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Updated: Mar 23, 2026

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

Sample size calculation while controlling false discovery rate for differential expression analysis with

Ran Bi¹, Peng Liu²

¹Department of Statistics, Iowa State University, Snedecor Hall, Ames, Iowa, 50011, USA.

BMC Bioinformatics

|April 1, 2016

Summary

This summary is machine-generated.

Calculating the appropriate sample size for RNA-Sequencing (RNA-seq) experiments is crucial for reliable differential expression analysis. This study introduces a novel method to determine sample size while controlling the false discovery rate (FDR), enhancing RNA-seq study design.

Keywords:

Experimental design FDR Power analysis RNA-seq Sample size calculation

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Area of Science:

Bioinformatics
Genomics
Statistical Genetics

Background:

RNA-Sequencing (RNA-seq) is widely used for transcriptome studies but remains costly, often necessitating small sample sizes.
Calculating statistical power and sample size for RNA-seq differential expression analysis is challenging due to the lack of closed-form formulas and the use of False Discovery Rate (FDR) control.

Purpose of the Study:

To develop a procedure for sample size calculation in RNA-seq experiments that effectively controls the False Discovery Rate (FDR).
To provide a practical tool for researchers to design RNA-seq studies with adequate statistical power.

Main Methods:

The proposed method utilizes a weighted linear model approach, leveraging the 'voom' technique known for its performance in RNA-seq differential expression analysis.
It derives a way to approximate average statistical power across differentially expressed genes and calculates the necessary sample size to achieve a target power while maintaining FDR control.

Main Results:

Simulation studies confirm that the sample sizes determined by the proposed method lead to actual statistical power close to the desired levels for RNA-seq differential expression tests.
The method demonstrates competitive performance in terms of both power and FDR control.

Conclusions:

A new, efficient algorithm is presented for sample size calculation in RNA-seq experimental design, with FDR control.
An accompanying R package, 'ssizeRNA', is available on CRAN to implement the proposed method.