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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/117779
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dc.contributor.authorLeemaqz, S.-
dc.contributor.authorMcLachlan, G.-
dc.contributor.authorPyne, S.-
dc.contributor.editorBRINKMAN, R.R.-
dc.contributor.editorAghaeepour, N.-
dc.contributor.editorFinak, G.-
dc.contributor.editorGottardo, R.-
dc.contributor.editorMosmann, T.-
dc.contributor.editorScheuermann, R.H.-
dc.date.issued2016-
dc.identifier.citationCytometry Part A, 2016; 89(1):30-43-
dc.identifier.issn1552-4922-
dc.identifier.issn1552-4930-
dc.identifier.urihttp://hdl.handle.net/2440/117779-
dc.description.abstractAbstract not available-
dc.description.statementofresponsibilitySharon X. Lee, Geoffrey J. McLachlan, Saumyadipta Pyne-
dc.language.isoen-
dc.publisherWiley-
dc.rights© 2015 International Society for Advancement of Cytometry-
dc.source.urihttp://dx.doi.org/10.1002/cyto.a.22789-
dc.subjectFlow cytometry; classification; class template; inter-sample variation; clustering; matching; skew mixture models; EM algorithm; JCM-
dc.titleModeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure-
dc.typeJournal article-
dc.identifier.doi10.1002/cyto.a.22789-
dc.relation.grantARC-
pubs.publication-statusPublished-
dc.identifier.orcidLeemaqz, S. [0000-0002-6391-135X]-
Appears in Collections:Aurora harvest 3
Mathematical Sciences publications

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