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

被引：348

作者：

Fang, Zhuoqing ^{[1
]}

Liu, Xinyuan ^{[2
]}

Peltz, Gary ^{[1
]}

机构：

[1] Stanford Univ, Dept Anesthesia Pain & Perioperat Med, Sch Med, Stanford, CA 94305 USA

[2] Stanford Univ, Sch Med, Dept Otolaryngol Head & Neck Surg, Stanford, CA 94305 USA

来源：

BIOINFORMATICS | 2023年 / 39卷 / 01期

关键词：

CELL; SIGNATURE; CANCER;

D O I：

10.1093/bioinformatics/btac757

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页数：3

共 19 条

[1] Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1 [J].

Barbie, David A. ;

Tamayo, Pablo ;

Boehm, Jesse S. ;

Kim, So Young ;

Moody, Susan E. ;

Dunn, Ian F. ;

Schinzel, Anna C. ;

Sandy, Peter ;

Meylan, Etienne ;

Scholl, Claudia ;

Froehling, Stefan ;

Chan, Edmond M. ;

Sos, Martin L. ;

Michel, Kathrin ;

Mermel, Craig ;

Silver, Serena J. ;

Weir, Barbara A. ;

Reiling, Jan H. ;

Sheng, Qing ;

Gupta, Piyush B. ;

Wadlow, Raymond C. ;

Le, Hanh ;

Hoersch, Sebastian ;

Wittner, Ben S. ;

Ramaswamy, Sridhar ;

Livingston, David M. ;

Sabatini, David M. ;

Meyerson, Matthew ;

Thomas, Roman K. ;

Lander, Eric S. ;

Mesirov, Jill P. ;

Root, David E. ;

Gilliland, D. Gary ;

Jacks, Tyler ;

Hahn, William C. .

NATURE, 2009, 462 (7269) :108-U122

[2] Enrichr: interactive and collaborative HTML']HTML5 gene list enrichment analysis tool [J].

Chen, Edward Y. ;

Tan, Christopher M. ;

Kou, Yan ;

Duan, Qiaonan ;

Wang, Zichen ;

Meirelles, Gabriela Vaz ;

Clark, Neil R. ;

Ma'ayan, Avi .

BMC BIOINFORMATICS, 2013, 14

[3] Stem cell-like ALDHbright cellular states in EGFR-mutant non-small cell lung cancer A novel mechanism of acquired resistance to erlotinib targetable with the natural polyphenol silibinin [J].

Corominas-Faja, Bruna ;

Oliveras-Ferraros, Cristina ;

Cuyas, Elisabet ;

Segura-Carretero, Antonio ;

Joven, Jorge ;

Martin-Castillo, Begona ;

Barrajon-Catalan, Enrique ;

Micol, Vicente ;

Bosch-Barrera, Joaquim ;

Menendez, Javier A. .

CELL CYCLE, 2013, 12 (21) :3390-3404

[4] BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis [J].

Durinck, S ;

Moreau, Y ;

Kasprzyk, A ;

Davis, S ;

De Moor, B ;

Brazma, A ;

Huber, W .

BIOINFORMATICS, 2005, 21 (16) :3439-3440

[5] A human multi-lineage hepatic organoid model for liver fibrosis [J].

Guan, Yuan ;

Enejder, Annika ;

Wang, Meiyue ;

Fang, Zhuoqing ;

Cui, Lu ;

Chen, Shih-Yu ;

Wang, Jingxiao ;

Tan, Yalun ;

Wu, Manhong ;

Chen, Xinyu ;

Johansson, Patrik K. ;

Osman, Issra ;

Kunimoto, Koshi ;

Russo, Pierre ;

Heilshorn, Sarah C. ;

Peltz, Gary .

NATURE COMMUNICATIONS, 2021, 12 (01)

[6] Multiplexed droplet single-cell RNA-sequencing using natural genetic variation [J].

Kang, Hyun Min ;

Subramaniam, Meena ;

Targ, Sasha ;

Michelle Nguyen ;

Maliskova, Lenka ;

McCarthy, Elizabeth ;

Wan, Eunice ;

Wong, Simon ;

Byrnes, Lauren ;

Lanata, Cristina M. ;

Gate, Rachel E. ;

Mostafavi, Sara ;

Marson, Alexander ;

Zaitlen, Noah ;

Criswell, Lindsey A. ;

Ye, Chun Jimmie .

NATURE BIOTECHNOLOGY, 2018, 36 (01) :89-+

[7] Challenges in unsupervised clustering of single-cell RNA-seq data [J].

Kiselev, Vladimir Yu ;

Andrews, Tallulah S. ;

Hemberg, Martin .

NATURE REVIEWS GENETICS, 2019, 20 (05) :273-282

[8]

Korotkevich G., 2021, bioRxiv, DOI DOI 10.1101/060012

[9] Enrichr: a comprehensive gene set enrichment analysis web server 2016 update [J].

Kuleshov, Maxim V. ;

Jones, Matthew R. ;

Rouillard, Andrew D. ;

Fernandez, Nicolas F. ;

Duan, Qiaonan ;

Wang, Zichen ;

Koplev, Simon ;

Jenkins, Sherry L. ;

Jagodnik, Kathleen M. ;

Lachmann, Alexander ;

McDermott, Michael G. ;

Monteiro, Caroline D. ;

Gundersen, Gregory W. ;

Ma'ayan, Avi .

NUCLEIC ACIDS RESEARCH, 2016, 44 (W1) :W90-W97

[10] Molecular profiling stratifies diverse phenotypes of treatment-refractory metastatic castration-resistant prostate cancer [J].

Labrecque, Mark P. ;

Coleman, Ilsa M. ;

Brown, Lisha G. ;

True, Lawrence D. ;

Kollath, Lori ;

Lakely, Bryce ;

Nguyen, Holly M. ;

Yang, Yu C. ;

da Costa, Rui M. Gil ;

Kaipainen, Arja ;

Coleman, Roger ;

Higano, Celestia S. ;

Yu, Evan Y. ;

Cheng, Heather H. ;

Mostaghel, Elahe A. ;

Montgomery, Bruce ;

Schweizer, Michael T. ;

Hsieh, Andrew C. ;

Lin, Daniel W. ;

Corey, Eva ;

Nelson, Peter S. ;

Morrissey, Colm .

JOURNAL OF CLINICAL INVESTIGATION, 2019, 129 (10) :4492-4505

← 1 2 →