README.md

metadata

license: mit
tags:
  - spatial-transcriptomics
  - graph-transformer
  - gene-expression
  - finetuned
  - mouse-stroke
  - pytorch
language:
  - en
library_name: transformers
pipeline_tag: feature-extraction

SpatialGT Finetuned Model - Mouse Stroke (PT)

Model Description

This is the finetuned checkpoint of SpatialGT on mouse stroke PT (photothrombotic stroke) spatial transcriptomics data.

This model is specifically finetuned for the mouse stroke perturbation simulation case study, trained on the PT1-1 slice.

Model Details

• Base Model: SpatialGT Pretrained
• Finetuning Data: Mouse stroke PT 4 slices (Visium)
• Finetuning Strategy: Full finetuning (8 transformer layers unfrozen)
• Epochs: 100
• Learning Rate: 1e-4

Usage

import torch
from pretrain.model_spatialpt import SpatialNeighborTransformer
from pretrain.Config import Config

# Load configuration
config = Config()

# Initialize model
model = SpatialNeighborTransformer(config)

# Load finetuned weights
from safetensors.torch import load_file
state_dict = load_file("model.safetensors")
model.load_state_dict(state_dict)

model.eval()

Intended Use

This model is intended for:

• Reconstructing gene expression in stroke-affected mouse brain tissue
• Simulating perturbation effects in ischemic regions (ICA, PIA)
• Comparative analysis with Sham (control) model

Files

• model.safetensors: Model weights in safetensors format
• training_args.bin: Training arguments

Related Models

• SpatialGT-Pretrained: Base pretrained model
• SpatialGT-MouseStroke-Sham: Finetuned on Sham (control) slice

Citation

If you use this model, please cite our paper (details to be added upon publication).

License

MIT License