Simulations are essential to find the best compromise between statistical power and cost effectiveness, but also help with the interpretation of conducted RNA-seq experiments. For example, researchers often wonder why their results differ from previous studies, powsim helps here by allowing the researcher to assess whether this inconsistency could simply be due to chance because the test was underpowered.
Researchers from Ludwig-Maximilians University have developed powsim, a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.
Powsim schematic overview
A The mean-dispersion relationship is estimated from RNA-seq data, which can be either single cell or bulk data. The users can provide their own count tables or one of our five example data sets. The plot shows the mean-dispersion estimated, assuming a negative binomial for the Kolodziejczyk-data, the red line is the loess fit, that we later use for the simulations. B These distribution parameters are then used to set-up the simulations. For better comparability, the parameters for the simulation of differential expression are set separately. C Finally, the TPR and FDR are calculated. Both can be either returned as marginal estimates per sample configuration (top), or stratified according to the estimates of mean expression, dispersion or dropout-rate (bottom).
Availability – The R package and associated tutorial are freely available at: https://github.com/bvieth/powsim.