Spatial Clustering of Molecular Localizations with Graph Neural Networks
This addresses the problem of accurate molecular organization analysis for researchers in fields like neuroscience and environmental science, though it appears incremental as it builds on existing clustering techniques with a novel transformation method.
The paper tackles the challenge of spatial cluster identification in single-molecule localization microscopy point clouds, which is difficult due to noise, high density, and complex structures, by introducing MIRO, an algorithm that uses recurrent graph neural networks to transform point clouds for improved clustering efficiency, demonstrating enhanced performance across varied datasets.
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.