FOCAL3D: Clustering Algorithm for SMLM

This lecture presents FOCAL3D, a density-based fast optimized clustering algorithm for Single-Molecule Localization Microscopy (SMLM). The algorithm addresses issues with parameter selection, run-time, and handling specific cluster configurations. It introduces the FOCAL algorithm, explaining its three key parameters and the improvements made in the 3D implementation with FOCAL3D. The lecture covers the iterative process of FOCAL3D, its performance on simulated and experimental data sets, and its run-time efficiency compared to other clustering algorithms. Pros and cons of FOCAL3D are discussed, highlighting its rapid parameter selection, noise robustness, and ability to handle hollow clusters. The lecture concludes with acknowledgements and information on the FOCAL3D package available for review on BiorXiv.