Owkin France, Paris, 75009, France.
Bioinformatics. 2023 Sep 2;39(9). doi: 10.1093/bioinformatics/btad547.
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.
摘要：我们介绍了 PyDESeq2，这是一个用于批量 RNA-seq 数据差异表达分析的 DESeq2 工作流程的 Python 实现。这个重新实现的结果相似，但不完全相同：它实现了更高的模型似然性，允许在大型数据集上提高速度，如在 TCGA 数据上的实验所示，并且可以更轻松地与现代基于 Python 的数据科学工具接口。
可用性和实现：PyDESeq2 作为 MIT 许可证下的开源软件发布。源代码可在 GitHub 上的 https://github.com/owkin/PyDESeq2 获得，并在 https://pydeseq2.readthedocs.io 上有文档说明。PyDESeq2 是 scverse 生态系统的一部分。
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