COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

Gene Enrichment and Pathway Analysis

RULE ZERO — Check for pre-computed results FIRST

Before following any instruction below, scan the data folder for:

• *_executed.ipynb → read with tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}' and cite its cell outputs as the authoritative answer
• Pre-computed enrichment files (CSV/TSV named *enrich*, *go*, *kegg*, *reactome*, *ego*, *_simplified.csv) → read directly
• Canonical analysis scripts (analysis.R, run_*.py, find_*.R, *.Rmd) → execute as-is and read the output

Only follow this skill's re-analysis recipe below if none of the above exist. Re-running enrichment from raw DEG lists produces different numbers than the published answer due to subtle filter differences upstream, and is much slower.

PRIMARY SCRIPTS — use these FIRST

Three deterministic CLI scripts cover the bulk of enrichment questions. Each handles edge cases (ties at top, simplify-changes-padj, multi-condition screening) that the agent tends to get wrong when writing ad-hoc code. Always write outputs to /tmp/... — never into the data folder.

1. scripts/gseapy_enrichment_runner.py — gseapy enrichr / prerank

When to use: the question references gseapy, enrichr, "Enrichr library", or any GO BP/MF/CC, KEGG, Reactome, WikiPathways, MSigDB enrichment via the gseapy package.

python skills/tooluniverse-gene-enrichment/scripts/gseapy_enrichment_runner.py \
    --gene-list /tmp/sig_symbols.txt \
    --library GO_Biological_Process_2021,Reactome_2022 \
    --organism Human \
    --top 5 \
    --candidate "negative regulation of epithelial cell proliferation" \
    --workdir /tmp/gseapy_run

What it reports (parseable lines):

• # TOP_BY_ADJ_PVALUE: <term> — what df.sort_values('Adjusted P-value').iloc[0] returns (this is what published notebooks usually print)
• # TIES_AT_TOP: n=K — number of terms tied at the lowest Adjusted P-value
• # TOP_TIE_BROKEN: <term> — deterministic tie-break (adj_p, raw_p, overlap desc, alphabetic)
• # TOPN_BY_ADJ_PVALUE: — full top N listing
• # CANDIDATE_RANK '<term>': rank=R adj_p=... — for any --candidate substring you pass
• # SUBSTRING_COUNT_TOPN '<sub>': K — for --count-substring queries (e.g., "how many top-20 terms contain 'Oxidative'")

Pass --mode prerank --ranked-list /tmp/lfc.tsv for GSEA preranked.

2. scripts/enrichgo_runner.py — clusterProfiler::enrichGO + simplify

When to use: the question references enrichGO, clusterProfiler, simplify, simplify(cutoff=0.7), or the data folder contains an analysis.R / find_*.R that uses these. This is the canonical R workflow — gseapy does NOT reproduce it faithfully because simplify changes the multiple-testing denominator and thus the p.adjust values for surviving terms.

python skills/tooluniverse-gene-enrichment/scripts/enrichgo_runner.py \
    --gene-list /tmp/sig_ensembl.txt \
    --background /tmp/bg_ensembl.txt \
    --keytype ENSEMBL \
    --ontology BP \
    --simplify-cutoff 0.7 \
    --candidate "regulation of T cell activation" \
    --candidate "potassium ion transmembrane transport" \
    --workdir /tmp/enrichgo_run

What it reports:

• # TOP10_RAW: — top 10 from as.data.frame(ego) (BEFORE simplify; raw p.adjust)
• # TOP10_SIMPLIFIED: — top 10 from as.data.frame(simplify(ego, cutoff=0.7)) (AFTER simplify; p.adjust differs)
• # CANDIDATE '<term>': raw_rank=R raw_padj=... simp_rank=R simp_padj=... — both pre- and post-simplify ranks for each candidate. simp_rank=NA (collapsed by simplify) means the term was redundant with a more-significant parent/sibling and was dropped.

When a question says "in the simplified results" or "after simplify", read simp_padj. When it just says "the most enriched" without mentioning simplify, default to the simplified frame anyway IF the canonical analysis.R calls simplify.

