Iterative stable alignment and clustering of 2D transmission electron microscope images.
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.
Department of Biochemistry and Molecular Biology, University of Texas-Houston Medical School, 6431 Fannin Street, MSB 6.218, Houston, TX 77030, USA.
Zhengfan
Z
Yang
Jia
J
Fang
Johnathan
J
Chittuluru
Francisco J
FJ
Asturias
Pawel A
PA
Penczek
D001426:Bacterial Proteins / Q000737:chemistry; D016000:Cluster Analysis; D020285:Cryoelectron Microscopy; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D046529:Microscopy, Electron, Transmission / Q000379:methods*; D008958:Models, Molecular; D008968:Molecular Conformation; D010444:Peptide Elongation Factor Tu / Q000737:chemistry; D012319:RNA Polymerase II / Q000737:chemistry; D012270:Ribosomes / Q000737:chemistry; D012984:Software; D016963:Thermus thermophilus
D001426:Bacterial Proteins; D012319:RNA Polymerase II; D010444:Peptide Elongation Factor Tu