Supplementary Figure 14: SCENIC analysis of >40000 single cells from the mouse retina.
From: SCENIC: single-cell regulatory network inference and clustering
Drop-seq data on cells from the mouse retina was analyzed with SCENIC by running GENIE3 on a subset of ~11K cells, and evaluating the resulting GRN on all cells. (a) tSNE colored according to the expected cell types as annotated by Macosko et al. (b, c). Main networks according to the activity of regulons, shown based in the red-green-blue coloring scheme. In these tSNEs, the cells shown in grey are not taking into account for the coloring. Logos corresponding to the significant motifs found in iRegulon for the regulons identified in Müller glia and the corresponding GO terms are included. The identified master regulators (such as Sox8/9, Hes1, Rax, Nr1h4, Srf, and Nr2e1 for the Muller glia, which are confirmed in literature58–61, illustrate that correct networks can be inferred even on sparse data. The sub-sampling approach is especially interesting on sparse datasets, such as the ones resulting from Drop-seq or 10x, because the best quality-cells can be used to infer the GRN, and then this high-quality GRN can be scored on all cells.