Fig. 4.

Fig. 4. A machine learning approach predicts putative regulators of resistance to Notch signaling blockade in deeply quiescent pallial NSCs of cluster q5.

(A) Levels of expression of major Notch targets represented in the low-dimensional embedding of qNSCs from the LY-treated dataset. Cells belonging to cluster q5 are circled and show higher and almost exclusive expression of these genes. (B) Barplots depicting the weight associated with putative regulator-target relationships inferred through gradient boosting and automatic filtering. The height of the bar is proportional to the contribution of the indicated transcription factor’s expression to the recovery of the pattern of expression of the target (her4.1, her8a, her9, and hey1). When transcription factors were anticorrelated with their putative target we assigned a negative sign to the link. Bars are color coded to highlight specific genes and gene families. (C and D) Levels of expression of the genes encoding the best candidates regulators of q5 resistance to activation, id1 (C) and nr2f1b (D), represented in the low-dimensional embedding of qNSCs from the control (left) and LY-treated (right) datasets.