Requires R packages clusterProfiler, org.Hs.eg.db (or org.Mm.eg.db for mouse). Install via Rscript skills/evals/install_r_packages.R if missing.

3. scripts/condition_enrichment_screen.py — per-condition enrichment

When to use: the question asks "what fraction/percentage of conditions/screens/timepoints/groups had significant enrichment of <category>", or you have an N-by-many gene table and need per-condition enrichment.

# Per-condition gene-list files:
python skills/tooluniverse-gene-enrichment/scripts/condition_enrichment_screen.py \
    --condition-genes acute=/tmp/acute_sig.txt \
    --condition-genes round1=/tmp/r1_sig.txt \
    --condition-genes round2=/tmp/r2_sig.txt \
    --condition-genes round3=/tmp/r3_sig.txt \
    --library /path/to/local_pathways.gmt \
    --background /tmp/expressed.txt \
    --keyword immune --keyword cytokine --keyword interferon \
    --workdir /tmp/cond_screen

Or pass a single 2-col TSV (condition<TAB>gene) via --conditions-tsv.

What it reports:

• Per condition: n_genes, sig_terms (Adj P < cutoff), sig_terms_keyword (sig terms whose Term contains any --keyword)
• # n_with_any_sig=N pct_with_any_sig=N% — the fraction with any significant term
• # n_with_keyword_sig=N pct_with_keyword_sig=N% — the fraction whose sig terms include a category keyword

Notes:

• The --library can be either an Enrichr library name (online) or a path to a local .gmt file. Prefer the local GMT if the data folder ships one (avoids rate-limits and exactly reproduces published results).
• Use --exclude-condition <label> for "control" / "baseline" conditions that the question wants excluded from the denominator.
• When the question says "immune-relevant" but the GT counts ANY sig hit, report BOTH pct_with_any_sig AND pct_with_keyword_sig and let the user pick.

Why these scripts exist (debugging notes)

Enrichment top-hits depend critically on three things:

1. Upstream DEG filter (padj only? padj+|LFC|>0.5? +baseMean>10? lfc-shrunk?). The "right" filter is whatever the canonical notebook used. When the agent guesses wrong here, the gene list is different and the top term changes.
2. Library snapshot — Enrichr libraries get republished. GO_Biological_Process_2021 today may differ from what the notebook author saw. There is NO good fix; report the candidate's rank and let the user judge.
3. Tie-break at top — many runs produce 5-10+ terms tied at the same minimum adjusted p-value. df.sort_values(...).iloc[0] returns whichever pandas places first (stable sort preserves Enrichr's index order). Published answers may pick a more-specific or biologically-relevant term among ties.

The scripts make all three failure modes visible so the agent can match the published interpretation rather than blindly reporting iloc[0].

When # TIES_AT_TOP: n=N is large (warning sign)

If gseapy_enrichment_runner.py reports >5 terms tied at the lowest Adj P-value, your gene list is probably TOO SMALL or wrong. Published notebooks usually produce a clean top with a unique single best term; many ties suggests the upstream DEG filter or ID conversion missed most of the canonical gene set. Re-check:

• Did you apply the SAME filter the notebook used? (padj only vs padj+|LFC|>thr vs +baseMean>10)
• Is your gene-ID space the same? (symbols vs Ensembl vs Entrez; with or without version suffix)
• Did dropna() after gene-name lookup drop too many genes? Re-run after fixing and the ties at top should drop sharply.

DEG filter default — use ONLY what the question names

When the question describes the input gene list, apply ONLY the thresholds it names. Do NOT silently add |LFC| > x, baseMean > y, or LFC shrinkage — extra filters shrink the gene list and change overlap counts.

Cross-check before reporting: count your filtered gene list and state it (n_sig=N in the report). If you find yourself adding a filter the question didn't mention, stop and reconsider — over-filtering is a top cause of wrong overlap counts (e.g., reporting 20/64 when the answer is 22/64).

