ArticleNature protocols2016
A Bayesian cluster analysis method for single-molecule localization microscopy data.
Article in Nature protocols, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Objective clustering protocol for single-molecule data: A lifetime vs. intensity study.Biophysical reports · 2026Article
- Modern RNA Quantification Methods: From RT-qPCR to Advanced Microscopy.The journal of physical chemistry. B · 2026Review
- Cluster parameter-based DBSCAN maps for image characterization.Computational and structural biotechnology journal · 2025Article
- Modeling Chickpea Productivity with Artificial Image Objects and Convolutional Neural Network.Plants (Basel, Switzerland) · 2024Article
- Recent Advances in Single-Molecule Tracking and Imaging Techniques.Annual review of analytical chemistry (Palo Alto, Calif.) · 2023Review
- Quantitative Single-Molecule Localization Microscopy.Annual review of biophysics · 2023Review
- The anatomy of transcriptionally active chromatin loops in Drosophila primary spermatocytes using super-resolution microscopy.PLoS genetics · 2023Article
- A framework for evaluating the performance of SMLM cluster analysis algorithms.Nature methods · 2023Article
- Cluster analysis for localisation-based data sets: dos and don'ts when quantifying protein aggregates.Frontiers in bioinformatics · 2023Review
- Quantitative Analysis of PcG-Associated Condensates by Stochastic Optical Reconstruction Microscopy (STORM).Methods in molecular biology (Clifton, N.J.) · 2023Article
- Unbiased choice of global clustering parameters for single-molecule localization microscopy.Scientific reports · 2022Article
- Correction of multiple-blinking artifacts in photoactivated localization microscopy.Nature methods · 2022Article
- Parameter-free molecular super-structures quantification in single-molecule localization microscopy.The Journal of cell biology · 2021Article
- Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data.Frontiers in bioinformatics · 2021Review
- An Efficient GUI-Based Clustering Software for Simulation and Bayesian Cluster Analysis of Single-Molecule Localization Microscopy Data.Frontiers in bioinformatics · 2021Article
- FOCAL3D: A 3-dimensional clustering package for single-molecule localization microscopy.PLoS computational biology · 2020Article
- Article
- A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods.Patterns (New York, N.Y.) · 2020Review
- The Decade of Super-Resolution Microscopy of the Presynapse.Frontiers in synaptic neuroscience · 2020Review
- Detecting Differences of Fluorescent Markers Distribution in Single Cell Microscopy: Textural or Pointillist Feature Space?Frontiers in robotics and AI · 2020Article
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Authors and funding
10 authors.
Funding
Abstract
Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in data generated by 2D single-molecule localization microscopy (SMLM)-for example, photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM). Three features of such data can cause standard cluster analysis approaches to be ineffective: (i) the data take the form of a list of points rather than a pixel array; (ii) there is a non-negligible unclustered background density of points that must be accounted for; and (iii) each localization has an associated uncertainty in regard to its position. These issues are overcome using a Bayesian, model-based approach. Many possible cluster configurations are proposed and scored against a generative model, which assumes Gaussian clusters overlaid on a completely spatially random (CSR) background, before every point is scrambled by its localization precision. We present the process of generating simulated and experimental data that are suitable to our algorithm, the analysis itself, and the extraction and interpretation of key cluster descriptors such as the number of clusters, cluster radii and the number of localizations per cluster. Variations in these descriptors can be interpreted as arising from changes in the organization of the cellular nanoarchitecture. The protocol requires no specific programming ability, and the processing time for one data set, typically containing 30 regions of interest, is ∼18 h; user input takes ∼1 h.
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