powsimR – power analysis for bulk and single cell RNA-seq experiments

Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes in RNA-seq data. PowsimR is 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.

 powsimR schematic overview.

rna-seq

(A) The mean-dispersion relationship is estimated from RNA-seq data, which can be either single cell or bulk data. The user can provide their own count tables or one of our five example datasets and choose whether to fit a negative binomial or a zero-inflated negative binomial. 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/powsimR

Vieth B, Ziegenhain C, Parekh S, Enard W, Hellmann I. (2017) powsimR: power analysis for bulk and single cell RNA-seq experiments. Bioinformatics [Epub ahead of print]. [abstract]

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