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Related Experiment Video

Updated: Jul 15, 2025

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Evaluating batch correction methods for image-based cell profiling.

John Arevalo¹, Ellen Su¹, Robert van Dijk¹

¹Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

Biorxiv : the Preprint Server for Biology

|September 25, 2023

Summary

This summary is machine-generated.

Batch effects hinder the integration of image-based profiling data. Harmony, a mixture-model method, effectively corrected these effects in large Cell Painting datasets, enabling better scientific discovery.

Keywords:

Batch correction Cell Painting Harmony high-throughput phenotypic screening image-based profiling machine learning morphological profiling pseudo-bulk analysis

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Last Updated: Jul 15, 2025

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

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A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

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Published on: March 1, 2017

Area of Science:

Computational biology
Biotechnology
Genomics

Background:

High-throughput image-based profiling generates vast cellular data for applications like drug discovery.
Batch effects from different labs and equipment limit data integration and interpretation.
Cellular imaging data is crucial for understanding biological mechanisms.

Approach:

Benchmarked seven scRNA-seq batch correction techniques on a large Cell Painting dataset.
Evaluated methods across five scenarios of varying complexity.
Utilized a newly released, largest-ever public image-based dataset.

Key Points:

Harmony, a mixture-model based method, demonstrated superior performance in batch effect correction.
The study provides a framework and metrics for assessing future batch correction methods.
Consistent performance of Harmony across diverse scenarios was observed.

Conclusions:

This work facilitates the integration and interpretation of public Cell Painting data.
Improved batch correction enables robust scientific discovery from large-scale imaging datasets.
The developed benchmark aids in advancing image-based profiling data analysis.