pathway-enrichment
Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results. Use whenever 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".
- Version
- 1.1
- License
- MIT
Pinned to revision 70a605012eee, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/pathway-enrichment/SKILL.md
- skills/pathway-enrichment/references/databases-and-gene-sets.md
- skills/pathway-enrichment/references/gseapy.md
- skills/pathway-enrichment/references/interpretation.md
- skills/pathway-enrichment/scripts/run_enrichment.py
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