A0225
Title: Probabilistic joint and individual variation explained
Authors: Benjamin Risk - Emory University (United States) [presenting]
Raphiel Murden - Emory (United States)
Gavin Tian - Emory University (United States)
Deqiang Qiu - Emory University (United States)
Abstract: Collecting multiple types of data on the same set of subjects is common in modern scientific applications including genomics, metabolomics, and neuroimaging. Joint and Individual Variation Explained (JIVE) seeks a low-rank approximation of the joint variation between two or more sets of features captured on common subjects and isolates this variation from that unique to each set of features. We propose a probabilistic model for the JIVE framework with subject random effects and develop an expectation-maximization (EM) algorithm to estimate the parameters of interest. The model extends probabilistic PCA to multiple data sets. Extensive simulation studies show that ProJIVE achieves greater accuracy compared to other methods and is robust to model misspecification. We apply ProJIVE to measures of brain morphometry and cognition from the Alzheimer's Disease Neuroimaging Initiative. ProJIVE learns biologically meaningful sources of variation in brain morphometry and cognition. The joint morphometry and cognition subject scores are related to existing biomarkers.