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Cytometry A. 2019 May 17. doi: 10.1002/cyto.a.23792. [Epub ahead of print]

Learning Single-Cell Distances from Cytometry Data.

Author information

1
KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Ghent, Belgium.
2
Center for Microbial Ecology and Technology, Department of Biotechnology, Ghent University, 9000 Ghent, Belgium.

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.

KEYWORDS:

flow cytometry; mass cytometry; metric learning; microbiology; synthetic microbial communities; transfer learning

PMID:
31099963
DOI:
10.1002/cyto.a.23792

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