ArticleFrontiers in bioinformatics2021
Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem.
Article in Frontiers in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Antigen properties shape organization of FcεRI aggregates to tune mast cell signaling.bioRxiv : the preprint server for biology · 2025Article
- Multiparatopic antibodies induce targeted downregulation of programmed death-ligand 1.Cell chemical biology · 2024Article
- Automated multi-target super-resolution microscopy with trust regions.Biomedical optics express · 2023Article
- SMITE: Single Molecule Imaging Toolbox Extraordinaire (MATLAB).Journal of open source software · 2023Article
- High-precision estimation of emitter positions using Bayesian grouping of localizations.Nature communications · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
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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