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Related Experiment Video

Updated: Feb 19, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

STCF: Multi-View Clustering for Spatial Transcriptomics Based on Cross-View Fusion.

Zeyu Zhu, Ke Liang, Lingyuan Meng

IEEE Transactions on Pattern Analysis and Machine Intelligence

|February 17, 2026

Summary

This summary is machine-generated.

This study introduces STCF, a novel spatial transcriptomics clustering framework. STCF effectively integrates highly variable genes and low-variability genes to enhance spatial domain identification and uncover intricate tissue patterns.

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Published on: July 6, 2022

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial transcriptomics (ST) enables gene expression analysis within tissue context.
Current ST clustering methods often rely on single gene sets (HVGs or SVGs), potentially missing complementary information from genes with different variability levels.
A unified approach to leverage both highly variable genes (HVGs) and low-variability genes (LVGs) for spatial domain identification is needed.

Purpose of the Study:

To develop a novel spatial transcriptomics clustering framework, STCF, that integrates information from both HVGs and LVGs.
To improve the resolution and accuracy of spatial domain identification in transcriptomic data.
To enhance the ability to uncover latent spatial patterns within tissue morphology.

Main Methods:

Proposed STCF, a framework for cross-view information fusion in spatial transcriptomics clustering.
Utilized HVGs and LVGs as two distinct gene-expression views.
Implemented a plug-and-play cross-view fusion strategy with reverse-scaled cosine error loss (R-SCE) to balance gene embedding alignment and separation.
Ensured robust representation learning and preserved spatial coherence for fine-grained structure resolution.

Main Results:

STCF demonstrated superior performance, effectiveness, and transferability across three benchmark datasets (DLPFC, HBC, and MBA).
The framework successfully resolved fine-grained spatial structures.
Case studies confirmed STCF's ability to identify latent spatial patterns and improve clustering precision.

Conclusions:

STCF offers a powerful new approach for spatial transcriptomics clustering by effectively integrating diverse gene expression profiles.
The framework enhances the understanding of tissue architecture and cellular organization through improved spatial domain identification.
STCF represents a significant advancement in computational methods for analyzing spatial transcriptomic data.