Cryo-electron microscopy image analysis using multi-frequency vector diffusion maps

Cryo-electron microscopy (EM) single particle reconstruction is an entirely general technique for 3D structure determination of macromolecular complexes. However, because the images are taken at low electron dose, it is extremely hard to visualize the individual particle with low contrast and high noise level. In this thesis, we propose a novel approach called multi-frequency vector diffusion maps (MFVDM) to improve the efficiency and accuracy of cryo-EM 2D image classification and denoising. This framework incorporates different irreducible representations of the estimated alignment between similar images. In addition, we propose a graph filtering scheme to denoise the images using the eigenvalues and eigenvectors of the MFVDM matrices. Through both simulated and publicly available real data, we demonstrate that our proposed method is efficient and robust to noise compared with the state-of-the-art cryo-EM 2D class averaging and image restoration algorithms.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Fan, Yifeng

Contributors dc:contributor

• Zhao, Zhizhen

• Cryo-Electron Microscopy
• Image denoising.

Statement dc:rights

• Copyright 2020 Yifeng Fan

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/107837

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/107837