Translation efficiency (TE) captures how effectively mRNA is translated into protein and represents a key regulatory layer of gene expression beyond mRNA abundance. Identifying genes with differential translation efficiency (DTE) across cell types and tissue regions is essential for understanding spatial variation in protein synthesis. Emerging spatial imaging technologies, such as STARmap and RIBOmap, enable measurement of transcriptional and translational signals at single-cell resolution within intact tissues, providing new opportunities to study translational regulation using spatial multi-omics data. However, most existing statistical methods for DTE gene detection are developed for bulk sequencing data. Current methods for DTE analysis in single-cell data remain limited and primarily rely on ratio-based metrics that are highly sensitive to data sparsity and modality-specific noise. In particular, RIBOmap signals are inherently sparser than STARmap signals, causing TE estimates to be undefined or unstable for the majority of gene-cell pairs, leading to biased or incomplete detection of differentially translated genes.
To address this, we propose a statistical framework for DTE gene detection that jointly models spatial transcriptomic and translatomic data within a hierarchical probabilistic model. Our approach explicitly accounts for excess zeros and measurement noise in both modalities, and uses the transcriptomic signal to inform the interpretation of translatomic zeros. This work contributes a statistical approach to DTE gene detection in spatial single-cell data, with potential applicability across spatial multi-omics platforms for studying spatially resolved translational regulation.

Co-authors: Heejung Shim