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dc.contributor.authorGuzel, Basak Esin Kokturk
dc.contributor.authorKaracali, Bilge
dc.date.accessioned2021-02-12T18:47:29Z
dc.date.available2021-02-12T18:47:29Z
dc.date.issued2020
dc.identifier.issn0169-7439
dc.identifier.issn1873-3239
dc.identifier.urihttps://doi.org/10.1016/j.chemolab.2019.103892
dc.identifier.urihttps://hdl.handle.net/11147/10201
dc.descriptionGuzel, Basak Esin Kokturk/0000-0002-9429-1149en_US
dc.description.abstractWe propose a new method for identification and visualization of cell-sub groups in uncompensated multi-color flow cytometry data. The method combines annealing-based model-free expectation-maximization to identify cell sub-groups and joint diagonalization on clustered data for better visualization. The proposed method was evaluated on a real, publicly available 8-color flow cytometry dataset manually gated beforehand for lymphocytes. The results obtained in three separable scenarios indicate that the method accurately identifies cell subgroups while properly adjusting visualization of identified cell groups by reducing the spectral overlap between the different fluorochrome channels.en_US
dc.language.isoengen_US
dc.publisherELSEVIERen_US
dc.relation.isversionof10.1016/j.chemolab.2019.103892en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAutomated FCM analysisen_US
dc.subjectAnnealing-based model-free expectation maximizationen_US
dc.subjectAutomated gatingen_US
dc.subjectFCM data visualizationen_US
dc.subjectQuasi-supervised learningen_US
dc.titleIdentification and visualization of cell subgroups in uncompensated flow cytometry dataen_US
dc.typearticleen_US
dc.typearticleen_US
dc.relation.journalChemometrics And Intelligent Laboratory Systemsen_US
dc.contributor.departmentIzmir Isntitute of Technologyen_US
dc.identifier.volume196en_US
dc.identifier.wosWOS:000510316100015
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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