MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration.
data analysis
diffuse large B-cell lymphoma
flow cytometry
mass cytometry
Journal
Patterns (New York, N.Y.)
ISSN: 2666-3899
Titre abrégé: Patterns (N Y)
Pays: United States
ID NLM: 101767765
Informations de publication
Date de publication:
12 Jul 2024
12 Jul 2024
Historique:
received:
30
10
2023
revised:
12
03
2024
accepted:
15
04
2024
medline:
31
7
2024
pubmed:
31
7
2024
entrez:
31
7
2024
Statut:
epublish
Résumé
Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level. Technical advances have substantially increased data complexity, but novel bioinformatical tools often show limitations in statistical testing, data sharing, cross-experiment comparability, or clinical data integration. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of metadata. MetaGate provides a data reduction algorithm based on a combinatorial gating system that produces a small, portable, and standardized data file. This is subsequently used to produce figures and statistical analyses through a fast web-based user interface. We demonstrate the utility of MetaGate through a comprehensive mass cytometry analysis of peripheral blood immune cells from 28 patients with diffuse large B cell lymphoma along with 17 healthy controls. Through MetaGate analysis, our study identifies key immune cell population changes associated with disease progression.
Identifiants
pubmed: 39081571
doi: 10.1016/j.patter.2024.100989
pii: S2666-3899(24)00104-1
pmc: PMC11284499
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100989Informations de copyright
© 2024 The Authors.
Déclaration de conflit d'intérêts
K-J.M. is a consultant at Fate Therapeutics and Vycellix and has research support from Oncopeptides for studies unrelated to this work.