Colocalization aims at characterizing spatial associations between two fluorescently-tagged biomolecules by quantifying the co-occurrence and correlation between the two channels acquired in fluorescence microscopy. Colocalization is presented either as the degree of overlap between the two channels or the overlays of the red and green images, with areas of yellow indicating colocalization of the molecules. This problem remains an open issue in diffraction-limited microscopy and raises new challenges with the emergence of super-resolution imaging, a microscopic technique awarded by the 2014 Nobel prize in chemistry. We propose GcoPS, for Geo-coPositioning System, an original method that exploits the random sets structure of the tagged molecules to provide an explicit testing procedure. Our simulation study shows that GcoPS unequivocally outperforms the best competitive methods in adverse situations (noise, irregularly shaped fluorescent patterns, different optical resolutions). GcoPS is also much faster, a decisive advantage to face the huge amount of data in super-resolution imaging. We demonstrate the performances of GcoPS on two biological real datasets, obtained by conventional diffraction-limited microscopy technique and by super-resolution technique, respectively. This article is protected by copyright. All rights reserved.

中文翻译：

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测试两个随机集之间的独立性以分析生物成像中的共定位

共定位旨在通过量化荧光显微镜中获得的两个通道之间的共现和相关性来表征两个荧光标记生物分子之间的空间关联。共定位表现为两个通道之间的重叠程度或红色和绿色图像的叠加，黄色区域表示分子的共定位。这个问题在衍射极限显微镜中仍然是一个悬而未决的问题，并且随着超分辨率成像的出现提出了新的挑战，超分辨率成像是一种由 2014 年诺贝尔化学奖授予的显微技术。我们为地理定位系统提出了 GcoPS，这是一种利用标记分子的随机集结构提供明确测试程序的原始方法。我们的模拟研究表明，GcoPS 在不利情况下（噪声、形状不规则的荧光图案、不同的光学分辨率）明显优于最佳竞争方法。GcoPS 的速度也快得多，这是在超分辨率成像中面对海量数据的决定性优势。我们展示了 GcoPS 在两个生物真实数据集上的性能，分别通过传统衍射极限显微镜技术和超分辨率技术获得。本文受版权保护。版权所有。我们展示了 GcoPS 在两个生物真实数据集上的性能，分别通过传统衍射极限显微镜技术和超分辨率技术获得。本文受版权保护。版权所有。我们展示了 GcoPS 在两个生物真实数据集上的性能，分别通过传统衍射极限显微镜技术和超分辨率技术获得。本文受版权保护。版权所有。