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--- |
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license: mit |
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tags: |
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- spatial-transcriptomics |
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- graph-transformer |
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- gene-expression |
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- finetuned |
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- mouse-stroke |
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- pytorch |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: feature-extraction |
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--- |
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# SpatialGT Finetuned Model - Mouse Stroke (PT) |
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## Model Description |
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This is the **finetuned checkpoint** of SpatialGT on mouse stroke PT (photothrombotic stroke) spatial transcriptomics data. |
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This model is specifically finetuned for the mouse stroke perturbation simulation case study, trained on the PT1-1 slice. |
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## Model Details |
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- **Base Model**: SpatialGT Pretrained |
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- **Finetuning Data**: Mouse stroke PT 4 slices (Visium) |
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- **Finetuning Strategy**: Full finetuning (8 transformer layers unfrozen) |
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- **Epochs**: 100 |
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- **Learning Rate**: 1e-4 |
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## Usage |
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```python |
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import torch |
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from pretrain.model_spatialpt import SpatialNeighborTransformer |
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from pretrain.Config import Config |
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# Load configuration |
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config = Config() |
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# Initialize model |
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model = SpatialNeighborTransformer(config) |
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# Load finetuned weights |
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from safetensors.torch import load_file |
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state_dict = load_file("model.safetensors") |
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model.load_state_dict(state_dict) |
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model.eval() |
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``` |
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## Intended Use |
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This model is intended for: |
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- Reconstructing gene expression in stroke-affected mouse brain tissue |
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- Simulating perturbation effects in ischemic regions (ICA, PIA) |
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- Comparative analysis with Sham (control) model |
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## Files |
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- `model.safetensors`: Model weights in safetensors format |
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- `training_args.bin`: Training arguments |
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## Related Models |
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- [SpatialGT-Pretrained](https://huggingface.co/Bgoood/SpatialGT-Pretrained): Base pretrained model |
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- [SpatialGT-MouseStroke-Sham](https://huggingface.co/Bgoood/SpatialGT-MouseStroke-Sham): Finetuned on Sham (control) slice |
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## Citation |
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If you use this model, please cite our paper (details to be added upon publication). |
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## License |
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MIT License |
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