In hcji/TarMet: Targeted Metabolic and Stable Isotope Tracer.

knitr::opts_chunk$set(echo = TRUE)

Introduction

This is a guide for using TarMet with R scripts.

Targeted metabolite analysis

Preparation

library(TarMet)

data("isotopes", package = "enviPat")
data("adducts", package = "enviPat")

# path of raw data
rawfiles <- list.files(system.file('rawfiles', package = 'TarMet'), full.names = TRUE)  # path of data files
samples <- sapply(strsplit(rawfiles, '/'), function(s) s[length(s)])                    # name of data files
rawDataset <- lapply(rawfiles, LoadData)                                                # load raw data

# path of config file
config <- read.csv(system.file('data/targets.csv', package = 'TarMet'))

# global parameters
fineness <- 'Medium'  # fineness of peak detection of MassSpecWavelet
resolution <- 50000   # resolution of your MS

shift <- 20           # parameter for alignment
segment <- 20         # parameter for alignment

# select the referance sample
sampleInd <- 1        # choose a reference file

Set targeted metabolite

i = 1                 # use the first metabolite

# set the parameters for the specific metabolite
threshold <- 0.01
name <- config$name[i]
formula <- config$formula[i]
mz <- config$mz[i]

# adduct type
if (is.na(config$adduct[i])){
  adduct <- 'M+H'
} else {
  adduct <- config$adduct[i]
}

  # ppm for EIC generation
if (is.na(config$ppm[i])){
  ppm <- 100
} else {
  ppm <- config$ppm[i]
}

  # scales for peak detection
if (is.na(config$scale[i])){
  scale <- 5
} else {
  scale <- config$scale[i]
}

  # start of retention time for EIC generation 
if (is.na(config$rtmin[i])){
  rtmin <- 0
} else {
  rtmin <- config$rtmin[i]
}

# end of retention time for EIC generation 
if (is.na(config$rtmax[i])){
  rtmax <- max(rawDataset[[1]]$times)
} else {
  rtmax <- config$rtmax[i]
} 

# minimum peak height of a peak
if (is.na(config$height[i])){
  height <- 0
} else {
  height <- config$height[i]
}

# minimum SNR of a peak
if (is.na(config$snr[i])){
  snr <- 5
} else {
  snr <- config$snr[i]
}

Calculate targeted m/z value

pattern <- getIsotopicPattern(formula, adduct, threshold, resolution)
mzs <- pattern[,1]
targetMzRanges <- getMzRanges(mzs, resolution=resolution, ppm=ppm)

Targeted EIC generation

targetEICs <- list()
rtranges <- c(rtmin, rtmax)
for (j in seq_along(rawfiles)){
  targetEICs[[j]] <- getMzEICs(rawDataset[[j]], rtranges=rtranges, mzranges=targetMzRanges, baseline=FALSE, smooth=FALSE)
}

theoretical <- as.numeric(pattern[,2])
targetPeaks <- getIsotopicPeaks(targetEICs[[sampleInd]], SNR.Th=snr, peakScaleRange=scale, peakThr=height, theoretical=theoretical, fineness=fineness)
plotEICs(targetEICs[[sampleInd]], targetPeaks)

Display peak detection results

outputPeakInfo <- targetPeaks$PeakInfo
knitr::kable(outputPeakInfo, format = "markdown")

Alignment

alignedEICs <- getAlignedEICs(targetEICs, sampleInd, shift, segment, align=TRUE)
WhtoPlot <- 1  # plot the monoisotopic feature of the targeted metabolite of all samples
EicstoPlot <- lapply(alignedEICs, function(eics){
  eics[[WhtoPlot]]
})
names(EicstoPlot) <- samples
plotEICs(EicstoPlot, rtrange=c(outputPeakInfo$Start[WhtoPlot], outputPeakInfo$End[WhtoPlot]))

Display peak area of all samples

whPeak <- which.max(outputPeakInfo$Similarity)  # Using the feature with most similar isotopic pattern
outputSamplesArea <- getQuantifiedResult(alignedEICs, outputPeakInfo$Start[whPeak], outputPeakInfo$End[whPeak])
colnames(outputSamplesArea) <- samples
knitr::kable(outputSamplesArea, format = "markdown")

