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

Nature reviews. Immunology, 2016-07, Vol.16 (7), p.449-462 [Peer Reviewed Journal]

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Title:
Computational flow cytometry: helping to make sense of high-dimensional immunology data
Author: Saeys, Yvan ; Van Gassen, Sofie ; Lambrecht, Bart N
Subjects: Analysis ; Animals ; Computational Biology - methods ; Flow cytometry ; Flow Cytometry - methods ; Humans ; Immunologic Techniques ; Immunology ; Technology application
Is Part Of: Nature reviews. Immunology, 2016-07, Vol.16 (7), p.449-462
Description: 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.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 1474-1733
EISSN: 1474-1741
DOI: 10.1038/nri.2016.56
PMID: 27320317
Source: MEDLINE
ProQuest Central

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