Tooluniverse Gene Enrichment

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.

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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).

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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.

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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

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Input Parameters

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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

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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

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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

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)

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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)

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Evidence Grading

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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

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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

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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

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Tool Reference

Primary Enrichment Tools

Cross-Validation Tools

ID Conversion Tools

See: references/tool_parameters.md for complete parameter documentation

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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

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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.

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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/

Complete Enrichr Library Guide

Comprehensive guide to all 225+ Enrichr libraries available via gseapy.

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Discovering Available Libraries

import gseapy as gp

# List all available libraries
all_libs = gp.get_library_name(organism='human')
print(f"Total libraries: {len(all_libs)}")
print(all_libs)

# Search for specific library
matching = [lib for lib in all_libs if 'GO_Biological' in lib]
print(matching)
# Output: ['GO_Biological_Process_2021', 'GO_Biological_Process_2023', 'GO_Biological_Process_2025']

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Gene Ontology (GO) Libraries

GO Biological Process

• GO_Biological_Process_2021 - 17,000+ terms (recommended)
• GO_Biological_Process_2023 - Updated version
• GO_Biological_Process_2025 - Latest version

Use for: Understanding biological processes (e.g., cell cycle, apoptosis, DNA repair)

GO Molecular Function

• GO_Molecular_Function_2021 - 4,000+ terms (recommended)
• GO_Molecular_Function_2023
• GO_Molecular_Function_2025

Use for: Understanding biochemical activities (e.g., kinase activity, DNA binding, transporter activity)

GO Cellular Component

• GO_Cellular_Component_2021 - 1,700+ terms (recommended)
• GO_Cellular_Component_2023
• GO_Cellular_Component_2025

Use for: Understanding subcellular localization (e.g., nucleus, mitochondria, membrane)

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Pathway Databases

KEGG Pathways

• KEGG_2021_Human - 327 human pathways (recommended)
• KEGG_2019_Mouse - Mouse pathways
• KEGG_2026 - Latest version

Use for: Canonical metabolic and signaling pathways

Examples:

• hsa04110: Cell cycle
• hsa04151: PI3K-Akt signaling
• hsa05200: Pathways in cancer

Reactome Pathways

• Reactome_2022 - 2,500+ pathways
• Reactome_Pathways_2024 - Latest version (recommended)

Use for: Detailed, curated pathway analysis with hierarchical structure

WikiPathways

• WikiPathways_2024_Human - Community-curated pathways (recommended)
• WikiPathways_2024_Mouse

Use for: Emerging pathways, disease-specific pathways, community contributions

BioCarta

• BioCarta_2016 - 249 pathways

Use for: Signal transduction pathways (legacy database, no longer updated)

BioPlanet

• BioPlanet_2019 - NCI Pathway Interaction Database

Use for: Comprehensive pathway coverage including regulatory pathways

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MSigDB Collections

Hallmark Gene Sets

• MSigDB_Hallmark_2020 - 50 hallmark gene sets

Use for: Cancer research, well-defined biological states

Examples:

• HALLMARK_APOPTOSIS
• HALLMARK_CELL_CYCLE
• HALLMARK_INFLAMMATORY_RESPONSE
• HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION

Computational Gene Sets

• MSigDB_Computational - Computational predictions

Use for: Predicted gene sets from computational methods

Oncogenic Signatures

• MSigDB_Oncogenic_Signatures - Cancer-related signatures

Use for: Cancer driver pathways, oncogene/tumor suppressor signatures

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Disease and Phenotype Libraries

Disease Associations

• DisGeNET - Gene-disease associations from text mining
• OMIM_Disease - Mendelian diseases from OMIM
• OMIM_Expanded - Extended OMIM annotations
• ClinVar_2025 - Clinical variants and associations
• Rare_Diseases_GeneRIF_Gene_Lists - Rare disease gene sets

Use for: Disease characterization, clinical interpretation

Human Phenotype Ontology

• HPO_2025 - Human phenotype terms

Use for: Phenotype enrichment, clinical feature analysis

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Drug and Chemical Libraries

Drug-Gene Interactions

• DGIdb_Drug_Targets_2024 - Drug-gene interaction database
• DrugMatrix - Toxicogenomics database

Use for: Drug mechanism, target identification, toxicity prediction

Drug Perturbations

• Drug_Perturbations_from_GEO_down - Genes down-regulated by drugs
• Drug_Perturbations_from_GEO_up - Genes up-regulated by drugs

Use for: Drug effect prediction, mechanism of action

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Cell Type and Tissue Libraries

Cell Type Markers

• CellMarker_2024 - Cell type marker genes (recommended)
• Azimuth_2023 - Single-cell reference-based cell type markers
• Descartes_Cell_Types_and_Tissue_2021 - Human developmental cell types
• Allen_Brain_Atlas_10x_scRNA_2021 - Brain cell types

Use for: Cell type identification from single-cell or bulk RNA-seq

Tissue Expression

• GTEx_Tissues_V8_2023 - Tissue-specific expression from GTEx
• ARCHS4_Tissues - Tissue signatures from ARCHS4
• Human_Gene_Atlas - Gene expression across tissues

Use for: Tissue specificity analysis

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Transcription Factor Libraries

TF ChIP-seq

• ChEA_2022 - Transcription factor targets (recommended)
• ENCODE_TF_ChIP-seq_2015 - ENCODE TF binding sites
• ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X - Consensus TF targets

Use for: Upstream regulator analysis, transcription factor binding

TF-Target Predictions

• TRANSFAC_and_JASPAR_PWMs - TF binding motifs
• TRRUST_Transcription_Factors_2019 - Human TF regulatory relationships

Use for: Motif analysis, regulatory network reconstruction

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Epigenomics Libraries

Histone Modifications

• ENCODE_Histone_Modifications_2015 - ENCODE histone ChIP-seq
• Epigenomics_Roadmap_HM_ChIP-seq - Roadmap Epigenomics histone marks

Use for: Chromatin state analysis, epigenetic regulation

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Protein Interaction and Complex Libraries

Protein Complexes

• CORUM - Comprehensive Resource of Mammalian protein complexes

Use for: Protein complex enrichment, functional module identification

Subcellular Localization

• COMPARTMENTS_Curated_2025 - Curated protein localization
• COMPARTMENTS_Experimental_2025 - Experimental protein localization
• COMPARTMENTS_Text_Mining_2025 - Text-mined protein localization

Use for: Subcellular localization analysis

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Specialized Libraries

Synaptic Gene Ontology

• SynGO_2022 - Synaptic gene ontology
• SynGO_2024 - Latest version

Use for: Neuroscience, synaptic function analysis

Cancer Dependencies

• DepMap_CRISPR_GeneDependency_CellLines_2023 - Cancer cell line gene dependencies

Use for: Cancer target identification, synthetic lethality

Elsevier Pathway Collection

• Elsevier_Pathway_Collection - Comprehensive pathway collection

Use for: Broad pathway coverage

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Library Selection Guide

For General Analysis

# Standard enrichment panel
standard_libraries = [
    'GO_Biological_Process_2021',
    'GO_Molecular_Function_2021',
    'GO_Cellular_Component_2021',
    'KEGG_2021_Human',
    'Reactome_Pathways_2024',
]

