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Bioinformatics Advance Access originally published online on January 29, 2004
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Bioinformatics 20(6) © Oxford University Press 2004; all rights reserved.

mdclust—exploratory microarray analysis by multidimensional clustering

M. Dugas 1,*, S. Merk 1, S. Breit 2 and P. Dirschedl 1

1 Department of Medical Informatics, Marchioninistr. 15, D-81377 Munich, Germany and 2 Department of Dermatology, Frauenlobstraße 9-11, D-80337 Munich, Germany

Received on September 24, 2003 ; accepted on November 3, 2003
Advance Access Publication January 29, 2004

Motivation: Unsupervised clustering of microarray data may detect potentially important, but not obvious characteristics of samples, for instance subgroups of diagnoses with distinct gene profiles or systematic errors in experimentation.

Results: Multidimensional clustering (mdclust) is a method, which identifies sets of sample clusters and associated genes. It applies iteratively two-means clustering and score-based gene selection.

For any phenotype variable best matching sets of clusters can be selected. This provides a method to identify gene–phenotype associations, suited even for settings with a large number of phenotype variables. An optional model based discriminant step may reduce further the number of selected genes.

Availability: R-code and supplemental information available from http://martin-dugas.de/mdclust/

Supplementary information: http://martin-dugas.de/mdclust/

Contact: dug{at}ibe.med.uni-muenchen.de

* To whom correspondence should be addressed.


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