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Authors: Verzelli, Pietro
Nold, Andreas
Sun, Chao
Heilemann, Mike
Schuman, Erin M.
Tchumatchenko, Tatjana
Title: Unbiased choice of global clustering parameters for single-molecule localization microscopy
Online publication date: 20-Apr-2023
Year of first publication: 2022
Language: english
Abstract: Single-molecule localization microscopy resolves objects below the diffraction limit of light via sparse, stochastic detection of target molecules. Single molecules appear as clustered detection events after image reconstruction. However, identification of clusters of localizations is often complicated by the spatial proximity of target molecules and by background noise. Clustering results of existing algorithms often depend on user-generated training data or user-selected parameters, which can lead to unintentional clustering errors. Here we suggest an unbiased algorithm (FINDER) based on adaptive global parameter selection and demonstrate that the algorithm is robust to noise inclusion and target molecule density. We benchmarked FINDER against the most common density based clustering algorithms in test scenarios based on experimental datasets. We show that FINDER can keep the number of false positive inclusions low while also maintaining a low number of false negative detections in densely populated regions.
DDC: 610 Medizin
610 Medical sciences
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 04 Medizin
Place: Mainz
Version: Published version
Publication type: Zeitschriftenaufsatz
Document type specification: Scientific article
License: CC BY
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Journal: Scientific reports
Pages or article number: 22561
Publisher: Macmillan Publishers Limited, part of Springer Nature
Publisher place: London
Issue date: 2022
ISSN: 2045-2322
Publisher URL:
Publisher DOI: 10.1038/s41598-022-27074-1
Appears in collections:DFG-491381577-G

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