论文标题
冷冻EM图像的快速主成分分析
Fast Principal Component Analysis for Cryo-EM Images
论文作者
论文摘要
主成分分析（PCA）在分析，分类，压缩，压缩和AB-Initio建模等各种任务的冷冻EM图像中起重要作用。我们介绍了一种快速方法，用于估计嘈杂的冷冻电子显微镜图像的2-D协方差矩阵的压缩表示，该图像可以实现快速的PCA计算。我们的方法基于一种新算法，用于以傅立叶式孔（磁盘上的谐波）扩展图像，该算法提供了一种方便的方法来处理对比度传输功能的效果。对于$ n $ size $ l \ times l $的图像，我们的方法具有时间复杂性$ o（n l^3 + l^4）$和空间复杂性$ o（nl^2 + l^3）$。与以前的工作相反，这些复杂性与图像的不同对比传递函数的数量无关。我们证明了我们在合成和实验数据方面的方法，并通过多达两个数量级的因素显示了加速度。
Principal component analysis (PCA) plays an important role in the analysis of cryo-EM images for various tasks such as classification, denoising, compression, and ab-initio modeling. We introduce a fast method for estimating a compressed representation of the 2-D covariance matrix of noisy cryo-electron microscopy projection images that enables fast PCA computation. Our method is based on a new algorithm for expanding images in the Fourier-Bessel basis (the harmonics on the disk), which provides a convenient way to handle the effect of the contrast transfer functions. For $N$ images of size $L\times L$, our method has time complexity $O(N L^3 + L^4)$ and space complexity $O(NL^2 + L^3)$. In contrast to previous work, these complexities are independent of the number of different contrast transfer functions of the images. We demonstrate our approach on synthetic and experimental data and show acceleration by factors of up to two orders of magnitude.
