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研究揭示整合单细胞数据集Harmony
作者:小柯机器人 发布时间:2019/11/19 12:23:39

美国布莱根妇女医院和哈佛医学院Soumya Raychaudhuri研究组揭示了快速、灵敏、准确地整合单细胞数据:Harmony。相关论文11月18日在线发表在《自然—方法学》上。

研究人员提供了Harmony(https://github.com/immunogenomics/harmony),一种按细胞类型而不是特定数据集进行分组的算法,其将不同细胞数据整合到一个数据集。Harmony解决了多个实验和生物学因素。在六项分析中,研究人员证明Harmony优于以前发布的算法,同时所需的计算资源更少。利用Harmony可以在个人计算机上集成约106个单元。研究人员利用Harmony整合了具有较大实验差异数据的外周血单核细胞聚集,这些数据集包括胰腺胰岛细胞的五项研究、小鼠胚胎发生数据集以及scRNA-seq与空间转录组学数据集。

据介绍,多样的单细胞RNA-seq数据集可以在多种生物学和临床条件下对细胞的转录进行完整的表征。但因为存在着生物学和技术上的差异,特别是在使用不同技术分析数据集时将它们一起分析仍是一个挑战。

附:英文原文

Title: Fast, sensitive and accurate integration of single-cell data with Harmony

Author: Ilya Korsunsky, Nghia Millard, Jean Fan, Kamil Slowikowski, Fan Zhang, Kevin Wei, Yuriy Baglaenko, Michael Brenner, Po-ru Loh, Soumya Raychaudhuri

Issue&Volume: 2019-11-18

Abstract: 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 ~106 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.

DOI: 10.1038/s41592-019-0619-0

Source: https://www.nature.com/articles/s41592-019-0619-0

期刊信息

Nature Methods:《自然—方法学》,创刊于2004年。隶属于施普林格·自然出版集团,最新IF:28.467
官方网址:https://www.nature.com/nmeth/
投稿链接:https://mts-nmeth.nature.com/cgi-bin/main.plex