Differential co-expression network centrality and machine learning feature selection for identifying susceptibility hubs in networks with scale-free structure

In co-expression analysis, the correlation between pairs of genes is typically combined into a network model of the correlation structure, which facilitates secondary network analysis such as community structure or centrality [1]. However, the correlation between pairs of genes in a co-expression network typically is assumed to be uniform across all samples (e.g., tissue types, treatment conditions, disease status, etc.). Yet it is often inter-group differences in correlated data that are of biological or clinical interest. For example, a gene co-expression network in microarray data for chronic lymphocytic leukemia using known biomarkers was able to predict treatment outcomes in an independent sample [2]. A differential co-expression network approach that leverages the genetic network information may yield novel biomarkers and improved prediction. Differential expression methods compute the mean difference between groups for each gene but typically do not incorporate conditional variation from other genes in the data that may help explain the between-group variation.