Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets
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.
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CytofRUV datasetFlowrepository.org database, FR-FCM-Z2L2.
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Immune Clock of Pregnancy Validation - ControlsFlowrepository.org database, FR-FCM-Z247.
Article and author information
Author details
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).
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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