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Bioinformatics Advance Access published online on April 21, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl148
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received January 25, 2006
Revised April 10, 2006
Accepted April 12, 2006

Article

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

G. J. McLachlan 1 *, R. W. Bean 2, and L. Ben-Tovim Jones 2

1 Department of Mathematics, University of Queensland, St. Lucia, Brisbane 4072, Australia; ARC Centre in Bioinformatics, Institute for Molecular Bioscience, St. Lucia, Brisbane, University of Queensland
2 ARC Centre in Bioinformatics, Institute for Molecular Bioscience, St. Lucia, Brisbane, University of Queensland

* To whom correspondence should be addressed.
G. J. McLachlan, E-mail: gjm{at}maths.uq.edu.au


  Abstract

Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive.

Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real data sets.

Availability: An R-program for implementing the approach is freely available at http://www.maths.uq.edu.au/~gjm/.

Supplemnetary Information: http://www.maths.uq.edu.au/~gjm/bioinf061supp_data.pdf.

Associate Editor: Martin Bishop
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