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.

单细胞基因组学的进步现在可以在两个或多个实验条件下对细胞状态进行大规模比较。许多统计工具可用于识别因条件而异的单个基因、蛋白质或染色质区域，但许多实验需要在细胞类型水平而非单个分析物水平上进行推断。我们开发了 Augur 来优先考虑复杂组织中对实验扰动最敏感的细胞类型。在此协议中，我们概述了 Augur 在单细胞 RNA-seq 数据中的应用，从逐个基因的细胞计数矩阵到根据扰动后的可分离性排名的细胞类型列表。我们提供了详细的说明，使在计算生物学方面经验有限的研究人员能够在他们自己的数据集中执行细胞类型优先排序并可视化结果。此外，我们展示了 Augur 在几个更专业的工作流程中的应用，包括使用 RNA 速度进行急性扰动、具有多种条件的实验设计、两次比较之间的差异优先级以及单细胞转录组成像数据。对于包含大约 20,000 个基因和 20 种细胞类型的数据集，此协议通常需要 1–4 小时才能完成。两次比较和单细胞转录组成像数据之间的差异优先级。对于包含大约 20,000 个基因和 20 种细胞类型的数据集，此协议通常需要 1–4 小时才能完成。两次比较和单细胞转录组成像数据之间的差异优先级。对于包含大约 20,000 个基因和 20 种细胞类型的数据集，此协议通常需要 1–4 小时才能完成。