pvals.end());
+
+            if (all_pvals.size()
+                && std::get<0>(all_pvals[0]) < config.family_wise_error_rate) {
+                common::logger->trace("Selecting significant k-mers");
+                common::logger->trace("Min observed p-value: {}", std::get<0>(all_pvals[0]));
+                size_t last_tig_id = std::numeric_limits<size_t>::max();
+
+                // Benjamini - Yekutieli
+                long double harm = 0.0;
+                for (size_t i = 1; i <= num_tests; ++i) {
+                    harm += 1.0 / i;
+                }
+
+                size_t cur_count = num_tests + 1;
+                auto it = all_pvals.rbegin();
+                for (; it != all_pvals.rend(); ++it) {
+                    const auto& [pval, tig_id, eff_size, node] = *it;
+                    if (tig_id != last_tig_id) {
+                        --cur_count;
+                        last_tig_id = tig_id;
+                    }
+
+                    // Benjamini-Yekutieli
+                    long double cutoff
+                        = config.family_wise_error_rate * cur_count / num_tests / harm;
 
-            size_t cur_count = num_tests + 1;
-            auto it = all_pvals.rbegin();
-            for (; it != all_pvals.rend(); ++it) {
-                const auto& [pval, tig_id, eff_size, node] = *it;
-                if (tig_id != last_tig_id) {
-                    --cur_count;
-                    last_tig_id = tig_id;
+                    // // Hochberg
+                    // long double cutoff
+                    //     = config.family_wise_error_rate / (num_tests + 1 - cur_count);
+
+                    if (pval <= cutoff)
+                        break;
                 }
 
-                // Benjamini-Yekutieli
-                long double cutoff
-                    = config.family_wise_error_rate * cur_count / num_tests / harm;
+                std::for_each(it, all_pvals.rend(), [&](const auto& b) {
+                    const auto& [pval, tig_id, eff_size, node] = b;
+                    num_sig += (eff_size != 0);
+                    if (eff_size > 0) {
+                        indicator_in[node] = true;
+                    } else if (eff_size < 0) {
+                        indicator_out[node] = true;
+                    }
+                });
 
-                // // Hochberg
-                // long double cutoff
-                //     = config.family_wise_error_rate / (num_tests + 1 - cur_count);
+                // Holm-Bonferroni or Sidak
+                // size_t cur_count = 0;
+                // common::logger->trace(
+                //     "First p-value cutoff: {}",
+                //     1.0L - pow(1.0L - config.family_wise_error_rate, 1.0L / num_tests));
+                // for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
+                //     if (tig_id != last_tig_id) {
+                //         ++cur_count;
+                //         last_tig_id = tig_id;
+                //     }
+
+                //     // holm bonferroni
+                //     // long double cutoff = config.family_wise_error_rate / (num_tests + 1 - cur_count);
+
+                //     // Sidak
+                //     long double cutoff = 1.0L
+                //         - pow(1.0L - config.family_wise_error_rate,
+                //               1.0L / (num_tests + 1 - cur_count));
+
+                //     if (pval > cutoff)
+                //         break;
+
+                //     if (eff_size > 0) {
+                //         indicator_in[node] = true;
+                //         ++num_sig;
+                //     } else if (eff_size < 0) {
+                //         indicator_out[node] = true;
+                //         ++num_sig;
+                //     }
+                // }
 
-                if (pval <= cutoff)
-                    break;
+                // Bonferroni
+                // for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
+                //     if (pval >= config.family_wise_error_rate / num_tests)
+                //         break;
+
+                //     if (eff_size > 0) {
+                //         indicator_in[node] = true;
+                //         ++num_sig;
+                //     } else if (eff_size < 0) {
+                //         indicator_out[node] = true;
+                //         ++num_sig;
+                //     }
+                // }
             }
-
-            std::for_each(it, all_pvals.rend(), [&](const auto& b) {
-                const auto& [pval, tig_id, eff_size, node] = b;
-                num_sig += (eff_size != 0);
-                if (eff_size > 0) {
-                    indicator_in[node] = true;
-                } else if (eff_size < 0) {
-                    indicator_out[node] = true;
+        } else {
+            std::vector<long double> numer;
+            numer.reserve(all_pvals.size() + 1);
+            numer.emplace_back(0.0);
+            std::vector<long double> denom;
+            denom.reserve(all_pvals.size() + 1);
+            denom.emplace_back(0.0);
+            for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
+                numer.emplace_back(numer.back() + 1.0L / num_tests);
+                denom.emplace_back(denom.back() + 1.0L / num_tests / pval);
+            }
+            std::vector<std::pair<size_t, size_t>> stack;
+            stack.emplace_back(0, all_pvals.size());
+            while (stack.size()) {
+                auto [begin, end] = stack.back();
+                stack.pop_back();
+                if (end - begin <= 1) {
+                    const auto& [pval, tig_id, eff_size, node] = all_pvals[begin];
+                    if (eff_size > 0) {
+                        indicator_in[node] = true;
+                        ++num_sig;
+                    } else if (eff_size < 0) {
+                        indicator_out[node] = true;
+                        ++num_sig;
+                    }
+                    continue;
                 }
-            });
 
-            // Holm-Bonferroni or Sidak
-            // size_t cur_count = 0;
-            // common::logger->trace(
-            //     "First p-value cutoff: {}",
-            //     1.0L - pow(1.0L - config.family_wise_error_rate, 1.0L / num_tests));
-            // for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
-            //     if (tig_id != last_tig_id) {
-            //         ++cur_count;
-            //         last_tig_id = tig_id;
-            //     }
-
-            //     // holm bonferroni
-            //     // long double cutoff = config.family_wise_error_rate / (num_tests + 1 - cur_count);
-
-            //     // Sidak
-            //     long double cutoff = 1.0L
-            //         - pow(1.0L - config.family_wise_error_rate,
-            //               1.0L / (num_tests + 1 - cur_count));
-
-            //     if (pval > cutoff)
-            //         break;
-
-            //     if (eff_size > 0) {
-            //         indicator_in[node] = true;
-            //         ++num_sig;
-            //     } else if (eff_size < 0) {
-            //         indicator_out[node] = true;
-            //         ++num_sig;
-            //     }
-            // }
-
-            // Bonferroni
-            // for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
-            //     if (pval >= config.family_wise_error_rate / num_tests)
-            //         break;
-
-            //     if (eff_size > 0) {
-            //         indicator_in[node] = true;
-            //         ++num_sig;
-            //     } else if (eff_size < 0) {
-            //         indicator_out[node] = true;
-            //         ++num_sig;
-            //     }
-            // }
+                size_t mid = begin + (end - begin) / 2;
+                long double hmp_right
+                    = (numer[end] - numer[mid]) / (denom[end] - denom[mid]);
+                long double hmp_left
+                    = (numer[mid] - numer[begin]) / (denom[mid] - denom[begin]);
+
+                if (hmp_right <= config.family_wise_error_rate * (numer[end] - numer[mid]))
+                    stack.emplace_back(mid, end);
+
+                if (hmp_left <= config.family_wise_error_rate * (numer[mid] - numer[begin]))
+                    stack.emplace_back(begin, mid);
+            }
         }
 
         common::logger->trace("Found {} / {} significant p-values.", num_sig, nelem);

From b7d061a7b7829c48c36935d28763e6faad92d7f7 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 11 Apr 2025 14:55:39 +0200
Subject: [PATCH 396/410] updated chi2 pval calculator

---
 .../src/graph/annotated_graph_algorithm.cpp   | 33 ++++++++++++++++---
 1 file changed, 29 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 8379595d4c..d0c9b7784a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -12,6 +12,7 @@
 #include <boost/math/distributions/students_t.hpp>
 #include <boost/math/tools/roots.hpp>
 #include <boost/math/tools/minima.hpp>
+#include <boost/math/special_functions/gamma.hpp>
 #include <boost/math/special_functions/digamma.hpp>
 #include <boost/math/special_functions/trigamma.hpp>
 #include <boost/math/distributions/cauchy.hpp>
@@ -241,6 +242,33 @@ std::pair<long double, long double> mann_whitneyu(const G1& generate_a, const G2
     return std::make_pair(p, eff_size);
 }
 
+long double get_chi2_pval(long double nu, long double chi_stat) {
+    if (chi_stat <= 0)
+        return 0;
+
+    long double pval
+        = boost::math::cdf(boost::math::complement(boost::math::chi_squared(nu), chi_stat));
+
+    if (pval == 0) {
+        long double a = nu / 2.0L;
+        long double pval_num = boost::math::tgamma(a, chi_stat / 2.0L);
+        if (pval_num == 0) {
+            pval_num = gammal(a) - boost::math::tgamma_lower(a, chi_stat / 2.0L);
+        }
+        long double log_pval_num = logl(pval_num);
+        long double log_pval_den = lgammal(a);
+        long double log_pval = log_pval_num - log_pval_den;
+        pval = expl(log_pval);
+        if (pval == 0) {
+            common::logger->error("p-value underflow: {}\tlogpval: {} = {} - {}",
+                                  chi_stat, log_pval, log_pval_num, log_pval_den);
+            throw std::runtime_error("Chi sq underflow");
+        }
+    }
+
+    return pval;
+}
+
 long double combine_pvals(const std::vector<long double>& pvals) {
     static const boost::math::cauchy dist;
     long double stat = 0.0;
@@ -1362,10 +1390,7 @@ mask_nodes_by_label_dual(
                 long double chi_stat
                     = pow(a - ab * ac / T, 2.0) / ab / ac / cd / bd * T * T * (T - 1.0L);
 
-                pval = chi_stat > 0
-                    ? boost::math::cdf(
-                          boost::math::complement(boost::math::chi_squared(1), chi_stat))
-                    : 1.0;
+                pval = get_chi2_pval(1.0L, chi_stat);
             } else {
                 pval = expl(lgammal(ab + 1) + lgammal(cd + 1) + lgammal(ac + 1)
                             + lgammal(bd + 1) - lgammal(a + 1) - lgammal(b + 1)

From 94cad1c6d254c7d13bb0832d597d70adc29ff3fe Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 11 Apr 2025 15:05:59 +0200
Subject: [PATCH 397/410] fix cutoff

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index d0c9b7784a..22304937d4 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1467,7 +1467,7 @@ mask_nodes_by_label_dual(
                     harm += 1.0 / i;
                 }
 
-                size_t cur_count = num_tests + 1;
+                size_t cur_count = all_pvals.size() + 1;
                 auto it = all_pvals.rbegin();
                 for (; it != all_pvals.rend(); ++it) {
                     const auto& [pval, tig_id, eff_size, node] = *it;

From 6781f591a195bea984135783b19a2ddfb2f694fc Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 11 Apr 2025 15:06:05 +0200
Subject: [PATCH 398/410] bh

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 22304937d4..5ac727d3ed 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1467,6 +1467,9 @@ mask_nodes_by_label_dual(
                     harm += 1.0 / i;
                 }
 
+                // Benjamini-Hochberg
+                harm = 1.0;
+
                 size_t cur_count = all_pvals.size() + 1;
                 auto it = all_pvals.rbegin();
                 for (; it != all_pvals.rend(); ++it) {

From c1c6e61f9cdcc19f416f69ef819a2cfdb65f5610 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 11 Apr 2025 19:06:36 +0200
Subject: [PATCH 399/410] fall back to LRT

---
 .../src/graph/annotated_graph_algorithm.cpp   | 43 ++++++++++++++++---
 1 file changed, 37 insertions(+), 6 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 5ac727d3ed..9ac5ddfd11 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -610,6 +610,7 @@ mask_nodes_by_label_dual(
     long double mu1 = static_cast<long double>(in_kmers) / nelem;
     long double mu2 = static_cast<long double>(out_kmers) / nelem;
     long double p = mu1 / (mu1 + mu2);
+    long double q = mu2 / (mu1 + mu2);
 
     // precompute p-values for poisson_binom test
     std::vector<std::vector<long double>> pb_pvals(num_labels_in + num_labels_out + 1);
@@ -1130,18 +1131,48 @@ mask_nodes_by_label_dual(
                         break;
                 }
 
-                if (s > 0)
-                    pval += boost::math::cdf(bdist, s - 1);
+                bool any_zero = false;
 
-                if (config.test_by_unitig || pval < config.family_wise_error_rate) {
-                    size_t sp = n;
+                if (s > 0) {
+                    pval = boost::math::cdf(bdist, s - 1);
+                    any_zero |= (pval == 0);
+                }
+
+                size_t sp = n;
+                if (!any_zero
+                    && (config.test_by_unitig || pval < config.family_wise_error_rate)) {
                     for (; sp >= s; --sp) {
                         if (devs[sp] < devs[in_sum])
                             break;
                     }
 
-                    if (sp < n)
-                        pval += boost::math::cdf(boost::math::complement(bdist, sp));
+                    if (sp < n) {
+                        long double pval_right
+                            = boost::math::cdf(boost::math::complement(bdist, sp));
+                        any_zero |= (pval_right == 0);
+                        pval += pval_right;
+                    }
+                }
+
+                if (any_zero) {
+                    std::ignore = q;
+                    // fall back to LRT
+                    auto poisson_log_prob = [&](long double k, long double lambda) {
+                        return lambda > 0 ? k * logl(lambda) - lambda - lgammal(k + 1) : 0.0L;
+                    };
+
+                    long double alt = poisson_log_prob(in_sum, in_sum)
+                        + poisson_log_prob(out_sum, out_sum);
+                    long double mn = static_cast<long double>(n) / total_kmers;
+                    long double null = poisson_log_prob(out_sum, mn * out_kmers)
+                        + poisson_log_prob(in_sum, mn * in_kmers);
+                    long double chi_stat = (alt - null) * 2.0L;
+                    pval = get_chi2_pval(1.0, chi_stat);
+                }
+
+                if (pval <= 0.0 || pval > 1.0) {
+                    common::logger->error("n: {}\t{},{}\tp: {}", n, in_sum, out_sum, pval);
+                    throw std::runtime_error("p-val fail, sum");
                 }
 
                 long double eff_size

From 4846ab46dfd1f472e79b6f0d896d5f2243c8e1bc Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 14 Apr 2025 18:49:34 +0200
Subject: [PATCH 400/410] test

