Iterative Stable Alignment and Clustering of 2D Transmission Electron Microscope Images

作者：Yang Zhengfan; Fang Jia; Chittuluru Johnathan; Asturias Francisco J^*; Penczek Pawel A

来源：Structure, 2012, 20(2): 237-247.

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.

• 出版日期2012-2-8