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

• Published in: European journal of immunology 2016-01, Vol.46 (1), p.34-43
• Main Authors: Mair, Florian, Hartmann, Felix J., Mrdjen, Dunja, Tosevski, Vinko, Krieg, Carsten, Becher, Burkhard
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Citrus ⋅ CyTOF ⋅ Data analysis ⋅ Flow cytometry ⋅ Mass cytometry ⋅ PSM ⋅ PhenoGraph ⋅ SPADE ⋅ t‐SNE ⋅ Wanderlust; Computational Biology - methods; Datasets; Flow cytometry; Flow Cytometry - methods; Humans; Software
• ISSN: 0014-2980; 1521-4141
• DOI: 10.1002/eji.201545774

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.