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Flow Cytometry

Flow Cytometry

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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Related Experiment Video

Updated: Jun 28, 2025

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

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

Published on: December 12, 2019

Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools.

Artuur Couckuyt^1,2, Benjamin Rombaut^1,2, Yvan Saeys^1,2

¹Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium.

Bioinformatics (Oxford, England)

|April 17, 2024

Summary

This summary is machine-generated.

A new Python version of FlowSOM, a cytometry data clustering tool, offers faster performance and better integration with single-cell omics data. This enhanced implementation is now available for researchers.

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Area of Science:

Computational Biology
Bioinformatics
Data Science

Background:

FlowSOM is a widely used clustering algorithm for cytometry data analysis.
Existing implementations may have limitations in speed and compatibility with modern single-cell omics workflows.

Purpose of the Study:

To introduce a novel, optimized Python implementation of the FlowSOM algorithm.
To enhance the usability and performance of FlowSOM for single-cell omics data analysis.

Main Methods:

Development of a new Python package for FlowSOM.
Benchmarking performance against the original R implementation.
Integration with common single-cell data structures.

Main Results:

The Python implementation demonstrates significantly faster execution times compared to the R version.
Improved compatibility and seamless integration with contemporary single-cell omics data formats.
Preservation of original FlowSOM visualization capabilities, including star and pie plots.

Conclusions:

The new Python FlowSOM implementation provides a faster and more versatile tool for cytometry data analysis.
This implementation facilitates advanced analysis of single-cell omics data, supporting researchers in the field.