stDyer enables spatial domain clustering with dynamic graph embedding

Thu, 20 Feb 2025 00:00:00 +0000

Overview

stDyer is an innovative deep learning framework designed for spatial domain clustering in spatially resolved transcriptomics (SRT) data. The framework integrates:

Gaussian Mixture Variational AutoEncoder (GM-VAE) for learning meaningful embeddings
Graph Attention Networks (GAT) for capturing spatial relationships
Dynamic Graph Construction that adaptively links spatial units based on Gaussian Mixture assignments
Mini-batch Strategy and Multi-GPU Support for handling large-scale datasets

Key Features

1. Dynamic Graph Embedding

Adaptively constructs graphs based on clustering assignments
Produces smoother spatial domain boundaries
Captures both transcriptomic similarity and spatial proximity

2. Scalability

Mini-batch processing strategy
Multi-GPU support for large datasets
Efficient handling of high-resolution SRT data

3. Superior Performance

Outperforms state-of-the-art tools in spatial domain clustering
Excellent performance in multi-slice analysis
Robust handling of large-scale datasets

Citation

If you use stDyer in your research, please cite:

@article{xu2025stdyer,
 title={stDyer enables spatial domain clustering with dynamic graph embedding},
 author={Xu, Ke and Xu, Yu and Wang, Zirui and Zhou, Xin Maizie and Zhang, Lu},
 journal={Genome Biology},
 volume={26},
 number={1},
 pages={34},
 year={2025},
 publisher={BioMed Central},
 doi={10.1186/s13059-025-03503-y}
}