ArticleCommunications biology2025
Bayesian optimized parameter selection for density-based clustering applied to single molecule localization microscopy.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Modern RNA Quantification Methods: From RT-qPCR to Advanced Microscopy.The journal of physical chemistry. B · 2026Review
- Enhanced spatial clustering of single-molecule localizations with graph neural networks.Nature communications · 2025Article
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Authors and funding
3 authors.
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Abstract
Density-based clustering is used in many contexts including in single molecule localization microscopy (SMLM), where it is commonly used to elucidate the nanoscale organization of molecules. However, little guidance is available for evaluating clustering performance, which is often dependent on user-input parameters. Here, we develop an efficient implementation of density-based cluster validation (DBCV) that can quantitatively evaluate clustering in SMLM-sized datasets. We demonstrate that maximizing DBCV scores accurately identifies clusters in noisy, simulated data. Coupling DBCV with Bayesian optimization, we outline a method, DBOpt, that selects input parameters in an unbiased manner for density-based clustering algorithms. We demonstrate that optimal parameters can be selected for popular algorithms (DBSCAN, HDBSCAN, OPTICS) with minimal user input. Finally, we show that DBOpt reports accurate feature sizes in 2D and 3D experimental data. In sum, DBOpt will improve the integrity, reproducibility, and quality of cluster analyses for SMLM and beyond.
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