Related Experiment Video
Updated: Nov 29, 2025
A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood
Published on: February 8, 2016
Multibatch Cytometry Data Integration for Optimal Immunophenotyping
Masato Ogishi1, Rui Yang2, Conor Gruber3,4,5,6
1St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, Rockefeller University, New York, NY 10065; mogishi@rockefeller.edu.
Insights
Batch effects in high-dimensional cytometry hinder comparisons. The iMUBAC computational framework integrates multibatch cytometry datasets for robust, unsupervised cell-type identification, enabling unified analysis of aberrant immunophenotypes across experiments.
Area of Science:
- Immunology
- Computational Biology
- Bioinformatics
Background:
- High-dimensional cytometry enables detailed immune system analysis.
- Batch effects from multi-site or multi-time experiments complicate data integration and comparison.
- Existing methods struggle with robust cell-type identification across diverse experimental batches.
Purpose of the Study:
- To develop a computational framework for integrating multibatch high-dimensional cytometry datasets.
- To enable unsupervised cell-type identification and aberrant immunophenotype detection across batches.
- To overcome challenges posed by batch effects in cytometry data analysis.
Main Methods:
- Integration of multibatch cytometry datasets (iMUBAC) framework.
- Unsupervised cell-type identification across multiple batches without technical replicates.
- Learning batch-specific cell-type classification boundaries using healthy control data.
- Unified identification of aberrant immunophenotypes in patient samples across batches.
Main Results:
- iMUBAC provides a flexible, scalable, and robust computational framework.
- Demonstrated unbiased and streamlined immunophenotyping on mass cytometry and spectral flow cytometry datasets.
- Successfully identified aberrant immunophenotypes in patient samples across multiple batches.
Conclusions:
- iMUBAC effectively integrates multibatch cytometry data, overcoming batch effects.
- The framework enables robust, unified cell-type identification and immunophenotyping.
- iMUBAC is available as an R package, facilitating broader application in immunology research.
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).
