JSM 2005 - Toronto

Abstract #303417

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 261
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #303417
Title: Mixed Factors Analysis with the Application to Clustering of DNA Microarray Experiments
Author(s): Ryo Yoshida*+ and Tomoyuki Higuchi and Seiya Imoto
Companies: Institute of Statistical Mathematics and Institute of Statistical Mathematics and University of Tokyo
Address: 467 Minamiazabu Minatoku, Tokyo, 106-8569, United States
Keywords: Model-based clustering ; Dimension reduction ; Overfitting of finite mixture distribution ; Gene expression profiling ; Fisher's linear discriminant analysis
Abstract:

Microarray gene expression data have a fairly small sample size, usually less than 100, whereas the dimension of data is more than several thousands. Under such a situation, the model-based clustering according to a conventional finite mixture distribution might fail due to the occurrence of overfitting in the density estimation. In this paper, we address the problem by extending the classical factor analysis to the mixed factors analysis. The mixed factors model we propose is stated by an observational equation with the inclusion of the low-dimensional mixed factors being a blind source of clusters. Such statistical modeling offers a parsimonious parameterization of the Gaussian mixture distribution for which the high-dimensional dataset follows. In this way, we can avoid the overfitting, even if the number of samples is much smaller than the dimension of data. The effectiveness of the mixed factors analysis is demonstrated with the real application to gene expression datasets.

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Revised March 2005