---
 .../src/graph/annotated_graph_algorithm.cpp   | 76 +++++++++++++++----
 1 file changed, 61 insertions(+), 15 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9ac5ddfd11..18b2339327 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -246,22 +246,53 @@ long double get_chi2_pval(long double nu, long double chi_stat) {
     if (chi_stat <= 0)
         return 0;
 
+    if (nu == 1) {
+        long double pval
+            = expl(logl(std::erfcl(sqrtl(chi_stat))) - lgammal(0.5L) + 0.5L * logl(M_PI));
+        return pval;
+        // if (pval != pval || pval <= 0 || pval > 1.0) {
+        //     common::logger->error("0.5 p-value fail: {}\t{} / {}", chi_stat,
+        //                           std::erfcl(sqrtl(chi_stat)), tgammal(0.5L));
+        //     throw std::runtime_error("Chi sq underflow");
+        // }
+    }
+
+    // gamma_q(nu / 2, x / 2)
     long double pval
         = boost::math::cdf(boost::math::complement(boost::math::chi_squared(nu), chi_stat));
 
     if (pval == 0) {
         long double a = nu / 2.0L;
         long double pval_num = boost::math::tgamma(a, chi_stat / 2.0L);
-        if (pval_num == 0) {
-            pval_num = gammal(a) - boost::math::tgamma_lower(a, chi_stat / 2.0L);
+        long double log_pval_num;
+        long double log_pval_den;
+        if (pval_num != pval_num || pval_num == 0) {
+            pval_num = tgammal(a) - boost::math::tgamma_lower(a, chi_stat / 2.0L);
+            if (pval_num <= 0 || pval_num != pval_num) {
+                common::logger->error("D:\tnu: {}\tstat: {}\t{} - {} = {}", a, chi_stat,
+                                      tgammal(a),
+                                      boost::math::tgamma_lower(a, chi_stat / 2.0L),
+                                      pval_num);
+                throw std::runtime_error("DjFKKF");
+            } else {
+                log_pval_num = logl(pval_num);
+                log_pval_den = lgammal(a);
+            }
+            // log_pval = log1pl(-boost::math::gamma_p(a, chi_stat / 2.0L));
+        } else {
+            log_pval_num = logl(pval_num);
+            log_pval_den = lgammal(a);
         }
-        long double log_pval_num = logl(pval_num);
-        long double log_pval_den = lgammal(a);
         long double log_pval = log_pval_num - log_pval_den;
+        // if (pval_num != pval_num || pval_num == 0) {
+        //     pval_num = gammal(a) - boost::math::tgamma_lower(a, chi_stat / 2.0L);
+        // }
+        // long double log_pval_num = logl(pval_num);
         pval = expl(log_pval);
-        if (pval == 0) {
-            common::logger->error("p-value underflow: {}\tlogpval: {} = {} - {}",
-                                  chi_stat, log_pval, log_pval_num, log_pval_den);
+        if (pval != pval || pval <= 0 || pval > 1.0) {
+            common::logger->error("p-value fail: {}\tlogpval: {} = {} - {}\t{}\t{},{}",
+                                  chi_stat, log_pval, log_pval_num, log_pval_den, pval_num,
+                                  tgammal(a), boost::math::tgamma_lower(a, chi_stat / 2.0L));
             throw std::runtime_error("Chi sq underflow");
         }
     }
@@ -1168,13 +1199,20 @@ mask_nodes_by_label_dual(
                         + poisson_log_prob(in_sum, mn * in_kmers);
                     long double chi_stat = (alt - null) * 2.0L;
                     pval = get_chi2_pval(1.0, chi_stat);
-                }
 
-                if (pval <= 0.0 || pval > 1.0) {
-                    common::logger->error("n: {}\t{},{}\tp: {}", n, in_sum, out_sum, pval);
-                    throw std::runtime_error("p-val fail, sum");
+                    // if (chi_stat != chi_stat || pval != pval) {
+                    //     common::logger->error(
+                    //         "FF:\tn: {}\tin: {}\tout: {}\tstat: {}\tpval: {}", n,
+                    //         in_sum, out_sum, chi_stat, pval);
+                    //     throw std::runtime_error("pval fail");
+                    // }
                 }
 
+                // if (pval <= 0.0 || pval > 1.0) {
+                //     common::logger->error("n: {}\t{},{}\tp: {}", n, in_sum, out_sum,
+                //     pval); throw std::runtime_error("p-val fail, sum");
+                // }
+
                 long double eff_size
                     = static_cast<long double>(in_sum) - boost::math::mode(bdist);
                 return std::make_pair(pval, eff_size);
@@ -1422,6 +1460,13 @@ mask_nodes_by_label_dual(
                     = pow(a - ab * ac / T, 2.0) / ab / ac / cd / bd * T * T * (T - 1.0L);
 
                 pval = get_chi2_pval(1.0L, chi_stat);
+
+                if (chi_stat != chi_stat || pval != pval) {
+                    common::logger->error(
+                        "FF:\tn: {}\tin: {}\tout: {}\tstat: {}\tpval: {}",
+                        in_sum + out_sum, in_sum, out_sum, chi_stat, pval);
+                    throw std::runtime_error("pval fail");
+                }
             } else {
                 pval = expl(lgammal(ab + 1) + lgammal(cd + 1) + lgammal(ac + 1)
                             + lgammal(bd + 1) - lgammal(a + 1) - lgammal(b + 1)
@@ -1498,8 +1543,8 @@ mask_nodes_by_label_dual(
                     harm += 1.0 / i;
                 }
 
-                // Benjamini-Hochberg
-                harm = 1.0;
+                // // Benjamini-Hochberg
+                // // harm = 1.0;
 
                 size_t cur_count = all_pvals.size() + 1;
                 auto it = all_pvals.rbegin();
@@ -1532,7 +1577,7 @@ mask_nodes_by_label_dual(
                     }
                 });
 
-                // Holm-Bonferroni or Sidak
+                // // Holm-Bonferroni or Sidak
                 // size_t cur_count = 0;
                 // common::logger->trace(
                 //     "First p-value cutoff: {}",
@@ -1544,7 +1589,8 @@ mask_nodes_by_label_dual(
                 //     }
 
                 //     // holm bonferroni
-                //     // long double cutoff = config.family_wise_error_rate / (num_tests + 1 - cur_count);
+                //     // long double cutoff
+                //     // = config.family_wise_error_rate / (num_tests + 1 - cur_count);
 
                 //     // Sidak
                 //     long double cutoff = 1.0L

From b066ec9d9bd4b0a395e583f248219fe49dbfc8d5 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Wed, 16 Apr 2025 18:45:56 +0200
Subject: [PATCH 401/410] effect sie

---
 .../src/graph/annotated_graph_algorithm.cpp   | 162 +++++++++++-------
 1 file changed, 98 insertions(+), 64 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 18b2339327..22a5990ef9 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -847,9 +847,9 @@ mask_nodes_by_label_dual(
     }
 
     common::logger->trace("Allocating p-value storage");
-    std::vector<std::tuple<long double, size_t, long double, node_index>> all_pvals;
+    std::vector<std::tuple<long double, size_t, long double, long double, node_index>> all_pvals;
     all_pvals.resize(nelem,
-                     std::make_tuple(1.0L, graph_ptr->max_index() + 1, Group::OTHER,
+                     std::make_tuple(1.0L, graph_ptr->max_index() + 1, 0.0L, 0.0L,
                                      graph_ptr->max_index() + 1));
 
     bool is_discrete = false && !config.test_by_unitig
@@ -1077,13 +1077,15 @@ mask_nodes_by_label_dual(
             return;
         }
 
-        auto set_pval = [&](long double pval, long double eff_size) {
-            size_t idx = kept.rank1(row_i) - 1;
-            node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            all_pvals[idx] = std::tie(pval, node, eff_size, node);
-        };
+        auto set_pval
+            = [&](long double pval, long double eff_size_in, long double eff_size_out) {
+                  size_t idx = kept.rank1(row_i) - 1;
+                  node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
+                  all_pvals[idx] = std::tie(pval, node, eff_size_in, eff_size_out, node);
+              };
 
-        long double eff_size = 0.0;
+        long double eff_size_in = 0.0;
+        long double eff_size_out = 0.0;
         long double pval = 1.0;
         if (config.test_type == "poisson_binom") {
             size_t n = 0;
@@ -1092,6 +1094,12 @@ mask_nodes_by_label_dual(
                 n += static_cast<int64_t>(groups[j] != Group::OTHER)
                     + (groups[j] == Group::BOTH);
                 s += (groups[j] == Group::IN);
+                if (groups[j] == Group::IN) {
+                    eff_size_in += 1.0L / n_kmers[j];
+                }
+                if (groups[j] == Group::OUT) {
+                    eff_size_out += 1.0L / n_kmers[j];
+                }
             }
 
             if (is_discrete) {
@@ -1108,18 +1116,22 @@ mask_nodes_by_label_dual(
             }
 
             pval = pb_pvals[n][s];
-            eff_size = static_cast<double>(s) - mid_points[n];
+            // eff_size = static_cast<double>(s) - mid_points[n];
         } else if (config.test_type == "poisson_exact") {
             size_t n = 0;
             int64_t in_sum = 0;
             int64_t out_sum = 0;
             for (const auto& [j, c] : row) {
                 n += c;
-                if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                     out_sum += c;
+                    eff_size_out += static_cast<long double>(c) / sums[j];
+                }
 
-                if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
                     in_sum += c;
+                    eff_size_in += static_cast<long double>(c) / sums[j];
+                }
             }
 
             boost::math::binomial bdist(n, p);
@@ -1218,7 +1230,7 @@ mask_nodes_by_label_dual(
                 return std::make_pair(pval, eff_size);
             };
 
-            std::tie(pval, eff_size) = get_pval_eff_size(in_sum, out_sum);
+            pval = get_pval_eff_size(in_sum, out_sum).first;
 
         } else if (config.test_type == "nbinom_exact") {
             size_t n = 0;
@@ -1228,10 +1240,12 @@ mask_nodes_by_label_dual(
                 n += c;
                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                     out_sum += c;
+                    eff_size_out += static_cast<long double>(c) / sums[j];
                 }
 
                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
                     in_sum += c;
+                    eff_size_in += static_cast<long double>(c) / sums[j];
                 }
             }
 
@@ -1268,7 +1282,7 @@ mask_nodes_by_label_dual(
             long double in_dev = get_deviance(in_sum, out_sum);
 
             if (in_dev > min_dev) {
-                eff_size = static_cast<long double>(in_sum) - midpoint;
+                // eff_size = static_cast<long double>(in_sum) - midpoint;
                 pval = 0.0;
                 long double base = nb_base
                     + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n)) / logl(2.0);
@@ -1386,6 +1400,7 @@ mask_nodes_by_label_dual(
                 if (std::min(pval0, pvaln) >= min_pval_cutoff)
                     return;
             }
+
             auto generate_a = [&](const auto& callback) {
                 sdsl::bit_vector found(groups.size());
                 for (const auto& [j, c] : row) {
@@ -1411,7 +1426,10 @@ mask_nodes_by_label_dual(
                 }
             };
 
-            std::tie(pval, eff_size) = mann_whitneyu(generate_a, generate_b);
+            generate_a([&](long double c) { eff_size_in += c; });
+            generate_b([&](long double c) { eff_size_out += c; });
+
+            pval = mann_whitneyu(generate_a, generate_b).first;
 
         } else if (config.test_type == "cmh" || config.test_type == "cmh_binary"
                    || config.test_type == "fisher_binary") {
@@ -1421,20 +1439,24 @@ mask_nodes_by_label_dual(
                 for (const auto& [j, c] : row) {
                     if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                         out_sum += c;
+                        eff_size_out += static_cast<long double>(c) / sums[j];
                     }
 
                     if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
                         in_sum += c;
+                        eff_size_in += static_cast<long double>(c) / sums[j];
                     }
                 }
             } else {
                 for (const auto& [j, c] : row) {
                     if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                         ++out_sum;
+                        eff_size_out += 1.0L / sums[j];
                     }
 
                     if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
                         ++in_sum;
+                        eff_size_in += 1.0L / sums[j];
                     }
                 }
             }
@@ -1454,7 +1476,7 @@ mask_nodes_by_label_dual(
                 throw std::runtime_error("Stat calc fail");
             }
 
-            eff_size = log2l(a) + log2l(d) - log2l(b) - log2l(c);
+            // eff_size = log2l(a) + log2l(d) - log2l(b) - log2l(c);
             if (config.test_type == "cmh" || config.test_type == "cmh_binary") {
                 long double chi_stat
                     = pow(a - ab * ac / T, 2.0) / ab / ac / cd / bd * T * T * (T - 1.0L);
@@ -1474,7 +1496,7 @@ mask_nodes_by_label_dual(
             }
         }
 
-        set_pval(pval, eff_size);
+        set_pval(pval, eff_size_in, eff_size_out);
     });
     std::atomic_thread_fence(std::memory_order_acquire);
 
@@ -1496,22 +1518,26 @@ mask_nodes_by_label_dual(
                         ranks.emplace_back(kept.rank1(row_i) - 1);
                     }
 
