gseapy-gene-enrichment
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 GSEA Java application for the official GUI-based analysis with full GSEA desktop interface
- Use fgsea (R) as an alternative with fast permutation-based p-values; GSEApy is preferred for Python-native pipelines
Prerequisites
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