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

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 data...

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Detalles Bibliográficos
Autores principales: Schodt, David J., Lidke, Keith A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581011/
https://www.ncbi.nlm.nih.gov/pubmed/36303762
http://dx.doi.org/10.3389/fbinf.2021.724325
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author Schodt, David J.
Lidke, Keith A.
author_facet Schodt, David J.
Lidke, Keith A.
author_sort Schodt, David J.
collection PubMed
description 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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spelling pubmed-95810112022-10-26 Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem Schodt, David J. Lidke, Keith A. Front Bioinform Bioinformatics 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. Frontiers Media S.A. 2021-10-20 /pmc/articles/PMC9581011/ /pubmed/36303762 http://dx.doi.org/10.3389/fbinf.2021.724325 Text en Copyright © 2021 Schodt and Lidke. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioinformatics
Schodt, David J.
Lidke, Keith A.
Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem
title Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem
title_full Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem
title_fullStr Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem
title_full_unstemmed Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem
title_short Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem
title_sort spatiotemporal clustering of repeated super-resolution localizations via linear assignment problem
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581011/
https://www.ncbi.nlm.nih.gov/pubmed/36303762
http://dx.doi.org/10.3389/fbinf.2021.724325
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