Differential expression was determined using edgeR, with the option of “tagwise” dispersion. Adjustment for multiple comparisons was performed using the SGOF procedure (de Uña-Alvarez, 2012). The threshold for significance was set at adjusted p-value < 0.05, although for module enrichment we utilized unadjusted p-values < 0.01. For gene differentially variable (DV), we utilized the “var.test” procedure in the R “stats” package; the threshold for significance was also set at adjusted p-value < 0.05. To mitigate the computational load for detecting differential wiring (DW), we restricted the search to Pearson correlations between individual genes that differed by >0.5. This general procedure has been used to quantify network rewiring in both genomic (Gill et al., 2010) and neural imaging studies (Hosseini et al., 2012). Using this procedure, we identified for each gene, the number of changed edges and then inquired as to whether some genes had a disproportionately high number of changing edges. For the latter, the binomial test was used with the following parameters. The average incidence of changing edges (the rate of the binomial test) was computed by dividing the
