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Title: Class Averaging in Cryo-EM Single Particle Reconstruction
Authors: Zhao, Zhizhen
Advisors: Singer, Amit
Aizenman, Michael
Contributors: Physics Department
Keywords: Bispectrum
Class averaging
Fourier-Bessel steerable PCA
Randomized algorithms
Vector diffusion maps
Subjects: Biophysics
Applied mathematics
Issue Date: 2013
Publisher: Princeton, NJ : Princeton University
Abstract: Single-particle reconstruction of vitrified samples (cryogenic electron microscopy, or cryo-EM) provides structural information for a large variety of biological molecules, ranging from small proteins to large macromolecular assemblies in their native state, without the need to produce crystals. However, for common-lines based ab initio modeling, it is a great challenge to produce density maps, because of the low signal to noise ratio (SNR) of the projection images. A crucial step is alignment and averaging of the 2D projection images, which are from similar viewing directions, a procedure known as "class averaging". Images from the same projection angles should be identified, aligned and averaged to achieve a higher signal-to-noise ratio. In Chapter 2, we introduce a set of rotationally invariant adaptive basis to compress and denoise the projection images. The global alignment followed by clustering has been a common practice to generate class averages for the past 20 years. In Chapter 3, we show that due to the geometry and topology of the image data set, it is impossible to globally align all images. The commonly used reference-free alignment procedure unavoidably introduces errors. We then introduce a fast in-plane rotationally invariant viewing angle classification method for identifying, among a large number of projection images, similar views without prior knowledge of the molecule to alleviate the computational burden for pairwise alignment and classification. To improve the initial classification results, we introduce the class averaging matrix in Chapter 4. The spectral properties of class averaging matrix allow us to define new metrics between projection images, which are more robust to noise. Class averaging operator is a special case of vector diffusion maps (VDM), which takes into account the linear transformation between data points. In Chapter 5, we discuss the application of VDM in class averaging. We are able to reconstruct ab initio 3D electron density maps with high resolution for experimental data sets, using the class averaging procedure described in this thesis and the common-lines reconstruction algorithm.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Physics

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