Classification of Trends in Dose-Response Microarray Experiments Using Information Theory Selection Methods

ABSTRACT

Dose-response microarray experiments consist of monitoring expression levels of thousands of genes with respect to increasing dose of the treatment under investigation. The primary goal of such an experiment is to establish a dose-response relationship, while the secondary goals are to determine the minimum effective dose level and to identify the shape of the dose-response curve. Recently, Lin et al. [1] discussed several testing procedures to test for monotone trend based on isotonic regression of the observed means [2,3]. Once a monotone relationship between the gene expression and dose is established, there is a set of R possible monotone models that can be fitted to the data. A selection of the best model from this set allows us to identify both the shape of dose-response curve and the minimum effective dose level. In this paper we focus on classification of dose-response curve shapes using the information theory model selection. In particular, the Order Restricted Information Criterion (ORIC) is discussed for the inference under order restriction. The posterior probability of the model is calculated using information criteria that take into account both the goodness-of-fit and the complexity of the models. The method is applied to a dose-response microarray experiment with 12 arrays (for three samples at each of the four dose levels) with 16,998 genes.

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