For Cancer Research

cancer_libraries = [
    'MSigDB_Hallmark_2020',
    'MSigDB_Oncogenic_Signatures',
    'Reactome_Pathways_2024',
    'DepMap_CRISPR_GeneDependency_CellLines_2023',
]

For Disease Research

disease_libraries = [
    'DisGeNET',
    'OMIM_Disease',
    'ClinVar_2025',
    'HPO_2025',
]

For Drug Discovery

drug_libraries = [
    'DGIdb_Drug_Targets_2024',
    'Drug_Perturbations_from_GEO_down',
    'Drug_Perturbations_from_GEO_up',
    'DrugMatrix',
]

For Single-Cell Analysis

scrnaseq_libraries = [
    'CellMarker_2024',
    'Azimuth_2023',
    'Allen_Brain_Atlas_10x_scRNA_2021',
    'Descartes_Cell_Types_and_Tissue_2021',
]

For Regulatory Analysis

regulatory_libraries = [
    'ChEA_2022',
    'ENCODE_TF_ChIP-seq_2015',
    'ENCODE_Histone_Modifications_2015',
]

---

Library Version Selection

When to Use Older Versions (2021-2022)

• More citations in literature
• Better validated
• More stable results
• Recommended for publication

When to Use Latest Versions (2024-2025)

• More comprehensive (newer annotations)
• Latest disease/drug associations
• More cell types (from recent atlases)
• For exploratory analysis

Recommendation

Default to 2021-2022 versions for primary analysis, validate with 2024-2025 versions

---

Usage Examples

Single Library

import gseapy

result = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets='GO_Biological_Process_2021',
    organism='human',
    outdir=None,
    no_plot=True,
)

Multiple Libraries

result = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets=[
        'GO_Biological_Process_2021',
        'KEGG_2021_Human',
        'Reactome_Pathways_2024',
        'MSigDB_Hallmark_2020',
    ],
    organism='human',
    outdir=None,
    no_plot=True,
)

# Results combined in single DataFrame
# Use 'Gene_set' column to distinguish libraries
go_results = result.results[result.results['Gene_set'] == 'GO_Biological_Process_2021']
kegg_results = result.results[result.results['Gene_set'] == 'KEGG_2021_Human']

All GO Categories at Once

result = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets=[
        'GO_Biological_Process_2021',
        'GO_Molecular_Function_2021',
        'GO_Cellular_Component_2021',
    ],
    organism='human',
    outdir=None,
    no_plot=True,
)

---

Library Update Frequency

---

Best Practices

1. Start with standard panel (GO + KEGG + Reactome) 2. Use consistent library versions across analyses 3. Document library versions in methods 4. Validate with multiple libraries (cross-database confirmation) 5. Check library date relative to your experiment date 6. Use organism-specific libraries when available 7. Report library in results (not just "GO enrichment") 8. Compare 2021 vs latest for robustness

---

Organism-Specific Libraries

Human

• All libraries available
• Use organism='human' or organism='Human'

Mouse

• KEGG_2019_Mouse
• WikiPathways_2024_Mouse
• GO_*_2021 (use organism='mouse')

Other Organisms

# Supported organisms
supported = ['human', 'mouse', 'fly', 'yeast', 'worm', 'fish', 'rat']

# For other organisms, use human libraries with ortholog conversion

---

See also:

• ora_workflow.md - How to use these libraries with ORA
• gsea_workflow.md - How to use these libraries with GSEA
• cross_validation.md - Multi-library validation strategies

Gene Set Enrichment Analysis (GSEA) Workflow

Complete guide to performing GSEA with gseapy for ranked gene lists.

---

When to Use GSEA

Use GSEA when:

• You have a ranked gene list with scores (e.g., log2FC, t-statistic, signal-to-noise ratio)
• You want to detect weak but consistent signals across a gene set
• Statistical test: Running enrichment score with permutation
• Question: "Are genes in this pathway consistently up/down-regulated?"

Key advantage over ORA: Detects coordinated changes even when individual genes don't pass significance thresholds.

---

Step-by-Step GSEA Workflow

Step 1: Prepare Ranked Gene List

import pandas as pd
import numpy as np

# Option 1: From dictionary
ranked_dict = {"TP53": 3.2, "BRCA1": 2.8, "EGFR": -1.5, "MYC": 4.1, ...}
ranked_series = pd.Series(ranked_dict).sort_values(ascending=False)

# Option 2: From DataFrame (e.g., DESeq2 results)
# df has columns: gene_symbol, log2FoldChange, pvalue, padj
ranked_series = df.set_index('gene_symbol')['log2FoldChange'].sort_values(ascending=False)

# Option 3: Signal-to-noise ratio
# snr = (mean_class1 - mean_class2) / (std_class1 + std_class2)
ranked_series = pd.Series(snr_dict).sort_values(ascending=False)

# Option 4: -log10(p) * sign(FC)
df['rank_metric'] = -np.log10(df['pvalue']) * np.sign(df['log2FoldChange'])
ranked_series = df.set_index('gene_symbol')['rank_metric'].sort_values(ascending=False)

Important:

• Series must be sorted in descending order (highest scores first)
• Remove NaN values and duplicates
• Use consistent gene symbols

Step 2: Run GSEA Preranked

import gseapy

# GSEA with GO Biological Process
gsea_bp = gseapy.prerank(
    rnk=ranked_series,
    gene_sets='GO_Biological_Process_2021',
    outdir=None,
    no_plot=True,
    seed=42,
    min_size=5,         # minimum gene set size
    max_size=500,       # maximum gene set size
    permutation_num=1000,  # number of permutations (1000 is standard)
)

# Result columns: Name, Term, ES, NES, NOM p-val, FDR q-val, FWER p-val, Tag %, Gene %, Lead_genes
gsea_bp_df = gsea_bp.res2d

# Filter significant (GSEA uses FDR < 0.25 as standard)
gsea_sig = gsea_bp_df[gsea_bp_df['FDR q-val'].astype(float) < 0.25]

# Key metrics:
# NES (Normalized Enrichment Score): positive = enriched in top of list, negative = enriched in bottom
# NOM p-val: nominal p-value (unadjusted)
# FDR q-val: false discovery rate (adjusted)
# FWER p-val: family-wise error rate (Bonferroni-like)
# Lead_genes: core genes driving enrichment

Step 3: GSEA with Multiple Databases

# KEGG GSEA
gsea_kegg = gseapy.prerank(
    rnk=ranked_series,
    gene_sets='KEGG_2021_Human',
    outdir=None,
    no_plot=True,
    seed=42,
    min_size=5,
    max_size=500,
    permutation_num=1000,
)

# Reactome GSEA
gsea_reactome = gseapy.prerank(
    rnk=ranked_series,
    gene_sets='Reactome_Pathways_2024',
    outdir=None,
    no_plot=True,
    seed=42,
    min_size=5,
    max_size=500,
    permutation_num=1000,
)

