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PRISM:用于多重组织微阵列的交互和集成分析的Python包

Rafael Tubelleza^1,2, Aaron Kilgallon^1,2, Chin Wee Tan^1,3,4

¹Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia.

NAR genomics and bioinformatics

|August 27, 2025

概括

PRISM是一个新的Python包,用于分析来自组织微阵列 (TMA) 的多重蛋白质组数据. 它提供了一个端到端的解决方案,用于空间奥米克分析,帮助在转化癌症研究中发现生物标志物.

科学领域:

计算生物学
生物信息学
蛋白质组学

背景情况:

组织微阵列 (TMA) 可以同时分析多个组织样本,节约资源,并为临床应用提供高效的查.
多复合成像提供单细胞分辨率的空间蛋白质概况,对于了解瘤微环境和疾病机制至关重要.
高复合空间蛋白质数据分析对于生物标志物发现至关重要,但缺乏全面的计算工具.

研究的目的:

介绍PRISM,这是一个Python包,用于使用多重化蛋白质组数据进行交互式端到端分析.
通过简化空间数据的分析来促进翻译和临床研究.
为研究人员提供直观的界面,将原始多重图像转化为可操作的临床见解.

主要方法:

PRISM使用 SpatialData 框架进行标准化数据存储和互操作.
包括TMA图像分析用于组织掩盖,脱皮,细胞细分和特征提取.
功能 AnnData分析用于质量控制,聚类,细胞类型注释和空间分析,集成在napari中进行交互使用.

主要成果:

PRISM 能够使用基于标记的组织掩盖,TMA 脱落和单细胞特征提取.
促进质量控制,聚类,细胞类型注释和蛋白质组数据的空间分析.
提供高效的多分辨率图像处理,并通过可扩展的数据结构,并行化和GPU加速加速生物信息工作流.

结论:

PRISM提供了一个模块化,计算效率高,以及用于空间数据分析的交互式解决方案.
简化了原始多重图像的转化为临床相关的见解.
使研究人员能够有效地探索和与生物标志物发现的复杂空间蛋白质组数据集进行交互.

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