Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

13.4K
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.
In...
13.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Microbiological spectrum and probability of transmission of infection from ocular tissues of enucleated eyes harvested from septicemic donors.

World journal of transplantation·2026
Same author

Response to "Bridging the implementation gap in AI-assisted flow cytometry".

Cytometry. Part B, Clinical cytometry·2026
Same author

Cyt-Geist: Current and Future Challenges in Cytometry: Reports of the CYTO 2025 Conference Workshops.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2025
Same author

Higher Promoter Methylation of the Ubiquitin-Associated and SH3 Domain Containing A (UBASH3A) Gene Is Associated With T-Lymphocyte Ontogeny and Reduced Susceptibility to Early-Onset Sepsis.

The Journal of infectious diseases·2025
Same author

Enhancing statistical analysis of real world data.

Database : the journal of biological databases and curation·2025
Same author

AI in flow cytometry: Current applications and future directions.

Cytometry. Part B, Clinical cytometry·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: May 1, 2026

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction
11:11

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Published on: August 15, 2013

17.8K

flowCore: a Bioconductor package for high throughput flow cytometry.

Florian Hahne1, Nolwenn LeMeur, Ryan R Brinkman

  • 1Life Sciences Department, Computational Biology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024, USA. fhahne@fhcrc.org

BMC Bioinformatics
|April 11, 2009
PubMed
Summary
This summary is machine-generated.

New open-source software, flowCore, enhances flow cytometry data analysis for high-throughput screening. It provides flexible tools and data structures for complex datasets in clinical trials and drug discovery.

More Related Videos

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.2K
Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

13.0K

Related Experiment Videos

Last Updated: May 1, 2026

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction
11:11

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Published on: August 15, 2013

17.8K
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.2K
Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

13.0K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Automation in flow cytometry generates large, complex datasets.
  • Current data management and analysis methods are insufficient for complex data with multiple covariates.
  • Challenges exist in handling high-throughput screening data from clinical trials and drug discovery.

Purpose of the Study:

  • To develop flexible, open-source computational tools for analyzing complex flow cytometry data.
  • To create suitable data structures for managing and analyzing collections of samples or clinical cohorts.
  • To establish a collaborative research platform for advancing flow cytometry data analysis.

Main Methods:

  • Development of the R package flowCore.
  • Implementation of flexible data structures within flowCore.
  • Creation of an extensible research platform for interdisciplinary collaboration.

Main Results:

  • flowCore provides tools for efficient analysis of complex flow cytometry datasets.
  • The software's data structures effectively capture and organize analytical workflows.
  • It supports the application of operations across multiple samples and clinical cohorts.

Conclusions:

  • The flowCore software is efficient for analyzing diverse flow cytometry datasets.
  • Its data structures streamline the analytical workflow.
  • It serves as a foundation for additional Bioconductor packages, enabling new avenues for flow data analysis.