Rotationally invariant image representation for viewing direction classification in cryo-EM.
Rotationally invariant image representation for viewing direction classification in cryo-EM.
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DOI：
10.1016/j.jsb.2014.03.003
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发表时间：
2014-04
影响因子：
3
通讯作者：
Singer, Amit
中科院分区：
文献类型：
--
作者：
Zhao, Zhizhen;Singer, Amit
我们介绍了一种新的旋转不变的视角分类方法，用于识别，在大量的冷冻EM投影图像，类似的意见，没有先验知识的分子。我们的旋转不变特征是基于双谱的。使用可操纵主成分分析（PCA）对每个图像进行去噪和压缩，使得旋转图像等效于对扩展系数进行相移。从而将一维周期信号的双谱理论推广到二维图像。然后使用随机PCA算法有效地降低双谱系数的维数，从而能够快速计算任何一对图像之间的相似性。最近的邻居提供相似视角的初始分类。以这种方式，仅对具有其最近邻居的图像执行旋转对准。通过一种称为矢量扩散映射的新的分类方法，进一步改进了初始最近邻分类和对齐。实验表明，我们的视角分类和对齐管道比旋转不变K均值聚类，MSA/MRA 2D分类及其现代近似的无参考对齐更快，更准确。
We introduce a new rotationally invariant viewing angle classification method for identifying, among a large number of cryo-EM projection images, similar views without prior knowledge of the molecule. Our rotationally invariant features are based on the bispectrum. Each image is denoised and compressed using steerable principal component analysis (PCA) such that rotating an image is equivalent to phase shifting the expansion coefficients. Thus we are able to extend the theory of bispectrum of 1D periodic signals to 2D images. The randomized PCA algorithm is then used to efficiently reduce the dimensionality of the bispectrum coefficients, enabling fast computation of the similarity between any pair of images. The nearest neighbors provide an initial classification of similar viewing angles. In this way, rotational alignment is only performed for images with their nearest neighbors. The initial nearest neighbor classification and alignment are further improved by a new classification method called vector diffusion maps. Our pipeline for viewing angle classification and alignment is experimentally shown to be faster and more accurate than reference-free alignment with rotationally invariant K-means clustering, MSA/MRA 2D classification, and their modern approximations.
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影响因子：
3
作者：
Hohn, Michael;Tang, Grant;Ludtke, Steven J.
通讯作者：
Ludtke, Steven J.
影响因子：
5.1
作者：
MICHAELIS, M;SOMMER, G
通讯作者：
SOMMER, G
影响因子：
10.6
作者：
Ponce, Colin;Singer, Amit
通讯作者：
Singer, Amit
影响因子：
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作者：
Frank, Joachim
通讯作者：
Frank, Joachim
影响因子：
48
作者：
Scheres, Sjors H. W.;Gao, Haixiao;Carazo, Jose-Maria
通讯作者：
Carazo, Jose-Maria