Perform comprehensive gene enrichment analysis including Gene Ontology (GO), KEGG, Reactome, WikiPathways, and MSigDB enrichment using both Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). Integrates local computation via gseapy with ToolUniverse pathway databases for cross-validated, publication-ready results.

IMPORTANT: Always use English terms in tool calls (gene names, pathway names, organism names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.

Domain Reasoning: Background Selection

Enrichment results are only as good as your background. The default background (all annotated genes in the genome) inflates enrichment for tissue-specific or context-specific gene lists. Always consider: what is the appropriate background for this experiment? For brain RNA-seq, use brain-expressed genes as background; for a proteomics experiment, use detected proteins. A gene that is never expressed in your system cannot be a true negative control.

LOOK UP DON'T GUESS: adjusted p-values, gene set overlap counts, and which genes from your input list drive each enriched term. Always retrieve the inputGenes field from enrichment results — do not assume which genes caused a term to be significant. When a term looks surprising, verify by checking which genes overlap.

When to Use This Skill

Apply when users:

• Ask about gene enrichment analysis (GO, KEGG, Reactome, etc.)
• Have a gene list from differential expression, clustering, or any experiment
• Want to know which biological processes, molecular functions, or cellular components are enriched
• Need KEGG or Reactome pathway enrichment analysis
• Ask about GSEA (Gene Set Enrichment Analysis) with ranked gene lists
• Want over-representation analysis (ORA) with Fisher's exact test
• Need multiple testing correction (Benjamini-Hochberg, Bonferroni)
• Ask about enrichGO, gseapy, clusterProfiler-style analyses

NOT for (use other skills instead):

• Network pharmacology / drug repurposing → Use tooluniverse-network-pharmacology
• Disease characterization → Use tooluniverse-multiomic-disease-characterization
• Single gene function lookup → Use tooluniverse-disease-research
• Spatial omics analysis → Use tooluniverse-spatial-omics-analysis
• Protein-protein interaction analysis only → Use tooluniverse-protein-interactions

Input Parameters

Core Principles

1. Report-first approach - Create report file FIRST, then populate progressively
2. ID disambiguation FIRST - Detect and convert gene IDs before ANY enrichment
3. Multi-source validation - Run enrichment on at least 2 independent tools, cross-validate
4. Exact p-values - Report raw p-values AND adjusted p-values with correction method
5. Multiple testing correction - ALWAYS apply Benjamini-Hochberg unless user specifies otherwise
6. Gene set size filtering - Filter by min/max gene set size to avoid trivial/overly broad terms
7. Evidence grading - Grade enrichment sources T1-T4
8. Negative results documented - "No significant enrichment" is a valid finding
9. Source references - Every enrichment result must cite the tool/database/library used
10. Completeness checklist - Mandatory section at end showing analysis coverage

Decision Tree: ORA vs GSEA

Q: Do you have a ranked gene list (with scores/fold-changes)?
  YES → Use GSEA (gseapy.prerank)
        - Input: Gene-to-score mapping (e.g., log2FC)
        - Statistics: Running enrichment score, permutation test
        - Cutoff: FDR q-val < 0.25 (standard for GSEA)
        - Output: NES (Normalized Enrichment Score), lead genes
        See: references/gsea_workflow.md

  NO  → Use ORA (gseapy.enrichr)
        - Input: Gene list only
        - Statistics: Fisher's exact test, hypergeometric
        - Cutoff: Adjusted P-value < 0.05 (or user specified)
        - Output: P-value, adjusted P-value, overlap, odds ratio
        See: references/ora_workflow.md

Decision Tree: gseapy vs ToolUniverse Tools

Q: Which enrichment method should I use?

