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bioRxiv · 10.64898/2026.01.12.698985

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

Sun, Y.·Marron, J. S.·Le Cao, K.-A.·Mao, J.
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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/.

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Sun, Y., Marron, J. S., Le Cao, K.-A., Mao, J.. 2026-01-13. DIVAS: an R package for identifying shared and individual variations of multiomics data. https://doi.org/10.64898/2026.01.12.698985

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