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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

You might also read

Related Articles

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

Sort by
Same author

The ITCC-P4 PDX Platform Enables Preclinical Testing of Pediatric Cancers.

Cancer research·2026
Same author

Unveiling Candidate Markers for Drug Resistance or Synthetic Lethality in Cervical Cancer: Integrative Analysis of Genetic and Pharmacoprofiling.

Cancer reports (Hoboken, N.J.)·2026
Same author

Mevalonate pathway activation in Ewing sarcoma reveals a 3D-specific synergy between statins and BCL-xL inhibition.

Molecular therapy. Oncology·2026
Same author

Neutralization of acyl-CoA-binding protein attenuates glucocorticoid-mediated suppression of cancer immunosurveillance.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Large scale compound selection guided by cell painting reveals activity cliffs and functional relationships.

Communications biology·2026
Same author

Ex vivo drug sensitivity profiling to complement molecular profiling in pediatric precision oncology.

NPJ precision oncology·2026

Related Experiment Video

Updated: May 19, 2026

High Content Screening in Neurodegenerative Diseases
13:32

High Content Screening in Neurodegenerative Diseases

Published on: January 6, 2012

A probabilistic model for cell population phenotyping using HCS data.

Edouard Pauwels1, Didier Surdez, Gautier Stoll

  • 1Mines ParisTech, Centre for Computational Biology, Fontainebleau Cedex, France. edouard.pauwels@ensmp.fr

Plos One
|August 29, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a probabilistic model to analyze High Content Screening (HCS) data, addressing cellular response heterogeneity and enabling better comparison of cell populations in biological experiments.

More Related Videos

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells
10:20

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

Related Experiment Videos

Last Updated: May 19, 2026

High Content Screening in Neurodegenerative Diseases
13:32

High Content Screening in Neurodegenerative Diseases

Published on: January 6, 2012

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
09:34

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells
10:20

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

Area of Science:

  • Cellular biology
  • Bioinformatics
  • Image analysis

Background:

  • High Content Screening (HCS) generates complex cellular image data.
  • Interpreting cellular response heterogeneity in HCS is challenging.
  • Defining population-level phenotypic classes aids experiment analysis.

Purpose of the Study:

  • To develop a probabilistic model for representing and comparing cell populations from HCS data.
  • To address the challenges of cellular response heterogeneity and population-level classification in HCS.
  • To improve the analysis and interpretation of HCS experiments.

Main Methods:

  • Utilized a probabilistic model to represent cell populations.
  • Incorporated HCS-specific information: dependence structure and within-population variability.
  • Input data included segmented cell images with pre-classified phenotypes.

Main Results:

  • The proposed model effectively leverages HCS-specific data features.
  • The model provides richer HCS data analysis compared to simpler averaging methods.
  • Validated an HCS data analysis method using control experiments, revealing biologically meaningful insights.

Conclusions:

  • The probabilistic model enhances High Content Screening data analysis by accounting for population-level variability and feature dependencies.
  • This approach offers a more comprehensive understanding of cellular responses than traditional methods.
  • Further biological validation of novel outputs is a promising future research direction.