低温电子显微镜异质性的最大割方法
A max-cut approach to heterogeneity in cryo-electron microscopy
摘要：
冷冻电子显微镜领域在过去几年中取得了惊人的进步，主要是由于电子探测器技术的进步。然而，该领域的一个关键的开放性挑战仍然是处理异质数据集，这些数据集是由包含几种不同构象状态的粒子的样品产生的。对于这样的数据集，算法必须包括一些分类程序，以识别数据中的同质组，以便每组中的图像对应于相同的底层结构。在低温电子显微镜的异质性问题的根本重要性，吸引了许多研究工作，并导致了显着的进展，在分类算法的异质性数据集。虽然这些算法在实践中非常有用和有效，但它们缺乏严格的数学分析和性能保证。在本文中，我们试图在冷冻电子显微镜的异质性问题进行严格的数学分析的第一步。为此，我们提出了一个算法，用于处理异构数据集，并证明其准确性和稳定性bounds.We还提出了该算法的扩展相结合的分类和重建步骤。我们在模拟数据上证明了它，并将其性能与RELION中最先进的算法进行了比较。
英文摘要：
The field of cryo-electron microscopy has made astounding advancements in the past few years, mainly due to advancements in electron detectors' technology. Yet, one of the key open challenges of the field remains the processing of heterogeneous data sets, produced from samples containing particles at several different conformational states. For such data sets, the algorithms must include some classification procedure to identify homogeneous groups within the data, so that the images in each group correspond to the same underlying structure. The fundamental importance of the heterogeneity problem in cryo-electron microscopy has drawn many research efforts, and resulted in significant progress in classification algorithms for heterogeneous data sets. While these algorithms are extremely useful and effective in practice, they lack rigorous mathematical analysis and performance guarantees. In this paper, we attempt to make the first steps towards rigorous mathematical analysis of the heterogeneity problem in cryo-electron microscopy. To that end, we present an algorithm for processing heterogeneous data sets, and prove accuracy and stability bounds for it. We also suggest an extension of this algorithm that combines the classification and reconstruction steps. We demonstrate it on simulated data, and compare its performance to the state-of-the-art algorithm in RELION.
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