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Nat Rev Immunol. 2016 Jul;16(7):449-62. doi: 10.1038/nri.2016.56. Epub 2016 Jun 20.

Computational flow cytometry: helping to make sense of high-dimensional immunology data.

Author information

1
VIB Inflammation Research Center, Technologiepark 927, Ghent B-9052, Belgium.
2
Department of Internal Medicine, Ghent University, De Pintelaan 185, Ghent B-9000, Belgium.
3
Department of Information Technology, Technologiepark 15, Ghent B-9052, Belgium.
4
Department of Pulmonary Medicine, Erasmus MC Rotterdam, Dr Molewaterplein 50, Rotterdam 3015 GE, The Netherlands.

Abstract

Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.

PMID:
27320317
DOI:
10.1038/nri.2016.56
[Indexed for MEDLINE]

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