RT Journal Article
SR Electronic
T1 Cumulus: a cloud-based data analysis framework for large-scale single-cell and single-nucleus RNA-seq
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 823682
DO 10.1101/823682
A1 Bo Li
A1 Joshua Gould
A1 Yiming Yang
A1 Siranush Sarkizova
A1 Marcin Tabaka
A1 Orr Ashenberg
A1 Yanay Rosen
A1 Michal Slyper
A1 Monika S Kowalczyk
A1 Alexandra-Chloé Villani
A1 Timothy Tickle
A1 Nir Hacohen
A1 Orit Rozenblatt-Rosen
A1 Aviv Regev
YR 2019
UL http://biorxiv.org/content/early/2019/10/30/823682.abstract
AB Massively parallel single-cell and single-nucleus RNA-seq (sc/snRNA-seq) have opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so does the need for computational pipelines for scaled analysis. Here, we developed Cumulus, a cloud-based framework for analyzing large scale sc/snRNA-seq datasets. Cumulus combines the power of cloud computing with improvements in algorithm implementations 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.