Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems by embedding cells in a product space of their own and the local neighborhood transcriptome, representing cell state and microenvironment, respectively. BANKSY’s spatial feature augmentation strategy improved performance on both tasks when tested on diverse RNA (imaging, sequencing) and protein (imaging) datasets. BANKSY revealed unexpected niche-dependent cell states in the mouse brain and outperformed competing methods on domain segmentation and cell typing benchmarks. BANKSY can also be used for quality control of spatial transcriptomics data and for spatially aware batch effect correction. Importantly, it is substantially faster and more scalable than existing methods, enabling the processing of millions of cell datasets. In summary, BANKSY provides an accurate, biologically motivated, scalable and versatile framework for analyzing spatially resolved omics data.

空间组学数据被聚类以定义细胞类型和组织域。我们提出了使用邻域内核和空间标尺构建聚合（BANKSY），这是一种通过将细胞嵌入到它们自己的乘积空间和本地邻域转录组（分别代表细胞状态和微环境）来统一这两个空间聚类问题的算法。在不同的 RNA（成像、测序）和蛋白质（成像）数据集上进行测试时，BANKSY 的空间特征增强策略提高了这两项任务的性能。BANKSY 揭示了小鼠大脑中意想不到的生态位依赖性细胞状态，并且在域分割和细胞分型基准方面优于竞争方法。BANKSY 还可用于空间转录组数据的质量控制和空间感知批次效应校正。重要的是，它比现有方法更快、更具可扩展性，能够处理数百万个细胞数据集。总之，BANKSY 提供了一个准确的、生物学驱动的、可扩展的和多功能的框架来分析空间解析的组学数据。