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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Apr 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

Power analysis and sample size estimation for RNA-Seq differential expression.

Travers Ching¹, Sijia Huang¹, Lana X Garmire²

¹University of Hawaii Cancer Center, Honolulu, Hawaii 96813, USA Graduate Program of Molecular Biosciences and Bioengineering, University of Hawaii-Manoa, Honolulu, Hawaii 96822, USA.

RNA (New York, N.Y.)

|September 24, 2014

Summary

This summary is machine-generated.

Researchers can optimize RNA-sequencing (RNA-seq) experiments by increasing sample size, which is more effective than sequencing depth for boosting statistical power. A free tool is available to guide experimental design under budget constraints.

Keywords:

RNA-Seq bioinformatics power analysis sample size simulation

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Last Updated: Apr 23, 2026

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Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

Accurate differential gene expression detection in RNA-sequencing (RNA-seq) is vital for biological research.
Current methods for estimating statistical power and sample size in complex RNA-seq designs are limited.
The negative binomial distribution is commonly used to model RNA-seq count data.

Purpose of the Study:

To develop general methods for estimating power and sample size in RNA-seq experiments.
To compare the performance of five differential expression analysis packages.
To evaluate the impact of experimental design factors on statistical power.

Main Methods:

Simulated RNA-seq count data based on parameters from diverse public datasets.
Calculated statistical power for paired and unpaired sample designs.
Evaluated differential expression analysis packages using power, ROC curves, AUC, MCC, and F-measures.

Main Results:

DESeq2 and edgeR packages generally exhibited superior performance.
Increasing sample size was more potent than increasing sequencing depth for enhancing power.
Paired-sample designs significantly improved statistical power compared to unpaired designs.
Long intergenic noncoding RNAs (lincRNAs) showed lower power due to lower expression levels.

Conclusions:

Sample size is the dominant factor for achieving optimal power within a budget.
A practical power analysis tool is provided to aid RNA-seq experimental design.
The tool accounts for data dispersion and budget constraints for RNA-seq studies.