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Updated: Jan 25, 2026
05:12
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
Published on: January 16, 2019
11.9K
Analysis of High-Dimensional Phenotype Data Generated by Mass Cytometry or High-Dimensional Flow Cytometry
Branko Cirovic1, Natalie Katzmarski1, Andreas Schlitzer2
1Myeloid Cell Biology, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany.
Methods in Molecular Biology (Clifton, N.J.)
|May 12, 2019
Summary
High-dimensional cytometry advances immunology research by enabling detailed cell analysis. This guide offers best practices for experimental setup and data processing to manage complex single-cell multi-omics data.
Area of Science:
- Immunology
- Bioinformatics
- Cell Biology
Background:
- Single-cell multi-omics technologies enhance understanding of cellular heterogeneity.
- Modern flow and mass cytometers analyze up to 50 parameters per cell.
- High-dimensional data presents significant experimental and data processing challenges.
Purpose of the Study:
- To provide guidelines for a high-dimensional cytometry workflow.
- To address the complexities of analyzing high-dimensional single-cell data.
- To facilitate the adoption of advanced bioinformatic tools in immunology.
Main Methods:
- Experimental setup optimization for high-dimensional cytometry.
- Panel design strategies for multi-parameter analysis.
- Fluorescent spillover compensation techniques.
- Bioinformatic approaches for high-dimensional data analysis.
Main Results:
- Development of novel bioinformatic tools for high-dimensional data analysis.
- Guidelines for comprehensive cell characterization in heterogeneous populations.
- Transition from manual gating to automated analysis methods.
Conclusions:
- High-dimensional cytometry workflows are essential for modern immunology.
- Advanced bioinformatic tools are crucial for exploiting multi-omics data.
- Standardized guidelines improve the reliability and efficiency of single-cell analysis.
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