We used Weighted Gene Coexpression Network Analysis (WGCNA) [60] to identify co-expressed gene modules from all of our RNA-seq data. From the WGCNA modules, we identified differentially expressed modules according to the recommendation by the developers of the software. To do that, we utilized the module preservation statistic (Z-summary), which takes into account both the overlap in module membership and the density and connectivity patterns of modules, to assess the module preservation between the control and SZ samples. Technically, Z-summary < 2 implies no evidence for module preservation, 2 < Z-summary < 10 implies weak to moderate evidence, and Z-summary > 10 implies strong module preservation. In order to obtain networks of high connectivity and minimize the adverse effect of a moderate sample size, we constructed networks as follows: first, we constructed a network from the combined case and control data and identified modules within it; then, we only tested modules with preservation Z-summary > 10 between control and SZ samples for differential expression.
