RT Journal Article
SR Electronic
T1 Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 002832
DO 10.1101/002832
A1 Michael I Love
A1 Wolfgang Huber
A1 Simon Anders
YR 2014
UL http://biorxiv.org/content/early/2014/05/27/002832.abstract
AB In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-Seq data, 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. DESeq2 uses shrinkage estimation for dispersions and fold changes to improve stability and interpretability of the estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression and facilitates downstream tasks such as gene ranking and visualization. DESeq2 is available as an R/Bioconductor package.