Classical detection theory and the cryo-EM particle selection problem.
Particle selection is an essential but tedious step in the determination of macromolecular structures by single particle reconstruction. This paper presents an automatic, multi-reference particle detection scheme that is based on the classical matched filter principle. It makes use of a pre-whitening filter to standardize the noise, a reduced representation of the references by means of principal component analysis, and a statistic for distinguishing particles from image artifacts. Standardizing the noise allows the noise-induced false-positive frequency to be estimated, and also allows the distribution of the discrimination statistic to be calculated a priori. The method is demonstrated with an annotated dataset of cryo-EM images.
Sigworth
Fred J
FJ
Department of Cellular and Molecular Physiology, Yale University, 333 Cedar Street, New Haven, CT 06520, USA. fred.sigworth@yale.edu
D000465:Algorithms; D000818:Animals; D020285:Cryoelectron Microscopy / Q000379:methods*; D003627:Data Interpretation, Statistical; D001330:Electronic Data Processing / Q000379:methods; D005583:Fourier Analysis; D006433:Hemocyanins / Q000737:chemistry / Q000648:ultrastructure; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008958:Models, Molecular; D008974:Mollusca; D010316:Particle Size; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012399:Rotation; D012985:Software Design; D016008:Statistical Distributions
D006433:Hemocyanins; C032808:keyhole-limpet hemocyanin