title: A novel approach to the clustering of microarray data via nonparametric density estimation.
abstract: BACKGROUND
  Cluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to dimensionality issues, since the number of variables can be much higher than the number of observations.
  
  
  RESULTS
  Here, we present a general framework to deal with the clustering of microarray data, based on a three-step procedure: (i) gene filtering; (ii) dimensionality reduction; (iii) clustering of observations in the reduced space. Via a nonparametric model-based clustering approach we obtain promising results both in simulated and real data.
  
  
  CONCLUSIONS
  The proposed algorithm is a simple and effective tool for the clustering of microarray data, in an unsupervised setting.
authors.affiliation: Department of Statistical Sciences, University of Padova, Padova, Italy.
authors.forename: Riccardo
authors.initials: R
authors.lastname: De Bin
authors.forename: Davide
authors.initials: D
authors.lastname: Risso
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025341:Principal Component Analysis; D012984:Software; D018709:Statistics, Nonparametric