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

You might also read

Related Articles

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

Sort by
Same author

Short-term hot spring balneotherapy ameliorates sleep disorders: wrist-worn wearable-assessed sleep improvement associated with neuroimmune, tryptophan metabolic and gut microbiome alterations.

International journal of biometeorology·2026
Same author

Analysis of the Validity of ChatGPT in the Assessment of Perceived Stress and Psychological Distress Among University Students: A Cross-Sectional Study.

The Psychiatric quarterly·2026
Same author

Translation, cross-cultural adaptation, and preliminary psychometric investigation of the Chinese version of the digital intervention barriers scale-7 (DIBS-7) among college students.

BMC psychology·2026
Same author

DNA Methylation at Birth Showing Age-Specific Association with Atopy in Children: A Prospective Longitudinal Study.

Epigenomes·2026
Same author

A novel ABCD1 frameshift mutation detected in a Chinese male with adrenomyeloneuropathy.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology·2026
Same author

Drivers of Rising Prevalence in Major Motor Neurodegenerative Diseases: Temporal Trends in Sweden and France (2003-2022).

Neurology·2026

Related Experiment Video

Updated: Sep 22, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.8K

Norm ISWSVR: A Data Integration and Normalization Approach for Large-Scale Metabolomics.

Xian Ding1, Fen Yang2, Yanhua Chen3,4,5

  • 1State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050 Beijing, China.

Analytical Chemistry
|May 18, 2022
PubMed
Summary

This study introduces Norm ISWSVR, a novel method to improve metabolomics data quality by removing systematic errors. This approach enhances reproducibility and reduces experimental burden in large-scale studies.

More Related Videos

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.1K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.7K

Related Experiment Videos

Last Updated: Sep 22, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.8K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.1K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.7K

Area of Science:

  • Metabolomics
  • Lipidomics
  • Bioinformatics

Background:

  • Large-scale metabolomics studies face challenges with systematic errors, impacting data quality and reproducibility.
  • Robust batch correction is crucial for quality control in metabolomics data analysis.

Purpose of the Study:

  • To develop and validate a novel data normalization and integration method, Norm ISWSVR, for large-scale metabolomics.
  • To comprehensively remove systematic errors, random errors, and matrix effects in metabolomics data.

Main Methods:

  • Norm ISWSVR combines internal standard correction with support vector regression normalization in a two-step process.
  • The method was tested on three untargeted lipidomics/metabolomics datasets and compared against 11 other normalization techniques.

Main Results:

  • Norm ISWSVR significantly reduced cross-validated relative standard deviation (cvRSD) and improved correlations between quality controls (QCs).
  • The method enhanced biomarker classification accuracy and demonstrated compatibility with quantitative data.
  • Norm ISWSVR enables a lower frequency of QCs, reducing the burden of large-scale experiments.

Conclusions:

  • Norm ISWSVR effectively improves the quality of large-scale metabolomics data by addressing systematic and random errors.
  • This novel method offers a robust solution for enhancing data reliability and statistical power in metabolomics research.