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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: Nov 11, 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

K-means quantization for a web-based open-source flow cytometry analysis platform.

Nathan Wong¹, Daehwan Kim², Zachery Robinson²

¹Department of Bioengineering and QB3, UC Berkeley, Berkeley, CA, 94720, USA. nathanwong@berkeley.edu.

Scientific Reports

|March 25, 2021

Summary

This summary is machine-generated.

Freecyto is a web application for analyzing flow cytometry (FCM) data. It uses k-means clustering to enable interactive visualization and analysis of large FCM datasets online.

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Last Updated: Nov 11, 2025

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Area of Science:

Computational Biology
Bioinformatics
Data Science

Background:

Flow cytometry (FCM) generates large datasets, making analysis computationally intensive.
Existing FCM analysis platforms have limitations in handling large data for online interactive processing.

Purpose of the Study:

To develop an effective web-based solution for interactive flow cytometry data analysis.
To overcome data size limitations in web browsers for FCM data visualization.

Main Methods:

Developed Freecyto, a Python-flask web application.
Utilized a weighted k-means clustering algorithm for data quantization.
Implemented interactive visualization features including scatterplots, histograms, and gating.

Main Results:

Freecyto enables interactive analysis of large FCM datasets online.
The application preserves standard FCM visualization features.
Data accuracy is maintained compared to conventional FCM software.

Conclusions:

Freecyto offers an effective and accessible platform for flow cytometry data analysis.
The tool addresses the bottleneck of large data handling in web-based applications.
Freecyto is applicable to diverse experimental setups requiring FCM analysis.