e2refinemulti

This is a variant of the standard e2refine program. This version performs so-called multi-model refinement. Rather than producing a single 3-D model based on a set of 2-D single particles, this program will create multiple 3-D models from a single set of 2-D particles. Several starting maps are provided, with (optionally) a different symmetry for each. Each particle will not only be classified based on orientation, but also based on which of the input models it best-matches. This process is iterative, so in theory, identical input models perturbed by a little random noise can be used as seeds, and eventually the refinement should find the greatest structural variations to base the different output maps on. However, if the input maps are nearly identical, this convergence could require many iterations. If the 'seed maps' are similar to the final expected structures, clearly the process will converge more quickly. Normally, after performing this multi-model refinement, in which particles are free to move between different output maps from iteration to iteration, the particles associated with each map would be separated into different 'sets', then normal single-model refinement would be performed on each of these particle sets, to achieve a more self-consistent result, and higher resolution.

The options for this program are basically identical to those for e2refine.py with 2 exceptions, instead of --model to specify the starting model, use --models= with a comma-separated list of inputs. --sym can be either a single symmetry for all models, or a comma separated list with the same number of symmetries as input models.

Command Line Arguments

General Options

--version

bool

show program's version number and exit

-h

--help

bool

show this help message and exit

-c

--check

bool

Checks the contents of the current directory to verify that e2refine.py command will work - checks for the existence of the necessary starting files and checks their dimensions.

-v

--verbose

int

verbose level [0-9], higner number means higher level of verboseness

Options impacting the overall refinement

--iter

int

The total number of refinement iterations to perform

--models

string

A set of 3-D maps to seed the refinement. Comma separated list. The number of outputs will match the number of inputs.

--input

string

The name of the image containing the particle data

--usefilt

string

Note: some unresolved bugs may exist with this option (6/2011) Specify a particle data file that has been low pass or Wiener filtered. Has a one to one correspondence with your particle data. If specified will be used in projection matching routines, and elsewhere.

--path

string

The name of a directory where results are placed. If not specified (suggested), will use a path of the form refine_xx.

--mass

float

The mass of the particle in kilodaltons, used to run normalize.bymass. If unspecified nothing happens. Requires the --apix argument.

--apix

float

The angstrom per pixel of the input particles. This argument is required if you specify the --mass argument. If unspecified, the convergence plot is generated using either the project apix, or an apix of 1.

--sym

string

Symmetry to be imposed throughout: c<n>, d<n>, h<n>, tet, oct, icos. Omit this option or specify 'c1' for asymmetric reconstructions. Either a single symmetry may be specified for all output maps, or the same number of (comma separated) symmetries as maps.

--lowmem

bool

Make limited use of memory when possible. Slight speed penalty.

-P

--parallel

string

Run in parallel, specify type:<option>=<value>:<option>:<value>. See EMAN2/Parallel

See also EMAN2/Programs/e2project3d

--projector

string

Projector to use. 'standard' is the default

--orientgen

string

The orientation generation argument for e2project3d.py. Typically something like: --orientgen=eman:delta=2.0:inc_mirror=0

Options for comparing particles to projections

See also EMAN2/Programs/e2simmx

--twostage

int

Optionally run a faster 2-stage similarity matrix, ~5-30x faster, almost identical results. Value specifies shrink factor for first stage, typ 1-3

--shrink

int

Optionally shrink the input particles by an integer amount prior to computing similarity scores. For speed. If used with --twostage, this specifies the second stage shrink factor.

--simcmp

string

The name of a comparator to be used in comparing the aligned images

--simalign

string

The name of an aligner to use prior to comparing the images

--simaligncmp

string

Name and options for a comparator to use in first stage alignment for classification

--simralign

string

The name and parameters of the second stage aligner which refines the results of the first alignment. Currently this is either not specified or is 'refine'.

--simraligncmp

string

The name and parameters of the comparator used by the second stage aligner. Default is dot.

--simmask

string

A file containing a single 0/1 image to apply as a mask before comparison but after alignment

--prefilt

bool

Filter each reference (c) to match the power spectrum of each particle (r) before alignment and comparison

Options for classifying particles based on similarity matrix

See also EMAN2/Programs/e2classify

--sep

int

The number of classes a particle can contribute towards (default is 1). This is a precursor to full maximum-liklihood.

