The future of rapid and automated single-cell data analysis using reference mapping

As the number of single-cell datasets continues to grow rapidly,workflows that map new data to well-curated reference atlases offer enormous promise for the biological community.In this perspective,we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms.We discuss how mapping algorithms will enable the integration of diverse datasets across disease states,molecular modalities,genetic perturbations,and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.