STING incorporates both graphs generated from the spatial proximity of tissue locations (or spots) and spot-specific graphs for related genes. This feature allows STING to better distinguish between clusters and identify meaningful gene-gene relations for knowledge discovery. It is a nested GNN framework that simultaneously models gene-gene and spatial relations. Using the gene expression, we generate a spot-specific gene-gene co-expression graph. We implement an inner GNN for these graphs to generate embeddings for each location. Next, we utilize these embeddings as features in a sample-wide graph generated using spatial information. We implement an outer GNN for this graph to reconstruct the original gene expression data. Finally, STING is trained end-to-end to generate embeddings that capture gene-gene and spatial information, which we input to a clustering algorithm to produce the spatial clusters. Experiments for 26 samples across 7 datasets and 5 spatial sequencing technologies show that STING outperforms the existing state-of-the-art techniques.
Preprint is available.
We suggest generating an environment (such as conda) to run the code. We have provided the requirements in requirements.txt.
You can create a conda environment directly by running this line in the shell.
conda create --name <env_name> --file requirements.txt