DiST：用自监督张量学习识别异质性空间转录组空间域
DiST: disentangling structure via self-supervised tensors for heterophilous spatial transcriptomics
作者提出 DiST，一个自监督张量学习框架，用于空间转录组数据的空间域识别。针对数据稀疏和基于图的方法在组织边界处过度平滑的问题，DiST 采用语义对齐的自我网络张量构建策略，把全局语境中语义相似的自我网络堆叠成高阶张量以保留局部异质性，再用低秩与稀疏分解把用于表示学习的连贯成分与测序丢失和技术伪影带来的稀疏偏差分开。在五个基准数据集上，DiST 的空间聚类表现优于代表性方法；在人类乳腺癌数据上的下游生物学验证显示，它能识别有生物学意义的分子特征和功能异质性。
来源：Briefings in Bioinformatics · doi.org
Spatially resolved transcriptomics (SRT) serves as a transformative technology for examining gene expression within tissue architecture. However, accurate spatial domain identification remains challenging due to the inherent data sparsity and the tendency of graph-based methods to over-smooth representations at tissue boundaries. To overcome these limitations, we propose DiST, a novel self-supervised tensor learning framework. Distinct from conventional approaches that rely on neighborhood propagation, DiST introduces a Semantically Aligned Ego-Network Tensor construction strategy. By stacking semantically similar ego-networks from the global context, we transform flat graph data into high-order tensor structures that preserve local heterogeneity. Furthermore, we employ a robust low-rank and sparse decomposition mechanism to separate a coherent component used for representation learning from sparse entrywise deviations associated with sequencing dropouts and technical artifacts. This decomposition yields sparsity-resilient representations that recover sharp tissue structures. Extensive experiments on five benchmark datasets demonstrate that DiST outperforms representative methods in spatial clustering. Moreover, downstream biological validation on human breast cancer data confirms DiST’s capability to uncover biologically meaningful molecular signatures and functional heterogeneity.