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Activity Number: 259 - Modern Statistical Methods for Structured Discovery in Large Biomedical Data
Type: Invited
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #319266
Title: Bayesian Cross-Study Factor Regression Approach
Author(s): Roberta De Vito*
Companies: Brown University
Keywords: multi-study; factor analysis; bayesian; nutrition

The Bayesian Multi-Study Factor model (De Vito et al., 2019, 2018) handles multiple high-dimensional studies simultaneously, achieving two goals: a) to capture the component shared across studies and b) to identify the sources of variation unique to each study. In this work, we propose a novel sparse Bayesian Multi-study Factor model by adopting a latent factor regression approach. This generalization recovers a covariance structure that identifies the common and study-specific covariances, keeping track of the observed variables, such as the demographic information. We consider spike and slab sparse priors (local and non-local) to detect the dimension of the latent factors. A user-defined prior dispersion for the regression coefficient accounts for population structure and other demographic characteristics for the subjects. We assess the characteristic of our method by a range of simulations settings, clarifying the benefit of using our model. We illustrate the advantages of the proposed method through a high dimensional application, the nutritional data application.

Authors who are presenting talks have a * after their name.

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