低温电子显微镜（cryoEM）已成为阐明生物大分子3D结构的成熟技术。来自数千个被假定为结构相同的大分子的投影图像被组合成表示所研究的大分子的库仑势的单个3D图。本文讨论了沿图像处理路径的可能警告，以及如何避免它们以获得可靠的3D结构。其中一些问题在社区中是众所周知的。这些可以被称为样品相关的(诸如在界面处的样本变性或导致不足表示的投影方向的不均匀投影几何形状)。其余的与使用的算法有关。虽然有些已经在文献中进行了深入的讨论，例如使用不正确的初始音量，其他人受到的关注要少得多。然而,它们是任何数据分析方法的基础。主要是其中,在估计许多关键参数的不稳定性，需要一个正确的三维重建，发生在整个处理工作流程的参考，这可能会显著影响整个过程的可靠性。在田野里,术语“过拟合”是指某些特定类型的工件。有人认为，过拟合是三维重建过程中关键参数估计步骤中的统计偏差，包括内在的算法偏差。还表明，通常用于检测或防止过拟合的常用工具（傅立叶壳相关性）和策略（黄金标准）并不能完全防止它。或者，有人提出，检测导致过拟合的偏差要容易得多，当解决在参数估计的水平，而不是一旦粒子图像被组合成3D地图就检测到它。比较来自多个算法的结果(或者至少，同一算法的独立执行)可以检测参数偏差。然后可以对这些多次执行求平均以给出基本参数的较低方差估计。
Cryo-electron microscopy (cryoEM) has become a well established technique to elucidate the 3D structures of biological macromolecules. Projection images from thousands of macromolecules that are assumed to be structurally identical are combined into a single 3D map representing the Coulomb potential of the macromolecule under study. This article discusses possible caveats along the image-processing path and how to avoid them to obtain a reliable 3D structure. Some of these problems are very well known in the community. These may be referred to as sample-related (such as specimen denaturation at interfaces or non-uniform projection geometry leading to underrepresented projection directions). The rest are related to the algorithms used. While some have been discussed in depth in the literature, such as the use of an incorrect initial volume, others have received much less attention. However, they are fundamental in any data-analysis approach. Chiefly among them, instabilities in estimating many of the key parameters that are required for a correct 3D reconstruction that occur all along the processing workflow are referred to, which may significantly affect the reliability of the whole process. In the field, the term overfitting has been coined to refer to some particular kinds of artifacts. It is argued that overfitting is a statistical bias in key parameter-estimation steps in the 3D reconstruction process, including intrinsic algorithmic bias. It is also shown that common tools (Fourier shell correlation) and strategies (gold standard) that are normally used to detect or prevent overfitting do not fully protect against it. Alternatively, it is proposed that detecting the bias that leads to overfitting is much easier when addressed at the level of parameter estimation, rather than detecting it once the particle images have been combined into a 3D map. Comparing the results from multiple algorithms (or at least, independent executions of the same algorithm) can detect parameter bias. These multiple executions could then be averaged to give a lower variance estimate of the underlying parameters.
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