Filling in the GUI

For calculating reference-free 2D class averages, select the run-type of 2D class averages from the drop-down menu at the top of the GUI.

I/O tab

See the Prepare input files page on how to prepare your input data.
The number of classes is the most important parameter of the 2D classification procedure. Often one performs multiple calculations with different values.

The pixel size (in Angstrom) should be the same as the one used to estimate the CTF parameters (unless you have rescaled your data afterwards, in which case the same scale factor should be applied)
If no CTF correction is to be performed, make sure you phase-flipped your data during preprocessing. See the Prepare input files page.
Tell the program whether the data have been phase flipped or not.

Often 25-50 iterations are necessary before the refinement converges to a stable solution. Note there is currently no convergence criterion implemented, so the user is responsible for monitoring the convergence.

The regularisation parameter determines the relative weight between the experimental data and the prior. Bayes' law dictates it should be 1, but sometimes better results are obtained using slightly higher values. Whereas 3D refinements (in cryo) may require values of 2-4, 2D classifications seem to go better with values of 1-2.

The particle diameter (in Angstroms) serves to define a soft spherical mask that will be applied to the reference images to reduce their background noise.

Translational search ranges may depend on how well-centered the particles were picked, but often 6 pixels will do the job (translational searches in subsequent iterations are centered at the optimal translation in the previous one, so that particles may "move" much more than the original search range during the course of an entire refinement. Note that pre-centering prior to RELION refinement is not necessary, and also not recommended (it often messes up the Gaussian distribution of origin offsets).

It is unlikely one needs threads for 2D class averaging, as this typically takes only modest amounts of memory. Therefore, in case multiple CPUs are available for this task, use the more efficient MPI parallelisation.