To explore the modular structures of the co-expression network, the adjacency matrix is further transformed into a topological overlap matrix118. Use of the topological overlap metric leads to more cohesive and biologically meaningful modules, since it not only represents the direct correlation between two genes but also incorporates their indirect interactions through other genes in the network 117,118. Next, to identify discrete modules of highly coregulated genes (either correlated or anti-correlated), average linkage hierarchical clustering of the genes is performed, followed by a dynamic tree-cut algorithm to dynamically cut clustering dendrogram branches into discrete subsets of gene modules119. Ordered from largest (the module containing the most genes) to smallest, each module is sequentially assigned: 1) a unique number (with higher numbers indicating smaller modules), 2) a color, and 3) a label of “c” or “s” for control or schizophrenia modules, respectively. The less well-connected genes are arbitrarily grouped in the “M0” module (grey color in the WGCNA package).
