Hi Dieter,

You can use relion_stack_create. Do change _rlnReferenceImage to
_rlnImageName in the star file of class average. 

Best wishes
Kai
On Wed, 2014-09-03 at 18:47 +0200, Dieter Blaas wrote:
> Hi Kai,
>    thank you very much for this ample explanation! Just one final question:
> How do I write out the images of the class averages? I can display them, 
> I can write out a star file referring to original image stacks but I 
> could not find a way of writing out an mrcs stack containing just the 
> image of each class average (as shown in 'Display'). Relion_preprocess 
> --operate_on star-file_of_class_averages.star --o 
> stack_of_class_average_images.mrcs does not work!
> Best, Dieter
> 
> ------------------------------------------------------------------------
> Dieter Blaas,
> Max F. Perutz Laboratories
> Medical University of Vienna,
> Inst. Med. Biochem., Vienna Biocenter (VBC),
> Dr. Bohr Gasse 9/3,
> A-1030 Vienna, Austria,
> Tel: 0043 1 4277 61630,
> Fax: 0043 1 4277 9616,
> e-mail: [log in to unmask]
> ------------------------------------------------------------------------
> 
> Am 03.09.2014 16:11, schrieb Kai Zhang:
> > Hi Dieter,
> >
> > Here's my experience to get rid of bad particles in the following.
> > Normally, I tend to deal with different types of bad particles in
> > different ways. Ice contamination, aggregation, carbon edge, obvious
> > different protein and so on are regarded as 100% rubbish and should be
> > excluded completely from data sets. To get rid of these rubbish, the
> > best way is manually screening after auto-picking. You can low-pass and
> > shrink all the particles, open the entire data set if possible and then
> > quickly screen. It's tedious but works very well(I believe it's better
> > than 2D classification); multiple rounds of 2D classification to get
> > comparable results also requires quite a long time. Also you can use CCC
> > or other values from auto-picking or projection match(if model
> > available) to rank all particles and open them from low quality to high
> > quality, then screening will be a bit faster.   There are other bad
> > particles simply because of the 'low-quality'(slightly distorted, drift,
> > dried, over-exposure, falling apart, low contrast ...)  rather than real
> > rubbish. It's not easy to get rid of them by eye and 2D classification
> > or 3D classification is more reliable than eye.  However, ranking by CCC
> > or phase error or maximum likelihood contribution doesn't really work
> > well at beginning because of the low-quality model and possible bias. In
> > 2D level you can do multiple rounds of classification. But I think it's
> > faster to do it in a hierarchical way(classification and
> > re-classification for each class). For the first round of entire data
> > sets, I tend to use shrinked and low-passed particles. And later, use
> > the original unfiltered particles. You might get rid of the very
> > low-quality particles roughly by doing this. But it's better to keep
> > suspicious classes that look like something in-between rubbish and good
> > particles(they may contain valuable rare views). I normally don't
> > completely get rid of these classes, but keep them for later 3D
> > classification. In 3D level, I will also completely get rid of the
> > really bad ones and keep suspicious classes like 2D.  Once I get a
> > better map, I will re-use these suspicious particles previously regarded
> > as bad and re-rank the quality of all the particles. This can be done
> > for multiple cycles. However if you have huge data sets, you don't need
> > to do it in this way, just keep the very high-quality particles :-).
> >
> > I myself is always struggling at find ways to do it better, but I still
> > hope this is helpful for you.
> >
> > Best wishes
> > Kai
> >
> > On Tue, 2014-09-02 at 12:35 +0200, Dieter Blaas wrote:
> >> Hi Kai,
> >>     thanks, 2D-classification is still running but so far I see that the
> >> use of both parameters results in all classes being populated with
> >> well-recognizable viral class averages. However, so far (iteration5) I
> >> do not get any trash particles. Since I do not think that I got rid of
> >> all (really all)  'bad' particles in the last run (#6), does this mean
> >> that they are now incorporated into the classes and will reduce the
> >> finally achieved resolution? I understand that there must be a tradeoff
> >> between separating class averages and inclusion of less good particles.
> >> However, what would be the best way to go for purification of the
> >> dataset? Many selective runs (10 or more) to get rid of the trash and a
> >> final run with these parameters set (for the sake of getting the class
> >> averages)?
> >> Best, Dieter
> >>
> >> ------------------------------------------------------------------------
> >> Dieter Blaas,
> >> Max F. Perutz Laboratories
> >> Medical University of Vienna,
> >> Inst. Med. Biochem., Vienna Biocenter (VBC),
> >> Dr. Bohr Gasse 9/3,
> >> A-1030 Vienna, Austria,
> >> Tel: 0043 1 4277 61630,
> >> Fax: 0043 1 4277 9616,
> >> e-mail: [log in to unmask]
> >> ------------------------------------------------------------------------
> >>
> >> Am 01.09.2014 00:20, schrieb Kai Zhang:
> >>> Hi Dieter,
> >>>
> >>> Did you tried --only_flip_phases(or --ctf_intact_first_peak) and/or
> >>> --strict_highres_exp? It helps a lot for uncleaned data sets with strong
> >>> orientation preference.
> >>>
> >>> Kai
> >>>
> >>> On Sun, 2014-08-31 at 14:52 +0200, Dieter Blaas wrote:
> >>>> Hi Sjors and all,
> >>>>       I am trying to get a reasonable number of 2D class averages by
> >>>> running a 2D-classification on an old icosahedral virus dataset (about
> >>>> 10,000 images, 50 classes). After each run I select the classes with
> >>>> features recognizable as viral, remove the trash, and run it again. I am
> >>>> now at run 5 and I am still getting only 2 classes recognizable as viral
> >>>> projections (in sum about 99% of the images) and about 40 classes
> >>>> containing between 1 and 20 'bad' images. I have tried T=1, 1.5, and 2
> >>>> and angular sampling 5 and 2.5 but regardless of these parameters it
> >>>> seems to go on for ever (each new run finds new 'trash images' and I do
> >>>> not get more than the 2 main classes (with the second one at about 3 %
> >>>> only)). I admit that the dataset is skewed towards one single view but
> >>>> shouldn't I get more than two main class averages ? Is there a way of
> >>>> removing the trash and getting a reasonable number of views once for ever?
> >>>> Thanks for hints, Dieter
> >>>>
> >>>> ------------------------------------------------------------------------
> >>>> Dieter Blaas,
> >>>> Max F. Perutz Laboratories
> >>>> Medical University of Vienna,
> >>>> Inst. Med. Biochem., Vienna Biocenter (VBC),
> >>>> Dr. Bohr Gasse 9/3,
> >>>> A-1030 Vienna, Austria,
> >>>> Tel: 0043 1 4277 61630,
> >>>> Fax: 0043 1 4277 9616,
> >>>> e-mail: [log in to unmask]
> >>>> ------------------------------------------------------------------------
> >
> >
> >
> >