Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape
Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape
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DOI：
10.1016/j.cels.2019.05.003
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发表时间：
2019-06-26
期刊：
影响因子：
9.3
通讯作者：
Berger, Bonnie
中科院分区：
文献类型：
--
作者：
Hie, Brian;Cho, Hyunghoon;Berger, Bonnie
大规模的单细胞RNA测序（scRNAseq）研究对数十万个细胞的分析越来越普遍，压倒了现有的分析管道。在这里，我们描述了如何通过使用一小部分细胞（我们称之为几何草图）总结数据集内的转录组异质性来增强和加速单细胞数据分析。我们的草图提供了更全面的转录多样性可视化，以高灵敏度捕获稀有细胞类型，并通过聚类揭示生物细胞类型。我们的脐带血细胞草图揭示了一种罕见的炎症巨噬细胞亚群，我们的实验验证。我们草图的构建速度非常快，这使我们能够加速其他关键的资源密集型任务，例如scRNA-seq数据集成，同时保持准确性。我们预计，在共享和分析快速增长的scRNA-seq数据量时，我们的算法将成为越来越重要的一步，并有助于实现单细胞组学的民主化。
Large-scale single-cell RNA sequencing (scRNAseq) studies that profile hundreds of thousands of cells are becoming increasingly common, overwhelming existing analysis pipelines. Here, we describe how to enhance and accelerate single-cell data analysis by summarizing the transcriptomic heterogeneity within a dataset using a small subset of cells, which we refer to as a geometric sketch. Our sketches provide more comprehensive visualization of transcriptional diversity, capture rare cell types with high sensitivity, and reveal biological cell types via clustering. Our sketch of umbilical cord blood cells uncovers a rare subpopulation of inflammatory macrophages, which we experimentally validated. The construction of our sketches is extremely fast, which enabled us to accelerate other crucial resource-intensive tasks, such as scRNA-seq data integration, while maintaining accuracy. We anticipate our algorithm will become an increasingly essential step when sharing and analyzing the rapidly growing volume of scRNA-seq data and help enable the democratization of single-cell omics.