SC3: consensus clustering of single-cell RNA-seq data

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dc.contributor.authorKiselev, VY
dc.contributor.authorKirschner, K
dc.contributor.authorSchaub, MT
dc.contributor.authorAndrews, T
dc.contributor.authorYiu, A
dc.contributor.authorChandra, T
dc.contributor.authorNatarajan, KN
dc.contributor.authorReik, W
dc.contributor.authorBarahona, M
dc.contributor.authorGreen, AR
dc.contributor.authorHemberg, M
dc.date.accessioned2017-05-24T09:13:39Z
dc.date.available2017-05-24T09:13:39Z
dc.date.issued2017-05-01
dc.identifier.issn1548-7091
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/264381
dc.description.abstractSingle-cell RNA-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/SC3). We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.
dc.description.sponsorshipV.Y.K., T.A., A.Y. and M.H. are supported by Wellcome Trust Grants. K.N.N. is supported by the Wellcome Trust Strategic Award 'Single cell genomics of mouse gastrulation'. M.T.S. acknowledges support from FRS-FNRS; the Belgian Network DYSCO (Dynamical Systems, Control and Optimisation), funded by the Interuniversity Attraction Poles Programme initiated by the Belgian State Science Policy Office; and the ARC (Action de Recherche Concerte) on Mining and Optimization of Big Data Models, funded by the Wallonia-Brussels Federation. M.B. acknowledges support from EPSRC (grant EP/N014529/1). T.C. was funded through a core funded fellowship by the Sanger Institute and a Chancellor′s fellowship from the University of Edinburgh. K.K. and A.R.G. are supported by Bloodwise (grant ref. 13003), the Wellcome Trust (grant ref. 104710/Z/14/Z), the Medical Research Council, the Kay Kendall Leukaemia Fund, the Cambridge NIHR Biomedical Research Center, the Cambridge Experimental Cancer Medicine Centre, the Leukemia and Lymphoma Society of America (grant ref. 07037) and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute. W.R. was supported by BBSRC (grant ref. BB/K010867/1), the Wellcome Trust (grant ref. 095645/Z/11/Z), EU BLUEPRINT and EpiGeneSys.
dc.languageeng
dc.language.isoen
dc.publisherNature Publishing Group
dc.subjectgene expression
dc.subjectmachine learning
dc.subjectRNA sequencing
dc.subjectsoftware
dc.titleSC3: consensus clustering of single-cell RNA-seq data
dc.typeArticle
prism.endingPage486
prism.issueIdentifier5
prism.publicationDate2017
prism.publicationNameNature Methods
prism.startingPage483
prism.volume14
dc.identifier.doi10.17863/CAM.9872
dcterms.dateAccepted2017-03-03
rioxxterms.versionofrecord10.1038/nmeth.4236
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-05-01
dc.contributor.orcidReik, Wolf [0000-0003-0216-9881]
dc.contributor.orcidGreen, Tony [0000-0002-9795-0218]
dc.identifier.eissn1548-7105
rioxxterms.typeJournal Article/Review
pubs.funder-project-idMedical Research Council (MC_PC_12009)
pubs.funder-project-idWellcome Trust (104710/Z/14/Z)
pubs.funder-project-idLeukaemia & Lymphoma Research (13003)
pubs.funder-project-idBlood Cancer UK (07037)
cam.issuedOnline2017-03-27
rioxxterms.freetoread.startdate2017-09-27


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