Figure 1.

The schematic architecture of GRAPHDeep. (A) Practicable spatial omics data in GRAPHDeep. The data are processed as the feature matrix and adjacency matrix to import the GDL module. (B) A crowd of GNNs. In the VGAE module, these GNNs are used as encoders and decoders to mimic the similarity between two graphs. In the DGI module, these GNNs are taken as encoders to represent the positive and negative samples. (C) Two GDL modules, VGAE, and DGI. These modules could enhance the learning ability of a single GNN. (D) The embedding space of latent representations. The generated latent embeddings are applied to segment the spatial domains. (E) The feasible downstream analysis of each spatial cluster. These analytical parameters could reflect each method’s clustering accuracy and declare the biological meaning of spatial domains.