1. Computational and Systems Biology
Download icon

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
  • Cited 8
  • Views 1,978
  • Annotations
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.

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

  • 1,978
    Page views
  • 181
    Downloads
  • 8
    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)

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

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

Further reading

    1. Computational and Systems Biology
    2. Medicine
    James A Timmons et al.
    Short Report

    Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

    1. Computational and Systems Biology
    2. Neuroscience
    Ling-Qi Zhang et al.
    Research Article

    We developed an image-computable observer model of the initial visual encoding that operates on natural image input, based on the framework of Bayesian image reconstruction from the excitations of the retinal cone mosaic. Our model extends previous work on ideal observer analysis and evaluation of performance beyond psychophysical discrimination, takes into account the statistical regularities of the visual environment, and provides a unifying framework for answering a wide range of questions regarding the visual front end. Using the error in the reconstructions as a metric, we analyzed variations of the number of different photoreceptor types on human retina as an optimal design problem. In addition, the reconstructions allow both visualization and quantification of information loss due to physiological optics and cone mosaic sampling, and how these vary with eccentricity. Furthermore, in simulations of color deficiencies and interferometric experiments, we found that the reconstructed images provide a reasonable proxy for modeling subjects' percepts. Lastly, we used the reconstruction-based observer for the analysis of psychophysical threshold, and found notable interactions between spatial frequency and chromatic direction in the resulting spatial contrast sensitivity function. Our method is widely applicable to experiments and applications in which the initial visual encoding plays an important role.