Individual skill
pathway-enrichment
Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Used when the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways — plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dotplots/enrichment maps, and publication-ready tables. Use this for "pathway analysis", "enrichment analysis", "GO enrichment", "KEGG/Reactome pathways", "GSEA", "over-representation", "functional annotation", or "what pathways are my genes in".
skills/pathway-enrichment/SKILL.md
Source commit 2d2065fb4520fa3f3f2bcf61cff26ec8b7eba4b5
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- Last checked
- Nov 10, 2026
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- Repository last updated
- Nov 9, 2026
- License
- MIT
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- ai-scientist Repository topic
- bioinformatics Repository topic
- chemoinformatics Repository topic
- claude Repository topic
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# Pathway Enrichment
## Overview
Enrichment analysis answers "what biology is over-represented in my genes?" It is the standard last step after differential expression, a screen, or clustering. There are two core methods, and choosing correctly is the single most important decision:
- **ORA (over-representation analysis)** — take a *thresholded* gene list (e.g., padj < 0.05) and test which gene sets it overlaps more than chance, using Fisher's exact / hypergeometric tests. Tools: Enrichr, g:Profiler.
- **GSEA (gene set enrichment analysis)** — take the *whole ranked list* of genes (no threshold) and test whether each gene set is concentrated toward the top or bottom. Preranked GSEA uses a per-gene score (e.g., the DESeq2 `stat`). Better when effects are broad and subtle.
This skill orchestrates these analyses, the gene-set databases behind them, and the interpretation pitfalls that make results wrong or unpublishable.
## When to Use This Skill
Use this skill when the user wants to: