Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data.

ABSTRACT: As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Herein we present Scarf, a modularly designed Python package that seamlessly interoperates with other single-cell toolkits and allows for memory-efficient single-cell analysis of millions of cells on a laptop or low-cost devices like single-board computers. We demonstrate Scarf's memory and compute-time efficiency by applying it to the largest existing single-cell RNA-Seq and ATAC-Seq datasets. Scarf wraps memory-efficient implementations of a graph-based t-stochastic neighbour embedding and hierarchical clustering algorithm. Moreover, Scarf performs accurate reference-anchored mapping of datasets while maintaining memory efficiency. By

SUBMITTER: Dhapola P 

PROVIDER: S-EPMC9360040 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature