We present scSVA (single-cell Scalable Visualization and Analytics), a lightweight R package for interactive two- and three-dimensional visualization and exploration of massive single-cell omics data. Building in part of methods originally developed for astronomy datasets, scSVA is memory efficient for more than hundreds of millions of cells, can be run locally or in a cloud, and generates high-quality figures. In particular, we introduce a numerically efficient method for single-cell data embedding in 3D which combines an optimized implementation of diffusion maps with a 3D force-directed layout, enabling generation of 3D data visualizations at the scale of a million cells. To facilitate reproducible research, scSVA supports interactive analytics in a cloud with containerized tools. scSVA is available online at https://github.com/klarman-cell-observatory/scSVA.

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