Jiang Xi, Luo Danni, Fern Ndez Esteban, Yang Jie, Li Huimin, Jin Kevin W, Zhan Yuanchun, Yao Bo, Bedi Suhana, Xiao Guanghua, Zhan Xiaowei, Li Qiwei, Xie Yang
bioRxiv. 2023 Mar 12:2023.03.10.532127. doi: 10.1101/2023.03.10.532127.
The emerging field of spatially resolved transcriptomics (SRT) has revolutionized biomedical research. SRT quantifies expression levels at different spatial locations, providing a new and powerful tool to interrogate novel biological insights. An essential question in the analysis of SRT data is to identify spatially variable (SV) genes; the expression levels of such genes have spatial variation across different tissues. SV genes usually play an important role in underlying biological mechanisms and tissue heterogeneity. Currently, several computational methods have been developed to detect such genes; however, there is a lack of unbiased assessment of these approaches to guide researchers in selecting the appropriate methods for their specific biomedical applications. In addition, it is difficult for researchers to implement different existing methods for either biological study or methodology development. Furthermore, currently available public SRT datasets are scattered across different websites and preprocessed in different ways, posing additional obstacles for quantitative researchers developing computational methods for SRT data analysis. To address these challenges, we designed Spatial Transcriptomics Arena (STAr), an open platform comprising 193 curated datasets from seven technologies, seven statistical methods, and analysis results. This resource allows users to retrieve high-quality datasets, apply or develop spatial gene detection methods, as well as browse and compare spatial gene analysis results. It also enables researchers to comprehensively evaluate SRT methodology research in both simulated and real datasets. Altogether, STAr is an integrated research resource intended to promote reproducible research and accelerate rigorous methodology development, which can eventually lead to an improved understanding of biological processes and diseases. STAr can be accessed at https://lce.biohpc.swmed.edu/star/ .
空间分辨转录组学（SRT）这一新兴领域彻底改变了生物医学研究。SRT可量化不同空间位置的表达水平，为探究新的生物学见解提供了一种全新且强大的工具。SRT数据分析中的一个关键问题是识别空间可变（SV）基因；此类基因的表达水平在不同组织间存在空间差异。SV基因通常在潜在生物学机制和组织异质性中发挥重要作用。目前，已开发出多种计算方法来检测此类基因；然而，缺乏对这些方法的无偏评估，难以指导研究人员为其特定的生物医学应用选择合适的方法。此外，研究人员难以实施不同的现有方法用于生物学研究或方法开发。再者，当前可用的公共SRT数据集分散在不同网站，且预处理方式各异，这给开发SRT数据分析计算方法的定量研究人员带来了额外障碍。为应对这些挑战，我们设计了空间转录组学竞技场（STAr），这是一个开放平台，包含来自七种技术的193个经过整理的数据集、七种统计方法及分析结果。该资源允许用户检索高质量数据集、应用或开发空间基因检测方法，以及浏览和比较空间基因分析结果。它还使研究人员能够在模拟和真实数据集中全面评估SRT方法学研究。总之，STAr是一个综合研究资源，旨在促进可重复研究并加速严谨的方法学开发，最终有助于增进对生物过程和疾病的理解。可通过https://lce.biohpc.swmed.edu/star/访问STAr。
