Introducing robustness to maximum-likelihood refinement of electron-microscopy data.
An expectation-maximization algorithm for maximum-likelihood refinement of electron-microscopy images is presented that is based on fitting mixtures of multivariate t-distributions. The novel algorithm has intrinsic characteristics for providing robustness against atypical observations in the data, which is illustrated using an experimental test set with artificially generated outliers. Tests on experimental data revealed only minor differences in two-dimensional classifications, while three-dimensional classification with the new algorithm gave stronger elongation factor G density in the corresponding class of a structurally heterogeneous ribosome data set than the conventional algorithm for Gaussian mixtures.
Scheres
Sjors H W
SH
Centro Nacional de Biotecnología-CSIC, Darwin 3, Cantoblanco, 28049 Madrid, Spain. scheres@cnb.csic.es
Carazo
José María
JM
D000465:Algorithms; D020285:Cryoelectron Microscopy; D004926:Escherichia coli / Q000737:chemistry; D016013:Likelihood Functions; D008958:Models, Molecular; D020653:Peptide Elongation Factor G / Q000737:chemistry / Q000648:ultrastructure; D017434:Protein Structure, Tertiary
D020653:Peptide Elongation Factor G