JSM 2005 Online Program

Abstract #303396

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	188
Type:	Contributed
Date/Time:	Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Biometrics Section
Abstract - #303396
Title:	Using Weighted Permutation Scores To Detect Differential Gene Expression with Microarray Data
Author(s):	Xu Guo*+ and Wei Pan
Companies:	Affymetrix, Inc. and University of Minnesota
Address:	3380 Central Expressway, Santa Clara, CA, 95051, United States
Keywords:	Empirical Bayes (EB) ; Microarray ; Mixture model ; Permutation ; Significance Analysis of Microarray (SAM)
Abstract:	A class of nonparametric statistical methods---including a nonparametric empirical Bayes (EB) method, the Significance Analysis of Microarray (SAM), and the mixture model method (MMM)---has been proposed to detect differential gene expression for replicated microarray experiments. They all depend on constructing a test statistic (e.g., a t-statistic) and using permutation to draw inferences. However, due to special features of microarray data, using standard permutation scores may not estimate the null distribution of the test statistic well, leading to possibly too conservative inferences. We propose a new method of constructing weighted permutation scores to overcome the problem: Posterior probabilities of having no differential expression from the EB method are used as weights for genes to better estimate the null distribution of the test statistic. We also propose a weighted method to estimate the false discovery rate (FDR) using the posterior probabilities. Using simulated data and real data for two-condition and time-course microarray experiments, we show the improved performance of the proposed methods when implemented in MMM, EB, and SAM.

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