Andén Joakim, Katsevich Eugene, Singer Amit
Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ.
Department of Statistics, Stanford University, Stanford, CA.
Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:200-204. doi: 10.1109/ISBI.2015.7163849.
Classifying structural variability in noisy projections of biological macromolecules is a central problem in Cryo-EM. In this work, we build on a previous method for estimating the covariance matrix of the three-dimensional structure present in the molecules being imaged. Our proposed method allows for incorporation of contrast transfer function and non-uniform distribution of viewing angles, making it more suitable for real-world data. We evaluate its performance on a synthetic dataset and an experimental dataset obtained by imaging a 70S ribosome complex.
对生物大分子的噪声投影中的结构变异性进行分类是冷冻电子显微镜中的一个核心问题。在这项工作中，我们基于之前的一种方法来估计成像分子中三维结构的协方差矩阵。我们提出的方法允许纳入对比度传递函数和视角的非均匀分布，使其更适合实际数据。我们在一个合成数据集和通过对70S核糖体复合物成像获得的实验数据集上评估了它的性能。
