Communications for Statistical Applications and Methods

  • 제14권1호
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  • Pages.205-213
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  • 2007
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Normal Mixture Model with General Linear Regressive Restriction: Applied to Microarray Gene Clustering

  • Kim, Seung-Gu (Department of Data Information, Sangji University)
  • 발행 : 2007.04.30
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초록

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.

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참고문헌

  1. 김승구 (2006). Use of Factor Analyzer Normal Mixture Model with Mean Pattern Modeling on Clustering Genes. <한국통계학회논문집>, 13, 113-123 https://doi.org/10.5351/CKSS.2006.13.1.113
  2. 김승구 (2007). Gene Clustering using Normal Mixture Model restricted Fold Change in Microarray data. Journal of the Korean Data Anlysis, 9, 127-135
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  7. McLachlan, G. J., Do, K-A. and Ambroise, C. (2004). Analyzing Microarray Gene Expression Data. John Wiely & Sons
  8. van't Veer, L. J., van de Vijver, M. J., He, Y. D., Hart, A. A., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., Schreiber, G. J., Kerkhoven, R. M., Roberts, C., Linsley, P. S., Bernards, R. and Friend, S. H. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530-536 https://doi.org/10.1038/415530a

피인용 문헌

  1. Identification of Cluster with Composite Mean and Variance vol.18, pp.3, 2011, https://doi.org/10.5351/CKSS.2011.18.3.391