BiGATAE: a bipartite graph attention auto-encoder enhancing spatial domain identification from single-slice to multi-slices
We show how to operate on the Human dataset in BiGATAE/Tutorial.ipynb, and the operations on other datasets are in BiGATAE/train. We also store the raw data, intermediate results and final results of BC data and cSCC data in BiGATAE/tmp_data. The Human_DLPFC dataset is too large, please refer to Tutorial to get final results. BiGATAE is a data augmentation method. After getting the enhanced data, it can be used for various spatial clustering methods, such as stLearn, SEDR.
- Human dorsolateral prefrontal cortex dataset
- Breast cancer dataset
- Human skin squamous cell carcinoma dataset
To execute the code, make sure to install the required packages listed below.
- python==3.8
- torch>=1.8.0
- scikit-learn==1.1.1
- matplotlib==3.4.2
- pandas==1.4.2
- numpy==1.22.3
- scanpy==1.9.1
- R==4.0.3