Clustering Samples Characterized by Time Course Gene Expression Profiles Using the Mixture of State Space Models

Osamu Hirose[1] (ochamu@ims.u-tokyo.ac.jp)
Ryo Yoshida[2] (yoshidar@ism.ac.jp)
Rui Yamaguchi[1] (ruiy@ims.u-tokyo.ac.jp)
Seiya Imoto[1] (imoto@ims.u-tokyo.ac.jp)
Tomoyuki Higuchi[2] (higuchi@ism.ac.jp)
Satoru Miyano[1] (miyano@ims.u-tokyo.ac.jp)

[1]Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
[2]Institute of Statistical Mathematics, Research Organization of Information and Systems, 4-6-7 Minami-Azabu, Minato-ku, Tokyo, 106-8569, Japan

Abstract

We propose a novel method to classify samples where each sample is characterized by a time course gene expression profile. By exploiting the mixture of state space model, the proposed method addresses the following tasks: (1) clustering samples according to temporal patterns of gene expressions, (2) automatic detection of genes that discriminate identified clusters, (3) estimation of a restricted autoregressive coefficient for each cluster. We demonstrate the proposed method along with the cluster analysis of 53 multiple sclerosis patients under recombinant interferon β therapy with the longitudinal time course expression profiles.

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