Welcome to MacSGP’s documentation!

MacSGP (GitHub repository) is a scalable statistical and computational approach for MApping Cell-type-specific Spatial Gene Programs (SGPs) in spatial transcriptomic (ST) data.

MacSGP’s effectiveness relies on our innovations in the seamless integration of deep graph neural networks (GNNs) and probabilistic models:

• MacSGP maps gene expressions and spatial information of spots into a shared latent space by leveraging deep GNNs, yielding low-dimensional representations of each spot that capture both gene expression similarity and spatial coherence.

• MacSGP utilizes the latent representation to generate cell-type-specific SGPs through a probabilistic model, which accounts for cell type mixtures and characterizes cell-type-specific SGPs using the low-rank structure.

• For large-scale high-resolution ST datasets, MacSGP adopts a batch-learning scheme that learns SGPs over small gene patches, enabling scalable training without sacrificing accuracy.

On this tutorial website, we provide guidelines for using MacSGP along with real data analysis examples. The source code for building the website can be found at https://github.com/statwangz/MacSGP-tutorial.

Contents

• Installation
  • Requirements
  • Installing MacSGP from PyPI
  • Installing MacSGP from Github
• Usage
  • Overview of MacSGP
  • Required data
  • Preprocessing
  • Cell type deconvolution
  • Cell-type-specific SGP identification
• MacSGP analysis
  • Simulation study
  • Visium adult mouse brain datasets containing two biological replicates
  • Visium kidney cancer dataset at the tumour-normal interface
  • Multiple human colorectal cancer datasets generated by different technologies

Reference

If you find the MacSGP package or any of the source code in this repository useful for your work, please cite:

Mapping Cell-Type-Specific Spatial Gene Programs Uncovers Tissue Architecture and Microenvironment Organization.

Yeqin Zeng, Zhiwei Wang, Yuyao Liu, Yuheng Chen, Jiguang Wang, Hao Chen, and Can Yang.

Submitted, 2025.

Development

The Python package MacSGP is developed and maintained by Yeqin Zeng.

Contact

Please feel free to contact Yeqin Zeng, Zhiwei Wang, or Prof. Can Yang if any inquiries.