Fig. 5: Application to human NSCLC dataset. | Nature Communications

Fig. 5: Application to human NSCLC dataset.

From: Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding

Fig. 5

a The comparison of cell type annotation accuracy; n = 20 independent samples; Center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range. Notably, the mean accuracy of SciBet is 0.98%, that is far below those shown and is therefore not shown. b Visualization of the ground truth cell types using UMAP embedding. c Spatial organization of the ground truth cell types of a sample. Field size is about 0.7 mm × 0.9 mm. Bar scale 100 µm. d Spatial organization of the predictions of Spatial-ID and the control methods. Bar scale 100 µm. e Visualization of the predictions of Spatial-ID for this sample using UMAP embedding. f Visualization of the unassigned cells of this sample using UMAP embedding. g Visualization of the clusters of the unassigned cells using UMAP embedding. h Spatial organization of the finally found new cell types. nc1: new class type 1; nc2: new class type 2. Bar scale 100 µm.

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