Abstract
In targeted spatial transcriptomics technologies, a key challenge is to select an informative gene panel that captures the complexity of cellular and spatial heterogeneity within tissues. Many existing methods use prior knowledge or heuristic selection rules, such as selecting highly variable genes, which overlook gene-gene correlations and may consequently result in suboptimal coverage. To address the limitations of the existing methods, we introduce single-cell informed Gene Panel Design (scGPD), a deep learning-based framework for gene panel design that leverages single-cell RNA-seq data to identify compact, nonredundant sets of genes for spatial profiling. scGPD uses a gene-gene correlation-aware gating mechanism to extract informative features from data, encouraging diversity among selected genes and eliminating redundancy. Across diverse single-cell datasets, scGPD outperforms existing gene panel design methods in recovering transcriptome-wide expression using a limited number of genes. When applied to spatial transcriptomics data, it achieves competitive and robust cell-type classification performance, demonstrating strong generalization across modalities. The gene panels selected by scGPD further exhibit well-defined spatial expression patterns, highlighting their robustness and relevance for spatial analysis. The scGPD framework is flexible and can be adapted to multiple use cases, enabling the prioritization of genes relevant to specific diseases or phenotypes. Together, these results demonstrate that scGPD provides a robust and adaptable solution to design efficient gene panels for spatial transcriptomics, with broad applicability to tissue mapping and disease characterization.