Lecture
This lecture covers the development of SuperNet, a software for super-resolution network analysis and quantification of single molecule clusters. The lecture discusses the motivation behind the software, challenges in analyzing super-resolution data, and the methodology used for network analysis. It also delves into the features of SuperNet, such as its platform independence, support for various microscopes, and machine learning capabilities. The presentation includes details on the software's modules for data loading, merging, filtering, and individual blob analysis. Additionally, it explores the proposed methods for fast, scalable, 3D analysis of clusters, robust to noise, flexible, and quantitative. The lecture concludes with acknowledgments of the groups involved in the research.