In the same way that read mapping tools have transformed genome sequence analysis1-3, the ability to map new datasets to established references represents an exciting opportunity for the field of single-cell genomics. As an alternative to fully unsupervised clustering, supervised mapping approaches leverage large and well-curated references to interpret and annotate query profiles. This strategy is enabled by the curation and public release of reference datasets, as well as the development of new computational tools, including statistical learning4-7 and deep learning-based approaches8,9 that have been successfully applied towards this goal.
