Please use this identifier to cite or link to this item: https://doi.org/10.1142/S0219720013410072
Title: Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions
Authors: Cozzini, A.
Jasra, A. 
Montana, G.
Keywords: Clustering
gene selection
microarray data
mixture models
skewed distributions
Issue Date: Jun-2013
Citation: Cozzini, A., Jasra, A., Montana, G. (2013-06). Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions. Journal of Bioinformatics and Computational Biology 11 (3) : -. ScholarBank@NUS Repository. https://doi.org/10.1142/S0219720013410072
Abstract: Cluster analysis of biological samples using gene expression measurements is a common task which aids the discovery of heterogeneous biological sub-populations having distinct mRNA profiles. Several model-based clustering algorithms have been proposed in which the distribution of gene expression values within each sub-group is assumed to be Gaussian. In the presence of noise and extreme observations, a mixture of Gaussian densities may over-fit and overestimate the true number of clusters. Moreover, commonly used model-based clustering algorithms do not generally provide a mechanism to quantify the relative contribution of each gene to the final partitioning of the data. We propose a penalized mixture of Student's t distributions for model-based clustering and gene ranking. Together with a resampling procedure, the proposed approach provides a means for ranking genes according to their contributions to the clustering process. Experimental results show that the algorithm performs well comparably to traditional Gaussian mixtures in the presence of outliers and longer tailed distributions. The algorithm also identifies the true informative genes with high sensitivity, and achieves improved model selection. An illustrative application to breast cancer data is also presented which confirms established tumor sub-classes. © Imperial College Press.
Source Title: Journal of Bioinformatics and Computational Biology
URI: http://scholarbank.nus.edu.sg/handle/10635/125064
ISSN: 02197200
DOI: 10.1142/S0219720013410072
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