Journal Article

Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data

Benhuai Xie, Wei Pan and Xiaotong Shen

in Bioinformatics

Volume 26, issue 4, pages 501-508
Published in print February 2010 | ISSN: 1367-4803
Published online December 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp707
Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data

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Motivation: Model-based clustering has been widely used, e.g. in microarray data analysis. Since for high-dimensional data variable selection is necessary, several penalized model-based clustering methods have been proposed tørealize simultaneous variable selection and clustering. However, the existing methods all assume that the variables are independent with the use of diagonal covariance matrices.

Results: To model non-independence of variables (e.g. correlated gene expressions) while alleviating the problem with the large number of unknown parameters associated with a general non-diagonal covariance matrix, we generalize the mixture of factor analyzers to that with penalization, which, among others, can effectively realize variable selection. We use simulated data and real microarray data to illustrate the utility and advantages of the proposed method over several existing ones.

Contact: weip@biostat.umn.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5763 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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