Consistent Bayesian Local Spatial Feature Selection with Application to Spatial Multimodal Omics
一致的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
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…