ISAC2 - 2D Clustering: Iterative Stable Alignment and Clustering (ISAC) of a 2D image stack.

Usage in command line

sxisac2.py stack_file output_directory --radius=particle_radius --img_per_grp=img_per_grp --CTF --xr=xr --thld_err=thld_err --target_radius=target_radius --target_nx=target_nx --VPP --ir=ir --rs=rs --yr=yr --ts=ts --maxit=maxit --center_method=center_method --dst=dst --FL=FL --FH=FH --FF=FF --init_iter=init_iter --iter_reali=iter_reali --stab_ali=stab_ali --minimum_grp_size --rand_seed=rand_seed --skip_prealignment --restart

sxisac2 exists only in MPI version.

1. Conventional TEM dataset:

mpirun -np 96 sxisac2.py bdb:stack isac2_outdir --radius=120 --CTF

Note: ISAC2 will change the size of input data such that they fit into box size 76×76 by default (see Description below).

2. Phase Plate TEM dataset:

mpirun -np 96 sxisac2.py bdb:stack isac2_outdir --radius=120 --VPP

Note: CTF and VPP options cannot be used together.

stack_file

Input image stack: The images must to be square (nx=ny). The stack can be either in bdb or hdf format. (default required string)

output_directory

Output directory: The automatically-created output directory will contain results. If the directory already exists, results will be written there, possibly overwriting previous runs. (default required string)

--radius

Particle radius [Pixels]: Radius of the particle (pixels). There is no default value and so a sensible number has to be provided. (default required int)

--img_per_grp

Images per class: Number of images per class in an ideal situation. In practice, it defines the maximum size of the classes or the number of classes K = N / img_per_grp, where N is the total number of images in the input stack. (default 200)

--CTF

CTF phase flipping: If set, the data will be phase-flipped using CTF information included in the image headers. (default False)

--VPP

Phase Plate data: Please use this option if the dataset is taken with Phase Plate. (default False)

--xr

Translation search range [Pixels]: The translational search range. Set by the program by default. (default 1)

--thld_err

Pixel error threshold [Pixels]: Used for checking stability. It is defined as the root mean square of distances between corresponding pixels from set of found transformations and theirs average transformation, depends linearly on square of radius (parameter ou). units - pixels. (default 0.7)

--target_radius

Target particle radius [Pixels]: Particle radius used by ISAC2 to process the data. The images will be resized to fit this radius (default 29)

--target_nx

Target particle image size [Pixels]: Image size used by ISAC2 to process the data. The images will be resized according to target particle radius and then cut/padded to achieve the target image size. When xr > 0, the final image size for ISAC2 processing is target_nx + xr - 1 (default 76)

--ir

Inner ring [Pixels]: Inner of the resampling to polar coordinates. (default 1)

--rs

Ring step [Pixels]: Step of the resampling to polar coordinates. (default 1)

--yr

Y search range [Pixels]: The translational search range in the y direction. Set as xr by default. (default -1)

--ts

Search step [Pixels]: Translational search step. (default 1.0)

--maxit

Reference-free alignment iterations: The number of iterations for reference-free alignment. (default 30)

--center_method

Centering method: Method to center global 2D average during the initial prealignment of the data (0: no centering; -1: average shift method; please see center_2D in utilities.py for methods 1-7). (default -1)

--dst

Discrete angle used for within-group alignment: Discrete angle used for within-group alignment. (default 90.0)

--FL

Lowest filter frequency [1/Pixel]: Lowest frequency used for the tangent filter. (default 0.2)

--FH

Highest filter frequency [1/Pixel]: Highest frequency used for the tangent filter. (default 0.45)

--FF

Tangent filter fall-off: The fall-off of the tangent filter. (default 0.2)

--init_iter

Maximum generations: Maximum number of generation iterations performed for a given subset. (default 7)

--iter_reali

SAC stability check interval: Defines every how many iterations the SAC stability checking is performed. (default 1)

--stab_ali

Number of alignments for stability check: The number of alignments when checking stability. (default 5)

--minimum_grp_size

Minimum size of reproducible class: Minimum size of reproducible class. (default 60)

--rand_seed

Seed: Random seed set before calculations. Useful for testing purposes. By default, ISAC2 sets a random seed number. (default none)

--skip_prealignment

Do pre-alignment: Indicate if pre-alignment should be used or not. Do not use pre-alignment if images are already centered. The 2dalignment directory will still be generated but the parameters will be zero. By default, do pre-alignment. (default False question reversed in GUI)

--restart

Restart run: 0: Restart ISAC2 after the last completed main iteration (i.e. the directory must contain finished file); k: Restart ISAC2 after k-th main iteration, it has to be completed (i.e. the directory must contain finished file), and higer iterations will be removed; Default: Do not restart. (default -1)

For each generation of running the program, there are two phases. The first phase is an exploratory phase. In this phase, we set the criteria to be very loose and try to find as much candidate class averages as possible. The candidate class averages are stored in class_averages_candidate_generation_n.hdf.

