Spatial domain identification requires jointly modeling molecular signatures and physical coordinates, yet current tools frequently over-smooth biological boundaries, require user-specified cluster numbers, and lack principled multimodal integration. We introduce BaySC, an integrative Bayesian spatial clustering framework for spatial domain identification. BaySC inherently learns the true number of spatial domains from the data by employing a Mixture of Finite Mixtures (MFM) prior. Tissue topology is modeled via a Markov Random Field (MRF) applied to discrete cellular assignments, a strategy that enforces local spatial coherence without distorting the underlying gene expression features. This enables BaySC to accurately map contiguous tissue layers as well as geographically scattered, transcriptionally identical cell populations. Furthermore, BaySC handles spatial multi-omics data through a weighted log-likelihood fusion mechanism executed via Gibbs sampling. This approach assigns interpretable weights to each modality, allowing users to quantify the biological relevance of different data layers to the final tissue map. Validated across ten single-modal spatial transcriptomics and two spatial multi-omics datasets, BaySC yields highly interpretable probabilistic outputs. It demonstrates competitive accuracy on standard clustering metrics and consistently outperforms existing tools in preserving spatial topography, as measured by spatially-aware Adjusted Rand Index (spARI).

翻译：空间域识别需要联合建模分子特征与物理坐标，但现有工具常过度平滑生物边界、需要用户指定聚类数量，且缺乏原理性多模态整合方法。我们提出BaySC——一种用于空间域识别的整合贝叶斯空间聚类框架。BaySC通过采用有限混合混合物先验，能够从数据中内在学习真正的空间域数量。组织拓扑结构通过应用于离散细胞分配的马尔可夫随机场进行建模，该策略在保持局部空间连贯性的同时，不会扭曲底层基因表达特征。这使得BaySC能够准确描绘连续组织层以及地理分散但转录一致的细胞群体。此外，BaySC通过吉布斯采样的加权对数似然融合机制处理空间多组学数据，为每个模态分配可解释权重，使研究人员能够量化不同数据层对最终组织图谱的生物学相关性。经过十个单模态空间转录组和两个空间多组学数据集的验证，BaySC生成高度可解释的概率输出。在标准聚类指标上展现出竞争性精度，并在通过空间感知调整兰德指数衡量的空间拓扑保持方面持续优于现有工具。