Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1784861319278624768 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9800574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT verzellipietro unbiasedchoiceofglobalclusteringparametersforsinglemoleculelocalizationmicroscopy AT noldandreas unbiasedchoiceofglobalclusteringparametersforsinglemoleculelocalizationmicroscopy AT sunchao unbiasedchoiceofglobalclusteringparametersforsinglemoleculelocalizationmicroscopy AT heilemannmike unbiasedchoiceofglobalclusteringparametersforsinglemoleculelocalizationmicroscopy AT schumanerinm unbiasedchoiceofglobalclusteringparametersforsinglemoleculelocalizationmicroscopy AT tchumatchenkotatjana unbiasedchoiceofglobalclusteringparametersforsinglemoleculelocalizationmicroscopy |