Design and power analysis for multi-sample single cell genomics experiments

Abstract Single cell RNA-seq revolutionizes transcriptomics by providing cell type resolution for interindividual differential gene expression and expression quantitative trait loci analyses. However, efficient power analysis methods accounting for the characteristics of single cell data and interindividual comparison are missing. Here we present a statistical framework for design and power analysis of multi-sample single cell genomics experiments. The model relates sample size, number of cells per individual and sequencing depth to the power of detecting differentially expressed genes within cell types. It enables fast systematic comparison of alternative experimental designs and optimization for a limited budget. We evaluated data driven priors for a range of applications and single cell platforms. In many settings, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model including priors is implemented as an R package scPower and is accessible as a web tool.