ScPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies.

ABSTRACT: Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high number

SUBMITTER: Schmid KT 

PROVIDER: S-EPMC8595682 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature