updateRI: Updating Time Index correction
In TargetSearch: A package for the analysis of GC-MS metabolite profiling data

Description Usage Arguments Details Value Note Author(s) See Also Examples

This function can be used to correct or adjust the detected retention time index (RI) markers or their location to specific retention times. This function adds on fixRI() as it also corrects the RI of the CDF files

1	updateRI(samples, rimLimits, RImatrix = NULL, quiet = TRUE)

`samples`	A tsSample object created by ImportSamples.
`rimLimits`	A tsRim object. See ImportFameSettings.
`RImatrix`	An optional matrix. It represents a retention time matrix of the detected retention time markers that was obtained after running RIcorrect
`quiet`	Logical. Do not print a list of converted files.

Sometimes the retention time of the RI markers are not detected correctly, either because there was a problem with the standard, or the time limits of the tsRim object were not set correctly, or simply because the markers are not injected with the samples.

In any case, the retention time correction can be fixed by calling this function. This function works almost exactly like fixRI(), in fact, it is called internally, and allows correction of RIfiles and CDFfiles at the same time. Check also the documentation of fixRI() for extra details.

The parameters are the tsSample and the tsRim object, with optionally a RImatrix to force the location of the markers. The parameter quiet can be unset to show what samples are corrected.

If only a subset of samples require correction, then they can be chosen by subsetting the object sample.

Neededless to say, this function expect that the CDF files exists and are in the TargetSearch format. If this is not the case, then use the function fixRI(), as this function deals only with RI files.

The retention index matrix. If RImatrix is not NULL, then the output is the same matrix.

It is required that all the sample names of samples are contained in the colnames of RImatrix, but the reverse is not necessary. The number of columns of the output matrix will match the number of samples. Extra columns in RImatrix will be ignored and not returned.

Alvaro Cuadros-Inostroza

fixRI(), RIcorrect(), ImportSamples(), ImportFameSettings()

require(TargetSearchData)
# import refLibrary, rimLimits and sampleDescription.
data(TSExample)
CDFpath(sampleDescription) <-
    file.path(find.package("TargetSearchData"), "gc-ms-data")

# convert a subset of files to netCDF4
smp <- ncdf4Convert(sampleDescription[1:6], path=".")

# make a copy of the RI markers object
fames <- rimLimits

# mess up the limits of marker 3 (real value is 369 seconds app.)
rimLimits(fames)[3,] <- c(375, 400)

# run RIcorrect (skip CDF-4 conversion)
RImat <- RIcorrect(smp, fames, Window = 15, IntThreshold = 200)

# fix the limits of marker 3
rimLimits(fames)[3,] <- c(360, 380)

# update RI files and CDF files
RImat <- updateRI(smp, fames)

# Pass a RI matrix for manual adjustment
RImat[, 3] <- c(252, 311, 369)
RImat <- updateRI(smp, fames, RImat)

# To select specific samples, simply use sample subsetting
# Note, RImat2 has only one column in this case.
( RImat2 <- updateRI(smp[3], fames, RImat) )