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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Updated: Nov 1, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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Analyzing high-dimensional cytometry data using FlowSOM.

Katrien Quintelier1,2,3, Artuur Couckuyt1,2, Annelies Emmaneel1,2

  • 1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.

Nature Protocols
|June 26, 2021
PubMed
Summary
This summary is machine-generated.

This protocol details using FlowSOM, a clustering algorithm, for analyzing high-dimensional cytometry data. It provides user-friendly guidelines and R code for unsupervised cell population identification in immunology and oncology research.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Immunology

Background:

  • High-dimensional cytometry data analysis presents challenges due to increasing data complexity.
  • Traditional manual analysis methods are becoming insufficient for complex datasets.
  • Automated approaches are crucial for efficient and accurate cytometry data interpretation.

Purpose of the Study:

  • To provide a user-friendly protocol for analyzing high-dimensional cytometry data using the FlowSOM algorithm.
  • To offer detailed guidelines, parameter descriptions, and troubleshooting for FlowSOM implementation.
  • To demonstrate a complete workflow from data preparation to biological question answering.

Main Methods:

  • Utilized FlowSOM, a self-organizing map-based clustering and visualization algorithm.
  • Developed comprehensive R functions to enhance user-friendliness and replace common scripts.
  • Included data preparation steps: compensation, transformation, and quality control.

Main Results:

  • Validated FlowSOM on diverse datasets, improving its usability.
  • Provided clearly annotated R code for accessible implementation.
  • Demonstrated the workflow for unsupervised cell population identification and statistical comparison.

Conclusions:

  • FlowSOM offers a powerful, unsupervised approach for analyzing complex, high-dimensional cytometry data.
  • This protocol empowers scientists with a user-friendly tool for deeper insights in immunology and oncology.
  • The workflow facilitates answering biological questions through robust data analysis and visualization.