DiffuScope: A diffusion-regularized autoencoder for spatial transcriptomic clustering

In recent years, the rapid advancement of spatial transcriptomics technologies has led to the public availability of a large and diverse collection of datasets spanning multiple species, organs, and tissue types. These datasets exhibit substantial biological and technical heterogeneity, highlighting...

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Bibliographic Details
Published in:	Computational biology and chemistry Vol. 120; no. Pt 2; p. 108746
Main Authors:	Shi, Hua, Yi, Ding, Cui, Yang, Wang, Ruheng, Li, Yan, Ao, Chunyan, Guo, Ruihua, Zhang, Weihang, Peng, Tao, Le, Yuying, Cui, Yaxuan, Wei, Leyi
Format:	Journal Article
Language:	English
Published:	England Elsevier Ltd 01.02.2026
Subjects:	Breast cancer Diffusion Consistency Loss Gastric cancer Graph Convolutional Variational Autoencoders Spatial transcriptomics Breast cancer Graph Convolutional Variational Autoencoders Spatial transcriptomics Gastric cancer Diffusion Consistency Loss
ISSN:	1476-9271, 1476-928X, 1476-928X
Online Access:	Get full text
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Summary:	In recent years, the rapid advancement of spatial transcriptomics technologies has led to the public availability of a large and diverse collection of datasets spanning multiple species, organs, and tissue types. These datasets exhibit substantial biological and technical heterogeneity, highlighting the urgent need for a generalizable clustering algorithm capable of adapting to such diversity. To address this challenge, we propose DiffuScope, a clustering framework based on Graph Convolutional Variational Autoencoders (GC-VAE). DiffuScope leverages self-supervised learning to extract informative latent representations from spatial transcriptomics data and incorporates two complementary loss functions — Reconstruction Loss and Diffusion Consistency Loss — to enhance feature learning, thereby improving clustering accuracy and cross-dataset generalization. We systematically and fairly benchmark DiffuScope against several state-of-the-art spatial transcriptomics clustering methods across 12 publicly available datasets. In addition, we assess the robustness of the proposed model by introducing random dropout noise into the spatial expression data. Finally, we apply DiffuScope to breast cancer and gastric cancer spatial transcriptomics datasets, demonstrating its ability to effectively delineate spatial domains and uncover biologically meaningful tissue structures. [Display omitted] •DiffuScope integrates graph autoencoders with diffusion loss for spatial clustering.•Achieves robust and accurate performance across diverse spatial transcriptomic datasets.•Reveals biologically meaningful domains in gastric and breast cancer tissues.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1476-9271 1476-928X 1476-928X
DOI:	10.1016/j.compbiolchem.2025.108746