StableMate: a new statistical method to select stable predictors in omics data
Identifying statistical associations between biological variables is crucial to understand 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 heterogenous datasets. Given datasets from different environments, such as experimental batches, StableMate selects environment-agnostic (stable) and environment-specific predictors in predicting the response of interest. Stable predictors represent robust functional dependencies with the response, and can be used to build regression models that make generalizable prediction in unseen environments. We applied StableMate to 1) RNA-seq data of breast cancer to discover genes that consistently predict estrogen receptor expression across disease status, 2) metagenomics data to identify microbial signatures that show persistent association with colon cancer across study cohorts and 3) scRNA-seq data of glioblastoma to discern signature genes associated with development of pro-tumour microglia regardless of cell location. Our case studies demonstrate that StableMate is adaptable to regression and classification analyses and achieves comprehensive characterisation of biological systems for different omics data types.