用图神经网络提升单分子定位显微的聚类精度。
Enhanced Spatial Clustering of Single-Molecule Localizations with Graph Neural Networks
- 引入递归图神经网络重构点云，优化聚类输入
- 可同时处理不同形状与尺度的簇，提升多场景性能
- 适合神经连接与生态空间分布等复杂结构分析
单分子定位显微生成对应荧光分子定位的点云。对这些点云进行空间聚类识别与分析，是揭示分子组织结构的关键。然而，在定位噪声、高点密度或复杂生物结构下，该任务极具挑战。本文提出MIRO（基于关系优化的多功能整合），利用递归图神经网络对点云进行转换，以提升传统聚类方法的效率。实验表明，MIRO能同时处理多种形状与尺度的聚类，在不同数据集上均表现更优。全面评估显示，MIRO在单分子定位应用中具有变革潜力，可精准可靠地揭示分子架构细节。其鲁棒聚类能力在神经科学（如神经连接模式分析）和环境科学（如生态数据空间分布研究）等领域亦具前景。
原文摘要 · Abstract (English)
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.
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