{"ID":2860381,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.04315","arxiv_id":"2510.04315","title":"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\u0026E) 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 clusters genes into hierarchical groups to expose cross-gene dependencies, models expression as codebook-free discrete token generation to directly predict raw counts, and conditions decoding on fused histological and spatial embeddings. From an information-theoretic perspective, the discrete formulation avoids log-induced biases and the coarse-to-fine factorization aligns with a principled conditional decomposition. Extensive experimental results on four Spatial Transcriptomics 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.","short_abstract":"Spatial Transcriptomics (ST) offers spatially resolved gene expression but remains costly. Predicting expression directly from widely available Hematoxylin and Eosin (H\u0026E) stained images presents a cost-effective alternative. However, most computational approaches (i) predict each gene independently, overlooking co-exp...","url_abs":"https://arxiv.org/abs/2510.04315","url_pdf":"https://arxiv.org/pdf/2510.04315v1","authors":"[\"Jiarui Ouyang\",\"Yihui Wang\",\"Yihang Gao\",\"Yingxue Xu\",\"Shu Yang\",\"Hao Chen\"]","published":"2025-10-05T18:28:21Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":608725,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2860381,"paper_url":"https://arxiv.org/abs/2510.04315","paper_title":"GenAR: Next-Scale Autoregressive Generation for Spatial Gene Expression Prediction","repo_url":"https://github.com/oyjr/genar","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}