CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders.

ABSTRACT: Correcting for confounding variables is often overlooked when computing RNA-RNA correlations, even though it can profoundly affect results. We introduce CorrAdjust, a method for identifying and correcting such hidden confounders. CorrAdjust selects a subset of principal components to residualize from expression data by maximizing the enrichment of "reference pairs" among highly correlated RNA-RNA pairs. Unlike traditional machine learning metrics, this novel enrichment-based metric is specifically designed to evaluate correlation data and provides valuable RNA-level interpretability. CorrAdjust outperforms current state-of-the-art methods when evaluated on 25 063 human RNA-seq datasets from The Cancer Genome Atlas, the Genotype-Tissue Expression project, and the Geuvadis collection. In par

SUBMITTER: Nersisyan S 

PROVIDER: S-EPMC12125544 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature