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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: May 23, 2026

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

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Published on: December 12, 2019

Multivariate analysis of flow cytometric data using decision trees.

Svenja Simon¹, Reinhard Guthke, Thomas Kamradt

¹Research Group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Germany.

Frontiers in Microbiology

|April 10, 2012

Summary

Machine learning, specifically induction of decision trees, can analyze complex flow cytometry data. This method reveals cytokine expression patterns, including dependencies on expression intensity, advancing immunological research.

Keywords:

cytokines flow cytometry imbalanced data induction of decision trees machine learning multidimensionality

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Last Updated: May 23, 2026

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

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Published on: December 12, 2019

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

Area of Science:

Immunology
Computational Biology
Data Science

Background:

Understanding host immune responses to pathogens is crucial.
Flow cytometry is a key tool in immunology for single-cell analysis.
High-dimensional flow cytometry data requires advanced statistical methods.

Purpose of the Study:

To evaluate the effectiveness of the "induction of decision trees" machine learning method for analyzing flow cytometry data.
To investigate cytokine co-expression patterns and dependencies in immune cells.
To explore novel analytical approaches for complex immunological datasets.

Main Methods:

Utilized supervised machine learning, specifically induction of decision trees.
Analyzed intracellular cytokine staining data for six cytokines.
Employed stratified fivefold cross-validation and quality criteria for tree selection.

Main Results:

Decision trees identified known cytokine co-expression patterns.
Revealed that cytokine expression depends not only on co-expression but also on expression intensity.
Successfully applied induction of decision trees to high-dimensional flow cytometry data.

Conclusions:

Induction of decision trees is a feasible method for analyzing complex flow cytometry data.
This approach can uncover intricate patterns in cytokine expression.
The method provides new insights into host immune system responses.