Welcome to MCube’s documentation!

The R package MCube (GitHub repository) implements the methods in the MMM paper. MMM, standing for the Mixture of Mixed Models, is a unified framework for the statistical identification of cell-type-specific spatially variable genes (SVGs) in spatial transcriptomic (ST) studies.

MMM’s effectiveness stems from our innovations in model and algorithm design:

• Beginning with the raw count data, MMM uses a log-mixture structure to account for cell type composition while simultaneously correcting for the spot and platform effects between ST and single‐cell RNA sequencing (scRNA-seq) data.

• The mixed-effects model decomposes the cell-type-specific gene expression in ST data into three components: the average gene expression of the same cell type obtained from scRNA-seq data, spatial variations, and non-spatial variations.

• The statistical significance of spatial variations is then examined using a powerful non-parametric test capable of detecting diverse spatial patterns.

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

Contents

• Installation
  • Installing MCube from GitHub
  • Suggested packages
• Usage
  • Required data
  • Pipeline overview
  • Cell type deconvolution
  • Cell-type-specific SVG identification
• Real data analysis
  • Visium human dorsolateral prefrontal cortex dataset
  • Multiple adult mouse brain datasets from different sources
  • Xenium human breast cancer dataset
  • Multiple human colorectal cancer datasets generated by different technologies
  • 3D Drosophila embryo model constructed from Stereo-seq dataset
• Integration with RCTD using one line of code
  • Direct integration with RCTD on the Visium adult mouse brain dataset
  • Direct integration with RCTD on the two human colorectal cancer datasets

Reference

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

A unified framework for identification of cell-type-specific spatially variable genes in spatial transcriptomic studies.

Zhiwei Wang, Yeqin Zeng, Ziyue Tan, Yuheng Chen, Xinrui Huang, Hongyu Zhao, Zhixiang Lin, and Can Yang.

Proceedings of the National Academy of Sciences of the United States of America, 2025.

DOI: 10.1073/pnas.2503952122

Development

The R package MCube is developed and maintained by Zhiwei Wang.

Contact

Please feel free to contact Zhiwei Wang, Prof. Hongyu Zhao, Prof. Zhixiang Lin, or Prof. Can Yang if any inquiries.