-                    long double comb_eff_size = 0.0;
+                    long double comb_eff_size_in = 0.0;
+                    long double comb_eff_size_out = 0.0;
                     for (size_t i = 0; i < path.size(); ++i) {
                         size_t idx = ranks[i];
-                        const auto& [pval, monotig_id, eff_size, stored_node]
+                        const auto& [pval, monotig_id, eff_size_in, eff_size_out, stored_node]
                             = all_pvals[idx];
                         pvals.emplace_back(pval);
-                        comb_eff_size += eff_size;
+                        comb_eff_size_in += eff_size_in;
+                        comb_eff_size_out += eff_size_out;
                     }
 
                     long double comb_pval = combine_pvals(pvals);
                     for (size_t i = 0; i < path.size(); ++i) {
                         size_t idx = ranks[i];
-                        auto& [pval, monotig_id, eff_size, stored_node] = all_pvals[idx];
+                        auto& [pval, monotig_id, eff_size_in, eff_size_out, stored_node]
+                            = all_pvals[idx];
                         pval = comb_pval;
                         monotig_id = cur_monotig_id;
-                        eff_size = comb_eff_size;
+                        eff_size_in = comb_eff_size_in / path.size();
+                        eff_size_out = comb_eff_size_out / path.size();
                     }
                 },
                 num_threads);
@@ -1549,7 +1575,7 @@ mask_nodes_by_label_dual(
                 size_t cur_count = all_pvals.size() + 1;
                 auto it = all_pvals.rbegin();
                 for (; it != all_pvals.rend(); ++it) {
-                    const auto& [pval, tig_id, eff_size, node] = *it;
+                    const auto& [pval, tig_id, eff_size_in, eff_size_out, node] = *it;
                     if (tig_id != last_tig_id) {
                         --cur_count;
                         last_tig_id = tig_id;
@@ -1568,7 +1594,9 @@ mask_nodes_by_label_dual(
                 }
 
                 std::for_each(it, all_pvals.rend(), [&](const auto& b) {
-                    const auto& [pval, tig_id, eff_size, node] = b;
+                    const auto& [pval, tig_id, eff_size_in, eff_size_out, node] = b;
+                    long double eff_size
+                        = eff_size_in / num_labels_in - eff_size_out / num_labels_out;
                     num_sig += (eff_size != 0);
                     if (eff_size > 0) {
                         indicator_in[node] = true;
@@ -1582,12 +1610,15 @@ mask_nodes_by_label_dual(
                 // common::logger->trace(
                 //     "First p-value cutoff: {}",
                 //     1.0L - pow(1.0L - config.family_wise_error_rate, 1.0L / num_tests));
-                // for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
+                // for (const auto& [pval, tig_id, eff_size_in, eff_size_out, node] : all_pvals) {
                 //     if (tig_id != last_tig_id) {
                 //         ++cur_count;
                 //         last_tig_id = tig_id;
                 //     }
 
+                //     long double eff_size
+                //         = eff_size_in / num_labels_in - eff_size_out / num_labels_out;
+
                 //     // holm bonferroni
                 //     // long double cutoff
                 //     // = config.family_wise_error_rate / (num_tests + 1 - cur_count);
@@ -1610,10 +1641,13 @@ mask_nodes_by_label_dual(
                 // }
 
                 // Bonferroni
-                // for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
+                // for (const auto& [pval, tig_id, eff_size_in, eff_size_out, node] : all_pvals) {
                 //     if (pval >= config.family_wise_error_rate / num_tests)
                 //         break;
 
+                //     long double eff_size
+                //         = eff_size_in / num_labels_in - eff_size_out / num_labels_out;
+
                 //     if (eff_size > 0) {
                 //         indicator_in[node] = true;
                 //         ++num_sig;
@@ -1624,45 +1658,45 @@ mask_nodes_by_label_dual(
                 // }
             }
         } else {
-            std::vector<long double> numer;
-            numer.reserve(all_pvals.size() + 1);
-            numer.emplace_back(0.0);
-            std::vector<long double> denom;
-            denom.reserve(all_pvals.size() + 1);
-            denom.emplace_back(0.0);
-            for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
-                numer.emplace_back(numer.back() + 1.0L / num_tests);
-                denom.emplace_back(denom.back() + 1.0L / num_tests / pval);
-            }
-            std::vector<std::pair<size_t, size_t>> stack;
-            stack.emplace_back(0, all_pvals.size());
-            while (stack.size()) {
-                auto [begin, end] = stack.back();
-                stack.pop_back();
-                if (end - begin <= 1) {
-                    const auto& [pval, tig_id, eff_size, node] = all_pvals[begin];
-                    if (eff_size > 0) {
-                        indicator_in[node] = true;
-                        ++num_sig;
-                    } else if (eff_size < 0) {
-                        indicator_out[node] = true;
-                        ++num_sig;
-                    }
-                    continue;
-                }
-
-                size_t mid = begin + (end - begin) / 2;
-                long double hmp_right
-                    = (numer[end] - numer[mid]) / (denom[end] - denom[mid]);
-                long double hmp_left
-                    = (numer[mid] - numer[begin]) / (denom[mid] - denom[begin]);
-
-                if (hmp_right <= config.family_wise_error_rate * (numer[end] - numer[mid]))
-                    stack.emplace_back(mid, end);
-
-                if (hmp_left <= config.family_wise_error_rate * (numer[mid] - numer[begin]))
-                    stack.emplace_back(begin, mid);
-            }
+            // std::vector<long double> numer;
+            // numer.reserve(all_pvals.size() + 1);
+            // numer.emplace_back(0.0);
+            // std::vector<long double> denom;
+            // denom.reserve(all_pvals.size() + 1);
+            // denom.emplace_back(0.0);
+            // for (const auto& [pval, tig_id, eff_size, node] : all_pvals) {
+            //     numer.emplace_back(numer.back() + 1.0L / num_tests);
+            //     denom.emplace_back(denom.back() + 1.0L / num_tests / pval);
+            // }
+            // std::vector<std::pair<size_t, size_t>> stack;
+            // stack.emplace_back(0, all_pvals.size());
+            // while (stack.size()) {
+            //     auto [begin, end] = stack.back();
+            //     stack.pop_back();
+            //     if (end - begin <= 1) {
+            //         const auto& [pval, tig_id, eff_size, node] = all_pvals[begin];
+            //         if (eff_size > 0) {
+            //             indicator_in[node] = true;
+            //             ++num_sig;
+            //         } else if (eff_size < 0) {
+            //             indicator_out[node] = true;
+            //             ++num_sig;
+            //         }
+            //         continue;
+            //     }
+
+            //     size_t mid = begin + (end - begin) / 2;
+            //     long double hmp_right
+            //         = (numer[end] - numer[mid]) / (denom[end] - denom[mid]);
+            //     long double hmp_left
+            //         = (numer[mid] - numer[begin]) / (denom[mid] - denom[begin]);
+
+            //     if (hmp_right <= config.family_wise_error_rate * (numer[end] - numer[mid]))
+            //         stack.emplace_back(mid, end);
+
+            //     if (hmp_left <= config.family_wise_error_rate * (numer[mid] - numer[begin]))
+            //         stack.emplace_back(begin, mid);
+            // }
         }
 
         common::logger->trace("Found {} / {} significant p-values.", num_sig, nelem);

From 4b9042d152443469f8c21ff13cedafd70c223df7 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 2 May 2025 09:30:58 +0200
Subject: [PATCH 402/410] wald test nb

---
 .../src/graph/annotated_graph_algorithm.cpp   | 500 +++++++++++-------
 1 file changed, 322 insertions(+), 178 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 22a5990ef9..d612346974 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -564,82 +564,88 @@ mask_nodes_by_label_dual(
     std::pair<long double, long double> nb_params_b;
     std::pair<long double, long double> nb_params_null;
     long double nb_base = 0.0;
-    if (config.test_type == "nbinom_exact") {
-        common::logger->trace("Fitting per-sample negative binomial distributions");
-
-#pragma omp parallel for num_threads(num_parallel_files)
-        for (size_t j = 0; j < groups.size(); ++j) {
-            const auto& hist = hists[j];
-            if (hist.size()) {
-                auto [r, p, mu, var] = get_rp([&](const auto& callback) {
-                    for (const auto& [k, c] : hist) {
-                        callback(k, c);
-                    }
-                });
-                nb_params[j] = std::make_pair(r, p);
-                common::logger->trace(
-                    "{}: size: {}\tmax_val: {}\tsample mean: {}\tsample var: "
-                    "{}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
-                    j, sums[j], (hist.end() - 1)->first, mu, var, r * (1 - p) / p,
-                    r * (1 - p) / p / p, r, p);
-            }
-        }
-        // based on this method for combining gamma distributions
-        // https://stats.stackexchange.com/questions/72479/generic-sum-of-gamma-random-variables
-        long double num_a = 0.0;
-        long double num_b = 0.0;
-
-        long double denom_a = 0.0;
-        long double denom_b = 0.0;
-
-        for (size_t j = 0; j < groups.size(); ++j) {
-            const auto& [r, p] = nb_params[j];
-            long double theta = (1.0 - p) / p;
-            if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
-                num_b += theta * r;
-                denom_b += theta * theta * r;
-            }
-
-            if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
-                num_a += theta * r;
-                denom_a += theta * theta * r;
-            }
-        }
-
-        long double r_a = num_a * num_a / denom_a;
-        long double theta_a = num_a / r_a;
-
-        long double r_b = num_b * num_b / denom_b;
-        long double theta_b = num_b / r_b;
-
-        long double r_null = pow(num_a + num_a, 2.0) / (denom_a + denom_b);
-        long double theta_null = (num_a + num_b) / r_null;
-
-        nb_params_a.first = r_a;
-        nb_params_b.first = r_b;
-        nb_params_null.first = r_null;
-
-        nb_params_a.second = 1.0 / (theta_a + 1.0);
-        nb_params_b.second = 1.0 / (theta_b + 1.0);
-        nb_params_null.second = 1.0 / (theta_null + 1.0);
-
-        common::logger->trace("In: r: {}\tp: {}", nb_params_a.first, nb_params_a.second);
-
-        common::logger->trace("Out: r: {}\tp: {}", nb_params_b.first, nb_params_b.second);
-
-        common::logger->trace("Null: r: {}\tp: {}", nb_params_null.first,
-                              nb_params_null.second);
-
-        nb_base = (lgammal(nb_params_null.first) - lgammal(nb_params_a.first)
-                   - lgammal(nb_params_b.first))
-            / logl(2.0);
-        nb_base += nb_params_a.first * log2l(nb_params_a.second)
-            + nb_params_b.first * log2l(nb_params_b.second)
-            - nb_params_null.first * log2l(nb_params_null.second);
-    }
+    //     if (config.test_type == "nbinom_exact") {
+    //         common::logger->trace("Fitting per-sample negative binomial distributions");
+
+    // #pragma omp parallel for num_threads(num_parallel_files)
+    //         for (size_t j = 0; j < groups.size(); ++j) {
+    //             const auto& hist = hists[j];
+    //             if (hist.size()) {
+    //                 auto [r, p, mu, var] = get_rp([&](const auto& callback) {
+    //                     for (const auto& [k, c] : hist) {
+    //                         callback(k, c);
+    //                     }
+    //                 });
+    //                 nb_params[j] = std::make_pair(r, p);
+    //                 common::logger->trace(
+    //                     "{}: size: {}\tmax_val: {}\tsample mean: {}\tsample var: "
+    //                     "{}\tmu: {}\tvar: {}\tmle: r: {}\tp: {}",
+    //                     j, sums[j], (hist.end() - 1)->first, mu, var, r * (1 - p) / p,
+    //                     r * (1 - p) / p / p, r, p);
+    //             }
+    //         }
+    //         // based on this method for combining gamma distributions
+    //         // https://stats.stackexchange.com/questions/72479/generic-sum-of-gamma-random-variables
+    //         long double num_a = 0.0;
+    //         long double num_b = 0.0;
+
+    //         long double denom_a = 0.0;
+    //         long double denom_b = 0.0;
+
+    //         for (size_t j = 0; j < groups.size(); ++j) {
+    //             const auto& [r, p] = nb_params[j];
+    //             long double theta = (1.0 - p) / p;
+    //             if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
+    //                 num_b += theta * r;
+    //                 denom_b += theta * theta * r;
+    //             }
+
+    //             if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
+    //                 num_a += theta * r;
+    //                 denom_a += theta * theta * r;
+    //             }
+    //         }
+
+    //         long double r_a = num_a * num_a / denom_a;
+    //         long double theta_a = num_a / r_a;
+
+    //         long double r_b = num_b * num_b / denom_b;
+    //         long double theta_b = num_b / r_b;
+
+    //         long double r_null = pow(num_a + num_a, 2.0) / (denom_a + denom_b);
+    //         long double theta_null = (num_a + num_b) / r_null;
+
+    //         nb_params_a.first = r_a;
+    //         nb_params_b.first = r_b;
+    //         nb_params_null.first = r_null;
+
+    //         nb_params_a.second = 1.0 / (theta_a + 1.0);
+    //         nb_params_b.second = 1.0 / (theta_b + 1.0);
+    //         nb_params_null.second = 1.0 / (theta_null + 1.0);
+
+    //         common::logger->trace("In: r: {}\tp: {}", nb_params_a.first, nb_params_a.second);
+
+    //         common::logger->trace("Out: r: {}\tp: {}", nb_params_b.first, nb_params_b.second);
+
+    //         common::logger->trace("Null: r: {}\tp: {}", nb_params_null.first,
+    //                               nb_params_null.second);
+
+    //         nb_base = (lgammal(nb_params_null.first) - lgammal(nb_params_a.first)
+    //                    - lgammal(nb_params_b.first))
+    //             / logl(2.0);
+    //         nb_base += nb_params_a.first * log2l(nb_params_a.second)
+    //             + nb_params_b.first * log2l(nb_params_b.second)
+    //             - nb_params_null.first * log2l(nb_params_null.second);
+    //     }
 
     long double mu1 = static_cast<long double>(in_kmers) / nelem;
     long double mu2 = static_cast<long double>(out_kmers) / nelem;
+    // long double mun = mu1 + mu2;
+
+    // long double var1 = static_cast<long double>(in_sq_kmers) / nelem - mu1 * mu1;
+    // long double var2 = static_cast<long double>(out_sq_kmers) / nelem - mu2 * mu2;
+    // long double varn = static_cast<long double>(total_sq_kmers) / nelem - mun * mun;
+
     long double p = mu1 / (mu1 + mu2);
     long double q = mu2 / (mu1 + mu2);
 
@@ -1249,122 +1255,261 @@ mask_nodes_by_label_dual(
                 }
             }
 
-            auto [r_a, p_a] = nb_params_a;
-            auto [r_b, p_b] = nb_params_b;
-            auto [r_n, p_n] = nb_params_null;
+            // int max_bits = std::numeric_limits<int>::max();
+            int max_bits = 8;
 
-            long double midpoint = r_a * n / (r_a + r_b);
+            auto get_loglik = [&](long double k, long double mu) {
+                if (k == 0 && mu == 0)
+                    return std::make_pair(0.0L, 0.0L);
 
