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Abstract Details

Activity Number: 289
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304179
Title: Bayesian Hierarchical Structured Variable Selection Methods with Application to MIP Studies in Breast Cancer
Author(s): Lin Zhang*+ and Veera Baladandayuthapani and Bani K Mallick and Ganiraju C Manyam and Patricia A Thompson and Melissa L Bondy and Kim-Anh Do
Companies: Texas A&M University and and Texas A&M University and MD Anderson Cancer Center and University of Arizona and Baylor University and MD Anderson Cancer Center
Address: Department of Statistics, College Station, TX, 77840, United States
Keywords: copy number alteration ; hierarchical variable selection ; lasso ; MIP data ; MCMC

Analysis of chromosomal copy number alterations has been a focus of research for identifying genetic markers of cancers. One recent high-throughput technique is the use of molecular inversion probes (MIPs) to measure probe copy number changes. The resulting data consist of high-dimensional copy number profiles that can be used to ascertain probe-specific copy number alterations for correlative studies with patient outcomes to guide risk stratification and future treatment. We propose a novel Bayesian method, the hierarchical structured variable selection (HSVS), which accounts for the natural gene and probe-within-gene architecture to identify important genes and probes associated with clinically relevant outcomes. The HSVS model conducts simultaneous selection at both group and within-group level by utilizing a discrete mixture prior distribution for group selection and group specific Bayesian lasso hierarchies for variable selection within groups. We provide methods for accounting for serial correlation within groups that incorporate Bayesian fused lasso methods for within-group selection. We demonstrate the performance of our method with both simulated and real MIP datasets.

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