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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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Updated: Apr 18, 2026

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

FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.

Sofie Van Gassen^1,2,3, Britt Callebaut¹, Mary J Van Helden^2,3

¹Department of Information Technology, Ghent University, iMinds, Ghent, Belgium.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|January 10, 2015

Summary

This summary is machine-generated.

Analyzing high-dimensional flow and mass cytometry data is challenging. FlowSOM, a novel visualization technique using Self-Organizing Maps, aids in identifying cell subsets and understanding marker behavior in complex datasets.

Keywords:

Key terms: polychromatic flow cytometry bioinformatics exploratory data analysis mass cytometry self-organizing map visualization method

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Area of Science:

Immunology
Computational Biology
Data Visualization

Background:

Flow and mass cytometry generate increasingly high-dimensional datasets.
Manual analysis of these complex datasets is becoming infeasible.
Traditional 2D scatter plots struggle with multi-marker data, risking missed insights.

Purpose of the Study:

Introduce FlowSOM, a new visualization technique for flow and mass cytometry data.
Address the challenge of analyzing large, multi-marker datasets.
Improve the detection of rare cell subsets and marker behavior.

Main Methods:

Utilizes a Self-Organizing Map (SOM) for data analysis.
Employs a two-level clustering approach.
Incorporates star charts for enhanced visualization.

Main Results:

FlowSOM provides a clear overview of marker behavior across all cells.
The algorithm effectively detects cell subsets that might be missed by other methods.
Facilitates the analysis of complex, high-dimensional cytometry data.

Conclusions:

FlowSOM offers an effective solution for visualizing and analyzing complex flow and mass cytometry data.
The technique aids researchers in uncovering hidden patterns and cell populations.
R code is available for implementation and further development.