Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq
© 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 pip...
| Main Authors: | , , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/135338 |
| _version_ | 1826189553038262272 |
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| author | 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 |
| author2 | Koch Institute for Integrative Cancer Research at MIT |
| author_facet | Koch Institute for Integrative Cancer Research at MIT 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 |
| author_sort | Li, Bo |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T08:16:50Z |
| format | Article |
| id | mit-1721.1/135338 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:16:50Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1353382023-12-08T20:59:07Z Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq 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 Koch Institute for Integrative Cancer Research at MIT Massachusetts Institute of Technology. Department of Biology Howard Hughes Medical Institute © 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. 2021-10-27T20:23:01Z 2021-10-27T20:23:01Z 2020 2021-07-22T15:03:57Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135338 en 10.1038/S41592-020-0905-X Nature Methods 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. application/pdf Springer Science and Business Media LLC PMC |
| spellingShingle | 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 Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq |
| title | Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq |
| title_full | Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq |
| title_fullStr | Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq |
| title_full_unstemmed | Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq |
| title_short | Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq |
| title_sort | cumulus provides cloud based data analysis for large scale single cell and single nucleus rna seq |
| url | https://hdl.handle.net/1721.1/135338 |
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