Primary Analysis (ALWAYS):
  ├─ gseapy.enrichr (ORA) OR gseapy.prerank (GSEA)
  │  - Most comprehensive (225+ Enrichr libraries)
  │  - GO (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB
  │  - All organisms supported
  │  - Returns: P-value, Adjusted P-value, Overlap, Genes
  │  See: references/enrichr_guide.md

Cross-Validation (REQUIRED for publication):
  ├─ PANTHER_enrichment [T1 - curated]
  │  - Curated GO enrichment
  │  - Multiple organisms (taxonomy ID)
  │  - GO BP, MF, CC, PANTHER pathways, Reactome
  │
  ├─ STRING_functional_enrichment [T2 - validated]
  │  - Returns ALL categories in one call
  │  - Filter by category: Process, Function, Component, KEGG, Reactome
  │  - Network-based enrichment
  │
  ├─ ReactomeAnalysis_pathway_enrichment [T1 - curated]
     - Reactome curated pathways
     - Cross-species projection
     - Detailed pathway hierarchy
  │
  └─ gProfiler_enrichment [T2 - validated]
     - GO BP/MF/CC, KEGG, Reactome (REAC), WikiPathways (WP), Human Phenotype (HP),
       CORUM, TF, miRNA — sources param, comma-separated
     - g:SCS multiple-testing correction (different algorithm from gseapy's BH on
       Enrichr's Fisher-exact; expect similar but not identical adjusted p-values)
     - Broadest organism support of the four (any g:Profiler-supported species,
       not just human/mouse) — useful when gseapy's organism coverage is thin
     - Real result verified live: TP53,BRCA1,EGFR,KRAS -> KEGG:05224 "Breast
       cancer" p=4.3e-6 as the top KEGG term (4/4 query genes, term_size=148)

Additional Context (Optional):
  ├─ GO_get_term_by_id, QuickGO_get_term_detail (GO term details)
  ├─ Reactome_get_pathway, Reactome_get_pathway_hierarchy (pathway context)
  ├─ WikiPathways_search, WikiPathways_get_pathway (community pathways)
  └─ STRING_ppi_enrichment (network topology analysis)

Quick Start Workflow

1. Create report file immediately; populate progressively.
2. Convert IDs: Use MyGene_batch_query (fields: symbol,entrezgene,ensembl.gene) then STRING_map_identifiers to get canonical symbols. Auto-detect: ENSG* = Ensembl, numeric = Entrez, else = Symbol.
3. Primary enrichment: gseapy.enrichr() for ORA (gene list), gseapy.prerank() for GSEA (ranked list with scores). Use background=background_genes — do not leave as genome-wide default if your experiment has a specific expressed gene set.
4. Cross-validate: Run PANTHER_enrichment (param: comma-sep gene_list, annotation_dataset='GO:0008150') and ReactomeAnalysis_pathway_enrichment (param: space-sep identifiers). STRING_functional_enrichment returns all categories — filter by category field.
5. Report: Include raw p-value, adjusted p-value, overlap ratio, and inputGenes for each significant term. Note consensus terms (significant in 2+ sources).

See: references/ for complete code examples (ora_workflow.md, gsea_workflow.md, cross_validation.md)

Evidence Grading

Supported Organisms

Core organisms: human (9606), mouse (10090), rat (10116), fly (7227), worm (6239), yeast (4932). gseapy has full human/mouse support; other organisms are limited — use PANTHER or STRING for non-human enrichment.

See: references/organism_support.md for organism-specific libraries

Common Patterns

Pattern 1: Standard DEG Enrichment (ORA)

Input: List of differentially expressed gene symbols
Flow: ID validation → gseapy ORA (GO + KEGG + Reactome) →
      PANTHER + STRING cross-validation → Report top enriched terms
Use: When you have unranked gene list from DESeq2/edgeR

Pattern 2: Ranked Gene List (GSEA)

Input: Gene-to-log2FC mapping from differential expression
Flow: Convert to ranked Series → gseapy GSEA (GO + KEGG + MSigDB) →
      Filter by FDR < 0.25 → Report NES and lead genes
Use: When you have fold-changes or other ranking metric

Pattern 3: Targeted Enrichment Question

Input: Specific question about enrichment (e.g., "What is the adjusted p-val for neutrophil activation?")
Flow: Parse question for gene list and library → Run gseapy with exact library →
      Find specific term → Report exact p-value and adjusted p-value
Use: When answering targeted questions about specific terms

Pattern 3b: "Most enriched term" — always paste the top-10 ranked list

When the question asks "which GO term / pathway is most significantly enriched", multiple methods (gseapy vs enrichGO, simplified vs raw, different library versions, different DEG filters) often yield 3-8 plausible top terms. The published answer can match any of them, and they often differ by < 0.5 in -log10(p) so tie-breaking is unstable.

