Clustering and averaging of images in single-particle analysis

Single particle analysis is a straightforward method for studying the structures of macromolecules that cannot be crystallized. It builds three-dimensional structures of particles by estimating the projection angles of their randomly oriented electron-microscopic images. The existing methods divide the images into clusters, build class averages for the clusters, and estimate the projection angle of each cluster. However, the clustering and the averaged images are highly sensitive to the choice of reference images and mask patterns for each cluster. Thus, the analyses are neither robust nor automatic, and their results depend heavily on the intuition and experience of researchers who set references. We have been developing a software system for single-particle analysis with new clustering and averaging algorithms for building the three-dimensional structures of target molecules. In this paper, we focus on the algorithms for the robust image-processing of the electron microscopic images in our system.