Abstract：
Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream technologies in the field of structural biology to determine the three-dimensional (3D) structures of biological macromolecules. Heterogeneous cryo-EM projection image classification is an effective way to discover conformational heterogeneity of biological macromolecules in different functional states. However, due to the low signal-to-noise ratio of the projection images, the classification of heterogeneous cryo-EM projection images is a very challenging task. In this paper, two novel distance measures between projection images integrating the reliability of common lines, pixel intensity and class averages are designed, and then a two-stage spectral clustering algorithm based on the two distance measures is proposed for heterogeneous cryo-EM projection image classification. In the first stage, the novel distance measure integrating common lines and pixel intensities of projection images is used to obtain preliminary classification results through spectral clustering. In the second stage, another novel distance measure integrating the first novel distance measure and class averages generated from each group of projection images is used to obtain the final classification results through spectral clustering. The proposed two-stage spectral clustering algorithm is applied on a simulated and a real cryo-EM dataset for heterogeneous reconstruction. Results show that the two novel distance measures can be used to improve the classification performance of spectral clustering, and using the proposed two-stage spectral clustering algorithm can achieve higher classification and reconstruction accuracy than using RELION and XMIPP.
摘要：
单粒子低温电子显微镜（cryo-EM）已成为结构生物学领域确定生物大分子三维（3D）结构的主流技术之一。异质低温-EM投影图像分类是发现生物大分子在不同功能状态下构象异质性的有效途径。然而,由于投影图像的信噪比低，异构低温电磁投影图像的分类是一项非常具有挑战性的任务。在本文中,投影图像之间的两种新颖的距离度量，集成了公共线的可靠性，像素强度和类平均设计，然后提出了一种基于两种距离度量的两阶段谱聚类算法，用于异构低温电磁投影图像分类。在第一阶段,结合投影图像的公共线和像素强度的新型距离度量用于通过谱聚类获得初步分类结果。在第二阶段,另一种新颖的距离度量，将第一种新颖的距离度量和从每组投影图像生成的类平均进行整合，以通过谱聚类获得最终的分类结果。将所提出的两阶段谱聚类算法应用于模拟和真实的低温EM数据集上，以进行异构重建。结果表明，两种新的距离度量可以用来提高谱聚类的分类性能，与使用RELION和XMIPP相比，使用所提出的两阶段谱聚类算法可以获得更高的分类和重建精度。