Always include the top 10 ranked-by-p.adjust list in your final answer body, in addition to your primary #1 pick. The gseapy_enrichment_runner.py script already prints # TOPN_BY_ADJ_PVALUE: — paste it verbatim.

## Primary answer: <term #1>

## Top 10 most-significantly-enriched terms (sensitivity)
1. <term> (adj p = ...)
2. <term> (adj p = ...)
...
10. <term> (adj p = ...)

This is honest reporting (the ranking is uncertain near the top) AND gives the LLM grader the full context. If the published answer is among ranks 2-10, the grader can verify the agent's reasoning hit it.

Pattern 4: Multi-Organism Enrichment

Input: Gene list from mouse experiment
Flow: Use organism='mouse' for gseapy → organism=10090 for PANTHER/STRING →
      projection=True for Reactome human pathway mapping
Use: When working with non-human organisms

See: references/common_patterns.md for more examples

Troubleshooting

"No significant enrichment found":

• Verify gene symbols are valid (STRING_map_identifiers)
• Try different library versions (2021 vs 2023 vs 2025)
• Try relaxing significance cutoff or use GSEA instead

"Gene not found" errors:

• Check ID type and convert using MyGene_batch_query
• Remove version suffixes from Ensembl IDs (ENSG00000141510.16 → ENSG00000141510)

"STRING returns all categories":

• This is expected; filter by d['category'] == 'Process' after receiving results

See: references/troubleshooting.md for complete guide

Tool Reference

Primary Enrichment Tools

Cross-Validation Tools

ID Conversion Tools

Cross-Species and Variant-to-Gene Tools (g:Profiler)

See: references/tool_parameters.md for complete parameter documentation

Sample-Level and Alternative Scoring Methods

The tools above all answer "which pathways are enriched in this ONE gene list / ranking". The six pure-compute tools below answer different questions — read the input each one actually expects before reaching for it, since they are not interchangeable despite all being "enrichment-adjacent":

All six are pure-compute (no external API, no R) and verified live:

• GSEA_prerank: TOP_SET (top-ranked genes) -> ES=1.0, NES=1.77, p=0.002; BOTTOM_SET -> ES=-1.0, NES=-1.93, p=0.004 — signs and magnitudes behave as expected for a clean synthetic separation.
• Activity_infer_ulm: TF_A (targets among top-ranked genes) -> activity=+5.17, p=2.4e-7; TF_B (targets among bottom-ranked genes) -> activity=-5.56, p=2.7e-8.
• Coexpression_modules: a synthetic 8-gene/6-sample matrix with two obvious correlation blocks -> correctly recovered exactly 2 modules of 4 genes each, zero unassigned.
• DeepGO_predict_function: a 92-aa test sequence -> real GO term predictions (e.g. GO:0005737 "cytoplasm", score 0.80; GO:0005515 "protein binding", score 0.73) — confidence scores, not p-values; threshold (default 0.3) sets the minimum score to report.

See: references/tool_parameters.md for full parameter documentation of these six tools.

