Transfer learning of multicellular organization via single-cell and spatial transcriptomics
Fig 6
Visualization of SpaRNA velocity on the DLPFC, human developing heart and mouse embryo datasets.
(a) Sample ID151674 shown in UMAP-reduction gene expression space annotated by different layers. In the low-dimensional gene space, the hierarchical organization of the cerebral cortex is arranged differently from the normal sequence. (b) Pseudo-time of sample ID151674 inferred by scTour shown in UMAP-reduction gene expression space. (c) RNA velocity of sample ID151674 inferred by scTour shown in UMAP-reduction gene expression space. Due to the misaligned hierarchical organization in the low-dimensional gene space, RNA velocity shows an incorrect trajectory, moving directly from layer 6 to layer 1 and continuing its evolution from layer 1. (d) Sample ID151674 displayed in physical space, annotated by different layers, illustrates the true hierarchical organization of the cerebral cortex. (e) Pseudo-time of sample ID151674 shown in physical space. (f) SpaRNA velocity of sample ID151674 inferred by iSORT shown in physical space. (g) Spatial transcriptomics of a human developing heart at 9 post-conception week. Different colors represent different cell types. (h) SpaRNA velocity on the human developing heart visualized by iSORT, characterizing the sequential appearance of different cell types in the human heart during development. (i) SpaRNA velocity inferred by iSORT on the mouse embryo dataset. iSORT successfully predicted the spatial differentiation trajectories of different cell types, revealing the patterns of spatial development.