Extended Data Fig. 4: Quality control of scRNA-seq data, relative to Fig. 2.

(a) tSNE-based visualization of clusters, cell types/states, number of counts (nCount) and number of genes (nGene) of the scRNA-seq data. The Adjusted Rand Index (ARI) between the clustering and cell type/state classification is indicated. (b) Clustering tree of the Seurat-based clustering results of the scRNA-seq data. It visualizes the relationship between clustering at increasing resolutions (top to bottom). The size of the circles represents the number of cells in that cluster, while the opacity of the arrows shows the proportion of the cells passing from one cluster to another at a different resolution. Note that the ASPCs do not split by treatment due to batch effect. The clustering was therefore independently adapted for the clustered ASPCs to correctly capture their state difference (see Methods). (c) Barplot showing the overlap in number of cells between the clustering (x-axis) and the ground truth, i.e. cell type/state, displayed in (a). (d) Heatmap showing the top differentially expressed genes stratified according to the five scRNA-seq clusters. (e) GO term enrichment analysis of the five scRNA-seq clusters using the top 100 differentially expressed genes. (f) Mouse gene atlas-based prediction of cell type/state of each cluster using the top 100 marker genes.