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dc.contributor.authorWang, Niya
dc.contributor.authorHoffman, Eric P.
dc.contributor.authorChen, Lulu
dc.contributor.authorChen, Li
dc.contributor.authorZhang, Zhen
dc.contributor.authorLiu, Chunyu
dc.contributor.authorYu, Guoqiang
dc.contributor.authorHerrington, David M.
dc.contributor.authorClarke, Robert
dc.contributor.authorWang, Yue
dc.date.accessioned2019-01-24T15:40:35Z
dc.date.available2019-01-24T15:40:35Z
dc.date.issued2016-01-07
dc.identifier.issn2045-2322
dc.identifier.other18909
dc.identifier.urihttp://hdl.handle.net/10919/86875
dc.description.abstractTissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised computational methods to deconvolute tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we describe convex analysis of mixtures (CAM), a fully unsupervised in silico method, for identifying subpopulation marker genes directly from the original mixed gene expressions in scatter space that can improve molecular analyses in many biological contexts. Validated with predesigned mixtures, CAM on the gene expression data from peripheral leukocytes, brain tissue, and yeast cell cycle, revealed novel marker genes that were otherwise undetectable using existing methods. Importantly, CAM requires no a priori information on the number, identity, or composition of the subpopulations present in mixed samples, and does not require the presence of pure subpopulations in sample space. This advantage is significant in that CAM can achieve all of its goals using only a small number of heterogeneous samples, and is more powerful to distinguish between phenotypically similar subpopulations.en_US
dc.description.sponsorshipNational Institutes of Health [NS029525, CA160036, CA184902, ES024988, CA149653, HL111362]
dc.format.extent12
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.subjectcycle-regulated genes
dc.subjectcell-cycle
dc.subjectexpression deconvolution
dc.subjectseparation
dc.subjectpatterns
dc.subjectcancer
dc.subjectbrain
dc.subjecttool
dc.titleMathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissuesen_US
dc.typeArticle - Refereed
dc.description.notesThis work was funded in part by the National Institutes of Health under Grants NS029525, CA160036, CA184902, ES024988, CA149653, and HL111362.
dc.title.serialScientific Reports
dc.identifier.doihttps://doi.org/10.1038/srep18909
dc.identifier.volume6
dc.type.dcmitypeText
dc.identifier.pmid26739359


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International