centWave handles data from a Bruker MicrOTOF-Q instrument and also from Thermo LTQ -Orbitrap data. We are really impressed with its feature detection performance. Is the acquisition of spectra information different for those machines and how does centWave compensate for that?
For example, we believe that Thermo uses an instantaneous model of aquisition, where there is biological sample missed between the centroids and they account for that by just integrating "a connect the centroids model". See the red area figure I made up. On the other hand there could be a different acquisition mode, right? We believe its also possible to have an "open door" acquisition mode, where each centroid represents the cumulative sum of ions acquired between the last scan and the current scan (blue area in figure).
And is centWave's normalization of scan rate to compensate for inter or intra sample scan rate variability? Please see recent post under "area under curve" of a guess at how you might do that normalization on an intra sample case.
I would like to evaluate how much area under the curve centWave actually obtains on a manually curated set. I have the raw data points corresponding to each feature detected, but when I sum the intensity values of the centroids it generally underestimates the intensity that is reported in the "into" and "intb" columns of the matrix returned by findPeaks.centWave(). Why are the intensities different? I read the following part of the documentation for findPeaks.centWave():
integrate: Integration method. If ‘=1’ peak limits are found through descent on the mexican hat filtered data, if ‘=2’ the descent is done on the real data. Method 2 is very accurate but prone to noise, while method 1 is more robust to noise but less exact.
I suppose that the mexican hat operation or the real descent alter the way centWave quantitates. I could not find an answer in the published paper. In other words, why doesn't it operate like this?
area under feature = int(1) + int(2) + ... + int(n)
I would like access to any peak's associated centroid (rt, m/z, int) triplet pairs. I have searched both forums and pdf documentation looking for how to use the function getEIC(). However, its been difficult to unlock the information within the object besides just plotting it. I don't necessarily need the plot. I want something like the following:
That would allow me to compare its performance to other algorithms on a centroid basis. I would prefer to avoid using the findPeaks.centWave() method that returns (among other things)
eic(1): { mzmin, mzmax, rtmin, rtmax }.
I could use this to get all points that fall in this region, but that sometimes will return more than one centroid per scan that would violate the ROI algorithm.
