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- Title
ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data.
- Authors
Opzoomer, James W.; Timms, Jessica A.; Blighe, Kevin; Mourikis, Thanos P.; Chapuis, Nicolas; Bekoe, Richard; Kareemaghay, Sedigeh; Nocerino, Paola; Apollonio, Benedetta; Ramsay, Alan G.; Tavassoli, Mahvash; Harrison, Claire; Ciccarelli, Francesca; Parker, Peter; Fontenay, Michaela; Barber, Paul R.; Arnold, James N.; Kordasti, Shahram
- 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.
- Subjects
CYTOMETRY; IMPORT quotas; TARIFF; FLOW cytometry; DATA quality
- Publication
eLife, 2021, p1
- ISSN
2050-084X
- Publication type
Article
- DOI
10.7554/eLife.62915