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 librari
npx skills add K-Dense-AI/scientific-agent-skills --skill pathway-enrichment --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Overview
- When to Use This Skill
- Choosing the Right Method
- Setup
- Quick Start
- ORA on a hit list (gseapy + Enrichr)
- Preranked GSEA from DESeq2 results
- Core Workflow
- Step 1 — Pin down inputs and pick the method
- Step 2 — Get gene IDs into the right namespace
- Step 3 — Choose gene-set libraries to match the question
- Step 4 — Set the background universe (ORA only)
- Step 5 — Run the analysis
- Step 6 — Filter on adjusted p-values
uv pip install gseapy gprofiler-official gseapy pulls pandas, numpy, scipy, matplotlib. Network access is needed for GMT file with gp.enrich() (see references/gseapy.md). ORA from a hit list (one gene symbol per line) python scripts/run_enrichment.py ora \ Preranked GSEA from a DESeq2 results CSV (auto-builds the rank from `stat`) python scripts/run_enrichment.py gsea \ Preranked GSEA from an explicit 2-column rank file (gene,score) python scripts/run_enrichment.py gsea --rnk ranked_genes.csv --outdir results/
What does the pathway-enrichment skill do?
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 librari
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill pathway-enrichment --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.