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Patil, S.* ; Heuser, C.* ; de Almeida, G.P.* ; Theis, F.J. ; Zielinski, C.E.*

Meeting the challenges of high-dimensional single-cell data analysis in immunology.

Front. Immunol. 10:1515 (2019)
Publ. Version/Full Text DOI PMC
Open Access Gold
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Recent advances in cytometry have radically altered the fate of single-cell proteomics by allowing a more accurate understanding of complex biological systems. Mass cytometry (CyTOF) provides simultaneous single-cell measurements that are crucial to understand cellular heterogeneity and identify novel cellular subsets. High-dimensional CyTOF data were traditionally analyzed by gating on bivariate dot plots, which are not only laborious given the quadratic increase of complexity with dimension but are also biased through manual gating. This review aims to discuss the impact of new analysis techniques for in-depths insights into the dynamics of immune regulation obtained from static snapshot data and to provide tools to immunologists to address the high dimensionality of their single-cell data.
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Publication type Article: Journal article
Document type Review
Keywords Cytof ; High-dimensional Data Analysis ; Single-cell Genomics ; Single-cell Profiling ; Systems Immunology ; Trajectory Inference ; Visualization; Flow-cytometry; Visualization; Hierarchy; Maps
Language
Publication Year 2019
HGF-reported in Year 2019
ISSN (print) / ISBN 1664-3224
e-ISSN 1664-3224
Journal Frontiers in Immunology
Quellenangaben Volume: 10, Issue: , Pages: , Article Number: 1515 Supplement: ,
Publisher Frontiers
Publishing Place Avenue Du Tribunal Federal 34, Lausanne, Ch-1015, Switzerland
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
DOI 10.3389/fimmu.2019.01515
WOS ID WOS:000473564900001
Scopus ID 85069040665
PubMed ID 31354705
Erfassungsdatum 2019-07-25
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