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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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Updated: Jun 18, 2026

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FlowFP: A Bioconductor Package for Fingerprinting Flow Cytometric Data.

Wade T Rogers¹, Herbert A Holyst

¹Department of Pathology and Laboratory Medicine, School of Medicine, University of Pennsylvania, 207 John Morgan Bldg., Philadelphia, PA 19104-6082, USA.

Advances in Bioinformatics

|December 4, 2009

Summary

This summary is machine-generated.

flowFP is a new software package for flow cytometry data analysis. It transforms raw data into a "fingerprint" for statistical modeling, efficiently handling large datasets for applications like Acute Myeloid Leukemia classification.

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Last Updated: Jun 18, 2026

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Published on: July 12, 2018

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Published on: December 12, 2019

Area of Science:

Computational Biology
Bioinformatics
Data Science

Background:

Flow cytometry generates high-dimensional data.
Existing analysis methods may rely on presumptive functional forms.
Efficient tools are needed for large-scale flow cytometry data analysis.

Purpose of the Study:

Introduce flowFP, a novel software package for flow cytometry data analysis.
Provide tools to transform raw flow cytometry data for statistical analysis.
Develop a method independent of presumptive distribution forms.

Main Methods:

Developed flowFP, a Bioconductor-integrated software package.
Implemented a "fingerprint" approach to describe multivariate probability distributions.
Ensured computational efficiency for large, high-dimensional datasets.

Main Results:

flowFP transforms raw flow cytometry data into a suitable format for statistical analysis.
The "fingerprint" method is independent of assumed distribution shapes.
The software efficiently handles large and complex flow cytometry datasets.

Conclusions:

flowFP offers a computationally efficient and flexible approach for flow cytometry data analysis.
The package facilitates integration with existing statistical and modeling tools.
flowFP demonstrates utility in data quality control and automated Acute Myeloid Leukemia classification.