基于统计流形学习的无监督单粒子深度聚类
Unsupervised single-particle deep clustering via statistical manifold learning
摘要：
动机：单粒子低温电子显微镜（cryo-EM）数据的结构异质性是高分辨率结构测定的主要挑战。无监督分类可以作为结构异质性评估的第一步。传统的无监督分类算法，如K-means聚类和极大似然优化等，在图像数据信噪比降低的情况下，可能会将图像错误地分类，同时也会增加计算量。克服这些局限性需要进一步开发用于高性能低温EM数据分析的聚类算法。结果：在这里，我们介绍了一种用于无监督单粒子深度聚类的统计流形学习算法。我们发现，统计流形学习在没有输入参考的情况下，将分类准确率提高了约40%。几个实验数据集的应用表明，我们的深度聚类方法可以检测类之间的细微结构差异。通过在英特尔高性能计算（HPC）处理器上进行代码优化，我们的软件实现可以在几个小时内从数十万个单粒子冷冻EM图像中生成数千个无参考类平均值，从而显著提高从头算3D重建分辨率和质量。我们的方法已成功地应用于几个结构测定项目。我们希望它提供了一个强大的计算工具，在分析高度异质性的结构数据，并协助计算纯化的单粒子数据集的高分辨率重建。
英文摘要：
Motivation: Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. Traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased cost in computation. Overcoming these limitations requires further development on clustering algorithms for high-performance cryo-EM data analysis. Results: Here we introduce a statistical manifold learning algorithm for unsupervised single-particle deep clustering. We show that statistical manifold learning improves classification accuracy by about 40% in the absence of input references for lower SNR data. Applications to several experimental datasets suggest that our deep clustering approach can detect subtle structural difference among classes. Through code optimization over the Intel high-performance computing (HPC) processors, our software implementation can generate thousands of reference-free class averages within several hours from hundreds of thousands of single-particle cryo-EM images, which allows significant improvement in ab initio 3D reconstruction resolution and quality. Our approach has been successfully applied in several structural determination projects. We expect that it provides a powerful computational tool in analyzing highly heterogeneous structural data and assisting in computational purification of single-particle datasets for high-resolution reconstruction.
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