The proposed SCGDL owns a two-levels architecture, as shown in Figure 1. The feature and adjacent matrices are regarded as two incentives in the higher-level framework. Based on the built-in gene expression profiles, the feature matrix is calculated. It indi-cates the inclusion relation of spots and genes. The adjacent matrix is derived according to the positional information of spots. A spa-tial neighbor graph (SNG) is capable of being delineated during the generation of an adjacent matrix. These two inputs are conducted by a DGI module with four layers of RGGCNN. Finally, the low-dimensional latent embeddings are acquired to imply the spots representation at the higher-level.
- Python (>=3.8)
- Scanpy
- Squidpy
- Pytorch_pyG
- matplotlib
- torch_geometric
- Using SCGDL to identify the spatial domains for spatial transcriptome
running SCGDL_Tutorial.ipynb to see the simulation results step by step
Distributed under the MIT License. See LICENSE.txt for more information.
-
data files
Please down load the spatial transcriptomics data from the provided links. -
Porch_pyg
Please follow the instruction to install pyG and geometric packages.