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ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data.

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journal contribution
posted on 19.05.2021, 08:45 by 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 Parker, Michaela Fontenay, Paul R Barber, James N Arnold, Shahram Kordasti
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.


Crick (Grant ID: 10130, Grant title: Parker FC001130)