After GLMs are fit for each gene, one may test whether each model coefficient differs significantly from zero. DESeq2 reports the standard error for each shrunken LFC estimate, obtained from the curvature of the coefficient’s posterior (dashed lines in Figure 2D) at its maximum. For significance testing, DESeq2 uses a Wald test: the shrunken estimate of LFC is divided by its standard error, resulting in a z-statistic, which is compared to a standard normal distribution. (See Materials and methods for details.) The Wald test allows testing of individual coefficients, or contrasts of coefficients, without the need to fit a reduced model as with the likelihood ratio test, though the likelihood ratio test is also available as an option in DESeq2. The Wald test P values from the subset of genes that pass an independent filtering step, described in the next section, are adjusted for multiple testing using the procedure of Benjamini and Hochberg [21].
