{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/7"},{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"}],"enrichment":{"capability":"Performs gene set enrichment analysis (GSEA) on genomic data using Python, supporting multiple methods including prerank, ssGSEA, GSVA, and Enrichr API integration.","skillfed_tags":["bioinformatics","genomics","enrichment-analysis"],"use_cases":["Identify significantly enriched biological pathways in RNA-seq differential expression results using prerank with gene rankings.","Perform single-sample enrichment scoring across multiple samples to assess pathway activity per sample.","Batch process multiple genomic datasets in a snakemake or Python workflow without manual GSEA desktop invocations.","Reproduce GSEA desktop results programmatically using the replot module on existing GSEA output directories.","Query Enrichr libraries for functional annotation of gene lists via the enrichr module API."],"what_it_does":"GSEApy is a Python/Rust implementation of gene set enrichment analysis that brings GSEA functionality and Enrichr API access into a Python environment. It supports multiple analysis methods\u2014standard GSEA, prerank (with a faithful Rust port of fgsea's multilevel algorithm for resolving p-values below 1/permutation_num), single-sample GSEA (ssGSEA), and GSVA\u2014making it suitable for RNA-seq, ChIP-seq, and microarray data. The package can work with expression matrices, ranked gene lists, or Enrichr libraries, and produces publication-quality figures via matplotlib.\n\nThe package is designed for both interactive Python workflows and command-line batch processing. It accepts standard GSEA file formats (GMT, GCT, RNK, CLS) and can work directly with pandas DataFrames and Series, eliminating the need to switch between Python and desktop GSEA tools. A biomart module also enables gene ID conversion. Runtime dependencies are numpy, scipy, pandas, matplotlib, and requests\u2014all widely used scientific Python libraries.","worth_installing":"Yes. GSEApy is actively maintained, has no known vulnerabilities, carries a permissive BSD-3-Clause license, and fills a genuine need for GSEA functionality within Python workflows. The medium install friction (Rust compilation) is a one-time cost offset by broad wheel availability for modern Python versions (3.10\u20133.13) and clear fallback instructions. Suitable for bioinformaticians and computational biologists working with genomic data."},"id":"gseapy","links":{"html":"https://skillfed.io/packages/gseapy","md":"https://skillfed.io/packages/gseapy.md","pypi":"https://pypi.org/project/gseapy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-26","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"gseapy","python_support":"supports_current","summary":"Gene Set Enrichment Analysis in Python"},"popularity":{"monthly_downloads":249903,"position":8643,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.3.1"}