Implementation method of single particle image clustering for cryo-EM based on graph convolutional autoencoder

Abstract

A method for realizing single-particle image clustering in cryo-EM based on graph convolution autoencoder, which generates KNN network by calculating the image similarity matrix of cryo-EM single-particle image set; The particle image is dimensionally reduced, the KNN network and the image feature matrix are input into the encoder in the graph convolution self-encoder, and the high-dimensional node features are embedded into the low-dimensional hidden layer space to obtain a low-dimensional hidden layer structure. After the point feature, the K-means clustering process is performed by the decoder in the graph convolution self-encoder to obtain the clustering result of the cryo-EM single particle image, and finally the images of each cluster are averaged to obtain the final class. Average image. The invention uses the networked similarity measurement method and the local linear embedding extraction method, and combines the hidden layer node features of the self-encoder to learn the structure information of the network and the image feature information of the node itself at the same time, so that the robustness of the clustering is improved. Boosted to significantly improve image quality.

Description

technical field [0001] The invention relates to a technology in the field of image processing, in particular to a method for realizing clustering of cryo-electron microscope single-particle images based on a graph convolution self-encoder. Background technique [0002] Cryo-electron microscopy is a technique in which macromolecules to be detected are placed in an ultra-low temperature environment and then observed with an electron microscope. The three-dimensional model of the biomacromolecule can be obtained by reconstructing the two-dimensional image obtained by the electron microscope. Cryo-EM single particle image reconstruction is the most commonly used cryo-EM reconstruction method. In the detection process of cryo-electron microscopy, in order to ensure that the activity of biological macromolecules is not affected by electron radiation as much as possible, the electrons used by the electron microscope must be kept at a very small dose, so the noise of single-particl...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the defect that the existing technology is greatly affected by noise, the present invention proposes a cryo-electron microscope single-particle image clustering method based on graph convolutional self-encoder, using a networked similarity measurement method and a local linear embedding extraction method, Combined with the hidden layer node features of the autoencoder, the network structure information and the image feature information of the node itself are simultaneously learned, which improves the robustness of the clustering and significantly improves the image quality

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