Figure 3

Human brain expression data illustrate how CPBA can be interpreted as a generalization of WGCNA. A) Hierarchical cluster tree based on WGCNA. Color bands show the WGCNA modules (first band), CPBA modules identified by propensity clustering (second band), and the modules identified by Oldham et al[9]. CPBA yields modules very similar to those identified by WGCNA. The overlap with the well annotated modules of Oldham et al[4] confirms that these clustering procedures yield meaningful modules. B) The intermodular adjacency calculated using CPBA (y-axis) is stronly correlated (r = 0.93) with its WGCNA counterpart, the correlation between eigengenes raised to the soft thresholding power. C) For nodes restricted to module 1 (turquoise in the color bands in panel A), CPBA propensity is highly correlated with its WGCNA counterpart, the module membership, kME (Eq. 3) raised to the soft thresholding power. D) and E) show analogous scatter plots for modules 2 (blue) and 3 (brown), respectively. F) The co-expression network exhibits approximate scale free topology (SFT). Specifically, the x-axis corresponds to equal width bins of the logarithm (base 10) of the connectivity $k_i=\sum _{j\ne i}{A}_{\mathit{\text{ij}}}$ (Eq. 1), and the y-axis reports the corresponding logarithm of the frequency. The approximate straight line relationship (linear model fitting index R²=0.91) indicates that SFT fits very well. G) evaluates SFT for CPBA connectivity defined by the right-hand side of Eq. 7. H) evaluates SFT for the propensity p _i only. I) The CPBA connectivity (y-axis) is highly correlated (r = 0.96) with connectivity k _i in the correlation network (x-axis). Genes are colored according to module assignment (PropClust color band in panel A. J) There is a high correlation (r = 0.88) between k _i (x-axis) and propensity (y-axis). K) There is a high correlation (r = 0.93) between CPBA based connectivity (x-axis) and propensity (y-axis).