Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem.

Front Bioinform

Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, United States.

Published: October 2021

Many fluorescence super-resolution techniques, such as (d)STORM, PALM, and DNA-PAINT, generate datasets wherein multiple localizations across many camera frames may arise from a single blinking event of an emitter. These repeated localizations not only hinder interpretation and analysis of such datasets, but also represent an incomplete use of the fluorescence photons. Such localizations are typically combined into a single localization either by clustering with hard distance and time thresholds, or by classical hypothesis testing assuming Gaussian localization errors. In this work, we describe a method for clustering that accounts for localization precision, local emitter density estimates, and a kinetic model for blinking which is used to optimize connections within a group of spatiotemporally colocated localizations.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581011PMC
http://dx.doi.org/10.3389/fbinf.2021.724325DOI Listing

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