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Modeling Microarray Data Using a Threshold Mixture Model

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Abstract:

Summary. 

An important goal of microarray studies is the detection of genes that show significant changes in expression when two classes of biological samples are being compared. We present an ANOVA-style mixed model with parameters for array normalization, overall level of gene expression, and change of expression between the classes. For the latter we assume a mixing distribution with a probability mass concentrated at zero, representing genes with no changes, and a normal distribution representing the level of change for the other genes. We estimate the parameters by optimizing the marginal likelihood. To make this practical, Laplace approximations and a backfitting algorithm are used. The performance of the model is studied by simulation and by application to publicly available data sets.

Keywords: Backfitting; Laplace approximation; Marginal likelihood; Microarray data; Mixed model

Document Type: Research Article

DOI: https://doi.org/10.1111/j.0006-341X.2004.00182.x

Publication date: 2004-06-01

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