Iterative stable alignment and clustering of 2D transmission electron microscope images

Structure. 2012 Feb 8;20(2):237-47. doi: 10.1016/j.str.2011.12.007.


Identification of homogeneous subsets of images in a macromolecular electron microscopy (EM) image data set is a critical step in single-particle analysis. The task is handled by iterative algorithms, whose performance is compromised by the compounded limitations of image alignment and K-means clustering. Here we describe an approach, iterative stable alignment and clustering (ISAC) that, relying on a new clustering method and on the concepts of stability and reproducibility, can extract validated, homogeneous subsets of images. ISAC requires only a small number of simple parameters and, with minimal human intervention, can eliminate bias from two-dimensional image clustering and maximize the quality of group averages that can be used for ab initio three-dimensional structural determination and analysis of macromolecular conformational variability. Repeated testing of the stability and reproducibility of a solution within ISAC eliminates heterogeneous or incorrect classes and introduces critical validation to the process of EM image clustering.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Bacterial Proteins / chemistry
  • Cluster Analysis
  • Cryoelectron Microscopy
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Microscopy, Electron, Transmission / methods*
  • Models, Molecular
  • Molecular Conformation
  • Peptide Elongation Factor Tu / chemistry
  • RNA Polymerase II / chemistry
  • Ribosomes / chemistry
  • Software*
  • Thermus thermophilus


  • Bacterial Proteins
  • RNA Polymerase II
  • Peptide Elongation Factor Tu