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

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.

Data availability

Flow Repository: The fcs files from this study are available at flow repository ID FR-FCM-Z2L2.

The following data sets were generated
The following previously published data sets were used

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)
  4. Version of Record published: September 18, 2020 (version 2)
  5. Version of Record updated: November 5, 2020 (version 3)

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.

Metrics

  • 2,277
    Page views
  • 214
    Downloads
  • 10
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Marie Trussart
  2. Charis E Teh
  3. Tania Tan
  4. Lawrence Leong
  5. Daniel HD Gray
  6. Terence P Speed
(2020)
Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets
eLife 9:e59630.
https://doi.org/10.7554/eLife.59630

Further reading

    1. Computational and Systems Biology
    Zhuang Liu et al.
    Research Article

    MicroRNAs (miR), as important epigenetic control factors, reportedly regulate wound repair. However, our insufficient knowledge of clinically relevant miRs hinders their potential therapeutic use. For this, we performed paired small RNA and long RNA sequencing and integrative omics analysis in human tissue samples, including matched skin and acute wounds collected at each healing stage and chronic non-healing venous ulcers (VU). On the basis of the findings, we developed a compendium (https://www.xulandenlab.com/humanwounds-mirna-mrna), which will be an open, comprehensive resource to broadly aid wound healing research. With this first clinical, wound-centric resource of miRs and mRNAs, we identified 17 pathologically relevant miRs that exhibited abnormal VU expression and displayed their targets enriched explicitly in the VU gene signature. Intermeshing regulatory networks controlled by these miRs revealed their high cooperativity in contributing to chronic wound pathology characterized by persistent inflammation and proliferative phase initiation failure. Furthermore, we demonstrated that miR-34a, miR-424, and miR-516, upregulated in VU, cooperatively suppressed keratinocyte migration and growth while promoting inflammatory response. By combining miR expression patterns with their specific target gene expression context, we identified miRs highly relevant to VU pathology. Our study opens the possibility of developing innovative wound treatment that targets pathologically relevant cooperating miRs to attain higher therapeutic efficacy and specificity.

    1. Computational and Systems Biology
    2. Neuroscience
    Emmanuelle Bioud et al.
    Research Article

    To decide whether a course of action is worth pursuing, individuals typically weigh its expected costs and benefits. Optimal decision-making relies upon accurate effort cost anticipation, which is generally assumed to be performed independently from goal valuation. In two experiments (n = 46), we challenged this independence principle of standard decision theory. We presented participants with a series of treadmill routes randomly associated to monetary rewards and collected both ‘accept’ versus ‘decline’ decisions and subjective estimates of energetic cost. Behavioural results show that higher monetary prospects led participants to provide higher cost estimates, although reward was independent from effort in our design. Among candidate cognitive explanations, they support a model in which prospective cost assessment is biased by the output of an automatic computation adjusting effort expenditure to goal value. This decision bias might lead people to abandon the pursuit of valuable goals that are in fact not so costly to achieve.