10.5061/DRYAD.4B8GTHTCF
Timms, Jessica
0000-0003-3687-9312
King's College London
Opzoomer, James
King's College London
Blighe, Kevin
King's College London
Mourikis, Thanos
King's College London
Chapuis, Nicolas
Centre national de la recherche scientifique
Bekoe, Richard
University College London
Kareemaghay, Sedigeh
King's College London
Nocerino, Paola
King's College London
Apollonio, Benedetta
King's College London
Ramsay, Alan
King's College London
Tavassoli, Mahvash
King's College London
Harrison, Claire
King's College London
Ciccarelli, Francesca
The Francis Crick Institute
Parker, Peter
King's College London
Fontenay, Michaela
Centre national de la recherche scientifique
Barber, Paul
King's College London
Arnold, James
King's College London
Kordasti, Shahram
King's College London
Flow cytometry data: healthy donor bone marrow taken during hip surgery
Dryad
dataset
2021
2021-05-07T00:00:00Z
2021-05-07T00:00:00Z
en
https://doi.org/10.1101/2020.09.09.289033
427644346 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.