2020Sharon NonUniformKam: Difference between revisions
Amit Singer (talk | contribs) No edit summary |
m (WikiSysop moved page 2020Singer NonUniformKam to 2020Sharon NonUniformKam: The index is constructed using the first author) |
(No difference)
|
Revision as of 10:34, 5 April 2021
Citation
N. Sharon, J. Kileel, Y. Khoo, B. Landa, A. Singer, "Method of moments for 3-D single particle ab initio modeling with non-uniform distribution of viewing angles", Inverse Problems, 36 (4) 044003 (2020). [1]
Abstract
Single-particle reconstruction in cryo-electron microscopy (cryo-EM) is an increasingly popular technique for determining the 3D structure of a molecule from several noisy 2D projections images taken at unknown viewing angles. Most reconstruction algorithms require a low-resolution initialization for the 3D structure, which is the goal of ab initio modeling. Suggested by Zvi Kam in 1980, the method of moments (MoM) offers one approach, wherein low-order statistics of the 2D images are computed and a 3D structure is estimated by solving a system of polynomial equations. Unfortunately, Kam's method suffers from restrictive assumptions, most notably that viewing angles should be distributed uniformly. Often unrealistic, uniformity entails the computation of higher-order correlations, as in this case first and second moments fail to determine the 3D structure. In the present paper, we remove this hypothesis, by permitting an unknown, non-uniform distribution of viewing angles in MoM. Perhaps surprisingly, we show that this case is statistically easier than the uniform case, as now first and second moments generically suffice to determine low-resolution expansions of the molecule. In the idealized setting of a known, non-uniform distribution, we find an efficient provable algorithm inverting first and second moments. For unknown, non-uniform distributions, we use non-convex optimization methods to solve for both the molecule and distribution.
Keywords
Cryo-EM, ab initio modeling, autocorrelation analysis, method of moments, spherical harmonics, Wigner matrices, non-convex optimization