Statistical Modelling 11 (2011), 71–88
Empirical and fully Bayesian approaches for random effects models in
microarray data analysis
Haim Y Bar
Department of Statistical Science,
Cornell University
Ithaca
USA
Elizabeth D Schifano
Department of Biostatistics,
Harvard School of Public Health,
655 Huntington Avenue
Boston, MA 02155
USA
eMail: eschifan@hsph.harvard.edu
Abstract:
A linear model involving a mixture distribution is considered for the
comparison of normalized microarray data from two treatment groups. Model
fitting using an empirical Bayes approach has been shown to be both accurate
and numerically stable. The posterior odds of treatment/gene interactions
derived from the model involve shrinkage estimates of both the interactions
and the gene-specific error variances, leading to powerful inference. We show
that the same model can easily be fit under a fully Bayesian framework,
allowing increased flexibility in terms of prior distributional assumptions
and posterior inference.
Keywords:
EM algorithm; empirical Bayes; Laplace approximation;
LEMMA; linear model; MCMC
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