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 provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq.
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作者:Li Bo, Gould Joshua, Yang Yiming, Sarkizova Siranush, Tabaka Marcin, Ashenberg Orr, Rosen Yanay, Slyper Michal, Kowalczyk Monika S, Villani Alexandra-Chloé, Tickle Timothy, Hacohen Nir, Rozenblatt-Rosen Orit, Regev Aviv
| 期刊: | Nature Methods | 影响因子: | 32.100 |
| 时间: | 2020 | 起止号: | 2020 Aug;17(8):793-798 |
| doi: | 10.1038/s41592-020-0905-x | ||
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