ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data
More about Open Access at the CrickAuthors list
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 Ciccarelli Peter Parker Michaela Fontenay Paul R Barber James N Arnold Shahram KordastiAbstract
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.
Full text links
Publisher website (DOI) 10.7554/eLife.62915
Figshare View on figshare
Europe PubMed Central 33929322
Pubmed 33929322