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

Opzoomer, JW; Timms, JA; Blighe, K; Mourikis, TP; Chapuis, N; Bekoe, R; Kareemaghay, S; ... Kordasti, S; + view all (2021) ImmunoCluster provides a computational framework for the non-specialist to profile high- dimensional cytometry data. eLife , 10 , Article e62915. 10.7554/eLife.62915. (In press). Green open access

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Abstract

High dimensional cytometry is an innovative tool for immune monitoring in health and disease, 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 high dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a non-specialist. 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.

Type: Article
Title: ImmunoCluster provides a computational framework for the non-specialist to profile high- dimensional cytometry data
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.7554/eLife.62915
Publisher version: https://doi.org/10.7554/eLife.62915
Language: English
Additional information: Copyright © 2021, Opzoomer et al. This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) permitting unrestricted use and redistribution provided that the original author and source are credited.
Keywords: computational biology, human, immunology, inflammation, systems biology
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Oncology
URI: https://discovery.ucl.ac.uk/id/eprint/10127234
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