ArticleNature methods2023
A framework for evaluating the performance of SMLM cluster analysis algorithms.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2026Article
- Modern RNA Quantification Methods: From RT-qPCR to Advanced Microscopy.The journal of physical chemistry. B · 2026Review
- Molecular mapping in DNA-PAINT via modified Gaussian Mixture Modeling.Nature communications · 2026Article
- Treatment of acute myeloid leukemia models by targeting a cell surface RNA-binding protein.Nature biotechnology · 2026Article
- Seeing the Unseen: Super-Resolution Microscopy in Protein Aggregation Research.Chemical & biomedical imaging · 2026Review
- Case Study: Illuminating the Nanoscale World of Microbiology.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Enhanced spatial clustering of single-molecule localizations with graph neural networks.Nature communications · 2025Article
- HDCluster: High-Degree Graph Clustering for Robust Analysis of Single Molecule Localization Microscopy.bioRxiv : the preprint server for biology · 2025Article
- Nanoscale restructuring of the immune synapse with an engager enhances NK cell function.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Mapping the nuclear landscape with multiplexed super-resolution fluorescence microscopy.Nature communications · 2025Article
- Bayesian optimized parameter selection for density-based clustering applied to single molecule localization microscopy.Communications biology · 2025Article
- Extracellular vesicles adhere to cells primarily by interactions of integrins and GM1 with laminin.The Journal of cell biology · 2025Article
- Closing the multichannel gap through computational reconstruction of interaction in super-resolution microscopy.Patterns (New York, N.Y.) · 2025Review
- Article
- Three-color single-molecule localization microscopy in chromatin.Light, science & applications · 2025Article
- Uptake of small extracellular vesicles by recipient cells is facilitated by paracrine adhesion signaling.Nature communications · 2025Article
- Cluster parameter-based DBSCAN maps for image characterization.Computational and structural biotechnology journal · 2025Article
- Article
- Anisotropic DBSCAN for 3D SMLM Data Clustering.The journal of physical chemistry. B · 2024Article
- Membrane topography and the overestimation of protein clustering in single molecule localisation microscopy - identification and correction.Communications biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
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.
Indexed as
Identifiers
36765136What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.