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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 9, 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

Computationally efficient multidimensional analysis of complex flow cytometry data using second order polynomial

John Zaunders^1,2, Junmei Jing³, Michael Leipold⁴

¹St Vincent's Centre for Applied Medical Research, St Vincent's Hospital, Darlinghurst, New South Wales, 2010, Australia.

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

|June 23, 2015

Summary

This summary is machine-generated.

A novel method, SOPHE, efficiently analyzes complex flow cytometry data. It accurately clusters millions of cells across many dimensions in seconds, improving cell mixture analysis.

Keywords:

clustering complex data data analysis high dimensions

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

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

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Published on: March 24, 2023

Area of Science:

Computational Biology
Bioinformatics
Data Science

Background:

Automated clustering of complex flow cytometry data remains a challenge, especially for high-dimensional datasets with millions of data points.
Existing methods struggle with efficient multivariate density estimation and mode identification.

Purpose of the Study:

To develop a novel, efficient method for automated clustering and analysis of complex flow cytometry data.
To accurately estimate multivariate densities and identify modes in high-dimensional datasets.

Main Methods:

Developed Second Order Polynomial Histogram Estimators (SOPHE) for mode description.
Data is divided into multivariate bins, with shapes analyzed using second-order polynomials.
Optimized binning reduces computational load, typically requiring only 4-8 bins per dimension.

Main Results:

SOPHE correctly identified all populations in defined mixtures of up to 17 fluorescent beads across 16 dimensions (<10s on a laptop).
Accurate clustering of immune cell subsets (granulocytes, lymphocytes, monocytes) in 9-color peripheral blood data was achieved within seconds.
Successfully clustered complex cell populations, including 36 memory CD4 T cell subsets in 14-color flow analysis and 65 PBMC subpopulations in 33-dimensional CyTOF data.

Conclusions:

SOPHE offers a computationally efficient and accurate approach for analyzing high-dimensional flow cytometry data.
The method demonstrates significant potential for accelerating discovery research by improving the analysis of complex cell mixtures.