云计算
计算机科学
可扩展性
水准点(测量)
巨量平行
数据集
比例(比率)
数据挖掘
分布式计算
计算生物学
生物
数据库
人工智能
并行计算
操作系统
物理
量子力学
地理
大地测量学
作者
Bo Li,Joshua Gould,Yiming Yang,Siranush Sarkizova,Marcin Tabaka,Orr Ashenberg,Yanay Rosen,Michal Slyper,Monika S. Kowalczyk,Alexandra‐Chloé Villani,Timothy L. Tickle,Nir Hacohen,Orit Rozenblatt–Rosen,Aviv Regev
出处
期刊:Nature Methods
[Nature Portfolio]
日期:2020-07-27
卷期号:17 (8): 793-798
被引量:197
标识
DOI:10.1038/s41592-020-0905-x
摘要
Massively parallel single-cell and single-nucleus RNA sequencing has opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so is the need for computational pipelines for scaled analysis. Here we developed Cumulus—a cloud-based framework for analyzing large-scale single-cell and single-nucleus RNA sequencing datasets. Cumulus combines the power of cloud computing with improvements in algorithm and implementation to achieve high scalability, low cost, user-friendliness and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies. Cumulus is a cloud-based framework enabling large-scale single-cell and single-nucleus RNA sequencing data analysis.
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