3D Clustering - SORT3D: Sort 3D heterogeneity based on the reproducible members of K-means and Equal K-means classification. It runs after 3D refinement where the alignment parameters are determined.
Usage in command line
sxsort3d.py stack outdir mask --focus=3Dmask --ir=inner_radius --radius=outer_radius --maxit=max_iter --rs=ring_step --xr=xr --yr=yr --ts=ts --delta=angular_step --an=angular_neighborhood --center=centring_method --nassign=nassign --nrefine=nrefine --CTF --stoprnct=stop_percent --sym=c1 --function=user_function --independent=indenpendent_runs --number_of_images_per_group=number_of_images_per_group --low_pass_filter=low_pass_filter --nxinit=nxinit --unaccounted --seed=random_seed --smallest_group=smallest_group --sausage --chunk0=CHUNK0_FILE_NAME --chunk1=CHUNK1_FILE_NAME --PWadjustment=PWadjustment --protein_shape=protein_shape --upscale=upscale --wn=wn --interpolation=method
sxsort3d exists only in MPI version.
mpirun -np 176 --host n1,n5,n6,n8,n9,n10,n0,n4,n3,n7 sxsort3d.py bdb:data sort3d_outdir1 mask.hdf --focus=ribosome_focus.hdf --chunkdir=/data/n10/pawel/ribosome_frank/ri3/main013 --radius=52 --CTF --number_of_images_per_group=2000 --low_pass_filter=.125 --stoprnct=5
The clustering algorithm in the program combines a couple of computational techniques, equal-Kmeans clustering, K-means clustering, and reproducibility of clustering such that it not only has a strong ability but also a high efficiency to sort out heterogeneity of cryo-EM images. The command sxsort3d.py is the protocol I {P1). In this protocol, the user defines the group size and thus defines the number of group K. Then the total data is randomly assigned into K group and an equal-size K-means (size restricted K-means) is carried out. N independent equal-Kmeans runs would give N partition of the K groups assignment. Then two-way comparison of these partitions gives the reproducible number of particles.
Zhong Huang
Category 1:: APPLICATIONS
applications.py
stable while under development:: Two programs (P1,P2) have been tested on both simulated and experimental ribosome data. For experimental ribosome data, P2 has a reproducible ratio-70-90%. P2 can 100%separate two conformations from the simulated ribosome data that contains 5 conformations.
None.