Abstract #300063

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JSM 2003 Abstract #300063
Activity Number: 475
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #300063
Title: Applications of the Split-Merge Markov Chain Monte Carlo Technique in the Analysis of Gene Expression Data
Author(s): Sonia Jain*+ and R. M. Neal
Companies: University of California, San Diego and University of Toronto
Address: 9500 Gilman Dr., MC 0622, La Jolla, CA, 92093-0622,
Keywords: Dirichlet process ; mixture model ; Metropolis-Hastings ; Gibbs sampling ; microarray data
Abstract:

The inferential problem of associating data in high dimensions to mixture components is difficult when components are nearby or overlapping. We introduce a new split-merge Markov chain Monte Carlo technique that efficiently classifies observations by splitting and merging mixture components of a nonconjugate Bayesian mixture model. Our method, which is a Metropolis-Hastings procedure with split-merge proposals, samples clusters of observations simultaneously rather than incrementally assigning observations to mixture components. Split-merge moves are produced by exploiting properties of a restricted Gibbs sampling scan. We apply our split-merge technique to a cancer classification problem, in which patients are clustered according to leukemia type based on their gene expression data obtained from DNA microarray experiments.

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