gseapy-gene-enrichment
GSEA and over-representation analysis (ORA) for RNA-seq and proteomics. Wraps Enrichr for ORA against MSigDB, KEGG, GO, and 200+ databases; runs preranked GSEA on ranked DE gene lists. Outputs enrichment tables and running-score plots. Use after DESeq2 or edgeR for pathway-level interpretation.
npx skills add BioTender-max/awesome-bio-agent-skills --skill gseapy-gene-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.
# GSEApy — Gene Set Enrichment Analysis in Python ## Overview GSEApy provides Python implementations of GSEA and over-representation analysis (ORA) for interpreting gene expression changes at the pathway level. The `enrich` module queries the Enrichr API to test a gene list against 200+ databases (GO, KEGG, MSigDB Hallmarks, Reactome, WikiPathways). The `prerank` and `gsea` modules run the GSEA algorithm on a pre-ranked gene list or expression matrix — computing normalized enrichment scores (NES) and FDR values for each gene set. GSEApy integrates directly with pandas DataFrames from DESeq2 or scanpy differential expression output, making it the standard Python tool for pathway analysis in RNA-seq workflows. ## When to Use - Interpreting DESeq2 or edgeR differential expression results at pathway/GO-term level - Running fast ORA (over-representation analysis) against Enrichr's 200+ databases including GO, KEGG, and MSigDB Hallmarks - Performing GSEA prerank analysis on a log2-fold-change-ranked gene list without an expression matrix - Identifying enriched pathways in scRNA-seq cluster marker genes - Generating publication-ready enrichment dot plots and GSEA running-score plots - Use
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Step 1: Over-Representation Analysis with Enrichr (ORA)
- Step 2: List Available Gene Set Databases
- Step 3: GSEA Prerank — Ranked Gene List Analysis
- Step 4: Plot GSEA Running Score
- Step 5: Enrichment Dot Plot for Multiple Terms
- Step 6: Integrate with DESeq2 / scanpy Output
- Key Parameters
- Common Recipes
- Recipe 1: Compare Enrichment Between Two Conditions
pip install gseapy Verify python -c "import gseapy; print(gseapy.__version__)"
What does the gseapy-gene-enrichment skill do?
GSEA and over-representation analysis (ORA) for RNA-seq and proteomics. Wraps Enrichr for ORA against MSigDB, KEGG, GO, and 200+ databases; runs preranked GSEA on ranked DE gene lists. Outputs enrichment tables and running-score plots. Use after DESeq2 or edgeR for pathway-level interpretation.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill gseapy-gene-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 BioTender-max/awesome-bio-agent-skills, a repository with 144 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.