Options for generating class-averages

See also EMAN2/Programs/e2classaverage

--classiter

int

The number of iterations to perform. Default is 1. Larger values reduce model/noise bias, but slightly decrease resolution.

--classcmp

string

The name and parameters of the comparitor used to generate similarity scores, when class averaging. Default is 'dot:normalize=1'

--classalign

string

If doing more than one iteration, this is the name and parameters of the aligner used to align particles to the previous class average.

--classaligncmp

string

This is the name and parameters of the comparator used by the fist stage aligner Default is dot.

--classralign

string

The second stage aligner which refines the results of the first alignment in class averaging. Currently this is either not specified or is 'refine'.

--classraligncmp

string

The comparator used by the second stage aligner in class averageing. Default is dot:normalize=1.

--classaverager

string

The averager used to generate the class averages. Default is 'mean'.

--classkeep

float

The fraction of particles to keep in each class, based on the similarity score generated by the --cmp argument (see also --classkeepsig).

--classkeepsig

bool

Change the keep ('--keep') criterion from fraction-based to sigma-based. eg - with this set, 1.0 would correspond to discarding particles more than 1 standard deviation from the mean

--classnormproc

string

Normalization processor and options applied during class averaging. Typically 'normalize.edgemean'

--classrefsf

bool

This will impose the 1-D structure factor of each model projection onto the corresponding class-average to improve its filtration. This is an alternative to Wiener filtration if the map resolution is regulated.

--classautomask

bool

Experimental. This will apply a 2-D automask to the class-average during iterative alignment for better accuracy. The final class averages are unmasked.

Options related to 3-D Reconstruction of Class-averages and post-processing

--pad

int

To reduce Fourier artifacts, the model is typically padded by ~25% - only applies to Fourier reconstruction. Please read EMAN2/BoxSize

--recon

string

reconstructor to use. Main choices are 'fourier' or 'wiener_fourier'

--m3dkeep

float

The percentage of slices to keep in e2make3d.py

--m3dkeepsig

bool

Similar to classkeepsig above, changes the meaning of --m3dkeep to be in terms of standard deviations

--m3dsetsf

bool

Filters the final 3-D map to match the precomputed structure factor (stored in the project database). Normally used with a --m3dpostprocess=filter.lowpass.* option

--m3diter

int

The number of times the 3D reconstruction should be iterated. 2 and 3 are the only valid values. 2 is faster and normally has sufficient accuracy.

--m3dpreprocess

string

Normalization processor applied before 3D reconstruction

--m3dpostprocess

string

Post processor to be applied to the 3D volume once the reconstruction is completed

--automask3d

string

The 5 parameters of the mask.auto3d processor, applied after 3D reconstruction. These parameters are, in order: isosurface threshold,radius,nshells and ngaussshells. From e2proc3d.py you could achieve the same thing using --process=mask.auto3d:threshold=1.1:radius=30:nshells=5:ngaussshells=5. e2help.py processors -v2 for more information on mask.auto3d.

The refinement results will normally go into directories called multi_XX rather than refine_XX. The output files from e2refinemulti are identical to those produced by e2refine, except instead of having threed_YY files for each iteration, threed_YY_ZZ files are produced representing iteration number and map number within that iteration. The projections, similarity matrix, classification matrix and class-averages are all combined into a single file for all output maps. Specifically, all projections for map #1 appear first, followed by all projections for map #2, etc., with the class-averages in an identical order. This is a very different structure than used in EMAN1's multirefine program, which separated maps into separate subdirectories. In addition to the class-averages following the same order as the projections, each class-average will have a 'model-id' header item identifying which map that average is associated with. This item is used by e2make3d.py to perform 3-D reconstructions on the appropriate subset of class-averages for each output map.

The most common usage pattern for e2refinemulti.py is to run it until pseudoconvergence is achieved, similar to single-model refinement. Once this is achieved (observed by looking at the map convergence FSC curves), generally the particles will be split into groups and refined independently using e2refine. This process is still less automated than it could be , but it is at least fairly straightforward:

Note: at present e2eotest.py cannot be run directly on the results of an e2refinemulti run. Resolution can only be assessed after the subsequent single-model refinements are complete.

EMAN2/Programs/e2refinemulti (last edited 2011-12-01 05:24:06 by SteveLudtke)