Adding Directionality

CONTRIBUTING.md

@@ -188,6 +188,15 @@ Follow the example for any of the other pathway reconstruction algorithm.
 First pull the image `<username>/local-neighborhood` from Docker Hub.
 Then build the Docker image using the `Dockerfile` that was completed in Step 2.
 
+Modify Generate Inputs:
+1. Include a key-value pair in the algo_exp_file dictionary that links the specific algorithm to its expected network file.
+2. Create or obtain the expected network file and save it to `/test/GenerateInputs/expected`. Name it as: `{algorithm name}-{network file name}-expected.txt`.
+
+Modify Parse Outputs:
+1. Obtain the raw-pathway output (ex. from the run function in your wrapper by running the Snakemake workflow) and save it to `/test/ParseOutputs/input`. Name it as  `{algorithm name}-raw-pathway.txt`.
+2. Create or obtain the expected universal output and save it to `test/ParseOutputs/expected` directory. Name it as: `{algorithm name}-pathway-expected.txt`.
+3. Add the new algorithm's name to the algorithms list in `test/ParseOutputs/test_parse_outputs.py`.
+
 ### Step 6: Work with SPRAS maintainers to revise the pull request
 Step 0 previously described how to create a `local-neighborhood` branch and create a pull request.
 Make sure to commit all of the new and modified files and push them to the `local-neighborhood` branch on your fork.
@@ -205,7 +214,7 @@ The pull request will be closed so that the `master` branch of the fork stays sy
 1. Import the new class in `src/runner.py` so the wrapper functions can be accessed
 1. Document the usage of the Docker wrapper and the assumptions made when implementing the wrapper
 1. Add example usage for the new algorithm and its parameters to the template config file
-1. Write test functions and provide example input data in a new test subdirectory `test/<algorithm>`
+1. Write test functions and provide example input data in a new test subdirectory `test/<algorithm>`. Provide example data and algorithm/expected files names to lists or dicts in `test/GenerateInputs` and `test/ParseOutputs`.
 1. Extend `.github/workflows/test-spras.yml` to pull and build the new Docker image
 
 When adding new algorithms, there are many other considerations that are not relevant with the simple Local Neighborhood example.

Snakefile

@@ -60,7 +60,8 @@ def make_final_input(wildcards):
     #TODO analysis could be parsed in the parse_config() function.
     if config["analysis"]["summary"]["include"]:
         # add summary output file for each pathway
-        final_input.extend(expand('{out_dir}{sep}{dataset}-{algorithm_params}{sep}summary.txt',out_dir=out_dir,sep=SEP,dataset=dataset_labels,algorithm_params=algorithms_with_params))
+        #TODO: reuse in the future once we make summary work for mixed graphs. See https://github.com/Reed-CompBio/spras/issues/128
+        # final_input.extend(expand('{out_dir}{sep}{dataset}-{algorithm_params}{sep}summary.txt',out_dir=out_dir,sep=SEP,dataset=dataset_labels,algorithm_params=algorithms_with_params))
         # add table summarizing all pathways for each dataset
         final_input.extend(expand('{out_dir}{sep}{dataset}-pathway-summary.txt',out_dir=out_dir,sep=SEP,dataset=dataset_labels))
 
@@ -219,14 +220,15 @@ rule parse_output:
     run:
         runner.parse_output(wildcards.algorithm, input.raw_file, output.standardized_file)
 
+#TODO: reuse in the future once we make summary work for mixed graphs. See https://github.com/Reed-CompBio/spras/issues/128
 # Collect summary statistics for a single pathway
-rule summarize_pathway:
-    input:
-        standardized_file = SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'pathway.txt'])
-    output:
-        summary_file = SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'summary.txt'])
-    run:
-        summary.run(input.standardized_file,output.summary_file,directed=algorithm_directed[wildcards.algorithm])
+# rule summarize_pathway:
+#     input:
+#         standardized_file = SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'pathway.txt'])
+#     output:
+#         summary_file = SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'summary.txt'])
+#     run:
+#         summary.run(input.standardized_file,output.summary_file)
 
