title: Model-based clustering and data transformations for gene expression data.
abstract: BACKGROUND
  Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated by a finite mixture of underlying probability distributions such as multivariate normal distributions. The issues of selecting a 'good' clustering method and determining the 'correct' number of clusters are reduced to model selection problems in the probability framework. Gaussian mixture models have been shown to be a powerful tool for clustering in many applications.
  
  
  RESULTS
  We benchmarked the performance of model-based clustering on several synthetic and real gene expression data sets for which external evaluation criteria were available. The model-based approach has superior performance on our synthetic data sets, consistently selecting the correct model and the number of clusters. On real expression data, the model-based approach produced clusters of quality comparable to a leading heuristic clustering algorithm, but with the key advantage of suggesting the number of clusters and an appropriate model. We also explored the validity of the Gaussian mixture assumption on different transformations of real data. We also assessed the degree to which these real gene expression data sets fit multivariate Gaussian distributions both before and after subjecting them to commonly used data transformations. Suitably chosen transformations seem to result in reasonable fits.
  
  
  BACKGROUND
  MCLUST is available at http://www.stat.washington.edu/fraley/mclust. The software for the diagonal model is under development.
  
  
  BACKGROUND
  kayee@cs.washington.edu.
  
  
  BACKGROUND
  http://www.cs.washington.edu/homes/kayee/model.
authors.affiliation: Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA. kayee@cs.washington.edu
authors.forename: K Y
authors.initials: KY
authors.lastname: Yeung
authors.forename: C
authors.initials: C
authors.lastname: Fraley
authors.forename: A
authors.initials: A
authors.lastname: Murua
authors.forename: A E
authors.initials: AE
authors.lastname: Raftery
authors.forename: W L
authors.initials: WL
authors.lastname: Ruzzo
mesh_terms: D000465:Algorithms; D002453:Cell Cycle / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D010053:Ovary / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics