Nonparametric Bayesian Bi-Clustering for Next Generation Sequencing Count Data

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dc.contributor.utaustinauthorMitra, Ritenen_US
dc.contributor.utaustinauthorMuller, Peteren_US
dc.creatorXu, Yanxunen_US
dc.creatorLee, Juheeen_US
dc.creatorYuan, Yuanen_US
dc.creatorMitra, Ritenen_US
dc.creatorLiang, Shoudanen_US
dc.creatorMuller, Peteren_US
dc.creatorJi, Yien_US
dc.date.accessioned2016-10-28T19:52:49Z
dc.date.available2016-10-28T19:52:49Z
dc.date.issued2013en_US
dc.description.abstractHistone modifications (HMs) play important roles in transcription through post-translational modifications. Combinations of HMs, known as chromatin signatures, encode specific messages for gene regulation. We therefore expect that inference on possible clustering of HMs and an annotation of genomic locations on the basis of such clustering can contribute new insights about the functions of regulatory elements and their relationships to combinations of HMs. We propose a nonparametric Bayesian local clustering Poisson model (NoB-LCP) to facilitate posterior inference on two-dimensional clustering of HMs and genomic locations. The NoB-LCP clusters HMs into HM sets and lets each HM set define its own clustering of genomic locations. Furthermore, it probabilistically excludes HMs and genomic locations that are irrelevant to clustering. By doing so, the proposed model effectively identifies important sets of HMs and groups regulatory elements with similar functionality based on HM patterns.en_US
dc.description.departmentMathematicsen_US
dc.description.sponsorshipNIH R01 CA132897en_US
dc.description.sponsorshipNCI 5 K25 CA123344en_US
dc.identifierdoi:10.15781/T2FQ9Q79X
dc.identifier.citationXu, Yanxun, Juhee Lee, Yuan Yuan, Riten Mitra, Shoudan Liang, Peter Müller, and Yuan Ji. "Nonparametric bayesian bi-clustering for next generation sequencing count data." Bayesian analysis (Online), Vol. 8, No. 4 (2013): 759.en_US
dc.identifier.doi10.1214/13-ba822en_US
dc.identifier.issn1931-6690en_US
dc.identifier.urihttp://hdl.handle.net/2152/43312
dc.language.isoEnglishen_US
dc.relation.ispartofen_US
dc.relation.ispartofserialBayesian Analysisen_US
dc.rightsAdministrative deposit of works to Texas ScholarWorks: This works author(s) is or was a University faculty member, student or staff member; this article is already available through open access or the publisher allows a PDF version of the article to be freely posted online. The library makes the deposit as a matter of fair use (for scholarly, educational, and research purposes), and to preserve the work and further secure public access to the works of the University.en_US
dc.rights.restrictionOpenen_US
dc.subjectchip-seqen_US
dc.subjecthistone modificationsen_US
dc.subjectnonparametric bayesen_US
dc.subjectbi-clusteringen_US
dc.subjectmarkov chain monte carloen_US
dc.subjectgene-expression dataen_US
dc.subjectmicroarray dataen_US
dc.subjecthuman genomeen_US
dc.subjecthistoneen_US
dc.subjectmodificationsen_US
dc.subjectstem-cellsen_US
dc.subjectmethylationsen_US
dc.subjectenhancersen_US
dc.subjectdomainsen_US
dc.subjectmathematics, interdisciplinary applicationsen_US
dc.subjectstatistics & probabilityen_US
dc.titleNonparametric Bayesian Bi-Clustering for Next Generation Sequencing Count Dataen_US
dc.typeArticleen_US

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