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dc.contributor.authorChristensen, Brock C
dc.contributor.authorYeh, Ru-Fang
dc.contributor.authorMarsit, Carmen J
dc.contributor.authorKaragas, Margaret R
dc.contributor.authorWrensch, Margaret
dc.contributor.authorNelson, Heather H
dc.contributor.authorWiemels, Joseph
dc.contributor.authorZheng, Shichun
dc.contributor.authorWiencke, John K
dc.contributor.authorKelsey, Karl T
dc.contributor.authorHouseman, Eugene Andres
dc.date.accessioned2010-11-29T18:41:35Z
dc.date.issued2008
dc.identifier.citationHouseman, E. Andres, Brock C. Christensen, Ru-Fang Yeh, Carmen J. Marsit, Margaret R. Karagas, Margaret Wrensch, Heather H. Nelson, et al. 2008. Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions. BMC Bioinformatics 9:365.en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4592392
dc.description.abstractBackground: Epigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. Arrays are now being used to study DNA methylation at a large number of loci; for example, the Illumina GoldenGate platform assesses DNA methylation at 1505 loci associated with over 800 cancer-related genes. Model-based cluster analysis is often used to identify DNA methylation subgroups in data, but it is unclear how to cluster DNA methylation data from arrays in a scalable and reliable manner. Results: We propose a novel model-based recursive-partitioning algorithm to navigate clusters in a beta mixture model. We present simulations that show that the method is more reliable than competing nonparametric clustering approaches, and is at least as reliable as conventional mixture model methods. We also show that our proposed method is more computationally efficient than conventional mixture model approaches. We demonstrate our method on the normal tissue samples and show that the clusters are associated with tissue type as well as age. Conclusion: Our proposed recursively-partitioned mixture model is an effective and computationally efficient method for clustering DNA methylation data.en_US
dc.language.isoen_USen_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofdoi:10.1186/1471-2105-9-365en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2553421/pdf/en_US
dash.licenseLAA
dc.titleModel-based Clustering of DNA Methylation Array Data: A Recursive-Partitioning Algorithm for High-dimensional Data Arising as a Mixture of Beta Distributionsen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalBMC Bioinformaticsen_US
dash.depositing.authorHouseman, Eugene Andres
dc.date.available2010-11-29T18:41:35Z
dash.affiliation.otherSPH^Biostatisticsen_US
dc.identifier.doi10.1186/1471-2105-9-365*
dash.authorsorderedfalse
dash.contributor.affiliatedHouseman, Eugene Andres


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