Hi,
I have previously talked about data set consistency. For example see
the post at https://mail.gna.org/public/relax-users/2007-06/msg00001.html
in which a few reasons for inconsistencies have been explained. I
have, from experience, noticed that small changes in protein
concentration can change the collected relaxation rates significantly
- most likely because of packing interactions. All samples should
essentially be identical in all respects for the relaxation rates to
be compared. And the temperate should always be fine tuned between
experiments and spectrometers using methanol (and always checked later
on if there is a large time between collecting the same experiment).
Therefore these tests would be quite useful. Data consistency is
essential for the model-free results to be correct (as well as reduced
spectral density mapping, SRLS, etc.) as this affects both the
optimisation and model selection and can result in artificial motions
appearing. However I don't know how these test would currently fit
within relax. Maybe a new type of analysis should be created for this
(see the pipe.create() user function in the 1.3 line or the
run.create() user function in the 1.2 line). These ideas should all
go into the 1.3 line (via a branch) as the 1.2 line is stable and no
new major features will be added to this code. What are the ideas you
have been playing with?
Cheers,
Edward
On 6/15/07, Sebastien Morin <sebastien.morin.1@xxxxxxxxx> wrote:
Hi everyone
During the last months, I was astonished to realize that some spin
relaxation data I had acquired at different fields were not consistent
between each other. The way I realized that was by seeing discrepancy
between J(0) values calculated with those different datasets.
I looked a little bit in the litterature and found some interesting
consistency tests in a paper by Fushman (Fushman et al., JACS, 1998,
120:10947-10952).
This paper present 2 consistency tests to compare datasets from
different magnetic fields / samples / time / etc.
I think it would be interesting to implement those simple tests in relax
so the user can, before trying to fit their data, know the quality of
those... Regrettably, very few people look at the consistency of their
datasets before analysis...
The underlying principle is the same as when looking at consistency for
J(0). Thus, I think that those two tests and a J(0) test should be
implemented altogether...
I'll try to work a bit on this. Mimicking the code for spectral density
should be a good starting point.
Am I right ?
Do you see any value in those tests ?
Cheers
Sébastien :)
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Re: Consistency tests