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

Verzelli, P., Nold, A., Sun, C., Heilemann, M., Schuman, E. M., & Tchumatchenko, T. (2022). Unbiased choice of global clustering parameters for single-molecule localization microscopy. Sci. Rep., 12: 22561. doi:10.1038/s41598-022-27074-1.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-495E-9 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-495F-8
Genre: Journal Article

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This 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/.
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 Creators:
Verzelli, Pietro1, Author
Nold, Andreas1, 2, Author
Sun, Chao3, Author
Heilemann, Mike4, Author
Schuman, Erin M.3, Author                 
Tchumatchenko, T.1, 2, 5, Author           
Affiliations:
1Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany., ou_persistent22              
2Theory of neural dynamics Group, Max Planck Institute for Brain Research, Max Planck Society, ou_2461711              
3Synaptic Plasticity Department, Max Planck Institute for Brain Research, Max Planck Society, ou_2461710              
4Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany., ou_persistent22              
5Institute for Physiological Chemistry, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany, ou_persistent22              

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Free keywords: Computational biology and bioinformatics Functional clustering Image processing Statistical methods
 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.

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Language(s): eng - English
 Dates: 2022-10-172022-12-232022-12-29
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/s41598-022-27074-1
PMID: 36581654
PMC: PMC9800574
 Degree: -

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Project name : -
Grant ID : EMBO ALTF 860-2018
Funding program : EMBO long-term postdoctoral fel- lowship
Funding organization : -
Project name : -
Grant ID : LT000737/2019-C
Funding program : HFSP Cross-Disciplinary Fellowship
Funding organization : -
Project name : CRC 1080: Molecular and Cellular Mechanisms of Neural Homeostasis
Grant ID : 221828878
Funding program : Collaborative Research Centres
Funding organization : Deutsche Forschungsgemeinschaft
Project name : CRC 902: Molecular Principles of RNA-based Regulation
Grant ID : 161793742
Funding program : Collaborative Research Centres
Funding organization : Deutsche Forschungsgemeinschaft
Project name : Specialized Ribosomes for Neuronal Protein Synthesis
Grant ID : 743216
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: Sci. Rep.
  Abbreviation : Sci. Rep.
Source Genre: Journal
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Publ. Info: London, UK : Nature Publishing Group
Pages: - Volume / Issue: 12 Sequence Number: 22561 Start / End Page: - Identifier: ISSN: 2045-2322
CoNE: https://pure.mpg.de/cone/journals/resource/2045-2322