Fast, sensitive and accurate integration of single-cell data with Harmony
Fast, sensitive and accurate integration of single-cell data with Harmony
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DOI：
10.1038/s41592-019-0619-0
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发表时间：
2019-12-01
期刊：
影响因子：
48
通讯作者：
Raychaudhuri, Soumya
中科院分区：
文献类型：
--
作者：
Korsunsky, Ilya;Millard, Nghia;Raychaudhuri, Soumya
新出现的单细胞RNA-seq数据集的多样性允许在广泛的生物和临床条件下对细胞类型进行全面的转录表征。然而，将它们放在一起分析是具有挑战性的，特别是当用不同的技术分析数据集时，因为生物学和技术差异是穿插的。我们提出了Harmony(https://github.com/immunogenomics/harmony)，)算法，该算法将单元格投影到共享嵌入中，其中单元格按单元格类型分组，而不是按特定于数据集的条件进行分组。和谐性同时解释了多种实验和生物因素。在六个分析中，我们证明了Harmony算法在需要更少计算资源的情况下具有比以前发表的算法更好的性能。Harmonity能够在个人计算机上集成类似于10(6)个电池。我们将Harmony应用于具有较大实验差异的数据集、五项胰岛细胞研究、小鼠胚胎发育数据集以及scRNA-seq与空间转录数据的整合。
The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony (https://github.com/immunogenomics/harmony), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of similar to 10(6) cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data.