Kimball Abigail K, Oko Lauren M, Bullock Bonnie L, Nemenoff Raphael A, van Dyk Linda F, Clambey Eric T
Department of Anesthesiology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045.
Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045; and.
J Immunol. 2018 Jan 1;200(1):3-22. doi: 10.4049/jimmunol.1701494.
Mass cytometry has revolutionized the study of cellular and phenotypic diversity, significantly expanding the number of phenotypic and functional characteristics that can be measured at the single-cell level. This high-dimensional analysis platform has necessitated the development of new data analysis approaches. Many of these algorithms circumvent traditional approaches used in flow cytometric analysis, fundamentally changing the way these data are analyzed and interpreted. For the beginner, however, the large number of algorithms that have been developed, as well as the lack of consensus on best practices for analyzing these data, raise multiple questions: Which algorithm is the best for analyzing a dataset? How do different algorithms compare? How can one move beyond data visualization to gain new biological insights? In this article, we describe our experiences as recent adopters of mass cytometry. By analyzing a single dataset using five cytometry by time-of-flight analysis platforms (viSNE, SPADE, X-shift, PhenoGraph, and Citrus), we identify important considerations and challenges that users should be aware of when using these different methods and common and unique insights that can be revealed by these different methods. By providing annotated workflow and figures, these analyses present a practical guide for investigators analyzing high-dimensional datasets. In total, these analyses emphasize the benefits of integrating multiple cytometry by time-of-flight analysis algorithms to gain complementary insights into these high-dimensional datasets.
质谱流式细胞术彻底改变了对细胞和表型多样性的研究，极大地扩展了在单细胞水平上可测量的表型和功能特征的数量。这个高维分析平台促使了新数据分析方法的发展。许多此类算法规避了流式细胞术分析中使用的传统方法，从根本上改变了这些数据的分析和解释方式。然而，对于初学者来说，已开发的大量算法以及在分析这些数据的最佳实践方面缺乏共识，引发了多个问题：哪种算法最适合分析数据集？不同算法如何比较？如何超越数据可视化以获得新的生物学见解？在本文中，我们描述了作为质谱流式细胞术新用户的经验。通过使用五个飞行时间分析平台（viSNE、SPADE、X-shift、PhenoGraph和Citrus）分析单个数据集，我们确定了用户在使用这些不同方法时应注意的重要考虑因素和挑战，以及这些不同方法可以揭示的共同和独特见解。通过提供带注释的工作流程和图表，这些分析为研究人员分析高维数据集提供了实用指南。总的来说，这些分析强调了整合多种飞行时间分析算法以获得对这些高维数据集的互补见解的好处。
