diff --git a/.github/workflows/Mouse.ONT_simulated.polyA.yml b/.github/workflows/Mouse.ONT_simulated.polyA.yml new file mode 100644 index 00000000..3c0c4744 --- /dev/null +++ b/.github/workflows/Mouse.ONT_simulated.polyA.yml @@ -0,0 +1,76 @@ +name: Mouse ONT simulated polyA prediction + +on: + workflow_dispatch: + schedule: + - cron: '0 6 * * 5' + +env: + RUN_NAME: Mouse.ONT_simulated.polyA + LAUNCHER: ${{github.workspace}}/isoquant_tests/github/run_pipeline.py + CFG_DIR: /abga/work/andreyp/ci_isoquant/data + BIN_PATH: /abga/work/andreyp/ci_isoquant/bin/ + OUTPUT_BASE: /abga/work/andreyp/ci_isoquant/output/${{github.ref_name}}/ + +concurrency: + group: ${{github.workflow}} + cancel-in-progress: false + +jobs: + check-changes: + runs-on: + labels: [isoquant] + name: 'Check for recent changes' + outputs: + has_changes: ${{steps.check.outputs.has_changes}} + steps: + - name: 'Checkout' + uses: actions/checkout@v3 + with: + fetch-depth: 0 + + - name: 'Check for commits in last 7 days' + id: check + run: | + # Always run on manual trigger + if [ "${{github.event_name}}" = "workflow_dispatch" ]; then + echo "has_changes=true" >> $GITHUB_OUTPUT + exit 0 + fi + # Check for commits in last 7 days + COMMITS=$(git log --oneline --since="7 days ago" | wc -l) + if [ "$COMMITS" -gt 0 ]; then + echo "Found $COMMITS commits in last 7 days" + echo "has_changes=true" >> $GITHUB_OUTPUT + else + echo "No commits in last 7 days, skipping" + echo "has_changes=false" >> $GITHUB_OUTPUT + fi + + launch-runner: + needs: check-changes + if: needs.check-changes.outputs.has_changes == 'true' + runs-on: + labels: [isoquant] + name: 'Running IsoQuant and QC' + + steps: + - name: 'Cleanup' + run: > + set -e && + shopt -s dotglob && + rm -rf * + + - name: 'Checkout' + uses: actions/checkout@v3 + with: + fetch-depth: 1 + + - name: 'IsoQuant polyA prediction' + if: always() + shell: bash + env: + STEP_NAME: Mouse.ONT_simulated.polyA + run: | + export PATH=$PATH:${{env.BIN_PATH}} + python3 ${{env.LAUNCHER}} ${{env.CFG_DIR}}/${{env.STEP_NAME}}.yaml -o ${{env.OUTPUT_BASE}} diff --git a/.gitignore b/.gitignore index 1be3a988..5e62d7ef 100644 --- a/.gitignore +++ b/.gitignore @@ -107,3 +107,7 @@ venv.bak/ # Claude Code documentation (private) .claude/ + +# polyA/TSS XGBoost training artifacts (regenerated by scripts/train_polya_tss_model.py) +isoquant_lib/data/model_df.csv +src/model_df.csv diff --git a/MANIFEST.in b/MANIFEST.in index 74ebe944..32f55689 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -7,6 +7,7 @@ include requirements_tests.txt recursive-include isoquant_lib *.py recursive-include isoquant_lib/test_data * +recursive-include isoquant_lib/data *.json prune isoquant_tests/out_* prune .claude diff --git a/README.md b/README.md index b7a712d0..21ff3c03 100644 --- a/README.md +++ b/README.md @@ -29,6 +29,13 @@ assigns reads to the annotated isoforms based on their intron and exon structure IsoQuant further performs annotated gene, isoform, exon, and intron quantification. If reads are grouped (e.g. according to a cell type), counts are reported according to the provided grouping. +When a reference annotation is supplied, IsoQuant additionally predicts polyA cleavage +sites per transcript by clustering read end positions, filtering peaks with a +pre-trained XGBoost classifier, and flagging each peak as `Known` (within 10 bp of the +annotated transcript end) or `Novel`. Passing `--fl_data` enables the same prediction +for transcription start sites. Results are written to `*.polyA_prediction.tsv` and +`*.TSS_prediction.tsv` alongside the standard counts files (see [docs/output.md](docs/output.md)). + The latest IsoQuant version can be downloaded from [github.com/ablab/IsoQuant/releases/latest](https://github.com/ablab/IsoQuant/releases/latest). Full IsoQuant documentation is available at [ablab.github.io/IsoQuant](https://ablab.github.io/IsoQuant/). diff --git a/docs/output.md b/docs/output.md index df6d32f3..09c0dc75 100644 --- a/docs/output.md +++ b/docs/output.md @@ -29,6 +29,29 @@ If `--count_exons` is set, exon and intron counts will be produced: * `SAMPLE_ID.exon_counts.tsv` - reference exon inclusion/exclusion read counts; * `SAMPLE_ID.intron_counts.tsv` - reference intron inclusion/exclusion read counts; +#### PolyA / TSS site prediction + +Whenever a gene annotation is supplied via `--genedb`, IsoQuant accumulates +per-transcript histograms of read polyA positions, detects peaks, filters them +with a pre-trained XGBoost classifier, and classifies each retained peak as +`Known` (within 10 bp of the annotated transcript end) or `Novel`: + +* `SAMPLE_ID.polyA_prediction.tsv` - predicted polyA cleavage sites per reference transcript. + +If `--fl_data` is also supplied (reads represent full-length transcripts), the +same machinery is applied to read start positions: + +* `SAMPLE_ID.TSS_prediction.tsv` - predicted transcription start sites per reference transcript. + +Columns: `chromosome`, `transcript_id`, `gene_id`, `prediction` (genomic +coordinate of the peak), `counts` (number of reads supporting the peak window), +`flag` (`Known` / `Novel`). When `--read_group` is set, an additional file per +grouping strategy is produced with two extra columns, `counts_byGroup` and +`group_id`: + +* `SAMPLE_ID.polyA_prediction_grouped_` +* `SAMPLE_ID.TSS_prediction_grouped_` (only with `--fl_data`) + If `--read_group` is set or multiple files are provided, the per-group expression values for reference features will be also computed: #### Default grouped counts in linear format diff --git a/isoquant.py b/isoquant.py index 6370aa5c..1b1fd8c4 100755 --- a/isoquant.py +++ b/isoquant.py @@ -338,6 +338,25 @@ def add_hidden_option(*args, **kwargs): # show command only with --full-help add_hidden_option("--cage", help="bed file with CAGE peaks", type=str, default=None) add_hidden_option("--cage-shift", type=int, default=50, help="interval before read start to look for CAGE peak") + # PolyA / TSS training-data collection (developer-only). + # When either flag is given to a normal IsoQuant run, the matching + # terminal-position counter switches from inference to dumping per-peak + # features (the eight FEATURE_COLUMNS plus `chromosome` and a `true_peak` + # 0/1 label computed against the annotated transcript end) to the CSV + # path supplied as the flag value. The XGBoost model is not consulted; + # rows are accumulated, not filtered. Feed the CSV to + # `misc/train_polya_tss_model.py` to fit a fresh model. + # python isoquant.py … --genedb GENEDB --collect_polya_training peaks.csv + # python misc/train_polya_tss_model.py --features peaks.csv \ + # --output isoquant_lib/data/model_polya.json + # For TSS, add --fl_data and --collect_tss_training tss_peaks.csv. + # These flags are intentionally hidden from --help/--full_help; the run + # emits a clearly marked warning when they are used. + add_hidden_option("--collect_polya_training", type=str, default=None, + help="Developer: dump per-peak features + true_peak label for polyA training to this CSV path.") + add_hidden_option("--collect_tss_training", type=str, default=None, + help="Developer: dump per-peak features + true_peak label for TSS training to this CSV path.") + isoquant_version = "3.12.0" try: with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), "VERSION")) as version_f: @@ -1187,6 +1206,15 @@ def main(cmd_args): parser.print_usage() sys.exit(IsoQuantExitCode.SUCCESS) set_logger(args) + if getattr(args, "collect_polya_training", None) or getattr(args, "collect_tss_training", None): + logger.warning("=" * 78) + logger.warning("DEVELOPER MODE: polyA/TSS training-data collection enabled.") + logger.warning("Per-peak features will be written to the supplied CSV path INSTEAD of") + logger.warning("running the production XGBoost peak filter. Predictions in") + logger.warning("*.polyA_prediction.tsv / *.TSS_prediction.tsv will be empty.") + logger.warning("This mode is intended only for retraining the shipped model; see") + logger.warning("misc/train_polya_tss_model.py and .claude/POLYA_TSS_TRAINING.md.") + logger.warning("=" * 78) args = check_and_load_args(args, parser) create_output_dirs(args) set_additional_params(args) diff --git a/isoquant_lib/assignment_aggregator.py b/isoquant_lib/assignment_aggregator.py index d2866384..d008ff6c 100644 --- a/isoquant_lib/assignment_aggregator.py +++ b/isoquant_lib/assignment_aggregator.py @@ -15,6 +15,7 @@ create_gene_counter, create_transcript_counter, ) +from .terminal_counter import PolyACounter, TSSCounter from .assignment_io import ( IOSupport, BEDPrinter, @@ -100,6 +101,18 @@ def __init__(self, args, sample, string_pools, gffutils_db=None, chr_id=None, gz self.intron_counter = IntronCounter(intron_counts_path) self.global_counter.add_counters([self.exon_counter, self.intron_counter]) + # polyA / TSS terminal-position prediction (ungrouped). PolyA requires + # only the gene annotation; TSS also requires --fl_data because read + # start coordinates without full-length evidence are unreliable. + if self.args.genedb: + polya_path = sample.get_polya_prediction_file(chr_id) if chr_id else sample.out_polya_prediction_tsv + self.polya_counter = PolyACounter(self.args, polya_path) + self.global_counter.add_counter(self.polya_counter) + if self.args.fl_data: + tss_path = sample.get_tss_prediction_file(chr_id) if chr_id else sample.out_tss_prediction_tsv + self.tss_counter = TSSCounter(self.args, tss_path) + self.global_counter.add_counter(self.tss_counter) + if self.args.read_group and self.args.genedb: for group_idx, strategy_name in enumerate(self.grouping_strategy_names): # Use chr-specific paths if chr_id is provided @@ -134,6 +147,29 @@ def __init__(self, args, sample, string_pools, gffutils_db=None, chr_id=None, gz intron_counter = IntronCounter(intron_out_file, string_pools=self.string_pools, group_index=group_idx) self.global_counter.add_counters([exon_counter, intron_counter]) + # Grouped polyA / TSS prediction (one file per grouping strategy). + # Skipped in training-collection mode -- only the ungrouped counter + # writes the per-chr training fragment, and grouped predictions + # are not produced in dev mode. + if not getattr(self.args, "collect_polya_training", None): + if chr_id: + polya_out_file = sample.get_grouped_counts_file(chr_id, "polyA_prediction", strategy_name) + else: + polya_out_file = f"{sample.out_polya_prediction_grouped_tsv}_{strategy_name}" + grouped_polya_counter = PolyACounter(self.args, polya_out_file, + string_pools=self.string_pools, + group_index=group_idx) + self.global_counter.add_counter(grouped_polya_counter) + if self.args.fl_data and not getattr(self.args, "collect_tss_training", None): + if chr_id: + tss_out_file = sample.get_grouped_counts_file(chr_id, "TSS_prediction", strategy_name) + else: + tss_out_file = f"{sample.out_tss_prediction_grouped_tsv}_{strategy_name}" + grouped_tss_counter = TSSCounter(self.args, tss_out_file, + string_pools=self.string_pools, + group_index=group_idx) + self.global_counter.add_counter(grouped_tss_counter) + if self.args.read_group and not self.args.no_model_construction: for group_idx, strategy_name in enumerate(self.grouping_strategy_names): if chr_id: diff --git a/isoquant_lib/data/model_polya.json b/isoquant_lib/data/model_polya.json new file mode 100755 index 00000000..5722e557 --- /dev/null +++ b/isoquant_lib/data/model_polya.json @@ -0,0 +1 @@ +{"learner":{"attributes":{"scikit_learn":"{\"_estimator_type\": 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\ No newline at end of file diff --git a/isoquant_lib/dataset_processor.py b/isoquant_lib/dataset_processor.py index d5e93b00..d2335733 100644 --- a/isoquant_lib/dataset_processor.py +++ b/isoquant_lib/dataset_processor.py @@ -739,6 +739,23 @@ def merge_assignments(self, sample, aggregator, chr_ids): merge_counts(counter, sample.prefix, chr_ids, unaligned) # counter.convert_counts_to_tpm(self.args.normalization_method) + # Developer training-data collection: concatenate per-chr training + # fragments (written by TerminalCounter._dump_training_features) into + # the user-supplied CSV path. Per-chr fragments live next to the + # per-chr prediction TSVs with a .training.csv suffix. + polya_csv = getattr(self.args, "collect_polya_training", None) + if polya_csv: + with open(polya_csv, "w") as fh: + merge_files(sample.out_polya_prediction_tsv + ".training.csv", + sample.prefix, chr_ids, fh, header_lines=1) + logger.info("PolyA training features written to %s", polya_csv) + tss_csv = getattr(self.args, "collect_tss_training", None) + if tss_csv and self.args.fl_data: + with open(tss_csv, "w") as fh: + merge_files(sample.out_tss_prediction_tsv + ".training.csv", + sample.prefix, chr_ids, fh, header_lines=1) + logger.info("TSS training features written to %s", tss_csv) + def merge_transcript_models(self, label, aggregator, chr_ids, gff_printer): merge_files(gff_printer.model_fname, label, chr_ids, gff_printer.out_gff, copy_header=False) if gff_printer.output_r2t: diff --git a/isoquant_lib/input_data_storage.py b/isoquant_lib/input_data_storage.py index 36704c0c..8562148a 100644 --- a/isoquant_lib/input_data_storage.py +++ b/isoquant_lib/input_data_storage.py @@ -73,6 +73,10 @@ def _init_paths(self): self.out_exon_grouped_counts_tsv = self._make_path(self.prefix + ".exon_grouped") self.out_intron_grouped_counts_tsv = self._make_path(self.prefix + ".intron_grouped") self.out_t2t_tsv = self._make_path(self.prefix + ".novel_vs_known.SQANTI-like.tsv") + self.out_polya_prediction_tsv = self._make_path(self.prefix + ".polyA_prediction.tsv") + self.out_tss_prediction_tsv = self._make_path(self.prefix + ".TSS_prediction.tsv") + self.out_polya_prediction_grouped_tsv = self._make_path(self.prefix + ".polyA_prediction_grouped") + self.out_tss_prediction_grouped_tsv = self._make_path(self.prefix + ".TSS_prediction_grouped") self.barcodes_tsv = self._make_path(self.prefix + ".barcoded_reads") self.barcodes_done = self._make_aux_path(self.prefix + ".barcodes_done") self.barcodes_split_reads = self._make_aux_path(self.prefix + ".split_barcodes") @@ -182,6 +186,14 @@ def get_t2t_tsv_file(self, chr_id: str) -> str: """Get path to SQANTI-like TSV for a chromosome.""" return self._make_path(self.get_chr_prefix(chr_id) + ".novel_vs_known.SQANTI-like.tsv") + def get_polya_prediction_file(self, chr_id: str) -> str: + """Get path to polyA prediction TSV for a chromosome.""" + return self._make_path(self.get_chr_prefix(chr_id) + ".polyA_prediction.tsv") + + def get_tss_prediction_file(self, chr_id: str) -> str: + """Get path to TSS prediction TSV for a chromosome.""" + return self._make_path(self.get_chr_prefix(chr_id) + ".TSS_prediction.tsv") + def get_grouped_counts_file(self, chr_id: str, feature: str, strategy_name: str) -> str: """Get path to grouped counts file for a chromosome. diff --git a/isoquant_lib/terminal_counter.py b/isoquant_lib/terminal_counter.py new file mode 100644 index 00000000..0879ed66 --- /dev/null +++ b/isoquant_lib/terminal_counter.py @@ -0,0 +1,492 @@ +############################################################################ +# Copyright (c) 2022-2026 University of Helsinki +# Copyright (c) 2020-2022 Saint Petersburg State University +# # All Rights Reserved +# See file LICENSE for details. +############################################################################ + +"""Terminal position (polyA / TSS) prediction. + +Per-transcript histogram of read end positions -> scipy peak detection -> +XGBoost peak filter (pre-trained model) -> classification against the +annotated transcript end. Predictions are written one row per accepted peak +(per group, when grouped) to a TSV alongside the standard counts files. + +The shipped XGBoost classifiers live in ``isoquant_lib/data/``; they are +trained offline with ``scripts/train_polya_tss_model.py``. +""" + +import logging +from pathlib import Path +from typing import Optional + +import numpy as np +import pandas as pd +from scipy import stats +from scipy.signal import find_peaks, peak_prominences, peak_widths +from xgboost import XGBClassifier + +from .isoform_assignment import ( + IsoformMatch, + ReadAssignment, + ReadAssignmentType, +) +from .long_read_counter import AbstractCounter +from .read_groups import AbstractReadGrouper + +logger = logging.getLogger('IsoQuant') + +_DATA_DIR = Path(__file__).parent / "data" +POLYA_MODEL_PATH = _DATA_DIR / "model_polya.json" +TSS_MODEL_PATH = _DATA_DIR / "model_tss.json" + +# Peak finding + classification parameters. Match values used to train the +# shipped XGBoost models -- changing these without retraining will break the +# feature alignment between fit time and inference time. +PEAK_DISTANCE = 10 +PEAK_REL_HEIGHT = 0.98 +HISTOGRAM_PAD = 10 +ANNOTATION_TOLERANCE = 10 + +_ACCEPTED_ASSIGNMENT_TYPES = frozenset({ + ReadAssignmentType.unique, + ReadAssignmentType.unique_minor_difference, + ReadAssignmentType.inconsistent, + ReadAssignmentType.inconsistent_non_intronic, +}) + +# XGBoost feature columns (must match training) +FEATURE_COLUMNS = ['var', 'skew', 'peak_count', 'peak_width', 'entropy', + 'mean_height', 'peak_heights', 'relative_height'] + +EMPTY_COLUMNS = ['chromosome', 'transcript_id', 'gene_id', 'prediction', + 'counts', 'flag'] +EMPTY_COLUMNS_GROUPED = EMPTY_COLUMNS + ['counts_byGroup', 'group_id'] + +# Columns dumped per peak when training-data collection is enabled. +# Trainer (misc/train_polya_tss_model.py) consumes exactly this layout. +TRAINING_COLUMNS = FEATURE_COLUMNS + ['chromosome', 'true_peak'] + +# Per-chr training CSV is placed next to the per-chr prediction TSV with this +# suffix; the sample-level merge concatenates them into the user-supplied path. +TRAINING_SUFFIX = ".training.csv" + + +class TerminalCounter(AbstractCounter): + """Base class for polyA / TSS peak prediction counters. + + One counter instance handles a single chromosome (when constructed inside a + worker) or the merged sample (when constructed at finalization). Each + instance accumulates positional histograms per transcript across the reads + it sees and emits its predictions in :meth:`dump`. + """ + + # Subclasses set the args attribute name (e.g. "collect_polya_training") + # to pick up their per-counter training-collection flag. + TRAINING_ARG: str = "" + + def __init__(self, args, output_prefix: str, model_path: Path, + string_pools=None, group_index: int = 0) -> None: + # Skip AbstractCounter.