Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/10494
Title: Identification and visualization of cell subgroups in uncompensated flow cytometry data
Authors: Güzel, Başak Esin Köktürk
Karaçalı, Bilge
Keywords: Automated FCM analysis
Annealing-based model-free expectation maximization
Automated gating
FCM data visualization
Quasi-supervised learning
Issue Date: 2020
Publisher: Elsevier Ltd.
Abstract: We 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.
URI: https://doi.org/10.1016/j.chemolab.2019.103892
https://hdl.handle.net/10494
ISSN: 0169-7439
1873-3239
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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