GenOT: Generative optimal transport for continuous spatiotemporal modeling of cross-platform spatial transcriptomics

Introduction

Spatial transcriptomics (ST) technologies have revolutionized the analysis of spatial gene expression patterns within tissues. However, existing computational methods still face challenges in integrating spatial information and generating cross-heterogeneous sample data. To address this, we developed GenOT - a spatial data generation framework based on graph self-supervised contrastive learning and optimal transport theory. The framework employs a multimodal feature learning architecture to dynamically identify important genes and hierarchically aggregate spatial neighborhood information, achieving high-precision spatial domain clustering and biologically interpretable feature extraction. The core innovation of GenOT lies in introducing an optimal transport barycenter-based interpolation algorithm, which mathematically models cross-sample spatial distribution differences to reconstruct spatiotemporal continuous gene expression dynamics. Experiments on multiple datasets including human dorsolateral prefrontal cortex (10x visium), mouse embryonic development (Stereo-seq), and olfactory bulb/hippocampal tissues (Slide-seqV2) demonstrate that GenOT significantly outperforms existing methods in spatial domain identification, cross-technology platform integration, and developmental trajectory reconstruction. This provides an innovative tool for tissue structure analysis at single-cell resolution and developmental process modeling.

Environment

First, clone and navigate to the repository.

git clone https://github.com/wrab12/GenOT
cd GenOT

This process can take several minutes, depending on network speed.

Create and activate a virtual environment using python 3.9 with conda,

# conda
conda create -n GenOT python=3.9
conda activate GenOT

Install dependencies and the local library with pip.

pip install -r requirements.txt

This process usually takes around 5 minutes.

Next, you need to install torch and torch_geometric. The installation depends on your computer's CUDA version. Please follow these steps:

1. Run nvidia-smi to check your CUDA version.
2. Install the appropriate version of torch according to your CUDA version. Refer to the official guide: PyTorch Installation.
3. After installing torch, install torch_geometric.
4. For detailed instructions, visit PyTorch Geometric Installation Guide.

Specifically, you can use the following command:

pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html
pip install torch_geometric

Replace ${TORCH} and ${CUDA} with your specific PyTorch and CUDA versions.

For more details, see the official documentation for PyTorch and PyTorch Geometric.

You also need to install R. You can download the installer and run it from the official CRAN website: https://cran.r-project.org/

Datasets

• Download all datasets from this Google Drive link.

Documentation

See detailed documentation and examples at https:/GenOT.readthedocs.io/en/latest/index.html.

Acknowledgements

We thank the developers of GraphST (GitHub), Somde (GitHub), Paste2 (GitHub), and POT (GitHub) for their valuable tools and resources.

References