Predictive response-relevant clustering of expression data provides insights into disease processes.

ABSTRACT: This article describes and illustrates a novel method of microarray data analysis that couples model-based clustering and binary classification to form clusters of `response-relevant' genes; that is, genes that are informative when discriminating between the different values of the response. Predictions are subsequently made using an appropriate statistical summary of each gene cluster, which we call the `meta-covariate' representation of the cluster, in a probit regression model. We first illustrate this method by analysing a leukaemia expression dataset, before focusing closely on the meta-covariate analysis of a renal gene expression dataset in a rat model of salt-sensitive hypertension. We explore the biological insights provided by our analysis of these data. In particular, we identif

SUBMITTER: Hopcroft LE 

PROVIDER: S-EPMC2978340 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature