Treffer: PyDESeq2: a python package for bulk RNA-seq differential expression analysis.
Title:
PyDESeq2: a python package for bulk RNA-seq differential expression analysis.
Authors:
Muzellec B; Owkin France, Paris, 75009, France., Teleńczuk M; Owkin France, Paris, 75009, France., Cabeli V; Owkin France, Paris, 75009, France., Andreux M; Owkin France, Paris, 75009, France.
Source:
Bioinformatics (Oxford, England) [Bioinformatics] 2023 Sep 02; Vol. 39 (9).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: Oxford : Oxford University Press, c1998-
MeSH Terms:
References:
Nat Methods. 2020 Mar;17(3):261-272. (PMID: 32015543)
Nat Rev Genet. 2019 Nov;20(11):631-656. (PMID: 31341269)
Nat Biotechnol. 2023 May;41(5):604-606. (PMID: 37037904)
Genome Biol. 2014;15(12):550. (PMID: 25516281)
Nat Methods. 2022 Feb;19(2):171-178. (PMID: 35102346)
Bioinformatics. 2019 Jun 1;35(12):2084-2092. (PMID: 30395178)
Genome Biol. 2018 Feb 6;19(1):15. (PMID: 29409532)
Nat Rev Genet. 2019 Nov;20(11):631-656. (PMID: 31341269)
Nat Biotechnol. 2023 May;41(5):604-606. (PMID: 37037904)
Genome Biol. 2014;15(12):550. (PMID: 25516281)
Nat Methods. 2022 Feb;19(2):171-178. (PMID: 35102346)
Bioinformatics. 2019 Jun 1;35(12):2084-2092. (PMID: 30395178)
Genome Biol. 2018 Feb 6;19(1):15. (PMID: 29409532)
Entry Date(s):
Date Created: 20230905 Date Completed: 20230918 Latest Revision: 20230918
Update Code:
20250114
PubMed Central ID:
PMC10502239
DOI:
10.1093/bioinformatics/btad547
PMID:
37669147
Database:
MEDLINE
Weitere Informationen
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.
(© The Author(s) 2023. Published by Oxford University Press.)