### R code from vignette source 'SIMAT-vignette.Rnw' ################################################### ### code chunk number 1: packageLoad ################################################### # load the package library(SIMAT) # load the extracted data from a CDF file of a SIM run data(Run) # load the target table information data(target.table) # load the background library to be used with fragment selection data(Library) # load retention index table from RI standards data(RItable) ################################################### ### code chunk number 2: examineRun ################################################### # check the names of different fields in Run names(Run) # show some values for the the first three fields head(as.data.frame(Run[c("rt", "sc", "tic")])) # see what is included in the scan information for the first scan Run$pk[[1]] ################################################### ### code chunk number 3: plotTIC ################################################### # plot the TIC of the selected Run plotTIC(Run = Run) ################################################### ### code chunk number 4: examineTargetTable ################################################### # check the name of included fields names(target.table) # check the first lines of the target.table head(as.data.frame(target.table[c("compound", "numFrag")])) # check the contents of the ms field target.table$ms[[1]] ################################################### ### code chunk number 5: examineLibrary ################################################### # check the name of included fields names(Library) # check the first lines of Library head(as.data.frame(Library[c("rt", "ri", "compound")])) # check the contents of the ms and sp fields related to the mass and intensity # of the fragments, i.e. spectral information Spectrum <- data.frame(ms = Library$ms[[1]], sp = Library$sp[[1]]) head(Spectrum) # plot the spectrum plot(x = Spectrum$ms, y = Spectrum$sp, type = "h", lwd = 2, col = "blue", xlab = "mass", ylab = "intensity", main = Library$compound[1]) ################################################### ### code chunk number 6: examineRItable ################################################### # check the name of included fields names(RItable) # check the first lines of RItable head(RItable) ################################################### ### code chunk number 7: getTarget ################################################### # get targets info using target table and provided library Targets <- getTarget(Method = "library", Library = Library, target.table = target.table) # check the fields of Targets names(Targets) # check the first lines of some fields head(as.data.frame(Targets[c("compound", "rt", "ri")])) ################################################### ### code chunk number 8: getPeak ################################################### # get the peaks for this run corresponding to Targets runPeaks <- getPeak(Run = Run, Targets = Targets) # check the length of runPeaks (number of targets) length(runPeaks) # check the fields for each peak names(runPeaks[[1]][[1]]) # area of the EIC of the first target runPeaks[[1]][[1]]$area ################################################### ### code chunk number 9: plotEIC ################################################### # plot the EIC of the first peak (target) on the list plotEIC(peakEIC = runPeaks[[1]][[1]]) ################################################### ### code chunk number 10: getRI ################################################### # create the RI calibration function calibRI <- getRI(RItable) # calculate the RI of an RT = 12.32min calibRI(12.32) # get the peaks for this run corresponding to Targets using RI calibration runPeaksRI <- getPeak(Run = Run, Targets = Targets, calibRI = calibRI) ################################################### ### code chunk number 11: getPeakScore ################################################### # find the similarity score of the found targets Scores <- getPeakScore(runPeaks = runPeaks, plot = TRUE) # check the value of scores print(Scores) ################################################### ### code chunk number 12: optFrag ################################################### # get the optimized version of the target list optTargets <- optFrag(Library = Library, target.table = target.table, forceOpt = TRUE) # check the fragments of the first target # the mass of fragments Targets$ms[[1]] # the intensity of fragments Targets$sp[[1]] # check them after optimization # the mass of fragments optTargets$ms[[1]] # the intensity of fragments optTargets$sp[[1]]