Single molecule localisation microscopy (SMLM) generates data in the form of Cartesian coordinates of localised fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite the range of developed cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance. Here, we use a systematic approach based on two metrics, the Adjusted Rand Index (ARI) and Intersection over Union (IoU), to score the success of clustering algorithms in diverse simulated clustering scenarios mimicking experimental data. We demonstrate the framework using three analysis algorithms: DBSCAN, ToMATo and KDE, show how to deduce optimal analysis parameters and how they are affected by fluorophore multiple blinking. We propose that these standard conditions and metrics become the basis for future analysis algorithm development and evaluation.

中文翻译：

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评估 SMLM 聚类分析算法性能的框架

单分子定位显微镜 (SMLM) 以定位荧光团的笛卡尔坐标形式生成数据。聚类分析是从此类数据中提取具有生物学意义的信息的一种有吸引力的途径，并已得到广泛应用。尽管开发的聚类分析算法范围很广，但不存在评估其性能的共识框架。在这里，我们使用基于两个指标的系统方法，即调整兰德指数 (ARI) 和联合交集 (IoU)，对模拟实验数据的各种模拟聚类场景中聚类算法的成功进行评分。我们使用三种分析算法演示了该框架：DBSCAN、ToMATo 和 KDE，展示了如何推导出最佳分析参数以及它们如何受荧光团多次闪烁的影响。