GSEApy: a comprehensive package for performing gene set enrichment analysis in Python

• Published in: Bioinformatics (Oxford, England) Vol. 39; no. 1
• Main Authors: Fang, Zhuoqing, Liu, Xinyuan, Peltz, Gary
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.01.2023; Oxford Publishing Limited (England)
• Subjects: Algorithms; Application programming interface; Applications Note; Availability; Bioinformatics; Datasets; Documentation; Enrichment; Gene expression; Gene set enrichment analysis; Software; Source code; Web services
• ISSN: 1367-4811; 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btac757

Abstract Motivation Gene set enrichment analysis (GSEA) is a commonly used algorithm for characterizing gene expression changes. However, the currently available tools used to perform GSEA have a limited ability to analyze large datasets, which is particularly problematic for the analysis of single-cell data. To overcome this limitation, we developed a GSEA package in Python (GSEApy), which could efficiently analyze large single-cell datasets. Results We present a package (GSEApy) that performs GSEA in either the command line or Python environment. GSEApy uses a Rust implementation to enable it to calculate the same enrichment statistic as GSEA for a collection of pathways. The Rust implementation of GSEApy is 3-fold faster than the Numpy version of GSEApy (v0.10.8) and uses >4-fold less memory. GSEApy also provides an interface between Python and Enrichr web services, as well as for BioMart. The Enrichr application programming interface enables GSEApy to perform over-representation analysis for an input gene list. Furthermore, GSEApy consists of several tools, each designed to facilitate a particular type of enrichment analysis. Availability and implementation The new GSEApy with Rust extension is deposited in PyPI: https://pypi.org/project/gseapy/. The GSEApy source code is freely available at https://github.com/zqfang/GSEApy. Also, the documentation website is available at https://gseapy.rtfd.io/. Supplementary information Supplementary data are available at Bioinformatics online.