PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments.
combinatorial protein expression
high-dimensional cytometry data
mass cytometry data
multi-parametric analysis
pattern perception
re-analysis
Journal
Frontiers in immunology
ISSN: 1664-3224
Titre abrégé: Front Immunol
Pays: Switzerland
ID NLM: 101560960
Informations de publication
Date de publication:
2022
2022
Historique:
received:
05
01
2022
accepted:
08
04
2022
entrez:
20
5
2022
pubmed:
21
5
2022
medline:
24
5
2022
Statut:
epublish
Résumé
Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4
Identifiants
pubmed: 35592315
doi: 10.3389/fimmu.2022.849329
pmc: PMC9110672
doi:
Substances chimiques
Transcription Factors
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
849329Informations de copyright
Copyright © 2022 Hoang, Gryzik, Hoppe, Rybak, Schädlich, Kadner, Walther, Vera, Radbruch, Groth, Baumgart and Baumgrass.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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