# MSigDB Hallmark (cancer hallmarks)
gsea_hallmark = gseapy.prerank(
    rnk=ranked_series,
    gene_sets='MSigDB_Hallmark_2020',
    outdir=None,
    no_plot=True,
    seed=42,
    min_size=5,
    max_size=500,
    permutation_num=1000,
)

Step 4: Multiple Gene Set Libraries

# Run GSEA across multiple libraries
gsea_multi = gseapy.prerank(
    rnk=ranked_series,
    gene_sets=['GO_Biological_Process_2021', 'KEGG_2021_Human', 'MSigDB_Hallmark_2020'],
    outdir=None,
    no_plot=True,
    seed=42,
    min_size=5,
    max_size=500,
    permutation_num=1000,
)

---

Understanding GSEA Results

Result DataFrame Columns

Interpreting NES (Normalized Enrichment Score)

Significance Thresholds

Note: GSEA uses FDR < 0.25 as standard (not 0.05 like ORA) because GSEA is more conservative.

---

Visualizing GSEA Results

Top Up-Regulated Pathways

# Positive NES (enriched in up-regulated genes)
gsea_up = gsea_sig[gsea_sig['NES'] > 0].sort_values('NES', ascending=False)
print("Top Up-Regulated Pathways:")
for _, row in gsea_up.head(10).iterrows():
    print(f"  {row['Term']}: NES={row['NES']:.2f}, FDR={row['FDR q-val']:.3e}")
    print(f"    Lead genes: {row['Lead_genes'][:100]}...")

Top Down-Regulated Pathways

# Negative NES (enriched in down-regulated genes)
gsea_down = gsea_sig[gsea_sig['NES'] < 0].sort_values('NES')
print("Top Down-Regulated Pathways:")
for _, row in gsea_down.head(10).iterrows():
    print(f"  {row['Term']}: NES={row['NES']:.2f}, FDR={row['FDR q-val']:.3e}")
    print(f"    Lead genes: {row['Lead_genes'][:100]}...")

---

GSEA vs ORA Comparison

Rule of thumb:

• Use ORA when you have clear gene lists (e.g., cluster markers, significant DEGs)
• Use GSEA when you have ranked data (e.g., all genes with log2FC from DESeq2)

---

GSEA Result Format Examples

gseapy.prerank Output

Term: regulation of cell cycle (GO:0051726)
ES: 0.623
NES: 2.14
NOM p-val: 0.001
FDR q-val: 0.023
FWER p-val: 0.045
Tag %: 26.7
Gene %: 12.3
Lead_genes: TP53,BRCA1,EGFR,MYC,AKT1,CCND1,CDK4,CDK6,RB1,E2F1

Interpretation:

• This pathway is strongly enriched (NES = 2.14) in the up-regulated genes
• FDR = 0.023 means 2.3% chance this is a false positive
• Lead genes (TP53, BRCA1, etc.) are the core genes driving enrichment
• Tag % = 26.7% means 26.7% of genes in this pathway appear before the peak enrichment point

---

Advanced GSEA Techniques

Custom Gene Sets

# Define custom gene set dictionary
custom_genesets = {
    'MyCustomPathway1': ['TP53', 'BRCA1', 'EGFR', 'MYC'],
    'MyCustomPathway2': ['AKT1', 'PTEN', 'PIK3CA', 'MTOR'],
    'MyCustomPathway3': ['KRAS', 'NRAS', 'HRAS', 'BRAF'],
}

# Run GSEA with custom gene sets
gsea_custom = gseapy.prerank(
    rnk=ranked_series,
    gene_sets=custom_genesets,
    outdir=None,
    no_plot=True,
    seed=42,
    min_size=3,  # allow smaller sets for custom gene sets
    max_size=500,
    permutation_num=1000,
)

Gene Set Size Filtering

# Filter gene sets by size before GSEA
# (gseapy does this automatically with min_size/max_size, but you can pre-filter)

from gseapy import get_library_name, parser

# Load gene set library
gene_sets = parser.read_gmt('path/to/geneset.gmt')  # or use Enrichr library

# Filter by size
filtered_genesets = {
    name: genes
    for name, genes in gene_sets.items()
    if 10 <= len(genes) <= 200  # custom size range
}

# Run GSEA
gsea_filtered = gseapy.prerank(
    rnk=ranked_series,
    gene_sets=filtered_genesets,
    outdir=None,
    no_plot=True,
    seed=42,
    permutation_num=1000,
)

Parameter Sensitivity Analysis

# Test different permutation numbers
for n_perm in [100, 500, 1000, 5000]:
    gsea_result = gseapy.prerank(
        rnk=ranked_series,
        gene_sets='GO_Biological_Process_2021',
        outdir=None,
        no_plot=True,
        seed=42,
        permutation_num=n_perm,
    )
    sig_count = (gsea_result.res2d['FDR q-val'].astype(float) < 0.25).sum()
    print(f"Permutations: {n_perm}, Significant terms: {sig_count}")

---

Common Issues and Solutions

Issue 1: No Significant Results

Problem: No terms pass FDR < 0.25

Solutions:

• Check ranked list quality: Are there extreme outliers?
• Try relaxing to FDR < 0.5 (exploratory)
• Check gene symbol mapping: Are genes recognized?
• Increase permutation_num to 5000 for more stable p-values
• Try ORA instead (different statistical framework)

Issue 2: Too Many Significant Results

Problem: Hundreds of significant terms

Solutions:

• Use stricter cutoff (FDR < 0.05 or FWER < 0.05)
• Filter by |NES| > 1.5 (only strong enrichments)
• Report top 20-50 terms by |NES|
• Use more specific gene set libraries (GO BP level 5-7 instead of all levels)

Issue 3: Unstable Results

Problem: Results change between runs

Solutions:

• Always set seed=42 for reproducibility
• Increase permutation_num from 1000 to 5000
• Check for ties in ranked list (same scores for many genes)

Issue 4: Warning: "No gene sets pass filtering"

Problem: Gene sets don't match gene names in ranked list

Solutions:

• Check gene symbol format (uppercase? with spaces?)
• Try different gene set library versions
• Convert gene symbols to common format (HGNC)
• Check min_size/max_size parameters (may be filtering all sets)

---

Best Practices

1. Always set random seed (seed=42) for reproducibility 2. Use entire ranked list (don't pre-filter by significance) 3. Sort in descending order (highest scores first) 4. Remove duplicates (average or max if gene appears multiple times) 5. Use appropriate ranking metric:

• log2FC: simple, interpretable
• -log10(p) × sign(FC): weights by significance
• Signal-to-noise ratio: classic GSEA metric

6. Use standard FDR < 0.25 (not 0.05 like ORA) 7. Report NES with FDR (not just NES alone) 8. Interpret lead genes (core enrichment, not all genes in set) 9. Compare up vs down (positive vs negative NES) 10. Cross-validate with ORA (run ORA on top/bottom genes as sanity check)

