Large Language Models as Empirical Bayes Priors for Spatial Gene Expression Prediction

Predicting spatial gene expression from histology images typically relies on supervised models trained on paired image-transcriptomics data, which limits accuracy when training samples are scarce. We propose BasilSup (empirical BAyes Spatial Integration of Llm prior with SUPervision), an empirical Bayes framework that uses large language models as priors for spatial gene expression prediction. A multimodal LLM reasons about associations between tissue morphology and gene expression to produce zero-shot predictions that are treated as prior distributions, which are then integrated with a supervised predictor built on pathology foundation model embeddings. The framework automatically balances the two sources of information, relying more on the LLM prior when supervised signal is weak. Across 28 spatial transcriptomics benchmark tasks, BasilSup achieves the highest performance overall, outperforming up to 6 existing state-of-the-art baselines. These results demonstrate that biological knowledge extracted from LLMs provides complementary signal to data-driven histology models, offering a principled way to incorporate domain knowledge into spatial gene expression prediction. Code will be released upon acceptance.