e2gmm - recommended steps to continue from kmeans

[Tim Schulte]

Hej Eman2 community,
I am a newbie to use Eman2/e2gmm to analyze heterogenity in my datasets.
I have explored a dataset based on the available tutorials, both the "Particle orientation refinement" and "e2gmm - A semi-friendly GUI for running GMM dynamics in EMAN2".
I have obtained interesting reconstructions from a latent space by kmeans clustering.
I also managed to save particle sets, as well as look at the binned (quick), unbinned volumes as well as gaussian representations
in the gmm gui, e2display or chimerax. These representations are all saved in set_maps.hdf (as well as their even and odd parts).

I would like to continue gmm or eventually Relion refinements of some of the kmeans-clustered particle stacks.
I managed to export the stack for use in Relion, following the tutorial. However, I would like to stay in Eman2 now and continue with another gmm refinement.

What would be your recommended steps to continue from the latent space-derived reconstructions? Can I make use of the unbinned reconstructions saved from the gui?

Best wishes,
Tim

[Steve Ludtke]

Hi Tim,

well, it's nice that that tutorial is still usable even though the interface has changed so much. I've been meaning to update it, but we keep making changes, so it hasn't felt like the right stopping point. 

If I'm understanding what you want to do, my advice would be to take one of the saved .lst files and run it through another ~3 iterations of refinement. e2gmm.py, unlike a couple of Muyuan's branched command-line workflow, does not do any particle orientation refinement. It assumes that particles are all aligned self-consistently to the common 3-D map. While this is true enough for classification in most cases, it isn't going to give really optimal alignments. So, once you've classified the particles, you can gain a fair bit by doing a refinement with just the subset. You can then run that subset refinement through e2gmm again.

cheers

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