dc.contributor.author | Opzoomer, JW | en_US |
dc.contributor.author | Timms, JA | en_US |
dc.contributor.author | Blighe, K | en_US |
dc.contributor.author | Mourikis, TP | en_US |
dc.contributor.author | Chapuis, N | en_US |
dc.contributor.author | Bekoe, R | en_US |
dc.contributor.author | Kareemaghay, S | en_US |
dc.contributor.author | Nocerino, P | en_US |
dc.contributor.author | Apollonio, B | en_US |
dc.contributor.author | Ramsay, AG | en_US |
dc.contributor.author | Tavassoli, M | en_US |
dc.contributor.author | Harrison, C | en_US |
dc.contributor.author | Ciccarelli, F | en_US |
dc.contributor.author | Parker, P | en_US |
dc.contributor.author | Fontenay, M | en_US |
dc.contributor.author | Barber, PR | en_US |
dc.contributor.author | Arnold, JN | en_US |
dc.contributor.author | Kordasti, S | en_US |
dc.date.accessioned | 2024-01-08T09:35:13Z | |
dc.date.available | 2021-04-22 | en_US |
dc.date.issued | 2021-04-30 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/93493 | |
dc.description.abstract | 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 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 nonspecialist. 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. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | Elife | en_US |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.subject | ImmunoCluster | en_US |
dc.subject | computational biology | en_US |
dc.subject | cytometry | en_US |
dc.subject | framework | en_US |
dc.subject | human | en_US |
dc.subject | immune monitoring | en_US |
dc.subject | immunology | en_US |
dc.subject | inflammation | en_US |
dc.subject | systems biology | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Allergy and Immunology | en_US |
dc.subject | B-Lymphocytes | en_US |
dc.subject | Computational Biology | en_US |
dc.subject | Data Analysis | en_US |
dc.subject | Flow Cytometry | en_US |
dc.subject | Humans | en_US |
dc.title | ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data. | en_US |
dc.type | Article | |
dc.identifier.doi | 10.7554/eLife.62915 | en_US |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/33929322 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
pubs.volume | 10 | en_US |
dcterms.dateAccepted | 2021-04-22 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |