Department Seminars

December 5, 2007
Identification of Differentially Expressed Gene Categories in Microarray Studies Using Multivariate Nonparametric Analysis

Dan Nettleton, the Laurence H. Baker Endowed Chair in Biological Statistics at Iowa State University, will deliver a CS seminar on Wednesday, December 5, at 3:30 PM in 322 ITTC.

Abstract

Microarray technology is frequently used to study the expression levels of genes in multiple biological samples. By studying how genes change their expression across biological samples of different types, researchers can gain clues about gene function and discover genes that play important roles in biological processes. As an example, we will discuss a microarray experiment that compared the gene expression levels of normal and mutant mice to identify genes involved in muscle development. For experiments like our mouse example, traditional analysis strategies consider the data on a gene-by-gene basis.

We will discuss limitations of such strategies and present a method for evaluating gene categories defined by a priori gene function information. We utilize a nonparametric multivariate method for identifying gene categories whose multivariate expression distribution differs across two or more conditions. We illustrate our approach and compare its performance to several existing procedures via the analysis of our example data set and a unique data-based simulation study designed to capture the challenges and complexities of practical data analysis. We show that our method has good power for differentiating between differentially expressed and non-differentially expressed gene categories, and we utilize a resampling based strategy for controlling the false discovery rate when testing multiple categories.

Dan Nettleton is the Laurence H. Baker Endowed Chair in Biological Statistics in the Department of Statistics at Iowa State University. He took his B.A. in Mathematics, with a minor in Computer Science, from Wartburg College in 1991. He took his M.S. and Ph.D. degrees in statistics from the University of Iowa. Dan's current research focuses on the development of statistical methods for the design and analysis of biological experiments. Most of his recent projects involve genomics research aimed at uncovering gene function or mapping genes that affect complex traits.



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