Liao Hstau Y, Frank Joachim
Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032.
Proc IEEE Int Symp Biomed Imaging. 2010 Apr 14;2010:169-172. doi: 10.1109/ISBI.2010.5490386.
In single-particle reconstruction methods, projections of macromolecules at random orientations are collected. Often, several classes of conformations or binding states coexist in a biological sample, which requires classification, so that each conformation can be reconstructed separately. In this work, we examine bootstrap techniques for classifying the projection data. When these techniques are applied to variance estimation, the projection images (particles) are randomly sampled with replacement from the data set and a bootstrap volume is reconstructed from each sample. In a recent extension of the bootstrap technique to classification, each particle is assigned to a volume in the space spanned by the bootstrap volumes, such that the projection of the assigned volume best matches the particle. In this work we explain the rationale of these techniques by discussing the nature of the bootstrap volumes and provide some statistical analyses.
在单颗粒重构方法中，会收集处于随机取向的大分子投影。通常，生物样品中会同时存在几类构象或结合状态，这就需要进行分类，以便能分别重构每种构象。在这项工作中，我们研究了用于对投影数据进行分类的自助法技术。当将这些技术应用于方差估计时，会从数据集中有放回地随机采样投影图像（颗粒），并从每个样本中重构一个自助体积。在自助法技术最近扩展到分类的应用中，每个颗粒会被分配到由自助体积所跨越的空间中的一个体积，使得所分配体积的投影与该颗粒最佳匹配。在这项工作中，我们通过讨论自助体积的性质来解释这些技术的基本原理，并提供一些统计分析。
