## Data preparation

### Spatial transcriptomics data

We used four spatial gene expression datasets. 

1. The first dataset is the LIBD human dorsolateral prefrontal cortex (DLPFC) with 12 tissue slices from the package [spatialLIBD](http://research.libd.org/spatialLIBD/). 

2. Mouse visual cortex STARmap data is obtained from the [STARMap](https://www.starmapresources.com/data). 

3. Two breast cancer datasets can be obtained from 10x Genomics Data Repository, including [V1](https://www.10xgenomics.com/cn/resources/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0) and [V2](https://www.10xgenomics.com/cn/resources/datasets/human-breast-cancer-block-a-section-2-1-standard-1-1-0). 

4. Human Lymph Node can be obtained from [10x Genomics Data Repository](https://www.10xgenomics.com/cn/resources/datasets/human-lymph-node-1-standard-1-1-0).

All data can be downloaded from [DeepGFT data](https://drive.google.com/drive/folders/1uzrXJXbtwFomQuEldagfyA0Z_wfNqEza?usp=sharing).

We recommend load Visium data by:
```python
import scanpy as sc
adata = sc.read_visium(path_to_visium_dataset)
```
For all spatial transcriptomics datasets, it should be pointed out that raw count matrix needs to be found at _adata.X_ and the spatial coordinate information needs to be found at _adata.obs_ or _adata.obsm_