In powsimR, we have implemented a flexible tool to assess power and sample size requirements for differential expression (DE) analysis of single cell and bulk RNA-seq experiments. For our read count simulations, we (1) reliably model the mean, dispersion and dropout distributions as well as the relationship between those factors from the data. (2) Simulate read counts from the empirical mean-variance- and dropout relations, while offering flexible choices of the number of differentially expressed genes, effect sizes and DE testing method. (3) Finally, we evaluate the power over various sample sizes.
In summary, powsimR can not only estimate sample sizes necessary to achieve a certain power, but also informs about the power to detect DE in a data set at hand. We believe that this type of posterior analysis will become more and more important, if results from different studies are compared. Often enough researchers are left to wonder why there is a lack of overlap in DE-genes when comparing similar experiments. powsimR will allow the researcher to distinguish between actual discrepancies and incongruities due to lack of power.