Clustering Replicated Microarray Data via Mixtures of Random Effects Models for Various Covariance Structures

Ng, S.K., McLachlan, G.J., Bean, R.W. and Ng, S.W.

A unified approach of mixed-effects model has been recently proposed for clustering correlated genes from different kinds of microarray experiments. With the so-called EM-based MIXture analysis WIth Random Effects (EMMIX-WIRE) model, both the gene-specific and tissue-specific random effects are taken into account in the (mixture) modelling of microarray data. In this paper, we focus on the applications of the EMMIX-WIRE model to the cluster analysis of microarray data with repeat4ed measurements. In particular, we investigate various forms of covariance structure commonly applicable for replicated microarray data and compare their impact on the final clustering results, using a real data set of microRNAA profile and a published yeast galactose data set with known Gene Ontology (GO) listings.

Cite as: Ng, S.K., McLachlan, G.J., Bean, R.W. and Ng, S.W. (2006). Clustering Replicated Microarray Data via Mixtures of Random Effects Models for Various Covariance Structures. In Proc. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006), Hobart, Australia. CRPIT, 73. Boden, M. and Bailey, T. L., Eds. ACS. 29-33.

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