Cross-Modal Imputation and Uncertainty Estimation for Spatial Transcriptomics
High-resolution spatial transcriptomics (ST) technologies can capture gene expression at the cellular level along with spatial information, but are limited in the number of genes that can be profiled. In contrast, single-cell RNA sequencing (SC) provides more comprehensive gene expression profiles but lacks spatial context. To bridge these gaps, existing methods typically focus on single-modality prediction tasks, leveraging complementary information from the other modality. Here, we propose an attention-based cross-modal framework that simultaneously imputes gene expression for ST and recovers spatial locations for SC, while also providing uncertainty estimates for the expression of the imputed genes. Our approach was evaluated on three real-world datasets, where it consistently outperformed state-of-the-art methods in spatial gene profile imputation. Moreover, our framework enhances latent embedding integration between the two modalities, resulting in more accurate spatial position estimates.
