{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:55:31Z","timestamp":1782280531343,"version":"3.54.5"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:00:00Z","timestamp":1669334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Health"},{"name":"National Institute for Drug Addiction","award":["5U01DA04439902"],"award-info":[{"award-number":["5U01DA04439902"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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 &amp;gt;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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>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\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac757","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T12:59:26Z","timestamp":1669381166000},"source":"Crossref","is-referenced-by-count":1086,"title":["GSEApy: a comprehensive package for performing gene set enrichment analysis in Python"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7418-1313","authenticated-orcid":false,"given":"Zhuoqing","family":"Fang","sequence":"first","affiliation":[{"name":"Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine , Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9754-0593","authenticated-orcid":false,"given":"Xinyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine , Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6191-7697","authenticated-orcid":false,"given":"Gary","family":"Peltz","sequence":"additional","affiliation":[{"name":"Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine , Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"2023010107541886800_btac757-B1","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1038\/nature08460","article-title":"Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1","volume":"462","author":"Barbie","year":"2009","journal-title":"Nature"},{"key":"2023010107541886800_btac757-B2","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1186\/1471-2105-14-128","article-title":"Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool","volume":"14","author":"Chen","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2023010107541886800_btac757-B3","doi-asserted-by":"crossref","first-page":"3390","DOI":"10.4161\/cc.26417","article-title":"Stem cell-like ALDH(bright) cellular states in EGFR-mutant non-small cell lung cancer: a novel mechanism of acquired resistance to erlotinib targetable with the natural polyphenol silibinin","volume":"12","author":"Corominas-Faja","year":"2013","journal-title":"Cell Cycle"},{"key":"2023010107541886800_btac757-B4","doi-asserted-by":"crossref","first-page":"3439","DOI":"10.1093\/bioinformatics\/bti525","article-title":"BioMart and bioconductor: a powerful link between biological databases and microarray data analysis","volume":"21","author":"Durinck","year":"2005","journal-title":"Bioinformatics"},{"key":"2023010107541886800_btac757-B6","doi-asserted-by":"crossref","first-page":"6138","DOI":"10.1038\/s41467-021-26410-9","article-title":"A human multi-lineage hepatic organoid model for liver fibrosis","volume":"12","author":"Guan","year":"2021","journal-title":"Nat. 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