Coexpression

Coexpression refers to patterns in which genes exhibit similar expression profiles across a set of samples, tissues, or conditions. When two or more genes rise and fall together, their expression is considered coexpressed. Coexpression is commonly assessed by correlating expression levels across samples using metrics such as Pearson or Spearman correlation, or by related distance measures. Data sources include bulk RNA sequencing, microarrays, and single-cell RNA sequencing, though single-cell data require special handling due to dropout and cellular heterogeneity.

Coexpression is widely used to infer functional relationships and to organize genes into networks or modules. Researchers build gene coexpression networks and identify modules of genes with highly correlated expression patterns, which can suggest shared regulation, participation in the same biological pathway, or coordinated cellular roles. A well-known approach for this analysis is weighted gene coexpression network analysis (WGCNA). Coexpression patterns can also help predict functions for poorly characterized genes or prioritize candidates for experimental validation.

Interpretation and limitations are important. Coexpression indicates similarity in expression patterns, which may arise from shared regulation or common involvement in similar processes, but it does not prove direct regulatory relationships or physical interactions. Correlation does not imply causation, and results can be influenced by confounding factors, batch effects, sample size, and tissue composition. In single-cell data, technical noise and dropout can complicate inference, requiring methods such as imputation or pseudobulk strategies. Despite these caveats, coexpression remains a fundamental tool in functional genomics and systems biology.