Gene Enrichment and Pathway Analysis

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. 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 Parameter Required Description Example gene_list Yes List of gene symbols, Ensembl IDs, or Entrez IDs ["TP53", "BRCA1", "EGFR"] organism No Organism (default: human). Supported: human, mouse, rat, fly, worm, yeast, zebrafish human analysis_type No ORA (default) or GSEA ORA enrichment_databases No Which databases to query. Default: all applicable ["GO_BP", "GO_MF", "GO_CC", "KEGG", "Reactome"] gene_id_type No Input ID type: symbol , ensembl , entrez , uniprot (auto-detected if omitted) symbol p_value_cutoff No Significance threshold (default: 0.05) 0.05 correction_method No Multiple testing: BH (Benjamini-Hochberg, default), bonferroni , fdr BH background_genes No Custom background gene set (default: genome-wide) ["GENE1", "GENE2", ...] ranked_gene_list No For GSEA: gene-to-score mapping (e.g., log2FC) {"TP53": 2.5, "BRCA1": -1.3, ...} Core Principles Report-first approach - Create report file FIRST, then populate progressively ID disambiguation FIRST - Detect and convert gene IDs before ANY enrichment Multi-source validation - Run enrichment on at least 2 independent tools, cross-validate Exact p-values - Report raw p-values AND adjusted p-values with correction method Multiple testing correction - ALWAYS apply Benjamini-Hochberg unless user specifies otherwise Gene set size filtering - Filter by min/max gene set size to avoid trivial/overly broad terms Evidence grading - Grade enrichment sources T1-T4 Negative results documented - "No significant enrichment" is a valid finding Source references - Every enrichment result must cite the tool/database/library used 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 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 Step 1: Create Report File (IMMEDIATE) report_path = f" { analysis_name } _enrichment_report.md"

Write header with placeholder sections

Update progressively as analysis proceeds

Step 2: ID Conversion and Validation from tooluniverse import ToolUniverse tu = ToolUniverse ( ) tu . load_tools ( )

Detect ID type

gene_list

[ "TP53" , "BRCA1" , "EGFR" ]

Auto-detect: ENSG* = Ensembl, numeric = Entrez, pattern = UniProt, else = Symbol

Convert if needed (Ensembl/Entrez → Symbol)

result

tu . tools . MyGene_batch_query ( gene_ids = gene_list , fields = "symbol,entrezgene,ensembl.gene" )

Extract symbols from results

Validate with STRING

mapped

tu . tools . STRING_map_identifiers ( protein_ids = gene_symbols , species = 9606

human

)

Use preferredName for canonical symbols

See

references/id_conversion.md for complete examples Step 3: Primary Enrichment with gseapy For ORA (gene list only) : import gseapy

GO Biological Process

go_bp

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

None = genome-wide

) go_bp_sig = go_bp . results [ go_bp . results [ 'Adjusted P-value' ] < 0.05 ] For GSEA (ranked gene list) : import pandas as pd

Ranked by log2FC

ranked_series

pd . Series ( gene_to_score ) . sort_values ( ascending = False ) gsea_result = gseapy . prerank ( rnk = ranked_series , gene_sets = 'GO_Biological_Process_2021' , outdir = None , no_plot = True , seed = 42 , min_size = 5 , max_size = 500 , permutation_num = 1000 ) gsea_sig = gsea_result . res2d [ gsea_result . res2d [ 'FDR q-val' ] < 0.25 ] See : references/ora_workflow.md for complete ORA examples references/gsea_workflow.md for complete GSEA examples references/enrichr_guide.md for all 225+ libraries Step 4: Cross-Validation with ToolUniverse

PANTHER [T1 - curated]

panther_bp

tu . tools . PANTHER_enrichment ( gene_list = ',' . join ( gene_symbols ) ,

comma-separated string

organism

9606 , annotation_dataset = 'GO:0008150'

biological_process

)

STRING [T2 - validated]

string_result

tu . tools . STRING_functional_enrichment ( protein_ids = gene_symbols , species = 9606 )

Filter by category: Process, Function, Component, KEGG, Reactome

Reactome [T1 - curated]

reactome_result

tu . tools . ReactomeAnalysis_pathway_enrichment ( identifiers = ' ' . join ( gene_symbols ) ,

space-separated

page_size

50

,

include_disease

=

True

)

See

references/cross_validation.md for comparison strategies Step 5: Report Compilation

Results

GO Biological Process (Top 10) | Term | P-value | Adj. P-value | Overlap | Genes | Evidence | |

|

|

|

|

|

| | regulation of cell cycle (GO:0051726) | 1.2e-08 | 3.4e-06 | 12/45 | TP53;BRCA1;... | [T2] gseapy |

Cross-Validation | GO Term | gseapy FDR | PANTHER FDR | STRING FDR | Consensus | |

|

|

|

|

| | GO:0051726 | 3.4e-06 | 2.1e-05 | 1.8e-05 | 3/3 ✓ |

Completeness Checklist

[x] ID Conversion (MyGene, STRING) - 95% mapped

[x] GO BP (gseapy, PANTHER, STRING) - 24 significant terms

[x] GO MF (gseapy, PANTHER, STRING) - 18 significant terms

[x] GO CC (gseapy, PANTHER, STRING) - 12 significant terms

[x] KEGG (gseapy, STRING) - 8 significant pathways

[x] Reactome (gseapy, ReactomeAPI) - 15 significant pathways

[x] Cross-validation - 12 consensus terms (2+ sources)

See

scripts/format_enrichment_output.py for automated formatting

Evidence Grading

Tier

Symbol

Criteria

Examples

T1

[T1]

Curated/experimental enrichment

PANTHER, Reactome Analysis Service

T2

[T2]

Computational enrichment, well-validated

gseapy ORA/GSEA, STRING functional enrichment

T3

[T3]

Text-mining/predicted enrichment

Enrichr non-curated libraries

T4

[T4]

Single-source annotation

Individual gene GO annotations from QuickGO

Supported Organisms

Organism

Taxonomy ID

gseapy

PANTHER

STRING

Reactome

Human

9606

Yes

Yes

Yes

Yes

Mouse

10090

Yes (

*_Mouse

)

Yes

Yes

Yes (projection)

Rat

10116

Limited

Yes

Yes

Yes (projection)

Fly

7227

Limited

Yes

Yes

Yes (projection)

Worm

6239

Limited

Yes

Yes

Yes (projection)

Yeast

4932

Limited

Yes

Yes

Yes

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: BixBench 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 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

Tool

Input

Output

Use For

gseapy.enrichr()

gene_list, gene_sets, organism

.results

DataFrame

ORA with 225+ libraries

gseapy.prerank()

rnk (ranked Series), gene_sets

.res2d

DataFrame

GSEA analysis

Cross-Validation Tools

Tool

Key Parameters

Evidence Grade

PANTHER_enrichment

gene_list (comma-sep), organism, annotation_dataset

[T1]

STRING_functional_enrichment

protein_ids, species

[T2]

ReactomeAnalysis_pathway_enrichment

identifiers (space-sep), page_size

[T1]

ID Conversion Tools

Tool

Input

Output

MyGene_batch_query

gene_ids, fields

Symbol, Entrez, Ensembl mappings

STRING_map_identifiers

protein_ids, species

Preferred names, STRING IDs

See

references/tool_parameters.md for complete parameter documentation 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: scripts/format_enrichment_output.py - Format results for reports scripts/compare_enrichment_sources.py - Cross-validation analysis scripts/filter_by_gene_set_size.py - Filter terms by size 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/