Sample size calculation based on exact test for assessing differential expression analysis in RNA-seq data.

Area of Science:

• Biostatistics
• Genomics
• Bioinformatics

Background:

• Accurate sample size calculation is vital for designing robust biomedical research, particularly for RNA-sequencing (RNA-seq) experiments.
• Existing methods often rely on the Poisson model, which inadequately handles over-dispersion common in RNA-seq data with biological replicates.
• The negative binomial model offers a more appropriate approach for RNA-seq data exhibiting over-dispersion.

Purpose of the Study:

• To develop and present a novel sample size calculation method for RNA-sequencing differential expression analysis.
• To address the limitation of existing methods in handling over-dispersed RNA-seq data.
• To provide a practical tool for researchers designing RNA-seq experiments.

Main Methods:

• A new sample size calculation method was developed based on the exact test.
• The method is specifically designed for differential expression analysis in RNA-seq data.
• It incorporates the negative binomial model to account for data over-dispersion.

Main Results:

• The proposed method provides a straightforward approach to sample size calculation.
• It is computationally efficient, making it accessible for practical use.
• Simulation studies confirmed the method's effectiveness in achieving desired statistical power.

Conclusions:

• The developed sample size calculation method is effective for RNA-seq differential expression analysis.
• It accurately addresses over-dispersion, a common challenge in such data.
• The method is practical, efficient, and achieves desired power, enhancing experimental design.