PT  - JOURNAL ARTICLE
AU  - Corey Nolet
AU  - Avantika Lal
AU  - Rajesh Ilango
AU  - Taurean Dyer
AU  - Rajiv Movva
AU  - John Zedlewski
AU  - Johnny Israeli
TI  - Accelerating single-cell genomic analysis with GPUs
AID  - 10.1101/2022.05.26.493607
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.05.26.493607
4099  - http://biorxiv.org/content/early/2022/05/28/2022.05.26.493607.short
4100  - http://biorxiv.org/content/early/2022/05/28/2022.05.26.493607.full
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.