Advances in single-cell genomics now enable large-scale comparisons of cell states across two or more experimental conditions. Numerous statistical tools are available to identify individual genes, proteins or chromatin regions that differ between conditions, but many experiments require inferences at the level of cell types, as opposed to individual analytes. We developed Augur to prioritize the cell types within a complex tissue that are most responsive to an experimental perturbation. In this protocol, we outline the application of Augur to single-cell RNA-seq data, proceeding from a genes-by-cells count matrix to a list of cell types ranked on the basis of their separability following a perturbation. We provide detailed instructions to enable investigators with limited experience in computational biology to perform cell-type prioritization within their own datasets and visualize the results. Moreover, we demonstrate the application of Augur in several more specialized workflows, including the use of RNA velocity for acute perturbations, experimental designs with multiple conditions, differential prioritization between two comparisons, and single-cell transcriptome imaging data. For a dataset containing on the order of 20,000 genes and 20 cell types, this protocol typically takes 1-4 h to complete.