Over het archief
Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.
Dit archief wordt uitgebouwd en beheerd volgens de principes van de Open Access Movement, en het daaruit ontstane Open Archives Initiative.
Basisinformatie over ‘Open Access to scholarly information'.
| one publication added to basket [321915] |
Learning single‐cell distances from cytometry data
Nguyen, B.; Rubbens, P.; Kerckhof, F.-M.; Boon, N.; De Baets, B.; Waegeman, W. (2019). Learning single‐cell distances from cytometry data. Cytometry A 95(7): 782-791. https://dx.doi.org/10.1002/cyto.a.23792
In: Cytometry Part A. Interscience/Wiley: Hoboken, N.J.. ISSN 1552-4922; e-ISSN 1552-4930, meer
|   |
| 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. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
