标题： 基于图神经网络的分子定位空间聚类 显示英文标题

标题： Spatial Clustering of Molecular Localizations with Graph Neural Networks

摘要： 单分子定位显微镜生成对应于荧光团定位的点云。 这些点云的空间聚类识别和分析对于提取关于分子组织的见解至关重要。 然而，在存在定位噪声、高点密度或复杂生物结构的情况下，这项任务变得具有挑战性。 在此，我们引入了MIRO（通过关系优化的多模式集成），一种使用循环图神经网络将点云转换以提高在应用传统聚类技术时的聚类效率的算法。 我们表明，MIRO支持不同形状和多个尺度的聚类的同步处理，在各种数据集上表现出改进的性能。 我们的全面评估展示了MIRO在单分子定位应用中的变革潜力，展示了其革新聚类分析并提供分子结构准确可靠细节的能力。 此外，MIRO强大的聚类能力在多个领域具有应用前景，例如神经科学中神经连接模式的分析，以及环境科学中生态数据的空间分布研究。 显示英文摘要

摘要： 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 (Multimodal 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.