-            auto get_deviance_a = [&](long double s) {
-                if (s == 0)
-                    return r_a * logl(p_a) * -2.0;
-
-                return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
-                        + r_a * (logl(r_a) - logl(p_a)))
-                    * 2.0;
-            };
+                long double lk = lgammal(k + 1);
+                auto nb_loglik = [&](long double r) {
+                    long double p = r / (r + mu);
+                    return lgammal(k + r) - lk - lgammal(r) + log1pl(-p) * k + r * logl(p);
+                };
 
-            auto get_deviance_b = [&](long double t) {
-                if (t == 0)
-                    return r_b * logl(p_b) * -2.0;
+                long double v = std::numeric_limits<long double>::max();
+                long double r = 0.0L;
+                try {
+                    auto [rt, vt] = boost::math::tools::brent_find_minima(
+                        [&](long double r) { return -nb_loglik(r); },
+                        static_cast<long double>(std::numeric_limits<double>::min()),
+                        1.0L - std::numeric_limits<double>::min(), max_bits);
+                    if (vt < v) {
+                        v = vt;
+                        r = rt;
+                    }
+                } catch (...) {
+                }
+                try {
+                    auto [at, vt] = boost::math::tools::brent_find_minima(
+                        [&](long double a) { return -nb_loglik(1.0L / a); },
+                        static_cast<long double>(std::numeric_limits<double>::min()),
+                        1.0L - std::numeric_limits<double>::min(), max_bits);
+
+                    if (vt < v) {
+                        v = vt;
+                        r = 1.0L / at;
+                    }
+                } catch (...) {
+                }
 
-                return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
-                        + r_b * (logl(r_b) - logl(p_b)))
-                    * 2.0;
+                return std::make_pair(-v, r);
             };
 
-            auto get_deviance = [&](long double s, long double t) {
-                return get_deviance_a(s) + get_deviance_b(t);
+            // wald test
+
+            // long double z_stat = 0.0;
+            // long double theta_a = static_cast<long double>(in_sum) / in_kmers;
+            // long double theta_b = static_cast<long double>(out_sum) / out_kmers;
+            // // long double theta_n = static_cast<long double>(n) / total_kmers;
+
+            auto get_wald_stat = [&](long double k, long double nl) {
+                if (k == 0)
+                    return std::make_pair(0.0L, 0.0L);
+
+                long double theta = k / nl;
+                long double r = get_loglik(k, k).second;
+                // long double var = theta / nl + theta * theta / r;
+                long double finf
+                    = r * nl * r * nl / (r + k) / (r + k) * (1.0L / k + 1.0L / r);
+                long double var = 1.0L / finf;
+                return std::make_pair(theta, var);
+                //     // long double var = theta_n / nl + theta_n * theta_n / r;
+                //     // return pow(theta - theta_n, 2.0) / var;
             };
 
-            long double min_dev
-                = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
-            long double in_dev = get_deviance(in_sum, out_sum);
-
-            if (in_dev > min_dev) {
-                // eff_size = static_cast<long double>(in_sum) - midpoint;
-                pval = 0.0;
-                long double base = nb_base
-                    + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n)) / logl(2.0);
-                double l1pa = log1pl(-p_a) / logl(2.0);
-                double l1pb = log1pl(-p_b) / logl(2.0);
-
-                if (is_discrete) {
-                    long double dev0 = get_deviance(0, n);
-                    long double devn = get_deviance(n, 0);
-
-                    long double min_pval = 0.0;
-                    if (dev0 >= devn) {
-                        size_t s = 0;
-                        size_t t = n;
-                        long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                             - lgammal(s + 1) - lgammal(t + 1))
-                                / logl(2.0)
-                            + s * l1pa + t * l1pb;
-                        min_pval += exp2(base + sbase);
-                    }
+            auto [theta_a, var_a] = get_wald_stat(in_sum, in_kmers);
+            auto [theta_b, var_b] = get_wald_stat(out_sum, out_kmers);
+            long double chi_stat = pow(theta_a - theta_b, 2.0) / (var_a + var_b);
+
+            // long double pval
+            //     = boost::math::cdf(
+            //           boost::math::complement(boost::math::normal_distribution(), wald_stat))
+            //     * 2;
+
+            // if (in_sum > 0)
+            //     chi_stat += get_wald_stat(in_sum, in_kmers);
+
+            // if (out_sum > 0)
+            //     chi_stat += get_wald_stat(out_sum, out_kmers);
+
+            // long double alt_loglik
+            //     = get_loglik(in_sum, in_sum).first + get_loglik(out_sum, out_sum).first;
+
+            // auto get_null_loglik = [&]() {
+            //     return -boost::math::tools::brent_find_minima(
+            //                 [&](long double theta) {
+            //                     return -get_loglik(in_sum, theta * in_kmers).first
+            //                         - get_loglik(out_sum, theta * out_kmers).first;
+            //                 },
+            //                 static_cast<long double>(std::numeric_limits<double>::min()),
+            //                 1.0L - std::numeric_limits<double>::min(), max_bits)
+            //                 .second;
+            //     // auto get_theta = [&](long double ra, long double rb, long double na,
+            //     //                      long double nb, long double xa, long double xb) {
+            //     //     long double a = na * nb * (ra + rb);
+            //     //     long double b = ra * nb * xa + na * rb * xb + ra * rb * (na + nb);
+            //     //     long double c = ra * rb * (xa + xb);
+            //     //     long double d = sqrtl(b * b - a * c * 4.0L);
+            //     //     long double r1 = (-b + d) / 2.0L / a;
+            //     //     long double r2 = (-b - d) / 2.0L / a;
+            //     //     if (r1 <= 0 && r2 <= 0) {
+            //     //         common::logger->error("r: {},{}\tx: {},{}\tn: {},{}", ra, rb, xa,
+            //     //                               xb, na, nb);
+            //     //         throw std::runtime_error("no lambda works");
+            //     //     }
+            //     //     return r1 > 0 ? r1 : r2;
+            //     // };
+
+            //     // long double theta = (static_cast<long double>(in_sum) / in_kmers
+            //     //                      + static_cast<long double>(out_sum) / out_kmers)
+            //     //     / 2;
+            //     // long double v = -std::numeric_limits<long double>::max();
+            //     // while (true) {
+            //     //     auto [va, ra] = get_loglik(in_sum, theta * in_kmers);
+            //     //     auto [vb, rb] = get_loglik(out_sum, theta * out_kmers);
+            //     //     theta = get_theta(ra, rb, in_kmers, out_kmers, in_sum, out_sum);
+            //     //     long double v_new = va + vb;
+            //     //     if (abs(v_new - v) / v_new < 1e-5)
+            //     //         break;
+
+            //     //     v = v_new;
+            //     // }
+
+            //     // return v;
+            // };
+
+            // long double null_loglik = get_null_loglik();
+            // long double chi_stat = (alt_loglik - null_loglik) * 2;
+
+            if (chi_stat <= 0.0) {
+                common::logger->error("{} = {} + {}\t->\t {}", n, in_sum, out_sum, chi_stat);
+                throw std::runtime_error("FOSDOF");
+            }
 
-                    if (dev0 <= devn) {
-                        size_t s = n;
-                        size_t t = 0;
-                        long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                             - lgammal(s + 1) - lgammal(t + 1))
-                                / logl(2.0)
-                            + s * l1pa + t * l1pb;
-                        min_pval += exp2(base + sbase);
-                    }
+            pval = get_chi2_pval(1.0, chi_stat);
 
-                    if (min_pval >= min_pval_cutoff)
-                        return;
-                }
+            if (pval == 0) {
+                common::logger->error("{} = {} + {}\t->\t {}", n, in_sum, out_sum, chi_stat);
+            }
 
-                {
-                    size_t s = 0;
-                    size_t t = n;
-                    long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                         - lgammal(s + 1) - lgammal(t + 1))
-                            / logl(2.0)
-                        + s * l1pa + t * l1pb;
-                    pval += exp2(base + sbase);
-                    for (++s; s <= n; ++s) {
-                        long double dev = get_deviance(s, t - 1);
-                        if (dev >= in_dev) {
-                            sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
-                                + log2l(t + 1) + l1pa - l1pb;
-                            --t;
-                            pval += exp2(base + sbase);
-                            if (!config.test_by_unitig && pval >= config.family_wise_error_rate)
-                                break;
-                        } else {
-                            break;
-                        }
-                    }
-                }
+            // auto [r_a, p_a] = nb_params_a;
+            // auto [r_b, p_b] = nb_params_b;
+            // auto [r_n, p_n] = nb_params_null;
+
+            // long double midpoint = r_a * n / (r_a + r_b);
+
+            // auto get_deviance_a = [&](long double s) {
+            //     if (s == 0)
+            //         return r_a * logl(p_a) * -2.0;
+
+            //     return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
+            //             + r_a * (logl(r_a) - logl(p_a)))
+            //         * 2.0;
+            // };
+
+            // auto get_deviance_b = [&](long double t) {
+            //     if (t == 0)
+            //         return r_b * logl(p_b) * -2.0;
+
+            //     return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
+            //             + r_b * (logl(r_b) - logl(p_b)))
+            //         * 2.0;
+            // };
+
+            // auto get_deviance = [&](long double s, long double t) {
+            //     return get_deviance_a(s) + get_deviance_b(t);
+            // };
+
+            // long double min_dev
+            //     = get_deviance(midpoint, static_cast<long double>(n) - midpoint);
+            // long double in_dev = get_deviance(in_sum, out_sum);
+
+            // if (in_dev > min_dev) {
+            //     // eff_size = static_cast<long double>(in_sum) - midpoint;
+            //     pval = 0.0;
+            //     long double base = nb_base
+            //         + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n)) / logl(2.0);
+            //     double l1pa = log1pl(-p_a) / logl(2.0);
+            //     double l1pb = log1pl(-p_b) / logl(2.0);
+
+            //     if (is_discrete) {
+            //         long double dev0 = get_deviance(0, n);
+            //         long double devn = get_deviance(n, 0);
+
+            //         long double min_pval = 0.0;
+            //         if (dev0 >= devn) {
+            //             size_t s = 0;
+            //             size_t t = n;
+            //             long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+            //                                  - lgammal(s + 1) - lgammal(t + 1))
+            //                     / logl(2.0)
+            //                 + s * l1pa + t * l1pb;
+            //             min_pval += exp2(base + sbase);
+            //         }
 
-                if ((config.test_by_unitig || pval < config.family_wise_error_rate)
-                    && get_deviance(n, 0) >= in_dev) {
-                    size_t s = n;
-                    size_t t = 0;
-                    long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                         - lgammal(s + 1) - lgammal(t + 1))
-                            / logl(2.0)
-                        + s * l1pa + t * l1pb;
-                    pval += exp2(base + sbase);
-                    for (++t; t <= n; ++t) {
-                        long double dev = get_deviance(s - 1, t);
-                        if (dev >= in_dev) {
-                            sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
-                                + log2l(t + 1) + l1pa - l1pb;
-                            --s;
-                            pval += exp2(base + sbase);
-                            if (!config.test_by_unitig && pval >= config.family_wise_error_rate)
-                                break;
-                        } else {
-                            break;
-                        }
-                    }
-                }
-            }
+            //         if (dev0 <= devn) {
+            //             size_t s = n;
+            //             size_t t = 0;
+            //             long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+            //                                  - lgammal(s + 1) - lgammal(t + 1))
+            //                     / logl(2.0)
+            //                 + s * l1pa + t * l1pb;
+            //             min_pval += exp2(base + sbase);
+            //         }
+
+            //         if (min_pval >= min_pval_cutoff)
+            //             return;
+            //     }
+
+            //     {
+            //         size_t s = 0;
+            //         size_t t = n;
+            //         long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+            //                              - lgammal(s + 1) - lgammal(t + 1))
+            //                 / logl(2.0)
+            //             + s * l1pa + t * l1pb;
+            //         pval += exp2(base + sbase);
+            //         for (++s; s <= n; ++s) {
+            //             long double dev = get_deviance(s, t - 1);
+            //             if (dev >= in_dev) {
+            //                 sbase += log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
+            //                     + log2l(t + 1) + l1pa - l1pb;
+            //                 --t;
+            //                 pval += exp2(base + sbase);
+            //                 if (!config.test_by_unitig && pval >= config.family_wise_error_rate)
+            //                     break;
+            //             } else {
+            //                 break;
+            //             }
+            //         }
+            //     }
+
+            //     if ((config.test_by_unitig || pval < config.family_wise_error_rate)
+            //         && get_deviance(n, 0) >= in_dev) {
+            //         size_t s = n;
+            //         size_t t = 0;
+            //         long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+            //                              - lgammal(s + 1) - lgammal(t + 1))
+            //                 / logl(2.0)
+            //             + s * l1pa + t * l1pb;
+            //         pval += exp2(base + sbase);
+            //         for (++t; t <= n; ++t) {
+            //             long double dev = get_deviance(s - 1, t);
+            //             if (dev >= in_dev) {
+            //                 sbase -= log2l(r_a + s) - log2l(r_b + t) - log2l(s + 1)
+            //                     + log2l(t + 1) + l1pa - l1pb;
+            //                 --s;
+            //                 pval += exp2(base + sbase);
+            //                 if (!config.test_by_unitig && pval >= config.family_wise_error_rate)
+            //                     break;
+            //             } else {
+            //                 break;
+            //             }
+            //         }
+            //     }
+            // }
         } else if (config.test_type == "mwu") {
             if (is_discrete) {
                 size_t n = 0;
@@ -1430,7 +1575,6 @@ mask_nodes_by_label_dual(
             generate_b([&](long double c) { eff_size_out += c; });
 
             pval = mann_whitneyu(generate_a, generate_b).first;
-
         } else if (config.test_type == "cmh" || config.test_type == "cmh_binary"
                    || config.test_type == "fisher_binary") {
             int64_t in_sum = 0;

From ec20f8af1576e70410027fef394f5ff0bfc87f50 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Tue, 6 May 2025 18:10:09 +0200
Subject: [PATCH 403/410] fix eff size

