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

16.5K
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...
16.5K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

821
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
821
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.7K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.7K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

1.1K
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
1.1K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.6K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.6K
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

357
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
357

You might also read

Related Articles

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

Sort by
Same author

A robust, high-content NAM for repeatable and predictive developmental and reproductive toxicity assessment in C. elegans.

Scientific reports·2026
Same author

HIV Transmission in a Declining African Epidemic.

medRxiv : the preprint server for health sciences·2026
Same author

A Robust, High-Content NAM for Repeatable and Predictive Developmental and Reproductive Toxicity Assessment in C. elegans.

Research square·2026
Same author

Inspiratory muscle training for people with spinal cord injury: An implementation study.

Clinical rehabilitation·2026
Same author

Patterns of HIV-1 Viral Load Suppression and Drug Resistance During the Dolutegravir Transition: A Population-based Longitudinal Study.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026
Same author

Decoding TNBC architecture.

Nature cancer·2026

Related Experiment Video

Updated: Feb 16, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.8K

High throughput automated analysis of big flow cytometry data.

Albina Rahim1, Justin Meskas2, Sibyl Drissler2

  • 1Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada; Department of Bioinformatics, University of British Columbia, Vancouver, BC, Canada.

Methods (San Diego, Calif.)
|December 31, 2017
PubMed
Summary

Manual analysis of complex flow cytometry data is challenging. This study presents an automated analysis pipeline to efficiently interpret large-scale, high-dimensional flow cytometry datasets, improving scientific discovery.

Keywords:
Automated analysisBioinformaticsFlow cytometry

More Related Videos

High-throughput Flow Cytometry Cell-based Assay to Detect Antibodies to N-Methyl-D-aspartate Receptor or Dopamine-2 Receptor in Human Serum
10:19

High-throughput Flow Cytometry Cell-based Assay to Detect Antibodies to N-Methyl-D-aspartate Receptor or Dopamine-2 Receptor in Human Serum

Published on: November 23, 2013

16.8K
Evaluation of Polymeric Gene Delivery Nanoparticles by Nanoparticle Tracking Analysis and High-throughput Flow Cytometry
08:51

Evaluation of Polymeric Gene Delivery Nanoparticles by Nanoparticle Tracking Analysis and High-throughput Flow Cytometry

Published on: March 1, 2013

16.7K

Related Experiment Videos

Last Updated: Feb 16, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.8K
High-throughput Flow Cytometry Cell-based Assay to Detect Antibodies to N-Methyl-D-aspartate Receptor or Dopamine-2 Receptor in Human Serum
10:19

High-throughput Flow Cytometry Cell-based Assay to Detect Antibodies to N-Methyl-D-aspartate Receptor or Dopamine-2 Receptor in Human Serum

Published on: November 23, 2013

16.8K
Evaluation of Polymeric Gene Delivery Nanoparticles by Nanoparticle Tracking Analysis and High-throughput Flow Cytometry
08:51

Evaluation of Polymeric Gene Delivery Nanoparticles by Nanoparticle Tracking Analysis and High-throughput Flow Cytometry

Published on: March 1, 2013

16.7K

Area of Science:

  • Immunology
  • Computational Biology
  • Data Science

Background:

  • Traditional manual analysis of flow cytometry data struggles with increasing dataset complexity and dimensionality.
  • High-dimensional datasets (e.g., 50-dimensional) in flow cytometry now rival mass cytometry, rendering manual interpretation infeasible.
  • Existing automated gating solutions do not address the full analytical pipeline, including data cleaning and immunophenotype extraction.

Purpose of the Study:

  • To review and present components of a customized automated analysis pipeline for large-scale flow cytometry data.
  • To provide a generalizable framework applicable to diverse flow cytometry datasets.
  • To demonstrate the pipeline's utility on real-world data from the International Mouse Phenotyping Consortium (IMPC).

Main Methods:

  • Review of essential components for an automated flow cytometry analysis pipeline.
  • Development of methodologies for data cleaning and event outlier detection.
  • Application of the automated pipeline to high-dimensional flow cytometry data.

Main Results:

  • The proposed automated pipeline effectively handles large-scale, high-dimensional flow cytometry data.
  • Demonstrated successful application of the pipeline to IMPC datasets, showcasing its practical utility.
  • The pipeline addresses critical analytical steps beyond automated gating, such as data preprocessing and feature extraction.

Conclusions:

  • Automated analysis pipelines are essential for interpreting complex, high-dimensional flow cytometry data.
  • The presented pipeline offers a robust and generalizable solution for large-scale flow cytometry analysis.
  • This approach facilitates more efficient and comprehensive interpretation of flow cytometry data in research settings like the IMPC.