Model-based approaches to synthesize microarray data: A unifying review using mixture of SEMs
Martella,F. Vermunt,J.K.
Martella,F.
Vermunt,J.K.
Abstract
Several statistical methods are nowadays available for the analysis of gene expression data recorded through microarray technology. In this article, we take a closer look at several Gaussian mixture models which have recently been proposed to model gene expression data. It can be shown that these are special cases of a more general model, called the mixture of structural equation models (mixture of SEMs), which has been developed in psychometrics. This model combines mixture modelling and SEMs by assuming that component-specific means and variances are subject to a SEM. The connection with SEM is useful for at least two reasons: (1) it shows the basic assumptions of existing methods more explicitly and (2) it helps in straightforward development of alternative mixture models for gene expression data with alternative mean/covariance structures. Different specifications of mixture of SEMs for clustering gene expression data are illustrated using two benchmark datasets. Keywords: biclustering, correlated data, microarray data, mixture of SEMs, simultaneous clustering and dimensional reduction
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2013
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Martella, F & Vermunt, J K 2013, 'Model-based approaches to synthesize microarray data : A unifying review using mixture of SEMs', Statistical Methods in Medical Research, vol. 22, no. 6, pp. 567-582. https://doi.org/10.1177/0962280211419482