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B0799
Title: Statistical multiscale analysis for molecules Authors:  Frank Werner - Max Planck Institute for Biophysical Chemistry (Germany)
Katharina Proksch - University of Twente (Netherlands) [presenting]
Jan Keller - Max Planck Institute for Biophysical Chemistry (Germany)
Abstract: In recent years, many new super-resolution microscopy techniques such as STED or RESOLFT have been developed such that inference on the level of single molecules is now a reasonable goal in fluorescence microscopy. We discuss options to infer on the number and locations of molecules in a given sample based on a hybrid algorithm, which offers both the segmentation of an image based on multiscale methods as well as estimates of the local numbers of molecules, while it preserves uniform confidence about all statements. The proposed method allows for the construction of a novel, automatized statistical analysis tool for scanning microscopy via a molecular map, that is, a graphical presentation of locations as well as local numbers of molecules and corresponding uniform confidence statements. It is based on a sound statistical model, which connects both the local brightness and molecule distributions with the fact that a single molecule can emit only one photon at a time (antibunching). More precisely, our method is built on rigorous statistical convolution modeling of higher order photon coincidences and an approach on hot spot detection in heterogeneous data via multiscale scan statistics. We demonstrate the functionality of the molecular map by means of data examples from STED fluorescence microscopy.