Protein Co-expression Network Analysis

Network analysis was performed in Banner and BLSA datasets separately to identify modules of co-expressed proteins. For the Banner network, missing proteins were imputed using the k-nearest neighbor imputation function in R. Then, batch effects were removed using Combat^{64 (link)}, and age at death, sex, and PMI were regressed from the proteomic profiles using Bootstrap regression. Weighted gene co-expression network analysis (WGCNA)^{68 (link)} was used on normalized protein abundance to define protein co-expression networks. For BLSA, we used the BLSA networks from Seyfried et al.^{23 (link)}, which were previously built using proteins measured from the precuneus and prefrontal cortex in the same individual. We defined hub proteins, i.e., highly connected proteins, for each of the modules as those with intramodular kME in the top 90th percentile among the proteins in the corresponding module^{68 (link)}. Gene ontology (GO) enrichment analysis was performed on each protein co-expression module using GO Elite and Fisher exact test^{69 (link)} to glean a deeper biological understanding of these modules.