title: Density of points clustering, application to transcriptomic data analysis.
abstract: 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.
authors.affiliation: LSIIT-ICPS (AXE E), UPRES-A CNRS 70005 Université Louis Pasteur, 67400 Illkirch, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Wicker
authors.forename: Doulaye
authors.initials: D
authors.lastname: Dembele
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Raffelsberger
authors.forename: Olivier
authors.initials: O
authors.lastname: Poch
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D020869:Gene Expression Profiling; D006801:Humans; D007938:Leukemia / Q000235:genetics / Q000473:pathology; D012441:Saccharomyces cerevisiae / Q000235:genetics; D014158:Transcription, Genetic / Q000235:genetics*