RT Journal Article
SR Electronic
T1 Accelerating single-cell genomic analysis with GPUs
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.05.26.493607
DO 10.1101/2022.05.26.493607
A1 Corey Nolet
A1 Avantika Lal
A1 Rajesh Ilango
A1 Taurean Dyer
A1 Rajiv Movva
A1 John Zedlewski
A1 Johnny Israeli
YR 2022
UL http://biorxiv.org/content/early/2022/05/28/2022.05.26.493607.abstract
AB 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 StatementC.N., R.I., T.D, J.Z., and J.I. are employees of NVIDIA Corporation.