A framework for evaluating the performance of SMLM cluster analysis algorithms
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Single-molecule localization microscopy (SMLM) generates data in the form of coordinates of localized fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite a range of cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance. Here, we use a systematic approach based on two metrics to score the success of clustering algorithms in simulated conditions mimicking experimental data. We demonstrate the framework using seven diverse analysis algorithms: DBSCAN, ToMATo, KDE, FOCAL, CAML, ClusterViSu and SR-Tesseler. Given that the best performer depended on the underlying distribution of localizations, we demonstrate an analysis pipeline based on statistical similarity measures that enables the selection of the most appropriate algorithm, and the optimized analysis parameters for real SMLM data. We propose that these standard simulated conditions, metrics and analysis pipeline become the basis for future analysis algorithm development and evaluation.
Date Issued
2023-02-01
Date Acceptance
2022-12-06
Citation
Nature Methods, 2023, 20 (2), pp.259-267
ISSN
1548-7091
Publisher
Nature Research
Start Page
259
End Page
267
Journal / Book Title
Nature Methods
Volume
20
Issue
2
Copyright Statement
Copyright © 2023 Springer-Verlag. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1038/s41592-022-01750-6
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/36765136
PII: 10.1038/s41592-022-01750-6
Subjects
Algorithms
Single Molecule Imaging
Cluster Analysis
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2023-02-10