Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Genome Biology
Michael I LoveSimon Anders

Abstract

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html webcite.

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Citations

Feb 26, 2016·Frontiers in Microbiology·Lilian Dos Santos CastroRoberto N Silva
Feb 18, 2016·Proceedings of the National Academy of Sciences of the United States of America·Katherine M Digianantonio, Michael H Hecht
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Nov 18, 2015·RNA Biology·Marta Lloret-LlinaresTorben Heick Jensen
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Datasets Mentioned

BETA
SRP001540
SRP004777

Methods Mentioned

BETA
immunoprecipitation
RNA-seq

Software Mentioned

baySeq
SAMseq
PoiClaClu package
cqn
count
BBSeq
robust
DSS
R
GenomicRanges

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