Welcome to STADiffuser’s documentation!¶
STADiffuser is a cutting-edge deep generative model designed to simulate high-fidelity spatial transcriptomic (ST) data. By providing a versatile simulation framework that can generate accurate and detailed ST data, this tool enables various downstream tasks, including: imputation, super-resolution, in silico experiments, and cell type-specific gene identification.
Please refer to the Tutorials for a step-by-step guide on how to use STADiffuser. We also provide a command-line interface (STADiffuser CLI documentation) for users who prefer to run STADiffuser from the terminal. For more advanced users, we offer an API Reference that allows for more flexibility and customization.
3D Marmoset Cerebellum Reconstruction¶
STADiffuser reconstructs a high-resolution 3D atlas of the marmoset cerebellum from sparse 2D slices, overcoming challenges posed by large-scale and anisotropic spatial data. Trained on over 1.9 million spatial spots, it enables dense, biologically meaningful virtual reconstruction and supports flexible slice generation from arbitrary viewing angles — including coronal, sagittal, and oblique planes. This empowers full-view spatial exploration beyond physical sectioning constraints.
👉 Interactive demo: https://zhanglab-amss.org/Omni-View-3D-Cerebellar/
Architecture¶
STADiffuser’s architecture is composed of a two-stage framework designed for high-fidelity simulation:
Autoencoder with Graph Attention Mechanism： The autoencoder learns embeddings for spatial spots using a graph attention mechanism, which captures the intricate spatial relationships and gene expression patterns in the data.
Latent Diffusion Model with Spatial Denoising Network： The latent diffusion model generates realistic ST data by diffusing the learned embeddings through a spatial denoising network, which refines the spatial patterns and gene expression profiles.
Functionality and Applications¶
STADiffuser offers a range of functionalities and applications that make it a powerful tool for simulating and analyzing spatial transcriptomic data. See Tutorials for more details.
