Fig 4: Causal inference in Scribe with RNA-velocity.

(A) RNA-velocity vector projected onto the first two latent dimensions. A small subset of arrows is used to visualize the velocity field of the cells. S: Sympathoblasts; C: Chromaffin. SCP: Schwann Cell Progenitor. The color of each cell corresponds to the cluster id from Fig 5B of ref. (Furlan et al., 2017). (B) A core causal network for chromaffin cell commitment inferred based on RNA-velocity. Gene set is collected from ref. (Furlan et al., 2017). CLR (context likelihood of relatedness) regularization is used to remove spurious causal edges in the network (see STAR Methods). (C) Two potential coherent FFL (feed-forward loop) motifs of chromaffin differentiation are discovered from the core network. Edge width corresponds to causal regulation strength. (D) Visualization of the six causal regulations pairs in the feedforward loops of Eya1-Phox2a-Erbb3 and Gata3-Phox2a-Notch1. (See STAR Methods for details). (E) Visualizing combinatorial regulation logic for the two feedforward loops in Panel C with Scribe. For both Panels D and E, a grid with 625 cells (25 on each dimension) is used. Similarly, expected values are scaled by the maximum to obtain a range from 0 to 1. (F) Scribe’s ability to detect causal regulatory interactions is limited by the single-cell measurement technology used. Technologies that provide measurements that are coupled across time and between genes provide more power for inference than conventional single-cell RNA-seq experiments.