Hi Martin,
From my experience, the best way to handle this is as follows:
- For the first spectrum in the time series, shift the peak list to
the tops of the peaks (i.e. 'pc' in Sparky).
- Copy this 1st spectrum list onto all spectra, shifting the peaks to
the top/centre.
- When the peak disappears into the noise, leave it at its current
position and do not type 'pc' or equivalent.
- Once all spectra are shifted, calculate an average peak list.
- Copy this average peak list onto fresh copies of all spectra.
- Measure peak heights using this averaged peak list.
This methodology will be discussed in a paper which is in preparation.
This is a special technique that I personally came up with (big
citation hint ;) which is designed to minimise the white-noise bias
talked about in the Viles et al., 2001 paper, though not solved there.
As the noise often decreases with the decrease in total spectral
power, using the tops of the peaks means that you are actually
measuring the real peak height plus noise in all cases. As this
additional noise contribution may not be constant across spectra, you
often end up with a double exponential in the measured data. The
technique above eliminates this as you then measure the peak height
+/- noise, rather than just + noise. It also nicely solves your
problem. Where the peaks disappear, you then are measuring the pure
baseplane noise. This is ok, as these white-noise data points centred
at zero will help with your subsequent exponential fit in relax.
Regards,
Edward
On 23 May 2012 14:46, Martin Ballaschk <ballaschk@xxxxxxxxxxxxx> wrote:
Hi Edward,
I have a general question regarding how to pick signals prior to relaxation
rate fitting.
Following the rationale of [1] fitting the data with a two-parameter
exponential decay is preferable. With relax, I can choose to do so. The
question remains if "picking noise" – i.e. picking signal intensities at
the position of the reference peaks, although the signal has decayed
already – is a good idea.
To me it seems only logical that "picking noise" should not be done. The
signal decays to zero whatsoever (given the pulse sequence is properly set
up) and by picking signals where no signal should be I may pick artifacts
that are not distributed evenly over the base plane of the spectrum. Which
means, I may be introduce artificial offsets.
However relax seems not be happy when I try to use peak lists for T1/T2
relaxation fitting if the lists have different lengths. I always have to do
"noise picking", otherwise already the grid_search fails when encountering
a incomplete time series.
Is the behaviour intended, do I need a complete time series of each amino
acid? Or am I missing something? Or am I completely misguided with my
not-picking-noise approach?
Cheers
Martin
[1] Viles, J. H., Duggan, B. M., Zaborowski, E., Schwarzinger, S., Huntley,
J. J., Kroon, G. J., Dyson, H. J., et al. (2001). Potential bias in NMR
relaxation data introduced by peak intensity analysis and curve fitting
methods. Journal of biomolecular NMR, 21(1), 1–9.
--
Martin Ballaschk
AG Schmieder
Leibniz-Institut für Molekulare Pharmakologie
Robert-Rössle-Str. 10
13125 Berlin
ballaschk@xxxxxxxxxxxxx
Tel.: +49-30-94793-234/315
Büro: A 1.26
Labor: C 1.10
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Re: [rx fitting] Dealing with intensities of decayed signals