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index d612346974..430d3b85b2 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1680,8 +1680,8 @@ mask_nodes_by_label_dual(
                             = all_pvals[idx];
                         pval = comb_pval;
                         monotig_id = cur_monotig_id;
-                        eff_size_in = comb_eff_size_in / path.size();
-                        eff_size_out = comb_eff_size_out / path.size();
+                        eff_size_in = comb_eff_size_in;
+                        eff_size_out = comb_eff_size_out;
                     }
                 },
                 num_threads);

From f0321d783efa58cf16be6b56f3376d9d81a5ce81 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 8 May 2025 17:48:14 +0200
Subject: [PATCH 404/410] better nbinom optim and scale values before wald test

---
 metagraph/external-libraries/eigen            |   2 +-
 .../src/graph/annotated_graph_algorithm.cpp   | 517 ++++++++++--------
 2 files changed, 287 insertions(+), 232 deletions(-)

diff --git a/metagraph/external-libraries/eigen b/metagraph/external-libraries/eigen
index a7c7d329d8..36b9596275 160000
--- a/metagraph/external-libraries/eigen
+++ b/metagraph/external-libraries/eigen
@@ -1 +1 @@
-Subproject commit a7c7d329d89e8484be58df6078a586c44523db37
+Subproject commit 36b95962756c1fce8e29b1f8bc45967f30773c00
diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 430d3b85b2..6143a7e1be 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -98,7 +98,7 @@ void push_back(PValStorage& v, double val) {
 }
 
 template <typename Generator>
-std::tuple<long double, long double, long double, long double>
+std::tuple<long double, long double, long double, long double, long double>
 get_rp(const Generator& generate) {
     long double mu = 0;
     long double var = 0;
@@ -115,7 +115,7 @@ get_rp(const Generator& generate) {
     if (mu >= var) {
         common::logger->warn("Fit failed, falling back to Poisson: mu: {} >= var: {}", mu,
                              var);
-        return { 0.0, 1.0, mu, var };
+        return { 0.0, 1.0, mu, var, 0.0 };
     }
 
     auto get_l = [&](long double r) {
@@ -158,20 +158,31 @@ get_rp(const Generator& generate) {
     }
 
     long double r = 0.0;
+    long double ll = 0.0;
     if (r1 == 0 && r2 == 0) {
         throw std::runtime_error("Failed to fit");
     } else if (r1 == 0) {
         r = r2;
+        ll = get_l(r);
     } else if (r2 == 0) {
         r = r1;
+        ll = get_l(r);
     } else {
-        r = get_l(r1) > get_l(r2) ? r1 : r2;
+        long double ll1 = get_l(r1);
+        long double ll2 = get_l(r2);
+        if (ll1 > ll2) {
+            r = r1;
+            ll = ll1;
+        } else {
+            r = r2;
+            ll = ll2;
+        }
     }
 
     long double p = r / (r + mu);
     // long double p = 0.1;
     // r = p * mu / (1.0 - p);
-    return { r, p, mu, var };
+    return { r, p, mu, var, ll };
 };
 
 template <class G1, class G2>
@@ -498,6 +509,8 @@ mask_nodes_by_label_dual(
     std::vector<size_t> n_kmers(groups.size());
     std::vector<uint64_t> sums(groups.size());
     std::vector<uint64_t> sq_sums(groups.size());
+    std::vector<long double> means(groups.size());
+    std::vector<long double> vars(groups.size());
     uint64_t max_in_obs_val = 0.0;
     uint64_t max_out_obs_val = 0.0;
 
@@ -534,6 +547,9 @@ mask_nodes_by_label_dual(
         common::logger->trace(
             "{}: n_unique: {}\tsum: {}\tmax_obs: {}\tmin_cutoff: {}\tmax_cutof: {}", j,
             n_kmers[j], sums[j], max_obs_vals[j], min_counts[j], check_cutoff[j]);
+
+        means[j] = sums[j] / nelem;
+        vars[j] = sq_sums[j] / nelem - sums[j] * sums[j];
     }
 
     common::logger->trace("Number of kept unique k-mers: {}\tNumber of kept k-mers: {}",
@@ -559,11 +575,11 @@ mask_nodes_by_label_dual(
         true, is_primary ? DeBruijnGraph::PRIMARY : DeBruijnGraph::BASIC);
 
     // precompute negative binomial fits for poisson_bayes and nbinom_exact tests
-    std::vector<std::pair<long double, long double>> nb_params(groups.size());
-    std::pair<long double, long double> nb_params_a;
-    std::pair<long double, long double> nb_params_b;
-    std::pair<long double, long double> nb_params_null;
-    long double nb_base = 0.0;
+    // std::vector<std::pair<long double, long double>> nb_params(groups.size());
+    // std::pair<long double, long double> nb_params_a;
+    // std::pair<long double, long double> nb_params_b;
+    // std::pair<long double, long double> nb_params_null;
+    // long double nb_base = 0.0;
     //     if (config.test_type == "nbinom_exact") {
     //         common::logger->trace("Fitting per-sample negative binomial distributions");
 
@@ -646,6 +662,37 @@ mask_nodes_by_label_dual(
     // long double var2 = static_cast<long double>(out_sq_kmers) / nelem - mu2 * mu2;
     // long double varn = static_cast<long double>(total_sq_kmers) / nelem - mun * mun;
 
+    std::vector<long double> scale_factors(groups.size(), 1.0);
+    std::vector<long double> scaled_sums(groups.size());
+    long double scaled_in_kmers = 0.0;
+    long double scaled_out_kmers = 0.0;
+    if (config.test_type == "nbinom_exact") {
+        long double target_p = 0.0;
+        for (size_t j = 0; j < groups.size(); ++j) {
+            long double p = means[j] / vars[j];
+            common::logger->trace("{}: p = {}", j, p);
+            target_p += 1.0L / p;
+        }
+        target_p = static_cast<long double>(groups.size()) / target_p;
+        common::logger->trace("Scaling distributions to p = {}", target_p);
+
+        for (size_t j = 0; j < groups.size(); ++j) {
+            long double p = means[j] / vars[j];
+            scale_factors[j] = p / target_p;
+            scaled_sums[j] = scale_factors[j] * sums[j];
+            if (groups[j] == Group::IN || groups[j] == Group::BOTH)
+                scaled_in_kmers += scaled_sums[j];
+
+            if (groups[j] == Group::OUT || groups[j] == Group::BOTH)
+                scaled_out_kmers += scaled_sums[j];
+        }
+
+        common::logger->trace("Scaling factors: {}", fmt::join(scale_factors, ","));
+    } else {
+        scaled_in_kmers = in_kmers;
+        scaled_out_kmers = out_kmers;
+    }
+
     long double p = mu1 / (mu1 + mu2);
     long double q = mu2 / (mu1 + mu2);
 
@@ -866,187 +913,187 @@ mask_nodes_by_label_dual(
 
     std::vector<std::pair<long double, size_t>> min_pvals;
     long double min_pval_cutoff = 1.0;
-    if (is_discrete) {
-        {
-            std::vector<std::vector<std::pair<long double, size_t>>> min_pvals_b(
-                num_threads + 1);
-            generate_clean_rows([&](uint64_t, const auto& row, uint64_t bucket_id) {
-                size_t n = 0;
-                int64_t in_sum = 0;
-                int64_t out_sum = 0;
-                if (config.test_type == "mwu" || config.test_type == "poisson_binom") {
-                    for (const auto& [j, c] : row) {
-                        n += static_cast<int64_t>(groups[j] != Group::OTHER)
-                            + (groups[j] == Group::BOTH);
-                    }
-                } else {
-                    for (const auto& [j, c] : row) {
-                        if (groups[j] == Group::IN || groups[j] == Group::OUT)
-                            in_sum += c;
-
-                        if (groups[j] == Group::OUT || groups[j] == Group::OUT)
-                            out_sum += c;
-                    }
-                    n = in_sum + out_sum;
-                }
-
-                auto& min_pval_b = min_pvals_b[bucket_id];
-
-                if (min_pval_b.size() <= n)
-                    min_pval_b.resize(n + 1, std::make_pair(1.0L, 0));
-
-                if (!min_pval_b[n].second) {
-                    // compute the min-attainable p-value
-                    if (config.test_type == "poisson_binom") {
-                        size_t front = n - std::min(n, num_labels_out);
-                        size_t back = std::min(n, num_labels_in);
-                        assert(s >= front);
-                        long double pval0 = pb_pvals[n][front];
-                        long double pvaln = pb_pvals[n][back];
-                        min_pval_b[n].first = std::min(pval0, pvaln)
-                            * (1 + (pval0 == pvaln && front != back));
-                    } else if (config.test_type == "poisson_exact") {
-                        boost::math::binomial bdist(n, p);
-                        auto get_deviance = [&](long double y, long double mu) {
-                            y += 1e-8;
-                            mu += 1e-8;
-                            return 2 * (y * log(y / mu) - y + mu);
-                        };
-
-                        long double dev0 = get_deviance(0, mu1) + get_deviance(n, mu2);
-                        long double devn = get_deviance(n, mu1) + get_deviance(0, mu2);
-
-                        min_pval_b[n].first = 0.0L;
-                        if (dev0 >= devn)
-                            min_pval_b[n].first += boost::math::pdf(bdist, 0);
-
-                        if (dev0 <= devn)
-                            min_pval_b[n].first += boost::math::pdf(bdist, n);
-
-                    } else if (config.test_type == "nbinom_exact") {
-                        auto [r_a, p_a] = nb_params_a;
-                        auto [r_b, p_b] = nb_params_b;
-                        auto [r_n, p_n] = nb_params_null;
-
-                        long double base = nb_base
-                            + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n))
-                                / logl(2.0);
-                        double l1pa = log1pl(-p_a) / logl(2.0);
-                        double l1pb = log1pl(-p_b) / logl(2.0);
-
-                        auto get_deviance_a = [&](long double s) {
-                            if (s == 0)
-                                return r_a * logl(p_a) * -2.0;
-
-                            return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
-                                    + r_a * (logl(r_a) - logl(p_a)))
-                                * 2.0;
-                        };
-
-                        auto get_deviance_b = [&](long double t) {
-                            if (t == 0)
-                                return r_b * logl(p_b) * -2.0;
-
-                            return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
-                                    + r_b * (logl(r_b) - logl(p_b)))
-                                * 2.0;
-                        };
-
-                        auto get_deviance = [&](long double s, long double t) {
-                            return get_deviance_a(s) + get_deviance_b(t);
-                        };
-
-                        long double dev0 = get_deviance(0, n);
-                        long double devn = get_deviance(n, 0);
-
-                        min_pval_b[n].first = 0.0;
-                        if (dev0 >= devn) {
-                            size_t s = 0;
-                            size_t t = n;
-                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                                 - lgammal(s + 1) - lgammal(t + 1))
-                                    / logl(2.0)
-                                + s * l1pa + t * l1pb;
-                            min_pval_b[n].first += exp2(base + sbase);
-                        }
+    // if (is_discrete) {
+    //     {
+    //         std::vector<std::vector<std::pair<long double, size_t>>> min_pvals_b(
+    //             num_threads + 1);
+    //         generate_clean_rows([&](uint64_t, const auto& row, uint64_t bucket_id) {
+    //             size_t n = 0;
+    //             int64_t in_sum = 0;
+    //             int64_t out_sum = 0;
+    //             if (config.test_type == "mwu" || config.test_type == "poisson_binom") {
+    //                 for (const auto& [j, c] : row) {
+    //                     n += static_cast<int64_t>(groups[j] != Group::OTHER)
+    //                         + (groups[j] == Group::BOTH);
+    //                 }
+    //             } else {
+    //                 for (const auto& [j, c] : row) {
+    //                     if (groups[j] == Group::IN || groups[j] == Group::OUT)
+    //                         in_sum += c;
+
+    //                     if (groups[j] == Group::OUT || groups[j] == Group::OUT)
+    //                         out_sum += c;
+    //                 }
+    //                 n = in_sum + out_sum;
+    //             }
 
