deprofile: De-profile a high-resolution MS scan in profile mode.
In RMassBank: Workflow to process tandem MS files and build MassBank records

Description Usage Arguments Details Value Note Author(s) References Examples

The deprofile functions convert profile-mode high-resolution input data to "centroid"-mode data amenable to i.e. centWave. This is done using full-width, half-height algorithm, spline algorithm or local maximum method.

deprofile.scan(scan, noise = NA, method = "deprofile.fwhm", 
					colnames = TRUE, ...)

				deprofile(df, noise, method, ...)

				deprofile.fwhm(df, noise = NA, cut = 0.5)

				deprofile.localMax(df, noise = NA)

				deprofile.spline(df, noise=NA, minPts = 5, step = 0.00001)

`df`	A dataframe with at least the columns `mz` and `int` to perform deprofiling on.
`noise`	The noise cutoff. A peak is not included if the maximum stick intensity of the peak is below the noise cutoff.
`method`	"deprofile.fwhm" for full-width half-maximum or "deprofile.localMax" for local maximum.
`...`	Arguments to the workhorse functions `deprofile.fwhm` etc.
`scan`	A matrix with 2 columns for m/z and intensity; from profile-mode high-resolution data. Corresponds to the spectra obtained with xcms::getScan or mzR::peaks.
`colnames`	For deprofile.scan: return matrix with column names (xcms-style, `TRUE`, default) or plain (mzR-style, `FALSE`).
`cut`	A parameter for `deprofile.fwhm` indicating where the peak flanks should be taken. Standard is 0.5 (as the algorithm name says, at half maximum.) Setting `cut = 0.75` would instead determine the peak width at 3/4 maximum, which might give a better accuracy for merged peaks, but could be less accurate if too few data points are present.
`minPts`	The minimal points count in a peak to build a spline; for peaks with less points the local maximum will be used instead. Note: The minimum value is 4!
`step`	The interpolation step for the calculated spline, which limits the maximum precision which can be achieved.

The deprofile.fwhm method is basically an R-semantic version of the "Exact Mass" m/z deprofiler from MZmine. It takes the center between the m/z values at half-maximum intensity for the exact peak mass. The logic is stolen verbatim from the Java MZmine algorithm, but it has been rewritten to use the fast R vector operations instead of loops wherever possible. It's slower than the Java implementation, but still decently fast IMO. Using matrices instead of the data frame would be more memory-efficient and also faster, probably.

The deprofile.localMax method uses local maxima and is probably the same used by e.g. Xcalibur. For well-formed peaks, "deprofile.fwhm" gives more accurate mass results; for some applications, deprofile.localMax might be better (e.g. for fine isotopic structure peaks which are not separated by a valley and also not at half maximum.) For MS2 peaks, which have no isotopes, deprofile.fwhm is probably the better choice generally.

deprofile.spline calculates the mass using a cubic spline, as the HiRes peak detection in OpenMS does.

The word "centroid" is used for convenience to denote not-profile-mode data. The data points are NOT mathematical centroids; we would like to have a better word for it.

The noise parameter was only included for completeness, I personally don't use it.

deprofile.fwhm and deprofile.localMax are the workhorses; deprofile.scan takes a 2-column scan as input. deprofile dispatches the call to the appropriate worker method.

deprofile.scan: a matrix with 2 columns for m/z and intensity

Known limitations: If the absolute leftmost stick or the absolute rightmost stick in a scan are maxima, they will be discarded! However, I don't think this will ever present a practical problem.

Michael Stravs, Eawag <michael.stravs@eawag.ch>

mzMine source code http://sourceforge.net/svn/?group_id=139835

## Not run: 
mzrFile <- openMSfile("myfile.mzML")
acqNo <- xraw@acquisitionNum[[50]]
scan.mzML.profile <- mzR::peaks(mzrFile, acqNo)
scan.mzML <- deprofile.scan(scan.mzML.profile) 
close(mzrFile)

## End(Not run)