Peak picking from CDF files and RI correction
This function reads from CDF files, finds the apex intensities, converts the retention time to retention time index (RI), and writes RI corrected text files (a.k.a. RI files).
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A two component vector of m/z range used by the GC-MS machine
The window used for smoothing. The number of points actually used
Apex intensities lower than this value will be removed from the RI files.
Peak picking method. Options are either "smoothing" or "ppc". See details.
Logical. Should the progress bar be displayed?
Logical. Should baseline correction be performed?
A list of options passed to
There are two pick picking methods available: "ppc" and "smoothing".
The "ppc" method (default) implements the peak detection method described in the
package. It looks for the local maxima within a
2*Window + 1 scans for
every mass trace.
The "smoothing" method calculates a moving average of
2*Window + 1 points
for every mass trace. Then it looks for a change of sign (from positive to negative) of
the difference between two consecutive points. Those points will be returned as
To work out a suitable
Window value, the following might be useful:
Window = (SR * PW - 1) / 2, where SR is the scan rate of the MS instrument and
PW is the peak width. Because
Window is an integer, the resulting value must be
rounded. For example, for SR = 20 scans per second, a PW = 1.5 seconds, then
Window = 14.5, which can be rounded to 15.
The RI file type is determined by the output of
method applied to the
tsSample input object. To
choose between the available formats ("binary" and "text"), select it
fileFormat method before calling
A retention time matrix of the found retention time markers. Every column represents a sample and rows RT markers.
Alvaro Cuadros-Inostroza, Matthew Hannah, Henning Redestig
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require(TargetSearchData) # import refLibrary, rimLimits and sampleDescription. data(TargetSearchData) # get the CDF files cdfpath <- file.path(find.package("TargetSearchData"), "gc-ms-data") cdfpath list.files(cdfpath) # update the CDF path CDFpath(sampleDescription) <- cdfpath # run RIcorrect (massScanRange = 85-320; Intensity Threshold = 50; # peak detection method = "ppc", window = 15) RImatrix <- RIcorrect(sampleDescription, rimLimits, massRange = c(85,320), Window = 15, pp.method = "ppc", IntThreshold = 50) # you can try other parameters and other peak picking algorithm. RImatrix <- RIcorrect(sampleDescription, rimLimits, massRange = c(85,320), Window = 15, pp.method = "smoothing", IntThreshold = 10) RImatrix <- RIcorrect(sampleDescription, rimLimits, massRange = c(85,320), Window = 15, pp.method = "ppc", IntThreshold = 100)
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