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: Jul 16, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

37.6K

Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data.

Hunyong Cho1, Yixiang Qu1, Chuwen Liu1

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, NC, United States.

Briefings in Bioinformatics
|September 22, 2023
PubMed
Summary

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

AdcBC-dependent zinc uptake influences physiological responses in <i>Streptococcus mutans</i>.

Journal of oral microbiology·2026
Same author

Evaluating Cross-Platform Batch Correction Methods for Integrated Microarray and RNA-seq Data Analysis.

bioRxiv : the preprint server for biology·2026
Same author

CD38⁺ endothelial remodeling marks spatially patterned vasculopathy in rapidly advancing periodontitis and peri-implantitis.

Nature communications·2026
Same author

Genetic diversity of collaborative cross mice enables the establishment of a novel <i>Chlamydia muridarum</i> female genital tract infection model.

Infection and immunity·2026
Same author

Structural design of a polarization beam splitter based on a thin-film lithium niobate photonic crystal and a multimode interference coupler.

Applied optics·2026
Same author

Cartridge-based flavored water bottles may pose a risk of developing dental erosion.

Journal of the American Dental Association (1939)·2026
Same journal

K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.

Briefings in bioinformatics·2026
Same journal

Accurate prediction of asparagine deamidation in biologics using advanced machine learning models.

Briefings in bioinformatics·2026
Same journal

scImmuneCo: a compendium of cell-type-specific functional modules for decoding immune responses from single-cell RNA-seq data.

Briefings in bioinformatics·2026
Same journal

scGenoByte: a GenoByte embedding transformer with biological priors for cell type annotation.

Briefings in bioinformatics·2026
Same journal

FerroScore: a statistical approach for quantifying tumor-related ferroptosis based on omics data.

Briefings in bioinformatics·2026
Same journal

METEOR: a data-adaptive Mendelian randomization method for powerful detection of shared and specific exposures underlying multiple outcomes.

Briefings in bioinformatics·2026
See all related articles
This summary is machine-generated.

The logistic-beta (LB) test offers the best sensitivity for analyzing microbial gene expression (metatranscriptomics) data, outperforming other methods. This evaluation provides guidance for selecting robust statistical tools for microbiome research.

Area of Science:

  • Microbiome research
  • Statistical genomics
  • Bioinformatics

Background:

  • Human microbiome function is crucial, but statistical methods for microbial gene expression (metatranscriptomics) are underdeveloped.
  • Existing differential expression analysis methods are often unsuitable for metatranscriptomics data and lack rigorous evaluation.

Purpose of the Study:

  • To comprehensively evaluate and benchmark 10 differential analysis methods for metatranscriptomics data.
  • To provide practical guidance for selecting appropriate statistical methods for metatranscriptomics analyses.

Main Methods:

  • Evaluated 10 methods (log-normal, logistic-beta, MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis, two-part Kruskal-Wallis) using simulated and real data.
  • Performance metrics included type I error, false discovery rate, and sensitivity.
Keywords:
benchmarkdifferential expressionearly childhood carieslogistic-betametagenomicsmetatranscriptomics

More Related Videos

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

12.1K
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.3K

Related Experiment Videos

Last Updated: Jul 16, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

37.6K
Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

12.1K
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.3K
  • Data sources included supragingival biofilm microbiome data from early childhood caries (ECC) and inflammatory bowel disease studies.
  • Main Results:

    • The logistic-beta (LB) test demonstrated the highest sensitivity across sample sizes and controlled type I error effectively.
    • MAST exhibited inflated type I error.
    • In the ECC study, LB and log-normal tests identified genes C8PHV7 and C8PEV7 from Campylobacter gracilis as strongly associated with childhood dental disease.

    Conclusions:

    • The logistic-beta test is a highly sensitive and reliable method for metatranscriptomics differential expression analysis.
    • This study offers essential guidance for researchers to choose optimal methods, enhancing the accuracy of microbiome gene expression studies.
    • Accurate method selection minimizes false positives and maximizes the detection of true biological signals.