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Unbiased choice of global clustering parameters for single-molecule localization microscopy

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 complica...

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Autores principales: Verzelli, Pietro, Nold, Andreas, Sun, Chao, Heilemann, Mike, Schuman, Erin M., Tchumatchenko, Tatjana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800574/
https://www.ncbi.nlm.nih.gov/pubmed/36581654
http://dx.doi.org/10.1038/s41598-022-27074-1
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author Verzelli, Pietro
Nold, Andreas
Sun, Chao
Heilemann, Mike
Schuman, Erin M.
Tchumatchenko, Tatjana
author_facet Verzelli, Pietro
Nold, Andreas
Sun, Chao
Heilemann, Mike
Schuman, Erin M.
Tchumatchenko, Tatjana
author_sort Verzelli, Pietro
collection PubMed
description 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.
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spelling pubmed-98005742022-12-31 Unbiased choice of global clustering parameters for single-molecule localization microscopy Verzelli, Pietro Nold, Andreas Sun, Chao Heilemann, Mike Schuman, Erin M. Tchumatchenko, Tatjana Sci Rep Article 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. Nature Publishing Group UK 2022-12-29 /pmc/articles/PMC9800574/ /pubmed/36581654 http://dx.doi.org/10.1038/s41598-022-27074-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Verzelli, Pietro
Nold, Andreas
Sun, Chao
Heilemann, Mike
Schuman, Erin M.
Tchumatchenko, Tatjana
Unbiased choice of global clustering parameters for single-molecule localization microscopy
title Unbiased choice of global clustering parameters for single-molecule localization microscopy
title_full Unbiased choice of global clustering parameters for single-molecule localization microscopy
title_fullStr Unbiased choice of global clustering parameters for single-molecule localization microscopy
title_full_unstemmed Unbiased choice of global clustering parameters for single-molecule localization microscopy
title_short Unbiased choice of global clustering parameters for single-molecule localization microscopy
title_sort unbiased choice of global clustering parameters for single-molecule localization microscopy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800574/
https://www.ncbi.nlm.nih.gov/pubmed/36581654
http://dx.doi.org/10.1038/s41598-022-27074-1
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