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 identify a highly influential cluster of 13 genes—including three transcription factors (Arntl, Bhlhe41 and Npas2)—that is implicated as being protective against hypertension in response to increased dietary sodium. Functional and canonical pathway analysis of this cluster using Ingenuity Pathway Analysis implicated transcriptional activation and circadian rhythm signalling, respectively. Although we illustrate our method using only expression data, the method is applicable to any high-dimensional datasets. Expression data are available at ArrayExpress (accession number E-MEXP-2514) and code is available at http://www.dcs.gla.ac.uk/inference/metacovariateanalysis/.