Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM
This work addresses a key bottleneck in cryo-EM analysis for structural biologists by improving the efficiency and accuracy of viewing direction classification, though it is an incremental advancement over prior methods.
The authors tackled the problem of classifying viewing angles in cryo-EM images without prior knowledge, introducing a rotationally invariant method based on bispectrum features and vector diffusion maps, which achieved faster and more accurate results compared to existing reference-free alignment techniques.
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.