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

Pietro Verzelli; Andreas Nold; Chao Sun; Mike Heilemann; Erin M Schuman; Tatjana Tchumatchenko

doi:10.1038/s41598-022-27074-1

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

Pietro Verzelli, Andreas Nold, Chao Sun, Mike Heilemann, Erin M Schuman, Tatjana Tchumatchenko

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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.

Original language	English
Article number	22561
Journal	Scientific Reports
Volume	12
Issue	1
Pages (from-to)	22561
ISSN	2045-2322
DOIs	https://doi.org/10.1038/s41598-022-27074-1
Publication status	Published - 29 Dec 2022
Externally published	Yes

Keywords

Microscopy/methods
Single Molecule Imaging/methods
Algorithms
Cluster Analysis
Nanotechnology

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10.1038/s41598-022-27074-1

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title = "Unbiased choice of global clustering parameters for single-molecule localization microscopy",

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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T1 - Unbiased choice of global clustering parameters for single-molecule localization microscopy

AU - Verzelli, Pietro

AU - Nold, Andreas

AU - Sun, Chao

AU - Heilemann, Mike

AU - Schuman, Erin M

AU - Tchumatchenko, Tatjana

PY - 2022/12/29

Y1 - 2022/12/29

N2 - 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.

AB - 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.

KW - Microscopy/methods

KW - Single Molecule Imaging/methods

KW - Algorithms

KW - Cluster Analysis

KW - Nanotechnology

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

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