 # Write GraphSpace JSON graphs
 rule viz_graphspace:
@@ -235,7 +237,7 @@ rule viz_graphspace:
         graph_json = SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'gs.json']),
         style_json = SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'gsstyle.json'])
     run:
-        graphspace.write_json(input.standardized_file,output.graph_json,output.style_json,directed=algorithm_directed[wildcards.algorithm])
+        graphspace.write_json(input.standardized_file,output.graph_json,output.style_json)
 
 
 # Write a Cytoscape session file with all pathways for each dataset

config/config.yaml

@@ -29,14 +29,12 @@
         - name: "pathlinker"
           params:
                 include: true
-                directed: true
                 run1:
                     k: range(100,201,100)
 
         - name: "omicsintegrator1"
           params:
                 include: true
-                directed: false
                 run1:
                     r: [5]
                     b: [5, 6]
@@ -47,7 +45,6 @@
         - name: "omicsintegrator2"
           params:
                 include: true
-                directed: false
                 run1:
                     b: [4]
                     g: [0]
@@ -58,7 +55,6 @@
         - name: "meo"
           params:
                 include: true
-                directed: true
                 run1:
                     max_path_length: [3]
                     local_search: ["Yes"]
@@ -67,20 +63,17 @@
         - name: "mincostflow"
           params:
                 include: true
-                directed: false
                 run1:
                     flow: [1] # The flow must be an int
                     capacity: [1]
 
         - name: "allpairs"
           params:
                 include: true
-                directed: false
 
         - name: "domino"
           params:
                 include: true
-                directed: false
                 run1:
                     slice_threshold: [0.3]
                     module_threshold: [0.05]

docker-wrappers/Cytoscape/cytoscape_util.py

@@ -115,7 +115,7 @@ def load_pathways(pathways: List[str], output: str) -> None:
         path, name = parse_name(pathway)
         suid = p4c.networks.import_network_from_tabular_file(
             file=path,
-            column_type_list='s,t,x',
+            column_type_list='s,t,x,ea',
             delimiters='\t'
         )
         p4c.networks.rename_network(name, network=suid)

input/README.md

@@ -46,7 +46,7 @@ The following files are very small toy datasets used to illustrate the supported
 This dataset represents protein phosphorylation changes in response to epidermal growth factor (EGF) treatment.
 The network includes protein-protein interactions from [iRefIndex](http://irefindex.org/) and kinase-substrate interactions from [PhosphoSitePlus](http://www.phosphosite.org/).
 The files are originally from the [Temporal Pathway Synthesizer (TPS)](https://github.com/koksal/tps) repository.
-They have been lightly modified for SPRAS by lowering one edge weight that was greater than 1, removing a PSEUDONODE prize, adding a prize of 10.0 to EGF_HUMAN, and converting all edges to undirected edges.
+They have been lightly modified for SPRAS by lowering one edge weight that was greater than 1, removing a PSEUDONODE prize, and adding a prize of 10.0 to EGF_HUMAN.
 The only source is EGF_HUMAN.
 All proteins with phosphorylation-based prizes are also labeled as targets.
 All nodes are considered active.

input/alternative-network.txt

@@ -1,9 +1,9 @@
-A	B	0.98
-B	C	0.77
-A	D	0.12
-C	D	0.89
-C	E	0.59
-C	F	0.50
-F	G	0.76
-G	H	0.92
-G	I	0.66
+A	B	0.98	U
+B	C	0.77	U
+A	D	0.12	U
+C	D	0.89	U
+C	E	0.59	U
+C	F	0.50	U
+F	G	0.76	U
+G	H	0.92	U
+G	I	0.66	U

input/network.txt

@@ -1,2 +1,2 @@
-A	B	0.98
-B	C	0.77
+A	B	0.98	U
+B	C	0.77	U