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).
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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-pp.ucl.ac.uk/id/eprint/10127234 |
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