RIcorrect {TargetSearch} | R Documentation |
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.
RIcorrect(samples, rimLimits = NULL, massRange, Window, IntThreshold, pp.method = "smoothing", showProgressBar = FALSE)
samples |
A tsSample object created by ImportSamples function. |
rimLimits |
A tsRim object. If set to NULL , no retention
time will be performed. See ImportFameSettings . |
massRange |
A two component vector of m/z range used by the GC-MS machine. |
Window |
The window used for smoothing. The number of points actually used
is 2*Window + 1 . |
IntThreshold |
Apex intensities lower than this value will be removed from the RI files. |
pp.method |
Peak picking method. Options are either "smoothing" or "ppc". See details. |
showProgressBar |
Logical. Should the progress bar be displayed? |
There are two pick picking methods available: "smoothing" and "ppc".
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
detected peaks.
The "ppc" method implements the peak detection method described in the ppc
package. It looks for the local maxima within a 2*Window + 1
scans for
every mass trace.
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
ImportSamples
, ImportFameSettings
,
NetCDFPeakFinding
, FAMEoutliers
,
tsSample
, tsRim
.
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)