RIcorrect {TargetSearch}R Documentation

Peak picking from CDF files and RI correction

Description

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.

Usage

RIcorrect(samples, rimLimits = NULL, massRange, Window, IntThreshold, 
        pp.method = "smoothing", showProgressBar = FALSE)

Arguments

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?

Details

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.

Value

A retention time matrix of the found retention time markers. Every column represents a sample and rows RT markers.

Author(s)

Alvaro Cuadros-Inostroza, Matthew Hannah, Henning Redestig

See Also

ImportSamples, ImportFameSettings, NetCDFPeakFinding, FAMEoutliers, tsSample, tsRim.

Examples

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)


[Package TargetSearch version 1.0.0 Index]