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Bioinformatics Advance Access originally published online on November 22, 2005
Bioinformatics 2006 22(3):326-331; doi:10.1093/bioinformatics/bti788
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Maximum significance clustering of oligonucleotide microarrays

Dick de Ridder 1,2,*, Frank J. T. Staal 2, Jacques J. M. van Dongen 2 and Marcel J. T. Reinders 1

1Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology PO Box 5031, 2600 GA Delft, The Netherlands
2Department of Immunology, Erasmus MC, University Medical Center PO Box 1738, 3000 DR Rotterdam, The Netherlands

*To whom correspondence should be addressed.

Motivation: Affymetrix high-density oligonucleotide microarrays measure the expression of DNA transcripts using probesets, i.e. multiple probes per transcript. Usually, these multiple measurements are transformed into a single probeset expression level before data analysis proceeds; any information on variability is lost. In this paper we demonstrate how individual probe measurements can be used in a statistic for differential expression. Furthermore, we show how this statistic can serve as a criterion for clustering microarrays.

Results: A novel clustering algorithm using this maximum significance criterion is demonstrated to be more efficient with the measured data than competing techniques for dealing with repeated measurements, especially when the sample size is small.

Availability: MATLAB source code can be found at http://ict.ewi.tudelft.nl/~dick

Contact: D.deRidder{at}ewi.tudelft.nl

Received on September 23, 2005; revised on November 3, 2005; accepted on November 16, 2005

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