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 newspaperJournal articleResearchpeer-review


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 languageEnglish
Article number22561
JournalScientific Reports
Pages (from-to)22561
Publication statusPublished - 29 Dec 2022
Externally publishedYes


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


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