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A structural mixed model for variances in differential gene expression studies

Published online by Cambridge University Press:  22 May 2007

Florence Jaffrézic*
Affiliation:
INRA, UR337 Station de Génétique Quantitative et Appliquée, Jouy-en-Josas 78350, France
Guillemette Marot
Affiliation:
INRA, UR337 Station de Génétique Quantitative et Appliquée, Jouy-en-Josas 78350, France
Séverine Degrelle
Affiliation:
INRA, UR337 Station de Génétique Quantitative et Appliquée, Jouy-en-Josas 78350, France
Isabelle Hue
Affiliation:
INRA, UMR 1198; ENVA; CNRS, FRE 2857, Biologie du Développement et Reproduction, Jouy-en-Josas 78350, France
Jean-Louis Foulley
Affiliation:
INRA, UR337 Station de Génétique Quantitative et Appliquée, Jouy-en-Josas 78350, France
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Summary

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The importance of variance modelling is now widely known for the analysis of microarray data. In particular the power and accuracy of statistical tests for differential gene expressions are highly dependent on variance modelling. The aim of this paper is to use a structural model on the variances, which includes a condition effect and a random gene effect, and to propose a simple estimation procedure for these parameters by working on the empirical variances. The proposed variance model was compared with various methods on both real and simulated data. It proved to be more powerful than the gene-by-gene analysis and more robust to the number of false positives than the homogeneous variance model. It performed well compared with recently proposed approaches such as SAM and VarMixt even for a small number of replicates, and performed similarly to Limma. The main advantage of the structural model is that, thanks to the use of a linear mixed model on the logarithm of the variances, various factors of variation can easily be incorporated in the model, which is not the case for previously proposed empirical Bayes methods. It is also very fast to compute and is adapted to the comparison of more than two conditions.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2007