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dc.contributor.authorHardcastle, Thomasen
dc.contributor.authorKelly, Krysen
dc.date.accessioned2011-06-16T15:52:27Z
dc.date.available2011-06-16T15:52:27Z
dc.date.issued2010-08-10en
dc.identifier.citationBMC Bioinformatics 2010, 11:422
dc.identifier.issn1471-2105
dc.identifier.urihttp://www.dspace.cam.ac.uk/handle/1810/237808
dc.description.abstractAbstract Background High throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key way of analysing such data is to look for elements of the data which display particular patterns of differential expression in order to take these forward for further analysis and validation. Results We propose a framework for defining patterns of differential expression and develop a novel algorithm, baySeq, which uses an empirical Bayes approach to detect these patterns of differential expression within a set of sequencing samples. The method assumes a negative binomial distribution for the data and derives an empirically determined prior distribution from the entire dataset. We examine the performance of the method on real and simulated data. Conclusions Our method performs at least as well, and often better, than existing methods for analyses of pairwise differential expression in both real and simulated data. When we compare methods for the analysis of data from experimental designs involving multiple sample groups, our method again shows substantial gains in performance. We believe that this approach thus represents an important step forward for the analysis of count data from sequencing experiments.
dc.languageEnglishen
dc.language.isoen
dc.titlebaySeq: Empirical Bayesian Methods For Identifying Differential Expression In Sequence Count Dataen
dc.typeArticle
dc.date.updated2011-06-16T15:52:28Z
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.holderHardcastle et al.; licensee BioMed Central Ltd.
prism.publicationDate2010en
dcterms.dateAccepted2010-08-10en
rioxxterms.versionofrecord10.1186/1471-2105-11-422en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2010-08-10en
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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