Abstract
A number of methods are being developed and used for analysis of gene expression data such as RNA-Seq data. Most of these tools focus on finding genes that are responsible for the disease conditions. Methods such as co-expression network generation, module detection and differential co-expression analysis are used to look into specific changes in the gene expression data among different conditions. In this paper, a comparative study of four differential co-expression analysis tools are presented, namely, WGCNA, DiffCorr, MODA and CEMiTool, for RNA-Seq data. The different methods used by these tools are studied and tested on schizophrenia and bipolar disorder datasets and their effectiveness in finding the related differentially co-expressed genes and pathways are being discussed. The relevancy of the resultant genes and pathways are decided on the basis of whether the genes and pathways are associated with the given disease conditions.
