ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data
James W. Opzoomer, Jessica A. Timms, Kevin Blighe, Thanos P. Mourikis, Nicolas Chapuis, Richard Bekoe, Sedigeh Kareemaghay, Paola Nocerino, Benedetta Apollonio, Alan G. Ramsay, Mahvash Tavassoli, Claire Harrison, Francesca D. Ciccarelli, Peter J. Parker, Michaëla Fontenay, Paul R. Barber, James N. Arnold, Shahram Kordasti
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 high-dimensional 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.