Prioritization of cell types responsive to biological perturbations in single-cell data with Augur

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. This protocol provides a step-by-step workflow for prioritizing the cell types most responsive to an experimental perturbation in single-cell data and describes various applications of the pipeline in five case studies.

Automated summary

This study introduces a method called Augur for prioritizing the cell types most responsive to an experimental perturbation in complex tissue. By analyzing single-cell RNA-seq data, a list of cell types ranked based on separability following perturbation can be obtained. The study also demonstrates the application of this method in various workflows, including experimental designs, prioritization, and single-cell transcriptome imaging data.