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A Study of HME Model in Time-Course Microarray Data
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 Title & Authors
A Study of HME Model in Time-Course Microarray Data
Myoung, Sung-Min; Kim, Dong-Geon; Jo, Jin-Nam;
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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).
Hierarchical Mixture of Experts;Mixture model;Linear Mixed Effect Model;Microarray;
 Cited by
Bridle J. (1989). Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Reconition, In Neurocomputing: Algorithms, Architectures, and Applications, Springer

Brown, M., Grundy, W., Lin, D., Cristianini, N., Sugnet, C., Furey, T., Ares, M. and Haussler, D. (2000). Knowledge-based analysis of microarray gene expression data by using support vector machines, Proceedings of the National Academy of Sciences of the United States of America, 97, 262-267. crossref(new window)

Costa, I. G., Carvalho, F. and Souto, M. (2004). Comparative analysis of clustering methods for gene expression time course data, Genetics and Molecular Biology, 27, 623-631. crossref(new window)

Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm, Journal of Royal Statistical Society Series B, 39, 1-38.

Draghici, S. (2003). Data Analysis Tools for DNA Microarrays, Chapman & Hall.

Eisen, M. B., Spellman, P. T., Brown, P. O. and Botstein, D. (1998). Cluster analysis and display of genomewide expression patterns, Proceedings of the National Academy of Sciences of the United States of America, 95, 14863-14868. crossref(new window)

Hartuv, E., Schmitt, A., Lange, J., Meirer-Ewert, S., Lehrach, H. and Shamir, R. (1999). An algorithm for clustering cDNAs for gene expression analysis, IN RECOMB99: Proceedings of the Third Annual International Conference on Computational Molecular Biology, Lyon, France, 188-197.

Hastie, T., Tibshirani, R. and Friedman, J. (2001). The Elements of Statistical Learning, Springer.

Iyer, V. R., Eisen, M. B., Ross, D. T., Schuler, G., Moore, T., Lee, J. C., Trent, J. M., Staudt, L., Hudson, J., Boguski, M., Lashkari, D., Shalon, D., Botstein, D. and Brown, P. O. (1999). The transcriptional program in the response of human fibroblasts to serum, Science, 283, 83-87. crossref(new window)

Jordan, M. I. and Jacobs, R. A. (1992). Hierarchies of adaptive experts, Advances in Neural Information Processing Systems, 4, 985-993.

Jordan, M. I. and Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm, Neural Computation, 6, 181-214. crossref(new window)

Kerr, M. K. and Churchill G. A. (2001). Experimental design for gene expression microarrays, Biostatistics, 2, 183-201. crossref(new window)

Laird, N. M. and Ware, J. H. (1982). Random effect models for longitudinal data, Biometrics, 38, 963-974. crossref(new window)

Lander, E. S. (1999). Array of hope, Nature Genetics, 21, 3-4. crossref(new window)

Little, R. J. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, Wiley.

Luan, Y. and Li, H. (2003). Clustering of time-course gene expression data using a mixed-effects model with B-splines, Bioinformatics, 19, 474-482. crossref(new window)

McCullagh, P. and Nelder, J. A. (1983). Generalized Linear Models, Chapman & Hall, London.

McLachlan, G. J. (2008). The EM Algorithm and Extensions, Wiley.

Pinheiro, J. and Bates, D. (2009). Mixed-Effects Models in S and S-PLUS 2nd Ed., Springer.

Quackenbush, J. (2001). Computational analysis of cDNA microarray data, Nature Review Genetics, 6, 418-428.

Schlattmann, P. (2009). Medical Applications of Finite Mixture Models, Springer.

Slonim, D. (2002). From patterns to pathways: Gene Expression data analysis come of age, Nature Genetics, 32, 502-508. crossref(new window)

Storey, J. D., Xiao, W., Leek, J., Tomkins, R. G. and Davis, R. W. (2005). Significance analysis of time course microarray experiments, Preceedings of the National Academy of Sciences, 102, 12837-12842. crossref(new window)

Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S. and Goulb, T. R. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentitation, Proceedings of the National Academy of Sciences of the United States of America, 96, 2907-2912. crossref(new window)

Tavazoie, S., Huges, J. D., Campbell, M. J., Cho, R. J. and Church, G. M. (1999). Systematic determination of genetic network architecture, Nature Genetics, 22, 281-285. crossref(new window)

Wang, L., Chen, X., Wolfinger, R. D., Franklin, J. L., Coffey, R. J. and Zhang, B. (2009). A unified mixed effects model for gene set analysis of time course microarray experiments, Statistical Applications in Genetics and Molecular Biology, 8, Article 47.

Yeung, K. Y., Fraley, C., Murua, A., Raftery, A. E. and Ruzzo, W. L. (2001). Model-based clustering and data transformations for gene expression data, Bioinformatics, 17, 977-987. crossref(new window)

Yeung, K. Y., Medvedovic, M. and Bumgarner, R. E. (2003). Clustering gene-expression data with repeated measurements, Genome Biology, 4, R34. crossref(new window)