1. Computational and Systems Biology
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Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets

  1. Marie Trussart  Is a corresponding author
  2. Charis E Teh
  3. Tania Tan
  4. Lawrence Leong
  5. Daniel HD Gray
  6. Terence P Speed
  1. The Walter and Eliza Hall Institute of Medical Research, Australia
Research Article
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Cite this article as: eLife 2020;9:e59630 doi: 10.7554/eLife.59630

Abstract

Mass cytometry (CyTOF) is a technology that has revolutionised single cell biology. By detecting over 40 proteins on millions of single cells, CyTOF allows the characterisation of cell subpopulations in unprecedented detail. However most CyTOF studies require the integration of data from multiple CyTOF batches usually acquired on different days and possibly at different sites. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches which includes an R-Shiny application with diagnostics plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes.

Article and author information

Author details

  1. Marie Trussart

    Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
    For correspondence
    trussart.m@wehi.edu.au
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7258-7272
  2. Charis E Teh

    Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
    Competing interests
    The authors declare that no competing interests exist.
  3. Tania Tan

    Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
    Competing interests
    The authors declare that no competing interests exist.
  4. Lawrence Leong

    Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
    Competing interests
    The authors declare that no competing interests exist.
  5. Daniel HD Gray

    Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8457-8242
  6. Terence P Speed

    Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Health and Medical Research Council (1054618)

  • Marie Trussart
  • Terence P Speed

National Health and Medical Research Council (1158024)

  • Daniel HD Gray

Cancer Council Victoria (1146518)

  • Daniel HD Gray

National Health and Medical Research Council (1089072)

  • Charis E Teh

Cancer Council Victoria (1146518)

  • Tania Tan

Perpetual Impact Philanthropy (IPAP2019/1437)

  • Charis E Teh

UROP Fellowship

  • Lawrence Leong

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: All patients provided written informed consent and the study was approved by Human Research Ethics Committees/Institutional Review Boards: RMH (2005.008, 2012.244, 2016.305,2016.066) and the Walter and Eliza Hall Institute (G15/05).

Reviewing Editor

  1. Greg Finak

Publication history

  1. Received: June 3, 2020
  2. Accepted: September 5, 2020
  3. Accepted Manuscript published: September 7, 2020 (version 1)

Copyright

© 2020, Trussart et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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