Sample size calculations and power analyses are essential components of experimental design in modern biomedical research. Designs that account for sample correlation, multiple testing, and other sources of variability inherent to specific studies are routinely employed for identifying differential expressions. Despite recent advances in methodologies and software tools for power analysis, there remains a lack of statistical packages capable of accommodating these complex designs in differential expression studies. To fill this gap, we provide the R package depower, which implements the simulation-based framework presented in our recent publications. This unified framework covers both independent and dependent group comparisons and controls false positive rates by employing a simulation-based approach to calculate the empirical null distribution of test statistics.
Klamer et al. (Sun,) studied this question.