新加坡科技研究局Jinmiao Chen研究组发现，可解释的空间感知降维方法STAMP可用于空间转录组学。这一研究成果于2024年10月15日在线发表在国际学术期刊《自然—方法学》上。

研究人员提出了空间转录组学分析与主题建模结合的方法（STAMP）。这是一种可解释的空间感知降维方法，基于深度生成模型，返回生物学相关的低维空间主题和相关基因模块。STAMP可以分析从单个切片到多个切片，以及不同技术和时间序列数据的数据，返回与已知生物学领域相匹配的主题，并包含高度排名的已建立标记的相关基因模块。

在一份肺癌样本中，STAMP以比原始注释更高的分辨率勾画了细胞状态及其支持标记，并发现了集中在肿瘤边缘外侧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解析了肝脏内红细胞-髓系造血和肝细胞的发育轨迹。STAMP具有很高的可扩展性，可以处理超过500000个细胞。

据介绍，空间转录组学生成了具有空间背景的高维基因表达测量。获得这种数据的生物学上有意义的低维表示对有效解释和后续分析至关重要。

附：英文原文

Title: Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP

Author: Zhong, Chengwei, Ang, Kok Siong, Chen, Jinmiao

Issue&Volume: 2024-10-15

Abstract: 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.

DOI: 10.1038/s41592-024-02463-8

Source: https://www.nature.com/articles/s41592-024-02463-8