---

GSEA Report Template

## GSEA Results

### Ranking Metric
- **Metric**: log2 Fold Change
- **Total genes ranked**: 15,234
- **Score range**: -8.5 to 12.3

### Top Up-Regulated Pathways (NES > 0)
| Rank | Pathway | NES | FDR q-val | Lead Genes |
|------|---------|-----|-----------|------------|
| 1 | Cell cycle (GO:0007049) | 2.34 | 0.001 | TP53, BRCA1, EGFR, MYC, CDK4 |
| 2 | DNA repair (GO:0006281) | 2.12 | 0.003 | BRCA1, RAD51, XRCC4, LIG4 |

### Top Down-Regulated Pathways (NES < 0)
| Rank | Pathway | NES | FDR q-val | Lead Genes |
|------|---------|-----|-----------|------------|
| 1 | Immune response (GO:0006955) | -2.01 | 0.012 | IL6, TNF, IFNG, CXCL10 |
| 2 | Inflammatory response (GO:0006954) | -1.89 | 0.018 | IL1B, TNF, IL6, CCL2 |

### Summary Statistics
- **Total gene sets tested**: 8,456
- **Significant at FDR < 0.25**: 234 (2.8%)
- **Positive NES**: 142 (up-regulated pathways)
- **Negative NES**: 92 (down-regulated pathways)
- **Permutations**: 1,000
- **Gene set size range**: 5-500 genes

---

See also:

• ora_workflow.md - For unranked gene lists
• enrichr_guide.md - All available libraries
• cross_validation.md - Multi-source validation strategies

Over-Representation Analysis (ORA) Workflow

Complete guide to performing ORA enrichment analysis with gseapy and ToolUniverse tools.

---

When to Use ORA

Use ORA when:

• You have an unranked gene list (e.g., DEGs from differential expression)
• No fold-change or score information available
• Statistical test: Fisher's exact test / hypergeometric test
• Question: "Are genes from my list over-represented in this pathway?"

---

Step-by-Step ORA Workflow

Step 1: GO Biological Process Enrichment

import gseapy
import pandas as pd

# GO Biological Process ORA
go_bp_result = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets='GO_Biological_Process_2021',  # or 2023, 2025
    organism='human',  # 'human', 'mouse', 'fly', 'yeast', 'worm', 'fish', 'rat'
    outdir=None,        # None = no file output
    no_plot=True,       # suppress plots for programmatic use
    background=None,    # None = Enrichr default background
)

# Result is Enrichr object with .results DataFrame
# Columns: Gene_set, Term, Overlap, P-value, Adjusted P-value,
#          Old P-value, Old Adjusted P-value, Odds Ratio, Combined Score, Genes
go_bp_df = go_bp_result.results

# Filter by significance
go_bp_sig = go_bp_df[go_bp_df['Adjusted P-value'] < 0.05].copy()

# Extract key info
for _, row in go_bp_sig.head(10).iterrows():
    term = row['Term']
    pval = row['P-value']
    adj_pval = row['Adjusted P-value']
    overlap = row['Overlap']
    genes = row['Genes']
    odds_ratio = row['Odds Ratio']
    combined_score = row['Combined Score']
    print(f"{term}: adj_p={adj_pval:.2e}, overlap={overlap}, genes={genes}")

Step 2: GO Molecular Function and Cellular Component

# GO Molecular Function
go_mf_result = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets='GO_Molecular_Function_2021',
    organism='human',
    outdir=None,
    no_plot=True,
)
go_mf_df = go_mf_result.results

# GO Cellular Component
go_cc_result = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets='GO_Cellular_Component_2021',
    organism='human',
    outdir=None,
    no_plot=True,
)
go_cc_df = go_cc_result.results

Step 3: KEGG Pathway Enrichment

# KEGG via gseapy
kegg_result = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets='KEGG_2021_Human',  # or KEGG_2026
    organism='human',
    outdir=None,
    no_plot=True,
)
kegg_df = kegg_result.results
kegg_sig = kegg_df[kegg_df['Adjusted P-value'] < 0.05]

Step 4: Reactome Pathway Enrichment

# Reactome via gseapy
reactome_gseapy = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets='Reactome_Pathways_2024',  # or Reactome_2022
    organism='human',
    outdir=None,
    no_plot=True,
)
reactome_df = reactome_gseapy.results

Step 5: MSigDB Hallmark and Other Libraries

# MSigDB Hallmark
hallmark_result = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets='MSigDB_Hallmark_2020',
    organism='human',
    outdir=None,
    no_plot=True,
)
hallmark_df = hallmark_result.results

# WikiPathways
wp_result = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets='WikiPathways_2024_Human',
    organism='human',
    outdir=None,
    no_plot=True,
)
wp_df = wp_result.results

# Multiple libraries at once
multi_result = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets=['GO_Biological_Process_2021', 'KEGG_2021_Human', 'Reactome_2022'],
    organism='human',
    outdir=None,
    no_plot=True,
)
# Results combined in single DataFrame with Gene_set column distinguishing libraries

Step 6: Custom Background Gene Set

# With custom background (e.g., all expressed genes)
background_genes = ["GENE1", "GENE2", ...]  # user-provided

# gseapy enrichr with background
result_with_bg = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets='GO_Biological_Process_2021',
    organism='human',
    outdir=None,
    no_plot=True,
    background=background_genes,  # custom background
)
# Note: background changes p-value calculation significantly

---

Understanding ORA Results

Result DataFrame Columns

Significance Thresholds

---

Cross-Validation with ToolUniverse

Always validate gseapy results with at least one independent tool:

PANTHER Enrichment (T1 - Curated)

from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()

# PANTHER ORA - GO Biological Process
panther_bp = tu.tools.PANTHER_enrichment(
    gene_list=','.join(gene_symbols),  # comma-separated string
    organism=9606,                      # NCBI taxonomy ID
    annotation_dataset='GO:0008150'     # biological_process
)
# Returns: {data: {gene_list, organism, annotation_dataset, result_count, enriched_terms: [
#   {term_id, term_label, number_in_list, number_in_reference, expected, fold_enrichment, pvalue, fdr, direction}
# ]}}
panther_terms = panther_bp.get('data', {}).get('enriched_terms', [])

# PANTHER - GO Molecular Function
panther_mf = tu.tools.PANTHER_enrichment(
    gene_list=','.join(gene_symbols),
    organism=9606,
    annotation_dataset='GO:0003674'  # molecular_function
)

# PANTHER - GO Cellular Component
panther_cc = tu.tools.PANTHER_enrichment(
    gene_list=','.join(gene_symbols),
    organism=9606,
    annotation_dataset='GO:0005575'  # cellular_component
)

# PANTHER - PANTHER Pathways
panther_pw = tu.tools.PANTHER_enrichment(
    gene_list=','.join(gene_symbols),
    organism=9606,
    annotation_dataset='ANNOT_TYPE_ID_PANTHER_PATHWAY'
)

