Open Access
Translator Disclaimer
March 2014 $\gamma$-SUP: A clustering algorithm for cryo-electron microscopy images of asymmetric particles
Ting-Li Chen, Dai-Ni Hsieh, Hung Hung, I-Ping Tu, Pei-Shien Wu, Yi-Ming Wu, Wei-Hau Chang, Su-Yun Huang
Ann. Appl. Stat. 8(1): 259-285 (March 2014). DOI: 10.1214/13-AOAS680

Abstract

Cryo-electron microscopy (cryo-EM) has recently emerged as a powerful tool for obtaining three-dimensional (3D) structures of biological macromolecules in native states. A minimum cryo-EM image data set for deriving a meaningful reconstruction is comprised of thousands of randomly orientated projections of identical particles photographed with a small number of electrons. The computation of 3D structure from 2D projections requires clustering, which aims to enhance the signal to noise ratio in each view by grouping similarly oriented images. Nevertheless, the prevailing clustering techniques are often compromised by three characteristics of cryo-EM data: high noise content, high dimensionality and large number of clusters. Moreover, since clustering requires registering images of similar orientation into the same pixel coordinates by 2D alignment, it is desired that the clustering algorithm can label misaligned images as outliers. Herein, we introduce a clustering algorithm $\gamma$-SUP to model the data with a $q$-Gaussian mixture and adopt the minimum $\gamma$-divergence for estimation, and then use a self-updating procedure to obtain the numerical solution. We apply $\gamma$-SUP to the cryo-EM images of two benchmark macromolecules, RNA polymerase II and ribosome. In the former case, simulated images were chosen to decouple clustering from alignment to demonstrate $\gamma$-SUP is more robust to misalignment outliers than the existing clustering methods used in the cryo-EM community. In the latter case, the clustering of real cryo-EM data by our $\gamma$-SUP method eliminates noise in many views to reveal true structure features of ribosome at the projection level.

Citation

Download Citation

Ting-Li Chen. Dai-Ni Hsieh. Hung Hung. I-Ping Tu. Pei-Shien Wu. Yi-Ming Wu. Wei-Hau Chang. Su-Yun Huang. "$\gamma$-SUP: A clustering algorithm for cryo-electron microscopy images of asymmetric particles." Ann. Appl. Stat. 8 (1) 259 - 285, March 2014. https://doi.org/10.1214/13-AOAS680

Information

Published: March 2014
First available in Project Euclid: 8 April 2014

zbMATH: 06302235
MathSciNet: MR3191990
Digital Object Identifier: 10.1214/13-AOAS680

Keywords: $\gamma$-divergence , $k$-means , $q$-Gaussian distribution , Clustering algorithm , cryo-EM images , mean-shift algorithm , multilinear principal component analysis , robust statistics , self-updating process

Rights: Copyright © 2014 Institute of Mathematical Statistics

JOURNAL ARTICLE
27 PAGES


SHARE
Vol.8 • No. 1 • March 2014
Back to Top