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Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline

Author

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  • Hao Chen
  • Mai Chan Lau
  • Michael Thomas Wong
  • Evan W Newell
  • Michael Poidinger
  • Jinmiao Chen

Abstract

Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in tissues. However, analysis and interpretation of these high-dimensional data poses a significant technical challenge. Here, we present cytofkit, a new Bioconductor package, which integrates both state-of-the-art bioinformatics methods and in-house novel algorithms to offer a comprehensive toolset for mass cytometry data analysis. Cytofkit provides functions for data pre-processing, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets. This pipeline also provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers. Applied to a CD14−CD19− PBMCs dataset, cytofkit accurately identified different subsets of lymphocytes; applied to a human CD4+ T cell dataset, cytofkit uncovered multiple subtypes of TFH cells spanning blood and tonsils. Cytofkit is implemented in R, licensed under the Artistic license 2.0, and freely available from the Bioconductor website, https://bioconductor.org/packages/cytofkit/. Cytofkit is also applicable for flow cytometry data analysis.

Suggested Citation

  • Hao Chen & Mai Chan Lau & Michael Thomas Wong & Evan W Newell & Michael Poidinger & Jinmiao Chen, 2016. "Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-17, September.
    • Handle: RePEc:plo:pcbi00:1005112
    DOI: 10.1371/journal.pcbi.1005112
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    References listed on IDEAS

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    1. Jinmiao Chen & Andreas Schlitzer & Svetoslav Chakarov & Florent Ginhoux & Michael Poidinger, 2016. "Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development," Nature Communications, Nature, vol. 7(1), pages 1-15, September.
    2. Wickham, Hadley, 2011. "The Split-Apply-Combine Strategy for Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i01).
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    2. Ross J Burton & Raya Ahmed & Simone M Cuff & Sarah Baker & Andreas Artemiou & Matthias Eberl, 2021. "CytoPy: An autonomous cytometry analysis framework," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-21, June.
    3. Víctor A. Arrieta & Andrew Gould & Kwang-Soo Kim & Karl J. Habashy & Crismita Dmello & Gustavo I. Vázquez-Cervantes & Irina Palacín-Aliana & Graysen McManus & Christina Amidei & Cristal Gomez & Silpol, 2024. "Ultrasound-mediated delivery of doxorubicin to the brain results in immune modulation and improved responses to PD-1 blockade in gliomas," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
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    7. Simone Puccio & Giorgio Grillo & Giorgia Alvisi & Caterina Scirgolea & Giovanni Galletti & Emilia Maria Cristina Mazza & Arianna Consiglio & Gabriele De Simone & Flavio Licciulli & Enrico Lugli, 2023. "CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    8. Chuan Shen & Yiyang Li & Boqian Wang & Zhipeng Zong & Tianfei Lu & Nokuzola Maboyi & Yuxiao Deng & Yongbing Qian & Jianjun Zhang & Xianting Ding & Qiang Xia, 2022. "HTLV-1 infection of donor-derived T cells might promote acute graft-versus-host disease following liver transplantation," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
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