Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

ABSTRACT: Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called gene

SUBMITTER: Wu J 

PROVIDER: S-EPMC5546606 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature