空间转录组学产生具有空间背景的高维基因表达测量。获得此类数据的具有生物学意义的低维表示对于有效解释和下游分析至关重要。在这里，我们提出了空间转录组学分析，主题建模揭示空间模式（STAMP），这是一种基于深度生成模型的可解释的空间感知降维方法，可返回生物相关的低维空间主题和相关基因模块。 STAMP 可以分析从单个部分到多个部分、从不同技术到时间序列数据的数据，返回与已知生物域和相关基因模块匹配的主题，其中包含已建立的高度排名的标记。在肺癌样本中，STAMP 以比原始注释更高的分辨率用支持标记描绘细胞状态，并发现集中在肿瘤边缘外部的癌症相关成纤维细胞。在小鼠胚胎发育的时间序列数据中，STAMP 解开了肝脏内红骨髓造血和肝细胞的发育轨迹。 STAMP 具有高度可扩展性，可以处理超过 500,000 个单元格。© 2024。作者。

Spatial transcriptomics produces high-dimensional gene expression measurements with spatial context. Obtaining a biologically meaningful low-dimensional representation of such data is crucial for effective interpretation and downstream analysis. Here, we present Spatial Transcriptomics Analysis with topic Modeling to uncover spatial Patterns (STAMP), an interpretable spatially aware dimension reduction method built on a deep generative model that returns biologically relevant, low-dimensional spatial topics and associated gene modules. STAMP can analyze data ranging from a single section to multiple sections and from different technologies to time-series data, returning topics matching known biological domains and associated gene modules containing established markers highly ranked within. In a lung cancer sample, STAMP delineated cell states with supporting markers at a higher resolution than the original annotation and uncovered cancer-associated fibroblasts concentrated on the tumor edge's exterior. In time-series data of mouse embryonic development, STAMP disentangled the erythro-myeloid hematopoiesis and hepatocytes developmental trajectories within the liver. STAMP is highly scalable and can handle more than 500,000 cells.© 2024. The Author(s).