alignments3Dkey of the input particles). Although these alignments may not be optimal, this reduces the dimensionality of the search space and can produce classes that may be missed by Heterogenous Refinement.
Number of classes: Number of classes to use in job. Note that this can be significantly larger than Heterogeneous Refinement for the same computational cost.
Target resolution: Desired resolution of each 3D map — this, combined with the extent of the particle images will determine 3D box size.
Number of O-EM epochs: Number of passes through the data to perform during classification.
Batch size per class: This parameter multiplied by the number of classes sets the batch size in each O-EM iteration
Initialization modedetermines the way in which initial 3D maps are set:
simple: for K classes, select K random subsets of particle images and back-project K structures;
PCA: for K classes, select M >> K subsets of particle images, back-project M structures, apply Principal Component Analysis (PCA) on the space of 3D voxels, cluster into K subsets in principal component space, average volumes in each cluster for K initial structures;
input: for K classes, use K input volumes (please note that initial volumes should be distinct or the job will throw an error).
Auto-tune initial class similarity: This can set the expected similarity of structures until an empirically observed ESS (effective sample size) matches a target. Typically we expect that ESS should be near the number of classes to start.