A framework for evaluating the performance of SMLM data clustering algorithms

Daniel Nieves, Jeremy Pike, Florian Levet, David J. Williamson, Mohammed Baragilly, Sandra Oloketuyi, Ario de Marco, Juliette Griffié, Daniel Sage, Edward A.K. Cohen, Jean-Baptiste Sibarita, Mike Heilemann, Dylan Owen

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)259-267
JournalNature Methods
Volume20
DOIs
Publication statusPublished - 10 Feb 2023

Bibliographical note

D.M.O. acknowledges funding from BBSRC grant BB/R007365/1. M.H. acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, Project-ID 259130777, SFB 1177; GRK 2566). D.M.O. and M.B. acknowledge funding from the Alan Turing Institute.

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