sampleRI: Sample specific RI detection

In acinostroza/TargetSearch: A package for the analysis of GC-MS metabolite profiling data

Sample specific RI detection

Description

Return a matrix of the sample specific retention indices (RI) based on the correlating selective masses.

Usage

sampleRI(samples, Lib, r_thres = 0.95, columns = NULL,
         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	Either NULL, a character vector, or an integer vector. In most cases, leave it as NULL. This parameter is used to configure the column names or positions of RI text files. See the documentation on the text2bin function for further details.
method	Normalization method. Options are "dayNorm", a day based median normalization, "medianNorm", normalization using the median of all the intensities of a given mass, and "none", no normalization 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.

Author(s)

Alvaro Cuadros-Inostroza, Matthew Hannah, Henning Redestig

See Also

ImportSamples, ImportLibrary, medianRILib, tsLib, tsSample

Examples

require(TargetSearchData)
data(TSExample)

# get RI file path
RI.path <- tsd_data_path()
# update RI file path
RIpath(sampleDescription) <- RI.path
# Import Library
refLibrary        <- ImportLibrary(tsd_file_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)
	

acinostroza/TargetSearch documentation built on April 3, 2024, 8:09 p.m.