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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: Mar 21, 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

flowAI: automatic and interactive anomaly discerning tools for flow cytometry data.

Gianni Monaco¹, Hao Chen², Michael Poidinger²

¹Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Singapore 138648, Singapore Integrative Genomics of Ageing Group, Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.

Bioinformatics (Oxford, England)

|May 7, 2016

Summary

This summary is machine-generated.

The flowAI R package offers automated and interactive tools to clean flow cytometry (FCM) data by removing unwanted events. This improves the accuracy of cell population analysis, especially for high-dimensional datasets.

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Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Flow cytometry (FCM) is crucial for cell analysis, generating high-dimensional data.
Automated analysis of FCM data requires robust quality control to prevent false discoveries.

Purpose of the Study:

To introduce flowAI, an R package for cleaning flow cytometry data.
To provide automated and interactive methods for identifying and removing anomalous events in FCM files.

Main Methods:

Developed an R package, flowAI, with two data cleaning methods: automatic anomaly detection and an interactive Shiny app.
Implemented algorithms to detect anomalies from flow rate changes, signal instability, and outlier events.

Main Results:

The flowAI package successfully identifies and removes unwanted events from FCM files.
Generates quality assessment summaries for each analyzed file, enhancing data reliability.

Conclusions:

flowAI offers an intuitive solution to improve both manual and automatic analyses of flow cytometry data.
The package aids in achieving more accurate and unbiased cell sub-population segregation.