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Data-Driven Flow Cytometry Analysis

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Mass Cytometry

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1989))

Abstract

The emergence of flow and mass cytometry technologies capable of generating 40-dimensional data has spurred research into automated methodologies that address bottlenecks across the entire analysis process from quality checking, data transformation, and cell population identification, to biomarker identification and visualizations. We review these approaches in the context of the stepwise progression through the different steps, including normalization, automated gating, outlier detection, and graphical presentation of results.

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Acknowledgments

This work was supported by GenomeCanada (252FLO Brinkman), NSERC, GenomeBC, and NIH (1 R01 GM118417-01A1).

Author information

Authors and Affiliations

  1. Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada

    Sherrie Wang & Ryan R. Brinkman

Authors
  1. Sherrie Wang
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  2. Ryan R. Brinkman
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Corresponding author

Correspondence to Ryan R. Brinkman .

Editor information

Editors and Affiliations

  1. Discipline of Pathology, The University of Sydney, Camperdown, NSW, Australia

    Helen M. McGuire

  2. Sydney Cytometry Facility, The University of Sydney and Centenary Institute, Camperdown, NSW, Australia

    Thomas M. Ashhurst

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Wang, S., Brinkman, R.R. (2019). Data-Driven Flow Cytometry Analysis. In: McGuire, H., Ashhurst, T. (eds) Mass Cytometry. Methods in Molecular Biology, vol 1989. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9454-0_16

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  • DOI: https://doi.org/10.1007/978-1-4939-9454-0_16

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9453-3

  • Online ISBN: 978-1-4939-9454-0

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