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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 |
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/11147/10494 |
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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