Gene co-expression analysis for functional classification and gene–disease predictions

Abstract

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. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.

Immune Gene Expression Profile in Hepatocellular Carcinoma and Surrounding Tissue Predicts Time to Tumor Recurrence

“…Since multiple interactions are suggestive of a central regulatory role, the genes/proteins with the highest degree of connectivity in the network are identified as hubs and expected to drive essential functions [34, 35]. …”

A Self‐Organizing Map of the Fathead Minnow Liver Transcriptome to Identify Consistent Toxicogenomic Patterns across Chemical Fingerprints

“…Both approaches, WGCNA and SOM, are applicable for data integration in studies and meta-studies with large amount of samples, for dimension reduction and for toxicogenomic fingerprinting. In contrast to single genes, gene clusters can be assigned to biological terms applying functional enrichment or over-representation analyses (van Dam et al 2018;Schüttler et al 2019).…”

Whole Transcriptome Profiling: An RNA‐Seq Primer and Implications for Pharmacogenomics Research

“…51 Going beyond the identification of singular genes or regulatory variants associated with disease or drug exposure, building coexpression networks can be used for candidate gene prioritization as a function of their position in network hubs, and functional gene annotation. 52 Guidance and further details about the various methods developed for this approach are available in recent reviews. 52,53 Because RNA-Seq also quantifies the expression of up to 70,000 noncoding RNAs, 54 not usually measured with microarrays, it permits a better understanding of regulatory networks driving biological processes including noncoding RNAs.…”

“…52 Guidance and further details about the various methods developed for this approach are available in recent reviews. 52,53 Because RNA-Seq also quantifies the expression of up to 70,000 noncoding RNAs, 54 not usually measured with microarrays, it permits a better understanding of regulatory networks driving biological processes including noncoding RNAs. Numerous noncoding RNAs are thought to have regulatory roles 55 and to play a role in disease processes.…”