Fig. 4
From: Universal prediction of cell-cycle position using transfer learning
A pre-learned weights matrix learned from proliferating cortical neurospheres enables cell-cycle position estimation in other proliferating datasets. a Different datasets (hippocampal NPCs, mouse pancreas, mouse retina, and HeLa set 2) projected into the cell-cycle embedding defined by the cortical neurosphere dataset. Cell-cycle position θ is estimated as the polar angle. b Inferred expression dynamics of Top2A (TOP2A for human), with a periodic loess line (Methods). c UMAP embeddings of top variable genes. All the cells are colored by cell-cycle position using a circular color scale. We put the discrete stage labels in approximated position on the circular legend to help relate the continuous θ to the discrete stages