title: Clustering.
abstract: Clustering techniques are used to arrange genes in some natural way, that is, to organize genes into groups or clusters with similar behavior across relevant tissue samples (or cell lines). These techniques can also be applied to tissues rather than genes. Methods such as hierarchical agglomerative clustering, k-means clustering, the self-organizing map, and model-based methods have been used. Here we focus on mixtures of normals to provide a model-based clustering of tissue samples (gene signatures) and of gene profiles, including time-course gene expression data.
authors.affiliation: Department of Mathematics, The University of Queensland, Brisbane, QLD, Australia. g.mclachlan@uq.edu.au.
authors.forename: G J
authors.initials: GJ
authors.lastname: McLachlan
authors.affiliation: Department of Health, The University of Queensland, Brisbane, QLD, Australia.
authors.forename: R W
authors.initials: RW
authors.lastname: Bean
authors.affiliation: School of Medicine, Griffith Health Institute, Griffith University, Brisbane, QLD, Australia.
authors.forename: S K
authors.initials: SK
authors.lastname: Ng
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software
keywords: Autoregressive random effects; Clustering of gene profiles; Clustering of tissue samples; Hierarchical agglomerative methods; Mixtures of factor analyzers; Mixtures of linear mixed-effects models; Model-based methods; Normal mixture models; Partitional methods; Time-course data; k-means