Bioinformatics Advance Access originally published online on November 2, 2005
Bioinformatics 2006 22(1):50-57; doi:10.1093/bioinformatics/bti750
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Using a calibration experiment to assess gene-specific information: full Bayesian and empirical Bayesian models for two-channel microarray data
1Department of Statistics G.Parenti, University of Florence & Biostatistic Unit CSPO, Florence
2Department Area Critica Medico Chirurgica University of Florence, Careggi Hospital, AOC Florence, Italy
3Cytogenetic and Genetic Unit, Careggi Hospital, AOC Florence, Italy
*To whom correspondence should be addressed at Imperal College School of Medicine, Norfolk Place W2 IPG. London, UK.
Motivation: Microarray studies permit to quantify expression levels on a global scale by measuring transcript abundance of thousands of genes simultaneously. A difficulty when analysing expression measures is how to model variability for the whole set of genes. It is usually unrealistic to assume a common variance for each gene. Several approaches to model gene-specific variances are proposed. We take advantage of calibration experiments, in which the probes hybridized on the two channels come from the same population (selfself experiment). In this case it is possible to estimate the gene-specific variance, to be incorporated in comparative experiments on the same tissue, cellular line or species.
Results: We present two approaches to introduce prior information on gene-specific variability from a calibration experiment: an empirical Bayes model and a full Bayesian hierarchical model. We apply the methods in the analysis of human lipopolysaccharide-stimulated leukocyte experiments.
Availability: The calculations are implemented in WinBugs. The codes are available on request from the authors.
Contact: m.blangiardo{at}imperial.ac.uk
Received on January 14, 2005; revised on August 4, 2005; accepted on October 27, 2005