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.

大规模并行单细胞和单核 RNA 测序为健康和疾病领域的系统组织图谱开辟了道路，但随着数据生成规模的不断增长，对用于大规模分析的计算管道的需求也在不断增长。在这里，我们开发了 Cumulus——一个基于云的框架，用于分析大规模单细胞和单核 RNA 测序数据集。Cumulus 将云计算的强大功能与算法和实现方面的改进相结合，以实现高可扩展性、低成本、用户友好性以及对全面功能集的集成支持。我们在骨髓细胞的人类细胞图谱免疫细胞普查数据集上对 Cumulus 进行了基准测试，结果表明它比传统框架显着提高了效率，同时保持或提高了结果的质量，从而实现了大规模研究。