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CytOpT: Optimal Transport with Domain Adaptation for Interpreting Flow Cytometry data

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorFREULON, Paul
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBIGOT, Jérémie
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierVaccine Research Institute [Créteil, France] [VRI]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHEJBLUM, Boris
dc.date.accessioned2024-04-04T02:47:47Z
dc.date.available2024-04-04T02:47:47Z
dc.date.created2020
dc.date.issued2023
dc.identifier.issn1932-6157
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191680
dc.description.abstractEnThe automated analysis of flow cytometry measurements is an active research field. We introduce a new algorithm, referred to as CytOpT, using regularized optimal transport to directly estimate the different cell population proportions from a biological sample characterized with flow cytometry measurements. We rely on the regularized Wasserstein metric to compare cytometry measurements from different samples, thus accounting for possible mis-alignment of a given cell population across sample (due to technical variability from the technology of measurements). In this work, we rely on a supervised learning technique based on the Wasserstein metric that is used to estimate an optimal re-weighting of class proportions in a mixture model from a source distribution (with known segmentation into cell sub-populations) to fit a target distribution with unknown segmentation. Due to the high-dimensionality of flow cytometry data, we use stochastic algorithms to approximate the regularized Wasserstein metric to solve the optimization problem involved in the estimation of optimal weights representing the cell population proportions in the target distribution. Several flow cytometry data sets are used to illustrate the performances of CytOpT that are also compared to those of existing algorithms for automatic gating based on supervised learning.
dc.language.isoen
dc.publisherInstitute of Mathematical Statistics
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enAutomatic gating
dc.subject.enFlow cytometry
dc.subject.enOptimal Transport
dc.subject.enStochastic Optimization
dc.title.enCytOpT: Optimal Transport with Domain Adaptation for Interpreting Flow Cytometry data
dc.typeArticle de revue
dc.identifier.doi10.1214/22-AOAS1660
dc.subject.halStatistiques [stat]/Machine Learning [stat.ML]
dc.subject.halSciences du Vivant [q-bio]/Biologie cellulaire
dc.identifier.arxiv2006.09003
bordeaux.journalAnnals of Applied Statistics
bordeaux.page1086-1104
bordeaux.volume17
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue2
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-03100405
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03100405v1
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