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

TLDR

RNA‑seq analysis of gene read counts must account for small sample sizes, discreteness, wide dynamic range, and outliers to detect systematic changes. The authors introduce DESeq2, a method for differential analysis of count data. DESeq2 applies shrinkage estimation to dispersions and fold changes, enhancing stability and interpretability. DESeq2 enables quantitative assessment of differential expression strength, aids gene ranking and visualization, and is available as an R/Bioconductor package.

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 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.