Modeling microarray data using a threshold mixture model.

Göran Kauermann; Paul Eilers

doi:10.1111/j.0006-341x.2004.00182.x

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Modeling microarray data using a threshold mixture model.

Göran Kauermann·Paul Eilers

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Abstract

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.

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Animals Apolipoprotein A-I Biometry Data Interpretation, Statistical Databases, Genetic Gene Expression Profiling Humans Leukemia Mice Mice, Knockout Models, Statistical Oligonucleotide Array Sequence Analysis Journal Article

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Göran Kauermann, Paul Eilers. 2004. Modeling microarray data using a threshold mixture model.. https://doi.org/10.1111/j.0006-341x.2004.00182.x

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