A Beginner’s Guide To Analyzing and Visualizing Mass Cytometry Data

Abstract

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, and 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? Here, we describe our experiences as recent adopters of mass cytometry. By analyzing a single dataset using five CyTOF analysis platforms (viSNE, SPADE, X-shift, PhenoGraph and Citrus), we identify: i) important considerations and challenges that users should be aware of when using these different methods, and ii) 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 CyTOF analysis algorithms to gain complementary insights into these high-dimensional datasets.