دورية أكاديمية

R/BHC: fast Bayesian hierarchical clustering for microarray data

التفاصيل البيبلوغرافية
العنوان: R/BHC: fast Bayesian hierarchical clustering for microarray data
المؤلفون: Grant Murray, Truman William M, Ghahramani Zoubin, Xu Yang, Heller Katherine, Savage Richard S, Denby Katherine J, Wild David L
المصدر: BMC Bioinformatics, Vol 10, Iss 1, p 242 (2009)
بيانات النشر: BMC, 2009.
سنة النشر: 2009
المجموعة: LCC:Computer applications to medicine. Medical informatics
LCC:Biology (General)
مصطلحات موضوعية: Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1471-2105
Relation: http://www.biomedcentral.com/1471-2105/10/242; https://doaj.org/toc/1471-2105
DOI: 10.1186/1471-2105-10-242
URL الوصول: https://doaj.org/article/39cf0cf8047944bea1bdc384c02d6126
رقم الأكسشن: edsdoj.39cf0cf8047944bea1bdc384c02d6126
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:14712105
DOI:10.1186/1471-2105-10-242