Abstract
Spatial transcriptomics is an emerging technology that aligns histopathology images with spatially resolved gene expression profiling. It holds the potential for understanding many diseases but faces significant bottlenecks such as specialized equipment and domain expertise. In this work, we present SEPAL, a new model for predicting genetic profiles from visual tissue appearance. Our method exploits the biological biases of the problem by directly supervising relative differences with respect to mean expression, and leverages local visual context at every coordinate to make predictions using a graph neural network. This approach closes the gap between complete locality and complete globality in current methods. In addition, we propose a novel benchmark that aims to better define the task by following current best practices in transcriptomics and restricting the prediction variables to only those with clear spatial patterns. Our extensive evaluation in two different human breast cancer datasets indicates that SEPAL outperforms previous state-of-the-art methods and other mechanisms of including spatial context.
- 8/7/2023: SEPAL has been accepted as an oral presentation in the ICCV workshop of Computer Vision for Automated Medical Diagnosis
- 9/7/2023: SEPAL preprint is available here
Run the following to define your environment in the terminal:
conda create -n st
conda activate st
conda install python==3.10.0
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
pip install torch_geometric
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
pip install squidpy
pip install wandb
pip install wget
pip install combat
pip install opencv-python
pip install --upgrade tbb
pip install positional-encodings[pytorch]The datasets and gene annotation files are downloaded automatically.
We use the GTF file of the basic gene annotation from GENCODE and use the set of functions provided by GTFtools to compute the genes length in TPM normalization.
To obtain the main results you must first train the image encoder using the following line:
python run_main_config.py --dataset_config configs/datasets/$dataset_name$_deltas.json --model_config configs/models/best_vit_$dataset_name$.json --train_config configs/training/best_vit_$dataset_name$.json Where the substring $dataset_name$ must be replaced by visium or stnet_dataset depending on what you want to try. This will train the image encoder and save the results inside the results/best_vit_$dataset_name$ directory. Now, to train the spatial module you must run:
python run_main_config.py --dataset_config configs/datasets/$dataset_name$_deltas.json --model_config configs/models/best_sepal_$dataset_name$.json --train_config configs/training/best_sepal_$dataset_name$.json This will train the graph neural network and save the results inside the results/best_sepal_$dataset_name$ directory. Your results might differ a little from the values reported in the paper. Consequently, we also provide our pre-trained models in the binary release of the code. The configurations are the same as before configs/models/best_sepal_$dataset_name$.json but you must change the paths where the image encoders are loaded accordingly. Retraining is mandatory when using another dataset since the specific predictor genes selected by Moran scores may vary.
When trained, the programs will prompt the possibility to log results into a weights and biases (W&B) account. We encourage its use since most of the logging performed inside was designed to interface with W&B.
@article{mejia2023sepal,
title={SEPAL: Spatial Gene Expression Prediction from Local Graphs},
author={Mejia, Gabriel and C{\'a}rdenas, Paula and Ruiz, Daniela and Castillo, Angela and Arbel{\'a}ez, Pablo},
journal={arXiv preprint arXiv:2309.01036},
year={2023}
}
