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feat: enable file output reporting #434

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d022c28
feat: enable file output reporting
tristan-f-r Nov 6, 2025
5362957
ci: enable reports at the end
tristan-f-r Nov 6, 2025
8105af9
ci: fix report handling
tristan-f-r Nov 6, 2025
7233826
feat: add analysis to report
tristan-f-r Nov 7, 2025
6945267
ci: use correct shell
tristan-f-r Nov 7, 2025
dfbf718
ci: mv to report.zip
tristan-f-r Nov 7, 2025
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3 changes: 3 additions & 0 deletions .github/workflows/test-spras.yml
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Expand Up @@ -94,6 +94,9 @@ jobs:
# race conditions from #268 and #279
# We also enforce strict DAG evaluation to catch DAG problems before they appear as user errors. (#359)
run: snakemake --cores 4 --configfile config/config.yaml --show-failed-logs --strict-dag-evaluation cyclic-graph --strict-dag-evaluation functions --strict-dag-evaluation periodic-wildcards
- name: Collect Snakemake workflow report
shell: bash --login {0}
run: snakemake --configfile config/config.yaml --report report.zip

# Run pre-commit checks on source files
pre-commit:
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2 changes: 2 additions & 0 deletions .gitignore
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Expand Up @@ -134,6 +134,8 @@ dmypy.json

# Snakemake
.snakemake/
report.html
report.zip

# Output files
output/
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82 changes: 46 additions & 36 deletions Snakefile
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Expand Up @@ -290,7 +290,12 @@ rule parse_output:
input:
raw_file = SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'raw-pathway.txt']),
dataset_file = SEP.join([out_dir, 'dataset-{dataset}-merged.pickle'])
output: standardized_file = SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'pathway.txt'])
output:
standardized_file = report(
SEP.join([out_dir, '{dataset}-{algorithm}-{params}', 'pathway.txt']),
category="dataset-{dataset}",
subcategory="Reconstructed Output"
)
run:
params = reconstruction_params(wildcards.algorithm, wildcards.params).copy()
params['dataset'] = input.dataset_file
Expand All @@ -311,7 +316,11 @@ rule parse_output:
rule viz_cytoscape:
input: pathways = expand('{out_dir}{sep}{{dataset}}-{algorithm_params}{sep}pathway.txt', out_dir=out_dir, sep=SEP, algorithm_params=algorithms_with_params)
output:
session = SEP.join([out_dir, '{dataset}-cytoscape.cys'])
session = report(
SEP.join([out_dir, '{dataset}-cytoscape.cys']),
category="dataset-{dataset}",
subcategory="Visualization"
)
run:
cytoscape.run_cytoscape(input.pathways, output.session, FRAMEWORK)

Expand All @@ -322,7 +331,8 @@ rule summary_table:
# Collect all pathways generated for the dataset
pathways = expand('{out_dir}{sep}{{dataset}}-{algorithm_params}{sep}pathway.txt', out_dir=out_dir, sep=SEP, algorithm_params=algorithms_with_params),
dataset_file = SEP.join([out_dir, 'dataset-{dataset}-merged.pickle'])
output: summary_table = SEP.join([out_dir, '{dataset}-pathway-summary.txt'])
output:
summary_table = report(SEP.join([out_dir, '{dataset}-pathway-summary.txt']), category="dataset-{dataset}", subcategory="Summary")
run:
# Load the node table from the pickled dataset file
node_table = Dataset.from_file(input.dataset_file).node_table
Expand All @@ -334,13 +344,13 @@ rule ml_analysis:
input:
pathways = expand('{out_dir}{sep}{{dataset}}-{algorithm_params}{sep}pathway.txt', out_dir=out_dir, sep=SEP, algorithm_params=algorithms_with_params)
output:
pca_image = SEP.join([out_dir, '{dataset}-ml', 'pca.png']),
pca_variance= SEP.join([out_dir, '{dataset}-ml', 'pca-variance.txt']),
pca_coordinates = SEP.join([out_dir, '{dataset}-ml', 'pca-coordinates.txt']),
hac_image_vertical = SEP.join([out_dir, '{dataset}-ml', 'hac-vertical.png']),
hac_clusters_vertical = SEP.join([out_dir, '{dataset}-ml', 'hac-clusters-vertical.txt']),
