American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
Columbia University
Department of Biostatistics Colloquium



GENE SELECTION FOR MICROARRAY DATA ANALYSIS
USING PRINCIPAL COMPONENT ANALYSIS

by

Dr. Antai Wang
Department of Biomathematics and Biostatistics
Georgetown University


Abstract

Principal component analysis (PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. It has become a useful tool in microarray data analysis. For a typical microarray data set, it is often difficult to compare the overall gene expression difference between observations from different groups or conduct the classification based on a very large number of genes. In this paper, we propose a gene selection method based on the strategy proposed by Krzanowski. We demonstrate the effectiveness of this procedure using a cancer gene expression data set and compare it with several other gene selection strategies. It turns out that the proposed method selects the best gene subset for preserving the original data structure.


Date: Thursday, December 3, 2009
Time: 11:00 A.M. - 12:00 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Room 532
New York, New York

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