This protocol explains how to perform a fast SCENIC analysis alongside standard best practices steps on single-cell RNA-sequencing data using software containers and Nextflow pipelines. SCENIC reconstructs regulons (i.e., transcription factors and their target genes) assesses the activity of these discovered regulons in individual cells and uses these cellular activity patterns to find meaningful clusters of cells. Here we present an improved version of SCENIC with several advances. SCENIC has been refactored and reimplemented in Python (pySCENIC), resulting in a tenfold increase in speed, and has been packaged into containers for ease of use. It is now also possible to use epigenomic track databases, as well as motifs, to refine regulons. In this protocol, we explain the different steps of SCENIC: the workflow starts from the count matrix depicting the gene abundances for all cells and consists of three stages. First, coexpression modules are inferred using a regression per-target approach (GRNBoost2). Next, the indirect targets are pruned from these modules using cis-regulatory motif discovery (cisTarget). Lastly, the activity of these regulons is quantified via an enrichment score for the regulon’s target genes (AUCell). Nonlinear projection methods can be used to display visual groupings of cells based on the cellular activity patterns of these regulons. The results can be exported as a loom file and visualized in the SCope web application. This protocol is illustrated on two use cases: a peripheral blood mononuclear cell data set and a panel of single-cell RNA-sequencing cancer experiments. For a data set of 10,000 genes and 50,000 cells, the pipeline runs in <2 h.

该协议说明了如何使用软件容器和Nextflow管道对单细胞RNA测序数据执行快速SCENIC分析以及标准最佳实践步骤。SCENIC重建调控因子（即转录因子及其靶基因），以评估这些发现的调控因子在单个细胞中的活性，并使用这些细胞活性模式来寻找有意义的细胞簇。在这里，我们介绍了SCENIC的改进版本，其中包括一些改进。SCENIC已在Python（pySCENIC）中进行了重构和重新实现，从而使速度提高了十倍，并且已打包到容器中以方便使用。现在也可以使用表观基因组轨迹数据库以及主题来完善规则。在此协议中，我们解释了SCENIC的不同步骤：工作流程从描述所有细胞的基因丰度的计数矩阵开始，包括三个阶段。首先，使用逐目标回归方法（GRNBoost2）推断共表达模块。接下来，使用顺式调控基序发现（cisTarget）从这些模块中修剪掉间接靶标。最后，这些调节子的活性是通过调节调节子的靶基因（AUCell）的富集分数来量化的。非线性投影方法可用于显示基于这些调节子的细胞活动模式的细胞视觉分组。可以将结果导出为织机文件，并在SCope Web应用程序中显示。在两个用例中说明了该协议：外周血单核细胞数据集和一组单细胞RNA测序癌症实验。对于10,000个基因和50个基因的数据集，