DisConST: Distribution-aware Contrastive Learning for Spatial Domain Identification Article Swipe
Peimeng Zhen
,
Xiaofeng Wang
,
Han Shu
,
Jialu Hu
,
Yongtian Wang
,
Jiajie Peng
,
Xuequn Shang
,
Jing Chen
,
Tao Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/gpbjnl/qzaf085
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/gpbjnl/qzaf085
Spatial transcriptomics (ST) is a cutting-edge technology that provides comprehensive insights into gene expression patterns from a spatial perspective. A key research focus within this field is spatial domain identification, which is essential for exploring tissue organization, biological development, and disease mechanisms. Although methods have been developed, they still face challenges in modeling the gene expression information together with the spatial locations, resulting in suboptimal accuracy. We introduce Distribution-aware Contrastive Learning for Spatial Transcriptomics (DisConST), a novel deep learning method designed to improve spatial domain detection within ST datasets. DisConST addresses key challenges, such as the high dropout rates and the complex integration of spatial and gene expression data, by incorporating contrastive learning strategies that are aware of the underlying data distributions. It employs the zero-inflated negative binomial (ZINB) distribution, along with graph contrastive learning, to generate more informative latent representations. These representations efficiently integrate spatial positions, transcriptomic profiles, and cell-type proportions within spots. We validated DisConST across diverse ST datasets of tissues, organs, and embryos from various sequencing platforms in both normal and disease states. Our results consistently demonstrated that DisConST achieves superior spatial domain recognition accuracy compared to existing state-of-the-art methods. Furthermore, our experiments highlighted the utility of DisConST in advancing research on tissue organization, embryonic development, and tumor immune microenvironment dissection. The source code for DisConST is freely available at https://github.com/Zhenpm/DisConST/.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/gpbjnl/qzaf085
- https://academic.oup.com/gpb/advance-article-pdf/doi/10.1093/gpbjnl/qzaf085/64372302/qzaf085.pdf
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https://openalex.org/W4414471726Canonical identifier for this work in OpenAlex
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https://doi.org/10.1093/gpbjnl/qzaf085Digital Object Identifier
- Title
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DisConST: Distribution-aware Contrastive Learning for Spatial Domain IdentificationWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-09-24Full publication date if available
- Authors
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Peimeng Zhen, Xiaofeng Wang, Han Shu, Jialu Hu, Yongtian Wang, Jiajie Peng, Xuequn Shang, Jing Chen, Tao WangList of authors in order
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https://doi.org/10.1093/gpbjnl/qzaf085Publisher landing page
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https://academic.oup.com/gpb/advance-article-pdf/doi/10.1093/gpbjnl/qzaf085/64372302/qzaf085.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://academic.oup.com/gpb/advance-article-pdf/doi/10.1093/gpbjnl/qzaf085/64372302/qzaf085.pdfDirect OA link when available
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