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Analyzing high-dimensional cytometry data using FlowSOM

Nature Protocols volume 16, pages 3775–3801 (2021)Cite this article

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Abstract

The dimensionality of cytometry data has strongly increased in the last decade, and in many situations the traditional manual downstream analysis becomes insufficient. The field is therefore slowly moving toward more automated approaches, and in this paper we describe the protocol for analyzing high-dimensional cytometry data using FlowSOM, a clustering and visualization algorithm based on a self-organizing map. FlowSOM is used to distinguish cell populations from cytometry data in an unsupervised way and can help to gain deeper insights in fields such as immunology and oncology. Since the original FlowSOM publication (2015), we have validated the tool on a wide variety of datasets, and to write this protocol, we made use of this experience to improve the user-friendliness of the package (e.g., comprehensive functions replacing commonly required scripts). Where the original paper focused mainly on the algorithm description, this protocol offers user guidelines on how to implement the procedure, detailed parameter descriptions and troubleshooting recommendations. The protocol provides clearly annotated R code, and is therefore relevant for all scientists interested in computational high-dimensional analyses without requiring a strong bioinformatics background. We demonstrate the complete workflow, starting from data preparation (such as compensation, transformation and quality control), including detailed discussion of the different FlowSOM parameters and visualization options, and concluding with how the results can be further used to answer biological questions, such as statistical comparison between groups of interest. An average FlowSOM analysis takes 1–3 h to complete, though quality issues can increase this time considerably.

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Fig. 1: FlowSOM analysis pipeline.
Fig. 2: Preprocessing summary.
Fig. 3: Fluorescence intensities per marker of interest for all files.
Fig. 4: FlowSOM troubleshooting.
Fig. 5: FlowSOM tree.
Fig. 6: Manual labels on the FlowSOM tree.
Fig. 7: Groups comparison.

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Data availability

The flow cytometry data files and additional files are publicly available on FlowRepository (https://flowrepository.org/id/FR-FCM-ZZQY) and (https://flowrepository.org/id/FR-FCM-Z2TQ).

Code availability

The FlowSOM code is publicly available on GitHub (https://github.com/saeyslab/FlowSOM) and on Bioconductor (https://bioconductor.org/packages/release/bioc/html/FlowSOM.html). A demo script including the protocol that was described here and the source code for the figures in this paper are available on GitHub (https://github.com/saeyslab/FlowSOM_protocol). A list of the used R libraries and the versions can be found in Supplementary Note 1.

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Acknowledgements

S.V.G. is an ISAC Marylou Ingram Scholar and supported by an FWO postdoctoral research grant (Research Foundation—Flanders). We thank the VIB Flow Core for training, support and access to the instrument park. This research received funding from the Flemish Government (AI Research program). A.E. is supported by the PID Grand Challenges Program of VIB. This VIB Program received support from the Flemish Government under the Management Agreement 2017–2021 (VR 2016 2312 Doc.1521/4).

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Authors and Affiliations

  1. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Katrien Quintelier, Artuur Couckuyt, Annelies Emmaneel, Yvan Saeys & Sofie Van Gassen

  2. Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium

    Katrien Quintelier, Artuur Couckuyt, Annelies Emmaneel, Yvan Saeys & Sofie Van Gassen

  3. Department of Pulmonary Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands

    Katrien Quintelier & Joachim Aerts

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Contributions

S.V.G and Y.S. conceptualized the FlowSOM algorithm. S.V.G., A.C., A.E. and K.Q. wrote the FlowSOM code. S.V.G., Y.S. and J.A. supervised the work. K.Q. wrote the manuscript. All authors edited, read and approved the manuscript.

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Correspondence to Sofie Van Gassen.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Nima Aghaeepour, Étienne Becht and Enrico Lugli for their contribution to the peer review of this work.

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Key references using this protocol

Van Gassen, S. et al. Cytometry A 87, 636–645 (2015): https://doi.org/10.1002/cyto.a.22625

Guilliams, M. et al. Immunity 45, 669–684 (2016): https://doi.org/10.1016/j.immuni.2016.08.015

Emmaneel, A. et al. Front. Immunol. 10, 2009 (2019): https://doi.org/10.3389/fimmu.2019.02009

Supplementary information

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Quintelier, K., Couckuyt, A., Emmaneel, A. et al. Analyzing high-dimensional cytometry data using FlowSOM. Nat Protoc 16, 3775–3801 (2021). https://doi.org/10.1038/s41596-021-00550-0

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