These analyses provide a conceptual framework and a resource for investigators interested in using CyTOF to gain high-dimensional insights into biological questions. Second, we supply annotated examples, illustrating how to use these different algorithms to gain complementary insights into a single dataset. First, we provide a detailed commentary on how to implement and interpret data using five established and widely used CyTOF analysis platforms (viSNE, SPADE, X-shift, Citrus, and PhenoGraph). We present a practical guide for CyTOF data analysis, a process that we have defined empirically as recent adopters of these methods. Although biological knowledge remains essential to interpret these high-dimensional data, understanding which tools to use when addressing specific biological questions remains a major challenge. Even for laboratories well-versed in multiparameter flow cytometry, analyzing mass cytometry data requires a major shift in how to approach these data, moving away from user-defined Boolean gating strategies to automated identification of cell clusters and phenotypes. For researchers with little to no computational background, entry into these data can represent a significant challenge. CyTOF data visualization and quantitation continues to be a rapidly evolving field (e.g., 18, 19).ĭespite the potential of mass cytometry, there remain multiple challenges to its widespread implementation, from instrument and reagent costs to determining optimal ways to visualize and quantify these high-dimensional data. These tools are typically developed by computational biologists or by laboratories that are leaders in the field of mass cytometry, using a variety of languages (e.g., R, Matlab, Java, Python), clustering methods (e.g., parametric, nonparametric), and dimensionality reduction approaches ( 15– 17). Many algorithms and software kits have been developed to facilitate analysis of CyTOF datasets, including, but not limited to SPADE ( 6), viSNE ( 7), Wanderlust ( 8), FlowSOM ( 9), PhenoGraph ( 10), Citrus ( 11), Scaffold ( 12), X-shift ( 13), and DensVM ( 14). The technology allows simultaneous quantification of >30 cellular parameters and when integrated with high-dimensional analysis algorithms, it has the potential to reveal extraordinary cellular diversity and heterogeneity ( 2, 4, 5). Since its inception, mass cytometry, or cytometry by time-of-flight (CyTOF), has allowed researchers to gain deep insights into cellular phenotype and function ( 1– 3). 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. By providing annotated workflow and figures, these analyses present a practical guide for investigators analyzing high-dimensional datasets. ![]() 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. 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. Many of these algorithms circumvent traditional approaches used in flow cytometric analysis, fundamentally changing the way these data are analyzed and interpreted. This high-dimensional analysis platform has necessitated the development of new data analysis approaches. ![]() 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.
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