Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq
Author(s)
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; ... Show more Show less
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© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. 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.
Date issued
2020Department
Koch Institute for Integrative Cancer Research at MIT; Massachusetts Institute of Technology. Department of Biology; Howard Hughes Medical InstituteJournal
Nature Methods
Publisher
Springer Science and Business Media LLC