-                        if (dev0 <= devn) {
-                            size_t s = n;
-                            size_t t = 0;
-                            long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
-                                                 - lgammal(s + 1) - lgammal(t + 1))
-                                    / logl(2.0)
-                                + s * l1pa + t * l1pb;
-                            min_pval_b[n].first += exp2(base + sbase);
-                        }
-                    } else if (config.test_type == "mwu") {
-                        std::vector<size_t> vals(groups.size());
-                        auto it = vals.begin();
-                        for (size_t i = n; i > 0; --i, ++it) {
-                            *it = i;
-                        }
-                        auto generate_a_in = [&](const auto& callback) {
-                            std::for_each(vals.begin(), vals.begin() + n,
-                                          [&](size_t i) { callback(i); });
-                        };
-                        auto generate_b_in = [&](const auto& callback) {
-                            std::for_each(vals.begin() + n, vals.end(),
-                                          [&](size_t i) { callback(i); });
-                        };
-                        auto generate_a_out = [&](const auto& callback) {
-                            std::for_each(vals.begin() + n, vals.end(),
-                                          [&](size_t i) { callback(i); });
-                        };
-                        auto generate_b_out = [&](const auto& callback) {
-                            std::for_each(vals.begin(), vals.begin() + n,
-                                          [&](size_t i) { callback(i); });
-                        };
-
-                        long double pval0
-                            = mann_whitneyu(generate_a_out, generate_b_out).first;
-                        long double pvaln = mann_whitneyu(generate_a_in, generate_b_in).first;
-                        min_pval_b[n].first = std::min(pval0, pvaln);
-                    }
-                }
-                ++min_pval_b[n].second;
-            });
-            size_t max_size = 0;
-            for (const auto& min_pvals : min_pvals_b) {
-                max_size = std::max(max_size, min_pvals.size());
-            }
-            min_pvals.resize(max_size, std::make_pair(1.0L, 0));
-            for (const auto& min_pval_b : min_pvals_b) {
-                for (size_t n = 0; n < min_pval_b.size(); ++n) {
-                    min_pvals[n].first = std::min(min_pvals[n].first, min_pval_b[n].first);
-                    min_pvals[n].second += min_pval_b[n].second;
-                }
-            }
-        }
+    //             auto& min_pval_b = min_pvals_b[bucket_id];
+
+    //             if (min_pval_b.size() <= n)
+    //                 min_pval_b.resize(n + 1, std::make_pair(1.0L, 0));
+
+    //             if (!min_pval_b[n].second) {
+    //                 // compute the min-attainable p-value
+    //                 if (config.test_type == "poisson_binom") {
+    //                     size_t front = n - std::min(n, num_labels_out);
+    //                     size_t back = std::min(n, num_labels_in);
+    //                     assert(s >= front);
+    //                     long double pval0 = pb_pvals[n][front];
+    //                     long double pvaln = pb_pvals[n][back];
+    //                     min_pval_b[n].first = std::min(pval0, pvaln)
+    //                         * (1 + (pval0 == pvaln && front != back));
+    //                 } else if (config.test_type == "poisson_exact") {
+    //                     boost::math::binomial bdist(n, p);
+    //                     auto get_deviance = [&](long double y, long double mu) {
+    //                         y += 1e-8;
+    //                         mu += 1e-8;
+    //                         return 2 * (y * log(y / mu) - y + mu);
+    //                     };
+
+    //                     long double dev0 = get_deviance(0, mu1) + get_deviance(n, mu2);
+    //                     long double devn = get_deviance(n, mu1) + get_deviance(0, mu2);
+
+    //                     min_pval_b[n].first = 0.0L;
+    //                     if (dev0 >= devn)
+    //                         min_pval_b[n].first += boost::math::pdf(bdist, 0);
+
+    //                     if (dev0 <= devn)
+    //                         min_pval_b[n].first += boost::math::pdf(bdist, n);
+
+    //                 } else if (config.test_type == "nbinom_exact") {
+    //                     auto [r_a, p_a] = nb_params_a;
+    //                     auto [r_b, p_b] = nb_params_b;
+    //                     auto [r_n, p_n] = nb_params_null;
+
+    //                     long double base = nb_base
+    //                         + (lgammal(n + 1) - lgammal(r_n + n) - n * log1pl(-p_n))
+    //                             / logl(2.0);
+    //                     double l1pa = log1pl(-p_a) / logl(2.0);
+    //                     double l1pb = log1pl(-p_b) / logl(2.0);
+
+    //                     auto get_deviance_a = [&](long double s) {
+    //                         if (s == 0)
+    //                             return r_a * logl(p_a) * -2.0;
+
+    //                         return ((logl(s) - log1pl(-p_a)) * s - (r_a + s) * logl(r_a + s)
+    //                                 + r_a * (logl(r_a) - logl(p_a)))
+    //                             * 2.0;
+    //                     };
+
+    //                     auto get_deviance_b = [&](long double t) {
+    //                         if (t == 0)
+    //                             return r_b * logl(p_b) * -2.0;
+
+    //                         return ((logl(t) - log1pl(-p_b)) * t - (r_b + t) * logl(r_b + t)
+    //                                 + r_b * (logl(r_b) - logl(p_b)))
+    //                             * 2.0;
+    //                     };
+
+    //                     auto get_deviance = [&](long double s, long double t) {
+    //                         return get_deviance_a(s) + get_deviance_b(t);
+    //                     };
+
+    //                     long double dev0 = get_deviance(0, n);
+    //                     long double devn = get_deviance(n, 0);
+
+    //                     min_pval_b[n].first = 0.0;
+    //                     if (dev0 >= devn) {
+    //                         size_t s = 0;
+    //                         size_t t = n;
+    //                         long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+    //                                              - lgammal(s + 1) - lgammal(t + 1))
+    //                                 / logl(2.0)
+    //                             + s * l1pa + t * l1pb;
+    //                         min_pval_b[n].first += exp2(base + sbase);
+    //                     }
 
-        std::sort(min_pvals.rbegin(), min_pvals.rend());
-        // long double harm = 0.0;
-        // for (size_t i = 1; i <= num_tests; ++i) {
-        //     harm += 1.0L / i;
-        // }
-        for (const auto& [pval, count] : min_pvals) {
-            if (!count)
-                continue;
+    //                     if (dev0 <= devn) {
+    //                         size_t s = n;
+    //                         size_t t = 0;
+    //                         long double sbase = (lgammal(r_a + s) + lgammal(r_b + t)
+    //                                              - lgammal(s + 1) - lgammal(t + 1))
+    //                                 / logl(2.0)
+    //                             + s * l1pa + t * l1pb;
+    //                         min_pval_b[n].first += exp2(base + sbase);
+    //                     }
+    //                 } else if (config.test_type == "mwu") {
+    //                     std::vector<size_t> vals(groups.size());
+    //                     auto it = vals.begin();
+    //                     for (size_t i = n; i > 0; --i, ++it) {
+    //                         *it = i;
+    //                     }
+    //                     auto generate_a_in = [&](const auto& callback) {
+    //                         std::for_each(vals.begin(), vals.begin() + n,
+    //                                       [&](size_t i) { callback(i); });
+    //                     };
+    //                     auto generate_b_in = [&](const auto& callback) {
+    //                         std::for_each(vals.begin() + n, vals.end(),
+    //                                       [&](size_t i) { callback(i); });
+    //                     };
+    //                     auto generate_a_out = [&](const auto& callback) {
+    //                         std::for_each(vals.begin() + n, vals.end(),
+    //                                       [&](size_t i) { callback(i); });
+    //                     };
+    //                     auto generate_b_out = [&](const auto& callback) {
+    //                         std::for_each(vals.begin(), vals.begin() + n,
+    //                                       [&](size_t i) { callback(i); });
+    //                     };
+
+    //                     long double pval0
+    //                         = mann_whitneyu(generate_a_out, generate_b_out).first;
+    //                     long double pvaln = mann_whitneyu(generate_a_in, generate_b_in).first;
+    //                     min_pval_b[n].first = std::min(pval0, pvaln);
+    //                 }
+    //             }
+    //             ++min_pval_b[n].second;
+    //         });
+    //         size_t max_size = 0;
+    //         for (const auto& min_pvals : min_pvals_b) {
+    //             max_size = std::max(max_size, min_pvals.size());
+    //         }
+    //         min_pvals.resize(max_size, std::make_pair(1.0L, 0));
+    //         for (const auto& min_pval_b : min_pvals_b) {
+    //             for (size_t n = 0; n < min_pval_b.size(); ++n) {
+    //                 min_pvals[n].first = std::min(min_pvals[n].first, min_pval_b[n].first);
+    //                 min_pvals[n].second += min_pval_b[n].second;
+    //             }
+    //         }
+    //     }
 
-            // common::logger->trace("Trying:\tc: {}\tpval: {}\tcutoff: {}", num_tests, pval,
-            //                       config.family_wise_error_rate / harm);
-            // if (pval >= config.family_wise_error_rate / harm) {
-            if (pval >= config.family_wise_error_rate / num_tests) {
-                min_pval_cutoff = pval;
-                // for (size_t i = num_tests; i > num_tests - count; --i) {
-                //     harm -= 1.0L / i;
-                // }
-                num_tests -= count;
-            }
-        }
-        common::logger->trace(
-            "Only running tests where min pval < {}. Keeping {} / {} tests",
-            min_pval_cutoff, num_tests, nelem);
-    }
+    //     std::sort(min_pvals.rbegin(), min_pvals.rend());
+    //     // long double harm = 0.0;
+    //     // for (size_t i = 1; i <= num_tests; ++i) {
+    //     //     harm += 1.0L / i;
+    //     // }
+    //     for (const auto& [pval, count] : min_pvals) {
+    //         if (!count)
+    //             continue;
+
+    //         // common::logger->trace("Trying:\tc: {}\tpval: {}\tcutoff: {}", num_tests, pval,
+    //         //                       config.family_wise_error_rate / harm);
+    //         // if (pval >= config.family_wise_error_rate / harm) {
+    //         if (pval >= config.family_wise_error_rate / num_tests) {
+    //             min_pval_cutoff = pval;
+    //             // for (size_t i = num_tests; i > num_tests - count; --i) {
+    //             //     harm -= 1.0L / i;
+    //             // }
+    //             num_tests -= count;
+    //         }
+    //     }
+    //     common::logger->trace(
+    //         "Only running tests where min pval < {}. Keeping {} / {} tests",
+    //         min_pval_cutoff, num_tests, nelem);
+    // }
 
     common::logger->trace("Starting tests");
     std::atomic_thread_fence(std::memory_order_release);
@@ -1218,6 +1265,10 @@ mask_nodes_by_label_dual(
                     long double chi_stat = (alt - null) * 2.0L;
                     pval = get_chi2_pval(1.0, chi_stat);
 
+                    if (pval == 0) {
+                        common::logger->warn("pval = 0\t{} = {} + {}\t->\t {}", n, in_sum, out_sum, chi_stat);
+                    }
+
                     // if (chi_stat != chi_stat || pval != pval) {
                     //     common::logger->error(
                     //         "FF:\tn: {}\tin: {}\tout: {}\tstat: {}\tpval: {}", n,
@@ -1239,62 +1290,66 @@ mask_nodes_by_label_dual(
             pval = get_pval_eff_size(in_sum, out_sum).first;
 
         } else if (config.test_type == "nbinom_exact") {
-            size_t n = 0;
-            int64_t in_sum = 0;
-            int64_t out_sum = 0;
-            for (const auto& [j, c] : row) {
+            long double n = 0;
+            long double in_sum = 0;
+            long double out_sum = 0;
+            for (const auto& [j, raw_c] : row) {
+                long double c = scale_factors[j] * raw_c;
                 n += c;
                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                     out_sum += c;
-                    eff_size_out += static_cast<long double>(c) / sums[j];
+                    eff_size_out += static_cast<long double>(raw_c) / sums[j];
                 }
 
                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
                     in_sum += c;
-                    eff_size_in += static_cast<long double>(c) / sums[j];
+                    eff_size_in += static_cast<long double>(raw_c) / sums[j];
                 }
             }
 
-            // int max_bits = std::numeric_limits<int>::max();
-            int max_bits = 8;
-
             auto get_loglik = [&](long double k, long double mu) {
                 if (k == 0 && mu == 0)
                     return std::make_pair(0.0L, 0.0L);
 
-                long double lk = lgammal(k + 1);
-                auto nb_loglik = [&](long double r) {
-                    long double p = r / (r + mu);
-                    return lgammal(k + r) - lk - lgammal(r) + log1pl(-p) * k + r * logl(p);
-                };
-
-                long double v = std::numeric_limits<long double>::max();
-                long double r = 0.0L;
-                try {
-                    auto [rt, vt] = boost::math::tools::brent_find_minima(
-                        [&](long double r) { return -nb_loglik(r); },
-                        static_cast<long double>(std::numeric_limits<double>::min()),
-                        1.0L - std::numeric_limits<double>::min(), max_bits);
-                    if (vt < v) {
-                        v = vt;
-                        r = rt;
-                    }
-                } catch (...) {
-                }
-                try {
-                    auto [at, vt] = boost::math::tools::brent_find_minima(
-                        [&](long double a) { return -nb_loglik(1.0L / a); },
-                        static_cast<long double>(std::numeric_limits<double>::min()),
-                        1.0L - std::numeric_limits<double>::min(), max_bits);
-
-                    if (vt < v) {
-                        v = vt;
-                        r = 1.0L / at;
-                    }
-                } catch (...) {
-                }
+                auto [r, p, m, v, l] = get_rp([&](const auto &callback) { callback(k, 1); });
+                return std::make_pair(l, r);
+
+                // int max_bits = std::numeric_limits<int>::max();
+                // int max_bits = 8;
+
+                // long double lk = lgammal(k + 1);
+                // auto nb_loglik = [&](long double r) {
+                //     long double p = r / (r + mu);
+                //     return lgammal(k + r) - lk - lgammal(r) + log1pl(-p) * k + r * logl(p);
+                // };
+
+                // long double v = std::numeric_limits<long double>::max();
+                // long double r = 0.0L;
+                // try {
+                //     auto [rt, vt] = boost::math::tools::brent_find_minima(
+                //         [&](long double r) { return -nb_loglik(r); },
+                //         static_cast<long double>(std::numeric_limits<double>::min()),
+                //         1.0L - std::numeric_limits<double>::min(), max_bits);
+                //     if (vt < v) {
+                //         v = vt;
+                //         r = rt;
+                //     }
+                // } catch (...) {
+                // }
+                // try {
+                //     auto [at, vt] = boost::math::tools::brent_find_minima(
+                //         [&](long double a) { return -nb_loglik(1.0L / a); },
+                //         static_cast<long double>(std::numeric_limits<double>::min()),
+                //         1.0L - std::numeric_limits<double>::min(), max_bits);
+
+                //     if (vt < v) {
+                //         v = vt;
+                //         r = 1.0L / at;
+                //     }
+                // } catch (...) {
+                // }
 
-                return std::make_pair(-v, r);
+                // return std::make_pair(-v, r);
             };
 
             // wald test
@@ -1319,8 +1374,8 @@ mask_nodes_by_label_dual(
                 //     // return pow(theta - theta_n, 2.0) / var;
             };
 
-            auto [theta_a, var_a] = get_wald_stat(in_sum, in_kmers);
-            auto [theta_b, var_b] = get_wald_stat(out_sum, out_kmers);
+            auto [theta_a, var_a] = get_wald_stat(in_sum, scaled_in_kmers);
+            auto [theta_b, var_b] = get_wald_stat(out_sum, scaled_out_kmers);
             long double chi_stat = pow(theta_a - theta_b, 2.0) / (var_a + var_b);
 
             // long double pval
@@ -1391,7 +1446,7 @@ mask_nodes_by_label_dual(
             pval = get_chi2_pval(1.0, chi_stat);
 
             if (pval == 0) {
-                common::logger->error("{} = {} + {}\t->\t {}", n, in_sum, out_sum, chi_stat);
+                common::logger->warn("pval = 0\t{} = {} + {}\t->\t {}", n, in_sum, out_sum, chi_stat);
             }
 
             // auto [r_a, p_a] = nb_params_a;

From a4cfa1ec27b6e985b80973b363ffe4844137f9cf Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Thu, 8 May 2025 19:00:45 +0200
Subject: [PATCH 405/410] minor fix for notest

