GenAR: Next-scale autoregressive generation for spatial gene expression prediction.

Abstract

Spatial Transcriptomics (ST) offers spatially resolved gene expression but remains costly. Predicting expression directly from widely available Hematoxylin and Eosin (H&E) stained images presents a cost-effective alternative. However, most computational approaches (i) predict each gene independently, overlooking co-expression structure, and (ii) cast the task as continuous regression despite expression being discrete counts. This mismatch can yield biologically implausible outputs and complicate downstream analyses. We introduce GenAR, a multi-scale autoregressive framework that refines predictions from coarse to fine. GenAR (a) clusters genes into hierarchical groups to expose cross-gene dependencies, (b) models expression as discrete token generation over a fixed vocabulary of integer count tokens to directly predict raw counts, and (c) conditions decoding on fused histological and spatial embeddings. By modeling expression on the physical count scale, GenAR avoids the limitations of continuous regression, while its coarse-to-fine factorization ensures a principled conditional decomposition. Extensive experimental results on five ST datasets across different tissue types demonstrate that GenAR achieves state-of-the-art performance, offering potential implications for precision medicine and cost-effective molecular profiling. Code is publicly available at https://github.com/oyjr/genar.