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Updated: Jan 17, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

A comprehensive comparison on clustering methods for multi-slice spatially resolved transcriptomics data analysis.

Caiwei Xiong¹, Shuai Huang¹, Muqing Zhou²

¹Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599-7420, United States.

Briefings in Bioinformatics

|September 18, 2025

Summary

This summary is machine-generated.

This study compares spatial transcriptomics clustering methods for analyzing multiple tissue slices. It evaluates single-slice and multi-slice approaches, offering guidance for selecting the best spatial domain detection techniques.

Keywords:

clustering evaluation multi-slice clustering spatial transcriptomics

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics (ST) enables gene expression and spatial pattern analysis within tissues.
Clustering is crucial for ST data, revealing spatial organizations with shared characteristics.
Multi-slice clustering methods are emerging for contiguous tissue sections.

Purpose of the Study:

To comprehensively compare single-slice and multi-slice clustering methods for spatial transcriptomics data.
To evaluate the impact of preprocessing techniques on clustering performance.
To provide a practical guide for selecting appropriate clustering methods for multi-slice ST data.

Main Methods:

Assessed seven single-slice and four multi-slice clustering algorithms.
Utilized two simulated and four real spatial transcriptomics datasets.
Investigated the effects of spatial coordinate alignment (e.g., PASTE) and batch effect removal (e.g., Harmony).

Main Results:

Performance varied across clustering methods depending on dataset characteristics.
Preprocessing techniques like spatial alignment and batch correction influenced clustering outcomes.
Multi-slice methods showed potential for improved spatial domain detection in integrated analyses.

Conclusions:

No single clustering method is universally optimal for all multi-slice ST data scenarios.
Method selection should consider data complexity, biological questions, and preprocessing steps.
This comparison serves as a valuable resource for researchers applying spatial transcriptomics.