docs: alg usage & impl details

docs/prms/allpairs.rst

@@ -8,3 +8,16 @@ with respect to a cartesian product.
 
 All Pairs does not take any arguments. Its source code is at https://github.com/Reed-CompBio/all-pairs-shortest-paths
 licensed under MIT.
+
+Dataset Usage
+-------------
+
+All Pairs Shortest Paths uses ``sources``, ``targets``, and edge weights.
+All Pairs Shortest Paths also considers graph directionality.
+
+Implementation Details
+----------------------
+
+When All Pairs Shortest Paths gets passed a mixed graph, it considers the entire graph to be
+directed, by converting all undirected edges to two directed edges pointing
+opposite of one another.

docs/prms/bowtiebuilder.rst

@@ -9,3 +9,17 @@ BowTieBuilder does not take in any arguments.
 
 * Repository: https://github.com/Reed-CompBio/BowTieBuilder-Algorithm
 * Paper: https://doi.org/10.1186/1752-0509-3-67
+
+Dataset Usage
+-------------
+
+BowTieBuilder uses ``sources``, ``targets``, and edge weights. Input graph
+directionality is considered
+
+Implementation Details
+----------------------
+
+BowTieBuilder's internal algorithm only takes in directed graphs.
+Any pathway inputted into BowTieBuilder gets converted into a directed graph,
+where undirected edges get converted into two directed edges pointing opposite of one
+another.

docs/prms/domino.rst

@@ -8,3 +8,15 @@ DOMINO has two optional parameters:
 
 * slice_threshold: the p-value threshold for considering a slice as relevant
 * module_threshold: the p-value threshold for considering a putative module as final module
+
+Dataset Usage
+-------------
+
+DOMINO requires the `active` column to be set. DOMINO does not consider edge weights,
+but DOMINO does consider graph directionality.
+
+Implementation Details
+----------------------
+
+If the input dataframe is empty or too 'small' (where no modules are found),
+SPRAS will instead emit an empty output file.

docs/prms/meo.rst

@@ -12,3 +12,9 @@ MEO takes in three optional parameters:
 * local_search: a "Yes"/"No" parameter that enables MEO's local search functionality. See "Improving approximations with local search" in
   the associated paper for more information. This should almost always be yes.
 * rand_restarts: the number (int) of random restarts to use.
+
+Dataset Usage
+-------------
+
+MEO uses ``sources``, ``targets``, and edge weights. MEO also considers
+input graph directionality.

docs/prms/mincostflow.rst

@@ -11,6 +11,19 @@ MinCostFlow takes two optional parameters:
 * flow: (int) the amount of flow going through the graph
 * capacity: the (float) max capacity for edges
 
+Dataset Usage
+-------------
+
+MinCostFlow uses the input's ``sources``, ``targets``, and edge weights. MinCostFlow also
+considers directionality.
+
+Implementation Details
+----------------------
+
+MinCostFlow converts all undirected edges into directed edges
+where undirected edges get converted into two directed edges
+pointing opposite of one another.
+
 External links
 ++++++++++++++
 

docs/prms/oi1.rst

@@ -24,3 +24,11 @@ OI1 takes some optional arguments:
   * ``all``: connect the dummy node to all nodes in the interactome (i.e. full set of nodes in graph)
   * ``others``: connect the dummy node to all nodes that are not terminal nodes (i.e. nodes without prizes)
   * ``file``: connect the dummy node to a specific list of nodes provided in a file
+
+Dataset Usage
+-------------
+
+OmicsIntegrator1 prefers ``prize``s, but will take the union of ``sources`` and ``targets``
+and set their 'prize' to 1 if ``prize`` is not specified.
+
+OmicsIntegrator1 considers graph directionality.

docs/prms/oi2.rst

@@ -18,3 +18,12 @@ OI2 takes a few optional arguments:
     * "others" = connect to all nodes except for terminals
     * "all" = connect to all nodes in the interactome.
 * seed: The random seed to use for this run.
+
+
+Dataset Usage
+-------------
+
+OmicsIntegrator2 prefers ``prize``s, but will take the union of ``sources`` and ``targets``
+and set their 'prize' to 1 if ``prize`` is not specified.
+
+OmicsIntegrator2 does not consider graph directionality.

docs/prms/pathlinker.rst

@@ -10,6 +10,21 @@ PathLinker takes one optional argument:
 
 * k: The number of paths to find (*k* shortest paths).
 
