Bayesian Hierarchical Functional Models for High-Dimensional Genomics Data

• Activity Number: 649
• Type: Topic Contributed
• Date/Time: Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Veera Baladandayuthapani*+
• Companies: The University of Texas MD Anderson Cancer Center
• Keywords: functional data analysis ; MCMC ; Bayesian ; copy number ; splines ; adaptive smoothing

Abstract

Recent advances in genomic profiling techniques such as array-based comparative genomic hybridization (array-CGH) and SNP arrays provide a high-throughput, high-resolution method to measure relative changes in DNA copy number. These experiments typically yield data consisting of profiles of copy number changes of hundreds/thousands of markers across the whole chromosomal map. Modeling and inference in such studies is challenging not only due to high-dimensionality but also due to presence of serial correlation of the markers along the genome. Using genome continuum models as a general principle we present a class of Bayesian methods to model these genomic profiles using functional data analysis approaches. Our methods allow for simultaneously characterization of these high-dimensional functions, borrowing strength between replicated functions and detection of local features in the data -- to answer several important biological questions using such data. The methods are illustrated using several real and simulated datasets.

(joint work with Jeffrey S. Morris)