Advantages of CEMiTool for gene co-expression analysis of RNA-seq data

Abstract

Gene co-expression analysis is widely applied to transcriptomics data to associate clusters of genes with biological functions or identify therapeutic targets in diseases. Recently, the emergence of high-throughput technologies for gene expression analyses allows researchers to establish connections through gene co-expression analysis to identify clinical disease markers. However, gene co-expression analysis is complex and may be a daunting task. Here, we evaluate three co-expression analysis packages (WGCNA, CEMiTool, and coseq) using published RNA-seq datasets derived from ischemic cardiomyopathy and chronic obstructive pulmonary disease. Results show that the packages produced consensus co-expression clusters using default parameters. CEMiTool package outperformed the other two packages and required less computational resource and bioinformatics experience. This evaluation provides a basis on which data analysts can select bioinformatics tools for gene co-expression analysis.