Topographic prominence as a method for cluster identification in single-molecule localisation data

Single-molecule localisation based super-resolution fluorescence imaging produces maps of the coordinates of fluorescent molecules in a region of interest. Cluster analysis algorithms provide information concerning the clustering characteristics of these molecules, often through the generation of cluster heat maps based on local molecular density. The goal of this study was to generate a new cluster analysis method based on a topographic approach. In particular, a topographic map of the level of clustering across a region is generated based on Getis' variant of Ripley's K-function. By using the relative heights (topographic prominence, TP) of the peaks in the map, cluster characteristics can be identified more accurately than by using previously demonstrated height thresholds. Analogous to geological TP, the concepts of wet and dry TP and topographic isolation are introduced to generate binary maps. The algorithm is validated using simulated and experimental data and found to significantly outperform previous cluster identification methods. Illustration of the topographic prominence based cluster analysis algorithm.