发表机构
OmixAI Co. Ltd.; Kyunghee University（OmixAI 有限公司; 庆熙大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MoSPR通过邻接感知的形态宏状态和低秩分子基础线性预测基因表达，在多个癌症队列中取得最佳性能，并支持可解释的分子贡献分解。
AI 中文摘要
从组织病理学预测分子谱仍然具有挑战性，因为全切片图像包含空间组织的异质组织模式，而基因表达包含数千个相关靶标。我们引入了MoSPR（形态-空间程序回归），这是一个线性框架，将邻接信息感知的组织学表示与低秩分子基础相结合。MoSPR将冻结的斑块嵌入聚类为形态微状态，聚合它们在训练队列中的空间邻接关系，并将具有相似邻接模式的微状态分组为共享的宏状态。然后，每个切片通过全局形态和宏状态特定偏差来表示，这些偏差被线性映射到训练派生的低秩基因表达基础的系数上。在来自癌症基因组图谱的三个癌症队列中，MoSPR在所有评估方法中实现了最高的平均基因表达预测分数。在没有通路级监督的情况下，从其预测的表达谱中得出的通路分数在三个通路集合的九次比较中排名第一。对乳腺癌队列的消融研究显示，邻接派生的宏状态表示和低秩分子预测带来了互补的增益。此外，在该队列使用一半训练数据的情况下，MoSPR超过了最强竞争基线的全数据基因预测分数。最后，其线性公式能够将每个预测的表达谱精确分解为全局和宏状态特定的分子贡献，为空间连贯的宏状态区域及其相关分子程序之间提供了可解释的联系。我们的代码可在https://this URL获取。
英文摘要
Predicting molecular profiles from histopathology remains challenging because whole-slide images contain spatially organized, heterogeneous tissue patterns, while gene expression comprises thousands of correlated targets. We introduce MoSPR (Morpho-Spatial Program Regression), a linear framework that couples an adjacency-informed histology representation with a low-rank molecular basis. MoSPR clusters frozen patch embeddings into morphology microstates, aggregates their spatial adjacencies across the training cohort, and groups microstates with similar adjacency patterns into shared macrostates. Each slide is then represented by global morphology and macrostate-specific deviations, which are linearly mapped to coefficients of a training-derived low-rank gene-expression basis. Across three cancer cohorts from The Cancer Genome Atlas, MoSPR achieves the highest mean gene-expression prediction scores among all evaluated methods. Without pathway-level supervision, pathway scores derived from its predicted expression profiles rank first in eight of nine comparisons across three pathway collections. Ablation studies on the breast cancer cohort show complementary gains from adjacency-derived macrostate representation and low-rank molecular prediction. Moreover, with half of the training data on this cohort, MoSPR exceeds the full-data gene-prediction score of the strongest competing baseline. Finally, its linear formulation enables exact decomposition of each predicted expression profile into global and macrostate-specific molecular contributions, providing an interpretable link between spatially coherent macrostate regions and their associated molecular programs. Our code is available at https://github.com/Radisen-Panthera/MoSPR.
