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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: Apr 21, 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

COPASutils: an R package for reading, processing, and visualizing data from COPAS large-particle flow cytometers.

Tyler C Shimko¹, Erik C Andersen¹

¹Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, United States of America.

|October 21, 2014

Summary

This summary is machine-generated.

The COPASutils R package simplifies processing and visualizing data from Complex Object Parametric Analyzer and Sorter (COPAS) and BioSorter flow cytometers. This tool aids researchers studying small organisms with high-throughput screening data.

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Area of Science:

Bioinformatics
Computational Biology
Flow Cytometry Data Analysis

Background:

Complex Object Parametric Analyzer and Sorter (COPAS) and BioSorter systems generate large, unwieldy datasets.
Existing tools often present challenges for processing and visualizing this high-throughput screening data.
Researchers studying small model organisms require efficient data handling methods.

Purpose of the Study:

To introduce the COPASutils R package for streamlined data analysis.
To provide a logical workflow for reading, processing, and visualizing flow cytometry data.
To enhance the analysis capabilities for researchers using COPAS and BioSorter platforms.

Main Methods:

Development of an extensible R package, COPASutils.
Implementation of functions for efficient data import and manipulation.
Integration of visualization tools tailored for flow cytometry outputs.

Main Results:

COPASutils enables rapid processing of large datasets from COPAS and BioSorter instruments.
The package facilitates straightforward data visualization for complex biological data.
Streamlined workflow improves efficiency for researchers analyzing small organisms.

Conclusions:

COPASutils offers a valuable solution for managing and analyzing large-scale flow cytometry data.
The R package supports researchers in fields like developmental biology and genetics.
This tool enhances the utility of COPAS and BioSorter systems in high-throughput screening.