# PANTHER - Reactome Pathways
panther_reactome = tu.tools.PANTHER_enrichment(
    gene_list=','.join(gene_symbols),
    organism=9606,
    annotation_dataset='ANNOT_TYPE_ID_PANTHER_REACTOME_PATHWAY'
)

STRING Functional Enrichment (T2 - Validated)

# STRING enrichment - returns ALL categories at once
# Categories: Process, Function, Component, KEGG, Reactome, COMPARTMENTS, DISEASES, etc.
string_enrichment = tu.tools.STRING_functional_enrichment(
    protein_ids=gene_symbols,
    species=9606
)
# Returns: {status: "success", data: [
#   {category, term, number_of_genes, number_of_genes_in_background, inputGenes, preferredNames, p_value, fdr, description}
# ]}

# NOTE: STRING returns ALL categories regardless of 'category' parameter
# Filter by category:
string_data = string_enrichment.get('data', [])
if isinstance(string_data, list):
    string_go_bp = [d for d in string_data if d.get('category') == 'Process']
    string_go_mf = [d for d in string_data if d.get('category') == 'Function']
    string_go_cc = [d for d in string_data if d.get('category') == 'Component']
    string_kegg = [d for d in string_data if d.get('category') == 'KEGG']
    string_reactome = [d for d in string_data if d.get('category') == 'Reactome']
    string_wp = [d for d in string_data if d.get('category') == 'WikiPathways']

Reactome Analysis Service (T1 - Curated)

# Reactome pathway overrepresentation analysis
reactome_result = tu.tools.ReactomeAnalysis_pathway_enrichment(
    identifiers=' '.join(gene_symbols),  # space-separated, NOT array
    page_size=50,                         # max pathways to return
    include_disease=True,                 # include disease pathways
    projection=True                       # project to human (for cross-species)
)
# Returns: {data: {token, analysis_type, identifiers_not_found, pathways_found, pathways: [
#   {pathway_id, name, species, is_disease, is_lowest_level, entities_found, entities_total,
#    entities_ratio, p_value, fdr, reactions_found, reactions_total}
# ]}}

reactome_pathways = reactome_result.get('data', {}).get('pathways', [])
# Filter significant
reactome_sig = [p for p in reactome_pathways if p.get('fdr', 1) < 0.05]

---

ORA Result Format Examples

gseapy.enrichr Output

Gene_set: GO_Biological_Process_2021
Term: regulation of cell cycle (GO:0051726)
Overlap: 12/45
P-value: 1.234e-08
Adjusted P-value: 3.456e-06
Odds Ratio: 8.7
Combined Score: 245.3
Genes: TP53;BRCA1;EGFR;MYC;AKT1;...

PANTHER Output

{
  "term_id": "GO:0051726",
  "term_label": "regulation of cell cycle",
  "number_in_list": 12,
  "number_in_reference": 450,
  "expected": 2.3,
  "fold_enrichment": 5.2,
  "pvalue": 2.1e-07,
  "fdr": 4.5e-05,
  "direction": "+"
}

STRING Output

{
  "category": "Process",
  "term": "GO:0051726",
  "description": "regulation of cell cycle",
  "number_of_genes": 12,
  "number_of_genes_in_background": 450,
  "p_value": 1.8e-07,
  "fdr": 3.2e-05,
  "inputGenes": "TP53,BRCA1,EGFR,MYC,AKT1,...",
  "preferredNames": "TP53,BRCA1,EGFR,MYC,AKT1,..."
}

---

Common Issues and Solutions

Issue 1: Small Gene Lists (<5 genes)

Problem: ORA has insufficient statistical power with very small lists

Solutions:

• Use PANTHER and STRING (handle small lists better)
• Consider gene-level annotation (GO_get_annotations_for_gene) instead
• Report as exploratory analysis only

Issue 2: Very Large Gene Lists (>500 genes)

Problem: Enrichment becomes less specific

Solutions:

• Use stricter significance cutoff (padj < 0.01)
• Recommend custom background (expressed genes only)
• Focus on most specific (lowest-level) GO terms
• Use GO Slim for high-level overview

Issue 3: No Significant Results

Problem: No terms pass significance threshold

Solutions:

• Verify gene symbols are valid (STRING_map_identifiers)
• Try different library versions (2021 vs 2023 vs 2025)
• Try relaxing cutoff (padj < 0.1)
• Consider GSEA as alternative
• Report as valid finding: "No significant enrichment"

Issue 4: Too Many Significant Results

Problem: Hundreds of significant terms

Solutions:

• Use stricter cutoff
• Filter by gene set size (remove very broad terms)
• Report top 20-50 terms only
• Use redundancy reduction (group similar GO terms)

---

Best Practices

1. Always apply multiple testing correction (default: Benjamini-Hochberg) 2. Cross-validate with at least 2 tools (gseapy + PANTHER + STRING) 3. Report exact p-values and adjusted p-values 4. Document background gene set (genome-wide or custom) 5. Filter by gene set size (5-500 genes recommended) 6. Report genes driving enrichment (not just term names) 7. Grade evidence (T1-T4 tiers) 8. Include negative results ("no significant enrichment" is informative) 9. Provide completeness checklist (show what was tested) 10. Reference tools and databases used

---

See also:

• gsea_workflow.md - For ranked gene lists
• enrichr_guide.md - All available libraries
• cross_validation.md - Multi-source validation strategies

Tool Parameters Reference

Complete parameter documentation for all enrichment tools.

---

gseapy Tools

gseapy.enrichr() - Over-Representation Analysis

Function: gseapy.enrichr()

Parameters:

Returns: Enrichr object with .results DataFrame

Result DataFrame Columns:

• Gene_set: Library name
• Term: GO term or pathway name
• Overlap: Format "X/Y" (X genes from list, Y total in pathway)
• P-value: Raw p-value from Fisher's exact test
• Adjusted P-value: Benjamini-Hochberg corrected p-value
• Old P-value: Legacy Enrichr p-value
• Old Adjusted P-value: Legacy corrected p-value
• Odds Ratio: Enrichment magnitude
• Combined Score: log(p-value) × z-score(odds ratio)
• Genes: Semicolon-separated gene list

Example:

result = gseapy.enrichr(
    gene_list=['TP53', 'BRCA1', 'EGFR'],
    gene_sets='GO_Biological_Process_2021',
    organism='human',
    outdir=None,
    no_plot=True,
)
df = result.results

---

gseapy.prerank() - Gene Set Enrichment Analysis

Function: gseapy.prerank()

Parameters:

Returns: Prerank object with .res2d DataFrame

Result DataFrame Columns:

• Name: Gene set ID
• Term: Gene set name
• ES: Enrichment Score (raw)
• NES: Normalized Enrichment Score (positive = up, negative = down)
• NOM p-val: Nominal p-value (unadjusted)
• FDR q-val: False Discovery Rate (use cutoff < 0.25)
• FWER p-val: Family-Wise Error Rate (Bonferroni-like)
• Tag %: Percentage of genes in set before peak
• Gene %: Position in ranked list
• Lead_genes: Core enrichment genes

