Details

Autor(en) / Beteiligte

• Opzoomer, James W
• Timms, Jessica A
• Blighe, Kevin
• Mourikis, Thanos P
• Chapuis, Nicolas
• Bekoe, Richard
• Kareemaghay, Sedigeh
• Nocerino, Paola
• Apollonio, Benedetta
• Ramsay, Alan G
• Tavassoli, Mahvash
• Harrison, Claire
• Ciccarelli, Francesca
• Parker, Peter
• Fontenay, Michaela
• Barber, Paul R
• Arnold, James N
• Kordasti, Shahram

Titel

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

Ist Teil von

• eLife, 2021-04, Vol.10

Ort / Verlag

England: eLife Sciences Publications Ltd

Erscheinungsjahr

2021

Quelle

MEDLINE

Beschreibungen/Notizen

• 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 (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 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.