Transfer learning of multicellular organization via single-cell and spatial transcriptomics
Fig 2
Spatial organization reconstruction by iSORT on the DLPFC, spatially resolved mouse embryo, and mouse brain datasets.
(a) Ground truth and spatial reconstruction results for ID151674 in DLPFC dataset by eleven algorithms: iSORT, scSpace, Tangram, novoSpaRc, CeLEry, CellTrek, RCTD, CARD, Redeconve, CytoSPACE, and Celloc, where Tangram, RCTD, CARD, Redeconve, CytoSPACE, and Celloc are designed for spot deconvolution, whereas novoSpaRc is designed for imputing the undetected genes. Different colors represent different cortical regions. (b) Bar charts to compare the performance of eleven algorithms based on four indicators: intra-layer similarity (), normalized density distribution (
), aggregative volume index (
) and aggregative perimeter index (
) of each layer. The indicators of iSORT (the red filled bars) are above 0.875 in average, larger than the other ten algorithms. (c) Spatial reconstruction of the spatially resolved mouse embryo dataset with iSORT. Different colors represent different cell types. iSORT can reorganize single cells based on their gene expressions in a continuous space. (d) Spatial reconstruction of the mouse brain dataset with iSORT. iSORT can recover the stratified structures in the mouse brain.