论文标题
概率polargmm：未知姿势非常嘈杂的投影图像的无监督聚类学习
Probabilistic PolarGMM: Unsupervised Cluster Learning of Very Noisy Projection Images of Unknown Pose
论文作者
论文摘要
低温电子显微镜（Cryo-EM），2D分类和比对的单个颗粒分析（SPA）的关键步骤，将嘈杂的粒子图像集合收集，以推导方向并将相似图像组合在一起。平均这些对齐和聚类的嘈杂图像会产生一组干净的图像，可以进行进一步的分析，例如3D重建。傅立叶贝塞尔可进入的主成分分析（FBSPCA）可实现有效的，适应性的，低级别的旋转操作员。我们将FBSPCA扩展到额外处理翻译。在此扩展的FBSPCA表示中，我们使用概率的极性坐标高斯混合模型，使用预期最大化（EM）算法以无监督的方式学习软簇。因此，获得的旋转簇还具有成对比对缺陷的存在。在与标准的单粒子冷冻EM工具（EMAN2和RELION）相比，模拟的冷冻EM数据集的多个基准表明，概率Polargmm的性能改善了性能，就各种聚类指标和对齐错误而言。
A crucial step in single particle analysis (SPA) of cryogenic electron microscopy (Cryo-EM), 2D classification and alignment takes a collection of noisy particle images to infer orientations and group similar images together. Averaging these aligned and clustered noisy images produces a set of clean images, ready for further analysis such as 3D reconstruction. Fourier-Bessel steerable principal component analysis (FBsPCA) enables an efficient, adaptable, low-rank rotation operator. We extend the FBsPCA to additionally handle translations. In this extended FBsPCA representation, we use a probabilistic polar-coordinate Gaussian mixture model to learn soft clusters in an unsupervised fashion using an expectation maximization (EM) algorithm. The obtained rotational clusters are thus additionally robust to the presence of pairwise alignment imperfections. Multiple benchmarks from simulated Cryo-EM datasets show probabilistic PolarGMM's improved performance in comparisons with standard single-particle Cryo-EM tools, EMAN2 and RELION, in terms of various clustering metrics and alignment errors.