Example:

import pandas as pd
ranked = pd.Series({'TP53': 3.2, 'BRCA1': 2.8, 'EGFR': -1.5}).sort_values(ascending=False)
result = gseapy.prerank(
    rnk=ranked,
    gene_sets='GO_Biological_Process_2021',
    outdir=None,
    no_plot=True,
    seed=42,
    min_size=5,
    max_size=500,
    permutation_num=1000,
)
df = result.res2d

---

ToolUniverse Enrichment Tools

PANTHER_enrichment

Tool: tu.tools.PANTHER_enrichment

Parameters:

Annotation Datasets:

Returns: {data: {enriched_terms: [...]}} structure

enriched_terms fields:

• term_id: Term ID (e.g., "GO:0051726")
• term_label: Term name
• number_in_list: Count in your gene list
• number_in_reference: Count in reference genome
• expected: Expected count by chance
• fold_enrichment: Observed/Expected
• pvalue: Raw p-value
• fdr: FDR-corrected p-value
• direction: "+" (over-represented) or "-" (under-represented)

Example:

result = tu.tools.PANTHER_enrichment(
    gene_list='TP53,BRCA1,EGFR,MYC',  # comma-separated string
    organism=9606,
    annotation_dataset='GO:0008150'
)
terms = result.get('data', {}).get('enriched_terms', [])

---

STRING_functional_enrichment

Tool: tu.tools.STRING_functional_enrichment

Parameters:

Important: STRING returns ALL categories regardless of category parameter. Filter by category after receiving results.

Categories Returned:

• Process: GO Biological Process
• Function: GO Molecular Function
• Component: GO Cellular Component
• KEGG: KEGG pathways
• Reactome: Reactome pathways
• WikiPathways: WikiPathways
• COMPARTMENTS: Protein localization
• DISEASES: Disease associations
• TISSUES: Tissue expression
• Keyword: UniProt keywords
• PMID: PubMed associations

Returns: {status: "success", data: [...]} structure

data fields:

• category: Category name (use for filtering)
• term: Term ID (e.g., "GO:0051726", "KEGG:04110")
• description: Term description
• number_of_genes: Count in your gene list
• number_of_genes_in_background: Count in STRING database
• p_value: Raw p-value
• fdr: FDR-corrected p-value
• inputGenes: Comma-separated input gene list
• preferredNames: Comma-separated preferred names

Example:

result = tu.tools.STRING_functional_enrichment(
    protein_ids=['TP53', 'BRCA1', 'EGFR'],
    species=9606
)
data = result.get('data', [])
go_bp = [d for d in data if d.get('category') == 'Process']
kegg = [d for d in data if d.get('category') == 'KEGG']

---

ReactomeAnalysis_pathway_enrichment

Tool: tu.tools.ReactomeAnalysis_pathway_enrichment

Parameters:

Returns: {data: {pathways: [...], identifiers_not_found: [...]}} structure

pathways fields:

• pathway_id: Reactome ID (e.g., "R-HSA-212436")
• name: Pathway name
• species: Species name
• is_disease: Boolean (disease pathway or not)
• is_lowest_level: Boolean (leaf pathway or parent)
• entities_found: Count in your gene list
• entities_total: Total entities in pathway
• entities_ratio: Found/Total ratio
• p_value: Raw p-value
• fdr: FDR-corrected p-value
• reactions_found: Reactions with your genes
• reactions_total: Total reactions

Example:

result = tu.tools.ReactomeAnalysis_pathway_enrichment(
    identifiers='TP53 BRCA1 EGFR MYC',  # space-separated string
    page_size=50,
    include_disease=True,
    projection=False
)
pathways = result.get('data', {}).get('pathways', [])
not_found = result.get('data', {}).get('identifiers_not_found', [])

---

ID Conversion Tools

MyGene_batch_query

Tool: tu.tools.MyGene_batch_query

Parameters:

Common Fields:

• symbol: Official gene symbol
• entrezgene: Entrez Gene ID
• ensembl.gene: Ensembl gene ID
• uniprot.Swiss-Prot: UniProt ID
• name: Gene name
• alias: Gene aliases

Returns: {results: [{query, _id, symbol, ...}]}

Example:

result = tu.tools.MyGene_batch_query(
    gene_ids=['ENSG00000141510', 'ENSG00000012048'],
    fields='symbol,entrezgene,ensembl.gene'
)
for hit in result.get('results', []):
    print(f"{hit['query']} -> {hit.get('symbol')}")

---

STRING_map_identifiers

Tool: tu.tools.STRING_map_identifiers

Parameters:

Returns: {status: "success", data: [{...}]}

data fields:

• queryItem: Your input ID
• preferredName: Official gene symbol (USE THIS)
• stringId: STRING database ID
• annotation: Additional info

Example:

result = tu.tools.STRING_map_identifiers(
    protein_ids=['TP53', 'BRCA1', 'EGFR'],
    species=9606
)
for item in result.get('data', []):
    print(f"{item['queryItem']} -> {item['preferredName']} ({item['stringId']})")

---

Organism Taxonomy IDs

---

Common Parameter Mistakes

1. Array vs String

WRONG:

# PANTHER expects string, not array
tu.tools.PANTHER_enrichment(gene_list=['TP53', 'BRCA1'], ...)

CORRECT:

tu.tools.PANTHER_enrichment(gene_list='TP53,BRCA1', ...)

2. Comma vs Space Separation

PANTHER: Comma-separated

gene_list='TP53,BRCA1,EGFR'

Reactome: Space-separated

identifiers='TP53 BRCA1 EGFR'

3. Organism Format

gseapy: String name

organism='human'  # NOT 9606

PANTHER/STRING: Taxonomy ID

organism=9606  # NOT 'human'
species=9606

4. Library Names

WRONG:

gene_sets='GO_Biological_Process'  # Missing year

CORRECT:

gene_sets='GO_Biological_Process_2021'  # Include year

5. Result Access

gseapy.enrichr: .results attribute

df = result.results  # NOT result.data

gseapy.prerank: .res2d attribute

df = result.res2d  # NOT result.results

---

See also:

• ora_workflow.md - ORA usage examples
• gsea_workflow.md - GSEA usage examples
• troubleshooting.md - Common issues

Troubleshooting Guide

Complete troubleshooting guide for gene enrichment analysis issues.

