SCGDL

Spatial Clustering for Spatial Omics Data via Geometric Deep Learning

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.

Contents

• Prerequisites
• Example usage
• Trouble shooting

Prerequisites

1. Python (>=3.8)
2. Scanpy
3. Squidpy
4. Pytorch_pyG
5. matplotlib
6. torch_geometric

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Example usage

• Using SCGDL to identify the spatial domains for spatial transcriptome

  running SCGDL_Tutorial.ipynb to see the simulation results step by step

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Trouble shooting

• 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.