Bayesian clustering of replicated time-course gene expression data with weak signals
نویسندگان
چکیده
منابع مشابه
Bayesian Clustering of Replicated Time-course Gene Expression Data with Weak Signals
To identify novel dynamic patterns of gene expression, we develop a statistical method to cluster noisy measurements of gene expression collected from multiple replicates at multiple time points, with an unknown number of clusters. We propose a random-effects mixture model coupled with a Dirichlet-process prior for clustering. The mixture model formulation allows for probabilistic cluster assig...
متن کاملBayesian Clustering of Replicated Time-course Gene Expression Data with Weak Signals by Audrey
To identify novel dynamic patterns of gene expression, we develop a statistical method to cluster noisy measurements of gene expression collected from multiple replicates at multiple time points, with an unknown number of clusters. We propose a random-effects mixture model coupled with a Dirichlet-process prior for clustering. The mixture model formulation allows for probabilistic cluster assig...
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Microarray experiments have been used to measure genes’ expression levels under different cellular conditions or along certain time course. Initial attempts to interpret these data begin with grouping genes according to similarity in their expression profiles. The widely adopted clustering techniques for gene expression data include hierarchical clustering, self-organizing maps, and K-means clu...
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Anne Badel-Chagnon , Gaëlle Lelandais , Serge Hazout and Pierre Vincens Equipe de Bioinformatique Génomique et Moléculaire, Inserm E0346, Université Paris 7, case 7113, 2 Place Jussieu, 75251 Paris, France Laboratoire de Génétique Moléculaire, CNRS UMR 8541, Ecole Normale Supérieure, 46 rue d’Ulm, 75230 Paris Cedex 05, France Département de Biologie (FR36), Ecole Normale Supérieure, 46 rue d’Ul...
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Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, t...
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ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2013
ISSN: 1932-6157
DOI: 10.1214/13-aoas650