2.7. SCENIC analysis

We used SCENIC (Single Cell rEgulatory Network Inference and Clustering) for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states. 16 SCENIC contains three main steps, including co‐expression analysis, target gene motif enrichment analysis and regulon activity evaluation. The main outcomes contain a list of regulons (each representing a transcription factor along with a set of co‐expressed and motif significantly enriched target genes), and the regulon activity scores for each cell. In detail, SCENIC analysis was run using the motif database for RcisTarget and GRNboost (SCENIC version 1.1.2.2, which corresponds to RcisTarget 1.2.1 and AUCell 1.4.1) with default parameters. We identified transcription factor binding motifs over‐represented on a gene list with RcisTarget package. The activity of each group of regulons in each cell was scored by AUCell package. To evaluate the specific regulon for each cell type, we calculated the regulon specificity score which is based on the Jensen‐Shannon divergence, a measure of the similarity between two probability distributions. To systematically characterize the combinatorial patterns, we compared the atlas‐wide similarity of regulon activity scores of every regulon pair based on the Connection Specificity Index (CSI). 17 , 18 , 19 The CSI for all regulons was calculated with scFunctions package.