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Demonstrates improved RNA-seq analysis precision using PyDESeq2, indicating benefits for data scientists.
Summary We present PyDESeq2, a python implementation of the DESeq2 workflow for differential expression analysis on bulk RNA-seq data. This implementation achieves better precision, 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 . Contact boris.muzellec@owkin.com
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Muzellec et al. (2022) studied this question.
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