Integrating single-cell transcriptomic data across different conditions, technologies, and species.
Integrating single-cell transcriptomic data across different conditions, technologies, and species.
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DOI：
10.1038/nbt.4096
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发表时间：
2018-06
影响因子：
46.9
通讯作者：
Satija R
中科院分区：
文献类型：
--
作者：
Butler A;Hoffman P;Smibert P;Papalexi E;Satija R
计算单细胞RNA-seq(scRNA-seq)方法已经成功地应用于代表单一条件、技术或物种的实验，以发现和定义细胞表型。然而，识别存在于多个数据集中的细胞亚群仍然具有挑战性。在这里，我们介绍了一种分析策略，用于基于共同的变异源整合scRNA-seq数据集，从而能够识别数据集上的共享种群并进行下游比较分析。在我们的R工具包Seurat(http://satijalab.org/seurat/)，)中，我们使用我们的方法来比对静息和刺激条件下的外周血单核细胞(PBMC)、使用两种图谱技术测序的造血祖细胞以及从人和小鼠胰岛生成的胰腺细胞‘图谱’的scRNA-seq数据集。在每一种情况下，我们都会在数据集中联合学习不同的或过渡的细胞状态，同时通过综合分析提高统计能力。我们的方法促进了scRNA-seq数据集的一般比较，潜在地加深了我们对不同的细胞状态如何对扰动、疾病和进化做出反应的理解。
Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple datasets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq datasets based on common sources of variation, enabling the identification of shared populations across datasets and downstream comparative analysis. Implemented in our R toolkit Seurat (http://satijalab.org/seurat/), we use our approach to align scRNA-seq datasets of peripheral blood monocytes (PBMCs) under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell ‘atlases’ generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across datasets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq datasets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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影响因子：
14.9
作者：
Kuleshov MV;Jones MR;Rouillard AD;Fernandez NF;Duan Q;Wang Z;Koplev S;Jenkins SL;Jagodnik KM;Lachmann A;McDermott MG;Monteiro CD;Gundersen GW;Ma'ayan A
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Ma'ayan A
影响因子：
9.3
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Yanai I
影响因子：
11.8
作者：
DeLaughter, Daniel M.;Bick, Alexander G.;Wakimoto, Hiroko;McKean, David;Gorham, Joshua M.;Kathiriya, Irian S.;Hinson, John T.;Homsy, Jason;Gray, Jesse;Pu, William;Bruneau, Benoit G.;Seidman, J. G.;Seidman, Christine E.
通讯作者：
Seidman, Christine E.
影响因子：
48
作者：
Kiselev, Vladimir Yu;Kirschner, Kristina;Hemberg, Martin
通讯作者：
Hemberg, Martin
影响因子：
64.5
作者：
Adolfsson, J;Månsson, R;Jacobsen, SEW
通讯作者：
Jacobsen, SEW