Abstract
In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.
Highlights
In this work, we describe a BAyesian Multiple-emitter Fitting (BAMF) analysis that uses Reversible Jump Markov Chain Monte Carlo (RJMCMC)[15,16]
The entire BAMF algorithm consists of several steps (Fig. 1a): (1) converting raw data to photon counts, (2) estimation of the intensity prior, (3) division of each image into subregions, (4) the core RJMCMC algorithm, (5) using the RJMCMC chain to initialize MCMC within the most probable space, (6) using the MCMC chain to calculate the parameters and their associated uncertainties, and (7) making the final reconstructions by removing the localizations in the overlapping areas of the subregions (Supplementary Video 1), and combining the results
Jaccard Index (JAC) and localization accuracy are two standard measures to assess the performance of SMLM fitting algorithms[20]
Summary
We describe a BAyesian Multiple-emitter Fitting (BAMF) analysis that uses Reversible Jump Markov Chain Monte Carlo (RJMCMC)[15,16]. The entire BAMF algorithm consists of several steps (Fig. 1a): (1) converting raw data to photon counts, (2) estimation of the intensity prior, (3) division of each image into subregions, (4) the core RJMCMC algorithm, (5) using the RJMCMC chain to initialize MCMC within the most probable space, (6) using the MCMC chain to calculate the parameters and their associated uncertainties, and (7) making the final reconstructions by removing the localizations in the overlapping areas of the subregions (Supplementary Video 1), and combining the results. Signal (background) converts an emitter from a PSF shaped kernel of a background structure to a detected emitter
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