> Namely, we would like to get out of a series of 2D spectra the variation
> in cross-peak intensity (height) and volume including where the cross-peak
> disappears into the noise.
This is similar to what is already possible in the rate measurements
section of Analysis. There is even an option to pick peaks at non-maxima
locations, according to a reference peak list, so you can follow peak
intensities into the noise.
> This task is essentially the same as deriving
> intensity series from a 15N relaxation data set, but in this case is
> actually from a different type of experiment.
The rates section of Analysis doesn't care what the type of experiments
are bing used, it just fits functions to the intensities of series
similarly located (or assigned) peaks. Hopefully we could expand this to
suit your needs. So maybe take a look at the rates functionality and let
us now where things need expanding.
> So, to be clear (hopefully), we want to set bounding boxes and
> cross-peak centre locations for each peak of interest in the initial
> spectrum, including a 'control region' of only noise, keep them fixed
> over the series of spectra, and extract the volumes and heights
> respectively. Importantly we do not want to be dependent upon whether
> the software 'finds' a cross-peak or not.
Most of this we can do already. The order of precedence for grouping peaks
is:
1, Find similarly assigned peaks
2, Locate existing peaks within reference tolerances
3, Pick new peaks at maxima within reference tolerances
4, Pick new peaks at exactly the reference position
Points 3, and 4, can be independently switched on or off.
The bounding box for peak locating is global; i.e. applies to all the
refence locations equally, so this might not be quite what you want.
We already have a function to get a noise value for a spectrum. In
analysis/Util.py we have getNoiseEstimate() - although at some point we'll
put this in a documented and more descriptive location. This function
automatically estimates noise for a spectrum by choosing fractions of a
random sample of points with a low standard deviation.
I'll let Wayne comment on how the peak volume measurement methods may work
in the noise...
Tim
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Dr Tim Stevens Email: [log in to unmask]
Department of Biochemistry [log in to unmask]
University of Cambridge Phone: +44 1223 766022 (office)
80 Tennis Court Road +44 7816 338275 (mobile)
Old Addenbrooke's Site +44 1223 364613 (home)
Cambridge CB2 1GA WWWeb: http://www.bio.cam.ac.uk/~tjs23
United Kingdom http://www.pantonia.co.uk
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