Multibatch Cytometry Data Integration for Optimal Immunophenotyping.
Journal
Journal of immunology (Baltimore, Md. : 1950)
ISSN: 1550-6606
Titre abrégé: J Immunol
Pays: United States
ID NLM: 2985117R
Informations de publication
Date de publication:
01 01 2021
01 01 2021
Historique:
received:
20
07
2020
accepted:
26
10
2020
pubmed:
25
11
2020
medline:
21
5
2021
entrez:
24
11
2020
Statut:
ppublish
Résumé
High-dimensional cytometry is a powerful technique for deciphering the immunopathological factors common to multiple individuals. However, rational comparisons of multiple batches of experiments performed on different occasions or at different sites are challenging because of batch effects. In this study, we describe the integration of multibatch cytometry datasets (iMUBAC), a flexible, scalable, and robust computational framework for unsupervised cell-type identification across multiple batches of high-dimensional cytometry datasets, even without technical replicates. After overlaying cells from multiple healthy controls across batches, iMUBAC learns batch-specific cell-type classification boundaries and identifies aberrant immunophenotypes in patient samples from multiple batches in a unified manner. We illustrate unbiased and streamlined immunophenotyping using both public and in-house mass cytometry and spectral flow cytometry datasets. The method is available as the R package iMUBAC (https://github.com/casanova-lab/iMUBAC).
Identifiants
pubmed: 33229441
pii: jimmunol.2000854
doi: 10.4049/jimmunol.2000854
pmc: PMC7855665
mid: NIHMS1643062
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
206-213Subventions
Organisme : NIAID NIH HHS
ID : P01 AI061093
Pays : United States
Organisme : NIAID NIH HHS
ID : R37 AI095983
Pays : United States
Organisme : Howard Hughes Medical Institute
Pays : United States
Informations de copyright
Copyright © 2020 by The American Association of Immunologists, Inc.
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