论文标题
使用概率PCA的低使粒子分类的无监督粒子分类
Unsupervised particle sorting for cryo-EM using probabilistic PCA
论文作者
论文摘要
单粒子冷冻电子显微镜（Cryo-EM）是解决分子结构的领先技术。在此过程的早期，用户检测到原始数据中的潜在粒子图像。通常，由于噪声和污染水平高，有许多错误检测。当前，删除虚假检测需要人类干预才能对数十万图像进行分类。我们提出了一种统计上建立的无监督算法来删除非颗粒图像。假设非颗粒图像被任意散布在高维空间中，我们将粒子图像建模为低维子空间的结合。该算法基于概率PCA框架的扩展，以稳健地学习子空间结合的非线性模型。这为冷冻EM数据提供了灵活的模型，并允许自动删除与纯噪声和污染相对应的图像。数值实验证实了排序算法的有效性。
Single-particle cryo-electron microscopy (cryo-EM) is a leading technology to resolve the structure of molecules. Early in the process, the user detects potential particle images in the raw data. Typically, there are many false detections as a result of high levels of noise and contamination. Currently, removing the false detections requires human intervention to sort the hundred thousands of images. We propose a statistically-established unsupervised algorithm to remove non-particle images. We model the particle images as a union of low-dimensional subspaces, assuming non-particle images are arbitrarily scattered in the high-dimensional space. The algorithm is based on an extension of the probabilistic PCA framework to robustly learn a non-linear model of union of subspaces. This provides a flexible model for cryo-EM data, and allows to automatically remove images that correspond to pure noise and contamination. Numerical experiments corroborate the effectiveness of the sorting algorithm.
