Computational Science and Informatics, Roche Diagnostics Solutions, Santa Clara, CA, 95050, USA.
Sci Rep. 2024 Nov 28;14(1):29585. doi: 10.1038/s41598-024-81383-1.
Understanding spatial dynamics within tissue microenvironments is crucial for deciphering cellular interactions and molecular signaling in living systems. These spatial characteristics govern cell distribution, extracellular matrix components, and signaling molecules, influencing local biochemical and biophysical conditions. Despite significant progress in analyzing digital pathology images, current methods for capturing spatial relationships are limited. They often rely on specific spatial features that only partially describe the complex spatial distributions of cells and are frequently tied to particular outcomes within predefined model frameworks. Furthermore, these methods are typically limited to field of view analysis, which restricts their capacity to capture spatial patterns across whole-slide images, thereby limiting their ability to fully address the complexities of tissue architecture. To address these limitations, we present SpatialQPFs (Spatial Quantitative Pathology Features), an R package designed to extract interpretable spatial features from cell imaging data using spatial statistical methodologies. Leveraging segmented cell information, our package offers a comprehensive toolkit for applying a range of spatial statistical methods within a stochastic process framework, including analyses of point process data, areal data, and geostatistical data. By decoupling feature extraction from specific outcome models, SpatialQPFs enables thorough large-scale spatial analyses applicable across diverse clinical and biological contexts. This approach enhances the depth and accuracy of spatial insights derived from tissue data, empowering researchers to conduct comprehensive spatial analyses efficiently and reproducibly. By providing a flexible and robust framework for spatial feature extraction, SpatialQPFs facilitates advanced spatial analyses, paving the way for new discoveries in tissue biology and pathology. SpatialQPFs code and documentation are publicly available at https://github.com/Genentech/SpatialQPFs .
理解组织微环境中的空间动态对于揭示活系统中的细胞相互作用和分子信号至关重要。这些空间特征决定了细胞的分布、细胞外基质成分和信号分子，影响局部生化和生物物理条件。尽管在分析数字病理学图像方面取得了重大进展，但目前捕获空间关系的方法仍然有限。这些方法通常依赖于仅部分描述细胞复杂空间分布的特定空间特征，并且常常与特定的模型框架内的特定结果相关联。此外，这些方法通常仅限于视野分析，这限制了它们捕获整个幻灯片图像中空间模式的能力，从而限制了它们充分解决组织架构复杂性的能力。为了解决这些限制，我们提出了 SpatialQPFs（空间定量病理学特征），这是一个 R 包，旨在使用空间统计方法从细胞成像数据中提取可解释的空间特征。利用分割的细胞信息，我们的软件包提供了一个全面的工具包，可在随机过程框架内应用一系列空间统计方法，包括点过程数据、面积数据和地质统计数据的分析。通过将特征提取与特定的结果模型分离，SpatialQPFs 能够在不同的临床和生物学背景下进行全面的大规模空间分析。这种方法增强了从组织数据中得出的空间洞察力的深度和准确性，使研究人员能够高效且可重复地进行全面的空间分析。通过提供灵活而强大的空间特征提取框架，SpatialQPFs 促进了高级空间分析，为组织生物学和病理学的新发现铺平了道路。SpatialQPFs 的代码和文档可在 https://github.com/Genentech/SpatialQPFs 上公开获取。
