Single cell RNA-seq has revolutionized transcriptomics. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. This paper presents scPower, a statistical framework for designing and performing power analysis of multi-sample single cell transcriptomic experiments. The relationship between sample size, cells per individual, sequencing depth, and power to detect differentially expressed genes within cell types is modeled. Optimal parameter combinations are evaluated for various single cell platforms, and broad recommendations are provided. Shallow sequencing of many cells offers higher power than deep sequencing of fewer cells. The model is implemented as an R package and a web tool.

Katharina T. Schmid, Barbara Höllbacher, Cristiana Cruceanu, Anika Böttcher, Heiko Lickert, Elisabeth B. Binder, Fabian J. Theis, Matthias Heinig