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TrackSOM: mapping immune response dynamics through sequential clustering of time- and disease-course single-cell cytometry data

View ORCID ProfileGivanna H. Putri, Jonathan Chung, Davis N. Edwards, View ORCID ProfileFelix Marsh-Wakefield, Suat Dervish, View ORCID ProfileIrena Koprinska, View ORCID ProfileNicholas J.C. King, View ORCID ProfileThomas M. Ashhurst, View ORCID ProfileMark N. Read
doi: https://doi.org/10.1101/2021.06.08.447468
Givanna H. Putri
1School of Computer Science, The University of Sydney
5Charles Perkins Centre, The University of Sydney
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  • For correspondence: givanna.haryonoputri@sydney.edu.au mark.read@sydney.edu.au
Jonathan Chung
1School of Computer Science, The University of Sydney
5Charles Perkins Centre, The University of Sydney
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Davis N. Edwards
1School of Computer Science, The University of Sydney
4Westmead Initiative, The University of Sydney
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Felix Marsh-Wakefield
3Viral Immunopathology Laboratory, Discipline of Pathology, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney
6School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney
7Vascular Immunology Unit, Department of Pathology, The University of Sydney
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Suat Dervish
4Westmead Initiative, The University of Sydney
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Irena Koprinska
1School of Computer Science, The University of Sydney
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Nicholas J.C. King
2Sydney Cytometry Core Research Facility, The University of Sydney and Centenary Institute
3Viral Immunopathology Laboratory, Discipline of Pathology, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney
5Charles Perkins Centre, The University of Sydney
8Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney
9Sydney Nano, The University of Sydney
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Thomas M. Ashhurst
2Sydney Cytometry Core Research Facility, The University of Sydney and Centenary Institute
5Charles Perkins Centre, The University of Sydney
8Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney
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Mark N. Read
1School of Computer Science, The University of Sydney
4Westmead Initiative, The University of Sydney
5Charles Perkins Centre, The University of Sydney
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  • ORCID record for Mark N. Read
  • For correspondence: givanna.haryonoputri@sydney.edu.au mark.read@sydney.edu.au
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Abstract

Mapping the dynamics of immune cell populations over time or disease-course is key to understanding immunopathogenesis and devising putative interventions. We present TrackSOM, an algorithm which delineates cellular populations and tracks their development over a time- or disease-course of cytometry datasets. We demonstrate TrackSOM-enabled elucidation of the immune response to West Nile Virus infection in mice, uncovering heterogeneous sub-populations of immune cells and relating their functional evolution to disease severity. TrackSOM is easy to use, encompasses few parameters, is quick to execute, and enables an integrative and dynamic overview of the immune system kinetics that underlie disease progression and/or resolution.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/ghar1821/TrackSOM

  • https://osf.io/8dvzu/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 09, 2021.
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TrackSOM: mapping immune response dynamics through sequential clustering of time- and disease-course single-cell cytometry data
Givanna H. Putri, Jonathan Chung, Davis N. Edwards, Felix Marsh-Wakefield, Suat Dervish, Irena Koprinska, Nicholas J.C. King, Thomas M. Ashhurst, Mark N. Read
bioRxiv 2021.06.08.447468; doi: https://doi.org/10.1101/2021.06.08.447468
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TrackSOM: mapping immune response dynamics through sequential clustering of time- and disease-course single-cell cytometry data
Givanna H. Putri, Jonathan Chung, Davis N. Edwards, Felix Marsh-Wakefield, Suat Dervish, Irena Koprinska, Nicholas J.C. King, Thomas M. Ashhurst, Mark N. Read
bioRxiv 2021.06.08.447468; doi: https://doi.org/10.1101/2021.06.08.447468

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