Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.

中文翻译：

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IAMSAM：使用 Segment Anything 模型对分子特征进行基于图像的分析


空间转录组学是一种将基因表达与空间信息相结合的尖端技术，使研究人员能够研究组织结构内的分子模式。在这里，我们介绍了 IAMSAM，这是一种用户友好的基于 Web 的工具，用于分析专注于形态学特征的空间转录组学数据。IAMSAM 使用 Segment Anything Model 准确分割组织图像，允许根据形态学特征半自动选择感兴趣的区域。此外，IAMSAM 还提供下游分析，例如鉴定差异表达基因、富集分析和选定区域内的细胞类型预测。凭借其简单的界面，IAMSAM 使研究人员能够以简化的方式探索和解释异质组织。