Disentangled Information Asymmetry in Spatial High-dimensional Data Interpretation

Recent advances in sensor technology have led to an explosion of Spatial High-Dimensional Data (SHD), including spatial transcriptomics and hyperspectral imaging, the two domains studied in this work. These data capture complex interactions between intrinsic sample properties and spatial microenvironments by incorporating location information through accumulation, embedding, or aggregation. Existing methods typically assume homogeneous spatial interaction patterns, overlooking critical asymmetries in SHD: (i) view asymmetry between spatial patterns and high-dimensional attributes, and (ii) spatial asymmetry in the internal pathways of high-dimensional data. To address this gap, we propose DIaS, a data-centric decoupling framework that disentangles spatial-induced, intrinsic, and harmonized information signals to better model the richness of SHD. Specifically, DIaS employs a Gaussian process for spatial embedding, leveraging uncertainty quantification to dynamically infer spatial pathways. During disentanglement, SHD is decomposed into three mutually regularized views to minimize dependence between spatial attributes and intrinsic features. To balance the asymmetric contributions of spatial and intrinsic information, we introduce an asymmetric harmonization mechanism, which adaptively aligns spatial and intrinsic representations to identify salient and diverse multi-view prototypes. Experiments on 18 benchmarks in spatial transcriptomics and hyperspectral imaging, together with downstream task evaluations, demonstrate state-of-the-art performance and the effectiveness of DIaS in alleviating information asymmetry within these two domains. The demo code of this work is publicly available at https://github.com/hexiao0275/DIaS.