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Bioinformatics Advance Access published online on November 30, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl612
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received October 1, 2006
Revised November 21, 2006
Accepted November 26, 2006

Article

Flexible empirical Bayes models for differential gene expression

Kenneth Lo 1 * and Raphael Gottardo 1

1 Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, BC, V6T 1Z2

* To whom correspondence should be addressed.
Kenneth Lo, E-mail: c.lo{at}stat.ubc.ca


  Abstract

Motivation: Inference about differential expression is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular for this type of problem. The two most common hierarchical models are the hierarchical Gamma-Gamma (GG) and Lognormal-Normal (LNN) models. However, to facilitate inference, some unrealistic assumptions have been made. One such assumption is that of a common coefficient of variation across genes, which can adversely affect the resulting inference.

Results: In this paper, we extend both the GG and LNN modeling frameworks to allow for gene-specific variances and propose EM based algorithms for parameter estimation. The proposed methodology is evaluated on three experimental datasets: one cDNA microarray experiment and two Affymetrix spike-in experiments. The two extended models significantly reduce the false positive rate while keeping a high sensitivity when compared to the originals. Finally, using a simulation study we show that the new frameworks are also more robust to model misspecification.

Availability: The R code for implementing the proposed methodology can be downloaded at http://www.stat.ubc.ca/~c.lo/FEBarrays.

Supplementary Information: The supplementary material is available at http://www.stat.ubc.ca/~c.lo/FEBarrays/supp.pdf.

Associate Editor: John Quackenbush
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