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  • Review Article
  • Published:

Computational flow cytometry: helping to make sense of high-dimensional immunology data

Nature Reviews Immunology volume 16, pages 449–462 (2016)Cite this article

Subjects

Key Points

  • Recent advances in flow and mass cytometry have spurred the development of novel computational tools to assist in data analysis and visualization. These techniques should be adopted, evaluated and improved upon by the broad immunological community.

  • Standardization is key to making computational flow cytometry work, and researchers should use standard procedures for data generation, analysis, interpretation and deposition. Standardized marker panels should be used as much as possible.

  • Computational flow cytometry allows the automation of population identification, biomarker discovery and predictive modelling to highlight potentially new and interesting cell types that correlate with clinical outcomes.

  • New algorithms allow the modelling of gradual changes that can shed new light on cell developmental processes.

  • Computational flow cytometry offers an additional toolbox, and young immunologists should be trained in basic programming and modelling skills to be able to adequately use these tools and interpret their outcome.

Abstract

Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.

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Figure 1: Evaluation of three alternative visualization techniques using a manually gated dataset.
Figure 2: Marker visualization of mouse splenocytes.
Figure 3: Cell development modelling.

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Acknowledgements

S.V.G. is funded by the Flanders Agency for Innovation by Science and Technology (IWT). Y.S. is an ISAC Marylou Ingram Scholar. B.N.L. is funded by a European Research Council (ERC) Consolidator grant and several FWO (Research Foundation Flanders) grants.

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Authors and Affiliations

  1. VIB Inflammation Research Center, Technologiepark 927, Ghent, B-9052, Belgium

    Yvan Saeys, Sofie Van Gassen & Bart N. Lambrecht

  2. Department of Internal Medicine, Ghent University, De Pintelaan 185, Ghent, B-9000, Belgium

    Yvan Saeys & Bart N. Lambrecht

  3. Department of Information Technology, Technologiepark 15, Ghent, B-9052, Belgium

    Sofie Van Gassen

  4. Department of Pulmonary Medicine, Erasmus MC Rotterdam, Dr Molewaterplein 50, Rotterdam, 3015 GE, The Netherlands

    Bart N. Lambrecht

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Correspondence to Yvan Saeys.

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Saeys, Y., Van Gassen, S. & Lambrecht, B. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat Rev Immunol 16, 449–462 (2016). https://doi.org/10.1038/nri.2016.56

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