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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 29, 2025

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

SuperCellCyto: enabling efficient analysis of large scale cytometry datasets.

Givanna H Putri¹, George Howitt², Felix Marsh-Wakefield³

¹The Walter and Eliza Hall Institute of Medical Research and The Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia. putri.g@wehi.edu.au.

|April 8, 2024

Summary

SuperCellCyto is a new R package that efficiently analyzes large cytometry datasets by grouping similar cells into supercells. This method avoids data loss from subsampling, preserving rare cell populations for accurate analysis.

Keywords:

Batch correction Bioinformatics CITEseq Clustering Computational analysis Cytof Cytometry Data compression Dimensionality reduction Supercell

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Published on: December 16, 2014

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Last Updated: Jun 29, 2025

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

Sample Preparation for Mass Cytometry Analysis

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Published on: April 29, 2017

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

Area of Science:

Computational Biology
Bioinformatics
Single-cell analysis

Background:

Cytometry technologies allow high-dimensional protein quantification in millions of cells.
Analyzing large cytometry datasets involves complex tasks like clustering and dimensionality reduction.
Current tools struggle with long runtimes on massive datasets, and subsampling risks losing rare cell subsets.

Purpose of the Study:

To introduce SuperCellCyto, an R package designed for efficient analysis of large-scale cytometry data.
To provide a computational solution that overcomes the limitations of existing tools for high-dimensional single-cell data.
To enable accurate analysis of cytometry data without compromising rare cell populations.

Main Methods:

Developed SuperCellCyto as an R package, leveraging the SuperCell algorithm.
Implemented a method for grouping highly similar cells into representative 'supercells'.
Focused on improving computational efficiency for large cytometry datasets.

Main Results:

SuperCellCyto offers an efficient approach to process millions of cells in cytometry data.
The supercell grouping strategy effectively retains information, including rare cell populations.
The package provides a viable alternative to computationally intensive methods and risky subsampling.

Conclusions:

SuperCellCyto enhances the analysis of large-scale cytometry data by improving computational efficiency.
This tool addresses the critical need for methods that preserve rare cell subsets during analysis.
SuperCellCyto is available on GitHub and Zenodo for broader research community adoption.