... but, back to the main point, my advice would be to only limit the
mosaicity, to get better completeness by avoiding overlaps.
Its not ideal, in the sense that you would be over-estimating the
partial fraction of most partial reflections, and thus systematically
underestimating intensities.
(I hope I got my overs and unders right here ...)
But these errors would not matter much for refinement purposes, where
you would rather have a slightly systematically wrong estimate
for all data, rather than not have the 15% of the data at all.
Or at least thats what I thought back in '99 refining MutS ... where I
did refine a lot with both datasets and liked the 'fixed mosaicity'
one better.
A.
On Jan 28, 2011, at 13:26, José Trincão wrote:
Ah, yes, I was missing that. The statistics will be wrong. But in
principle I will get an mtz with better data, because I am
integrating more observations which would have been rejected by
being missed at low resolution if the mosaicity was set too low or
being rejected by overlaps at high resolution if the mosaicity is
increased.
So the question is - can I use this data for refinement? Or should I
stick with the best of the datasets (the one with the highest
completeness and multiplicity)?
Thanks!
Jose
On Jan 28, 2011, at 28/1/11 - 11:59, Ian Tickle wrote:
Jose - you're missing the fact that the same dataset processed in
different ways are not statistically independent datasets! Increasing
the multiplicity for independent data reduces the uncertainty because
the calculation of the SU assumes statistical independence.
Cheers
-- Ian
On Fri, Jan 28, 2011 at 11:46 AM, José Trincão
<[email protected]> wrote:
Hello all,
I have been trying to squeeze the most out of a bad data set (P1,
anisotropic, crystals not reproducible). I had very incomplete data
due to high mosaicity and lots of overlaps. The completeness was
about 80% overall to ~3A. Yesterday I noticed that I could process
the data much better fixing the mosaicity to 0.5 in imosflm. I got
about 95% complete up to 2.5A but with a multiplicity of 1.7. I
tried to integrate the same data fixing the mosaicity at different
values ranging from 0.2 to 0.6 and saw the trend in completeness,
Rmerge and multiplicity.
Now, is there any reason why I should not just merge all these
together and feed them to scala in order to increase multiplicity?
Am I missing something?
Thanks for any comments!
Jose
José Trincão, PhD CQFB@FCT-UNL
2829-516 Caparica, Portugal
"It's very hard to make predictions... especially about the future"
- Niels Bohr
José Trincão, PhD CQFB@FCT-UNL
2829-516 Caparica, Portugal
"It's very hard to make predictions... especially about the future"
- Niels Bohr
José Trincão, PhD CQFB@FCT-UNL
2829-516 Caparica, Portugal
"It's very hard to make predictions... especially about the future"
- Niels Bohr
P please don't print this e-mail unless you really need to
Anastassis (Tassos) Perrakis, Principal Investigator / Staff Member
Department of Biochemistry (B8)
Netherlands Cancer Institute,
Dept. B8, 1066 CX Amsterdam, The Netherlands
Tel: +31 20 512 1951 Fax: +31 20 512 1954 Mobile / SMS: +31 6 28 597791
