StormGraph: An automated graph-based algorithm for quantitative clustering analysis of single-molecule localization microscopy data

Clustering of proteins is crucial for many cellular processes and can be imaged at nanoscale resolution using single-molecule localization microscopy (SMLM). Existing cluster analysis methods for SMLM data suffer from major limitations, such as unsuitability for heterogeneous datasets, failure to account for uncertainties in localization data, excessive computation time, or inability to analyze three-dimensional data. To address these shortcomings, we developed StormGraph, an algorithm using graph theory and community detection to identify and quantify clusters in heterogeneous 2D and 3D SMLM datasets. StormGraph accounts for localization uncertainties and, by determining thresholds adaptively, it allows many heterogeneous samples to be analyzed using identical parameters. Consequently, StormGraph improves the potential accuracy, objectivity, and throughput of cluster analysis. Furthermore, StormGraph generates a hierarchical clustering, and it quantifies cluster colocalization for two-color SMLM data. We use simulated data to show that StormGraph is superior to existing algorithms. Finally, we demonstrate its application to two-dimensional B-cell antigen receptor clustering and three-dimensional intracellular LAMP-1 clustering.