title: Dynamic Bayesian clustering.
abstract: Clusters of time series data may change location and memberships over time; in gene expression data, this occurs as groups of genes or samples respond differently to stimuli or experimental conditions at different times. In order to uncover this underlying temporal structure, we consider dynamic clusters with time-dependent parameters which split and merge over time, enabling cluster memberships to change. These interesting time-dependent structures are useful in understanding the development of organisms or complex organs, and could not be identified using traditional clustering methods. In cell cycle data, these time-dependent structure may provide links between genes and stages of the cell cycle, whilst in developmental data sets they may highlight key developmental transitions.
authors.affiliation: Department of Mathematics, Imperial College London, 180 Queens Gate, London SW7 2AZ, UK.
authors.forename: Anna
authors.initials: A
authors.lastname: Fowler
authors.forename: Vilas
authors.initials: V
authors.lastname: Menon
authors.forename: Nicholas A
authors.initials: NA
authors.lastname: Heard
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001921:Brain / Q000254:growth & development / Q000378:metabolism; D002453:Cell Cycle / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D018507:Gene Expression Regulation, Developmental; D008390:Markov Chains; D051379:Mice; D015233:Models, Statistical; D009010:Monte Carlo Method