Hi Relion Users,
I have been trying to classify a heterogeneous negative stain data. The
starting references (5 different volumes) are from RCT reconstructions.
The input dataset is 70,000 particles.
mpirun -np 108 -machinefile cluster.txt `which relion_refine_mpi` --o
Class3D/r3_k5_7p5d --i all_untilt.star --particle_diameter 320 --angpix
5.71 --ref ref_volumes.star --firstiter_cc --ini_high 60 --iter 25
--tau2_fudge 3 --K 5 --flatten_solvent --zero_mask --strict_highres_exp
10 --oversampling 1 --healpix_order 2 --offset_range 5 --offset_step 2
--sym C1 --norm --scale --j 1 --memory_per_thread 4
The output volumes are much distorted as compared to initial ones and
the particle distributions are almost equal between the 3d classes.
Moreover, when i compared the 3d projections of some of the refined
volumes (auto_refine on the classified sub-pool of particles) against
the reference free 2d class averages, the projections show more
features than actual 2d class averages.
Is there a better sort out the heterogeneous data set in relion?
(classification based on the consensus reference model did not work
also).
Is it true that 3d classification runs only on the selected angular
sampling?
Is it possible make it like in '3d_auto_refine' where angular sampling
is calculated by the program for every iteration?
thanking you.
with kind regards
Mani.
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