Journal
BIOINFORMATICS
Volume 39, Issue 1, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btac757
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This study developed a GSEA package (GSEApy) in Python that efficiently analyzes large datasets, using a Rust implementation to calculate the same enrichment statistic as GSEA. GSEApy is 3-fold faster than the Numpy version, has a smaller memory footprint, and provides interfaces with Enrichr and BioMart for additional enrichment analysis.
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/. Contact: gpeltz@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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