Our approach is similar to the one used by DSS [6], in that both methods sequentially estimate a prior distribution for the true dispersion values around the fit, and then provide the maximum a posteriori (MAP) as the final estimate. It differs from the previous implementation of DESeq, which used the maximum of the fitted curve and the gene-wise dispersion estimate as the final estimate and tended to overestimate the dispersions (Additional file 1: Figure S2). The approach of DESeq2 differs from that of edgeR [3], as DESeq2 estimates the width of the prior distribution from the data and therefore automatically controls the amount of shrinkage based on the observed properties of the data. In contrast, the default steps in edgeR require a user-adjustable parameter, the prior degrees of freedom, which weighs the contribution of the individual gene estimate and edgeR’s dispersion fit.
