SPEX:一个模块化的端到端平台,用于高复杂性组织空间组学分析。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Xiao Li, Ximo Pechuan-Jorge, Tyler Risom, Conrad Foo, Alexander Prilipko, Artem Zubkov, Caleb Chan, Patrick Chang, Frank Peale, James Ziai, Sandra Rost, Derrek Hibar, Lisa McGinnis, Evgeniy Tabatsky, Xin Ye, Hector Corrada Bravo, Zhen Shi, Malgorzata Nowicka, Jon Scherdin, James Cowan, Jennifer Giltnane, Darya Orlova, Rajiv Jesudason
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引用次数: 0

摘要

转录组学和蛋白质组学的最新进展为组织结构的空间解析分子表征提供了可能性,并有望在稳态或疾病中更深入地了解组织生物学。这些技术产生的大量数据最近推动了各种计算方法的发展。这些方法在整个空间组学分析过程中都需要高级的编码流畅性,因此对生物研究界的广泛采用提出了障碍。为了解决这个问题,我们引入了SPEX(空间表达浏览器),这是一个基于web的分析平台,采用模块化分析管道设计,通过用户友好的界面进行访问。SPEX的基础设施允许对开源图像数据管理系统、分析模块和完全集成的数据可视化解决方案进行简化访问。分析模块包括基本步骤,涵盖图像处理,单细胞分析和空间分析。我们证明了SPEX能够促进从健康组织到肿瘤样本的空间分辨组学数据集的生物学见解的发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SPEX: A modular end-to-end platform for high-plex tissue spatial omics analysis.

Recent advancements in transcriptomics and proteomics have opened the possibility for spatially resolved molecular characterization of tissue architecture with the promise of enabling a deeper understanding of tissue biology in either homeostasis or disease. The wealth of data generated by these technologies has recently driven the development of a wide range of computational methods. These methods have the requirement of advanced coding fluency to be applied and integrated across the full spatial omics analysis process, thus presenting a hurdle for widespread adoption by the biology research community. To address this, we introduce SPEX (Spatial Expression Explorer), a web-based analysis platform that employs modular analysis pipeline design, accessible through a user-friendly interface. SPEX's infrastructure allows for streamlined access to open-source image data management systems, analysis modules, and fully integrated data visualization solutions. Analysis modules include essential steps covering image processing, single-cell analysis, and spatial analysis. We demonstrate SPEX's ability to facilitate the discovery of biological insights in spatially resolved omics datasets from healthy tissue to tumor samples.

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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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