pipeline:sort3d:sxsort3d

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pipeline:sort3d:sxsort3d [2018/08/22 11:53]
fmerino
pipeline:sort3d:sxsort3d [2019/04/02 10:49] (current)
lusnig
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 ~~NOTOC~~ ~~NOTOC~~
  
-===== sxsort3d ​=====+===== sp_sort3d ​=====
 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. 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.
  
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 Usage in command line Usage in command line
  
-  ​sxsort3d.py  stack  outdir ​ mask  --focus=3Dmask ​ --radius=outer_radius ​ --delta=angular_step ​ --CTF  --sym=c1 ​ --number_of_images_per_group=number_of_images_per_group ​ --nxinit=nxinit ​ --smallest_group=smallest_group ​ --chunk0=CHUNK0_FILE_NAME ​ --chunk1=CHUNK1_FILE_NAME ​ --ir=inner_radius ​ --maxit=max_iter ​ --rs=ring_step ​ --xr=xr ​ --yr=yr ​ --ts=ts ​ --an=angular_neighborhood ​ --center=centring_method ​ --nassign=nassign ​ --nrefine=nrefine ​ --stoprnct=stop_percent ​ --function=user_function ​ --independent=indenpendent_runs ​ --low_pass_filter=low_pass_filter ​ --unaccounted ​ --seed=random_seed ​ --sausage ​ --PWadjustment=PWadjustment ​ --protein_shape=protein_shape ​ --upscale=upscale ​ --wn=wn ​ --interpolation=method+  ​sp_sort3d.py  stack  outdir ​ mask  --focus=3Dmask ​ --radius=outer_radius ​ --delta=angular_step ​ --CTF  --sym=c1 ​ --number_of_images_per_group=number_of_images_per_group ​ --nxinit=nxinit ​ --smallest_group=smallest_group ​ --chunk0=CHUNK0_FILE_NAME ​ --chunk1=CHUNK1_FILE_NAME ​ --ir=inner_radius ​ --maxit=max_iter ​ --rs=ring_step ​ --xr=xr ​ --yr=yr ​ --ts=ts ​ --an=angular_neighborhood ​ --center=centring_method ​ --nassign=nassign ​ --nrefine=nrefine ​ --stoprnct=stop_percent ​ --function=user_function ​ --independent=indenpendent_runs ​ --low_pass_filter=low_pass_filter ​ --unaccounted ​ --seed=random_seed ​ --sausage ​ --PWadjustment=PWadjustment ​ --protein_shape=protein_shape ​ --upscale=upscale ​ --wn=wn ​ --interpolation=method
  
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 ===== Typical usage ===== ===== Typical usage =====
  
-sxsort3d ​exists only in MPI version.+sp_sort3d ​exists only in MPI version.
  
-  mpirun -np 192 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+  mpirun -np 192 sp_sort3d.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
  
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 ===== Description ===== ===== Description =====
-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.+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 ​sp_sort3d.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.
  
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 ==== Files ==== ==== Files ====
-sparx/bin/sxsort3d.py+sparx/bin/sp_sort3d.py
  
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 ==== See also ==== ==== See also ====
-[[pipeline:​meridien:​sxmeridien|sxmeridien]], [[pipeline:​utilities:​sxheader|sxheader]], [[[pipeline:​sort3d:​sx3dvariability|sx3dvariability]], [[pipeline:​sort3d:​sxrsort3d|sxrsort3d]], and [[pipeline:​sort3d:​sxsort3d_depth|sxsort3d_depth]].+[[pipeline:​meridien:​sxmeridien|sp_meridien]], [[pipeline:​utilities:​sxheader|sp_header]], [[[pipeline:​sort3d:​sx3dvariability|sp_3dvariability]], [[pipeline:​sort3d:​sxrsort3d|sp_rsort3d]], and [[pipeline:​sort3d:​sxsort3d_depth|sp_sort3d_depth]].
  
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  • pipeline/sort3d/sxsort3d.txt
  • Last modified: 2019/04/02 10:49
  • by lusnig