Cryo-forum: A framework for orientation recovery with uncertainty measure with the application in cryo-EM image analysis
This work addresses a critical bottleneck in cryo-EM image analysis for researchers in structural biology, offering a more streamlined and accurate method with uncertainty quantification, though it is incremental as it builds on existing deep learning solutions.
The paper tackles the challenge of efficiently determining orientation parameters for 2D projection images in cryo-EM, which is crucial for 3D structure reconstruction, by introducing a novel approach that uses a 10-dimensional feature vector and a Quadratically-Constrained Quadratic Program to predict orientation as a unit quaternion with an uncertainty metric, resulting in effective end-to-end orientation recovery and enabling direct dataset clean-up at the 3D level.
In single-particle cryo-electron microscopy (cryo-EM), the efficient determination of orientation parameters for 2D projection images poses a significant challenge yet is crucial for reconstructing 3D structures. This task is complicated by the high noise levels present in the cryo-EM datasets, which often include outliers, necessitating several time-consuming 2D clean-up processes. Recently, solutions based on deep learning have emerged, offering a more streamlined approach to the traditionally laborious task of orientation estimation. These solutions often employ amortized inference, eliminating the need to estimate parameters individually for each image. However, these methods frequently overlook the presence of outliers and may not adequately concentrate on the components used within the network. This paper introduces a novel approach that uses a 10-dimensional feature vector to represent the orientation and applies a Quadratically-Constrained Quadratic Program to derive the predicted orientation as a unit quaternion, supplemented by an uncertainty metric. Furthermore, we propose a unique loss function that considers the pairwise distances between orientations, thereby enhancing the accuracy of our method. Finally, we also comprehensively evaluate the design choices involved in constructing the encoder network, a topic that has not received sufficient attention in the literature. Our numerical analysis demonstrates that our methodology effectively recovers orientations from 2D cryo-EM images in an end-to-end manner. Importantly, the inclusion of uncertainty quantification allows for direct clean-up of the dataset at the 3D level. Lastly, we package our proposed methods into a user-friendly software suite named cryo-forum, designed for easy accessibility by the developers.