A Bayesian Framework for Joint Analysis of Heterogeneous Data

• Activity Number: 414
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Esther Salazar*+
• Companies: Duke University
• Keywords: Bayesian Analysis ; Factor Model ; Neuroscience ; Graphical model

Abstract

A new model is developed for joint analysis of ordered, real and count data. In the motivating application, the ordered data are answers to questionnaires, the count data correspond to the text questions from the questionnaires, and the real data correspond to fMRI responses for each subject. We also combine the analysis of these data with single-nucleotide polymorphism (SNP) data from each individual. The questionnaires considered here correspond to standard psychological surveys, and the study is motivated by psychology and neuroscience. The proposed Bayesian model infers sparse graphical models jointly across people, questions, fMRI stimuli and brain region, integrated within a new matrix factorization based on latent binary features. We demonstrate how the learned model may take fMRI and SNP data from a subject as inputs, and predict how the individual would answer a psychological questionnaire. We also use an individual's SNP data and answers from questionnaires to impute unobserved fMRI data. Each of these two imputation settings have practical clinical value and applications, which are discussed.