Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Use of Factor Analyzer Normal Mixture Model with Mean Pattern Modeling on Clustering Genes

  • Kim Seung-Gu (Department of Applied Statistics, SangJi University)
  • Published : 2006.04.01
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Abstract

Normal mixture model(NMM) frequently used to cluster genes on microarray gene expression data. In this paper some of component means of NMM are modelled by a linear regression model so that its design matrix presents the pattern between sample classes in microarray matrix. This modelling for the component means by given design matrices certainly has an advantage that we can lead the clusters that are previously designed. However, it suffers from 'overfitting' problem because in practice genes often are highly dimensional. This problem also arises when the NMM restricted by the linear model for component-means is fitted. To cope with this problem, in this paper, the use of the factor analyzer NMM restricted by linear model is proposed to cluster genes. Also several design matrices which are useful for clustering genes are provided.

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References

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  1. Variable Selection in Normal Mixture Model Based Clustering under Heteroscedasticity vol.24, pp.6, 2011, https://doi.org/10.5351/KJAS.2011.24.6.1213