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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| Veröffentlicht in: | Computational biology and chemistry Jg. 120; H. Pt 2; S. 108746 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.02.2026
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| ISSN: | 1476-9271, 1476-928X, 1476-928X |
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| Abstract | 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.
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•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. |
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| AbstractList | 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. 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.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. 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. |
| ArticleNumber | 108746 |
| Author | Zhang, Weihang Le, Yuying Peng, Tao Ao, Chunyan Li, Yan Guo, Ruihua Wang, Ruheng Wei, Leyi Cui, Yaxuan Yi, Ding Shi, Hua Cui, Yang |
| Author_xml | – sequence: 1 givenname: Hua orcidid: 0000-0001-8812-5737 surname: Shi fullname: Shi, Hua organization: School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China – sequence: 2 givenname: Ding surname: Yi fullname: Yi, Ding organization: Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan – sequence: 3 givenname: Yang surname: Cui fullname: Cui, Yang organization: Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan – sequence: 4 givenname: Ruheng surname: Wang fullname: Wang, Ruheng organization: University of Texas Southwestern Medical Center, Dallas, TX, USA – sequence: 5 givenname: Yan surname: Li fullname: Li, Yan organization: School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China – sequence: 6 givenname: Chunyan surname: Ao fullname: Ao, Chunyan organization: Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China – sequence: 7 givenname: Ruihua surname: Guo fullname: Guo, Ruihua organization: School of computer science, the University of Sydney, Australia – sequence: 8 givenname: Weihang surname: Zhang fullname: Zhang, Weihang organization: Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan – sequence: 9 givenname: Tao surname: Peng fullname: Peng, Tao organization: School of Future Science and Engineering, Soochow University, China – sequence: 10 givenname: Yuying surname: Le fullname: Le, Yuying email: yuyinle@126.com organization: Department of Radiation Oncology, Fuzhou Pulmonary Hospital of Fujian Province, Teaching Hospital of Fujian Medical University, China – sequence: 11 givenname: Yaxuan surname: Cui fullname: Cui, Yaxuan email: s2236008@u.tsukuba.ac.jp organization: Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan – sequence: 12 givenname: Leyi orcidid: 0000-0003-1444-190X surname: Wei fullname: Wei, Leyi email: weileyi@sdu.edu.cn organization: Centre for Artificial Intelligence driven Drug Discovery, Faculty of Applied Science, Macao Polytechnic University, SAR, Macao, China |
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| Issue | Pt 2 |
| Keywords | Breast cancer Graph Convolutional Variational Autoencoders Spatial transcriptomics Gastric cancer Diffusion Consistency Loss |
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