Bioinformatics Advance Access originally published online on April 21, 2006
Bioinformatics 2006 22(13):1608-1615; doi:10.1093/bioinformatics/btl148
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A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays
1 Department of Mathematics, University of Queensland St Lucia, Brisbane 4072, Australia
2 ARC Centre in Bioinformatics, Institute for Molecular Bioscience, University of Queensland St Lucia, Brisbane 4072, Australia
*To whom correspondence should be addressed.
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 datasets.
Availability: An R-program for implementing the approach is freely available at http://www.maths.uq.edu.au/~gjm/
Contact: gjm{at}maths.uq.edu.au
Supplementary information: http://www.maths.uq.edu.au/~gjm/bioinf061supp_data.pdf
Received on January 25, 2006; revised on April 10, 2006; accepted on April 12, 2006
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