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dc.contributor.authorFu, Yi
dc.contributor.authorYu, Guoqiang
dc.contributor.authorLevine, Douglas A.
dc.contributor.authorWang, Niya
dc.contributor.authorShih, Ie-Ming
dc.contributor.authorZhang, Zhen
dc.contributor.authorClarke, Robert
dc.contributor.authorWang, Yue
dc.date.accessioned2019-01-24T18:24:47Z
dc.date.available2019-01-24T18:24:47Z
dc.date.issued2015-09-09
dc.identifier.issn2045-2322
dc.identifier.other13955
dc.identifier.urihttp://hdl.handle.net/10919/86882
dc.description.abstractMost published copy number datasets on solid tumors were obtained from specimens comprised of mixed cell populations, for which the varying tumor-stroma proportions are unknown or unreported. The inability to correct for signal mixing represents a major limitation on the use of these datasets for subsequent analyses, such as discerning deletion types or detecting driver aberrations. We describe the BACOM2.0 method with enhanced accuracy and functionality to normalize copy number signals, detect deletion types, estimate tumor purity, quantify true copy numbers, and calculate average-ploidy value. While BACOM has been validated and used with promising results, subsequent BACOM analysis of the TCGA ovarian cancer dataset found that the estimated average tumor purity was lower than expected. In this report, we first show that this lowered estimate of tumor purity is the combined result of imprecise signal normalization and parameter estimation. Then, we describe effective allele-specific absolute normalization and quantification methods that can enhance BACOM applications in many biological contexts while in the presence of various confounders. Finally, we discuss the advantages of BACOM in relation to alternative approaches. Here we detail this revised computational approach, BACOM2.0, and validate its performance in real and simulated datasets.en_US
dc.description.sponsorshipNational Institutes of Health [CA149653, CA160036, HL111362, CA184902, NS029525, ES024988]
dc.format.extent11
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherSpringer Nature
dc.rightsCreative Commons Attribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectinformation-theoretic criteria
dc.subjectgeneration sequencing data
dc.subjectsignificant aberrations
dc.subjectcancer
dc.subjectsamples
dc.subjectidentification
dc.subjectgenome
dc.subjectpurity
dc.subjectaneuploidy
dc.subjectcarcinoma
dc.titleBACOM2.0 facilitates absolute normalization and quantification of somatic copy number alterations in heterogeneous tumoren_US
dc.typeArticle - Refereed
dc.description.notesThis work was supported in part by the National Institutes of Health under Grants CA149653, CA160036, HL111362, CA184902, NS029525, and ES024988.
dc.title.serialScientific Reports
dc.identifier.doihttps://doi.org/10.1038/srep13955
dc.identifier.volume5
dc.type.dcmitypeText
dc.identifier.pmid26350498


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