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应对免疫学中高维单细胞数据分析的挑战。

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

机构信息

TranslaTUM, Technical University of Munich, Munich, Germany.

Institute of Virology, Technical University of Munich, Munich, Germany.

出版信息

Front Immunol. 2019 Jul 3;10:1515. doi: 10.3389/fimmu.2019.01515. eCollection 2019.

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.

摘要

近年来,细胞术的发展极大地改变了单细胞蛋白质组学的命运,使人们能够更准确地了解复杂的生物系统。质谱流式细胞术(CyTOF)提供了单细胞的同时测量,这对于理解细胞异质性和鉴定新的细胞亚群至关重要。传统上,高维 CyTOF 数据是通过在二元散点图上进行门控分析来进行分析的,这种方法不仅由于复杂性随维度呈二次增加而非常繁琐,而且通过手动门控还存在偏差。本综述旨在讨论新的分析技术对从静态快照数据中获得的免疫调节动态的深入了解的影响,并为免疫学家提供工具来解决他们的单细胞数据的高维性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/009a/6634245/d4195145c36a/fimmu-10-01515-g0001.jpg

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