A BAYESIAN NONPARAMETRIC APPROACH TO SUPER-RESOLUTION SINGLE-MOLECULE LOCALIZATION

Mariano I. Gabitto, Herve Marie-Nelly, Ari Pakman, Andras Pataki, Xavier Darzacq, Michael I. Jordan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We consider the problem of single-molecule identification in superresolution microscopy. Super-resolution microscopy overcomes the diffraction limit by localizing individual fluorescing molecules in a field of view. This is particularly difficult since each individual molecule appears and disappears randomly across time and because the total number of molecules in the field of view is unknown. Additionally, data sets acquired with superresolution microscopes can contain a large number of spurious fluorescent fluctuations caused by background noise. To address these problems, we present a Bayesian nonparametric framework capable of identifying individual emitting molecules in super-resolved time series. We tackle the localization problem in the case in which each individual molecule is already localized in space. First, we collapse observations in time and develop a fast algorithm that builds upon the Dirichlet process. Next, we augment the model to account for the temporal aspect of fluorophore photophysics. Finally, we assess the performance of our methods with ground-truth data sets having known biological structure.

Original languageEnglish
Pages (from-to)1742-1766
Number of pages25
JournalAnnals of Applied Statistics
Volume15
Issue number4
DOIs
StatePublished - 1 Dec 2021
Externally publishedYes

Keywords

  • Bayesian nonparametrics
  • Super-resolution microscopy
  • Variational inference

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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