MINT Block: multivariate integration across independent studies to identify multi-omics signatures

Abstract

Integrative multi-omics analyses can identify molecular signatures spanning multiple layers of a biological system. However, signatures derived from datasets with small sample sizes may not generalise. Combining data from multiple independent studies can increase the available sample size, but may also introduce unwanted variation arising from study-specific differences. Existing approaches typically address this variation sequentially by correcting the data before downstream model fitting, which can inadvertently distort the biological signals of interest. We introduce MINT Block, a framework that integrates multi-omics data across independent studies while simultaneously accounting for study-specific unwanted variation. MINT Block supports multivariate regression and classification analyses, enabling the identification of generalisable multi-omics signatures that capture relationships between different types of molecules and predict sample phenotypes. We evaluated MINT Block using bulk and single-cell multi-omics datasets from studies of human disease and marine biology. Across these applications, we showed that MINT Block identified stable, interpretable molecular signatures and improves prediction accuracy compared with existing sequential approaches.