DIVAS: an R package for identifying shared and individual variations of multiomics data

Abstract

MotivationMultiomics data integration aims to identify biological patterns shared across molecular modalities. Most existing methods detect either jointly shared variation, across all modalities, or individual variation, unique to a single modality, but overlook partially shared variation, shared by only a subset of modalities. This is a critical limitation, because many biological mechanisms manifest in some but not all molecular modalities. ResultsWe present an open-source R package implementing DIVAS, a framework for systematically identifying jointly shared, partially shared and individual variations across multiple data types. DIVAS combines angle-based subspace analysis with inference through rotational bootstrap, hierarchically searching all combinations of modalities to decompose multiomics data into interpretable components with scores and loadings. In simulations with a known sharing structure, DIVAS recovered every component across a wide range of noise levels, whereas AJIVE and MOFA+ did not. Applied to multi-modal COVID-19 data, it reveals partially shared immune and metabolic dysregulation patterns underpinning disease severity that conventional approaches would miss. Availability and implementationDIVAS is available as an R package on GitHub (https://github.com/ByronSyun/DIVAS), with documentation and vignettes at https://byronsyun.github.io/DIVAS/. A step-by-step vignette for the COVID-19 case study is available at https://byronsyun.github.io/DIVAS_COVID19_CaseStudy/.