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Activity Number: 441 - The Key to Integrative Analysis for Precision Medicine: Statistics!
Type: Invited
Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #321895 View Presentation
Title: Bayesian Variable Selection for Multi-Layer Overlapping Group Structure with Applications to Multi-Level Omics Data Integration
Author(s): George Tseng* and Li Zhu
Companies: University of Pittsburgh and University of Pittsburgh
Keywords: Bayesian hierarchical model ; variable selection ; multi-layer group structure ; indicator variable selection model
Abstract:

Variable selection is a pervasive question in small-n-large-p problem. Incorporation of group structure to improve variable selection has been widely studied. In this paper, we consider incorporation of a multi-layer overlapping group structure to improve variable selection in regression setting. For example, a biological pathway contains tens to hundreds of genes and a gene can contain multiple experimentally measured features (such as its mRNA expression, copy number variation and possibly methylation level of multiple sites). In addition to the hierarchical structure, the groups may be overlapped (e.g. two pathways may contain overlapped genes). We propose a Bayesian hierarchical indicator model that can conveniently incorporate the multi-layer overlapping group structure in variable selection. We discuss properties of the proposed prior and prove selection consistency and asymptotic normality of the posterior median estimator of the method. We apply the model to two simulations and a TCGA breast cancer example to demonstrate its superiority over other existing methods. The results not only enhance prediction accuracy but also improve variable selection and model interpretation.


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