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Nevertheless, there still exist multiple challenges to accurately detect spatial functional regions in tissue. Here, we present a novel contrastive learning framework, SPAtially Contrastive variational AutoEncoder (SpaCAE), which contrasts transcriptomic signals of each spot and its spatial neighbors to achieve fine-grained tissue structures detection. By employing a graph embedding variational autoencoder and incorporating a deep contrastive strategy, SpaCAE achieves a balance between spatial local information and global information of expression, enabling effective learning of representations with spatial constraints. Particularly, SpaCAE provides a graph deconvolutional decoder to address the smoothing effect of local spatial structure on expression\u2019s self-supervised learning, an aspect often overlooked by current graph neural networks. We demonstrated that SpaCAE could achieve effective performance on SRT data generated from multiple technologies for spatial domains identification and data denoising, making it a remarkable tool to obtain novel insights from SRT studies.<\/jats:p>","DOI":"10.1093\/bib\/bbae016","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T17:51:43Z","timestamp":1707328303000},"source":"Crossref","is-referenced-by-count":18,"title":["Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7651-3712","authenticated-orcid":false,"given":"Yaofeng","family":"Hu","sequence":"first","affiliation":[{"name":"Key Laboratory of Systems Health Science of Zhejiang Province , School of Life Science, Hangzhou Institute for Advanced Study, , China"},{"name":"Hangzhou 310024; University of Chinese Academy of Sciences , School 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