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dc.contributor.authorHardcastle, Thomasen
dc.contributor.authorKelly, Krysen
dc.date.accessioned2013-05-20T11:05:30Z
dc.date.available2013-05-20T11:05:30Z
dc.date.issued2013-04-23en
dc.identifier.issn1471-2105
dc.identifier.urihttp://www.dspace.cam.ac.uk/handle/1810/244596
dc.description.abstractAbstract Background Pairing of samples arises naturally in many genomic experiments; for example, gene expression in tumour and normal tissue from the same patients. Methods for analysing high-throughput sequencing data from such experiments are required to identify differential expression, both within paired samples and between pairs under different experimental conditions. Results We develop an empirical Bayesian method based on the beta-binomial distribution to model paired data from high-throughput sequencing experiments. We examine the performance of this method on simulated and real data in a variety of scenarios. Our methods are implemented as part of the RbaySeq package (versions 1.11.6 and greater) available from Bioconductor (http://www.bioconductor.org). Conclusions We compare our approach to alternatives based on generalised linear modelling approaches and show that our method offers significant gains in performance on simulated data. In testing on real data from oral squamous cell carcinoma patients, we discover greater enrichment of previously identified head and neck squamous cell carcinoma associated gene sets than has previously been achieved through a generalised linear modelling approach, suggesting that similar gains in performance may be found in real data. Our methods thus show real and substantial improvements in analyses of high-throughput sequencing data from paired samples.
dc.languageEnglishen
dc.language.isoen
dc.titleEmpirical Bayesian analysis of paired high-throughput sequencing data with a beta-binomial distributionen
dc.typeArticle
dc.date.updated2013-05-20T11:05:31Z
dc.description.versionRIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.en
dc.rights.holderThomas J Hardcastle et al.; licensee BioMed Central Ltd.
prism.publicationDate2013en
dcterms.dateAccepted2013-03-21en
rioxxterms.versionofrecord10.1186/1471-2105-14-135en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2013-04-23en
dc.contributor.orcidHardcastle, Thomas [0000-0002-9328-5011]
dc.contributor.orcidKelly, Krys [0000-0001-9820-4068]
dc.identifier.eissn1471-2105
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEuropean Research Council (233325)


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