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 implementing a subsampling algorithm, Scarf additionally has the capacity to generate representative sampling of cells from a given dataset wherein rare cell populations and lineage differentiation trajectories are conserved. Together, Scarf provides a framework wherein any researcher can perform advanced processing, subsampling, reanalysis, and integration of atlas-scale datasets on standard laptop computers. Scarf is available on Github: https://github.com/parashardhapola/scarf.

Single-cell RNAseq is a powerful tool for the dissection of cell populations. Multiple dimension reduction (DR) tools are available to project cells on to 3-dimensional space that allow one to visualise the heterogeneity within the assayed population, often forming complex cellular maps. Thus far, visualisation methods for 3D embedded maps are poor, and the lack of intuitive point/cell selection often hinders a rapid exploration of finer details contained in the data. Moreover, directly comparing the output from several DR methods is not possible. Here we present CellexalVR (www.cellexalvr.med.lu.se), a feature-rich, fully interactive virtual reality environment for the visualisation and analysis of single-cell RNAseq experiments that allows researchers to intuitively and collaboratively gain an understanding of their data.Single-cell RNAseq (scRNAseq) is a routinely used method to explore the heterogeneity of

HighlightsSingle-cell experiments are often visualized when embedded into three dimensions CellexalVR is a virtual reality environment to visualize all data simultaneously Teams can analyze singlecell experiments together in VR regardless of location Legetth et al., iScience --, 103251 --, 2021 ª 2021 The Author(s).

The increasing capacity to perform large-scale single-cell genomic experiments continues to outpace the ability to efficiently handle growing datasets. 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 implementing a novel data downsampling algorithm, Scarf additionally has the capacity to generate representative sampling of cells from a given dataset wherein rare cell populations and lineage differentiation trajectories are conserved. Together, Scarf provides a framework wherein any researcher can perform advanced processing, downsampling, reanalysis and integration of atlas-scale datasets on standard laptop computers.