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Related Experiment Video

Updated: Nov 29, 2025

A Semi-automated Approach to Preparing Antibody Cocktails for Immunophenotypic Analysis of Human Peripheral Blood

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 Gruber^3,4,5,6

¹St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, Rockefeller University, New York, NY 10065; mogishi@rockefeller.edu.

Journal of Immunology (Baltimore, Md. : 1950)

|November 24, 2020

Summary

This summary is machine-generated.

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.

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Last Updated: Nov 29, 2025

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Published on: February 8, 2018

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.