PyDESeq2:用于批量RNA-seq差异表达分析的python包。

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad547

Boris Muzellec, Maria Teleńczuk, Vincent Cabeli, Mathieu Andreux

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引用次数: 6

摘要

摘要:我们提出PyDESeq2，一个python实现的DESeq2工作流，用于对大量RNA-seq数据进行差异表达分析。这种重新实现产生了类似但不相同的结果:它实现了更高的模型可能性，允许在大型数据集上提高速度，如在TCGA数据上的实验所示，并且可以更容易地与现代基于python的数据科学工具接口。可用性和实现:PyDESeq2在MIT许可下作为开源软件发布。源代码可在GitHub上获得https://github.com/owkin/PyDESeq2，文档在https://pydeseq2.readthedocs.io。PyDESeq2是逆向生态系统的一部分。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

PyDESeq2: a python package for bulk RNA-seq differential expression analysis.

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PyDESeq2: a python package for bulk RNA-seq differential expression analysis.

Summary: We present PyDESeq2, a python implementation of the DESeq2 workflow for differential expression analysis on bulk RNA-seq data. This re-implementation yields similar, but not identical, results: it achieves higher model likelihood, allows speed improvements on large datasets, as shown in experiments on TCGA data, and can be more easily interfaced with modern python-based data science tools.

Availability and implementation: PyDESeq2 is released as an open-source software under the MIT license. The source code is available on GitHub at https://github.com/owkin/PyDESeq2 and documented at https://pydeseq2.readthedocs.io. PyDESeq2 is part of the scverse ecosystem.

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来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.