StableMate: a statistical method to select stable predictors in omics data

Abstract

Identifying statistical associations between biological variables is crucial to understanding molecular mechanisms. Most association studies are based on correlation or linear regression analyses, but the identified associations often lack reproducibility and interpretability due to the complexity and variability of omics datasets, making it difficult to translate associations into meaningful biological hypotheses. We developed StableMate, a regression framework, to address these challenges through a process of variable selection across heterogeneous datasets. Given datasets from different environments, such as experimental batches, StableMate selects environment-agnostic (stable) and enviro..