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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: Feb 20, 2026

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

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

Published on: January 16, 2019

High-Dimensional Modeling for Cytometry: Building Rock Solid Models Using GemStone™ and Verity Cen-se'™

C Bruce Bagwell¹

¹Verity Software House, P.O. Box 247, 45A Augusta Road, Topshsam, ME, 04086, USA. cbb@vsh.com.

Methods in Molecular Biology (Clifton, N.J.)

|October 27, 2017

Summary

This summary is machine-generated.

This chapter introduces automated modeling for high-dimensional cytometry data, improving upon traditional gating methods. A new technique, high-definition t-SNE mapping, simplifies the creation of accurate cellular population models.

Keywords:

Cen-se’ mapping High-dimensional modeling Probability state model t-SNE

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Area of Science:

Computational Biology
Data Science
Immunology

Background:

High-dimensional cytometry generates complex datasets requiring sophisticated analysis.
Traditional gating methods are manual, time-consuming, and struggle with overlapping cell populations.
Automated modeling offers a more efficient and robust approach to cytometry data analysis.

Purpose of the Study:

To provide a guide for designing automated models for high-dimensional cytometry data.
To highlight the advantages of modeling over traditional gating techniques.
To introduce high-definition t-SNE mapping as a tool for model design.

Main Methods:

Utilizing modeling approaches for cytometry data analysis.
Implementing high-definition t-SNE mapping for enhanced visualization and model design.
Developing and validating models against complex datasets.

Main Results:

Demonstrated that modeling automates analysis and accounts for measurement overlap.
Showcased the utility of high-definition t-SNE mapping in simplifying model design.
Provided nontrivial examples illustrating consistent model creation.

Conclusions:

Automated modeling, particularly with high-definition t-SNE, offers a powerful and accessible method for analyzing high-dimensional cytometry data.
This approach overcomes limitations of manual gating, enabling more accurate and reproducible results.
The provided examples serve as a practical guide for researchers in the field.