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September 10, 2025Briefings in BioinformaticsOpen Access

MuST: multiple-modality structure transformation for single-cell spatial transcriptomics

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The methodology tackles modality bias in spatial transcriptomics data, suggesting enhanced integration and analysis.

MuST improves the precision of identifying tissue structures and biomarkers, thus enhancing analysis.
This approach utilizes a latent space to better coordinate multimodal data for various tasks.
It employs topology discovery strategies to mitigate the adverse impacts of modality bias on data interpretation.
The methodology demonstrates clear advantages over existing state-of-the-art techniques in spatial transcriptomics.

Zang et al. (2025) studied this question.

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September 10, 2025Briefings in BioinformaticsOpen Access

View Full Paper

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Authors

The methodology tackles modality bias in spatial transcriptomics data, suggesting enhanced integration and analysis.

MuST improves the precision of identifying tissue structures and biomarkers, thus enhancing analysis.
This approach utilizes a latent space to better coordinate multimodal data for various tasks.
It employs topology discovery strategies to mitigate the adverse impacts of modality bias on data interpretation.
The methodology demonstrates clear advantages over existing state-of-the-art techniques in spatial transcriptomics.

Zang et al. (2025) studied this question.

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