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An Adaptive Empirical Bayesian Thresholding Procedure for Analysing Microarray Experiments with Replication

Rebecca E. Walls, Stuart Barber, John T. Kent and Mark S. Gilthorpe
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 56, No. 3 (2007), pp. 271-291
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/4626767
Page Count: 21
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An Adaptive Empirical Bayesian Thresholding Procedure for Analysing Microarray Experiments with Replication
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Abstract

A typical microarray experiment attempts to ascertain which genes display differential expression in different samples. We model the data by using a two-component mixture model and develop an empirical Bayesian thresholding procedure, which was originally introduced for thresholding wavelet coefficients, as an alternative to the existing methods for determining differential expression across thousands of genes. The method is built on sound theoretical properties and has easy computer implementation in the R statistical package. Furthermore, we consider improvements to the standard empirical Bayesian procedure when replication is present, to increase the robustness and reliability of the method. We provide an introduction to microarrays for those who are unfamilar with the field and the proposed procedure is demonstrated with applications to two-channel complementary DNA microarray experiments.

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