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
- 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.
- organism: human
- assay: scRNA-seq
- assay: snRNA-seq
- research focus: benchmarking
- research focus: dimensionality reduction
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
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.
Related papers
- scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data
Combiz Khozoie, Nurun Nahar Fancy, … Nathan Skene·bioRxiv·Aug 16, 2021
- A cloud-based platform for the analysis of single cell RNA sequencing data.
Nithin Joshy, Kyuson Yun·bioRxiv·Sep 28, 2020
- SSCC: a computational framework for rapid and accurate clustering of large-scale single cell RNA-seq data
Xianwen Ren, Liangtao Zheng, Zemin Zhang·Genomics Proteomics & BioinformaticsPeer-reviewed·Apr 1, 2019
- scOmnom: scalable end-to-end single-cell RNA-seq analysis with auditable resolution selection
Iman Man Hu, Artemiy Kovynev, … Koen H.M. Prange·bioRxiv·Oct 19, 2026
- Falco: A quick and flexible single-cell RNA-seq processing framework on the cloud
Andrian Yang, Michael Troup, … Joshua W. K. Ho·BioinformaticsPeer-reviewed·Nov 16, 2016
- ScRNAbox: Empowering Single-Cell RNA Sequencing on High Performance Computing Systems
Rhalena A. Thomas, Michael R. Fiorini, … Sali M.K. Farhan·BMC BioinformaticsPeer-reviewed·Oct 1, 2024