Bayesian Models for Integrative Analysis of High-Dimensional Genomics Data

• Activity Number: 118
• Type: Topic Contributed
• Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Veera Baladandayuthapani*+ and Jeffrey S. Morris and wenting wang and Kim-Ahn Do
• Companies: The University of Texas MD Anderson Cancer Center and The University of Texas MD Anderson Cancer Center and UT MD Anderson Cancer Center and MD Anderson Cancer Center
• Keywords: bayesian ; genomics ; integromics ; sparse regression ; high dimensional ; MCMC

Abstract

Due to rapid technological advances, various types of genomic, epigenomic, transcriptomic and proteomic data with different sizes and structures have become available. Each of these distinct data types provides a different, partly independent and complementary view of the whole genome. Modeling and inference in such studies is challenging, not only due to high dimensionality, but also due to presence of structured dependencies (e.g. regulatory mechanisms). We propose an integrative Bayesian framework for modeling such data using a hierarchical approach to model the fundamental biological relationships underlying the molecular features obtained by different platforms -- thus accounting for both the influences of different platforms, and their mechanistic information, in one model to predict patients' clinical outcomes. Our models are based on sparse regression-based approaches that allow simultaneous high-dimensional variable selection flexibly models the different intrinsic structures of biological relationships for different high-throughput platforms. We exemplify our approaches using several real and synthetic datasets.

(joint work with W. Wang, Jeff Morris and K.Ahn-Do).