RE2DC: a robust and efficient 2D classifier with visualization for processing massive and heterogeneous cryo-EM data

Despite the fact that single particle cryo-EM has become a powerful method of structural biology, processing cryo-EM images are challenging due to the low SNR, high-dimension and un-label nature of the data. Selecting the best subset of particle images relies on 2D classification--a process that involves iterative image alignment and clustering. This process, however, represents a major time sink, particularly when the data is massive or overly heterogeneous. Popular approaches to this process often trade its robustness for efficiency. Here, we introduced a new unsupervised 2D classification method termed RE2DC. It is built upon a highly efficient variant of{gamma} -SUP, a robust statistical cryo-EM clustering algorithm resistant to the attractor effect. To develop this efficient variant, we employed a tree-based approximation to reduce the computation complexity from O(N2) to O(N), with N as the number of images. In addition, we exploited t-SNE visualization to unveil the process of 2D classification. Our tests of RE2DC using various datasets demonstrate it is both robust and efficient, with the potential to reveal subtle structural intermediates. Using RE2DC to curate a dataset of sub-millions of COVID-19 spike particles picked from 3,511 movies only takes 8 hours, suggesting its capability of accelerating cryo-EM structural determination. Currently, RE2DC is available with both CPU and GPU versions, where the implementation only requires modest hardware resources.