Affiliation:
1. Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, China
2. Britton Chance Centerand MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
Abstract
Single molecule localization microscopy (SMLM) enables the analysis and quantification of protein complexes at the nanoscale. Using clustering analysis methods, quantitative information about protein complexes (for example, the size, density, number, and the distribution of nearest neighbors) can be extracted from coordinate-based SMLM data. However, since a final super-resolution image in SMLM is usually reconstructed from point clouds that contain millions of localizations, current popular clustering methods are not fast enough to enable daily quantification on such a big dataset. Here, we provide a fast and accurate clustering analysis method called FACAM, which is modified from the Alpha Shape method (a point dataset analysis method used in many fields). By taking advantage of parallel computation, FACAM is able to process millions of localizations in less than an hour, which is at least 10 times faster than the popular DBSCAN method. Furthermore, FACAM adaptively determines the segmentation threshold, and thus overcomes the problem of user-defined parameters. Using simulation and experimental datasets, we verified the advantages of FACAM over other reported clustering methods (including Ripley’s H, DBSCAN, and ClusterViSu).
Funder
National Natural Science Foundation of China
Start-up Fund from Hainan University
Subject
Radiology, Nuclear Medicine and imaging,Instrumentation,Atomic and Molecular Physics, and Optics