Display barplot of isotopes

plotStackBar(outputSamplesArea)

Isotope tracer analysis

Preparation

if (!'AssayRdata' %in% installed.packages()){
  install.packages(
    "https://gitlab.com/jimiwills/assay.R/raw/master/AssayR_0.1.5.tar.gz", 
    repos = NULL, type = "source")
  install.packages(
    "https://gitlab.com/jimiwills/assay.R/raw/2190202fd4bc0325421bb10c4ec42089d45e1952/AssayRdata_0.1.3.tar.gz", 
    repos = NULL, type = "source")
}

rawfiles <- list.files(system.file("extdata", package = "AssayRdata"), full.names = TRUE)
samples <- sapply(strsplit(rawfiles, '/'), function(s) s[length(s)])
rawDataset <- lapply(rawfiles, LoadData)

Set targeted metabolite

name <- 'glucose'
formula <- 'C6H12O6'
adduct <- 'M-H'

ppm <- 50
scale <- 10
rtmin <- 0
rtmax <- max(rawDataset[[1]]$times)
height <- 0
snr <- 10

Calculate m/z value

tracer_element <- 'C'       # type of element
tracer_isotope <- '13C'     # type of isotope tracer
tracer_number <- 6          # maximum number of isotope tagger
mzs <- getMzWithTracer(formula, adduct, tracer_element, tracer_isotope, tracer_number)
targetMzRanges <- getMzRanges(mzs, resolution=resolution, ppm=ppm)

Targeted EIC generation

targetEICs <- list()
rtranges <- c(rtmin, rtmax)
for (j in seq_along(rawfiles)){
  targetEICs[[j]] <- getMzEICs(rawDataset[[j]], rtranges=rtranges, mzranges=targetMzRanges, baseline=FALSE, smooth=FALSE)
}

theoretical <- as.numeric(pattern[,2])
targetPeaks <- getIsotopicPeaks(targetEICs[[sampleInd]], SNR.Th=snr, peakScaleRange=scale, peakThr=height, theoretical=theoretical, fineness=fineness)
plotEICs(targetEICs[[sampleInd]], targetPeaks)

Display peak information

outputPeakInfo <- targetPeaks$PeakInfo
knitr::kable(outputPeakInfo, format = "markdown")

Set peak bounds manually

Set peak bounds manually to combine peak 1 and peak 2

targetRtLeft <- 900.492
targetRtRight <- 976.229
targetRtAxis <- 928.768
userInfo <- data.frame(Name='User', Position=targetRtAxis, Start=targetRtLeft, End=targetRtRight)
outputPeakInfo <- rbind(targetPeaks$PeakInfo[,1:ncol(userInfo)], userInfo)
knitr::kable(outputPeakInfo, format = "markdown")

Calculate peak area

userArea <- round(getArea(targetEICs[[sampleInd]], targetRtLeft, targetRtRight), 2)
outputPeakArea <- {
    res <- targetPeaks$PeakArea
    res <- cbind(rownames(res), res)
    res <- cbind(res, userArea, round(userArea/sum(userArea),3))
    colnames(res)[1] <- 'mzRange'
    colnames(res)[(ncol(res)-1) : ncol(res)] <- c('User','Abundance')
    res
}

knitr::kable(outputPeakArea, format = "markdown")

Alignment

alignedEICs <- getAlignedEICs(targetEICs, sampleInd, shift, segment, align=TRUE)
WhtoPlot <- 7   # plot the M+6 feature of the targeted metabolite of all samples
EicstoPlot <- lapply(alignedEICs, function(eics){
  eics[[WhtoPlot]]
})
names(EicstoPlot) <- samples
plotEICs(EicstoPlot, rtrange=c(outputPeakInfo$Start[WhtoPlot], outputPeakInfo$End[WhtoPlot]))

Display peak area of all samples

whPeak <- which(outputPeakInfo$Name == 'User')
outputSamplesArea <- getQuantifiedResult(alignedEICs, outputPeakInfo$Start[whPeak], outputPeakInfo$End[whPeak])
colnames(outputSamplesArea) <- samples
knitr::kable(outputSamplesArea, format = "markdown")

Display barplot of isotopes

plotStackBar(outputSamplesArea)

hcji/TarMet documentation built on Nov. 22, 2021, 1:07 p.m.