+Dataset Usage
+-------------
+
+PathLinker uses ``sources``, ``targets``, and edge weights. PathLinker
+considers graph directionality.
+
+Implementation Details
+----------------------
+
+PathLinker's internal algorithm only takes in directed graphs.
+Any pathway inputted into PathLinker gets converted into a directed graph,
+where undirected edges get converted into two directed edges pointing opposite of one
+another.
+
+
 External links
 ++++++++++++++
 

docs/prms/prms.rst

@@ -6,6 +6,12 @@ Pathway Reconstruction Methods
    there are other sub-types of algorithms that have more specialized capabilities than the rest.
    Currently, SPRAS also supports edge orientation algorithms (e.g. MEO) and active module identifiers/disease module mining methods (e.g. DOMINO).
 
+This is the list of SPRAS's supported pathway reconstruction methods. Each subpage comes with a description of the algorithm,
+its source code and associated paper (if one exists), and its 'dataset usage,' or parts of a dataset that it will utilize when
+running pathway reconstruction. Implementation details are also provided, for users wondering about any
+important decisions that differentriate the SPRAS-wrapped version from the actual
+algorithm.
+
 .. toctree::
    :maxdepth: 1
    :caption: All Pairs

docs/prms/responsenet.rst

@@ -9,3 +9,17 @@ https://github.com/Reed-CompBio/ResponseNet.
 ResponseNet takes one optional parameter:
 
 * gamma: (int) controls the size of the output graph: more gamma means more 'flow' gets passed along starting from the sources.
+
+Dataset Usage
+-------------
+
+ResponseNet uses ``sources``, ``targets``, and edge weights. ResponseNet
+considers graph directionality.
+
+Implementation Details
+----------------------
+
+ResponseNet's internal algorithm only takes in directed graphs.
+Any pathway inputted into ResponseNet gets converted into a directed graph,
+where undirected edges get converted into two directed edges pointing opposite of one
+another.

docs/prms/rwr.rst

@@ -1,5 +1,5 @@
 RWR
-==========
+===
 
 RWR, or random walk with restarts, is a source and target independent pathway reconstruction algorithm
 that performs PathRank on the input interactome, using the provided prizes.
@@ -11,3 +11,16 @@ RWR takes in two parameters:
 * alpha: The damping factor of the internal PathRank algorithm. This is the probability that RWR randomly chooses a neighbor instead of restarting.
 
 RWR is implemented at https://github.com/reed-compbio/rwr.
+
+Dataset Usage
+-------------
+
+RWR considers the union of ``sources`` and ``targets`` as the
+input active nodes.
+
+
+Implementation Details
+----------------------
+
+RWR returns a ranked list of nodes: SPRAS returns the induced subgraph
+from the number of nodes corresponding to the user-specified ``threshold``.

docs/prms/strwr.rst

@@ -1,7 +1,7 @@
 ST_RWR
-==========
+======
 
-ST_RWR, or random walk with restarts, is a source and target dependent pathway reconstruction algorithm
+ST_RWR, or source-target random walk with restarts, is a source and target dependent pathway reconstruction algorithm
 that performs PathRank on the input interactome, using its edge weights, prizes, sources, and targets.
 
 For a random walk with restarts implementation that does not use  sources and targets, see RWR.
@@ -10,3 +10,14 @@ For a random walk with restarts implementation that does not use  sources and ta
 * alpha: The damping factor of the internal PathRank algorithm. This is the probability that RWR randomly chooses a neighbor instead of restarting.
 
 ST_RWR is implemented at https://github.com/reed-compbio/rwr.
+
+Dataset Usage
+-------------
+
+ST_RWR considers ``sources`` and ``targets``.
+
+Implementation Details
+----------------------
+
+ST_RWR returns a ranked list of nodes: SPRAS returns the induced subgraph
+from the number of nodes corresponding to the user-specified ``threshold``.