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A Study of HME Model in Time-Course Microarray Data

Myoung, Sung-Min;Kim, Dong-Geon;Jo, Jin-Nam

  • Received : 2012.01.25
  • Accepted : 2012.04.20
  • Published : 2012.06.30

Abstract

For statistical microarray data analysis, clustering analysis is a useful exploratory technique and offers the promise of simultaneously studying the variation of many genes. However, most of the proposed clustering methods are not rigorously solved for a time-course microarray data cluster and for a fitting time covariate; therefore, a statistical method is needed to form a cluster and represent a linear trend of each cluster for each gene. In this research, we developed a modified hierarchical mixture of an experts model to suggest clustering data and characterize each cluster using a linear mixed effect model. The feasibility of the proposed method is illustrated by an application to the human fibroblast data suggested by Iyer et al. (1999).

Keywords

Hierarchical Mixture of Experts;Mixture model;Linear Mixed Effect Model;Microarray

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Acknowledgement

Supported by : National Research Foundation of Korea(NRF)