This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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242
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #307150 |
Title:
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Semiparametric Bayesian Analysis of High-Throughput Array CGH Data
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Author(s):
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Subharup Guha*+
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Companies:
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University of Missouri
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Address:
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209C Middlebush Hall, Columbia, MO, 65211, United States
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Keywords:
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Generalized Polya urn process ;
Copy number ;
Genomic alterations ;
MCMC ;
Tumor ;
Lung cancer
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Abstract:
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Array CGH has emerged as a powerful technique for identifying genomic alterations potentially linked to various kinds of cancer. Adopting a semiparametric Bayesian approach, we model the dependence among neighboring copy number alterations using a first-order infinite-dimensional generalized Polya urn process. The model overcomes several shortcomings of existing approaches based on the hidden Markov model, while accounting for multi-modality and skewness.
For high-throughput array CGH data, current MCMC techniques take an order of magnitude longer than alternative approaches that are often less accurate. We apply a cost-effective MCMC strategy to perform a fully Bayesian analysis of high-resolution data on lung cancer. The results demonstrate the ability of the proposed approach to easily detect large-scale trends as well as localized changes in copy number.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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