hac_image_horizontal = SEP.join([out_dir, '{dataset}-ml', 'hac-horizontal.png']),
hac_clusters_horizontal = SEP.join([out_dir, '{dataset}-ml', 'hac-clusters-horizontal.txt']),
pca_image = report(SEP.join([out_dir, '{dataset}-ml', 'pca.png']), category="dataset-{dataset}", subcategory="ML"),
pca_variance = report(SEP.join([out_dir, '{dataset}-ml', 'pca-variance.txt']), category="dataset-{dataset}", subcategory="ML"),
pca_coordinates = report(SEP.join([out_dir, '{dataset}-ml', 'pca-coordinates.txt']), category="dataset-{dataset}", subcategory="ML"),
hac_image_vertical = report(SEP.join([out_dir, '{dataset}-ml', 'hac-vertical.png']), category="dataset-{dataset}", subcategory="ML"),
hac_clusters_vertical = report(SEP.join([out_dir, '{dataset}-ml', 'hac-clusters-vertical.txt']), category="dataset-{dataset}", subcategory="ML"),
hac_image_horizontal = report(SEP.join([out_dir, '{dataset}-ml', 'hac-horizontal.png']), category="dataset-{dataset}", subcategory="ML"),
hac_clusters_horizontal = report(SEP.join([out_dir, '{dataset}-ml', 'hac-clusters-horizontal.txt']), category="dataset-{dataset}", subcategory="ML"),
run:
summary_df = ml.summarize_networks(input.pathways)
ml.hac_vertical(summary_df, output.hac_image_vertical, output.hac_clusters_vertical, **hac_params)
Expand All @@ -353,8 +363,8 @@ rule jaccard_similarity:
pathways = expand('{out_dir}{sep}{{dataset}}-{algorithm_params}{sep}pathway.txt',
out_dir=out_dir, sep=SEP, algorithm_params=algorithms_with_params)
output:
jaccard_similarity_matrix = SEP.join([out_dir, '{dataset}-ml', 'jaccard-matrix.txt']),
jaccard_similarity_heatmap = SEP.join([out_dir, '{dataset}-ml', 'jaccard-heatmap.png'])
jaccard_similarity_matrix = report(SEP.join([out_dir, '{dataset}-ml', 'jaccard-matrix.txt']), category="dataset-{dataset}", subcategory="ML"),
jaccard_similarity_heatmap = report(SEP.join([out_dir, '{dataset}-ml', 'jaccard-heatmap.png']), category="dataset-{dataset}", subcategory="ML"),
run:
summary_df = ml.summarize_networks(input.pathways)
ml.jaccard_similarity_eval(summary_df, output.jaccard_similarity_matrix, output.jaccard_similarity_heatmap)
Expand All @@ -365,7 +375,7 @@ rule ensemble:
input:
pathways = expand('{out_dir}{sep}{{dataset}}-{algorithm_params}{sep}pathway.txt', out_dir=out_dir, sep=SEP, algorithm_params=algorithms_with_params)
output:
ensemble_network_file = SEP.join([out_dir,'{dataset}-ml', 'ensemble-pathway.txt'])
ensemble_network_file = report(SEP.join([out_dir,'{dataset}-ml', 'ensemble-pathway.txt']), category="dataset-{dataset}", subcategory="ML"),
run:
summary_df = ml.summarize_networks(input.pathways)
ml.ensemble_network(summary_df, output.ensemble_network_file)
Expand All @@ -381,13 +391,13 @@ rule ml_analysis_aggregate_algo:
input:
pathways = collect_pathways_per_algo
output:
pca_image = SEP.join([out_dir, '{dataset}-ml', '{algorithm}-pca.png']),
pca_variance= SEP.join([out_dir, '{dataset}-ml', '{algorithm}-pca-variance.txt']),
pca_coordinates = SEP.join([out_dir, '{dataset}-ml', '{algorithm}-pca-coordinates.txt']),
hac_image_vertical = SEP.join([out_dir, '{dataset}-ml', '{algorithm}-hac-vertical.png']),
hac_clusters_vertical = SEP.join([out_dir, '{dataset}-ml', '{algorithm}-hac-clusters-vertical.txt']),
hac_image_horizontal = SEP.join([out_dir, '{dataset}-ml', '{algorithm}-hac-horizontal.png']),
hac_clusters_horizontal = SEP.join([out_dir, '{dataset}-ml', '{algorithm}-hac-clusters-horizontal.txt']),
pca_image = report(SEP.join([out_dir, '{dataset}-ml', '{algorithm}-pca.png']), category="dataset-{dataset}", subcategory="ML"),
pca_variance = report(SEP.join([out_dir, '{dataset}-ml', '{algorithm}-pca-variance.txt']), category="dataset-{dataset}", subcategory="ML"),
pca_coordinates = report(SEP.join([out_dir, '{dataset}-ml', '{algorithm}-pca-coordinates.txt']), category="dataset-{dataset}", subcategory="ML"),
hac_image_vertical = report(SEP.join([out_dir, '{dataset}-ml', '{algorithm}-hac-vertical.png']), category="dataset-{dataset}", subcategory="ML"),
