A Beginner’s Guide to Analyzing and Visualizing Mass Cytometry Data
| Autor: | Lauren M. Oko, Raphael A. Nemenoff, Bonnie L. Bullock, Linda F. van Dyk, Eric T. Clambey, Abigail K. Kimball |
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| Rok vydání: | 2018 |
| Předmět: |
Diagnostic Imaging
0301 basic medicine business.industry Computer science Extramural Immunology Computational Biology Cell Separation Flow Cytometry Data science Article Immunophenotyping 03 medical and health sciences 030104 developmental biology Data visualization Workflow Practice Guidelines as Topic Image Processing Computer-Assisted Cell separation Animals Humans Immunology and Allergy Mass cytometry business Algorithms |
| Zdroj: | The Journal of Immunology. 200:3-22 |
| ISSN: | 1550-6606 0022-1767 |
| DOI: | 10.4049/jimmunol.1701494 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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