The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
GSEApy: a comprehensive package for performing gene set enrichment analysis in Python
Open accessAbstract
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.
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Metadata
- Authors
- Fang, Z., Liu, X., & Peltz, G.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-11-25
- Volume
- 39
- Issue
- 1
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W4309944312
- Cited by
- 1,170
- References
- 6
- FWCI
- 52.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Funders
- National Institute of Health
- National Institute for Drug Addiction (5U01DA04439902)
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