Exploring qpcr data with weighted gene coexpression network analysis (WGCNA)

Abstract: Differently expressed genes e.g. in a disease may play a role in the etiology or progression of the disease. The traditional approach of finding differentially expressed genes is to compare the expression levels in the groups, and produce a list of differentially expressed candidate genes. With many pairwise comparisons, the risk of introducing type I and type II errors is high. One solution is to group together genes that are co-expressed into modules. Weighted gene coexpression network analysis (WGCNA) uses a topological overlap module approach and has been proved to find patterns that have been undetected by gene-to-gene comparison methods. qPCR has high sensitivity and specificity, and advances in technology has increased its throughput. The goal of the project was to construct WGCNA modules from qPCR data and evaluate the WGCNA method in five previously published qPCR data sets. There was little overlap between the differentially expressed genes found in the published articles and the candidates found by WGCNA. In three data sets WGCNA failed to produce any significant genes. In one of the data set significant genes were found where the original article failed. In one data set, 19 out of 60 genes that are top-ranked by the original authors were found in significant WGCNA modules. The biggest challenge with this type of comparison is to determine whether results that differ from the published studies are more or less biologically relevant. It is difficult to draw conclusions on whether the method is suitable for use for analysis of qPCR data based on this study.