ArticleScientific reports2022
Unbiased choice of global clustering parameters for single-molecule localization microscopy.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Objective clustering protocol for single-molecule data: A lifetime vs. intensity study.Biophysical reports · 2026Article
- Enhanced spatial clustering of single-molecule localizations with graph neural networks.Nature communications · 2025Article
- Closing the multichannel gap through computational reconstruction of interaction in super-resolution microscopy.Patterns (New York, N.Y.) · 2025Review
- Cluster parameter-based DBSCAN maps for image characterization.Computational and structural biotechnology journal · 2025Article
- Anisotropic DBSCAN for 3D SMLM Data Clustering.The journal of physical chemistry. B · 2024Article
- Quantitative Super-Resolution Imaging of ER-Phagy Initiation in Cells.Methods in molecular biology (Clifton, N.J.) · 2024Article
- The prevalence and specificity of local protein synthesis during neuronal synaptic plasticity.Science advances · 2021Article
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Authors and funding
6 authors.
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Abstract
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.
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