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Activity Number: 108 - Junior Research in Bayesian Modeling for High-Dimensional Data
Type: Topic Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #323750 View Presentation
Title: Bayesian Multi-Study Factor Analysis in High-Dimensional Biological Data
Author(s): Roberta De Vito*
Companies: Princeton
Keywords: Factor Analysis ; Adaptive Gibbs sampling ; Shrinkage ; High-dimensional data ; Gene expression ; Meta-analysis
Abstract:

High-dimensional data are the norm in current statistical research. Gaining systematic knowledge from these data is a cumulative process that greatly benefits from integration of multiple studies and technologies, and that relies critically on methods of analysis. In this work we introduce the `` Bayesian Multi-study Factor Analysis'' (BMFA), a generalized version of Bayesian factor analysis able to handle multiple studies and to derive in a single analysis (1) factors that capture common information, shared across studies, and (2) study-specific factors. Our fundamental challenge is estimation of common features shared among studies and identifying the variation specific to each study. We use sparse modeling of high-dimensional factor loadings matrices, both common and specific, using shrinkage priors. We describe a computationally efficient algorithm to estimate the parameters and to select the number of relevant common and study-specific factors. We assess the operating characteristics of our method with simulation studies, and we present an application in ovarian cancer with four gene expression studies.


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

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