__init__ -- we don't want counts_file_name's + # suffix machinery; the prediction TSV path is already the full name. + self.ignore_read_groups = string_pools is None + self.output_prefix = output_prefix + self.output_file = output_prefix + self.output_counts_file_name = output_prefix + self.output_tpm_file_name = None + self.output_stats_file_name = None + self.usable_file_name = None + # Clear any stale per-chr file before we start appending in dump(). + open(self.output_file, "w").close() + + self.args = args + self.string_pools = string_pools + self.group_index = group_index + + # Model loading is deferred until dump() to keep XGBoost's OpenMP + # runtime out of the parent process -- otherwise the inherited state + # deadlocks fork() workers in ProcessPoolExecutor. + self._model_path = model_path + self._model = None + + # Developer training-data collection mode (off by default). + self._collecting_training = bool( + self.TRAINING_ARG and getattr(args, self.TRAINING_ARG, None)) + # Per-chr fragment lives next to the per-chr prediction TSV; the + # sample-level merge will concatenate fragments into the user's + # requested CSV path. + self._training_csv_path = (output_prefix + TRAINING_SUFFIX + if self._collecting_training else None) + if self._training_csv_path: + open(self._training_csv_path, "w").close() + + # transcript_id -> {'chr', 'gene_id', 'data', 'annotated', + # int_group_id -> list[int]} + self.transcripts: dict = {} + + @property + def model(self) -> XGBClassifier: + if self._model is None: + self._model = XGBClassifier() + self._model.load_model(str(self._model_path)) + return self._model + + # -- AbstractCounter interface -------------------------------------------- + # Most of these are no-ops: the terminal counter only consumes finished + # read assignments and emits its own TSV; it does not participate in the + # confirmed-feature / unassigned / raw-feature flows. + + def add_read_info_raw(self, read_id, feature_ids, group_ids) -> None: + return + + def add_confirmed_features(self, features) -> None: + return + + def add_unassigned(self, read_assignment) -> None: + return + + def add_unaligned(self, n_reads: int = 1) -> None: + return + + def finalize(self, args=None) -> None: + return + + def get_output_file_handler(self): + return open(self.output_file, "a") + + # -- subclass hooks ------------------------------------------------------- + + def _passes_filter(self, read_assignment: ReadAssignment) -> bool: + raise NotImplementedError + + def _extract_position(self, read_assignment: ReadAssignment) -> Optional[int]: + """Return the position of interest (genomic coordinate) or None.""" + raise NotImplementedError + + def _annotated_position(self, read_assignment: ReadAssignment, + transcript_id: str) -> int: + raise NotImplementedError + + # -- accumulation --------------------------------------------------------- + + def add_read_info(self, read_assignment: Optional[ReadAssignment] = None) -> None: + if read_assignment is None: + return + if read_assignment.assignment_type not in _ACCEPTED_ASSIGNMENT_TYPES: + return + if not self._passes_filter(read_assignment): + return + if not read_assignment.corrected_exons or not read_assignment.isoform_matches: + return + + position = self._extract_position(read_assignment) + if position is None: + return + + isoform_match: IsoformMatch = read_assignment.isoform_matches[0] + transcript_id = isoform_match.assigned_transcript + if transcript_id is None: + return + + entry = self.transcripts.get(transcript_id) + if entry is None: + entry = { + 'chr': read_assignment.chr_id, + 'gene_id': isoform_match.assigned_gene, + 'data': [], + 'annotated': self._annotated_position(read_assignment, transcript_id), + } + self.transcripts[transcript_id] = entry + + position = int(position) + entry['data'].append(position) + + if not self.ignore_read_groups: + group_id = self._read_group_id(read_assignment) + entry.setdefault(group_id, []).append(position) + + def _read_group_id(self, read_assignment: ReadAssignment) -> int: + if not read_assignment.read_group_ids: + return 0 + return read_assignment.read_group_ids[self.group_index] + + # -- emission ------------------------------------------------------------- + + def dump(self) -> None: + if not self.transcripts: + self._write_empty() + return + + df = self._build_peak_dataframe() + zero_peaks, peaks = self._split_zero_and_real_peaks(df) + if not peaks.empty: + peaks = self._rank_and_explode(peaks) + + # Developer training-data collection: dump features + true_peak label + # for every detected peak, skip the XGBoost filter, emit an empty + # prediction TSV so the rest of the pipeline still sees the file. + if self._collecting_training: + self._dump_training_features(zero_peaks, peaks) + self._write_empty() + return + + if not peaks.empty: + peaks[FEATURE_COLUMNS] = peaks[FEATURE_COLUMNS].astype(float, errors='ignore') + predicted = self.model.predict(peaks[FEATURE_COLUMNS].astype(float)) + peaks = peaks[predicted == 1].copy() + peaks['prediction'] = peaks['peak_location'] + + frames = [f for f in (zero_peaks, peaks) if not f.empty] + if not frames: + self._write_empty() + return + result = (pd.concat(frames, axis=0, ignore_index=True, sort=False) + if len(frames) > 1 else frames[0].reset_index(drop=True)) + + # Convert peak position back to genomic coordinate and add flag. + result['prediction'] = (result['prediction'].astype(int) + + result['start'].astype(int)) + result['flag'] = ( + (result['prediction'] - result['annotated']).abs() + .gt(ANNOTATION_TOLERANCE).map({True: 'Novel', False: 'Known'})) + result['counts'] = result.apply(self._counts_for_peak, axis=1) + + if self.ignore_read_groups: + self._write_ungrouped(result) + else: + self._write_grouped(result) + + def _build_peak_dataframe(self) -> pd.DataFrame: + """One row per transcript with histogram, summary stats, and raw peaks.""" + records = [] + for tid, entry in self.transcripts.items(): + data = entry['data'] + data_min = min(data) + data_max = max(data) + hist_counts = np.histogram( + data, + bins=(data_max - data_min) + 1, + range=(data_min, data_max), + )[0] + padded = ([0] * HISTOGRAM_PAD + list(hist_counts) + + [0] * HISTOGRAM_PAD) + records.append({ + 'transcript_id': tid, + 'chromosome': entry['chr'], + 'gene_id': entry['gene_id'], + 'annotated': entry['annotated'], + 'start': data_min, + 'mode': int(stats.mode(data, keepdims=False).mode) - data_min, + 'mean': float(np.mean(data)) - data_min, + 'range': data_max - data_min + 1, + 'var': float(np.var(data)), + 'skew': (float(stats.skew(data)) + if len(data) > 3 and np.var(data) >= 1e-12 else None), + 'entropy': float(stats.entropy(padded)), + 'mean_height': float(np.mean(padded)), + 'histogram': padded, + }) + df = pd.DataFrame.from_records(records) + + # find_peaks returns padded-array indices; subtract HISTOGRAM_PAD to + # convert to start-relative coordinates. + peaks_idx = df['histogram'].apply( + lambda h: find_peaks(h, distance=PEAK_DISTANCE)[0]) + df['peak_count'] = peaks_idx.apply(len) + df['peak_location'] = peaks_idx.apply( + lambda p: [int(j - HISTOGRAM_PAD) for j in p]) + df['peak_prominence'] = df.apply( + lambda r: peak_prominences(r['histogram'], peaks_idx.loc[r.name])[0], + axis=1) + widths = df.apply( + lambda r: peak_widths(r['histogram'], peaks_idx.loc[r.name], + rel_height=PEAK_REL_HEIGHT), + axis=1) + df['peak_width'] = widths.apply(lambda w: w[0]) + df['peak_heights'] = df.apply( + lambda r: np.array([r['histogram'][int(p) + HISTOGRAM_PAD] + for p in r['peak_location']]), + axis=1) + df['peak_left'] = widths.apply( + lambda w: [int(j - HISTOGRAM_PAD) for j in w[2]]) + df['peak_right'] = widths.apply( + lambda w: [int(j - HISTOGRAM_PAD) for j in w[3]]) + return df + + @staticmethod + def _split_zero_and_real_peaks(df: pd.DataFrame): + """Split out transcripts with zero detected peaks (mode fallback) from + those with one or more real peaks.""" + zero_peaks = df[df['peak_count'] == 0].copy() + if not zero_peaks.empty: + zero_peaks['prediction'] = zero_peaks['mode'] + zero_peaks['peak_location'] = zero_peaks['mode'] + zero_peaks['peak_heights'] = zero_peaks.apply( + lambda r: r['histogram'][int(r['prediction']) + HISTOGRAM_PAD], + axis=1) + zero_peaks['peak_left'] = zero_peaks['mode'] + zero_peaks['peak_right'] = zero_peaks['mode'] + peaks = df[df['peak_count'] > 0].copy() + return zero_peaks, peaks + + def _rank_and_explode(self, peaks: pd.DataFrame) -> pd.DataFrame: + peaks = peaks.apply(self._rank_peaks, axis=1) + return peaks.explode(['peak_location', 'peak_prominence', 'peak_width', + 'peak_heights', 'peak_left', 'peak_right', + 'rank', 'relative_height']).reset_index(drop=True) + + def _dump_training_features(self, zero_peaks: pd.DataFrame, + peaks: pd.DataFrame) -> None: + """Append per-peak features + ``true_peak`` label to the per-chr + training CSV. The sample-level merge concatenates fragments into the + user-supplied path.""" + frames = [f for f in (zero_peaks, peaks) if not f.empty] + if not frames: + return + rows = (pd.concat(frames, axis=0, ignore_index=True, sort=False) + if len(frames) > 1 else frames[0].reset_index(drop=True)) + genomic_prediction = (rows['peak_location'].astype(int) + + rows['start'].astype(int)) + rows = rows.assign(true_peak=( + (genomic_prediction - rows['annotated']).abs() + .le(ANNOTATION_TOLERANCE).astype(int))) + rows[TRAINING_COLUMNS].to_csv( + self._training_csv_path, sep=",", index=False, mode="w", + header=True) + + def _rank_peaks(self, row: pd.Series) -> pd.Series: + heights = np.asarray(row['peak_heights'], dtype=float) + if heights.size > 1: + order = np.argsort(-heights) + for key in ('peak_location', 'peak_prominence', 'peak_width', + 'peak_heights', 'peak_left', 'peak_right'): + row[key] = np.asarray(row[key])[order].tolist() + row['rank'] = list(range(1, heights.size + 1)) + top = row['peak_heights'][0] + row['relative_height'] = ( + [h / top if top else 0.0 for h in row['peak_heights']]) + else: + row['rank'] = [0] + row['relative_height'] = [1.0] + return row + + def _counts_for_peak(self, row: pd.Series) -> int: + hist = row['histogram'] + # peak_left / peak_right are start-relative; +HISTOGRAM_PAD to index + # into the padded histogram. Clamp to its bounds in case of edge peaks. + low = max(0, int(row['peak_left']) + HISTOGRAM_PAD) + high = min(len(hist), int(row['peak_right']) + HISTOGRAM_PAD + 1) + if high <= low: + return 0 + return int(np.asarray(hist[low:high]).sum()) + + def _counts_for_group(self, row: pd.Series, group_id: int) -> int: + positions = self.transcripts[row['transcript_id']].get(group_id, []) + if not positions: + return 0 + # Count read ends falling within the genomic window of this peak. + start = row['start'] + low = start + int(row['peak_left']) + high = start + int(row['peak_right']) + return int(sum(1 for p in positions if low <= p <= high)) + + def _write_empty(self) -> None: + cols = EMPTY_COLUMNS if self.ignore_read_groups else EMPTY_COLUMNS_GROUPED + pd.DataFrame(columns=cols).to_csv( + self.output_file, sep="\t", index=False, mode="w", header=True) + + def _write_ungrouped(self, result: pd.DataFrame) -> None: + out = result[EMPTY_COLUMNS].copy() + out.to_csv(self.output_file, sep="\t", index=False, mode="w", header=True) + + def _write_grouped(self, result: pd.DataFrame) -> None: + # Collect all integer group IDs we have seen across the chromosome. + seen_groups = sorted({gid for entry in self.transcripts.values() + for gid in entry.keys() + if isinstance(gid, int)}) + if not seen_groups: + self._write_empty() + return + + rows = [] + for _, peak in result.iterrows(): + for gid in seen_groups: + count = self._counts_for_group(peak, gid) + if count == 0: + continue + rows.append({ + 'chromosome': peak['chromosome'], + 'transcript_id': peak['transcript_id'], + 'gene_id': peak['gene_id'], + 'prediction': peak['prediction'], + 'counts': peak['counts'], + 'flag': peak['flag'], + 'counts_byGroup': count, + 'group_id': self._group_name(gid), + }) + out = pd.DataFrame(rows, columns=EMPTY_COLUMNS_GROUPED) + out.to_csv(self.output_file, sep="\t", index=False, mode="w", header=True) + + def _group_name(self, group_id: int) -> str: + if self.string_pools is None: + return AbstractReadGrouper.default_group_id + pool = self.string_pools.get_read_group_pool(self.group_index) + return pool.get_str(group_id) + + +class PolyACounter(TerminalCounter): + """Predicts polyA cleavage sites per transcript.""" + + TRAINING_ARG = "collect_polya_training" + + def __init__(self, args, output_prefix: str, + string_pools=None, group_index: int = 0) -> None: + super().__init__(args, output_prefix, POLYA_MODEL_PATH, + string_pools=string_pools, group_index=group_index) + + def _passes_filter(self, read_assignment: ReadAssignment) -> bool: + return bool(read_assignment.polyA_found and read_assignment.polya_info) + + def _extract_position(self, read_assignment: ReadAssignment) -> Optional[int]: + info = read_assignment.polya_info + if read_assignment.strand == '+' and info.external_polya_pos != -1: + return info.external_polya_pos + if read_assignment.strand == '-' and info.external_polyt_pos != -1: + return info.external_polyt_pos + return None + + def _annotated_position(self, read_assignment: ReadAssignment, + transcript_id: str) -> int: + exons = read_assignment.gene_info.all_isoforms_exons[transcript_id] + if read_assignment.strand == '+': + return exons[-1][1] + 1 + return exons[0][0] - 1 + + +class TSSCounter(TerminalCounter): + """Predicts transcription start sites per transcript.""" + + TRAINING_ARG = "collect_tss_training" + + def __init__(self, args, output_prefix: str, + string_pools=None, group_index: int = 0) -> None: + super().__init__(args, output_prefix, TSS_MODEL_PATH, + string_pools=string_pools, group_index=group_index) + + def _passes_filter(self, read_assignment: ReadAssignment) -> bool: + return read_assignment.strand in ('+', '-') + + def _extract_position(self, read_assignment: ReadAssignment) -> Optional[int]: + exons = read_assignment.corrected_exons + if not exons: + return None + if read_assignment.strand == '+': + return exons[0][0] + return exons[-1][1] + + def _annotated_position(self, read_assignment: ReadAssignment, + transcript_id: str) -> int: + exons = read_assignment.gene_info.all_isoforms_exons[transcript_id] + if read_assignment.strand == '+': + return exons[0][0] - 1 + return exons[-1][1] + 1 diff --git a/isoquant_tests/console_test.py b/isoquant_tests/console_test.py index 7c24a89b..2b3471a5 100644 --- a/isoquant_tests/console_test.py +++ b/isoquant_tests/console_test.py @@ -114,9 +114,46 @@ def test_with_bam_and_polya(): "intron_counts.tsv", "read_assignments.tsv.gz", "novel_vs_known.SQANTI-like.tsv", "transcript_counts.tsv", - "discovered_transcript_counts.tsv", "transcript_models.gtf"] + "discovered_transcript_counts.tsv", "transcript_models.gtf", + "polyA_prediction.tsv"] for f in resulting_files: assert os.path.exists(os.path.join(sample_folder, sample_name + "." + f)) + # polyA prediction should produce at least a header row and some data. + polya_tsv = os.path.join(sample_folder, sample_name + ".polyA_prediction.tsv") + with open(polya_tsv) as f: + lines = f.read().splitlines() + assert lines[0].split("\t")[:3] == ["chromosome", "transcript_id", "gene_id"] + assert any(ln and not ln.startswith("chromosome") for ln in lines), \ + "polyA prediction TSV is empty" + + +def test_with_bam_polya_and_fl_data(): + source_dir = os.path.dirname(os.path.realpath(__file__)) + data_dir = os.path.join(source_dir, 'simple_data/') + out_dir = os.path.join(source_dir, "out_polya_fl/") + shutil.rmtree(out_dir, ignore_errors=True) + sample_name = "ONT_Simulated.chr9.4M.polyA.fl" + + result = subprocess.run(["python3", "isoquant.py", + "-o", out_dir, + "--data_type", "nanopore", + "--bam", os.path.join(data_dir, "chr9.4M.ont.sim.polya.bam"), + "--genedb", os.path.join(data_dir, "chr9.4M.gtf.gz"), + "--complete_genedb", + "-r", os.path.join(data_dir, "chr9.4M.fa.gz"), + "-t", "2", + "--fl_data", + "--prefix", sample_name]) + + assert result.returncode == 0 + sample_folder = os.path.join(out_dir, sample_name) + assert os.path.isdir(sample_folder) + for f in ["polyA_prediction.tsv", "TSS_prediction.tsv"]: + path = os.path.join(sample_folder, sample_name + "." + f) + assert os.path.exists(path), f"expected {f} to be produced" + with open(path) as fh: + lines = fh.read().splitlines() + assert lines[0].split("\t")[:3] == ["chromosome", "transcript_id", "gene_id"] def