title: Model-based approaches to synthesize microarray data: a unifying review using mixture of SEMs.
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.
authors.affiliation: 1Dipartimento di Scienze Statistiche, Sapienza University of Rome, P.le Aldo Moro, 5-I00185 Rome, Italy.
authors.forename: F
authors.initials: F
authors.lastname: Martella
authors.forename: J K
authors.initials: JK
authors.lastname: Vermunt
mesh_terms: D016000:Cluster Analysis; D008962:Models, Theoretical; D005810:Multigene Family; D020411:Oligonucleotide Array Sequence Analysis