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 6143a7e1be..ee1d1bb687 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1106,12 +1106,12 @@ mask_nodes_by_label_dual(
             for (const auto& [j, c] : row) {
                 if (groups[j] == Group::OUT || groups[j] == Group::BOTH) {
                     ++count_out;
-                    rel_out_count += static_cast<long double>(c) / out_kmers;
+                    rel_out_count += static_cast<long double>(c) / sums[j];
                 }
 
                 if (groups[j] == Group::IN || groups[j] == Group::BOTH) {
                     ++count_in;
-                    rel_in_count += static_cast<long double>(c) / in_kmers;
+                    rel_in_count += static_cast<long double>(c) / sums[j];
                 }
             }
 
@@ -1121,10 +1121,10 @@ mask_nodes_by_label_dual(
                 && count_out <= config.max_out_recurrence;
 
             node_index node = AnnotatedDBG::anno_to_graph_index(row_i);
-            if (in_kmer && rel_in_count > rel_out_count)
+            if (in_kmer && rel_in_count / num_labels_in > rel_out_count / num_labels_out)
                 set_bit(indicator_in.data(), node, true, std::memory_order_relaxed);
 
-            if (out_kmer && rel_in_count < rel_out_count)
+            if (out_kmer && rel_in_count / num_labels_in < rel_out_count / num_labels_out)
                 set_bit(indicator_out.data(), node, true, std::memory_order_relaxed);
 
             return;

From 8ed7c494751452c392fe73a263f3354dd8886d14 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 9 May 2025 10:23:20 +0200
Subject: [PATCH 406/410] switch to Holm

---
 .../src/graph/annotated_graph_algorithm.cpp   | 120 +++++++++---------
 1 file changed, 60 insertions(+), 60 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ee1d1bb687..9b40415d55 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1762,82 +1762,82 @@ mask_nodes_by_label_dual(
                 common::logger->trace("Min observed p-value: {}", std::get<0>(all_pvals[0]));
                 size_t last_tig_id = std::numeric_limits<size_t>::max();
 
-                // Benjamini - Yekutieli
-                long double harm = 0.0;
-                for (size_t i = 1; i <= num_tests; ++i) {
-                    harm += 1.0 / i;
-                }
-
-                // // Benjamini-Hochberg
-                // // harm = 1.0;
-
-                size_t cur_count = all_pvals.size() + 1;
-                auto it = all_pvals.rbegin();
-                for (; it != all_pvals.rend(); ++it) {
-                    const auto& [pval, tig_id, eff_size_in, eff_size_out, node] = *it;
-                    if (tig_id != last_tig_id) {
-                        --cur_count;
-                        last_tig_id = tig_id;
-                    }
-
-                    // Benjamini-Yekutieli
-                    long double cutoff
-                        = config.family_wise_error_rate * cur_count / num_tests / harm;
-
-                    // // Hochberg
-                    // long double cutoff
-                    //     = config.family_wise_error_rate / (num_tests + 1 - cur_count);
+                // // Benjamini - Yekutieli
+                // long double harm = 0.0;
+                // for (size_t i = 1; i <= num_tests; ++i) {
+                //     harm += 1.0 / i;
+                // }
 
-                    if (pval <= cutoff)
-                        break;
-                }
+                // // // Benjamini-Hochberg
+                // // // harm = 1.0;
 
-                std::for_each(it, all_pvals.rend(), [&](const auto& b) {
-                    const auto& [pval, tig_id, eff_size_in, eff_size_out, node] = b;
-                    long double eff_size
-                        = eff_size_in / num_labels_in - eff_size_out / num_labels_out;
-                    num_sig += (eff_size != 0);
-                    if (eff_size > 0) {
-                        indicator_in[node] = true;
-                    } else if (eff_size < 0) {
-                        indicator_out[node] = true;
-                    }
-                });
-
-                // // Holm-Bonferroni or Sidak
-                // size_t cur_count = 0;
-                // common::logger->trace(
-                //     "First p-value cutoff: {}",
-                //     1.0L - pow(1.0L - config.family_wise_error_rate, 1.0L / num_tests));
-                // for (const auto& [pval, tig_id, eff_size_in, eff_size_out, node] : all_pvals) {
+                // size_t cur_count = all_pvals.size() + 1;
+                // auto it = all_pvals.rbegin();
+                // for (; it != all_pvals.rend(); ++it) {
+                //     const auto& [pval, tig_id, eff_size_in, eff_size_out, node] = *it;
                 //     if (tig_id != last_tig_id) {
-                //         ++cur_count;
+                //         --cur_count;
                 //         last_tig_id = tig_id;
                 //     }
 
-                //     long double eff_size
-                //         = eff_size_in / num_labels_in - eff_size_out / num_labels_out;
+                //     // Benjamini-Yekutieli
+                //     long double cutoff
+                //         = config.family_wise_error_rate * cur_count / num_tests / harm;
 
-                //     // holm bonferroni
+                //     // // Hochberg
                 //     // long double cutoff
-                //     // = config.family_wise_error_rate / (num_tests + 1 - cur_count);
-
-                //     // Sidak
-                //     long double cutoff = 1.0L
-                //         - pow(1.0L - config.family_wise_error_rate,
-                //               1.0L / (num_tests + 1 - cur_count));
+                //     //     = config.family_wise_error_rate / (num_tests + 1 - cur_count);
 
-                //     if (pval > cutoff)
+                //     if (pval <= cutoff)
                 //         break;
+                // }
 
+                // std::for_each(it, all_pvals.rend(), [&](const auto& b) {
+                //     const auto& [pval, tig_id, eff_size_in, eff_size_out, node] = b;
+                //     long double eff_size
+                //         = eff_size_in / num_labels_in - eff_size_out / num_labels_out;
+                //     num_sig += (eff_size != 0);
                 //     if (eff_size > 0) {
                 //         indicator_in[node] = true;
-                //         ++num_sig;
                 //     } else if (eff_size < 0) {
                 //         indicator_out[node] = true;
-                //         ++num_sig;
                 //     }
-                // }
+                // });
+
+                // Holm-Bonferroni or Sidak
+                size_t cur_count = 0;
+                common::logger->trace(
+                    "First p-value cutoff: {}",
+                    1.0L - pow(1.0L - config.family_wise_error_rate, 1.0L / num_tests));
+                for (const auto& [pval, tig_id, eff_size_in, eff_size_out, node] : all_pvals) {
+                    if (tig_id != last_tig_id) {
+                        ++cur_count;
+                        last_tig_id = tig_id;
+                    }
+
+                    long double eff_size
+                        = eff_size_in / num_labels_in - eff_size_out / num_labels_out;
+
+                    // holm bonferroni
+                    long double cutoff
+                    = config.family_wise_error_rate / (num_tests + 1 - cur_count);
+
+                    // Sidak
+                    // long double cutoff = 1.0L
+                    //     - pow(1.0L - config.family_wise_error_rate,
+                    //           1.0L / (num_tests + 1 - cur_count));
+
+                    if (pval > cutoff)
+                        break;
+
+                    if (eff_size > 0) {
+                        indicator_in[node] = true;
+                        ++num_sig;
+                    } else if (eff_size < 0) {
+                        indicator_out[node] = true;
+                        ++num_sig;
+                    }
+                }
 
                 // Bonferroni
                 // for (const auto& [pval, tig_id, eff_size_in, eff_size_out, node] : all_pvals) {

From 71401a42cbc4f0058167ae22b4b2afea3a98a752 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 9 May 2025 13:05:00 +0200
Subject: [PATCH 407/410] Revert "switch to Holm"

This reverts commit 8ed7c494751452c392fe73a263f3354dd8886d14.
---
 .../src/graph/annotated_graph_algorithm.cpp   | 120 +++++++++---------
 1 file changed, 60 insertions(+), 60 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 9b40415d55..ee1d1bb687 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1762,82 +1762,82 @@ mask_nodes_by_label_dual(
                 common::logger->trace("Min observed p-value: {}", std::get<0>(all_pvals[0]));
                 size_t last_tig_id = std::numeric_limits<size_t>::max();
 
-                // // Benjamini - Yekutieli
-                // long double harm = 0.0;
-                // for (size_t i = 1; i <= num_tests; ++i) {
-                //     harm += 1.0 / i;
-                // }
-
-                // // // Benjamini-Hochberg
-                // // // harm = 1.0;
-
-                // size_t cur_count = all_pvals.size() + 1;
-                // auto it = all_pvals.rbegin();
-                // for (; it != all_pvals.rend(); ++it) {
-                //     const auto& [pval, tig_id, eff_size_in, eff_size_out, node] = *it;
-                //     if (tig_id != last_tig_id) {
-                //         --cur_count;
-                //         last_tig_id = tig_id;
-                //     }
-
-                //     // Benjamini-Yekutieli
-                //     long double cutoff
-                //         = config.family_wise_error_rate * cur_count / num_tests / harm;
-
-                //     // // Hochberg
-                //     // long double cutoff
-                //     //     = config.family_wise_error_rate / (num_tests + 1 - cur_count);
+                // Benjamini - Yekutieli
+                long double harm = 0.0;
+                for (size_t i = 1; i <= num_tests; ++i) {
+                    harm += 1.0 / i;
+                }
 
-                //     if (pval <= cutoff)
-                //         break;
-                // }
+                // // Benjamini-Hochberg
+                // // harm = 1.0;
 
-                // std::for_each(it, all_pvals.rend(), [&](const auto& b) {
-                //     const auto& [pval, tig_id, eff_size_in, eff_size_out, node] = b;
-                //     long double eff_size
-                //         = eff_size_in / num_labels_in - eff_size_out / num_labels_out;
-                //     num_sig += (eff_size != 0);
-                //     if (eff_size > 0) {
-                //         indicator_in[node] = true;
-                //     } else if (eff_size < 0) {
-                //         indicator_out[node] = true;
-                //     }
-                // });
-
-                // Holm-Bonferroni or Sidak
-                size_t cur_count = 0;
-                common::logger->trace(
-                    "First p-value cutoff: {}",
-                    1.0L - pow(1.0L - config.family_wise_error_rate, 1.0L / num_tests));
-                for (const auto& [pval, tig_id, eff_size_in, eff_size_out, node] : all_pvals) {
+                size_t cur_count = all_pvals.size() + 1;
+                auto it = all_pvals.rbegin();
+                for (; it != all_pvals.rend(); ++it) {
+                    const auto& [pval, tig_id, eff_size_in, eff_size_out, node] = *it;
                     if (tig_id != last_tig_id) {
-                        ++cur_count;
+                        --cur_count;
                         last_tig_id = tig_id;
                     }
 
-                    long double eff_size
-                        = eff_size_in / num_labels_in - eff_size_out / num_labels_out;
-
-                    // holm bonferroni
+                    // Benjamini-Yekutieli
                     long double cutoff
-                    = config.family_wise_error_rate / (num_tests + 1 - cur_count);
+                        = config.family_wise_error_rate * cur_count / num_tests / harm;
 
-                    // Sidak
-                    // long double cutoff = 1.0L
-                    //     - pow(1.0L - config.family_wise_error_rate,
-                    //           1.0L / (num_tests + 1 - cur_count));
+                    // // Hochberg
+                    // long double cutoff
+                    //     = config.family_wise_error_rate / (num_tests + 1 - cur_count);
 
-                    if (pval > cutoff)
+                    if (pval <= cutoff)
                         break;
+                }
 
+                std::for_each(it, all_pvals.rend(), [&](const auto& b) {
+                    const auto& [pval, tig_id, eff_size_in, eff_size_out, node] = b;
+                    long double eff_size
+                        = eff_size_in / num_labels_in - eff_size_out / num_labels_out;
+                    num_sig += (eff_size != 0);
                     if (eff_size > 0) {
                         indicator_in[node] = true;
-                        ++num_sig;
                     } else if (eff_size < 0) {
                         indicator_out[node] = true;
-                        ++num_sig;
                     }
-                }
+                });
+
+                // // Holm-Bonferroni or Sidak
+                // size_t cur_count = 0;
+                // common::logger->trace(
+                //     "First p-value cutoff: {}",
+                //     1.0L - pow(1.0L - config.family_wise_error_rate, 1.0L / num_tests));
+                // for (const auto& [pval, tig_id, eff_size_in, eff_size_out, node] : all_pvals) {
+                //     if (tig_id != last_tig_id) {
+                //         ++cur_count;
+                //         last_tig_id = tig_id;
+                //     }
+
+                //     long double eff_size
+                //         = eff_size_in / num_labels_in - eff_size_out / num_labels_out;
+
+                //     // holm bonferroni
+                //     // long double cutoff
+                //     // = config.family_wise_error_rate / (num_tests + 1 - cur_count);
+
+                //     // Sidak
+                //     long double cutoff = 1.0L
+                //         - pow(1.0L - config.family_wise_error_rate,
+                //               1.0L / (num_tests + 1 - cur_count));
+
+                //     if (pval > cutoff)
+                //         break;
+
+                //     if (eff_size > 0) {
+                //         indicator_in[node] = true;
+                //         ++num_sig;
+                //     } else if (eff_size < 0) {
+                //         indicator_out[node] = true;
+                //         ++num_sig;
+                //     }
+                // }
 
                 // Bonferroni
                 // for (const auto& [pval, tig_id, eff_size_in, eff_size_out, node] : all_pvals) {

From 8704ee8399ae2c14a0826ee2ebde1d383497ad09 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 9 May 2025 19:29:30 +0200
Subject: [PATCH 408/410] simplify

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index ee1d1bb687..4ea06ad86a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -1365,13 +1365,8 @@ mask_nodes_by_label_dual(
 
                 long double theta = k / nl;
                 long double r = get_loglik(k, k).second;
-                // long double var = theta / nl + theta * theta / r;
-                long double finf
-                    = r * nl * r * nl / (r + k) / (r + k) * (1.0L / k + 1.0L / r);
-                long double var = 1.0L / finf;
+                long double var = theta / r / nl * (k + r);
                 return std::make_pair(theta, var);
-                //     // long double var = theta_n / nl + theta_n * theta_n / r;
-                //     // return pow(theta - theta_n, 2.0) / var;
             };
 
             auto [theta_a, var_a] = get_wald_stat(in_sum, scaled_in_kmers);

From db81e728e5f007ca3a2d69b8efbd0a34618b27dd Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Fri, 9 May 2025 19:43:15 +0200
Subject: [PATCH 409/410] fit nb distribution first, then scale