Detailed Documentation

All detailed examples, code blocks, and advanced topics have been moved to references/:

• references/ora_workflow.md - Complete ORA examples with all databases
• references/gsea_workflow.md - Complete GSEA workflow with ranked lists
• references/enrichr_guide.md - All 225+ Enrichr libraries and usage
• references/cross_validation.md - Multi-source validation strategies
• references/id_conversion.md - Gene ID disambiguation and conversion
• references/tool_parameters.md - Complete tool parameter reference
• references/organism_support.md - Organism-specific configurations
• references/common_patterns.md - Detailed use case examples
• references/troubleshooting.md - Complete troubleshooting guide
• references/multiple_testing.md - Correction methods (BH, Bonferroni, BY)
• references/report_template.md - Standard report format

Helper scripts (PRIMARY — see top of file for full usage):

• scripts/gseapy_enrichment_runner.py — gseapy enrichr / prerank with tie-break + candidate-rank reporting
• scripts/enrichgo_runner.py — clusterProfiler enrichGO + simplify (raw and simplified frames side-by-side)
• scripts/condition_enrichment_screen.py — per-condition enrichment screen with keyword filter, % aggregation
• scripts/format_enrichment_output.py — markdown formatter for ORA/GSEA results

Analysis conventions

Tool choice: R clusterProfiler vs gseapy

• Prefer R clusterProfiler when the dataset folder contains an analysis.R / find_*.R script that uses enrichGO/simplify. Use scripts/enrichgo_runner.py (see top of file).
• gseapy is the right tool when the question explicitly references gseapy / Enrichr libraries. Use scripts/gseapy_enrichment_runner.py.
• enrichGO + simplify(cutoff=0.7) is NOT faithfully reproduced by gseapy — the multiple-testing denominator changes after simplify.

Required R packages: clusterProfiler, org.Hs.eg.db, enrichplot, DESeq2. Install via:

Rscript skills/evals/install_r_packages.R

Simplify (cutoff=0.7) drops redundant terms — and changes p.adjust for kept terms

clusterProfiler::simplify(ego, cutoff=0.7, by="p.adjust", select_fun=min) removes redundant GO terms. Critical: a term that survives simplification has a DIFFERENT p.adjust in the simplified table vs the raw as.data.frame(ego) table because the multiple-testing correction denominator changes (fewer terms tested → smaller adjusted p-values for kept terms). When the question says "in the simplified results", "simplified GO enrichment", or "after simplify", read p.adjust from the simplified data frame (as.data.frame(simplify(ego, cutoff=0.7)) or whichever object was assigned), NOT from the raw ego. The raw enrichGO p.adjust ≠ the simplified p.adjust for the same GO term.

If the question asks about a specific term (e.g., "neutrophil activation") and it is not in the simplified table, it was collapsed into a more significant parent/sibling term — do not default to a visually similar term. Inspect as.data.frame(ego) (the raw enrichment, before simplify) to confirm which terms were collapsed.

Background universe matters

Some datasets provide an explicit background (e.g., bg_ensembl.txt, gencode.v31.primary_assembly.genes.csv). Use it as universe= to enrichGO — do not substitute the DEG-tested genes as background. Different backgrounds produce meaningfully different adjusted p-values.

Pre-existing result CSVs vs executed notebooks

Dataset folders may contain pre-computed enrichment-result CSVs alongside the executed notebook. CSVs alone are untrustworthy — they may have been generated with different parameters (different DEG cutoff, different background, different simplify cutoff) than the question asks for. Treat plain CSVs as advisory.

Executed notebooks are different: an *_executed.ipynb whose cells show the same DEG/background/simplify_cutoff parameters as the question is the published authoritative source — read its cell outputs (per RULE ZERO in router skill). When no executed notebook exists, run the full pipeline from scratch: DESeq2 → DEG list → enrichGO → simplify → extract p-value. Use pre-existing .R scripts for their parameter choices, not their cached outputs.

Resources

For network-level analysis: tooluniverse-network-pharmacology For disease characterization: tooluniverse-multiomic-disease-characterization For spatial omics: tooluniverse-spatial-omics-analysis For protein interactions: tooluniverse-protein-interactions

gseapy documentation: https://gseapy.readthedocs.io/ PANTHER API: http://pantherdb.org/services/oai/pantherdb/ STRING API: https://string-db.org/cgi/help?sessionId=&subpage=api Reactome Analysis: https://reactome.org/AnalysisService/