hac_clusters_vertical = report(SEP.join([out_dir, '{dataset}-ml', '{algorithm}-hac-clusters-vertical.txt']), category="dataset-{dataset}", subcategory="ML"),
hac_image_horizontal = report(SEP.join([out_dir, '{dataset}-ml', '{algorithm}-hac-horizontal.png']), category="dataset-{dataset}", subcategory="ML"),
hac_clusters_horizontal = report(SEP.join([out_dir, '{dataset}-ml', '{algorithm}-hac-clusters-horizontal.txt']), category="dataset-{dataset}", subcategory="ML"),
run:
summary_df = ml.summarize_networks(input.pathways)
ml.hac_vertical(summary_df, output.hac_image_vertical, output.hac_clusters_vertical, **hac_params)
Expand All @@ -399,7 +409,7 @@ rule ensemble_per_algo:
input:
pathways = collect_pathways_per_algo
output:
ensemble_network_file = SEP.join([out_dir,'{dataset}-ml', '{algorithm}-ensemble-pathway.txt'])
ensemble_network_file = report(SEP.join([out_dir,'{dataset}-ml', '{algorithm}-ensemble-pathway.txt']), category="dataset-{dataset}", subcategory="ML"),
run:
summary_df = ml.summarize_networks(input.pathways)
ml.ensemble_network(summary_df, output.ensemble_network_file)
Expand All @@ -409,8 +419,8 @@ rule jaccard_similarity_per_algo:
input:
pathways = collect_pathways_per_algo
output:
jaccard_similarity_matrix = SEP.join([out_dir, '{dataset}-ml', '{algorithm}-jaccard-matrix.txt']),
jaccard_similarity_heatmap = SEP.join([out_dir, '{dataset}-ml', '{algorithm}-jaccard-heatmap.png'])
jaccard_similarity_matrix = report(SEP.join([out_dir, '{dataset}-ml', '{algorithm}-jaccard-matrix.txt']), category="dataset-{dataset}", subcategory="ML"),
jaccard_similarity_heatmap = report(SEP.join([out_dir, '{dataset}-ml', '{algorithm}-jaccard-heatmap.png']), category="dataset-{dataset}", subcategory="ML"),
run:
summary_df = ml.summarize_networks(input.pathways)
ml.jaccard_similarity_eval(summary_df, output.jaccard_similarity_matrix, output.jaccard_similarity_heatmap)
Expand Down Expand Up @@ -439,8 +449,8 @@ rule evaluation_pr_per_pathways:
node_gold_standard_file = get_gold_standard_pickle_file,
pathways = collect_pathways_per_dataset
output:
node_pr_file = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', "pr-per-pathway-nodes.txt"]),
node_pr_png = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-per-pathway-nodes.png']),
node_pr_file = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', "pr-per-pathway-nodes.txt"]), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
node_pr_png = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-per-pathway-nodes.png']), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
run:
node_table = Evaluation.from_file(input.node_gold_standard_file).node_table
pr_df = Evaluation.node_precision_and_recall(input.pathways, node_table)
Expand All @@ -458,8 +468,8 @@ rule evaluation_per_algo_pr_per_pathways:
node_gold_standard_file = get_gold_standard_pickle_file,
pathways = collect_pathways_per_algo_per_dataset,
output:
node_pr_file = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', "pr-per-pathway-for-{algorithm}-nodes.txt"]),
node_pr_png = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-per-pathway-for-{algorithm}-nodes.png']),
node_pr_file = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', "pr-per-pathway-for-{algorithm}-nodes.txt"]), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
node_pr_png = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-per-pathway-for-{algorithm}-nodes.png']), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
run:
node_table = Evaluation.from_file(input.node_gold_standard_file).node_table
pr_df = Evaluation.node_precision_and_recall(input.pathways, node_table)
Expand All @@ -484,8 +494,8 @@ rule evaluation_pca_chosen:
pca_coordinates_file = collect_pca_coordinates_per_dataset,
pathway_summary_file = collect_summary_statistics_per_dataset
output:
node_pca_chosen_pr_file = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-pca-chosen-pathway-nodes.txt']),
node_pca_chosen_pr_png = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-pca-chosen-pathway-nodes.png']),
