Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images - Simulation Data

The datasets contain simulated Single Particle Tracking (SPT) data consisting of sequences of camera images of a single fluorescent sub-diffraction limit-sized particle undergoing two-dimensional diffusion. We simulated a variety of experimental conditions, including different Signal-to-Background Ratios (SBRs), two different camera types, different diffusion speeds, and two different settings for motion blur. SPT is a class of experimental methods and data analysis techniques for exploring the motion of individual biological macromolecules. Typical estimation algorithms split the problem into two parts: first localize the particle at each data point to generate a trajectory and then estimate model parameters from that trajectory. We have recently introduced a class of algorithms for jointly estimating both trajectory and model parameters. In this study, we used the data to perform quantitative comparisons between two variants of our approach, one relying on a Sequential Monte Carlo met...