test_with_illumina(): diff --git a/isoquant_tests/github/run_pipeline.py b/isoquant_tests/github/run_pipeline.py index 092d98e3..5a713e90 100755 --- a/isoquant_tests/github/run_pipeline.py +++ b/isoquant_tests/github/run_pipeline.py @@ -34,6 +34,7 @@ RT_QUANTIFICATION_GENES = "quantification_genes" RT_PERFORMANCE = "performance" RT_ALLINFO = "allinfo" +RT_POLYA_PREDICTION = "polya_prediction" log = logging.getLogger('GitHubRunner') @@ -315,6 +316,64 @@ def run_assignment_quality(args, config_dict, baselines=None): return exit_code +def run_polya_prediction(args, config_dict, baselines=None): + log.info('== Running polyA prediction quality assessment ==') + config_file = args.config_file + source_dir = os.path.dirname(os.path.realpath(__file__)) + isoquant_dir = os.path.join(source_dir, "../../") + + label = config_dict["label"] + output_folder = os.path.join(args.output if args.output else config_dict["output"], label) + prediction_tsv = os.path.join(output_folder, "%s/%s.polyA_prediction.tsv" % (label, label)) + if not os.path.exists(prediction_tsv): + log.error("polyA prediction TSV not found at %s" % prediction_tsv) + return -5 + + if "reference_polya_gtf" not in config_dict: + log.error("reference_polya_gtf is not set in the config") + return -4 + reference_gtf = fix_path(config_file, config_dict["reference_polya_gtf"]) + + quality_report = os.path.join(output_folder, "polya_report.tsv") + qa_command_list = ["python3", os.path.join(isoquant_dir, "misc/assess_polya_prediction.py"), + "--prediction", prediction_tsv, + "--reference_gtf", reference_gtf, + "--output", quality_report] + if "simulated_counts" in config_dict: + qa_command_list += ["--transcript_counts", + fix_path(config_file, config_dict["simulated_counts"])] + if "qa_options" in config_dict: + log.info("Appending additional options: %s" % config_dict["qa_options"]) + opts = config_dict["qa_options"].replace('"', '').split() + qa_command_list += opts + + log.info("QA command line: " + " ".join(qa_command_list)) + result = subprocess.run(qa_command_list) + if result.returncode != 0: + log.error("QA exited with non-zero status: %d" % result.returncode) + return -11 + + if not baselines or "polya_prediction" not in baselines: + return 0 + etalon_dict = {k: str(v) for k, v in baselines["polya_prediction"].items()} + + log.info('== Checking polyA prediction metrics ==') + quality_report_dict = load_tsv_config(quality_report) + exit_code = 0 + new_etalon_outf = open(os.path.join(output_folder, "new_polya_prediction_etalon.tsv"), "w") + for k, v in etalon_dict.items(): + if k not in quality_report_dict: + log.error("Metric %s was not found in the report" % k) + exit_code = -12 + continue + new_etalon_outf.write("%s\t%.4f\n" % (k, float(quality_report_dict[k]))) + err_code = check_value(float(v), float(quality_report_dict[k]), k) + if err_code != 0: + exit_code = err_code + new_etalon_outf.close() + return exit_code + + def parse_gffcomapre(stats_file): for l in open(stats_file): if l.find("Transcript level") == -1: @@ -626,6 +685,8 @@ def main(): err_codes.append(run_performance_assessment(args, config_dict, baselines)) if RT_ALLINFO in run_types: err_codes.append(run_allinfo_quality(args, config_dict, baselines)) + if RT_POLYA_PREDICTION in run_types: + err_codes.append(run_polya_prediction(args, config_dict, baselines)) if "check_input_files" in config_dict: files_list = config_dict["check_input_files"].split() diff --git a/isoquant_tests/test_ci_config.py b/isoquant_tests/test_ci_config.py index cc7dc12a..e5277928 100644 --- a/isoquant_tests/test_ci_config.py +++ b/isoquant_tests/test_ci_config.py @@ -145,6 +145,25 @@ def test_empty_file(self): self.assertEqual(config, {}) self.assertEqual(baselines, {}) + def test_polya_prediction_baselines_extracted(self): + # Schema check for the polya_prediction run type added alongside + # misc/assess_polya_prediction.py. Only precision and recall are + # baselined (see Mouse.ONT_simulated.polyA.yaml). + path = self._write_yaml({ + "name": "polya_test", + "run_type": "polya_prediction", + "reference_polya_gtf": "/abga/work/coroari/thesis_model/polya/updated_mouse_polya_1.gtf", + "baselines": { + "polya_prediction": {"precision": 79.41, "recall": 50.00}, + }, + }) + config, baselines = pipeline_load_yaml(path) + self.assertEqual(config["run_type"], "polya_prediction") + self.assertIn("reference_polya_gtf", config) + self.assertIn("polya_prediction", baselines) + self.assertAlmostEqual(baselines["polya_prediction"]["precision"], 79.41) + self.assertAlmostEqual(baselines["polya_prediction"]["recall"], 50.00) + class TestLoadConfigDispatch(unittest.TestCase): def setUp(self): diff --git a/isoquant_tests/test_polya_prediction.py b/isoquant_tests/test_polya_prediction.py new file mode 100644 index 00000000..a5c9b04e --- /dev/null +++ b/isoquant_tests/test_polya_prediction.py @@ -0,0 +1,350 @@ +############################################################################ +# Copyright (c) 2022-2026 University of Helsinki +# # All Rights Reserved +# See file LICENSE for details. +############################################################################ + +"""Unit tests for the polyA / TSS prediction counters. + +The XGBoost classifier is patched with a stub that returns ``1`` for every +peak. This keeps the tests independent of the shipped model file and lets us +exercise the counter's bookkeeping, feature extraction, and output layout. +""" + +import os +import types +from argparse import Namespace + +import numpy as np +import pandas as pd +import pytest + +from isoquant_lib import terminal_counter as tc +from isoquant_lib.isoform_assignment import ReadAssignmentType + + +class _StubModel: + """Minimal stand-in for ``XGBClassifier`` used at inference time.""" + + def __init__(self, accept=True): + self._accept = accept + + def load_model(self, _path): + return None + + def predict(self, X): + value = 1 if self._accept else 0 + return np.full(len(X), value, dtype=int) + + +@pytest.fixture +def stub_model(monkeypatch): + model = _StubModel(accept=True) + monkeypatch.setattr(tc, "XGBClassifier", lambda *a, **kw: model) + return model + + +def _make_args(): + return Namespace(genedb=True, fl_data=True, read_group=None) + + +def _make_gene_info(transcript_id="T1", exons=((100, 200), (300, 400))): + return types.SimpleNamespace(all_isoforms_exons={transcript_id: list(exons)}) + + +def _make_isoform_match(transcript_id="T1", gene_id="G1"): + return types.SimpleNamespace(assigned_transcript=transcript_id, + assigned_gene=gene_id) + + +def _make_polya_info(external_polya_pos=-1, external_polyt_pos=-1): + return types.SimpleNamespace( + external_polya_pos=external_polya_pos, + external_polyt_pos=external_polyt_pos, + internal_polya_pos=-1, + internal_polyt_pos=-1, + ) + + +def _make_read_assignment( + *, strand="+", chr_id="chr1", polya_pos=420, exons=None, + assignment_type=ReadAssignmentType.unique, polyA_found=True, + transcript_id="T1", gene_id="G1", read_group_ids=None, + gene_info=None, +): + exons = exons or [(100, 200), (300, polya_pos)] + polya_info = _make_polya_info( + external_polya_pos=polya_pos if strand == "+" else -1, + external_polyt_pos=polya_pos if strand == "-" else -1, + ) + return types.SimpleNamespace( + assignment_type=assignment_type, + strand=strand, + chr_id=chr_id, + polyA_found=polyA_found, + polya_info=polya_info, + corrected_exons=exons, + isoform_matches=[_make_isoform_match(transcript_id, gene_id)], + read_group_ids=list(read_group_ids) if read_group_ids else [], + gene_info=gene_info or _make_gene_info(transcript_id, exons), + ) + + +def _read_tsv(path): + return pd.read_csv(path, sep="\t") + + +def test_polya_counter_filters_invalid_reads(stub_model, tmp_path): + counter = tc.PolyACounter(_make_args(), str(tmp_path / "p.tsv")) + + # polyA_found=False is rejected + counter.add_read_info(_make_read_assignment(polyA_found=False)) + # Wrong assignment type is rejected + counter.add_read_info(_make_read_assignment( + assignment_type=ReadAssignmentType.noninformative)) + # Strand without matching polya_info position is rejected + bad = _make_read_assignment(strand="+", polya_pos=420) + bad.polya_info = _make_polya_info(external_polya_pos=-1) + counter.add_read_info(bad) + + assert counter.transcripts == {} + + +def test_polya_counter_accumulates_positions_per_group(stub_model, tmp_path): + counter = tc.PolyACounter(_make_args(), str(tmp_path / "p.tsv"), + string_pools=object(), group_index=0) + # Single transcript, three reads in two groups + for pos, gid in [(410, 0), (411, 0), (455, 1)]: + counter.add_read_info(_make_read_assignment( + polya_pos=pos, read_group_ids=[gid])) + + entry = counter.transcripts["T1"] + assert