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:

• Find enriched GO terms / KEGG / Reactome / WikiPathways / MSigDB Hallmark sets in a gene list.
• Run GSEA / preranked GSEA on DESeq2, edgeR, limma, or Scanpy rank_genes_groups output.
• Score pathway activity per sample/cell (ssGSEA, GSVA).
• Interpret, deduplicate, and visualize enrichment results, or build a publication table/figure.
• Decide between ORA and GSEA, pick gene-set libraries, choose a background, or fix gene-ID problems.