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dc.date.accessioned2018-03-17T16:05:03Z
dc.date.available2018-03-17T16:05:03Z
dc.date.created2016-09-02T11:15:00Z
dc.date.issued2016
dc.identifier.citationHellton, Kristoffer Herland Thoresen, Magne . Integrative clustering of high-dimensional data with joint and individual clusters. Biostatistics. 2016, 17(3), 537-548
dc.identifier.urihttp://hdl.handle.net/10852/61092
dc.description.abstractWhen measuring a range of genomic, epigenomic, and transcriptomic variables for the same tissue sample, an integrative approach to analysis can strengthen inference and lead to new insights. This is also the case when clustering patient samples, and several integrative cluster procedures have been proposed. Common for these methodologies is the restriction to a joint cluster structure, equal in all data layers. We instead present a clustering extension of the Joint and Individual Variance Explained algorithm (JIVE), Joint and Individual Clustering (JIC), enabling the construction of both joint and data type-specific clusters simultaneously. The procedure builds on the connection between k-means clustering and principal component analysis, and hence, the number of clusters can be determined by the number of relevant principal components. The proposed procedure is compared with iCluster, a method restricted to only joint clusters, and simulations show that JIC is clearly advantageous when both individual and joint clusters are present. The procedure is illustrated using gene expression and miRNA levels measured in breast cancer tissue from The Cancer Genome Atlas. The analysis suggests a division into three joint clusters common for both data types and two expression-specific clusters. The final version of this research has been published in Biostatistics. © 2016 Oxford University Pressen_US
dc.languageEN
dc.publisherOxford University Press
dc.titleIntegrative clustering of high-dimensional data with joint and individual clustersen_US
dc.typeJournal articleen_US
dc.creator.authorHellton, Kristoffer Herland
dc.creator.authorThoresen, Magne
cristin.unitcode185,51,15,0
cristin.unitnameAvdeling for biostatistikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2
dc.identifier.cristin1377555
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Biostatistics&rft.volume=17&rft.spage=537&rft.date=2016
dc.identifier.jtitleBiostatistics
dc.identifier.volume17
dc.identifier.issue3
dc.identifier.startpage537
dc.identifier.endpage548
dc.identifier.doihttp://dx.doi.org/10.1093/biostatistics/kxw005
dc.identifier.urnURN:NBN:no-63714
dc.type.documentTidsskriftartikkelen_US
dc.type.peerreviewedPeer reviewed
dc.source.issn1465-4644
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/61092/1/Revision4.pdf
dc.type.versionAcceptedVersion


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