Ospina Oscar E, Manjarres-Betancur Roberto, Gonzalez-Calderon Guillermo, Soupir Alex C, Smalley Inna, Tsai Kenneth Y, Markowitz Joseph, Khaled Mariam L, Vallebuona Ethan, Berglund Anders E, Eschrich Steven A, Yu Xiaoqing, Fridley Brooke L
Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida.
Biostatistics and Bioinformatics Shared Resource, Moffitt Cancer Center, Tampa, Florida.
Cancer Res. 2025 Mar 3;85(5):848-858. doi: 10.1158/0008-5472.CAN-24-2346.
Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, the advanced data analysis and programming skills required can hinder researchers from realizing the full potential of ST. To address this, we developed spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provided a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enabled comparative analysis among samples and supported various ST technologies. The utility of spatialGE was demonstrated through its application in studying the tumor microenvironment of two data sets: 10× Visium samples from a cohort of melanoma metastasis and NanoString CosMx fields of vision from a cohort of Merkel cell carcinoma samples. These results support the ability of spatialGE to identify spatial gene expression patterns that provide valuable insights into the tumor microenvironment and highlight its utility in democratizing ST data analysis for the wider scientific community. Significance: The spatialGE web application enables user-friendly exploratory analysis of spatial transcriptomics data by using a point-and-click interface to guide users from data input to discovery of spatial patterns, facilitating hypothesis generation.
空间转录组学(ST)是理解组织生物学和疾病机制的强大工具。然而,所需的先进数据分析和编程技能可能会阻碍研究人员充分发挥ST的潜力。为了解决这个问题,我们开发了spatialGE,这是一个简化ST数据分析的网络应用程序。spatialGE应用程序提供了一个用户友好的界面,可引导没有编程专业知识的用户完成各种分析流程,包括质量控制、标准化、区域检测、表型分析和多种空间分析。它还支持样本间的比较分析,并支持各种ST技术。通过将spatialGE应用于研究两个数据集的肿瘤微环境,证明了其效用:一组黑色素瘤转移的10× Visium样本和一组默克尔细胞癌样本的NanoString CosMx视野。这些结果支持spatialGE识别空间基因表达模式的能力,这些模式可为肿瘤微环境提供有价值的见解,并突出了其在为更广泛的科学界普及ST数据分析方面的效用。意义:spatialGE网络应用程序通过使用点击界面引导用户从数据输入到发现空间模式,实现对空间转录组学数据的用户友好型探索性分析,促进假设生成。