1. Meeting the challenges of high-dimensional single-cell data analysis in immunology
- Author
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Subarna, Palit, Christoph, Heuser, Gustavo P, de Almeida, Fabian J, Theis, and Christina E, Zielinski
- Subjects
single-cell genomics ,Immunology ,Computational Biology ,Review ,trajectory inference ,systems immunology ,Flow Cytometry ,Cytof ,High-dimensional Data Analysis ,Single-cell Genomics ,Single-cell Profiling ,Systems Immunology ,Trajectory Inference ,Visualization ,single-cell profiling ,Humans ,high-dimensional data analysis ,CyTOF ,Single-Cell Analysis ,Algorithms ,visualization - Abstract
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.
- Published
- 2019