What-if effects: A tale of entangled covariates.
Where this comes from
- Record sourced from PubMed, PMID 42746522.
- Also identified by DOI 10.1016/j.patter.2026.101616 and PMC identifier 13576651.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Single-cell epigenomic studies promise to reveal how disease states, donor traits, environmental exposures, and technical factors shape cellular regulatory programs, yet these variables are often highly collinear and difficult to disentangle, particularly in human cohorts. The recent study by Møller and Madsen in <i>Patterns</i> introduces DeepDive, a probabilistic deep-learning framework that disentangles known covariate effects and residual variations in single-nucleus ATAC-seq data, enabling counterfactual "what-if" analyses of chromatin accessibility.