SpaIM: single-cell spatial transcriptomics imputation via style transfer.

ABSTRACT: Spatial transcriptomics (ST) technologies have transformed our understanding of cellular organization but are limited by sparse signals and restricted gene coverage. To address these challenges, we introduce SpaIM, a style transfer learning model that leverages single-cell RNA sequencing (scRNA-seq) data to predict unmeasured gene expressions in ST profiles. By disentangling shared content and modality-specific styles, SpaIM effectively integrates scRNA-seq's rich gene expression with the spatial context of ST. Evaluated across 53 datasets spanning sequencing- and imaging-based spatial technologies in various tissue types, SpaIM consistently outperforms 12 state-of-the-art methods in improving gene coverage and expression accuracy. Furthermore, SpaIM enhances downstream analyses, including ligand-receptor interaction inference, spatial domain characterization, and differential gene expression analysis. Released as open-source software, SpaIM expands accessibility and utility in ST research. Overall, SpaIM represents a robust and generalizable framework for enriching ST data with single-cell information, enabling deeper insights into tissue architecture and cellular function.

SUBMITTER: Li B 

PROVIDER: S-EPMC12375071 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature