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Pathway Enrichment

Run and interpret ORA/GSEA pathway enrichment (GO, KEGG, Reactome, MSigDB) from gene lists or ranked gene tables.

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 8/10Last updated: Aug 9, 2026

What it does

Takes a gene set — DE genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster markers, proteomics hits — and tells you which biological pathways are over-represented or enriched.

  • ORA: Fisher/hypergeometric testing on thresholded lists (Enrichr, g:Profiler)
  • Preranked GSEA: full ranked list using e.g. the DESeq2 stat column
  • ssGSEA / GSVA: per-sample or per-cell pathway activity scores
  • Plus gene-ID mapping, background-universe selection, FDR correction, GO redundancy collapsing, dotplots/enrichment maps, and publication-ready tables

A bundled scripts/run_enrichment.py runs ORA or GSEA end-to-end and writes a table plus a dotplot.

Who it's for

  • RNA-seq / single-cell researchers who need the biology-interpretation step after differential expression
  • Students unsure whether to use ORA or GSEA, or how to pick a background universe
  • Anyone who needs a reproducible, reviewer-defensible enrichment workflow

Example uses

  1. "Run preranked GSEA on deseq2_results.csv against Hallmark and GO:BP" → builds the rank from stat, runs gp.prerank, returns an NES/FDR table
  2. "ORA these 350 DEGs on KEGG and Reactome using all expressed genes as background" → switches to g:Profiler with domain_scope='custom'
  3. "My GO output has 40 near-duplicate terms" → collapses via enrichment map / leading-edge overlap and reports representatives

· · · Install guide · · ·

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/pathway-enrichment folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/pathway-enrichment/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills && mkdir -p ~/.claude/skills && cp -r /tmp/scientific-agent-skills/skills/pathway-enrichment ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal.
  2. Clone the repo: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Copy the skill: mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/pathway-enrichment ~/.claude/skills/
  4. Install Python deps: uv pip install gseapy gprofiler-official (or use pip).
  5. Network access is required for Enrichr, g:Profiler and MSigDB downloads; for fully offline ORA use a local GMT file with gp.enrich().
  6. Restart Claude Code and ask something like "run GO enrichment on my DEG list" to trigger the skill.
View source on GitHub ↗License: MIT