A flexible framework for transformer-based analysis of spatial transcriptomics data.
Project description
stFormer
A flexible framework for transformer-based analysis of spatial transcriptomics data. stFormer provides tools for data tokenization, pretraining, embedding extraction, in silico perturbation, and downstream classification.
- Keep up to date on Github
- Browse model files on Hugging-Face
Install
pip install --upgrade stformer
Prerequisites: Python3.8+, OpenMPI (for deepspeed only)
Features
- Data Tokenization
- Pretraining
- Token/Sequence Classification
- In Silico Perturbation
- Network Dynamic Predictions
Contributing
Contributions are welcome! Please open issues at our Github or submit pull requests for bug fixes and new features.
Models
Pretrained models and datasets are available through our hugging-face model card
License
This project is licensed under the MIT License. See LICENSE for details.
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