Pathway Enrichment Analysis

Runs ORA and (pre)ranked GSEA on gene lists or ranked gene tables across GO, KEGG, Reactome and MSigDB, then interprets and visualizes the results.

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

What it does

Determines which biological pathways, GO terms, or gene sets are over-represented in your genes (DE genes, CRISPR screen hits, cluster markers, proteomics hits).
Picks the right method: ORA (Enrichr, g:Profiler, Fisher/hypergeometric), preranked GSEA, or per-sample/per-cell scoring (ssGSEA/GSVA), with a decision table to avoid the classic mistakes.
Covers gene-ID mapping, background-universe selection, BH/FDR multiple-testing correction, GO redundancy reduction, dotplots and enrichment maps, and publication-ready tables.
Ships scripts/run_enrichment.py for end-to-end ORA or GSEA runs that emit a results table plus a dotplot.
Includes three deep-dive reference files on the gseapy API, gene-set databases, and statistical interpretation.

Who it's for

Researchers who just finished differential expression with PyDESeq2, edgeR, or limma and need the functional follow-up.
Single-cell analysts interpreting Scanpy rank_genes_groups marker output.
Grad students and bioinformaticians who need enrichment results defensible enough for a manuscript.

Example uses

"Run preranked GSEA on deseq2_results.csv" → builds the rank from the stat column, runs GSEA against Hallmark and GO:BP, reports NES/FDR and leading-edge genes.
"Check GO/KEGG enrichment for these 300 DEGs" → cleans symbols, runs Enrichr ORA, and sets the expressed-gene set as background so p-values aren't inflated.
"My GO output has 40 near-duplicate terms" → collapses them via enrichment map / leading-edge overlap and reports representative terms in a publication table.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/pathway-enrichment/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)

Download the ZIP with the button below.
In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
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 && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/pathway-enrichment ~/.claude/skills/

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

Open a terminal and change into the folder where you keep skills.
Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
Create the skills directory: mkdir -p ~/.claude/skills
Copy just this skill: cp -r scientific-agent-skills/skills/pathway-enrichment ~/.claude/skills/
Install Python dependencies: uv pip install gseapy gprofiler-official (or pip install gseapy gprofiler-official).
Make sure you have internet access — Enrichr, g:Profiler, and MSigDB downloads require it (offline ORA needs a local GMT file).
Restart Claude Code and ask something like "run pathway enrichment on my DE gene list" to trigger the skill.

View source on GitHub ↗License: MIT