entry["data"] == [410, 411, 455] + assert entry[0] == [410, 411] + assert entry[1] == [455] + + +def test_tss_counter_extracts_strand_aware_start(stub_model, tmp_path): + counter = tc.TSSCounter(_make_args(), str(tmp_path / "t.tsv")) + counter.add_read_info(_make_read_assignment(strand="+", + exons=[(105, 200), (300, 400)])) + counter.add_read_info(_make_read_assignment(strand="-", + exons=[(100, 200), (300, 405)])) + assert counter.transcripts["T1"]["data"] == [105, 405] + + +def test_dump_empty_writes_header_only(stub_model, tmp_path): + out = tmp_path / "empty.tsv" + counter = tc.PolyACounter(_make_args(), str(out)) + counter.dump() + + df = _read_tsv(out) + assert list(df.columns) == tc.EMPTY_COLUMNS + assert df.empty + + +def test_dump_ungrouped_emits_one_row_per_peak(stub_model, tmp_path): + out = tmp_path / "ungrouped.tsv" + counter = tc.PolyACounter(_make_args(), str(out)) + # Cluster 30 reads tightly around 420 to form a clear peak that the stub + # model accepts; annotated end is exon[-1][1]+1 = 401, so the peak is Novel. + exons = [(100, 200), (300, 400)] + for offset in range(30): + counter.add_read_info(_make_read_assignment( + polya_pos=420 + offset % 3, exons=exons)) + counter.dump() + + df = _read_tsv(out) + assert list(df.columns) == tc.EMPTY_COLUMNS + assert len(df) >= 1 + assert (df["transcript_id"] == "T1").all() + assert df["counts"].sum() == 30 + assert df["flag"].iloc[0] == "Novel" + + +def test_dump_flags_peak_as_known_within_tolerance(stub_model, tmp_path): + out = tmp_path / "known.tsv" + counter = tc.PolyACounter(_make_args(), str(out)) + # Annotated polyA for strand '+' is exons[-1][1]+1 = 401, so reads at 402 + # land within ANNOTATION_TOLERANCE (10). + exons = [(100, 200), (300, 400)] + for _ in range(30): + counter.add_read_info(_make_read_assignment(polya_pos=402, exons=exons)) + counter.dump() + + df = _read_tsv(out) + assert (df["flag"] == "Known").all() + + +def test_dump_grouped_emits_group_id_column(stub_model, tmp_path): + # Minimal stub for string_pools: an object that returns a pool with a + # ``get_str`` method translating integer ids to names. + pool = types.SimpleNamespace(get_str=lambda i: f"g{i}") + string_pools = types.SimpleNamespace(get_read_group_pool=lambda _idx: pool) + + out = tmp_path / "grouped.tsv" + counter = tc.PolyACounter(_make_args(), str(out), + string_pools=string_pools, group_index=0) + exons = [(100, 200), (300, 400)] + for offset in range(30): + counter.add_read_info(_make_read_assignment( + polya_pos=420, exons=exons, read_group_ids=[offset % 2])) + counter.dump() + + df = _read_tsv(out) + assert list(df.columns) == tc.EMPTY_COLUMNS_GROUPED + assert set(df["group_id"]) == {"g0", "g1"} + # Total counts_byGroup should match the 30 contributing reads. + assert df["counts_byGroup"].sum() == 30 + + +def test_model_rejection_yields_empty_output(monkeypatch, tmp_path): + # When the model predicts 0 for every peak, only zero-peak fallback rows + # could survive. A 30-read cluster produces a peak, so output is empty. + monkeypatch.setattr(tc, "XGBClassifier", lambda *a, **kw: _StubModel(accept=False)) + out = tmp_path / "rejected.tsv" + counter = tc.PolyACounter(_make_args(), str(out)) + exons = [(100, 200), (300, 400)] + for offset in range(30): + counter.add_read_info(_make_read_assignment( + polya_pos=420 + offset % 3, exons=exons)) + counter.dump() + + df = _read_tsv(out) + assert df.empty + + +def test_rank_peaks_orders_by_height_descending(): + counter_cls = tc.TerminalCounter + row = pd.Series({ + "peak_heights": np.array([3.0, 8.0, 5.0]), + "peak_location": [10, 20, 30], + "peak_prominence": [0.3, 0.8, 0.5], + "peak_width": [1.0, 2.0, 3.0], + "peak_left": [9, 19, 29], + "peak_right": [11, 21, 31], + }) + ranked = counter_cls._rank_peaks(None, row) + assert ranked["peak_location"] == [20, 30, 10] + assert ranked["rank"] == [1, 2, 3] + assert ranked["relative_height"][0] == 1.0 + assert ranked["relative_height"][1] == pytest.approx(5.0 / 8.0) + + +def test_rank_peaks_handles_single_peak(): + counter_cls = tc.TerminalCounter + row = pd.Series({ + "peak_heights": np.array([7.0]), + "peak_location": [42], + "peak_prominence": [0.7], + "peak_width": [1.5], + "peak_left": [40], + "peak_right": [44], + }) + ranked = counter_cls._rank_peaks(None, row) + assert ranked["rank"] == [0] + assert ranked["relative_height"] == [1.0] + + +def test_counts_for_peak_clamps_to_histogram_bounds(): + counter_cls = tc.TerminalCounter + histogram = ([0] * tc.HISTOGRAM_PAD + [1, 2, 3, 4, 5] + + [0] * tc.HISTOGRAM_PAD) + row = pd.Series({ + "histogram": histogram, + "peak_left": -2, # before histogram start, must clamp to 0 + "peak_right": 10, # past histogram end, must clamp + }) + assert counter_cls._counts_for_peak(None, row) == sum([1, 2, 3, 4, 5]) + + +@pytest.mark.parametrize("strand,expected", [("+", 401), ("-", 99)]) +def test_polya_counter_annotated_position(stub_model, tmp_path, strand, expected): + counter = tc.PolyACounter(_make_args(), str(tmp_path / "p.tsv")) + read = _make_read_assignment(strand=strand, + exons=[(100, 200), (300, 400)]) + assert counter._annotated_position(read, "T1") == expected + + +@pytest.mark.parametrize("strand,expected", [("+", 99), ("-", 401)]) +def test_tss_counter_annotated_position(stub_model, tmp_path, strand, expected): + counter = tc.TSSCounter(_make_args(), str(tmp_path / "t.tsv")) + read = _make_read_assignment(strand=strand, + exons=[(100, 200), (300, 400)]) + assert counter._annotated_position(read, "T1") == expected + + +def test_tss_counter_rejects_dot_strand(stub_model, tmp_path): + counter = tc.TSSCounter(_make_args(), str(tmp_path / "t.tsv")) + counter.add_read_info(_make_read_assignment(strand=".")) + assert counter.transcripts == {} + + +def test_tss_counter_rejects_empty_exons(stub_model, tmp_path): + counter = tc.TSSCounter(_make_args(), str(tmp_path / "t.tsv")) + read = _make_read_assignment() + read.corrected_exons = [] + counter.add_read_info(read) + assert counter.transcripts == {} + + +def test_polya_counter_rejects_negative_strand_without_polyt(stub_model, tmp_path): + counter = tc.PolyACounter(_make_args(), str(tmp_path / "p.tsv")) + bad = _make_read_assignment(strand="-", polya_pos=420) + bad.polya_info = _make_polya_info(external_polyt_pos=-1) + counter.add_read_info(bad) + assert counter.transcripts == {} + + +def test_dump_emits_rows_for_multiple_transcripts(stub_model, tmp_path): + out = tmp_path / "multi.tsv" + counter = tc.PolyACounter(_make_args(), str(out)) + exons_a = [(100, 200), (300, 400)] + exons_b = [(1000, 1100), (1300, 1500)] + gene_info = types.SimpleNamespace( + all_isoforms_exons={"T1": exons_a, "T2": exons_b}) + for _ in range(30): + counter.add_read_info(_make_read_assignment( + transcript_id="T1", gene_id="G1", + exons=exons_a, polya_pos=420, gene_info=gene_info)) + counter.add_read_info(_make_read_assignment( + transcript_id="T2", gene_id="G2", + exons=exons_b, polya_pos=1520, gene_info=gene_info)) + counter.dump() + + df = _read_tsv(out) + assert set(df["transcript_id"]) == {"T1", "T2"} + assert set(df["gene_id"]) == {"G1", "G2"} + + +def test_dump_zero_peak_fallback_uses_histogram_mode(stub_model, tmp_path): + # A single read produces a one-bin histogram; find_peaks returns nothing, + # so the zero-peak branch fires and emits a row using the mode position. + out = tmp_path / "mode.tsv" + counter = tc.PolyACounter(_make_args(), str(out)) + counter.add_read_info(_make_read_assignment( + polya_pos=420, exons=[(100, 200), (300, 400)])) + counter.dump() + + df = _read_tsv(out) + assert len(df) == 1 + assert df.iloc[0]["prediction"] == 420 + + +def test_no_op_methods_do_not_raise(stub_model, tmp_path): + counter = tc.PolyACounter(_make_args(), str(tmp_path / "p.tsv")) + counter.add_read_info(None) + counter.add_read_info_raw("rid", ["F1"], [0]) + counter.add_confirmed_features({"F1"}) + counter.add_unassigned(_make_read_assignment()) + counter.add_unaligned(3) + counter.finalize() + counter.finalize(_make_args()) + assert counter.transcripts == {} + + +def test_init_truncates_existing_output_file(stub_model, tmp_path): + path = tmp_path / "stale.tsv" + path.write_text("garbage from a previous run\n") + tc.PolyACounter(_make_args(), str(path)) + assert path.read_text() == "" diff --git a/misc/assess_polya_prediction.py b/misc/assess_polya_prediction.py new file mode 100644 index 00000000..fc557baf --- /dev/null +++ b/misc/assess_polya_prediction.py @@ -0,0 +1,371 @@ +#!