2D class averaging

[Lana S]

Hello,

I am trying to perform 2D class averaging for a set of particles that picked. I am not sure what is the input file supposed to be: all.lst or ...fullres.lst, or bispec.lst?

Thank you

[Lana S]

this is the error message I get: 

Error running:

e2classaverage.py --input=sets/all__ctf_flip_fullres.lst --classmx=r2d_01/classmx_00.hdf --output=r2d_01/classes_init.hdf --iter=8 --force --storebad --center=xform.center --align=rotate_translate_tree:maxshift=60 --averager=ctf.weight.autofilt  --keep=0.850000 --cmp=ccc --aligncmp=ccc --normproc=normalize.edgemean

my eman2 version is 

EMAN 2.22 final (GITHUB: 2018-08-20 20:32 - commit: f4f3952 )

Your EMAN2 is running on: Linux-3.10.0-957.21.2.el7.x86_64-x86_64-with-redhat-7.6-Maipo 3.10.0-957.21.2.el7.x86_64

Your Python version is: 2.7.14

[Steven Ludtke]

Hi. You aren’t using the correct program. That program makes averages, but only after particles have been classified. Please follow the standard eman2 tutorial at eman2.org using your own data. The first few steps will take you through the process of making class averages. I would also suggest upgrading to 2.3, as the new class averaging scheme introduced in 2.22 has been significantly improved. It can make class averages from 25,000 particles in a few minutes on a typical laptop

Sent from my iPhone

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[Lana S]

Steven, thank you for your speedy reply! I changed the ncls (number of classes to lower, from 32 to 3) and it worked. No error.

I guess I never specified that I have only 50 total particles picked...

Have a good day!

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[Steve Ludtke]

Ahh,

I see. I was reading your original message on my phone, so it wasn't clear that this error was the result of running e2refine2d.py rather than e2classaverage.py. While I'm glad that solved it for you, unless you have a very strongly preferred orientation, 2-D classification isn't useful until you have at least several hundred boxed out particles (many projects nowadays have 1,000,000+ particles in them). The point is that each particle is in a random orientation. If you make only 3 classes that means you are averaging together particles from many completely different orientations (unless, again, you have a strongly preferred orientation). 

--------------------------------------------------------------------------------------
Steven Ludtke, Ph.D. <slu...@bcm.edu>                      Baylor College of Medicine 
Charles C. Bell Jr., Professor of Structural Biology
Dept. of Biochemistry and Molecular Biology                      (www.bcm.edu/biochem)
Academic Director, CryoEM Core                                        (cryoem.bcm.edu)
Co-Director CIBR Center                                    (www.bcm.edu/research/cibr)

To view this discussion on the web visit https://groups.google.com/d/msgid/eman2/CAHn_UzdUr6uRQ62a3FUqWx22DziZvjAz7whfEacpaPE48a5EvQ%40mail.gmail.com.

[Lana Segizbayeva]

Hello Dr. Ludtke,

In your opinion is it possible to get a decent 2d classification based only on 50 particles in total? Do I need export stuff to Relion to achieve a better classification? The thing is our protein aggregates very quickly at higher concentrations. 

Thank you,

Lana.

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[Steve Ludtke]

Hi Lana,

The problem is not with the software. The problem is that 50 particles just isn't a useful number, unless your particles exhibit a strongly preferred orientation. The whole point of single particle analysis is that you have many identical objects which are randomly oriented in solution. If you think about a particle in some orientation, then consider how a projection of that particle would change if you rotate it, say, 10 degrees out of the plane. The particle appearance changes quite a bit. If you were to average these two particles together you would be smearing out most of the features. 

Even with this large angular step, there are over 200 different unique views of an asymmetric particle!  If you reduce the step to 5 degrees, this number jumps to over 800, and 5 degrees is still a pretty significant change. So, if you have randomly oriented asymmetric particle and want to have on average 5 particles per class average, this would mean you would need to start with at least 1000 particles, and your class averages would still be pretty fuzzy.

Now, if you have a strongly preferred orientation, you may be able to do 2-D averages with far fewer particles, but in that case, you will not be able to do 3-D well, even if you incorporate tilting into your data collection. 

If your particle has symmetry, say a 4-fold rotational symmetry, then the minimum number of required particles is reduced by this factor. So people looking, for example, at icosahedral viruses (60-fold symmetry) can make class-averages with a pretty small number of particles.

No, you should not get any better 2-D classification results in Relion. EMAN2's new 2-D classification works extremely well, though there are differences between the goals of EMAN2 and Relion in this respect, so results will not be identical. It's probably not worth getting into the details of that here. 

Aggregation is a very common problem in TEM, but it is largely a problem to solve experimentally rather than computationally. ie - come up with a better buffer, make sure you are diluting the specimen following methods which limit aggregation, alter grid conditions, etc.  In the end it is unlikely that you are going to be able to answer useful biological questions with fewer than a few thousand particles. (there are exceptions to this rule, of course)

--------------------------------------------------------------------------------------
Steven Ludtke, Ph.D. <slu...@bcm.edu>                      Baylor College of Medicine 
Charles C. Bell Jr., Professor of Structural Biology
Dept. of Biochemistry and Molecular Biology                      (www.bcm.edu/biochem)
Academic Director, CryoEM Core                                        (cryoem.bcm.edu)
Co-Director CIBR Center                                    (www.bcm.edu/research/cibr)

To view this discussion on the web visit https://groups.google.com/d/msgid/eman2/CAHn_UzdHPge%3DouJDc89VoFd1-ODKQFJc78eXZVbOw3PNoGrSsA%40mail.gmail.com.