tsLib-class: Class for representing a reference library
In TargetSearch: A package for the analysis of GC-MS metabolite profiling data

Description Objects from the Class Slots Methods Note Author(s) See Also Examples

This is a class representation of a reference library.

Objects can be created by the function ImportLibrary.

Name:: "character", the metabolite or analyte names.
RI:: "numeric", the expected retention time indices (RI) of the metabolites/analytes.
medRI:: "numeric", the median RI calculated from the samples.
RIdev:: "matrix", the RI deviation windows, k = 1,2,3. A three column matrix
selMass:: "list", every component is a numeric vector containing the selective masses.
topMass:: "list", every component is a numeric vector containing the top masses.
quantMass:: "numeric", the mass used for quantification.
libData:: "data.frame", additional library information.
spectra:: "list", the metabolite spectra. Each component is a two column matrix: m/z and intensity.

[: signature(x = "tsLib"): Selects a subset of metabolites from the library.
$name: signature(x = "tsLib"): Access column name of libData slot.
libId: signature(obj = "tsLib"): Returns a vector of indices.
length: signature(x = "tsLib"): returns the length of the library. i.e., number of metabolites.
libData: signature(obj = "tsLib"): gets/sets the libData slot.
libName: signature(obj = "tsLib"): gets the Name slot.
libRI: signature(obj = "tsLib"): gets the RI slot.
medRI: signature(obj = "tsLib"): gets the medRI slot.
refLib: signature(obj = "tsLib"): Low level method to create a matrix representation of the library.
RIdev: signature(obj = "tsLib"): gets the RI deviations.
RIdev<-: signature(obj = "tsLib"): sets the RI deviations.
quantMass: signature(obj = "tsLib"): gets the quantification mass.
quantMass<-: signature(obj = "tsLib"): sets the quantification mass.
selMass: signature(obj = "tsLib"): gets the selective masses.
show: signature(object = "tsLib"): show method.
spectra: signature(obj = "tsLib"): gets the spectra.
topMass: signature(obj = "tsLib"): gets the top masses.

Some care is needed when using the methods quantMass<-, selMass<-, topMass<-. In order to be consistent, the first m/z value of the slot topMass and selMass are equal to quantMass, and the values of selMass are equal to the first couple of values of topMass. In other words, the following constrain is applied.

1
2
3

    quantMass : x[0]
    selMass   : x[0], x[1], ..., x[k]
    topMass   : x[0], x[1], ..., x[k], x[k+1], ..., x[n]

where 1 <= k <= n and x[i] is a m/z value. Thus, using one these methods will change the values of the other slots. In the future, these methods will be deprecated, so it is better to not rely on them.

See the last example on how this can lead to unexpected results.

Alvaro Cuadros-Inostroza, Matthew Hannah, Henning Redestig

ImportLibrary

showClass("tsLib")

# define some metabolite names
libNames   <- c("Metab1", "Metab2", "Metab3")
# the expected retention index
RI         <- c(100,200,300)
# selective masses to search for. A list of vectors.
selMasses  <- list(c(95,204,361), c(87,116,190), c(158,201,219))
# define the retention time windows to look for the given selective masses.
RIdev      <- matrix(rep(c(10,5,2), length(libNames)), ncol = 3, byrow = TRUE)
# Set the mass spectra. A list object of two-column matrices, or set to
# NULL if the spectra is not available
spectra    <- NULL
# some extra information about the library
libData    <- data.frame(Name = libNames, Lib_RI = RI)
# create a reference library object
refLibrary <- new("tsLib", Name = libNames, RI = RI, medRI = RI, RIdev = RIdev,
                  selMass = selMasses, topMass = selMasses, spectra = spectra, libData = libData)

# get the metabolite names
libName(refLibrary)
# set new names
libName(refLibrary) <- c("Metab01", "Metab02", "Metab03")

# get the expected retention times
libRI(refLibrary)
# set the retention time index for metabolite 3 to 310 seconds
libRI(refLibrary)[3] <- 310
# change the seleccion and top masses of metabolite 3
selMass(refLibrary)[[3]] <- c(158,201,219,220,323)
topMass(refLibrary)[[3]] <- c(158,201,219,220,323)
# change the retention time deviations
RIdev(refLibrary)[3,] <- c(8,4,1)

#####################################################################
#####################################################################
# These examples show how changing a quantitaive or selective mass
# could lead to unexpected results.

# show quantMasses
quantMass(refLibrary)

# suposse that we want to change the quant mass of metabolite 1 to 96 due
# to a typo in the library. We could do just
quantMass(refLibrary)[1] <- 96

# however, we still see the mass 95 in the selective and top masses.
selMass(refLibrary)[[1]]
topMass(refLibrary)[[1]]

# to remove the mass 95, set the topMass and selMass explicitely, noting that
# the first masses coincides with 96 (the quantMass)
selMass(refLibrary)[[1]] <- c(96, 204, 361)
topMass(refLibrary)[[1]] <- c(96, 204, 361)