Comparison of Methods for Differential Co-expression Analysis for Disease Biomarker Prediction

In the recent past, a number of methods have been developed for analysis of biological data. Among these methods, gene co-expression networks have the ability to mine functionally related genes with similar co-expression patterns, because of which such networks have been most widely used. However, gene co-expression networks cannot identify genes, which undergo condition specific changes in their relationships with other genes. In contrast, differential co-expression analysis enables finding co-expressed genes exhibiting significant changes across disease conditions. In this paper, we present some significant outcomes of a comparative study of four co-expression network module detection techniques, namely, THD-Module Extractor, DiffCoEx, MODA, and WGCNA, which can perform differential co-expression analysis on both gene and miRNA expression data (microarray and RNA-seq) and discuss the applications to Alzheimer's disease and Parkinson's disease research. Our observations reveal that compared to other methods, THD-Module Extractor is the most effective in finding modules with higher functional relevance and biological significance.