Flow cytometry data: healthy donor bone marrow taken during hip surgery

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.