A considerable amount of di®erent of approaches have been proposed for synthesizing gene expression data obtained from microarray experiments. In this paper, we have a closer look at various types of Gaussian mixture models which have recently been proposed in the gene expression level literature. It is shown that these are, in fact, special cases of a more general model; that is, the mixture structural equation model developed in psychometrics (Arminger and Stein, 1997; Dolan and van der Maas, 1998). This model combines mixture modeling and SEM by assuming that within each mixture component the model parameters are subject to a structural equation model (SEM). A SEM is a very general model for a multivariate Gaussian mean vector and covariance matrix. The various Gaussian mixture methodologies for microarray analysis { such as mixture factor analyzers { are special case of mixture SEM which can be obtained by imposing speci¯c restrictions on the SEM model parameters; item intercepts,

Model-based approaches to synthesize microarray data: a unifying review using mixture SEM / Martella, Francesca; J. K., Vermunt. - (2009). (Intervento presentato al convegno IFCS 2009 Conference: Data Science and Classification, Section on Mixture Analysis - Mixture models in genetics tenutosi a Dresden, Germany).

Model-based approaches to synthesize microarray data: a unifying review using mixture SEM

MARTELLA, Francesca;
2009

Abstract

A considerable amount of di®erent of approaches have been proposed for synthesizing gene expression data obtained from microarray experiments. In this paper, we have a closer look at various types of Gaussian mixture models which have recently been proposed in the gene expression level literature. It is shown that these are, in fact, special cases of a more general model; that is, the mixture structural equation model developed in psychometrics (Arminger and Stein, 1997; Dolan and van der Maas, 1998). This model combines mixture modeling and SEM by assuming that within each mixture component the model parameters are subject to a structural equation model (SEM). A SEM is a very general model for a multivariate Gaussian mean vector and covariance matrix. The various Gaussian mixture methodologies for microarray analysis { such as mixture factor analyzers { are special case of mixture SEM which can be obtained by imposing speci¯c restrictions on the SEM model parameters; item intercepts,
2009
IFCS 2009 Conference: Data Science and Classification, Section on Mixture Analysis - Mixture models in genetics
04 Pubblicazione in atti di convegno::04d Abstract in atti di convegno
Model-based approaches to synthesize microarray data: a unifying review using mixture SEM / Martella, Francesca; J. K., Vermunt. - (2009). (Intervento presentato al convegno IFCS 2009 Conference: Data Science and Classification, Section on Mixture Analysis - Mixture models in genetics tenutosi a Dresden, Germany).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/500729
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