G-sup: a clustering algorithm for cryo-electron microscopy images of asymmetric particles

Ting-Li Chen Institute of Statistical Science, Academia Sinica Dai-Ni Hsieh Institute of Statistical Science, Academia Sinica Hung Hung Institute of Epidemiology and Preventive Medicine I-Ping Tu Institute of Statistical Science, Academia Sinica Pei-Shien Wu Dept. of Biostatistics, Duke University Yi-Ming Wu Institute of Chemistry, Academia Sinica Wei-Hau Chang Institute of Chemistry, Academia Sinica Su-Yun Huang Institute of Statistical Science, Academia Sinica

Statistics Theory and Methods Data Analysis, Bio-Statistics, Bio-Mathematics mathscidoc:2004.33002

The Annals of Applied Statistics , 8, (1), 259-285, 2014
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 γ-SUP to model the data with a q-Gaussian mixture and adopt the minimum γ-divergence for estimation, and then use a self-updating procedure to obtain the numerical solution. We apply γ-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 γ-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 γ-SUP method eliminates noise in many views to reveal true structure features of ribosome at the projection level.
Clustering algorithm, cryo-EM images, γ -divergence, k-means, meanshift algorithm, multilinear principal component analysis, q-Gaussian distribution, robust statistics, self-updating process.
[ Download ] [ 2020-04-22 14:03:32 uploaded by ipingtu ] [ 274 downloads ] [ 0 comments ]
@inproceedings{ting-li2014g-sup:,
  title={g-SUP: A CLUSTERING ALGORITHM FOR CRYO-ELECTRON MICROSCOPY IMAGES OF ASYMMETRIC PARTICLES},
  author={Ting-Li Chen, Dai-Ni Hsieh, Hung Hung, I-Ping Tu, Pei-Shien Wu, Yi-Ming Wu, Wei-Hau Chang, and Su-Yun Huang},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20200422140332553360629},
  booktitle={The Annals of Applied Statistics },
  volume={8},
  number={1},
  pages={259-285},
  year={2014},
}
Ting-Li Chen, Dai-Ni Hsieh, Hung Hung, I-Ping Tu, Pei-Shien Wu, Yi-Ming Wu, Wei-Hau Chang, and Su-Yun Huang. g-SUP: A CLUSTERING ALGORITHM FOR CRYO-ELECTRON MICROSCOPY IMAGES OF ASYMMETRIC PARTICLES. 2014. Vol. 8. In The Annals of Applied Statistics . pp.259-285. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20200422140332553360629.
Please log in for comment!
 
 
Contact us: office-iccm@tsinghua.edu.cn | Copyright Reserved