Sample size calculation based on exact test for assessing differential expression analysis in RNA-seq data
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Updated: May 5, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
Published on: September 18, 2021
Chung-I Li, Pei-Fang Su, Yu Shyr1
1Center for Quantitative Sciences, Vanderbilt University, 571 Preston Building Nashville, TN, USA. yu.shyr@vanderbilt.edu.
Background:
Sample size calculation is an important issue in the experimental design of biomedical research. For RNA-seq experiments, the sample size calculation method based on the Poisson model has been proposed; however, when there are biological replicates, RNA-seq data could exhibit variation significantly greater than the mean (i.e. over-dispersion). The Poisson model cannot appropriately model the over-dispersion, and in such cases, the negative binomial model has been used as a natural extension of the Poisson model. Because the field currently lacks a sample size calculation method based on the negative binomial model for assessing differential expression analysis of RNA-seq data, we propose a method to calculate the sample size.
Results:
We propose a sample size calculation method based on the exact test for assessing differential expression analysis of RNA-seq data.
Conclusions:
The proposed sample size calculation method is straightforward and not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size method are presented; the results indicate our method works well, with achievement of desired power.
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