Tessellation: SMLM Data Analysis

This lecture covers the principles of tessellation for single molecule localization microscopy (SMLM) data analysis, focusing on Voronoi tessellation as a space subdividing technique and colocalization analysis using Pearson and Manders' coefficients. The instructor presents applications in various fields, such as synaptic proteins and plant biology, highlighting the benefits and limitations of the method. Pros include robustness to localization density and multiscale segmentation, with fast 2D and 3D Voronoi generation. Cons involve challenges with non-homogeneous density, addressed through region of interest selection. The lecture also discusses the integration of state-of-the-art quantification methods and future developments in 3D analysis tools.