Due to the large number of tests performed in the analysis of RNA-seq and other genome-wide experiments, the multiple testing problem needs to be addressed. A popular objective is control or estimation of the FDR. Multiple testing adjustment tends to be associated with a loss of power, in the sense that the FDR for a set of genes is often higher than the individual P values of these genes. However, the loss can be reduced if genes that have little or no chance of being detected as differentially expressed are omitted from the testing, provided that the criterion for omission is independent of the test statistic under the null hypothesis [22] (see Materials and methods). DESeq2 uses the average expression strength of each gene, across all samples, as its filter criterion, and it omits all genes with mean normalized counts below a filtering threshold from multiple testing adjustment. DESeq2 by default will choose a threshold that maximizes the number of genes found at a user-specified target FDR. In Figures 2A,B and 3, genes found in this way to be significant
