title: Bayesian hierarchical clustering for studying cancer gene expression data with unknown statistics.
abstract: Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC) algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC) algorithm represents data as a mixture of Gaussian distributions. It uses normal-gamma distribution as a conjugate prior on the mean and precision of each of the Gaussian components. We tested GBHC over 11 cancer and 3 synthetic datasets. The results on cancer datasets show that in sample clustering, GBHC on average produces a clustering partition that is more concordant with the ground truth than those obtained from other commonly used algorithms. Furthermore, GBHC frequently infers the number of clusters that is often close to the ground truth. In gene clustering, GBHC also produces a clustering partition that is more biologically plausible than several other state-of-the-art methods. This suggests GBHC as an alternative tool for studying gene expression data. The implementation of GBHC is available at https://sites.google.com/site/gaussianbhc/
authors.affiliation: Department of Computer Science, The University of Warwick, Coventry, United Kingdom.
authors.forename: Korsuk
authors.initials: K
authors.lastname: Sirinukunwattana
authors.forename: Richard S
authors.initials: RS
authors.lastname: Savage
authors.forename: Muhammad F
authors.initials: MF
authors.lastname: Bari
authors.forename: David R J
authors.initials: DR
authors.lastname: Snead
authors.forename: Nasir M
authors.initials: NM
authors.lastname: Rajpoot
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics*; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D016011:Normal Distribution