HDCluster: High-Degree Graph Clustering for Robust Analysis of Single Molecule Localization Microscopy

Abstract

Clustering is a fundamental task in data analysis: grouping similar objects together into distinguishable subsets. Here, we introduce HDCluster, a novel high-degree graph-based clustering algorithm designed to effectively and rapidly handle various real-world clustering applications, particularly in the context of super-resolution single molecule localization microscopy (SMLM). HDCluster efficiently handles datasets with large and variable numbers of clusters, without requiring prior knowledge of the cluster count, relying on only one parameter. The high speed and efficiency of HDCluster allow it to handle large SMLM datasets with millions of localizations. A comprehensive quantitative comparison shows that HDCluster is equivalent to state-of-the-art clustering methods based on clustering performance measures, such as ARI and AMI, on simulated datasets. In terms of time efficiency, HDCluster significantly outperforms other clustering algorithms on large DNA-PAINT datasets. Application of HDCluster to a neuronal DNA-PAINT dataset on synapse biology identified and removed multiple blinking artifacts and effectively identified excitatory and inhibitory synapses, highlighting its utility for real experimental DNA-PAINT data. HDCluster is particularly robust to noise, making it a promising and effective tool for various clustering tasks in big-data settings, such as SMLM DNA-PAINT datasets.