Derek Shyr and Chung-I Li
Power and sample size calculation is an essential component of experimental design in biomedical research. For RNA-sequencing experiments, sample size calculations have been proposed based on mathematical models such as Poisson and negative binomial; however, RNA-seq data has exhibited variations, i.e. over-dispersion, that has caused past calculation methods to be under- or over-power. Because of this issue and the field’s lack of a simulation-based sample size calculation method for assessing differential expression analysis of RNA-seq data, we developed this method and applied it to three cancer sites from the Tumor Cancer Genome Atlas. Our results showed that each cancer site had its own unique dispersion distribution, which influenced the power and sample size calculation.
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