High-Dimensional Mixed Model for Repeated Measures for Reliable and Replicable Inference in Imaging and Omics
This Biostatistics seminar will feature Shuo Chen, MPower Professor of Biostatistics and Bioinformatics at the University of Maryland at Baltimore.
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Abstract
Accounting for dependence among high-dimensional variables in omics, imaging, and phenomics data analysis is critical to obtain accurate and reliable statistical inference. Although latent, omics variables often exhibit structured correlation/co-expression patterns. However, there are few methods explicitly accounting for such structured dependence in the statistical analysis of omics data (e.g., differential expression analysis). To address this methodological gap, we propose a Coexpression network multivariate Regression (CoReg), which integrates co-expression network structure into multivariate regression analysis to precisely account for the inter-correlations (dependence) among omics variables. CoReg is an efficient high-dimensional Mixed Model for Repeated Measures. We show in simulations that CoReg substantially improves the accuracy of statistical inference and replicability across studies. These findings suggest that CoReg provides an alternative approach for omics data association analysis with dependence adjustment, analogous to the role of mixed-effects models in handling repeated measures in lower-dimensional settings.
The Biostatistics seminar series invites researchers from across the nation to discuss methodological research and its implications for a variety of health issues.
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Andy Ni