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 numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget.
Highlights
Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci analyses
We systematically investigated the evolution of optimal parameters for increasing budgets in four prototypic scenarios for DEG (Fig. 5a) and expression quantitative trait loci (eQTL) analysis (Fig. 5b), four scenarios based on prior DEG (Fig. 5c) and two scenarios on prior eQTL (Fig. 5d) experiments on fluorescence activated cell sorting (FACS) sorted cells
Our model generalizes across different tissues and scRNAseq technologies and provides the means to design experiments that maximize the number of biological discoveries
Summary
Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. Understanding the molecular basis of phenotypic variation, such as disease susceptibility, is a key goal of contemporary biomedical research To this end, researchers use transcriptomic profiling to identify changes of gene expression levels (differentially expressed genes; DEGs) between sets of samples, e.g., patients and healthy controls[1,2,3,4,5]. Researchers use transcriptomic profiling to identify changes of gene expression levels (differentially expressed genes; DEGs) between sets of samples, e.g., patients and healthy controls[1,2,3,4,5] Combining this with genetic information leads to the analysis of differential expression between genotypes and the identification of expression quantitative trait loci (eQTLs)[6,7,8,9], supplying the molecular link between genome and phenotype[10]. The overall number of transcripts as well as the number of transcripts of individual genes can be highly cell type specific[33]
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