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Bioinformatics ; 39(9)2023 09 02.
Artigo em Inglês | MEDLINE | ID: mdl-37669147

RESUMO

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.


Assuntos
Ciência de Dados , Ecossistema , RNA-Seq , Probabilidade , Software
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