Clustering samples characterized by time course gene expression profiles using the mixture of state space models.
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 beta therapy with the longitudinal time course expression profiles.
Hirose
Osamu
O
Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo, 108-8639, Japan. ochamu@ims.u-tokyo.ac.jp
Yoshida
Ryo
R
Yamaguchi
Rui
R
Imoto
Seiya
S
Higuchi
Tomoyuki
T
Miyano
Satoru
S
D016000:Cluster Analysis; D020869:Gene Expression Profiling; D006801:Humans; D008957:Models, Genetic; D009103:Multiple Sclerosis / Q000235:genetics