High-dimensional cytometry experiments measuring 20–50 cellular markers have become routine in many laboratories. The increased complexity of these datasets requires added rigor during the experimental planning and the subsequent manual and computational data analysis to avoid artefacts and misinterpretation of results. Here we discuss pitfalls frequently encountered during high-dimensional cytometry data analysis and aim to provide a basic framework and recommendations for reporting and analyzing these datasets.
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Code availability
The code used to generate plots in Fig. 2d–f can be found at https://github.com/immunedynamics/Liechti-2021. Code to reproduce Fig. 3 is available from https://github.com/lmweber/cytometry-perplexed.
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Acknowledgements
We thank M. Roederer (Vaccine Research Center, NIH) for critical reading and feedback. We thank D. Shinko and N. J. C. King for assistance in generating flow, spectral and mass cytometry data. T.L was supported by the Intramural Research Program of the Vaccine Research Center, NIAID, NIH. L.M.W. was supported by the National Institutes of Health (NIH) grant R01CA237170 from the National Cancer Institute; NIH grant U01MH122849 from the National Institute of Mental Health to the Lieber Institute for Brain Development; and CZF2019-002443 from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation. M.P was supported by NIH grants R01AI123323 and R21AI144677. F.M was supported by an AAI Intersect Fellowship. S.V.G. was supported by an FWO postdoctoral research grant (Research Foundation, Flanders). T.L., T.M.A., S.V.G. and F.M. are ISAC Marylou Ingram Scholars.
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Liechti, T., Weber, L.M., Ashhurst, T.M. et al. An updated guide for the perplexed: cytometry in the high-dimensional era. Nat Immunol 22, 1190–1197 (2021). https://doi.org/10.1038/s41590-021-01006-z
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DOI: https://doi.org/10.1038/s41590-021-01006-z
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