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ImmunoCluster provides a computational framework for the non-specialist to profile high- dimensional cytometry data

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article numbere62915
JournaleLife
Volume10
Early online date30 Apr 2021
DOIs
Accepted/In press22 Apr 2021
E-pub ahead of print30 Apr 2021
PublishedApr 2021

Bibliographical note

Funding Information: We would like to thank the following funding bodies for their support: Guy?s and St Thomas Hospital Charity: grant number: G170701, Jessica A Timms, Shahram Kordasti. Cancer Research UK (CRUK), King?s Health Partners Centre: Jessica A Timms, Shahram Kordasti C604/A25135. Aplastic Anemia and MDS International Foundation (AAMDSIF): Shahram Kordasti. Blood Cancer UK: Shahram Kordasti. European Research Council (ERC): James N Arnold Start up grant 335326. Medical Research Council (MRC): James W Opzoomer MR/N013700/1. Medical Research Council (MRC): James W Opzoomer Doctoral Training Partnership in Biomedical Sciences. Rosetrees Trust (Rosetrees): Sedigeh Kareemaghay, Mahvash Tavassoli M117-F2. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The research was supported by the Cancer Research UK King?s Health Partners Centre and Experimental Cancer Medicine Centre at King?s College London, and the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy?s and St Thomas? NHS Foundation Trust and King?s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. LifeArc 1118406 Jessica Timms Shahram Kordasti. Funding Information: We would like to thank the following funding bodies for their support: Guy’s and St Thomas Hospital Charity: grant number: G170701, Jessica A Timms, Shahram Kordasti. Cancer Research UK (CRUK), King’s Health Partners Centre : Jessica A Timms, Shahram Kordasti C604/A25135. Aplastic Anemia and MDS International Foundation (AAMDSIF): Shahram Kordasti. Blood Cancer UK: Shahram Kordasti. European Research Council (ERC): James N Arnold Start up grant 335326. Medical Research Council (MRC): James W Opzoomer MR/N013700/1. Medical Research Council (MRC): James W Opzoomer Doctoral Training Partnership in Biomedical Sciences. Rosetrees Trust (Rosetrees): Sedigeh Kareemaghay, Mahvash Tavassoli M117-F2. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The research was supported by the Cancer Research UK King’s Health Partners Centre and Experimental Cancer Medicine Centre at King’s College London, and the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. Publisher Copyright: © Opzoomer et al. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

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Abstract

High-dimensional cytometry is an innovative tool for immune monitoring in health and disease, and it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here, we describe ImmunoCluster (https://github.com/ kordastilab/ImmunoCluster), an R package for immune profiling cellular heterogeneity in highdimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a nonspecialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users’ needs. The protocol consists of three core computational stages: (1) data import and quality control; (2) dimensionality reduction and unsupervised clustering; and (3) annotation and differential testing, all contained within an R-based open-source framework.

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