Meeting the Challenges of High-Dimensional Single-Cell Data Analysis in Immunology

被引：51
|
作者：
Patil, Subarea [1 ,2 ,3 ]
Heuser, Christoph [1 ,2 ,3 ]
de Almeida, Gustavo P. [1 ,2 ,3 ]
Theis, Fabian J. [4 ,5 ]
Zielinski, Christina E. [1 ,2 ,3 ]
机构：
[1] Tech Univ Munich, TranslaTUM, Munich, Germany
[2] Tech Univ Munich, Inst Virol, Munich, Germany
[3] German Ctr Infect Res, Partner Site Munich, Munich, Germany
[4] Helmholtz Zenbum Munchen, Inst Computat Biol, Neuherberg, Germany
[5] Tech Univ Munich, Dept Math, Munich, Germany
来源：
FRONTIERS IN IMMUNOLOGY | 2019年 / 10卷
关键词：
high-dimensional data analysis; CyTOF; single-cell profiling; single-cell genomics; visualization; trajectory inference; systems immunology; FLOW-CYTOMETRY; VISUALIZATION; HIERARCHY; MAPS;
D O I：
10.3389/fimmu.2019.01515
中图分类号：
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号：
100102 ;
摘要：
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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页数：12
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