Abstract
Single-cell genomic technologies are rapidly improving our understanding of cellular heterogeneity in biological systems. In recent years, technological and computational improvements have continuously increased the scale of single-cell experiments, and now allow for millions of cells to be analyzed in a single experiment. However, existing software tools for single-cell analysis do not scale well to such large datasets. RAPIDS is an open-source suite of Python libraries that use GPU computing to accelerate data science workflows. Here, we report the use of RAPIDS and GPU computing to accelerate single-cell genomic analysis workflows and present open-source examples that can be reused by the community.
Competing Interest Statement
C.N., R.I., T.D, J.Z., and J.I. are employees of NVIDIA Corporation.
