Coexpress

Coexpress, in the context of genomics, refers to the phenomenon in which two or more genes exhibit correlated expression patterns across samples, tissues, or experimental conditions. This correlation can indicate shared regulatory mechanisms, functional relationships, or participation in the same biological pathway. The concept is central to transcriptomics and systems biology and is often analyzed using large-scale expression profiles.

Expression data are typically obtained by RNA sequencing or microarrays. For each gene, expression levels are quantified across a set of samples, and pairwise similarity is assessed using correlation metrics such as Pearson or Spearman coefficients. When many gene pairs show strong coexpression, researchers may construct a gene coexpression network in which nodes represent genes and edges reflect significant correlations. Clustering or network-module detection methods then identify modules of coexpressed genes that may correspond to regulatory programs or biological processes.

Interpreting coexpression patterns relies on context; high correlation can result from shared regulation, participation in the same pathway, or simply coincidental co-variation due to confounding factors such as cell-type composition. Therefore, experimental validation is often required to establish causal or functional relationships.

Applications include functional annotation of poorly characterized genes, discovery of regulatory modules, construction of disease-associated gene networks, and identification of potential biomarkers or drug targets. Limitations include sensitivity to data quality, batch effects, sample heterogeneity, and the challenge of distinguishing direct from indirect associations.