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Related Experiment Video

Updated: Jan 10, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

PHD-MS: Multiscale Domain Identification for Spatial Transcriptomics via Persistent Homology.

Perry Beamer¹, Zixuan Cang^1,2

¹Department of Mathematics, North Carolina State University, Raleigh, NC, USA.

|November 26, 2025

Summary

This summary is machine-generated.

This study introduces Persistent Homology for Domains at Multiple Scales (PHD-MS), a new method using topological data analysis to identify spatial domains across various scales in tissues. PHD-MS enhances spatial transcriptomics analysis by revealing multiscale tissue structures.

Keywords:

Multiscale domains Spatial transcriptomics Topological data analysis

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Area of Science:

Genomics
Computational Biology
Biotechnology

Background:

Spatial transcriptomics (ST) reveals gene expression within tissue microenvironments.
Current ST clustering methods often focus on single scales, missing multiscale spatial domain interactions.
Understanding multiscale tissue organization is crucial for biological insights.

Purpose of the Study:

To develop a novel method for identifying spatial domains at multiple morphological scales.
To address limitations of single-scale clustering in spatial transcriptomics.
To provide a tool for exploring persistent tissue structures across scales.

Main Methods:

Application of Topological Data Analysis (TDA) to spatial transcriptomics data.
Development of Persistent Homology for Domains at Multiple Scales (PHD-MS).
Utilizing TDA to identify tissue structures that persist across various geometric scales.

Main Results:

PHD-MS successfully identified multiscale spatial domains in diverse tissue types and ST technologies.
The method highlights persistent tissue structures across different morphological scales.
PHD-MS demonstrated superior performance compared to traditional clustering methods against expert-annotated ground truth.

Conclusions:

PHD-MS effectively captures multiscale spatial organization in tissues.
The developed method offers a significant advancement for spatial transcriptomics data analysis.
PHD-MS is available as an open-source package with a user-friendly interface for broader accessibility.