---

Common Issues Index

1. No Significant Enrichment Found 2. Gene Not Found Errors 3. gseapy Library Not Found 4. PANTHER Returns Empty Results 5. STRING Returns All Categories 6. Reactome Identifiers Not Found 7. enrichr_gene_enrichment_analysis Returns Wrong Format 8. Results Don't Match Between Tools 9. GSEA Gives Unstable Results 10. Memory Errors with Large Gene Lists

---

No Significant Enrichment Found

Symptoms

go_bp_result = gseapy.enrichr(gene_list=genes, gene_sets='GO_Biological_Process_2021', ...)
go_bp_sig = go_bp_result.results[go_bp_result.results['Adjusted P-value'] < 0.05]
print(len(go_bp_sig))  # Output: 0

Possible Causes & Solutions

1. Gene Symbols Are Invalid

Diagnosis:

from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()

# Check if genes are recognized
mapped = tu.tools.STRING_map_identifiers(
    protein_ids=genes,
    species=9606
)
print(f"Mapped: {len(mapped['data'])}/{len(genes)}")

Solution:

• Convert IDs using MyGene: tu.tools.MyGene_batch_query(gene_ids=genes, fields='symbol')
• Use official HGNC symbols
• Remove version suffixes from Ensembl IDs

2. Library Version Too Old/New

Solution:

# Try different library versions
for lib_version in ['GO_Biological_Process_2021', 'GO_Biological_Process_2023', 'GO_Biological_Process_2025']:
    result = gseapy.enrichr(gene_list=genes, gene_sets=lib_version, ...)
    sig = result.results[result.results['Adjusted P-value'] < 0.05]
    print(f"{lib_version}: {len(sig)} significant terms")

3. Significance Threshold Too Stringent

Solution:

# Try relaxing threshold
for cutoff in [0.05, 0.1, 0.15, 0.2]:
    sig = go_bp_result.results[go_bp_result.results['Adjusted P-value'] < cutoff]
    print(f"Cutoff {cutoff}: {len(sig)} significant terms")

4. Wrong Statistical Method

Solution:

# If ORA fails, try GSEA
import pandas as pd

# Create ranked list (even if you don't have fold-changes)
# Use presence/absence scoring: 1 for genes in list, 0 for others
ranked_dict = {gene: 1 for gene in genes}
ranked_series = pd.Series(ranked_dict).sort_values(ascending=False)

gsea_result = gseapy.prerank(
    rnk=ranked_series,
    gene_sets='GO_Biological_Process_2021',
    ...
)

5. Organism Mismatch

Solution:

# Verify organism parameter
result = gseapy.enrichr(
    gene_list=genes,
    gene_sets='GO_Biological_Process_2021',
    organism='human',  # NOT 'homo_sapiens' or 9606
    ...
)

6. Gene List Too Small or Too Large

Diagnosis:

print(f"Gene list size: {len(genes)}")
# Small (<5): insufficient power
# Large (>500): too non-specific

Solution:

• Small lists (<5 genes): Use PANTHER/STRING (better for small lists) or switch to gene-level annotation
• Large lists (>500 genes): Use custom background or stricter DEG cutoff

7. No True Enrichment

Valid Result: Sometimes there genuinely is no enrichment. Document this:

## Results
No significant enrichment was found at FDR < 0.05.
- Gene list size: 45 genes
- Libraries tested: GO BP, GO MF, GO CC, KEGG, Reactome
- Alternative cutoff (FDR < 0.1): 3 weak hits (exploratory only)

---

Gene Not Found Errors

Symptoms

Error: Gene 'TP-53' not found
Warning: 15 out of 50 genes not mapped

Solutions

1. Check ID Type

# Detect ID type
sample = genes[0]
if sample.startswith('ENSG'):
    id_type = 'ensembl'
elif sample.isdigit():
    id_type = 'entrez'
elif len(sample) == 6 and sample[0].isalpha():
    id_type = 'uniprot'
else:
    id_type = 'symbol'

print(f"Detected ID type: {id_type}")

2. Convert IDs

from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()

# Convert Ensembl -> Symbol
if id_type == 'ensembl':
    result = tu.tools.MyGene_batch_query(
        gene_ids=genes,
        fields='symbol,entrezgene,ensembl.gene'
    )
    symbol_map = {hit['query']: hit.get('symbol', hit['query'])
                  for hit in result.get('results', [])}
    gene_symbols = [symbol_map.get(g, g) for g in genes]

3. Remove Version Suffixes

# ENSG00000141510.16 -> ENSG00000141510
genes_clean = [g.split('.')[0] for g in genes]

4. Fix Common Typos

# Common issues
fixes = {
    'TP-53': 'TP53',
    'BRCA 1': 'BRCA1',
    'p53': 'TP53',
    'egfr': 'EGFR',  # case-sensitive
}

genes_fixed = [fixes.get(g, g) for g in genes]

5. Try Aliases

# Use MyGene to find aliases
for gene in unmapped_genes:
    result = tu.tools.MyGene_query_genes(query=gene)
    if result.get('hits'):
        official_symbol = result['hits'][0].get('symbol')
        print(f"{gene} -> {official_symbol}")

---

gseapy Library Not Found

Symptoms

ValueError: gene_set 'GO_Biological_Process' not found

Solutions

1. List Available Libraries

import gseapy as gp

all_libs = gp.get_library_name(organism='human')
print(f"Total libraries: {len(all_libs)}")

# Search for library
matching = [lib for lib in all_libs if 'GO_Biological' in lib]
print("Available GO BP libraries:", matching)

2. Use Exact Library Name

# Wrong (will fail)
gene_sets='GO_Biological_Process'

# Correct
gene_sets='GO_Biological_Process_2021'  # or 2023, 2025

3. Check Case Sensitivity

# Library names are case-sensitive
gene_sets='KEGG_2021_Human'  # NOT 'kegg_2021_human'

4. Organism-Specific Libraries

# Wrong for mouse
gene_sets='KEGG_2021_Human'  # Will fail for mouse genes

# Correct for mouse
gene_sets='KEGG_2019_Mouse'

---

PANTHER Returns Empty Results

Symptoms

panther_result = tu.tools.PANTHER_enrichment(gene_list=genes, organism=9606, ...)
print(panther_result.get('data', {}).get('enriched_terms', []))  # Output: []

Solutions

1. Check Input Format

# Wrong (array)
gene_list=["TP53", "BRCA1", "EGFR"]

# Correct (comma-separated string)
gene_list="TP53,BRCA1,EGFR"

2. Check Organism ID

# Wrong
organism='human'  # PANTHER uses taxonomy IDs

# Correct
organism=9606  # NCBI Taxonomy ID for human

3. Check Annotation Dataset

# Valid datasets
annotation_datasets = [
    'GO:0008150',  # Biological Process
    'GO:0003674',  # Molecular Function
    'GO:0005575',  # Cellular Component
    'ANNOT_TYPE_ID_PANTHER_PATHWAY',
    'ANNOT_TYPE_ID_PANTHER_REACTOME_PATHWAY',
]

# Wrong
annotation_dataset='biological_process'

# Correct
annotation_dataset='GO:0008150'

4. Gene Symbols Not Recognized

# PANTHER uses official gene symbols
# Convert using STRING first
mapped = tu.tools.STRING_map_identifiers(
    protein_ids=genes,
    species=9606
)
official_symbols = [item['preferredName'] for item in mapped.get('data', [])]
gene_list = ','.join(official_symbols)

---

STRING Returns All Categories

Symptoms

string_result = tu.tools.STRING_functional_enrichment(
    protein_ids=genes,
    species=9606,
    category='Process'  # Only want GO BP
)
# But result includes Process, Function, Component, KEGG, Reactome, etc.

