Construction and Comparison of Gene Co-expression Networks Based on Immunity Microarray Data from Arabidopsis, Rice, Soybean, Tomato and Cassava

Abstract

A big challenge in gene expression data analyses is to reveal the coordinated expression of different genes. Gene co-expression networks (GCNs) are graphic representations where nodes symbolize genes while edges reconstruct the coordinated transcription of genes to certain external stimuli. In this paper, an enhanced novel methodology for construction and comparison of GCNs is proposed. Microarray datasets from pathogen infected plants (Arabidopsis, rice, soybean, tomato and cassava) were used. Initially, similarity metrics that find linear and non-linear correlations between gene expression profiles were evaluated. A similarity threshold was chosen and GCNs were constructed. Afterwards, GCNs were characterized by graph variables and a principal component analysis on these variables was applied to differentiate them. The results allowed the discovery of topologically and non-topologically similar networks among species. Potentially conserved biological processes, like those related to immunity in plants could be studied from this work.