Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm.

ABSTRACT: In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive c

SUBMITTER: Xu Y 

PROVIDER: S-EPMC5154524 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature