Wang Shulei, Arena Ellen T, Becker Jordan T, Bement William M, Sherer Nathan M, Eliceiri Kevin W, Yuan Ming
IEEE Trans Image Process. 2019 Apr 4. doi: 10.1109/TIP.2019.2909194.
Colocalization analysis aims to study complex spatial associations between bio-molecules via optical imaging techniques. However, existing colocalization analysis workflows only assess an average degree of colocalization within a certain region of interest and ignore the unique and valuable spatial information offered by microscopy. In the current work, we introduce a new framework for colocalization analysis that allows us to quantify colocalization levels at each individual location and automatically identify pixels or regions where colocalization occurs. The framework, referred to as spatially adaptive colocalization analysis (SACA), integrates a pixel-wise local kernel model for colocalization quantification and a multi-scale adaptive propagation-separation strategy for utilizing spatial information to detect colocalization in a spatially adaptive fashion. Applications to simulated and real biological datasets demonstrate the practical merits of SACA in what we hope to be an easily applicable and robust colocalization analysis method. In addition, theoretical properties of SACA are investigated to provide rigorous statistical justification.
共定位分析旨在通过光学成像技术研究生物分子之间复杂的空间关联。然而，现有的共定位分析流程仅评估感兴趣的特定区域内共定位的平均程度，而忽略了显微镜提供的独特且有价值的空间信息。在当前工作中，我们引入了一种新的共定位分析框架，该框架使我们能够量化每个单独位置的共定位水平，并自动识别发生共定位的像素或区域。该框架被称为空间自适应共定位分析（SACA），它集成了用于共定位量化的逐像素局部核模型和用于以空间自适应方式利用空间信息来检测共定位的多尺度自适应传播-分离策略。在模拟和真实生物数据集上的应用证明了SACA作为一种易于应用且稳健的共定位分析方法的实际优点。此外，我们还研究了SACA的理论特性，以提供严格的统计依据。
