Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.
In...

Test for Homogeneity

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...

You might also read

Related Articles

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

Sort by

Same author

A quantitative coordinate system for developmental dynamics.

bioRxiv : the preprint server for biology·2026

Same author

Pathogen-specific immune responses might underlie divergent outcomes of coronavirus and influenza infection in the natural porcine host.

Communications biology·2026

Same author

Toward informed batch correction for single-cell transcriptome integration.

Nature computational science·2026

Same author

Correction: Tracking early mammalian organogenesis - prediction and validation of differentiation trajectories at whole organism scale.

Development (Cambridge, England)·2026

Same author

Constructing the spatiotemporal atlas of single-cell lineage trajectories in stereotypic biological structures.

iScience·2026

Same author

Spatiotemporal cellular map of the developing human reproductive tract.

Nature·2025

Same journal

ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.

Nature methods·2026

Same journal

RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.

Nature methods·2026

Same journal

Spatio-DARLIN enables robust and efficient in situ lineage tracing in mice at single-cell resolution.

Nature methods·2026

Same journal

EasyGrid: a versatile platform for automated cryo-EM sample preparation and quality control.

Nature methods·2026

Same journal

Cloud-based microscope enables live neuroimaging for 24 h and beyond with worldwide access.

Nature methods·2026

Same journal

Deep molecular profiling in three dimensions.

Nature methods·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 2, 2026

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Testing for differential abundance in mass cytometry data.

Aaron T L Lun¹, Arianne C Richard^1,2, John C Marioni^1,3,4

¹Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.

|May 16, 2017

Summary

This summary is machine-generated.

Identifying cellular populations that change between biological conditions is crucial. Our new computational method, cydar, effectively detects differentially abundant cell populations using hypersphere assignment and spatial false discovery rate control, outperforming existing approaches.

More Related Videos

Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry

Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry

Published on: June 26, 2018

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

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

Published on: July 12, 2018

Related Experiment Videos

Last Updated: Mar 2, 2026

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry

Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry

Published on: June 26, 2018

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

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

Published on: July 12, 2018

Area of Science:

Immunology
Computational Biology
Biostatistics

Background:

Mass cytometry enables high-dimensional analysis of immune cell populations.
Identifying shifts in cell population abundance between biological conditions is a key challenge in cytometry data analysis.

Purpose of the Study:

To develop a robust computational strategy for detecting differentially abundant cell populations in mass cytometry data.
To improve the accuracy and reliability of identifying cellular changes across different biological conditions.

Main Methods:

Assigning cells to hyperspheres in high-dimensional space.
Implementing statistical tests to identify significant differences in population abundance between conditions.
Controlling the spatial false discovery rate to minimize false positives.

Main Results:

The proposed method, cydar, demonstrated superior performance compared to existing approaches in simulation studies.
Novel patterns of differential cell population abundance were identified in real-world mass cytometry datasets.
The method provides a reliable way to detect changes in cellular composition.

Conclusions:

The cydar package offers an effective computational solution for identifying differentially abundant cell populations in mass cytometry data.
This approach enhances the ability to discover biologically relevant cellular changes across experimental conditions.
The method is validated through simulations and real data analysis, suggesting broad applicability.