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Related Experiment Video

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

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

Zhiwei Wang¹, Yeqin Zeng¹, Ziyue Tan¹

¹Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China.

Proceedings of the National Academy of Sciences of the United States of America

|November 12, 2025

Summary

We developed the Mixture of Mixed Models (MMM) to identify cell-type-specific spatially variable genes (SVGs) in spatial transcriptomic data. MMM effectively reveals SVGs linked to complex traits and tissue microenvironments.

Keywords:

complex traits and diseases penalized quasi-likelihood spatially variable genes tumor microenvironment variance component testing

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Area of Science:

Genomics
Bioinformatics
Systems Biology

Background:

Spatial transcriptomics (ST) studies require methods to identify cell-type-specific spatially variable genes (SVGs) within tissue context.
Existing methods may not adequately account for cell type composition or platform effects in ST data.

Purpose of the Study:

To present a unified framework, the Mixture of Mixed Models (MMM), for identifying cell-type-specific SVGs.
To directly model RNA count data while accounting for cell type composition and correcting for platform effects.

Main Methods:

Developed the Mixture of Mixed Models (MMM) framework.
Applied MMM to eight publicly available ST datasets and a high-resolution Xenium human breast cancer dataset.
Integrated MMM results with genome-wide association studies (GWAS).

Main Results:

MMM effectively identifies cell-type-specific SVGs across diverse ST datasets and technologies.
Identified SVGs in mouse brain ST data show significant heritability enrichment for brain-related phenotypes.
Analysis of breast cancer ST data suggests SVGs contribute to cell-cell communication and regulate the tissue microenvironment.
Demonstrated MMM's versatility in analyzing 3D ST data.

Conclusions:

MMM is a robust and effective tool for identifying cell-type-specific SVGs in ST studies.
Cell-type-specific SVGs are crucial for understanding complex traits, diseases, and tissue microenvironment regulation.
MMM facilitates deeper insights into biological systems using ST data, including 3D analyses.