## ----style-knitr, eval=TRUE, echo=FALSE, results="asis"-------------------- BiocStyle::latex() ## ----ImportSample---------------------------------------------------------- library(TargetSearchData) library(TargetSearch) cdf.path <- system.file("gc-ms-data", package = "TargetSearchData") sample.file <- file.path(cdf.path, "samples.txt") samples <- ImportSamples(sample.file, CDFpath = cdf.path, RIpath = ".") ## ----ImportSample2--------------------------------------------------------- cdffiles <- dir(cdf.path, pattern="cdf$") # take the measurement day info from the cdf file names. days <- sub("^([[:digit:]]+).*$","\\1",cdffiles) # sample names smp_names <- sub("\\.cdf", "", cdffiles) # add some sample info smp_data <- data.frame(CDF_FILE =cdffiles, GROUP = gl(5,3)) # create the sample object samples <- new("tsSample", Names = smp_names, CDFfiles = cdffiles, CDFpath = cdf.path, RIpath = ".", days = days, data = smp_data) ## ----ImportFameSettings---------------------------------------------------- rim.file <- file.path(cdf.path, "rimLimits.txt") rimLimits <- ImportFameSettings(rim.file, mass = 87) ## ----checkRimLim, fig.cap="Example of the RI markers $m/z$ traces in one sample"---- checkRimLim(samples, rimLimits) ## ----ncdf4----------------------------------------------------------------- samples <- ncdf4Convert(samples, path=".") ## ----baseline, eval=FALSE-------------------------------------------------- # samples <- ncdf4Convert(samples, path=".", baseline=TRUE, bsline_method="quantiles", ...) ## ----RIcorrection---------------------------------------------------------- RImatrix <- RIcorrect(samples, rimLimits, IntThreshold = 100, pp.method = "ppc", Window = 15) ## ----PeakIdentification---------------------------------------------------- outliers <- FAMEoutliers(samples, RImatrix, threshold = 3) ## ----plotFAME, fig.cap="Retention Index Marker 1.", fig.small='TRUE'------- plotFAME(samples, RImatrix, 1) ## ----ImportLibrary--------------------------------------------------------- lib.file <- file.path(cdf.path, "library.txt") lib <- ImportLibrary(lib.file, RI_dev = c(2000,1000,200), TopMasses = 15, ExcludeMasses = c(147, 148, 149)) ## ----LibrarySearch1-------------------------------------------------------- lib <- medianRILib(samples, lib) ## ----medianLib, fig.cap="RI deviation of first 9 metabolites in the library", fig.dim=c(8,8), fig.wide=TRUE, out.width='95%', echo=FALSE---- resPeaks <- FindPeaks(RIfiles(samples), refLib(lib, w = 1, sel = TRUE)) plotRIdev(lib, resPeaks, libId = 1:9) ## ----LibrarySearch2-------------------------------------------------------- cor_RI <- sampleRI(samples, lib, r_thres = 0.95, method = "dayNorm") ## ----LibrarySearch3-------------------------------------------------------- peakData <- peakFind(samples, lib, cor_RI) ## ----LibrarySearch4-------------------------------------------------------- met.RI <- retIndex(peakData) met.Intensity <- Intensity(peakData) # show the intensity values of the first metabolite. met.Intensity[[1]] ## ----MetaboliteProfile----------------------------------------------------- MetabProfile <- Profile(samples, lib, peakData, r_thres = 0.95, method = "dayNorm") ## ----MetaboliteProfile2---------------------------------------------------- finalProfile <- ProfileCleanUp(MetabProfile, timeSplit = 500, r_thres = 0.95) ## ----plotSpectra, fig.cap="Spectra comparison of ``Valine''"--------------- grep("Valine", libName(lib)) plotSpectra(lib, peakData, libId = 3, type = "ht") ## ----plotPeak, fig.cap="Chromatographic peak of Valine."------------------- # we select the first sample sample.id <- 1 cdf.file <- file.path(cdf.path, cdffiles[sample.id]) rawpeaks <- peakCDFextraction(cdf.file) # which.met=3 (id of Valine) plotPeak(samples, lib, MetabProfile, rawpeaks, which.smp=sample.id, which.met=3, corMass=FALSE) ## ----untargeted1----------------------------------------------------------- metRI <- seq(200000, 300000, by = 5000) metMZ <- 85:250 metNames <- paste("Metab",format(metRI,digits=6), sep = "_") ## ----untargeted2----------------------------------------------------------- metLib <- new("tsLib", Name = metNames, RI = metRI, selMass = rep(list(metMZ), length(metRI)), RIdev = c(3000, 1500, 500)) ## ----untargeted3----------------------------------------------------------- metLib <- medianRILib(samples, metLib) metCorRI <- sampleRI(samples, metLib) metPeakData <- peakFind(samples, metLib, metCorRI) metProfile <- Profile(samples, metLib, metPeakData) metFinProf <- ProfileCleanUp(metProfile, timeSplit = 500) ## ----untargeted4----------------------------------------------------------- sum( profileInfo(metFinProf)$Mass_count > 5) tmp <- profileInfo(metFinProf)$Mass_count > 5 metRI <- profileInfo(metFinProf)$RI[tmp] metNames <- as.character( profileInfo(metFinProf)$Name[tmp] ) metMZ <- sapply(profileInfo(metFinProf)$Masses[tmp], function(x) as.numeric(unlist(strsplit(x,";"))) ) metLib <- new("tsLib", Name = metNames, RI = metRI, selMass = metMZ, RIdev = c(1500, 750, 250)) ## ----sessionInfo, results="asis", echo=FALSE------------------------------- toLatex(sessionInfo())