====== Coexpression analysis ======


[[Gene]] co-expression [[network]]s can be used to associate [[gene]]s of unknown [[function]] with biological processes, to prioritize candidate disease genes or to discern [[transcription]]al 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. 

van Dam  et al., introduced 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, they discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis
((van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP. Gene
co-expression analysis for functional classification and gene-disease
predictions. Brief Bioinform. 2018 Jul 20;19(4):575-592. doi: 10.1093/bib/bbw139.
PubMed PMID: 28077403; PubMed Central PMCID: PMC6054162.
)).
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see [[Glioma coexpression analysis]].