Solution

This is expected behavior. STRING always returns all categories. Filter after receiving results:

string_data = string_result.get('data', [])

# Filter by category
string_go_bp = [d for d in string_data if d.get('category') == 'Process']
string_go_mf = [d for d in string_data if d.get('category') == 'Function']
string_go_cc = [d for d in string_data if d.get('category') == 'Component']
string_kegg = [d for d in string_data if d.get('category') == 'KEGG']
string_reactome = [d for d in string_data if d.get('category') == 'Reactome']
string_wp = [d for d in string_data if d.get('category') == 'WikiPathways']

print(f"GO BP terms: {len(string_go_bp)}")
print(f"KEGG pathways: {len(string_kegg)}")

---

Reactome Identifiers Not Found

Symptoms

reactome_result = tu.tools.ReactomeAnalysis_pathway_enrichment(identifiers=genes, ...)
identifiers_not_found = reactome_result.get('data', {}).get('identifiers_not_found', [])
print(f"Not found: {len(identifiers_not_found)}")  # High number

Solutions

1. Use Space-Separated String

# Wrong (array)
identifiers=["TP53", "BRCA1", "EGFR"]

# Correct (space-separated string)
identifiers="TP53 BRCA1 EGFR"
# or
identifiers=' '.join(genes)

2. Use Official Gene Symbols

# Use STRING to get official names
mapped = tu.tools.STRING_map_identifiers(
    protein_ids=genes,
    species=9606
)
official_symbols = [item['preferredName'] for item in mapped.get('data', [])]
identifiers = ' '.join(official_symbols)

3. Try UniProt IDs

# Convert to UniProt IDs
mygene_result = tu.tools.MyGene_batch_query(
    gene_ids=genes,
    fields='uniprot.Swiss-Prot'
)
uniprot_ids = [hit['uniprot']['Swiss-Prot']
               for hit in mygene_result.get('results', [])
               if 'uniprot' in hit]
identifiers = ' '.join(uniprot_ids)

---

enrichr_gene_enrichment_analysis Returns Wrong Format

Symptoms

result = tu.tools.enrichr_gene_enrichment_analysis(genes=genes, ...)
# Returns: connected_paths JSON string, NOT standard enrichment results

Solution

DO NOT USE `enrichr_gene_enrichment_analysis` for standard enrichment analysis.

This ToolUniverse tool returns path analysis, not standard ORA enrichment.

Use `gseapy.enrichr()` directly instead:

import gseapy

# Correct approach
result = gseapy.enrichr(
    gene_list=genes,
    gene_sets='GO_Biological_Process_2021',
    organism='human',
    outdir=None,
    no_plot=True,
)
# Returns standard enrichment DataFrame

---

Results Don't Match Between Tools

Symptoms

# gseapy finds 50 significant terms
# PANTHER finds 30 significant terms
# Only 10 overlap

Explanation & Solutions

This is Normal

Different tools use:

• Different gene set annotations (versions, curation)
• Different backgrounds (genome-wide vs database-specific)
• Different statistical methods (Fisher's exact vs hypergeometric)
• Different multiple testing corrections

Focus on Consensus

# Extract GO IDs from each source
import re

gseapy_terms = set()
for term in go_bp_sig['Term']:
    match = re.search(r'(GO:\d+)', term)
    if match:
        gseapy_terms.add(match.group(1))

panther_terms = set(t['term_id'] for t in panther_bp_terms if t.get('fdr', 1) < 0.05)

string_terms = set(d['term'] for d in string_go_bp if d.get('fdr', 1) < 0.05)

# Consensus terms (in 2+ sources)
all_terms = gseapy_terms | panther_terms | string_terms
consensus = []
for term in all_terms:
    sources = []
    if term in gseapy_terms: sources.append('gseapy')
    if term in panther_terms: sources.append('PANTHER')
    if term in string_terms: sources.append('STRING')
    if len(sources) >= 2:
        consensus.append((term, sources))

print(f"Consensus terms: {len(consensus)}")

Report Both

| GO Term | gseapy FDR | PANTHER FDR | STRING FDR | Consensus |
|---------|-----------|-------------|-----------|-----------|
| GO:0051726 | 3.4e-06 | 4.5e-05 | 3.2e-05 | 3/3 ✓ |
| GO:0006281 | 1.2e-05 | 2.1e-04 | - | 2/3 |
| GO:0007049 | 5.6e-06 | - | 8.9e-06 | 2/3 |

---

GSEA Gives Unstable Results

Symptoms

# Run 1: 50 significant terms
# Run 2: 45 significant terms (different terms!)

Solutions

1. Set Random Seed

gsea_result = gseapy.prerank(
    rnk=ranked_series,
    gene_sets='GO_Biological_Process_2021',
    seed=42,  # CRITICAL for reproducibility
    permutation_num=1000,
    ...
)

2. Increase Permutation Number

# More permutations = more stable p-values
gsea_result = gseapy.prerank(
    rnk=ranked_series,
    gene_sets='GO_Biological_Process_2021',
    seed=42,
    permutation_num=5000,  # instead of 1000
    ...
)

3. Check for Ties

# Check for many genes with same score
print(ranked_series.value_counts().head())
# If many ties, add small noise
import numpy as np
np.random.seed(42)
ranked_series = ranked_series + np.random.normal(0, 0.001, len(ranked_series))

---

Memory Errors with Large Gene Lists

Symptoms

MemoryError: Unable to allocate array

Solutions

1. Run Libraries Sequentially

# Wrong (runs all at once)
result = gseapy.enrichr(
    gene_list=genes,
    gene_sets=['GO_Biological_Process_2021', 'GO_Molecular_Function_2021', ...],  # 10+ libraries
    ...
)

# Correct (one at a time)
results = {}
for lib in ['GO_Biological_Process_2021', 'GO_Molecular_Function_2021', 'KEGG_2021_Human']:
    results[lib] = gseapy.enrichr(gene_list=genes, gene_sets=lib, ...)

2. Filter Gene Sets by Size

gsea_result = gseapy.prerank(
    rnk=ranked_series,
    gene_sets='GO_Biological_Process_2021',
    min_size=10,    # instead of 5
    max_size=200,   # instead of 500
    ...
)

3. Use Smaller Libraries

# Instead of GO_Biological_Process_2021 (17,000 terms)
# Use GO Slim (high-level terms only)
gene_sets='ANNOT_TYPE_ID_PANTHER_GO_SLIM_BP'  # via PANTHER

---

Getting Help

If you encounter an issue not covered here:

1. Check gseapy documentation: https://gseapy.readthedocs.io/ 2. Check tool parameter documentation: references/tool_parameters.md 3. Test with known good example:

   # Known working example
   genes = ["TP53", "BRCA1", "EGFR", "MYC", "AKT1", "PTEN"]
   result = gseapy.enrichr(
       gene_list=genes,
       gene_sets='GO_Biological_Process_2021',
       organism='human',
       outdir=None,
       no_plot=True,
   )

---

See also:

• ora_workflow.md - Complete ORA examples
• gsea_workflow.md - Complete GSEA examples
• tool_parameters.md - Complete parameter reference