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A crucial step in single particle analysis (SPA) of cryogenic electronmicroscopy (Cryo-EM), 2D classification and alignment takes a collection ofnoisy particle images to infer orientations and group similar images together.Averaging these aligned and clustered noisy images produces a set of cleanimages, ready for further analysis such as 3D reconstruction. Fourier-Besselsteerable principal component analysis (FBsPCA) enables an efficient,adaptable, low-rank rotation operator. We extend the FBsPCA to additionallyhandle translations. In this extended FBsPCA representation, we use aprobabilistic polar-coordinate Gaussian mixture model to learn soft clusters inan unsupervised fashion using an expectation maximization (EM) algorithm. Theobtained rotational clusters are thus additionally robust to the presence ofpairwise alignment imperfections. Multiple benchmarks from simulated Cryo-EMdatasets show probabilistic PolarGMM's improved performance in comparisons withstandard single-particle Cryo-EM tools, EMAN2 and RELION, in terms of variousclustering metrics and alignment errors.

Probabilistic PolarGMM: Unsupervised Cluster Learning of Very Noisy Projection Images of Unknown Pose