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.
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DOI：
10.1038/s41467-021-26779-7
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发表时间：
2021-11-16
影响因子：
16.6
通讯作者：
Heinig M
中科院分区：
文献类型：
--
作者：
Schmid KT;Höllbacher B;Cruceanu C;Böttcher A;Lickert H;Binder EB;Theis FJ;Heinig M
单细胞 RNA-seq 通过为差异基因表达和表达数量性状位点 (eQTL) 分析提供细胞类型分辨率，彻底改变了转录组学。然而，缺乏有效的单细胞数据和个体间比较的功效分析方法。在这里，我们介绍 scPower；用于多样本单细胞转录组实验的设计和功效分析的统计框架。我们对样本大小、每个个体的细胞数量、测序深度以及检测细胞类型内差异表达基因的能力之间的关系进行了建模。我们系统地评估了几个单细胞分析平台的这些最佳参数组合，并提出了广泛的建议。一般来说，大量细胞的浅层测序比少量细胞的深度测序具有更高的总体功效。该模型（包括先验）作为 R 包实现，并且可以作为 Web 工具访问。 scPower 是一种高度可定制的工具，实验人员可以使用它快速比较多种实验设计并针对有限的预算进行优化。 scRNASeq 数据正在彻底改变我们对生物系统的理解，但生成成本仍然很高。在这里，作者提出了一个统计框架，促进知情的多样本实验设计，以减少不必要的成本并最大限度地提高生成数据的效用。
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. scRNASeq data is revolutionizing our understanding of biological systems, but is still expensive to generate. Here, the authors present a statistical framework that facilitates informed multi-sample experimental design to reduce unnecessary costs and maximize the utility of the generated data.
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遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
12.3
作者：
Chen W;Li Y;Easton J;Finkelstein D;Wu G;Chen X
通讯作者：
Chen X
DOI：
10.1038/nrg3575
发表时间：
2014-01
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
通讯作者：
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DOI：
10.1088/1742-5468/2008/10/p10008
发表时间：
2008-10-01
影响因子：
2.4
作者：
Blondel, Vincent D.;Guillaume, Jean-Loup;Lefebvre, Etienne
通讯作者：
Lefebvre, Etienne
影响因子：
16.6
作者：
Cuomo, Anna S. E.;Seaton, Daniel D.;Stegle, Oliver
通讯作者：
Stegle, Oliver