Advanced search
1 file | 2.70 MB

Learning Single‐Cell Distances from Cytometry Data

Bac Nguyen Cong (UGent) , Peter Rubbens (UGent) , Frederiek-Maarten Kerckhof (UGent) , Nico Boon (UGent) , Bernard De Baets (UGent) and Willem Waegeman (UGent)
(2019) Cytometry Part A. 95. p.782-791
Author
Organization
Abstract
Recent years have seen an increased interest in employing data analysis techniques for the automated identification of cell populations in the field of cytometry. These techniques highly depend on the use of a distance metric, a function that quantifies the distances between single‐cell measurements. In most cases, researchers simply use the Euclidean distance metric. In this article, we exploit the availability of single‐cell labels to find an optimal Mahalanobis distance metric derived from the data. We show that such a Mahalanobis distance metric results in an improved identification of cell populations compared with the Euclidean distance metric. Once determined, it can be used for the analysis of multiple samples that were measured under the same experimental setup. We illustrate this approach for cytometry data from two different origins, that is, flow cytometry applied to microbial cells and mass cytometry for the analysis of human blood cells. We also illustrate that such a distance metric results in an improved identification of cell populations when clustering methods are employed. Generally, these results imply that the performance of data analysis techniques can be improved by using a more advanced distance metric. © 2019 International Society for Advancement of Cytometry
Keywords
Pathology and Forensic Medicine, Cell Biology, Histology

Downloads

  • KERMIT-A1-529.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 2.70 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Nguyen Cong, Bac, Peter Rubbens, Frederiek-Maarten Kerckhof, Nico Boon, Bernard De Baets, and Willem Waegeman. 2019. “Learning Single‐Cell Distances from Cytometry Data.” Cytometry Part A 95: 782–791.
APA
Nguyen Cong, B., Rubbens, P., Kerckhof, F.-M., Boon, N., De Baets, B., & Waegeman, W. (2019). Learning Single‐Cell Distances from Cytometry Data. Cytometry Part A, 95, 782–791.
Vancouver
1.
Nguyen Cong B, Rubbens P, Kerckhof F-M, Boon N, De Baets B, Waegeman W. Learning Single‐Cell Distances from Cytometry Data. Cytometry Part A. 2019;95:782–91.
MLA
Nguyen Cong, Bac et al. “Learning Single‐Cell Distances from Cytometry Data.” Cytometry Part A 95 (2019): 782–791. Print.
@article{8626850,
  abstract     = {Recent years have seen an increased interest in employing data analysis techniques for the automated identification of cell populations in the field of cytometry. These techniques highly depend on the use of a distance metric, a function that quantifies the distances between single‐cell measurements. In most cases, researchers simply use the Euclidean distance metric. In this article, we exploit the availability of single‐cell labels to find an optimal Mahalanobis distance metric derived from the data. We show that such a Mahalanobis distance metric results in an improved identification of cell populations compared with the Euclidean distance metric. Once determined, it can be used for the analysis of multiple samples that were measured under the same experimental setup. We illustrate this approach for cytometry data from two different origins, that is, flow cytometry applied to microbial cells and mass cytometry for the analysis of human blood cells. We also illustrate that such a distance metric results in an improved identification of cell populations when clustering methods are employed. Generally, these results imply that the performance of data analysis techniques can be improved by using a more advanced distance metric. © 2019 International Society for Advancement of Cytometry},
  author       = {Nguyen Cong, Bac and Rubbens, Peter and Kerckhof, Frederiek-Maarten and Boon, Nico and De Baets, Bernard and Waegeman, Willem},
  issn         = {1552-4922},
  journal      = {Cytometry Part A},
  keywords     = {Pathology and Forensic Medicine,Cell Biology,Histology},
  language     = {eng},
  pages        = {782--791},
  title        = {Learning Single‐Cell Distances from Cytometry Data},
  url          = {http://dx.doi.org/10.1002/cyto.a.23792},
  volume       = {95},
  year         = {2019},
}

Altmetric
View in Altmetric