This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 406
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #307745
Title: Multiple Hypothesis Testing by Clustering of Gene Profiles
Author(s): Leesa Francis Wockner*+ and Geoffrey J. McLachlan and Ian Wood
Companies: University of Queensland and University of Queensland and University of Queensland
Address: School of Mathematics and Physics, Brisbane, International, 4072, Australia
Keywords: Differential expression ; Model-based clustering ; Local and global false discovery rate ; Mixture linear models

There are many problems that require the testing of thousands of hypotheses simultaneously. For example, often the first goal of a microarray experiment is the detection of differentially expressed genes in a given number of classes. More recently, consideration has been given to the clustering of gene profiles in order to improve the power in detecting differentially expressed genes in experiments with small sample sizes. The initial aim is to cluster the gene profiles into a number of tight clusters in order to share information about the genes. We use a mixture of linear models to effect this clustering from which we are able to assess the probability of no differential expression for each gene, the local false discovery rate (FDR). A threshold is imposed on these posterior probabilities so as to control the global FDR. We demonstrate this approach on a number of real data sets.

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