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arXiv · 2210.12811

Unsupervised particle sorting for cryo-EM using probabilistic PCA

Gili Weiss-Dicker·Amitay Eldar·Yoel Shkolinsky·Tamir Bendory
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Abstract

Single-particle cryo-electron microscopy (cryo-EM) is a leading technology to resolve the structure of molecules. Early in the process, the user detects potential particle images in the raw data. Typically, there are many false detections as a result of high levels of noise and contamination. Currently, removing the false detections requires human intervention to sort the hundred thousands of images. We propose a statistically-established unsupervised algorithm to remove non-particle images. We model the particle images as a union of low-dimensional subspaces, assuming non-particle images are arbitrarily scattered in the high-dimensional space. The algorithm is based on an extension of the probabilistic PCA framework to robustly learn a non-linear model of union of subspaces. This provides a flexible model for cryo-EM data, and allows to automatically remove images that correspond to pure noise and contamination. Numerical experiments corroborate the effectiveness of the sorting algorithm.

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Gili Weiss-Dicker, Amitay Eldar, Yoel Shkolinsky, Tamir Bendory. 2022-10-23. Unsupervised particle sorting for cryo-EM using probabilistic PCA. https://arxiv.org/abs/2210.12811

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