Predicting Spatial Gene Expression from Histology with a Single-Cell Reference and Orthogonal Residuals

H&E slides are routinely available, whereas spatial transcriptomics (ST) is costly and limited in scale. This has motivated models that predict spatial gene expression from histology. Most existing methods, however, predict spot-level expression directly from image features without modeling the cell types that contribute to each spot. We introduce ScComp, which predicts composition weights over a fixed single-cell reference and combines the reference profiles accordingly. A residual captures expression variation not represented by the reference and is restricted to the orthogonal complement of the reference space. Across three patient-disjoint ST benchmarks, ScComp achieves the best equal-dataset macro performance among the compared methods with only 0.87M trainable parameters. More importantly, the composition weights can be applied beyond the genes used during training. They predict approximately 11,000 reference-covered genes never used as training targets, at both the gene and pathway levels, and agree with cell-type proportions measured by Xenium without composition supervision. Ablation results further show that the orthogonal projection helps preserve this zeroshot behavior by keeping the residual separate from variation represented by the reference.