henhaobio
Sign in

Nature MethodsBioinformaticsPeer-reviewed journal article

Cumulus: a cloud-based data analysis framework for large-scale single-cell and single-nucleus RNA-seq

Bo LiiD, Joshua GouldiD, Yiming YangiD, Siranush SarkizovaiD, Marcin TabakaiD, Orr AshenbergiD, Yanay RoseniD, Michal SlyperiD, Monika S. KowalczykiD, Alexandra–Chloé VillaniiD, Timothy L. TickleiD, Nir HacoheniD, Orit Rozenblatt–RoseniD, Aviv RegeviD

PDFDOIPreprint
Citations
256
FWCI
9.72
Field percentile
top 1%

Abstract

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.

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISKhozoie '21Joshy '20Ren '19Hu '26Yang '16Thomas '24
71% semantic matchBioinformatics
Combiz Khozoie, Nurun Nahar Fancy, Mahdi Moradi Marjaneh+3 more·bioRxiv·Aug 16, 2021· 21 citations