Enhanced spatial clustering of single-molecule localizations with graph neural networks.

ABSTRACT: 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.

SUBMITTER: Pineda J 

PROVIDER: S-EPMC12583556 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature