title: mdclust--exploratory microarray analysis by multidimensional clustering.
abstract: BACKGROUND
  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.
authors.affiliation: Department of Medical Informatics, Marchioninistr. 15, D-81377 Munich, Germany. dug@ibe.med.uni-muenchen.de
authors.forename: M
authors.initials: M
authors.lastname: Dugas
authors.forename: S
authors.initials: S
authors.lastname: Merk
authors.forename: S
authors.initials: S
authors.lastname: Breit
authors.forename: P
authors.initials: P
authors.lastname: Dirschedl
mesh_terms: D016000:Cluster Analysis; D003196:Computer Graphics; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D007938:Leukemia / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software