Ospina Oscar E, Manjarres-Betancur Roberto, Gonzalez-Calderon Guillermo, Soupir Alex C, Smalley Inna, Tsai Kenneth, Markowitz Joseph, Vallebuona Ethan, Berglund Anders, Eschrich Steven, Yu Xiaoqing, Fridley Brooke L
Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA.
Biostatistics and Bioinformatics Shared Resource, Moffitt Cancer Center, Tampa, FL, USA.
bioRxiv. 2024 Jul 2:2024.06.27.601050. doi: 10.1101/2024.06.27.601050.
Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides 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 enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.
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,由于需要先进的数据分析和编程技能,其潜力常常未得到充分利用。为了解决这个问题,我们推出了spatialGE,一个简化ST数据分析的网络应用程序。应用程序spatialGE提供了一个用户友好的界面,引导没有编程专业知识的用户通过各种分析流程,包括质量控制、归一化、区域检测、表型分析和多种空间分析。它还能够进行样本间的比较分析,并支持各种ST技术。我们通过将spatialGE应用于研究黑色素瘤脑转移和默克尔细胞癌的肿瘤微环境来证明其效用。我们的结果突出了spatialGE识别空间基因表达模式和富集的能力,为肿瘤微环境提供了有价值的见解,以及它在使更广泛的科学界能够进行ST数据分析方面的效用。