Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis.
Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis.
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DOI：
10.1038/s41467-020-19365-w
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发表时间：
2020-10-30
影响因子：
16.6
通讯作者：
Halperin E
中科院分区：
文献类型：
--
作者：
Mandric I;Schwarz T;Majumdar A;Hou K;Briscoe L;Perez R;Subramaniam M;Hafemeister C;Satija R;Ye CJ;Pasaniuc B;Halperin E
单细胞RNA测序（scRNA-Seq）是一种直接同时测量细胞组成和状态的引人注目的方法，否则只能通过将去卷积方法应用于批量RNA-Seq估计来估计。然而，由于其高昂的成本，它尚未成为人口规模分析中广泛使用的工具。在这里，我们表明，在相同的预算下，细胞类型特异性表达数量性状基因座（eQTL）定位的统计能力可以通过更多样品的低覆盖率每细胞测序而不是更少样品的高覆盖率测序来增加。我们使用从来自120个个体的最大的可用真实的单细胞RNA-Seq数据之一开始的模拟，也表明具有不同数量的样品，每个样品的细胞和每个细胞的读数的多个实验设计可能具有相似的统计功效，并且选择适当的设计可以节省大量的成本，特别是当考虑多重工作流程时。最后，我们提供了一个实用的方法来选择成本效益的设计，以最大限度地提高细胞类型特定的eQTL的权力，这是一个网络工具的形式。单细胞RNA测序可以是表征细胞群体中的细胞组成的有力方法，但被认为对于群体规模分析来说过于昂贵。在这里，作者展示了更多样本的较低覆盖率如何增加检测细胞类型特异性eQTL的能力。
Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool. Single cell RNA-sequencing can be a powerful approach to characterizing cell composition in a population of cells but is thought to be too expensive for population-scale analyses. Here, the authors show how lower coverage of more samples can increase the power to detect cell-type-specific eQTL.
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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
单细胞拓扑RNA-seq分析揭示了对细胞分化和发育的见解。
影响因子：
46.9
作者：
Rizvi AH;Camara PG;Kandror EK;Roberts TJ;Schieren I;Maniatis T;Rabadan R
通讯作者：
Rabadan R
影响因子：
48
作者：
McGinnistm, Christopher S.;Patterson, David M.;Gartner, Zev J.
通讯作者：
Gartner, Zev J.
影响因子：
21.3
作者：
Gao, Shuai;Yan, Liying;Tang, Fuchou
通讯作者：
Tang, Fuchou
影响因子：
30.5
作者：
Kakaradov B;Arsenio J;Widjaja CE;He Z;Aigner S;Metz PJ;Yu B;Wehrens EJ;Lopez J;Kim SH;Zuniga EI;Goldrath AW;Chang JT;Yeo GW
通讯作者：
Yeo GW