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

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Systematic Error: Methodological and Sampling Errors

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...

You might also read

Related Articles

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

Sort by

Same author

APAdeg enhances differentially expressed gene inference by leveraging site-specific signals in APA-seq data.

Briefings in bioinformatics·2026

Same author

PepAnno: A structure-aware deep learning framework for bioactive peptide prediction, structural visualization, and physicochemical profiling.

PLoS computational biology·2026

Same author

AWmeta Empowers Adaptively Weighted Transcriptomic Meta-Analysis.

Current issues in molecular biology·2026

Same author

T2T-Hub: a central platform for analyzing plant and animal telomere-to-telomere genomes.

Nucleic acids research·2026

Same author

UK BioCoin: swift trait-specific summary statistics regression for UK Biobank.

Nature communications·2026

Same author

The potential of wheat spatial omics.

Nature genetics·2026

Same journal

Entamoeba histolytica Gal/GalNAc lectin intermediate subunit as a potential driver of inflammation and epithelial damage in intestinal amebiasis.

Communications biology·2026

Same journal

OMIDIENT: Multiomics Integration for Cancer by Dirichlet Auto-Encoder Networks.

Communications biology·2026

Same journal

KCTD3 deficiency disrupts axon initial segment organization and neurite outgrowth in a neurodevelopmental disorder mouse model.

Communications biology·2026

Same journal

A two-pronged strategy eliminates dissociation artifacts for high-fidelity neuroimmune single-cell transcriptomics.

Communications biology·2026

Same journal

Prospects of DNA nanotechnology in stroke repair and regeneration.

Communications biology·2026

Same journal

A human epithelial co-culture system reveals distinct host cell interaction behaviours for Treponema pallidum.

Communications biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Reference-informed evaluation of batch correction for single-cell omics data with overcorrection awareness.

Xiaoyue Hu^1,2, He Li¹, Ming Chen³

¹Center for Data Science, Zhejiang University, Hangzhou, China.

Communications Biology

|March 29, 2025

Summary

This summary is machine-generated.

Evaluating batch effect correction (BEC) is crucial for single-cell data integration. We introduce RBET, a new statistical framework that reliably assesses BEC performance, preventing overcorrection and ensuring accurate biological insights from sequencing data.

More Related Videos

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

Published on: June 16, 2020

Related Experiment Videos

Last Updated: Jun 17, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

Published on: June 16, 2020

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Integrating multiple single-cell datasets requires effective batch effect correction (BEC).
Existing evaluation metrics lack sensitivity to overcorrection, risking false biological discoveries.
Accurate BEC performance assessment is vital for reliable single-cell data analysis.

Purpose of the Study:

To develop a robust and sensitive metric for evaluating batch effect correction (BEC) performance.
To address the limitations of current methods in detecting data overcorrection.
To provide a reliable framework for selecting appropriate BEC strategies in single-cell studies.

Main Methods:

Development of RBET, a reference-informed statistical framework for BEC evaluation.
Extensive simulations and validation on six real-world datasets (scRNA-seq and scATAC-seq).
Assessment across varying numbers of batches, batch effect sizes, and cell types.

Main Results:

RBET provides a fairer evaluation of BEC methods compared to existing metrics.
RBET is sensitive to overcorrection, which can erase true biological variations.
RBET demonstrates computational efficiency and robustness, even with large batch effects.

Conclusions:

RBET offers a reliable guideline for selecting case-specific BEC methods.
The RBET framework is extendable to other single-cell data modalities.
RBET enhances the accuracy of biological insights derived from integrated single-cell data.