title: Modeling microarray data using a threshold mixture model.
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.
authors.affiliation: Department of Economics and Business Administration, University of Bielefeld, 33501 Bielefeld, Germany. gkauermann@wiwi.uni-bielefeld.de
authors.forename: Göran
authors.initials: G
authors.lastname: Kauermann
authors.forename: Paul
authors.initials: P
authors.lastname: Eilers
mesh_terms: D000818:Animals; D016632:Apolipoprotein A-I / Q000235:genetics; D001699:Biometry; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D007938:Leukemia / Q000235:genetics; D051379:Mice; D018345:Mice, Knockout; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*
chemical_list: D016632:Apolipoprotein A-I