Fast principal component analysis for cryo-electron microscopy images
Fast principal component analysis for cryo-electron microscopy images
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DOI：
10.1017/s2633903x23000028
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发表时间：
2023-02
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通讯作者：
Nicholas F. Marshall;Oscar Mickelin;Yunpeng Shi;A. Singer
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作者：
Nicholas F. Marshall;Oscar Mickelin;Yunpeng Shi;A. Singer
摘要主成分分析（PCA）在低温电子显微镜（cryo-EM）图像的分类、去噪、压缩和从头算建模等分析中起着重要的作用。我们介绍了一种快速的方法来估计压缩表示的2-D的噪声cryo-EM投影图像的协方差矩阵的径向点扩散函数，使快速PCA计算的影响。我们的方法是基于一种新的算法，用于扩展图像中的傅里叶-贝塞尔基础（磁盘上的谐波），这提供了一种方便的方式来处理的对比度传递函数的效果。对于$ N $个大小为$ L\times L $的图像，我们的方法的时间复杂度为$ O\left（{NL}^3+{L}^4\right）$，空间复杂度为$ O\left（{NL}^2+{L}^3\right）$。与以前的工作相比，这些复杂性与图像的不同对比度传递函数的数量无关。我们证明了我们的方法的合成和实验数据，并显示加速高达两个数量级的因素。
Abstract Principal component analysis (PCA) plays an important role in the analysis of cryo-electron microscopy (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-EM projection images affected by radial point spread functions 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\left({NL}^3+{L}^4\right) $ and space complexity $ O\left({NL}^2+{L}^3\right) $ . 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.