Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes.
Example of a co-expression network analysis
First, pairwise correlation is determined for each possible gene pair in the expression data. These pairwise correlations can then be represented as a network. Modules within these networks are defined using clustering analysis. The network and modules can be interrogated to identify regulators, functional enrichment and hub genes. Differential co-expression analysis can be used to identify modules that behave differently under different conditions. Potential disease genes can be identified using a guilt-by-association (GBA) approach that highlights genes that are co-expressed with multiple disease genes.
Here, researchers from UMC Groningen introduce and guide researchers through a (differential) co-expression analysis. They provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and they explain how these can be used to identify genes with a regulatory role in disease. Furthermore, the researchers discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.