Unbiased choice of global clustering parameters for single-molecule localization microscopy.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
29 12 2022
29 12 2022
Historique:
received:
17
10
2022
accepted:
23
12
2022
entrez:
29
12
2022
pubmed:
30
12
2022
medline:
3
1
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36581654
doi: 10.1038/s41598-022-27074-1
pii: 10.1038/s41598-022-27074-1
pmc: PMC9800574
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
22561Informations de copyright
© 2022. The Author(s).
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