Comprehensive Integration of Single-Cell Data
Comprehensive Integration of Single-Cell Data
复制标题
单元格数据的全面集成
DOI：
10.1016/j.cell.2019.05.031
复制
发表时间：
2019-06-13
期刊：
影响因子：
64.5
通讯作者：
Satija, Rahul
中科院分区：
文献类型：
--
作者：
Stuart, Tim;Butler, Andrew;Satija, Rahul
单细胞转录组学已经改变了我们表征细胞状态的能力，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，一个关键的分析挑战是整合这些数据集，以更好地了解细胞的身份和功能。在这里，我们开发了一种策略，将不同的数据集“锚”在一起，使我们能够不仅在scRNA-seq技术中，而且在不同的模式中整合单细胞测量。在证明了对整合scRNA-seq数据的现有方法的改进后，我们用scATAC-seq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，并将蛋白质表达测量投射到骨髓图谱上以表征淋巴细胞群体。最后，我们协调了原位基因表达和scRNA-seq数据集，允许对空间基因表达模式进行转录组范围的插补。我们的工作提出了一个战略，协调的参考资料和跨数据集的信息传输的组装。
Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.