Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK.
Nat Methods. 2023 Feb;20(2):259-267. doi: 10.1038/s41592-022-01750-6. Epub 2023 Feb 10.
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.
单分子定位显微镜（SMLM）以定位荧光团的坐标形式生成数据。聚类分析是从这类数据中提取有生物学意义的信息的一种很有吸引力的方法，已被广泛应用。尽管存在多种聚类分析算法，但对于评估它们的性能却没有共识框架。在这里，我们使用一种基于两个指标的系统方法，对模拟实验数据的聚类算法的成功程度进行评分。我们使用七种不同的分析算法（DBSCAN、ToMATo、KDE、FOCAL、CAML、ClusterViSu 和 SR-Tesseler）来演示该框架。由于表现最好的算法取决于定位的基础分布，我们展示了一个基于统计相似性度量的分析管道，该管道能够选择最合适的算法，以及针对真实 SMLM 数据的优化分析参数。我们建议将这些标准的模拟条件、指标和分析管道作为未来分析算法开发和评估的基础。
