JSM 2005 - Toronto

Abstract #304301

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 14
Type: Topic Contributed
Date/Time: Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #304301
Title: Screening for Differentially Expressed Genes Using the Bayes Factor
Author(s): Fang Yu*+ and Ming-Hui Chen and Lynn Kuo
Companies: University of Connecticut and University of Connecticut and University of Connecticut
Address: 215 Glenbrook Road Unit 4120, Storrs, CT, 06269, United States
Keywords: Gene Selection ; Bayes factor ; Calibrating value ; Multilevel model ; Prior predictive distribution
Abstract:

A common problem in microarray data analysis is to identify genes having different gene intensities between two conditions. The existing methods include using two-sampled t-statistics, a modified t-statistics (SAM), Bayesian t-statistics (Cyber-T), semiparametric version, and nonparametric permutation tests. All these essentially compare two population means. In this paper, we propose a new test based on the Bayes factor that evaluates the evidence for a gene to be differentially expressed from the marginal likelihood. We build several Bayesian multilevel models to fit the experimental data we encounter. For choosing the cut-off value for the Bayes factor, we propose a new calibration approach that weighs two types of error probabilities from the prior predictive distribution of the Bayes factor. We will compare the results of our method to several existing methods based on several simulated datasets.

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Revised March 2005