JSM 2004 - Toronto

Abstract #301002

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Activity Number: 198
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301002
Title: Gene Selection Using a Two-level Hierarchical Bayesian Model
Author(s): Kyounghwa Bae*+ and Bani K. Mallick
Companies: Texas A&M University and Texas A&M University
Address: 1 Hensel Dr. #U3G, College Station, TX, 77840,
Keywords: gene selection ; Markov chain Monte Carlo ; two-level hierarchical model ; cDNA data
Abstract:

The fundamental problem in gene selection via cDNA data is to identify which genes have different gene expression between two tissue types (e.g., normal and cancer). CDNA data has a large number of genes for a single sample while sample sizes are relatively small. Therefore, estimates which incorporate variable sparsity are desirable for this kind of problem. This paper proposes a two-level hierarchical Bayesian model for variable selection which assumes a prior that favors sparseness in parameters. We adopt a Markov chain Monte Carlo (MCMC) based computation technique to simulate the parameters from the posteriors. The method is applied to leukemia data from Golub et al. (1999) and a published dataset of Hendenfalk et al. (2001) on breast cancer.

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