title: COVARIANCE ESTIMATION USING CONJUGATE GRADIENT FOR 3D CLASSIFICATION IN CRYO-EM.
abstract: Classifying structural variability in noisy projections of biological macromolecules is a central problem in Cryo-EM. In this work, we build on a previous method for estimating the covariance matrix of the three-dimensional structure present in the molecules being imaged. Our proposed method allows for incorporation of contrast transfer function and non-uniform distribution of viewing angles, making it more suitable for real-world data. We evaluate its performance on a synthetic dataset and an experimental dataset obtained by imaging a 70S ribosome complex.
authors.affiliation: Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ.
authors.forename: Joakim
authors.initials: J
authors.lastname: Andén
authors.affiliation: Department of Statistics, Stanford University, Stanford, CA.
authors.forename: Eugene
authors.initials: E
authors.lastname: Katsevich
authors.affiliation: Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ.
authors.forename: Amit
authors.initials: A
authors.lastname: Singer
keywords: 3D reconstruction; Cryo-EM; classification; conjugate gradient; covariance; heterogeneity; single particle reconstruction; structural variability