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 Experiment Video

Updated: Jan 3, 2026

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota
07:15

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

Published on: July 31, 2019

10.2K

Managing batch effects in microbiome data.

Yiwen Wang1, Kim-Anh LêCao1

  • 1Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, 3052, Australia.

Briefings in Bioinformatics
|November 29, 2019
PubMed
Summary

Batch effects in microbiome studies introduce unwanted variation, challenging reproducibility. This review defines batch effects, discusses their sources, and evaluates computational methods for correction, offering practical guidelines for microbiome data analysis.

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

Integrated multi-omics reveals coordinated <i>Staphylococcus aureus</i> metabolic, iron transport, and stress responses to human serum.

mSystems·2026
Same author

Linderangolides A-D, four new butanolides from the roots of Lindera angustifolia and their cytotoxic activity.

Fitoterapia·2024
Same author

Efficacy of cordycepin against Neospora caninum infection in vitro and in vivo.

Veterinary parasitology·2024
Same author

Combining hierarchical drift-diffusion model and event-related potentials to reveal how do natural sounds nudge green product purchases.

Physiology & behavior·2024
Same author

Reexamining the Kuleshov effect: Behavioral and neural evidence from authentic film experiments.

PloS one·2024
Same author

Integrative genomics reveals the polygenic basis of seedlessness in grapevine.

Current biology : CB·2024

Area of Science:

  • Microbiology
  • Bioinformatics
  • Ecological Sciences

Background:

  • Microbiome studies are crucial for understanding ecological roles but face reproducibility challenges.
  • Unavoidable confounding factors introduce unwanted variation, termed batch effects, in microbiome data.
  • These batch effects originate from biological, technical, or computational sources.

Purpose of the Study:

  • To define and identify common sources of batch effects in microbiome research.
  • To review and critically assess computational methods for batch effect correction in microbiome data.
  • To provide practical guidelines and a tutorial for evaluating and applying these methods.

Main Methods:

  • Literature review of batch effect sources and computational methods.
Keywords:
batch sourcesmethods assessmentmethods selectionsystematic batch effectsunwanted variation

More Related Videos

Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems
06:58

Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems

Published on: August 23, 2019

7.4K
A Method for Targeted 16S Sequencing of Human Milk Samples
09:09

A Method for Targeted 16S Sequencing of Human Milk Samples

Published on: March 23, 2018

10.2K

Related Experiment Videos

Last Updated: Jan 3, 2026

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota
07:15

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

Published on: July 31, 2019

10.2K
Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems
06:58

Analysis of Interactions between Endobiotics and Human Gut Microbiota Using In Vitro Bath Fermentation Systems

Published on: August 23, 2019

7.4K
A Method for Targeted 16S Sequencing of Human Milk Samples
09:09

A Method for Targeted 16S Sequencing of Human Milk Samples

Published on: March 23, 2018

10.2K
  • Case studies illustrating common batch effect scenarios.
  • Development of practical guidelines for method assessment and a reproducible tutorial.

    • Main Results:

    • Batch effects are a significant challenge due to inherent microbiome data characteristics (sparsity, compositionality, multivariate nature).
    • Current computational methods often have unmet assumptions when applied to microbiome data.
    • Guidelines and a tutorial are provided for assessing method efficiency and reproducing analyses.

    Conclusions:

    • Addressing batch effects is critical for accurate and reproducible microbiome research.
    • Careful consideration of data characteristics and method assumptions is necessary.
    • Standardized assessment and application of correction methods will improve microbiome study reliability.