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Cytometree: A binary tree algorithm for automatic gating in cytometry analysis

dc.rights.licenseopenen_US
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorCOMMENGES, Daniel
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorALKHASSIM, Chariff
dc.contributor.authorGOTTARDO, R.
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHEJBLUM, Boris
ORCID: 0000-0003-0646-452X
IDREF: 189970316
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorTHIEBAUT, Rodolphe
dc.date.accessioned2020-11-02T09:47:25Z
dc.date.available2020-11-02T09:47:25Z
dc.date.issued2018-11
dc.identifier.issn1552-4930 (Electronic) 1552-4922 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/11565
dc.description.abstractEnFlow cytometry is a powerful technology that allows the high-throughput quantification of dozens of surface and intracellular proteins at the single-cell level. It has become the most widely used technology for immunophenotyping of cells over the past three decades. Due to the increasing complexity of cytometry experiments (more cells and more markers), traditional manual flow cytometry data analysis has become untenable due to its subjectivity and time-consuming nature. We present a new unsupervised algorithm called "cytometree" to perform automated population identification (aka gating) in flow cytometry. cytometree is based on the construction of a binary tree, the nodes of which are subpopulations of cells. At each node, the marker distributions are modeled by mixtures of normal distributions. Node splitting is done according to a model selection procedure based on a normalized difference of Akaike information criteria between two competing models. Post-processing of the tree structure and derived populations allows us to complete the annotation of the populations. The algorithm is shown to perform better than the state-of-the-art unsupervised algorithms previously proposed on panels introduced by the Flow Cytometry: Critical Assessment of Population Identification Methods project. The algorithm is also applied to a T-cell panel proposed by the Human Immunology Project Consortium (HIPC) program; it also outperforms the best unsupervised open-source available algorithm while requiring the shortest computation time. (c) 2018 International Society for Advancement of Cytometry.
dc.language.isoENen_US
dc.subject.enBiostatistics
dc.subject.enSISTM
dc.title.enCytometree: A binary tree algorithm for automatic gating in cytometry analysis
dc.title.alternativeCytometry Aen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1002/cyto.a.23601
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed30277649en_US
bordeaux.journalCytometry Part Aen_US
bordeaux.page1132-1140en_US
bordeaux.volume93en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue11en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamSISTM_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03161874
hal.version2
hal.date.transferred2021-03-08T09:26:02Z
hal.exportfalse
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