DESeq2 offers a comprehensive and general solution for gene-level analysis of RNA-seq data. Shrinkage estimators substantially improve the stability and reproducibility of analysis results compared to maximum-likelihood-based solutions. Empirical Bayes priors provide automatic control of the amount of shrinkage based on the amount of information for the estimated quantity available in the data. This allows DESeq2 to offer consistent performance over a large range of data types and makes it applicable for small studies with few replicates as well as for large observational studies. DESeq2’s heuristics for outlier detection help to recognize genes for which the modeling assumptions are unsuitable and so avoids type-I errors caused by these. The embedding of these strategies in the framework of GLMs enables the treatment of both simple and complex designs.
