Refereed journal article or data article (A1)
Multibatch Cytometry Data Integration for Optimal Immunophenotyping
List of Authors: Ogishi Masato, Yang Rui, Gruber Conor, Zhang Peng, Pelham Simon J, Spaan András N, Rosain Jérémie, Chbihi Marwa, Han Ji Eun, Rao V Koneti, Kainulainen Leena, Bustamante Jacinta, Boisson Bertrand, Bogunovic Dusan, Boisson-Dupuis Stéphanie, Casanova Jean-Laurent
Publisher: AMER ASSOC IMMUNOLOGISTS
Publication year: 2021
Journal: Journal of Immunology
Journal name in source: JOURNAL OF IMMUNOLOGY
Journal acronym: J IMMUNOL
Volume number: 206
Issue number: 1
Start page: 206
End page: 213
Number of pages: 13
ISSN: 0022-1767
eISSN: 1550-6606
DOI: http://dx.doi.org/10.4049/jimmunol.2000854
URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7855665/
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 highdimensional 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).
