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Nucleic Acids Research, 2002, Vol. 30, No. 18 3992-4000
© 2002 Oxford University Press

Density of points clustering, application to transcriptomic data analysis

Nicolas Wicker1,2, Doulaye Dembele2, Wolfgang Raffelsberger2 and Olivier Poch*,2

1 LSIIT-ICPS (AXE E), UPRES-A CNRS 70005 Université Louis Pasteur, 67400 Illkirch, France and 2 Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS/INSERM/ULP, BP 10142, 67404 Illkirch Cedex, France

*To whom correspondence should be addressed. Tel: +33 3 88 65 32 94; Fax: +33 3 88 65 32 76; Email: poch{at}igbmc.u-strasbg.fr

With the increasing amount of data produced by high-throughput technologies in many fields of science, clustering has become an integral step in exploratory data analysis in order to group similar elements into classes. However, many clustering algorithms can only work properly if aided by human expertise. For example, one parameter which is crucial and often manually set is the number of clusters present in the analyzed set. We present a novel stopping rule to find the optimal number of clusters based on the comparison of the density of points inside the clusters and between them. The method is evaluated on synthetic as well as on real transcriptomic data and compared with two current methods. Finally, we illustrate its usefulness in the analysis of the expression profiles of promyelocytic cells before and after treatment with all-trans retinoic acid. Simultaneous clustering for gene regulation and absolute initial expression levels allowed the identification of numerous genes associated with signal transduction revealing the complexity of retinoic acid signaling.


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