TargetSearch (version 1.28.1)

sampleRI: Sample especific RI detection

Description

Return a matrix of the sample specific RIs based on the correlating selective masses.

Usage

sampleRI(samples, Lib, r_thres = 0.95, columns = c("SPECTRUM", "RETENTION_TIME_INDEX", "RETENTION_TIME"), method = "dayNorm", minPairObs = 5, showProgressBar = FALSE, makeReport = FALSE, pdfFile = "medianLibRep.pdf")

Arguments

samples

A tsSample object created by ImportSamples function.

Lib

A tsLib object created by ImportLibrary function with corrected RI values. See medianRILib.

r_thres

A correlation threshold.

columns

A numeric vector with the positions of the columns SPECTRUM, RETENTION_TIME_INDEX, and RETENTION_TIME or a character vector with the header names of those columns.

method

Normalisation method. Options are "dayNorm", a day based median normalisation, "medianNorm", normalisation using the median of all the intensities of a given mass, and "none", no normalisation at all.

minPairObs

Minimum number of pair observations. Correlations between two variables are computed using all complete pairs of observations in those variables. If the number of observations is too small, you may get high correlations values just by chance, so this parameters is used to avoid that. Cannot be set lower than 5.

showProgressBar

Logical. Should the progress bar be displayed?

makeReport

Logical. If TRUE will report the RI deviations for every metabolite in the library.

pdfFile

The file name where the report will be saved.

Value

A matrix of correlating selective masses RI. Columns represent samples and rows the median RI of the selective masses.

See Also

ImportSamples, ImportLibrary, medianRILib, tsLib, tsSample

Examples

require(TargetSearchData)
data(TargetSearchData)

# get RI file path
RI.path <- file.path(find.package("TargetSearchData"), "gc-ms-data")
# update RI file path
RIpath(sampleDescription) <- RI.path
# Import Library
refLibrary        <- ImportLibrary(file.path(RI.path,'library.txt'))

# get the sample RI
corRI <- sampleRI(sampleDescription, refLibrary, r_thres = 0.95)

# same as above, but changing the correlation threshold and the minimum number
# of observations
corRI <- sampleRI(sampleDescription, refLibrary, r_thres = 0.9,
			minPairObs = 10)