Hyun Yoonsuk, Kim Doory
Department of Mathematics, Inha University, Republic of Korea.
Department of Chemistry, Hanyang University, Republic of Korea.
Comput Struct Biotechnol J. 2023 Jan 9;21:879-888. doi: 10.1016/j.csbj.2023.01.006. eCollection 2023.
With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.
随着超分辨率成像技术的发展，从细胞内蛋白质的聚集和组织方面在纳米尺度上理解蛋白质结构至关重要。然而，单分子定位显微镜（SMLM）图像的聚类分析仍然具有挑战性，因为为传统显微镜图像开发的经典计算聚类分析方法不适用于点彩派风格的SMLM数据，因此需要开发用于从SMLM图像进行聚类分析的独特方法。在本综述中，我们通过将计算聚类分析方法分为经典方法和基于机器学习的方法来讨论SMLM图像计算聚类分析方法的发展。最后，我们探讨了基于机器学习的SMLM数据聚类分析方法未来可能的发展方向。
