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This task is complicated by the inherent batch effects and the need for precise gene expression characterization to accurately reflect spatial information. To address these challenges, we developed SELF-Former, a transformer-based framework that utilizes multi-scale structures to learn gene representations, while designing spatial correlation constraints for the reconstruction of corresponding ST data. SELF-Former excels in recovering the spatial information of ST data and effectively mitigates batch effects between scRNA-seq and ST data. A novel aspect of SELF-Former is the introduction of a gene filtration module, which significantly enhances the spatial reconstruction task by selecting genes that are crucial for accurate spatial positioning and reconstruction. The superior performance and effectiveness of SELF-Former\u2019s modules have been validated across four benchmark datasets, establishing it as a robust and effective method for spatial reconstruction tasks. SELF-Former demonstrates its capability to extract meaningful gene expression information from scRNA-seq data and accurately map it to the spatial context of real ST data. Our method represents a significant advancement in the field, offering a reliable approach for spatial reconstruction.<\/jats:p>","DOI":"10.1093\/bib\/bbae523","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T21:56:52Z","timestamp":1729115812000},"source":"Crossref","is-referenced-by-count":3,"title":["SELF-Former: multi-scale gene filtration transformer for single-cell spatial reconstruction"],"prefix":"10.1093","volume":"25","author":[{"given":"Tianyi","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science , City University of Hong Kong, Kowloon 999077,","place":["Hong Kong"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xindian","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Computer Science , City University of Hong Kong, Kowloon 999077,","place":["Hong 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