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Updated: Dec 21, 2025

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

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

Published on: September 18, 2021

Power analysis for RNA-Seq differential expression studies using generalized linear mixed effects models.

Lianbo Yu¹, Soledad Fernandez², Guy Brock²

¹Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Dr., Columbus, 43210, OH, USA. lianbo.yu@osumc.edu.

BMC Bioinformatics

|May 21, 2020

Summary

This summary is machine-generated.

This study introduces simulation-based methods for power analysis in RNA-Seq differential expression studies with complex designs. The novel procedures accurately control false positive rates, aiding experimental design.

Keywords:

Bivariate negative binomial Generalized linear mixed effects Model Power analysis RNA-Seq

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Last Updated: Dec 21, 2025

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

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Published on: September 18, 2021

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Published on: July 29, 2022

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Biostatistics
Genomics
Bioinformatics

Background:

Power analysis is crucial for biomedical research, especially for complex RNA-Seq experimental designs.
Current statistical methods are insufficient for sample size and power estimation in RNA-Seq studies with correlated data.
Simulation-based methods offer a viable solution due to the unavailability of theoretical distributions for complex designs.

Purpose of the Study:

To develop novel simulation-based procedures for power estimation in RNA-Seq differential expression analysis.
To address the need for statistical methods in complex experimental designs with correlated expression data.
To provide a framework for accurate power estimation in RNA-Seq studies.

Main Methods:

Proposed a simulation-based procedure using generalized linear mixed-effects models for correlated RNA-Seq data.
Developed a new procedure for paired designs utilizing a bivariate negative binomial distribution.
Compared likelihood ratio and Wald tests, estimating null distributions via simulation for false positive control.

Main Results:

The proposed procedures effectively control the false positive rate at the nominal level.
Simulation scenarios demonstrated the performance of the new power estimation methods.
The procedure for paired designs was successfully applied to the TCGA breast cancer dataset.

Conclusions:

A comprehensive framework for RNA-Seq differential expression power estimation under complex designs has been established.
The developed simulation-based procedures provide reliable power estimation and false positive rate control.
This work enhances the statistical rigor of RNA-Seq experimental design.