node_pca_chosen_pr_file = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-pca-chosen-pathway-nodes.txt']), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
node_pca_chosen_pr_png = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-pca-chosen-pathway-nodes.png']), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
run:
node_table = Evaluation.from_file(input.node_gold_standard_file).node_table
pca_chosen_pathway = Evaluation.pca_chosen_pathway(input.pca_coordinates_file, input.pathway_summary_file, out_dir)
Expand All @@ -505,8 +515,8 @@ rule evaluation_per_algo_pca_chosen:
pca_coordinates_file = collect_pca_coordinates_per_algo_per_dataset,
pathway_summary_file = collect_summary_statistics_per_dataset
output:
node_pca_chosen_pr_file = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-pca-chosen-pathway-per-algorithm-nodes.txt']),
node_pca_chosen_pr_png = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-pca-chosen-pathway-per-algorithm-nodes.png']),
node_pca_chosen_pr_file = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-pca-chosen-pathway-per-algorithm-nodes.txt']), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
node_pca_chosen_pr_png = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-pca-chosen-pathway-per-algorithm-nodes.png']), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
run:
node_table = Evaluation.from_file(input.node_gold_standard_file).node_table
pca_chosen_pathways = Evaluation.pca_chosen_pathway(input.pca_coordinates_file, input.pathway_summary_file, out_dir)
Expand All @@ -530,8 +540,8 @@ rule evaluation_ensemble_pr_curve:
dataset_file = get_dataset_pickle_file,
ensemble_file = collect_ensemble_per_dataset
output:
node_pr_curve_png = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-curve-ensemble-nodes.png']),
node_pr_curve_file = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-curve-ensemble-nodes.txt']),
node_pr_curve_png = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-curve-ensemble-nodes.png']), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
node_pr_curve_file = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-curve-ensemble-nodes.txt']), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
run:
node_table = Evaluation.from_file(input.node_gold_standard_file).node_table
node_ensemble_dict = Evaluation.edge_frequency_node_ensemble(node_table, input.ensemble_file, input.dataset_file)
Expand All @@ -549,8 +559,8 @@ rule evaluation_per_algo_ensemble_pr_curve:
dataset_file = get_dataset_pickle_file,
ensemble_files = collect_ensemble_per_algo_per_dataset
output:
node_pr_curve_png = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-curve-ensemble-nodes-per-algorithm-nodes.png']),
node_pr_curve_file = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-curve-ensemble-nodes-per-algorithm-nodes.txt']),
node_pr_curve_png = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-curve-ensemble-nodes-per-algorithm-nodes.png']), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
node_pr_curve_file = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'pr-curve-ensemble-nodes-per-algorithm-nodes.txt']), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
run:
node_table = Evaluation.from_file(input.node_gold_standard_file).node_table
node_ensembles_dict = Evaluation.edge_frequency_node_ensemble(node_table, input.ensemble_files, input.dataset_file)
Expand All @@ -560,7 +570,7 @@ rule evaluation_edge_dummy:
input:
edge_gold_standard_file = get_gold_standard_pickle_file,
output:
dummy_file = SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'dummy-edge.txt']),
dummy_file = report(SEP.join([out_dir, '{dataset_gold_standard_pair}-eval', 'dummy-edge.txt']), category="dgs-{dataset_gold_standard_pair}", subcategory="Evaluation"),
run:
mixed_edge_table = Evaluation.from_file(input.edge_gold_standard_file).mixed_edge_table
undirected_edge_table = Evaluation.from_file(input.edge_gold_standard_file).undirected_edge_table
Expand Down
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