title: A fully Bayesian model to cluster gene-expression profiles.
abstract: BACKGROUND
  With cDNA or oligonucleotide chips, gene-expression levels of essentially all genes in a genome can be simultaneously monitored over a time-course or under different experimental conditions. After proper normalization of the data, genes are often classified into co-expressed classes (clusters) to identify subgroups of genes that share common regulatory elements, a common function or a common cellular origin. With most methods, e.g. k-means, the number of clusters needs to be specified in advance; results depend strongly on this choice. Even with likelihood-based methods, estimation of this number is difficult. Furthermore, missing values often cause problems and lead to the loss of data.
  
  
  RESULTS
  We propose a fully probabilistic Bayesian model to cluster gene-expression profiles. The number of classes does not need to be specified in advance; instead it is adjusted dynamically using a Reversible Jump Markov Chain Monte Carlo sampler. Imputation of missing values is integrated into the model. With simulations, we determined the speed of convergence of the sampler as well as the accuracy of the inferred variables. Results were compared with the widely used k-means algorithm. With our method, biologically related co-expressed genes could be identified in a yeast transcriptome dataset, even when some values were missing.
  
  
  BACKGROUND
  The code is available at http://genome.tugraz.at/BayesianClustering/
authors.affiliation: Institute of Animal Breeding and Genetics, Veterinärmedizinische Universität Wien, Vienna, Austria. claus.vogl@vu-wien.ac.at
authors.forename: C
authors.initials: C
authors.lastname: Vogl
authors.forename: F
authors.initials: F
authors.lastname: Sanchez-Cabo
authors.forename: G
authors.initials: G
authors.lastname: Stocker
authors.forename: S
authors.initials: S
authors.lastname: Hubbard
authors.forename: O
authors.initials: O
authors.lastname: Wolkenhauer
authors.forename: Z
authors.initials: Z
authors.lastname: Trajanoski
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D005810:Multigene Family / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*