Comprehensive Integration of Single-Cell Data
Comprehensive Integration of Single-Cell Data
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DOI：
10.1101/460147
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发表时间：
2018-11
期刊：
影响因子：
64.5
通讯作者：
Tim Stuart;Andrew Butler;Paul J. Hoffman;Christoph Hafemeister;Efthymia Papalexi;William M. Mauck;
中科院分区：
文献类型：
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作者：
Tim Stuart;Andrew Butler;Paul J. Hoffman;Christoph Hafemeister;Efthymia Papalexi;William M. Mauck;
单细胞转录组学（scRNA-seq）已经改变了我们发现和注释细胞类型和状态的能力，但深入的生物学理解需要的不仅仅是聚类的分类列表。随着测量不同细胞形态的新方法的出现，包括高维免疫表型、染色质可及性和空间定位，一个关键的分析挑战是将这些数据集整合到一个协调的图谱中，以更好地了解细胞的身份和功能。在这里，我们开发了一种计算策略，将不同的数据集“锚定”在一起，使我们不仅能够整合和比较scRNA-seq技术中的单细胞测量，还能够整合和比较不同模式中的单细胞测量。锚在一起。在证明了对现有数据整合方法的实质性改进后，我们使用scATAC-seq数据集进行锚scRNA-seq实验，以探索密切相关的中间神经元亚群中的染色质差异，并将单细胞蛋白测量结果投射到人骨髓图谱上，以注释和表征淋巴细胞群体。最后，我们展示了锚定如何协调原位基因表达和scRNA-seq数据集，允许空间基因表达模式的转录组范围内的插补，以及视觉皮层中映射细胞类型之间的空间关系的识别。我们的工作提出了一种全面整合单细胞数据的策略，包括协调参考的组装以及跨数据集的信息传输。可用性：安装说明、文档和教程可从以下网址获得：https://www.satijalab.org/seurat
Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to “anchor” diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets. Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat