JSM 2004 - Toronto

Abstract #302163

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Activity Number: 114
Type: Contributed
Date/Time: Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #302163
Title: Generalized Models and Design Issues in Genomic and Proteomic Experiments
Author(s): Mark Carpenter*+
Companies: Auburn University
Address: Dept. of Mathematics and Statistics, Auburn , AL, 36849,
Keywords: generalized models ; generalized gamma ; missing at random ; microarray ; 2D-gels
Abstract:

Because modern high-throughput genomic and proteomic experiments can potentially generate tens of thousands of response variables, research in statistical methodology for these experiments have understandably focused on issues of data screening, data reduction, adjustments for multiple testing, and other high-end data analyses. We develop methods for separately modeling each response variable using generalized models. This approach increases the sensitivity for discovering biologically significant results while minimizing false discovery due to model choice. Since issues of experimental design depend heavily on the statistical methods and models to be applied, we also address issues of sample size estimation for simple designs. In particular, we discuss the appropriateness of technical replication (same-sample repeated measures) when there is a fixed experiment budget where increase in technical replications would reduce the number of actual biological replicates (independent observations). The methods are applied to data from microarray and 2D-gel proteomic experiments.

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