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2D classification
Stage
2
of 8
Included on all plans
AutoClass2D
2D classes selected by correlating them with projections of a reference, so class selection is made on a score rather than by eye.
Reference-based
Classes scored against projections of a reference
Correlation score
Classes above a set threshold are taken forward
What it does
2D classification aligns and averages particles into groups. Averaging thousands of noisy particles produces class averages with real detail, and those averages are one of the first points where you can can assess the quality of the sample.
Blurred or featureless classes are the picks that were never particles, or particles too damaged to contribute to a meaningful class average. AutoClass2D automatically selects true particle classes and leaves behind junk, reducing the need for manual and subjective class selection.
How it works
Selection is made against a reference map you provide. The reference is projected at a range of viewing angles, those projections are cross-correlated with the experimental class averages, and the resulting correlation coefficients become the selection criterion: classes scoring above a set threshold are taken forward.
Because the criterion is a number rather than a judgement, the same standard applies to every dataset and the decision can be repeated. The job also accepts particle files from classification or selection jobs, so a stack that has already been filtered another way can be fed straight in.
Schematic, not measured data. Nine classes carry structure and are kept. Three are diffuse, have no recognisable features, and fall below the selection threshold.
Where it sits
AutoClass2D sits between 2D classification and 3D classification (or additional rounds of 2D classification).
At a glance
Stage
2D classification
Runs
Following 2D classification
Hardware
GPU
Inputs
Model and data star files from 2D classification
Outputs
Selected class averages and corresponding particles
Replaces
Availability
Included
Let the selection be made on a score, the same way every time.
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