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.
Immunocluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data / Opzoomer, J. W.; Timms, J. A.; Blighe, K.; Mourikis, T. P.; Chapuis, N.; Bekoe, R.; Kareemaghay, S.; Nocerino, P.; Apollonio, B.; Ramsay, A. G.; Tavassoli, M.; Harrison, C.; Ciccarelli, F.; Parker, P.; Fontenay, M.; Barber, P. R.; Arnold, J. N.; Kordasti, S.. - In: ELIFE. - ISSN 2050-084X. - ELETTRONICO. - 10:(2021). [10.7554/ELIFE.62915]