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. Single-cell datasets are increasing in number and size. To leverage this rich resource, new workflows can reveal novel insights and discoveries. This perspective discusses the computational challenges and opportunities for single-cell reference-mapping algorithms that may eventually replace manual and laborious unsupervised clustering pipelines.