GSEApy: a comprehensive package for performing gene set enrichment analysis in Python.
GSEApy: a comprehensive package for performing gene set enrichment analysis in Python.
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DOI：
10.1093/bioinformatics/btac757
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发表时间：
2023-01-01
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基因集富集分析（GSEA）是一种常用的算法，用于表征基因表达的变化。然而，目前用于执行GSEA的工具分析大型数据集的能力有限，这对于单细胞数据的分析尤其成问题。为了克服这个限制，我们在Python中开发了一个GSEA包（GSEApy），它可以有效地分析大型单细胞数据集。我们提供了一个在命令行或Python环境中执行GSEA的包（GSEApy）。GSEApy使用Rust实现，使其能够为路径集合计算与GSEA相同的富集统计。GSEApy的Rust实现比GSEApy的Numpy版本（v0.10.8）快3倍，并且使用的内存少4倍。GSEApy还提供了Python和Enrichr Web服务之间的接口，以及BioMart。Enrichr应用程序编程接口使GSEApy能够对输入基因列表进行过度表达分析。此外，GSEApy由若干工具组成，每种工具都旨在促进特定类型的富集分析。带有Rust扩展的新GSEApy存放在PyPI：https://pypi.org/project/gseapy/。GSEApy源代码可以在https://github.com/zqfang/GSEApy上免费获得。此外，文档网站也可在https://gseapy.rtfd.io/上查阅。 补充数据可在Bioinformatics在线获得。
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. 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. 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 data are available at Bioinformatics online.
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影响因子：
14.9
作者：
Kuleshov MV;Jones MR;Rouillard AD;Fernandez NF;Duan Q;Wang Z;Koplev S;Jenkins SL;Jagodnik KM;Lachmann A;McDermott MG;Monteiro CD;Gundersen GW;Ma'ayan A
通讯作者：
Ma'ayan A
影响因子：
16.6
作者：
Guan Y;Enejder A;Wang M;Fang Z;Cui L;Chen SY;Wang J;Tan Y;Wu M;Chen X;Johansson PK;Osman I;Kunimoto K;Russo P;Heilshorn SC;Peltz G
通讯作者：
Peltz G
影响因子：
7
作者：
Lakkis J;Wang D;Zhang Y;Hu G;Wang K;Pan H;Ungar L;Reilly MP;Li X;Li M
通讯作者：
Li M
影响因子：
5.8
作者：
Lachmann, Alexander;Xie, Zhuorui;Ma'ayan, Avi
通讯作者：
Ma'ayan, Avi
影响因子：
4
作者：
Wang, Zhe;Wang, Zhongmiao;Qin, Baoli
通讯作者：
Qin, Baoli