CLUSTERING SAMPLES CHARACTERIZED BY TIME COURSE GENE EXPRESSION PROFILES USING THE MIXTURE OF STATE SPACE MODELS

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.