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

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. Overall design: We performed 10X single cell RNA seq on PBMCs of pools of a total of 14 human study participants. The goal of the experiment was to benchmark different pooling and demultiplexing strategies for multisample single cell transcriptomics experiments. We performed 3 runs of sequencing on a pool of all 14 samples with targeted 8000 cells each. In addition we performed one run with overloading the 10X reaction with 25000 cells. Finally, for assesing the precision of the demultiplexing we split the pool into samples 1-7 and 8-14.

单细胞RNA测序（single cell RNA-seq）通过为差异基因表达分析与表达数量性状基因座（expression quantitative trait loci, eQTL）分析提供细胞类型分辨率，极大革新了转录组学研究。然而，当前仍缺乏针对单细胞数据及个体间比较的高效功效分析方法。
本研究提出scPower——一款面向多样本单细胞转录组实验设计与功效分析的统计框架。我们构建了样本量、单一个体的细胞数、测序深度与细胞类型内差异表达基因检测功效之间的关系模型。我们系统评估了多款单细胞测序平台的最优参数组合，并给出了普适性建议。总体而言，对大量细胞进行浅度测序，相较于对少量细胞进行深度测序，可获得更高的整体检测功效。
该模型（包含先验项）已封装为R语言工具包，同时可通过网页工具访问。scPower是一款高度可定制化的工具，实验研究者可借助其快速对比多种实验设计方案，并在有限预算下实现实验优化。
整体实验设计：我们对总计14名人类研究受试者的外周血单个核细胞（peripheral blood mononuclear cells, PBMCs）混合样本开展了10X单细胞RNA测序。本实验旨在对多样本单细胞转录组实验中的不同混合与解复用策略进行基准测试。我们对全部14个样本的混合池进行了3轮测序，每轮目标捕获8000个细胞。此外，我们还开展了1轮测序，对10X反应体系进行过载样处理，目标细胞数为25000个。最后，为评估解复用的精度，我们将混合池拆分为样本1-7与样本8-14两组。