Document Type
Article
Publication Date
5-22-2025
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 particular, CorrAdjust excels at integrating small RNA and mRNA sequencing data, significantly enhancing the enrichment of experimentally validated miRNA targets among negatively correlated miRNA-mRNA pairs. CorrAdjust, with accompanying documentation and tutorials, is available at https://tju-cmc-org.github.io/CorrAdjust.
Recommended Citation
Nersisyan, Stepan; Loher, Phillipe; and Rigoutsos, Isidore, "Corradjust Unveils Biologically Relevant Transcriptomic Correlations by Efficiently Eliminating Hidden Confounders" (2025). Computational Medicine Center Faculty Papers. Paper 61.
https://jdc.jefferson.edu/tjucompmedctrfp/61
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
PubMed ID
40448503
Language
English
Comments
This article is the author's final published version in Nucleic Acids Research, Volume 53, Issue 10, May 2025, Article number gkaf444.
The published version is available at https://doi.org/10.1093/nar/gkaf444. Copyright © The Authors.