  Spatial transcriptomics (ST) captures gene expression within distinct regions
(i.e., windows) of a tissue slide. Traditional supervised learning frameworks
applied to model ST are constrained to predicting expression from slide image
windows for gene types seen during training, failing to generalize to unseen
gene types. To overcome this limitation, we propose a semantic guided network
(SGN), a pioneering zero-shot framework for predicting gene expression from
slide image windows. Considering a gene type can be described by functionality
and phenotype, we dynamically embed a gene type to a vector per its
functionality and phenotype, and employ this vector to project slide image
windows to gene expression in feature space, unleashing zero-shot expression
prediction for unseen gene types. The gene type functionality and phenotype are
queried with a carefully designed prompt from a pre-trained large language
model (LLM). On standard benchmark datasets, we demonstrate competitive
zero-shot performance compared to past state-of-the-art supervised learning
approaches.
