Devariation: a robust approach to improve statistical power in high-dimensional multi-view association testing

Music City Center 

Understanding the interplay between high-dimensional data from different views is essential in biomedical research, in fields like genomics and neuroimaging. Existing statistical association tests for two random vectors often do not fully capture dependencies between views due to limitations in modeling within-view dependencies, particularly in unstructured high-dimensional data without clear dependency patterns, leading to a potential loss of statistical power. In this work, we propose a novel approach termed devariation which is considered as a simple yet effective preprocessing method to address the limitations by adopting a penalized low-rank factor model
to flexibly capture within-view dependencies. Theoretical asymptotic power analysis shows that devariation increases statistical power, especially when within-view correlations impact signal-to-noise ratios, while maintaining robustness in Scenarios without strong internal correlations. Simulation studies highlight devariation's superior performance over existing methods in various Scenarios. We further validated devariation in neuroimaging data from the UK Biobank study, examining the associations between imaging-driven phenotypes (IDPs) derived from functional, structural, and diffusion magnetic resonance imaging (MRI).

Association testing

Neuroimaging data

Within-view dependencies

Statistical power

Penalized low-rank factor model