coexpressed

Coexpressed refers to transcripts whose expression levels correlate across samples, conditions, or time points. They are often interpreted as sharing regulatory controls or participating in the same biological processes. The term is widely used in functional genomics and transcriptomics to describe gene sets that respond together under certain stimuli or in particular tissues.

Coexpression is typically inferred by computing pairwise correlations, such as Pearson or Spearman, from high-throughput expression data like microarrays or RNA-seq. Clustering methods group genes into modules of coexpression, while network approaches such as weighted gene coexpression network analysis (WGCNA) identify modules and hub genes. Validation commonly involves enrichment analyses for known pathways or experimental follow-up.

Applications include functional annotation of poorly characterized genes via guilt-by-association, reconstruction of gene regulatory networks, discovery of tissue- or condition-specific programs, and identification of biomarkers or therapeutic targets. Coexpression also enables cross-species comparisons of module preservation and pathway activity.

Limitations include that correlation does not imply direct regulation or physical interaction, and patterns can reflect indirect effects or confounding factors. Analyses depend on data quality, preprocessing, and sampling, and results may vary across tissues, conditions, or time. Time-series data can help resolve dynamics.