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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 10, 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

Data reduction for spectral clustering to analyze high throughput flow cytometry data.

Habil Zare¹, Parisa Shooshtari, Arvind Gupta

¹Terry Fox Laboratory, BC Cancer Agency, 675 W 10th Ave, Vancouver, BC, Canada.

BMC Bioinformatics

|July 30, 2010

Summary

This summary is machine-generated.

SamSPECTRAL enhances spectral clustering for large biological datasets. This new algorithm effectively identifies complex cell populations in flow cytometry data, overcoming previous computational limitations.

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Last Updated: Jun 10, 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

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

Published on: January 16, 2019

Area of Science:

Computational biology
Data science
Bioinformatics

Background:

Clustering large biological datasets is crucial for biological discovery.
Spectral clustering is a powerful technique but limited by computational constraints on large datasets.
Existing methods struggle with complex cell population identification in high-dimensional data.

Purpose of the Study:

To adapt spectral clustering for efficient analysis of large-scale biological data.
To overcome the time and memory limitations of standard spectral clustering.
To develop a robust algorithm for flow cytometry data analysis.

Main Methods:

Developed SamSPECTRAL, an algorithm modifying spectral clustering.
Incorporated an information-preserving sampling procedure.
Applied a post-processing stage to refine clustering results.

Main Results:

SamSPECTRAL successfully applied to large, multidimensional flow cytometry data.
Outperformed state-of-the-art methods in identifying non-elliptical, low-density, and rare cell populations.
Demonstrated superior performance in resolving subpopulations within major populations.

Conclusions:

SamSPECTRAL represents the first successful application of spectral clustering to flow cytometry data.
The algorithm addresses key limitations in analyzing complex biological datasets.
An R package implementation is available via BioConductor for public use.