GenAR: Next-Scale Autoregressive Generation for Spatial Gene Expression Prediction

• URL: http://arxiv.org/abs/2510.04315v1
• Date: Sun, 05 Oct 2025 18:28:21 GMT
• Title: GenAR: Next-Scale Autoregressive Generation for Spatial Gene Expression Prediction
• Authors: Jiarui Ouyang, Yihui Wang, Yihang Gao, Yingxue Xu, Shu Yang, Hao Chen,
• Abstract summary: GenAR is a multi-scale autoregressive framework that refines predictions from coarse to fine.<n>We introduce GenAR, a multi-scale autoregressive framework that refines predictions from coarse to fine.<n>GenAR achieves principled state-of-the-art performance, offering potential implications for precision medicine and cost-effective molecular profiling.
• Score: 15.143858141542532
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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