Clustering Enhancement of Noisy Cryo-Electron Microscopy Single-Particle Images with a Network Structural Similarity Metric.

The reconstruction of a three-dimensional model from cryo-electron microscopy (cryo-EM) two-dimensional images is currently a mainstream technology for revealing the structure of biomacromolecules. In this structure solution protocol, an important step is to identify each particle's projection orientation. Because the obtained single-particle images are often too noisy, clustering is an important step to mitigate noise by averaging images within the same class. The core of clustering is to place similar cryo-EM images into the same class; hence, measurement of similarity between data samples is an essential element in any clustering algorithm. As the cryo-EM images are highly noisy, directly measuring the similarity of two images will be easily biased by the hidden noise. In this study, we propose a new network structural similarity metric-based clustering protocol NCEM for clustering the noisy cryo-EM images. We first construct an image complex network for all of the cryo-EM single-particle images, where each image is represented as a node in the network. Then the similarity between two images is refined from the network structural geometry. By extending the similarity measurement from two independent images to their corresponding neighboring sets in the network, this new NCEM has typical advantages over direct measurement of two images for its noise resistance by using the structural information on the network. Our experimental results for both synthetic and real data sets demonstrate the efficacy of the protocol.