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September 10, 2025Nature CommunicationsOpen Access

SpaIM: single-cell spatial transcriptomics imputation via style transfer

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SpaIM improves gene expression accuracy in spatial transcriptomics, suggesting enhanced integration with single-cell data.

SpaIM significantly improves gene coverage and expression accuracy in spatial transcriptomics.
Evaluated across 53 datasets, SpaIM outperformed 12 methods, enhancing overall analysis capabilities.
SpaIM utilizes style transfer learning to integrate single-cell RNA sequencing with spatial transcriptomics.
Released as open-source software, SpaIM expands accessibility and enables deeper tissue architecture insights.

Li et al. (2025) studied this question.

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