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

You might also read

Related Articles

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

Sort by

Same author

MSR1 Drives MASLD Progression Via Disrupting FoxO3a-SOD3 Mediated Redox Balance in Liver Macrophages.

Liver international : official journal of the International Association for the Study of the Liver·2026

Same author

Eco-Nanozymology: A Catalytic Paradigm Integrating Energy, Environment, and Ecology.

Nano-micro letters·2026

Same author

Targeting the Myocardial Microenvironment: Novel Antiviral Strategies and Therapeutic Perspectives for Coxsackievirus B-Induced Myocarditis.

Journal of the American Heart Association·2026

Same author

Genome-wide identification and characterization of the Groucho/Tup1-like corepressor family identifies a potential role in the epigenetic regulation of abiotic stress responses in soybean.

Frontiers in plant science·2026

Same author

Qi Wei Zhi Gan formulation alleviates progressive fibrosis in metabolic dysfunction-associated steatohepatitis through suppressing Peroxidasin-collagen IV crosslinking.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026

Same author

Comparative metabolomic analysis of flavor and metabolite profiles across cultivated kiwifruit species.

Food chemistry·2026

Same journal

Glycoform engineering of a mammalian platform to sculpt a humanized recombinant bioscavenger.

Cell systems·2026

Same journal

Targeted genomic editing of human gut Bacteroides species based on CRISPR-associated transposases.

Cell systems·2026

Same journal

Scalable enumeration and sampling of minimal metabolic pathways for organisms and communities.

Cell systems·2026

Same journal

Deciphering protein mutation-phenotype linkages from CRISPR-based tiling mutagenesis screens.

Cell systems·2026

Same journal

High-throughput machine learning-aided antibody discovery for cell surface antigens.

Cell systems·2026

Same journal

Quantitative cytokine profiling of primary human macrophages reveals distinct single-cell modes of trained immunity.

Cell systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 10, 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

Data-driven batch detection enhances single-cell omics data analysis.

Ziqi Zhang¹, Xiuwei Zhang¹

¹School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

|October 17, 2024

Summary

This summary is machine-generated.

Single-cell omics studies face batch effects from data collection. This research addresses challenges in correcting these technical confounders for improved data integration and analysis.

More Related Videos

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

Related Experiment Videos

Last Updated: Jun 10, 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

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell omics data generation often involves multiple experimental batches.
Batch effects are technical variations that introduce noise and distort biological signals.
These effects complicate downstream data analysis and interpretation.

Purpose of the Study:

To investigate the challenges associated with correcting batch effects in single-cell omics data.
To explore the impact of unknown sources and nonlinear distortions on data integration.
To develop strategies for accurate data assignment to optimal batches for integration.

Main Methods:

Review of existing batch correction methodologies for single-cell omics.
Analysis of data characteristics contributing to batch effect complexity.
Evaluation of data assignment strategies for integration.

Main Results:

Batch effects present significant hurdles due to unknown origins and nonlinear distortions.
Accurate batch assignment is critical but difficult for effective data integration.
Current methods may struggle with complex, non-linear batch variations.

Conclusions:

Effective batch effect correction is crucial for reliable single-cell omics analysis.
Addressing the challenges of unknown sources and nonlinearities is key for future methods.
Improved data integration strategies are needed to overcome batch-related artifacts.