一致贝叶斯局部空间特征选择及其在空间多模态组学中的应用
Consistent Bayesian Local Spatial Feature Selection with Application to Spatial Multimodal Omics
AI总结：
针对空间多模态组学中的高维回归问题，提出一种贝叶斯局部空间特征选择框架，通过随机域划分先验和局部特征选择先验实现域划分与特征选择的一致性理论，并开发高效的可逆跳跃MCMC算法。
摘要：
受空间多模态组学（SMO）中高维回归问题的启发，我们提出了一种用于局部空间特征选择的贝叶斯框架，其中引入随机域划分先验将空间域划分为若干形状灵活且数量未知的连续簇，并在每个簇内施加局部特征选择先验。“特征”的概念是广义的，可能包括协变量和函数基，使得该框架既能执行局部变量选择，也能执行局部基选择，后者对于自适应逼近具有局部特性的空间变化函数至关重要。我们推导了连接域划分和局部特征选择先验的耦合超参数条件，在此条件下建立了域划分和特征选择的一致性理论及后验收缩率。我们开发了一种高效的信息化可逆跳跃马尔可夫链蒙特卡洛算法，以解决域划分和选定特征的联合后验采样中遇到的计算挑战。模拟研究证明了所提模型和算法的有效性，突出了其相对于现有方法的优势。将该模型应用于SMO数据集，揭示了乳腺癌组织中具有生物学意义的空间模式。
英文摘要：
Motivated by a high-dimensional regression problem in spatial multimodal omics (SMO), we propose a Bayesian framework for local spatial feature selection, where a random domain partition prior is introduced to divide the spatial domain into several contiguous clusters with flexible shapes and an unknown number of clusters, conditional on which a local feature selection prior is imposed within each cluster. The notion of "feature" is general and may include both covariates and functional bases, allowing the framework to perform both local variable selection and local basis selection, the latter being essential for adaptively approximating spatially varying functions with localized characteristics. We derive coupled hyperparameter conditions linking domain partition and local feature selection priors, under which the consistency theory and posterior contraction rates of both the domain partition and feature selection are established. We develop an efficient informed reversible jump Markov chain Monte Carlo algorithm to address the computational challenges encountered in joint posterior sampling of domain partitions and selected features. Simulation studies demonstrate the effectiveness of the proposed model and algorithm, highlighting its advantages over existing methods. The application of our model to an SMO dataset reveals biologically meaningful spatial patterns within breast cancer tissue.
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