Classification of Tissue Samples using Mixture Modeling of Microarray Gene Expression Data

• Activity Number: 258
• Type: Contributed
• Date/Time: Tuesday, August 13, 2002 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section*
• Author(s): Roxana Alexandridis*+ and Shili Lin
• Affiliation(s): Ohio State University and Ohio State University
• Keywords: classification ; gene expression levels ; gene selection ; microarrays ; mixture modelling ; posterior probabilities

Abstract

Accurate classification of tissue samples is an essential tool in disease diagnosis and treatment. The DNA microarray technology enables disease classification based only on gene expression analysis, without prior biological insights. We present a multi-type classification method based on modeling the distribution of the gene expression profile of a test sample as a mixture of distributions, each of which characterizes the levels of gene expression within a class. Class assignment for a test sample is based on the predictive probabilities of class memberships. Since most of the thousands of genes whose expression levels are measured do not contribute to the separation between types of tissue samples, we also explore several measures for gene selection, including T, NPT, BW, NPBW, and a mixture modeling approach based on Markov chain Monte Carlo estimation of parameters. For a classifier based on a gene selection measure, such as the T classifier, the number of genes selected is achieved by cross validation. The methods are applied to a leukemia dataset; our results are comparable with the best results achieved in a comparative study by Dudoit et al. (JASA 97:77-87, 2002).