R/BHC: fast Bayesian hierarchical clustering for microarray data Export

@article{citeulike:5390965, abstract = {BACKGROUND:Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, little attention has been paid to uncertainty in the results obtained.RESULTS:We present an R/Bioconductor port of a fast novel algorithm for Bayesian agglomerative hierarchical clustering and demonstrate its use in clustering gene expression microarray data. The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge.CONCLUSION:Biologically plausible results are presented from a well studied data set: expression profiles of A. thaliana subjected to a variety of biotic and abiotic stresses. Our method avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric.}, author = {Savage, Richard and Heller, Katherine and Xu, Yang and Ghahramani, Zoubin and Truman, William and Grant, Murray and Denby, Katherine and Wild, David}, citeulike-article-id = {5390965}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-10-242}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19660130}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19660130}, day = {6}, doi = {10.1186/1471-2105-10-242}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {bayesian, clustering, microarray}, month = {August}, number = {1}, pages = {242+}, posted-at = {2009-08-07 18:40:25}, priority = {2}, title = {R/BHC: fast Bayesian hierarchical clustering for microarray data}, url = {http://dx.doi.org/10.1186/1471-2105-10-242}, volume = {10}, year = {2009} }

TY - JOUR ID - citeulike:5390965 L3 - citeulike-article-id:5390965 N2 - BACKGROUND:Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, little attention has been paid to uncertainty in the results obtained.RESULTS:We present an R/Bioconductor port of a fast novel algorithm for Bayesian agglomerative hierarchical clustering and demonstrate its use in clustering gene expression microarray data. The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge.CONCLUSION:Biologically plausible results are presented from a well studied data set: expression profiles of A. thaliana subjected to a variety of biotic and abiotic stresses. Our method avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 TI - R/BHC: fast Bayesian hierarchical clustering for microarray data VL - 10 SP - 242 KW - bayesian KW - clustering KW - microarray AU - Savage, Richard AU - Heller, Katherine AU - Xu, Yang AU - Ghahramani, Zoubin AU - Truman, William AU - Grant, Murray AU - Denby, Katherine AU - Wild, David PY - 2009/08/06/ UR - http://dx.doi.org/10.1186/1471-2105-10-242 ER -