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However, we identify the modality bias phenomenon in ST data species, i.e. the inconsistent contribution of different modalities to the labels leads to a tendency for the analysis methods to retain the information of the dominant modality. How to mitigate the adverse effects of modality bias to satisfy various downstream tasks remains a fundamental challenge. This paper introduces Multiple-modality Structure Transformation, named MuST, a novel methodology to tackle the challenge. MuST integrates the multi-modality information contained in the ST data effectively into a uniform latent space to provide a foundation for all the downstream tasks. It learns intrinsic local structures by topology discovery strategy and topology fusion loss function to solve the inconsistencies among different modalities. Thus, these topology-based and deep learning techniques provide a solid foundation for a variety of analytical tasks while coordinating different modalities. The effectiveness of MuST is assessed by performance metrics and biological significance. The results show that it outperforms existing state-of-the-art methods with clear advantages in the precision of identifying and preserving structures of tissues and biomarkers. MuST offers a versatile toolkit for the intricate analysis of complex biological systems.<\/jats:p>","DOI":"10.1093\/bib\/bbaf405","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T12:19:44Z","timestamp":1756383584000},"source":"Crossref","is-referenced-by-count":2,"title":["MuST: multiple-modality structure transformation for single-cell spatial transcriptomics"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2831-5437","authenticated-orcid":false,"given":"Zelin","family":"Zang","sequence":"first","affiliation":[{"name":"Westlake Institute for Advanced Studies, Westlake University , HangZhou, 310000 ,","place":["China"]},{"name":"Centre for Artificial Intelligence and Robotics (CAIR) , HKISI-CAS, 310000"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangyu","family":"Li","sequence":"additional","affiliation":[{"name":"Westlake Institute for Advanced Studies, Westlake University , HangZhou, 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