Microarrays Research Today is a free monthly online journal that collates and summarizes the latest research about Microarrays, including details on experiments, designs, statistics, analysis, software.

Hierarchical Bayes models for cDNA microarray gene expression.

Lönnstedt I, Britton T

Department of Mathematics, Uppsala University, Box 480, Uppsala SE-751 06, Sweden. ingrid@math.uu.se

cDNA microarrays are used in many contexts to compare mRNA levels between samples of cells. Microarray experiments typically give us expression measurements on 1000-20 000 genes, but with few replicates for each gene. Traditional methods using means and standard deviations to detect differential expression are not satisfactory in this context. A handful of alternative statistics have been developed, including several empirical Bayes methods. In the present paper we present two full hierarchical Bayes models for detecting gene expression, of which one (D) describes our microarray data very well. We also compare the full Bayes and empirical Bayes approaches with respect to model assumptions, false discovery rates and computer running time. The proposed models are compared to existing empirical Bayes models in a simulation study and for a set of data (Yuen et al., 2002), where 27 genes have been categorized by quantitative real-time PCR. It turns out that the existing empirical Bayes methods have at least as good performance as the full Bayes ones.

Published 17 March 2005 in Biostatistics, 6(2): 279-91.
Full-text of this article is available online (may require subscription).

© 2004-2008 Microarrays Research Today. All Rights Reserved.