Single-molecule localization microscopy generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, high point density, or complex biological structures. Here, we introduce MIRO (Multifunctional Integration through Relational Optimization), an algorithm that uses recurrent graph neural networks to transform the point clouds in order to improve clustering efficiency when applying conventional clustering techniques. We show that MIRO supports simultaneous processing of clusters of different shapes and at multiple scales, demonstrating improved performance across varied datasets. Our comprehensive evaluation demonstrates MIRO's transformative potential for single-molecule localization applications, showcasing its capability to revolutionize cluster analysis and provide accurate, reliable details of molecular architecture. In addition, MIRO's robust clustering capabilities hold promise for applications in various fields such as neuroscience, for the analysis of neural connectivity patterns, and environmental science, for studying spatial distributions of ecological data.

翻译：单分子定位显微技术生成对应于荧光团定位的点云数据。对这些点云进行空间聚类识别与分析对于提取分子组织信息至关重要。然而，在存在定位噪声、高密度点云或复杂生物结构的情况下，该任务变得极具挑战性。本文提出MIRO（通过关系优化的多功能集成）算法，该算法利用循环图神经网络对点云进行变换，以提高应用传统聚类技术时的聚类效率。我们证明MIRO能够同时处理不同形状和多个尺度的聚类，并在多种数据集上展现出改进的性能。我们的综合评估表明MIRO在单分子定位应用中具有变革性潜力，展示了其革新聚类分析、提供准确可靠分子结构细节的能力。此外，MIRO强大的聚类能力在多个领域具有应用前景，例如神经科学中用于分析神经连接模式，以及环境科学中用于研究生态数据的空间分布。