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Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells. Common data analysis pipelines include a dimensionality reduction step for visualising the data in two dimensions, most frequently performed using t-distributed stochastic neighbour embedding (t-SNE). It excels at revealing local structure in high-dimensional data, but naive applications often suffer from severe shortcomings, e.g. the global structuredoi:10.1038/s41467-019-13056-x pmid:31780648 pmcid:PMC6882829 fatcat:t5m5mzmzdvg27icxomglkzutqu