JSM 2004 - Toronto

Abstract #301479

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Activity Number: 88
Type: Contributed
Date/Time: Monday, August 9, 2004 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #301479
Title: MAOSA: A New Procedure for Detection of Differential Gene Expression
Author(s): Greg Dyson*+ and C.F. Jeff Wu
Companies: University of Michigan and Georgia Institute of Technology
Address: , , ,
Keywords: Multiplicity ; Microarray ; Shrinkage ; Data Mining
Abstract:

The Multiplicity-Adjusted Order Statistics Analysis (MAOSA) is a new technique to determine differential expression in gene expression data. The MAOSA technique assumes the normality of the middle 1-delta of the distribution of the test statistics. The test statistics are adjusted by adding a constant c to the denominator to ensure that genes with low variability are not erroneously called significant. Then, after a transformation of these test statistics to the uniform scale, known features of differences in uniform order statistics are used to facilitate analysis. The inherent multiplicity problem (thousands of genes to be tested) will be eased by performing a Bonferroni correction on a small number of effects. This is a reasonable approach since the majority of genes are not differentially expressed. The MAOSA method can incorporate prior biological knowledge in the analysis with the selection of c. Datasets derived from a spotted glass array and a gene chip are analyzed using MAOSA and the results compared to other techniques.

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