---
 metagraph/src/graph/annotated_graph_algorithm.cpp | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index 4ea06ad86a..a4e402072a 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -667,9 +667,20 @@ mask_nodes_by_label_dual(
     long double scaled_in_kmers = 0.0;
     long double scaled_out_kmers = 0.0;
     if (config.test_type == "nbinom_exact") {
+        common::logger->trace("Computing scaling factors");
+        std::vector<long double> sample_p(groups.size());
+        #pragma omp parallel for num_threads(num_threads) schedule(dynamic)
+        for (size_t j = 0; j < groups.size(); ++j) {
+            sample_p[j] = std::get<1>(get_rp([&](const auto &callback) {
+                const auto& hist = hists[j];
+                for (const auto& [k, c] : hist) {
+                    callback(k, c);
+                }
+            }));
+        }
         long double target_p = 0.0;
         for (size_t j = 0; j < groups.size(); ++j) {
-            long double p = means[j] / vars[j];
+            long double p = sample_p[j];
             common::logger->trace("{}: p = {}", j, p);
             target_p += 1.0L / p;
         }
@@ -677,7 +688,7 @@ mask_nodes_by_label_dual(
         common::logger->trace("Scaling distributions to p = {}", target_p);
 
         for (size_t j = 0; j < groups.size(); ++j) {
-            long double p = means[j] / vars[j];
+            long double p = sample_p[j];
             scale_factors[j] = p / target_p;
             scaled_sums[j] = scale_factors[j] * sums[j];
             if (groups[j] == Group::IN || groups[j] == Group::BOTH)

From 8ec120b03a7d25b16683e1f8e796269055acee28 Mon Sep 17 00:00:00 2001
From: Harun Mustafa <bilgisayaradami@gmail.com>
Date: Mon, 12 May 2025 19:58:50 +0200
Subject: [PATCH 410/410] changes

---
 .../src/graph/annotated_graph_algorithm.cpp   | 194 ++++++++++++++++--
 1 file changed, 179 insertions(+), 15 deletions(-)

diff --git a/metagraph/src/graph/annotated_graph_algorithm.cpp b/metagraph/src/graph/annotated_graph_algorithm.cpp
index a4e402072a..3ea4dbbaa3 100644
--- a/metagraph/src/graph/annotated_graph_algorithm.cpp
+++ b/metagraph/src/graph/annotated_graph_algorithm.cpp
@@ -132,11 +132,12 @@ get_rp(const Generator& generate) {
     };
 
     auto get_dl_a = [&](long double a) {
-        long double dl = mu * mu * total / (1.0 + a * mu) + total * logl(1 + a * mu) / a / a
-            + total / a / a * boost::math::digamma(1.0 / a);
-        generate(
-            [&](auto k, auto c) { dl -= boost::math::digamma(k + 1.0 / a) / a / a * c; });
-        return dl;
+        return get_dl_r(1.0L / a);
+        // long double dl = mu * mu * total / (1.0 + a * mu) + total * logl(1 + a * mu) / a / a
+        //     + total / a / a * boost::math::digamma(1.0 / a);
+        // generate(
+        //     [&](auto k, auto c) { dl -= boost::math::digamma(k + 1.0 / a) / a / a * c; });
+        // return dl;
     };
 
     long double r1 = 0.0;
@@ -153,7 +154,7 @@ get_rp(const Generator& generate) {
         auto [a_min, a_max]
             = boost::math::tools::bisect(get_dl_a, std::numeric_limits<double>::min(), 1.0,
                                          boost::math::tools::eps_tolerance<long double>(5));
-        r2 = (1.0 / a_min + 1.0 / a_max) / 2.0;
+        r2 = (1.0L / a_min + 1.0L / a_max) / 2.0;
     } catch (boost::wrapexcept<boost::math::evaluation_error>& e) {
     }
 
@@ -183,7 +184,124 @@ get_rp(const Generator& generate) {
     // long double p = 0.1;
     // r = p * mu / (1.0 - p);
     return { r, p, mu, var, ll };
-};
+}
+
+std::tuple<long double, long double, long double, long double, long double>
+get_rp_theta(long double k, long double nl, long double theta_alpha, long double theta_theta) {
+    long double total = 1;
+
+    long double a = -nl / theta_theta;
+    auto get_theta = [&](long double r) {
+        long double b = nl * (theta_alpha - 1 - r) - r / theta_theta;
+        long double c = r * (k + theta_alpha - 1);
+        long double disc = b * b - 4.0L * a * c;
+        try {
+            if (disc < 0) {
+                // common::logger->error("Failed to fit on r: {}\tk: {}\tN: {} -> {},{},{} -> d: {}", r, k, nl, a,b,c, disc);
+                throw std::runtime_error("FIAJS");
+            }
+            disc = sqrtl(disc);
+            long double theta_pos = (-b + disc) / 2.0L / a;
+            long double theta_neg = (-b - disc) / 2.0L / a;
+            if (theta_pos <= 0 && theta_neg <= 0) {
+                // common::logger->error("No solutions for r: {}\tk: {}\tN: {} -> {},{}\t{},{},{} -> {}", r, k, nl, theta_pos, theta_neg, a, b, c, disc);
+                throw std::runtime_error("FIAJS");
+            }
+
+            if (theta_pos > 0 && theta_neg > 0) {
+                // common::logger->error("Two solutions for r: {}\tk: {}\tN: {} -> {},{}\t{},{},{} -> {}", r, k, nl, theta_pos, theta_neg,a,b,c,disc);
+                throw std::runtime_error("FIAJS");
+            }
+
+            return std::max(theta_pos, theta_neg);
+        } catch (std::runtime_error& e) {
+            if (r > 1e100)
+                return (k + theta_alpha - 1) / (nl + 1.0L / theta_theta);
+
+            if (r < 1e-100)
+                return theta_theta * (theta_alpha - 1.0L);
+
+            common::logger->error("Weird case for r: {}\tk: {}\tN: {}\t{},{},{} -> {}", r, k, nl, a,b,c,disc);
+            throw std::runtime_error("FHDKF");
+        }
+
+    };
+
+    auto get_l = [&](long double r) {
+        long double theta = get_theta(r);
+        long double mu = nl * theta;
+        long double p = r / (r + mu);
+        long double l = -lgammal(r) * total + mu * total * log1pl(-p) + total * r * logl(p) - lgammal(theta_alpha) - theta_alpha * logl(theta_theta) + (theta_alpha - 1) * logl(theta) - theta / theta_theta + lgammal(k + r);
+        return l;
+    };
+
+    auto get_dl_r = [&](long double r) {
+        long double mu = nl * get_theta(r);
+        long double dl = log(r) - log(r + mu) + 1.0L - boost::math::digamma(r) + boost::math::digamma(k + r) + (k + r) / (r + mu);
+        return dl;
+    };
+
+    auto get_dl_a = [&](long double a) {
+        return get_dl_r(1.0L / a);
+        // long double mu = nl * get_theta(r);
+        // long double dl = -r * r * (boost::math::digamma(k + r) - boost::math::digamma(r)) + r * r * logl(1 + mu / r) + k * r - (k + r) * mu / (1.0L + mu / r);
+        // return dl;
+    };
+
+    long double r1 = 0.0;
+    long double r2 = 0.0;
+    try {
+        auto [r_min, r_max]
+            = boost::math::tools::bisect(get_dl_r, std::numeric_limits<double>::min(), 1.0,
+                                         boost::math::tools::eps_tolerance<long double>(5));
+        r1 = (r_min + r_max) / 2.0;
+    } catch (boost::wrapexcept<boost::math::evaluation_error>& e) {
+    } catch (std::runtime_error& e) {
+    }
+
+    try {
+        auto [a_min, a_max]
+            = boost::math::tools::bisect(get_dl_a, std::numeric_limits<double>::min(), 1.0,
+                                         boost::math::tools::eps_tolerance<long double>(5));
+        r2 = (1.0L / a_min + 1.0L / a_max) / 2.0;
+    } catch (boost::wrapexcept<boost::math::evaluation_error>& e) {
+    } catch (std::runtime_error& e) {
+    }
+
+    long double r = 0.0;
+    long double ll = 0.0;
+    long double theta = 0.0;
+    if (r1 == 0 && r2 == 0) {
+        common::logger->error("Failed to fit k: {}\tN: {}", k, nl);
+        throw std::runtime_error("Failed to fit");
+    } else if (r1 == 0) {
+        r = r2;
+        ll = get_l(r);
+        theta = get_theta(r);
+    } else if (r2 == 0) {
+        r = r1;
+        ll = get_l(r);
+        theta = get_theta(r);
+    } else {
+        long double ll1 = get_l(r1);
+        long double ll2 = get_l(r2);
+        if (ll1 > ll2) {
+            r = r1;
+            ll = ll1;
+            theta = get_theta(r);
+        } else {
+            r = r2;
+            ll = ll2;
+            theta = get_theta(r);
+        }
+    }
+
+    long double mu = nl * theta;
+    long double p = r / (r + mu);
+    // long double p = 0.1;
+    // r = p * mu / (1.0 - p);
+    return { r, p, mu, ll, theta };
+}
 
 template <class G1, class G2>
 std::pair<long double, long double> mann_whitneyu(const G1& generate_a, const G2& generate_b) {
@@ -666,6 +784,8 @@ mask_nodes_by_label_dual(
     std::vector<long double> scaled_sums(groups.size());
     long double scaled_in_kmers = 0.0;
     long double scaled_out_kmers = 0.0;
+    // long double theta_theta = 0.0;
+    // long double theta_alpha = 0.0;
     if (config.test_type == "nbinom_exact") {
         common::logger->trace("Computing scaling factors");
         std::vector<long double> sample_p(groups.size());
@@ -699,6 +819,42 @@ mask_nodes_by_label_dual(
         }
 
         common::logger->trace("Scaling factors: {}", fmt::join(scale_factors, ","));
+
+        // long double scaled_total_kmers = scaled_in_kmers + scaled_out_kmers;
+
+        // std::vector<long double> xlnxs(num_threads + 1);
+        // std::vector<long double> lnxs(num_threads + 1);
+        // std::vector<long double> xs(num_threads + 1);
+        // generate_clean_rows([&](uint64_t, const auto& row, uint64_t bucket_idx) {
+        //     long double in_sum = 1.0L / nelem;
+        //     long double out_sum = 1.0L / nelem;
+        //     for (const auto &[j, raw_c] : row) {
+        //         if (groups[j] == Group::IN) {
+        //             in_sum += scale_factors[j] * raw_c;
+        //         }
+
+        //         if (groups[j] == Group::OUT) {
+        //             out_sum += scale_factors[j] * raw_c;
+        //         }
+        //     }
+
+        //     long double theta_a = in_sum / (scaled_in_kmers + 1);
+        //     long double theta_b = out_sum / (scaled_out_kmers + 1);
+        //     xs[bucket_idx] += theta_a + theta_b;
+        //     lnxs[bucket_idx] += logl(theta_a);
+        //     xlnxs[bucket_idx] += logl(theta_a) * theta_a;
+        //     lnxs[bucket_idx] += logl(theta_b);
+        //     xlnxs[bucket_idx] += logl(theta_b) * theta_b;
+        // });
+        // size_t count = nelem * 2;
+        // long double mean_x = std::accumulate(xs.begin(), xs.end(), 0.0L) / count;
+        // theta_theta = std::accumulate(xlnxs.begin(), xlnxs.end(), 0.0L) / count
+        //                 - mean_x * std::accumulate(lnxs.begin(), lnxs.end(), 0.0L) / count;
+        // theta_alpha = mean_x / theta_theta;
+        // common::logger->trace("Gamma fit to theta: alpha: {}\ttheta: {}", theta_alpha, theta_theta);
+        // if (theta_alpha <= 0 || theta_theta <= 0) {
+        //     throw std::runtime_error("Gamma fail");
+        // }
     } else {
         scaled_in_kmers = in_kmers;
         scaled_out_kmers = out_kmers;
@@ -1318,12 +1474,17 @@ mask_nodes_by_label_dual(
                 }
             }
 
-            auto get_loglik = [&](long double k, long double mu) {
-                if (k == 0 && mu == 0)
-                    return std::make_pair(0.0L, 0.0L);
+            auto get_loglik = [&](long double k, long double nl) {
+                // auto [r, p, m, l, theta] = get_rp_theta(k, nl, theta_alpha, theta_theta);
+                // long double ddl = -(k + theta_alpha - 1) / theta / theta + nl * nl * (k + r) / (r + nl * theta) / (r + nl * theta);
+                // long double var = -1.0L / ddl;
 
                 auto [r, p, m, v, l] = get_rp([&](const auto &callback) { callback(k, 1); });
-                return std::make_pair(l, r);
+                long double theta = k / nl;
+                long double var = theta / r / nl * (k + r);
+
+                return std::make_tuple(l, r, theta, var);
+                // return std::make_tuple(l, r, theta);
 
                 // int max_bits = std::numeric_limits<int>::max();
                 // int max_bits = 8;
@@ -1374,9 +1535,12 @@ mask_nodes_by_label_dual(
                 if (k == 0)
                     return std::make_pair(0.0L, 0.0L);
 
-                long double theta = k / nl;
-                long double r = get_loglik(k, k).second;
-                long double var = theta / r / nl * (k + r);
+                auto [l, r, theta, var] = get_loglik(k, nl);
+
+                // long double var = -(k + theta_alpha - 1) / theta / theta + nl * nl * (k + r) / (r + nl * theta) / (r + nl * theta);
+
+                // long double theta = k / nl;
+                // long double var = theta / r / nl * (k + r);
                 return std::make_pair(theta, var);
             };
 
@@ -1445,7 +1609,7 @@ mask_nodes_by_label_dual(
             // long double chi_stat = (alt_loglik - null_loglik) * 2;
 
             if (chi_stat <= 0.0) {
-                common::logger->error("{} = {} + {}\t->\t {}", n, in_sum, out_sum, chi_stat);
+                common::logger->error("{} = {} + {}\t->\t {}\t{},{}\t{},{}", n, in_sum, out_sum, chi_stat, theta_a, var_a, theta_b, var_b);
                 throw std::runtime_error("FOSDOF");
             }