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Abstract Details
Activity Number:
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649
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #302822 |
Title:
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Bayesian Hierarchical Functional Models for High-Dimensional Genomics Data
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Author(s):
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Veera Baladandayuthapani*+
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Companies:
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The University of Texas MD Anderson Cancer Center
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Address:
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1515 Holcombe Blvd, houston, TX, 77035, USA
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Keywords:
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functional data analysis ;
MCMC ;
Bayesian ;
copy number ;
splines ;
adaptive smoothing
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Abstract:
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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)
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Authors who are presenting talks have a * after their name.
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