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The end of gating? An introduction to automated analysis of high dimensional cytometry data


Mair, Florian; Hartmann, Felix J; Mrdjen, Dunja; Tosevski, Vinko; Krieg, Carsten; Becher, Burkhard (2016). The end of gating? An introduction to automated analysis of high dimensional cytometry data. European Journal of Immunology, 46(1):34-43.

Abstract

Ever since its invention half a century ago, flow cytometry has been a major tool for single-cell analysis, fueling advances in our understanding of a variety of complex cellular systems, in particular the immune system. The last decade has witnessed significant technical improvements in available cytometry platforms, such that more than 20 parameters can be analyzed on a single-cell level by fluorescence-based flow cytometry. The advent of mass cytometry has pushed this limit up to, currently, 50 parameters. However, traditional analysis approaches for the resulting high-dimensional datasets, such as gating on bivariate dot plots, have proven to be inefficient. Although a variety of novel computational analysis approaches to interpret these datasets are already available, they have not yet made it into the mainstream and remain largely unknown to many immunologists. Therefore, this review aims at providing a practical overview of novel analysis techniques for high-dimensional cytometry data including SPADE, t-SNE, Wanderlust, Citrus and PhenoGraph, and how these applications can be used advantageously not only for the most complex datasets, but also for standard 14-parameter cytometry datasets. This article is protected by copyright. All rights reserved.

Abstract

Ever since its invention half a century ago, flow cytometry has been a major tool for single-cell analysis, fueling advances in our understanding of a variety of complex cellular systems, in particular the immune system. The last decade has witnessed significant technical improvements in available cytometry platforms, such that more than 20 parameters can be analyzed on a single-cell level by fluorescence-based flow cytometry. The advent of mass cytometry has pushed this limit up to, currently, 50 parameters. However, traditional analysis approaches for the resulting high-dimensional datasets, such as gating on bivariate dot plots, have proven to be inefficient. Although a variety of novel computational analysis approaches to interpret these datasets are already available, they have not yet made it into the mainstream and remain largely unknown to many immunologists. Therefore, this review aims at providing a practical overview of novel analysis techniques for high-dimensional cytometry data including SPADE, t-SNE, Wanderlust, Citrus and PhenoGraph, and how these applications can be used advantageously not only for the most complex datasets, but also for standard 14-parameter cytometry datasets. This article is protected by copyright. All rights reserved.

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > Institute of Experimental Immunology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:January 2016
Deposited On:11 Dec 2015 14:11
Last Modified:05 Apr 2016 19:36
Publisher:Wiley-VCH Verlag Berlin
ISSN:0014-2980
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1002/eji.201545774
PubMed ID:26548301

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