Performance of deep learning methods for spatial gene expression prediction from histology images: a comprehensive assessment.

MOTIVATION: Spatial transcriptomics enables spatially-resolved measurement of gene expression in tissues, but its widespread adoption has been limited by high cost. Predicting gene expression profiles from histology images using deep learning has recently attracted broad interest as a promising and cost-effective spatial transcriptomics solution, yet the strengths and limitations of existing methods have not been comprehensively assessed. RESULTS: We reviewed current approaches and compared seven algorithms across six datasets spanning four cancer types and a non-cancer disease, covering three spatial protocols and a comprehensive set of performance measures. Unlike previous studies that focused only on highly variable genes, we also analysed the predictability of genes functionally relevant to disease. We further assessed out-of-domain performance, the effect of data transformation techniques, the use of predicted gene expression for detecting tissue domains, and the benefit of integrating histopathology foundation models. Our results provide new quantitative insights into the limitations and promise of deep learning based spatial transcriptomics methods, and suggest challenges and future directions. AVAILABILITY AND IMPLEMENTATION: The benchmarking pipeline and source code are available at https://github.com/BiomedicalMachineLearning/DeepHis2Exp.