Here we present DESeq2, a successor to our DESeq method [4]. DESeq2 integrates methodological advances with several novel features to facilitate a more quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change. We demonstrate the advantages of DESeq2’s new features by describing a number of applications possible with shrunken fold changes and their estimates of standard error, including improved gene ranking and visualization, hypothesis tests above and below a threshold, and the regularized logarithm transformation for quality assessment and clustering of overdispersed count data. We furthermore compare DESeq2’s statistical power with existing tools, revealing that our methodology has high sensitivity and precision, while controlling the false positive rate. DESeq2 is available [10] as an R/Bioconductor package [11].
