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research article

A framework for evaluating the performance of SMLM cluster analysis algorithms

Nieves, Daniel J.
•
Pike, Jeremy A.
•
Levet, Florian
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February 1, 2023
Nature Methods

This analysis compares the performance of seven algorithms for cluster analysis of single-molecule localization microscopy data. The results provide a framework for comparing these types of methods and point users to the best tools.

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.

  • Details
  • Metrics
Type
research article
DOI
10.1038/s41592-022-01750-6
Web of Science ID

WOS:000935657300011

Author(s)
Nieves, Daniel J.
•
Pike, Jeremy A.
•
Levet, Florian
•
Williamson, David J.
•
Baragilly, Mohammed
•
Oloketuyi, Sandra
•
de Marco, Ario
•
Griffie, Juliette  
•
Sage, Daniel  
•
Cohen, Edward A. K.
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Date Issued

2023-02-01

Publisher

NATURE PORTFOLIO

Published in
Nature Methods
Volume

20

Issue

2

Start page

259

End page
Subjects

Biochemical Research Methods

•

Biochemistry & Molecular Biology

•

localization microscopy

•

superresolution microscopy

•

binding

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LIB  
Available on Infoscience
March 27, 2023
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/196565
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