Fast principal component analysis for cryo-electron microscopy images.

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 × L, our method has time complexity O(NL³ + L⁴) and space complexity O(NL

SUBMITTER: Marshall NF 

PROVIDER: S-EPMC10465116 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature