Key Points
iMUBAC integrates multibatch cytometry datasets without shared technical replicates.
Streamlined immunophenotyping identifies disease-associated phenotypes across batches.
Abstract
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).
Footnotes
This study was supported in part by grants from the St. Giles Foundation, Rockefeller University, INSERM, the Université de Paris, the Howard Hughes Medical Institute, the National Institute of Allergy and Infectious Diseases/National Institutes of Health (R37AI095983 to J.-L.C., U19AI142737 to S.B.-D., and R01AI127372 and R01AI148963 to D.B.), the French Foundation for Medical Research (EQU201903007798), the French National Research Agency under the Investments for the Future Program (ANR-10-IAHU-01), the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence (ANR-10-LABX-62-IBEID), the GENMSMD project (ANR-16-CE17-0005-01 to J.B.), the SCOR Corporate Foundation for Science, and Fondation du Souffle (SRC2017 to J.B.). M.O. was supported by the David Rockefeller Graduate Program, the Funai Foundation for Information Technology, the Honjo International Scholarship Foundation, and the New York Hideyo Noguchi Memorial Society, Inc. R.Y. was supported by the Immune Deficiency Foundation and the Stony Wold-Herbert Fund. A.N.S. was supported by the European Commission (Horizon 2020 Marie Skłodowska-Curie Individual Fellowship 789645), the Dutch Research Council (Rubicon Grant 019.171LW.015), and the European Molecular Biology Organization (Long-Term Fellowship ALTF 84-2017, nonstipendiary). J.R. was supported by the INSERM Ph.D. Program (Poste d’Accueil INSERM).
The cytometry datasets presented in this article have been submitted to FlowRepository (https://flowrepository.org/id/FR-FCM-Z3YK) under accession numbers FR-FCM-Z3YK and FR-FCMZ3YL.
The online version of this article contains supplemental material.
- Received July 20, 2020.
- Accepted October 26, 2020.
- Copyright © 2020 by The American Association of Immunologists, Inc.
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