Seed oversampling

Seed oversampling is a technique used in models that parametrise overall region where actual particles will be located, as membranes or pseudo crystals. The crop_points generated by the models after the geometric computation stage are not supposed to realistically be centered on a copy of the macromolecule of interest.

One possible procedure is to define a distribution of boxes denser than the expected distribution of physical particles. This way, after particle extraction you will end up end up with more subtomograms than actual particles, but you'll make sure that all the particles are actually included in some of the subtomograms. This might require some planning ahead, playing with the relationship between the expected physical distance between the particles, the sampling distance that you impose through the model, the physical size of the particle and the sidelength that you ask for the particle cropping.

Oversampling in an alignment project

When you crop your particles using seed oversampling, you should try to avoid carrying all the "extra" subtomograms along the iteration procedure.

You can run a couple of alignment iterations to allow the subtomograms to converge to positions with actual particles, and then elliminate repetitions (we call this procedure trimming). This can be done manually or through the alignment project.

Oversampling trimming through the alignment project

The parameter Template:Separation in tomogram can be tuned (in pixels) at the end of each iteration to ellminate particles that are closer to each other more than a given threshold. The particle with the highest correlation will be kept, and any other particle in the particle radius will be eliminated.

Manual oversampling trimming

In order to get a closer view on how the alignment handles the particles, it is advisable to run a few iterations with the full oversampled data set, then explore visually the refined table and trim it manually, i.e., impose manually a threshold

Command tools

The function that "filters" the refined table created by an iteration is dpktbl. exclusionPerVolume. You can use it to analize the results of an iteration manually.

Note that this function has not been optimized

function [newTable,o] = exclusionPerVolume(table,distanceThreshold,columnVolume,columnCC)