Robust nonparametric descriptors for clustering quantification in single-molecule localization microscopy

We report a robust nonparametric descriptor, J'(r), for quantifying the spatial organization of molecules in singlemolecule localization microscopy. J'(r), based on nearest neighbor distribution functions, does not require any parameter as an input for analyzing point patterns. We show that J'(r) displays a valley shape in the presence of clusters of molecules, and the characteristics of the valley reliably report the clustering features in the data. More importantly, the position of the J'(r) valley (rJ'm) depends exclusively on the density of clustering molecules ({rho}c). Therefore, it is ideal for direct measurements of clustering density of molecules in single-molecule localization microscopy.