/usr/bin/env python3 +############################################################################ +# Copyright (c) 2022-2026 University of Helsinki +# # All Rights Reserved +# See file LICENSE for details. +############################################################################ + +"""Evaluate IsoQuant's polyA-site predictions against a ground-truth GTF. + +Designed to be called by ``isoquant_tests/github/run_pipeline.py`` under the +``polya_prediction`` run type. Emits a TSV with one ``metricvalue`` row +per metric so ``check_value()`` in the launcher can compare against the +baselines block in the YAML config. + +Ground truth comes from a GTF (typically a synthetic GTF with alternative +transcript ends used to simulate the reads). The GTF is loaded via +``gffutils`` and a ``.db`` cache is built next to it on first use so the +~156 K-record file is only parsed once per CI host. Prediction file is the +six-column ungrouped TSV emitted by ``isoquant_lib/terminal_counter.py``. + +Annotated polyA position formula matches +``isoquant_lib.terminal_counter.PolyACounter._annotated_position``: +``last_exon_end + 1`` on ``+`` strand, ``first_exon_start - 1`` on ``-``. +""" + +import argparse +import logging +import os +import sys +from collections import defaultdict +from traceback import print_exc +from typing import Optional + +import gffutils + +logger = logging.getLogger("assess_polya_prediction") + +# IsoQuant's prediction TSV column layout (see EMPTY_COLUMNS in +# isoquant_lib/terminal_counter.py). The grouped variant adds two more +# columns; this script ignores them. +TSV_COLUMNS = ["chromosome", "transcript_id", "gene_id", + "prediction", "counts", "flag"] + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + p.add_argument("--prediction", required=True, + help="IsoQuant polyA_prediction.tsv (or .gz)") + p.add_argument("--reference_gtf", required=True, + help="Ground-truth GTF (synthetic) or .db file") + p.add_argument("--output", "-o", required=True, + help="Output TSV with one metricvalue per row") + p.add_argument("--transcript_counts", + help="IsoQuant transcript_counts.tsv — when provided, " + "only transcripts with count > 0 are included in " + "the recall denominator (filters out unexpressed).") + p.add_argument("--tolerance", type=int, default=10, + help="bp tolerance for matching a prediction to a truth " + "end coordinate. Default 10 matches " + "ANNOTATION_TOLERANCE in terminal_counter.py.") + return p.parse_args() + + +def get_or_build_db(gtf_path: str) -> gffutils.FeatureDB: + """Return a FeatureDB for the supplied GTF, building a sibling .db cache + on first call. Mirrors the flags used by ``isoquant_lib/gtf2db.py`` so + the cache stays compatible with any other consumer in the project.""" + if gtf_path.endswith(".db"): + return gffutils.FeatureDB(gtf_path, keep_order=True) + db_path = gtf_path + ".db" + if not os.path.exists(db_path): + logger.info("Building gffutils DB at %s (one-time cache)", db_path) + gffutils.create_db( + gtf_path, db_path, + force=False, keep_order=True, merge_strategy="error", + sort_attribute_values=True, + disable_infer_transcripts=True, + disable_infer_genes=True, + ) + return gffutils.FeatureDB(db_path, keep_order=True) + + +def annotated_polya(strand: str, exon_blocks: list) -> Optional[int]: + """Match PolyACounter._annotated_position. exon_blocks is a list of + ``(start, end)`` tuples sorted by genomic coordinate.""" + if not exon_blocks: + return None + if strand == "+": + return exon_blocks[-1][1] + 1 + if strand == "-": + return exon_blocks[0][0] - 1 + return None + + +def _is_novel_id(tid: str) -> bool: + """True when *tid* looks like ``ENSMUST00000000033.12_142204503`` + (base GENCODE ID + ``_`` + novel polyA coordinate).""" + parts = tid.rsplit("_", 1) + return len(parts) == 2 and parts[1].isdigit() + + +def _base_id(tid: str) -> str: + return tid.rsplit("_", 1)[0] + + +def load_truth(db: gffutils.FeatureDB) -> dict: + """Return ``{transcript_id: [(chr, polya_pos, is_novel), ...]}`` keyed + by base GENCODE ID. Novel transcript entries (IDs with a ``_`` + suffix) are merged into the base transcript's list so that a prediction + for the base ID can match either the known or the novel site.""" + truth: dict = defaultdict(list) + n_known = n_novel = 0 + for t in db.features_of_type("transcript"): + tid = t.id + if not tid: + continue + exon_blocks = sorted( + ((e.start, e.end) for e in db.children(t, featuretype="exon")), + key=lambda b: b[0]) + polya = annotated_polya(t.strand, exon_blocks) + if polya is None: + continue + novel = _is_novel_id(tid) + key = _base_id(tid) if novel else tid + truth[key].append((t.seqid, polya, novel)) + if novel: + n_novel += 1 + else: + n_known += 1 + logger.info("Loaded %d known + %d novel truth entries (%d base IDs)", + n_known, n_novel, len(truth)) + return truth + + +def load_expressed_transcripts(counts_path: str) -> set: + """Return the set of *base* transcript IDs with count > 0. Novel IDs + (``base_``) are mapped to their base GENCODE ID.""" + expressed: set = set() + with open(counts_path) as fh: + header = fh.readline() + for line in fh: + parts = line.rstrip("\n").split("\t") + if len(parts) >= 2 and float(parts[1]) > 0: + tid = parts[0] + expressed.add(_base_id(tid) if _is_novel_id(tid) else tid) + logger.info("Loaded %d expressed base transcript IDs from %s", + len(expressed), counts_path) + return expressed + + +def read_predictions(path: str) -> list: + """Yield prediction dicts from the polyA_prediction.tsv file.""" + rows = [] + if path.endswith(".gz"): + import gzip + opener = lambda p: gzip.open(p, "rt") + else: + opener = open + with opener(path) as fh: + header = fh.readline().rstrip("\n").split("\t") + if header[:len(TSV_COLUMNS)] != TSV_COLUMNS: + raise SystemExit( + "Unexpected polyA_prediction.tsv header. Expected %s, got %s" + % (TSV_COLUMNS, header)) + for line in fh: + if not line.strip(): + continue + v = line.rstrip("\n").split("\t") + rows.append({ + "chromosome": v[0], + "transcript_id": v[1], + "gene_id": v[2], + "prediction": int(v[3]), + "counts": int(v[4]), + "flag": v[5], + }) + return rows + + +def _prf(tp: int, n_pred: int, n_truth: int) -> tuple: + precision = 100.0 * tp / n_pred if n_pred else 0.0 + recall = 100.0 * tp / n_truth if n_truth else 0.0 + f1 = (2.0 * precision * recall / (precision + recall) + if (precision + recall) > 0 else 0.0) + return precision, recall, f1 + + +def compute_metrics(predictions: list, truth: dict, tolerance: int, + expressed: Optional[set] = None) -> dict: + """Site-level evaluation. Each truth polyA site is an independent target. + + Precision: fraction of predictions that match some truth site. + Recall: fraction of truth sites matched by some prediction. + Known/novel are tracked as subsets of truth sites. + """ + if expressed is not None: + truth = {tid: entries for tid, entries in truth.items() + if tid in expressed} + predictions = [p for p in predictions if p["transcript_id"] in expressed] + logger.info("After expression filter: %d truth transcripts, " + "%d predictions", len(truth), len(predictions)) + + # Group predictions by transcript. + preds_by_tid: dict = defaultdict(list) + for p in predictions: + preds_by_tid[p["transcript_id"]].append(p) + + # Count truth sites. + n_sites_known = n_sites_novel = 0 + for entries in truth.values(): + for _, _, novel in entries: + if novel: + n_sites_novel += 1 + else: + n_sites_known += 1 + n_sites = n_sites_known + n_sites_novel + + # Per-prediction: does it match any truth site? + # The closest truth site determines known/novel category. + tp_pred = fp_pred = unmatched = 0 + tp_pred_known = tp_pred_novel = 0 + fp_pred_known = fp_pred_novel = 0 + abs_distances: list = [] + for p in predictions: + tid = p["transcript_id"] + if tid not in truth: + unmatched += 1 + continue + entries = truth[tid] + best_d: Optional[int] = None + best_novel: bool = False + for chr_id, pos, novel in entries: + if chr_id != p["chromosome"]: + continue + d = abs(p["prediction"] - pos) + if best_d is None or d < best_d: + best_d = d + best_novel = novel + if best_d is None: + unmatched += 1 + elif best_d <= tolerance: + tp_pred += 1 + abs_distances.append(best_d) + if best_novel: + tp_pred_novel += 1 + else: + tp_pred_known += 1 + else: + fp_pred += 1 + if best_novel: + fp_pred_novel += 1 + else: + fp_pred_known += 1 + + # Per-site: is the truth site matched by any prediction? + recovered_known = 0 + recovered_novel = 0 + for tid, entries in truth.items(): + tid_preds = preds_by_tid.get(tid, []) + for chr_id, pos, novel in entries: + matched = any( + p["chromosome"] == chr_id + and abs(p["prediction"] - pos) <= tolerance + for p in tid_preds) + if matched: + if novel: + recovered_novel += 1 + else: + recovered_known += 1 + recovered = recovered_known + recovered_novel + + n_predictions = len(predictions) + precision = 100.0 * tp_pred / n_predictions if n_predictions else 0.0 + recall = 100.0 * recovered / n_sites if n_sites else 0.0 + f1 = (2.0 * precision * recall / (precision + recall) + if (precision + recall) > 0 else 0.0) + mean_abs_distance = (sum(abs_distances) / len(abs_distances) + if abs_distances else 0.0) + + n_pred_known = tp_pred_known + fp_pred_known + n_pred_novel = tp_pred_novel + fp_pred_novel + precision_known = 100.0 * tp_pred_known / n_pred_known if n_pred_known else 0.0 + recall_known = 100.0 * recovered_known / n_sites_known if n_sites_known else 0.0 + f1_known = (2.0 * precision_known * recall_known / (precision_known + recall_known) + if (precision_known + recall_known) > 0 else 0.0) + precision_novel = 100.0 * tp_pred_novel / n_pred_novel if n_pred_novel else 0.0 + recall_novel = 100.0 * recovered_novel / n_sites_novel if n_sites_novel else 0.0 + f1_novel = (2.0 * precision_novel * recall_novel / (precision_novel + recall_novel) + if (precision_novel + recall_novel) > 0 else 0.0) + + return { + "precision": precision, + "recall": recall, + "f1": f1, + "mean_abs_distance": mean_abs_distance, + "n_predictions": n_predictions, + "n_truth_sites": n_sites, + "unmatched_predictions": unmatched, + "tp": tp_pred, + "fp": fp_pred, + "precision_known": precision_known, + "recall_known": recall_known, + "f1_known": f1_known, + "n_truth_known": n_sites_known, + "precision_novel": precision_novel, + "recall_novel": recall_novel, + "f1_novel": f1_novel, + "n_truth_novel": n_sites_novel, + } + + +REPORT_KEYS = [ + "precision", "recall", "f1", "mean_abs_distance", + "n_predictions", "n_truth_sites", + "unmatched_predictions", "tp", "fp", + "precision_known", "recall_known", "f1_known", "n_truth_known", + "precision_novel", "recall_novel", "f1_novel", "n_truth_novel", +] + + +def write_report(metrics: dict, output_path: str) -> None: + with open(output_path, "w") as fh: + for k in REPORT_KEYS: + v = metrics[k] + if isinstance(v, float): + fh.write("%s\t%.4f\n" % (k, v)) + else: + fh.write("%s\t%d\n" % (k, v)) + + +def main() -> int: + logging.basicConfig(level=logging.INFO, + format="%(asctime)s %(levelname)s %(message)s") + args = parse_args() + if not os.path.exists(args.prediction): + logger.error("Prediction file not found: %s", args.prediction) + return 2 + if not os.path.exists(args.reference_gtf): + logger.error("Reference GTF/DB not found: %s", args.reference_gtf) + return 2 + + db = get_or_build_db(args.reference_gtf) + truth = load_truth(db) + predictions = read_predictions(args.prediction) + expressed = None + if args.transcript_counts: + if not os.path.exists(args.transcript_counts): + logger.error("Transcript counts file not found: %s", + args.transcript_counts) + return 2 + expressed = load_expressed_transcripts(args.transcript_counts) + metrics = compute_metrics(predictions, truth, args.tolerance, expressed) + write_report(metrics, args.output) + + logger.info("precision=%.4f recall=%.4f f1=%.4f n_pred=%d truth_sites=%d " + "(known=%d novel=%d) recall_known=%.4f recall_novel=%.4f", + metrics["precision"], metrics["recall"], metrics["f1"], + metrics["n_predictions"], metrics["n_truth_sites"], + metrics["n_truth_known"], metrics["n_truth_novel"], + metrics["recall_known"], metrics["recall_novel"]) + logger.info("Report written to %s", args.output) + return 0 + + +if __name__ == "__main__": + try: + sys.exit(main()) + except Exception: + print_exc() + sys.exit(1) diff --git a/misc/train_polya_tss_model.py b/misc/train_polya_tss_model.py new file mode 100644 index 00000000..300b1a04 --- /dev/null +++ b/misc/train_polya_tss_model.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python +############################################################################ +# Copyright (c) 2022-2026 University of Helsinki +# # All Rights Reserved +# See file LICENSE for details. +############################################################################ + +"""Train the XGBoost peak-filter used by ``isoquant_lib/terminal_counter.py``. + +Inputs a per-peak feature CSV (the same format IsoQuant emits when training +data collection is enabled via the hidden ``--collect_polya_training`` / +``--collect_tss_training`` flags), trains an XGBoost classifier with the +hyperparameters used for the shipped model, and writes the JSON model file. + +Typical use:: + + # 1. Collect features from a real run (hidden dev-only flag): + python isoquant.py --genedb ANNOTATION.gtf --bam READS.bam --data_type nanopore \\ + -o train_run --prefix run --collect_polya_training peaks.csv + + # 2. Train a fresh model: + python misc/train_polya_tss_model.py \\ + --features peaks.csv \\ + --output isoquant_lib/data/model_polya.json + +The features CSV must contain at least the columns referenced by +``FEATURE_COLUMNS`` plus a binary ``true_peak`` label column and a +``chromosome`` column for train/test splitting. +""" + +import argparse +import logging +import random +import sys +from pathlib import Path + +import pandas as pd +from sklearn.metrics import f1_score, roc_auc_score +from xgboost import XGBClassifier + +# Importable so we always train on the same feature set the runtime expects. +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) +from isoquant_lib.terminal_counter import FEATURE_COLUMNS # noqa: E402 + +logger = logging.getLogger("train_polya_tss_model") + +# Hyperparameters used for the shipped polyA / TSS models. +DEFAULT_PARAMS = { + 'booster': 'dart', + 'learning_rate': 0.25989024187450366, + 'gamma': 4.087502269121285, + 'max_depth': 6, + 'min_child_weight': 5.722951381412213, + 'reg_lambda': 4.84758559371017, + 'reg_alpha': 0.03795296107941127, + 'subsample': 0.8809954546952385, + 'colsample_bytree': 0.7483035972922923, + 'n_estimators': 111, + 'scale_pos_weight': 0.297061977156673, +} + + +def parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + p.add_argument("--features", required=True, type=Path, + help="CSV with per-peak features and 'true_peak' label.") + p.add_argument("--output", required=True, type=Path, + help="Path to write the trained XGBoost JSON model.") + p.add_argument("--seed", type=int, default=42, + help="Random seed for the chromosome-level train/test split.") + p.add_argument("--test-fraction", type=float, default=0.5, + help="Fraction of chromosomes to use for the test split.") + return p.parse_args() + + +def main() -> None: + logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") + args = parse_args() + + df = pd.read_csv(args.features) + if 'true_peak' not in df.columns: + raise SystemExit("features CSV is missing required 'true_peak' label column") + missing = [c for c in FEATURE_COLUMNS if c not in df.columns] + if missing: + raise SystemExit(f"features CSV is missing feature columns: {missing}") + + chrs = sorted(df['chromosome'].dropna().unique().tolist()) + if len(chrs) < 2: + raise SystemExit("need at least two distinct chromosomes for a train/test split") + rng = random.Random(args.seed) + n_test = max(1, int(round(len(chrs) * args.test_fraction))) + test_chrs = set(rng.sample(chrs, n_test)) + train_chrs = [c for c in chrs if c not in test_chrs] + + X = df[FEATURE_COLUMNS].astype(float) + y = df['true_peak'].astype(int) + train_mask = df['chromosome'].isin(train_chrs) + test_mask = df['chromosome'].isin(test_chrs) + + model = XGBClassifier(**DEFAULT_PARAMS) + model.fit(X[train_mask], y[train_mask]) + + y_pred = model.predict(X[test_mask]) + y_score = model.predict_proba(X[test_mask])[:, 1] + logger.info("test F1: %.4f", f1_score(y[test_mask], y_pred)) + try: + logger.info("test ROC-AUC: %.4f", roc_auc_score(y[test_mask], y_score)) + except ValueError as e: + logger.warning("ROC-AUC undefined: %s", e) + + args.output.parent.mkdir(parents=True, exist_ok=True) + model.save_model(str(args.output)) + logger.info("wrote model to %s", args.output) + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml index 1cb5fb56..46d4c18f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,6 +34,8 @@ dependencies = [ "editdistance>=0.8.1", "biopython>=1.76", "numba>=0.58", + "xgboost>=2.1.4", + "scikit-learn>=1.3.2", ] [project.optional-dependencies] @@ -62,4 +64,4 @@ py-modules = ["isoquant", "isoquant_detect_barcodes", "isoquant_visualize"] version = {file = "VERSION"} [tool.setuptools.package-data] -isoquant_lib = ["test_data/*"] +isoquant_lib = ["test_data/*", "data/*.json"] diff --git a/requirements.txt b/requirements.txt index 49010324..040aa34c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,3 +11,5 @@ seaborn>=0.10.0 editdistance>=0.8.1 biopython>=1.76 numba>=0.58 +xgboost>=2.1.4 +scikit-learn>=1.3.2 diff --git a/requirements_tests.txt b/requirements_tests.txt index 6344a661..ea56195e 100644 --- a/requirements_tests.txt +++ b/requirements_tests.txt @@ -18,5 +18,7 @@ biopython>=1.76 numpy>=1.24 scipy>=1.10 numba>=0.58 +xgboost>=2.1.4 +scikit-learn>=1.3.2 coverage>=7.6