Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq
Name
nihms-1605144.pdf
Description
Accepted version
Size
3.62 MB
Format
Adobe PDF
Checksum (MD5)
7440aa2f1d89fb1d513d7535b7a6eb1f
Author(s)
Li, Bo
Gould, Joshua
Yang, Yiming
Sarkizova, Siranush
Tabaka, Marcin
Ashenberg, Orr
Rosen, Yanay
Slyper, Michal
Kowalczyk, Monika S
Villani, Alexandra-Chloé
Date Issued
2020
Journal
Nature Methods
Publisher
Springer Science and Business Media LLC
Version
Author's final manuscript
Abstract
© 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.
MIT Department
Koch Institute for Integrative Cancer Research at MIT
Massachusetts Institute of Technology. Department of Biology
Howard Hughes Medical Institute
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41592-020-0905-X
