A framework for evaluating the performance of SMLM cluster analysis algorithms.

Autor: Nieves DJ; Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.; Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK., Pike JA; Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK.; Institute of Cardiovascular Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK., Levet F; Interdisciplinary Institute for Neuroscience, CNRS, IINS, UMR 5297, Université de Bordeaux, Bordeaux, France.; Bordeaux Imaging Center, CNRS, INSERM, BIC, UMS 3420, US 4, Université de Bordeaux, Bordeaux, France., Williamson DJ; Department of Infectious Diseases, School of Immunology and Microbial Sciences, King's College London, London, UK., Baragilly M; Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.; Department of Mathematics, Insurance and Applied Statistics, Helwan University, Helwan, Egypt., Oloketuyi S; Laboratory of Environmental and Life Sciences, University of Nova Gorica, Rožna Dolina, Slovenia., de Marco A; Laboratory of Environmental and Life Sciences, University of Nova Gorica, Rožna Dolina, Slovenia., Griffié J; Laboratory of Experimental Biophysics, Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland., Sage D; Biomedical Imaging Group, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland., Cohen EAK; Department of Mathematics, Imperial College London, London, UK., Sibarita JB; Interdisciplinary Institute for Neuroscience, CNRS, IINS, UMR 5297, Université de Bordeaux, Bordeaux, France., Heilemann M; Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany., Owen DM; Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK. d.owen@bham.ac.uk.; Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK. d.owen@bham.ac.uk.; School of Mathematics, University of Birmingham, Birmingham, UK. d.owen@bham.ac.uk.
Jazyk: angličtina
Zdroj: Nature methods [Nat Methods] 2023 Feb; Vol. 20 (2), pp. 259-267. Date of Electronic Publication: 2023 Feb 10.
DOI: 10.1038/s41592-022-01750-6
Abstrakt: 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.
(© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Databáze: MEDLINE
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