The second phase is where the actual class averages are generated. The candidate groups are tested for their stability by repeated randomly initialized reference-free alignment and those deemed stable as set aside as output and member images of those that did not pass the test are returned to the overall pool of data and processed again starting from the first phase.

The program will perform the following steps (to save computation time, in case of inadvertent termination, i.e. power failure or other causes, the program can be restarted from any saved step location, see options) :

1. The images in the input stacked will be phase-flipped.
2. The data stack will be pre-aligned (output is in subdirectory 2dalignment, in particular it contains the overall 2D average aqfinal.hdf, it is advisable to confirm it is correctly centered).
   • In case 2dalignment directory exists steps 1 and 2 are skipped.
3. The alignment shift parameters will be applied to the input data.
4. IMPORTANT: Input aligned images will be resized such that the original user-provided radius will be now target_radius and the box size target_nx + xr - 1. The pixel size of the modified data is thus original_pixel_size * original_radius_size / target_radius.
   • The pseudo-code for adjusting the size of the radius and the size of the images is as follows:
   • shrink_ratio = target_radius / original_radius_size
   • new_pixel_size = original_pixel_size * shrink_ratio
   • if shrink_ratio is different than 1: resample images using shrink_ratio
   • if new_pixel_size > target_nx : cut image to be target_nx in size
   • if new_pixel_size < target_nx : pad image to be target_nx in size
   • The target_radius and target_nx options allow the user to finely adjust the image so that it contains enough background information.
5. The program will iterate through generations of ISAC2 by alternating two steps. The outcome of these two steps is in subdirectory generation_*** (stars replaced by the current generation number).
   • Calculation of candidate class averages.
   • Calculation of validated class averages.
6. The program will terminate when it cannot find any more reproducible class averages.
7. If no restart option is given the program will pick-up from the last saved point.

Also see the reference below.

ISAC2 is very time- and memory-consuming. For example, it may take 15 hours to process 50,000 particles using 256 processors. Therefore, before embarking on the big dataset, it is recommended to run a test dataset (about 20,000 particles) first to get a rough idea of timing.

1. Open in e2display.py file class_averages.hdf located in the main directory.
2. Delete averages whose member particles should not be included in the output.
3. Save the selected subset under a new name,say select1.hdf
4. Retrieve IDs of member particles and store them in a text file ohk.txt:
   • $ sxprocess.py –isacselect class_averages.hdf ok.txt
5. Create a vritual stack containng selected particles:
   • $ e2bdb.py bdb:data –makevstack:bdb:select1 –list=ohk.txt

The same steps can be performed on files containing candidate class averages.

Let us assume we would want to generate a RCT reconstruction using as a basis group number 12 from ISAC2 generation number 3. We have to do the following steps:

1. Retrieve original image numbers in the selected ISAC2 group. The output is list3_12.txt, which will contain image numbers in the main stack (bdb:test) and thus of the tilted counterparts in the tilted stack. First, change directory to the subdirectory of the main run that contains results of the generation 3. Note bdb:../data is the file in the main output directory containing original (reduced size) particles.
   • $ cd generation_0003
   • $ sxprocess.py bdb:../data class_averages_generation_3.hdf list3_12.txt –isacgroup=12 –params=originalid
2. Extract the identified images from the main stack (into subdirectory RCT, has to be created):
   • $ e2bdb.py bdb:test –makevstack=bdb:RCT/group3_12 –list=list3_12.txt
3. Extract the class average from the stack (NOTE the awkward numbering of the output file!).
   • $ e2proc2d.py –split=12 –first=12 –last=12 class_averages_generation3.hdf group3_12.hdf
4. Align particles using the corresponding class average from ISAC2 as a template (please adjust the parameters):
   • $ sxali2d.py bdb:RCT/group3_12 None –ou=28 –xr=3 –ts=1 –maxit=1 –template=group3_12.12.hdf
5. Extract the needed alignment parameters. The order is phi,sx,sy,mirror. sx and mirror are used to transfer to tilted images.
   • $ sxheader.py group3_12.12.hdf –params=xform.align2d –export=params_group3_12.txt

• The meaning of the following option might changed from ISAC to ISAC2.
  • init_iter:: SAC initialization iterations: Number of ab-initio-within-cluster alignment runs used for stability evaluation during SAC initialization. (default 3)
• The removal of the following option have to be reflected to the Tutorial
  • stop_after_candidates:: Stop after candidates step: The run stops after the 'candidate_class_averages' section is created. (default False)

Yang, Z., Fang, J., Chittuluru, F., Asturias, F. and Penczek, P. A.: Iterative Stable Alignment and Clustering of 2D Transmission Electron Microscope Images, Structure 20, 237-247, February 8, 2012.

Horatiu Voicu, Zhengfan Yang, Jia Fang, Francisco Asturias, and Pawel A. Penczek

Category 1:: APPLICATIONS

sparx/bin/sxisac2.py, sparx/bin/sxisac.py, sparx/bin/isac.py

sxcompute_isac_avg, sxpipe isac_substack, and sxisac

Beta:: Under evaluation and testing. Please let us know if there are any bugs.

None right now.