1 Introduction

This is a guide for using TarMet with R scripts.

2 Targeted metabolite analysis

2.1 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

2.2 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]
}

2.3 Calculate targeted m/z value

pattern <- getIsotopicPattern(formula, adduct, threshold, resolution)

##  done.
##  Calculate profiles ... done.
##  Detect centroids...  done.

mzs <- pattern[,1]
targetMzRanges <- getMzRanges(mzs, resolution=resolution, ppm=ppm)

2.4 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)

2.5 Display peak detection results

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

Name	Position	Start	End	Similarity
Peak 1	540.466	534.073	550.221	0.988

2.6 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]))

2.7 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")

	MM14_20um.mzxml	Mspos_Leaf_MM48_20uM_1-A,6_01_14611.mzdata
285.0615-285.09	649934.82	607414.37
286.0648-286.0934	110295.40	112381.49
287.0671-287.0958	15524.51	12008.88
288.0696-288.0985	1599.30	1352.85
289.0723-289.1012	72.45	91.65

2.8 Display barplot of isotopes

plotStackBar(outputSamplesArea)

3 Isotope tracer analysis

3.1 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)

3.2 Set targeted metabolite

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

ppm <- 50
scale <- 5
rtmin <- 0
rtmax <- max(rawDataset[[1]]$times)
height <- 0
snr <- 10

3.3 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)

3.4 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)

3.5 Display peak information

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

Name	Position	Start	End	Similarity
Peak 1	908.756	900.492	914.107	NA
Peak 2	928.768	914.473	976.229	NA
Peak 3	992.366	983.439	1003.931	NA

3.6 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")

Name	Position	Start	End
Peak 1	908.756	900.492	914.107
Peak 2	928.768	914.473	976.229
Peak 3	992.366	983.439	1003.931
User	928.768	900.492	976.229

3.7 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")

	mzRange	Peak 1	Peak 2	Peak 3	User	Abundance
179.0516-179.0606	179.0516-179.0606	1156033.85	4612776.52	15866939.16	5749613.18	0.059
180.055-180.064	180.055-180.064	34193.77	92606.14	884895.83	126649.13	0.001
181.0583-181.0673	181.0583-181.0673	75354.17	464183.24	201373.60	541305.15	0.006
182.0616-182.0707	182.0616-182.0707	0.00	6310.11	1989.10	6310.11	0.000
183.065-183.0741	183.065-183.0741	63231.63	373169.99	129932.96	435033.23	0.004
184.0683-184.0775	184.0683-184.0775	799966.50	3058292.11	118941.63	3872913.99	0.039
185.0716-185.0809	185.0716-185.0809	19486037.41	67679800.39	1317331.94	87534708.58	0.891

3.8 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]))

3.9 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")

	02_n1-60minutepulse1.mzML	03_n1-5minutepulse2.mzML	04_n1-60minutepulse2.mzML	08_n1-60minutepulse4.mzML	09_n1-5minutepulse5.mzML	11_n1-5minutepulse6.mzML
179.0516-179.0606	5749613.18	7690342.68	3974550.17	6972055.88	6613996.47	9918241.30
180.055-180.064	126649.13	198314.72	80314.51	155238.23	149387.68	240990.81
181.0583-181.0673	541305.15	386873.34	336773.71	367260.01	342136.57	209746.41
182.0616-182.0707	6310.11	1303.91	1561.42	3999.24	0.00	5213.95
183.065-183.0741	435033.23	292361.46	278744.00	146440.55	135493.90	115178.45
184.0683-184.0775	3872913.99	5108263.25	3169319.83	5869383.85	4884388.55	6046015.02
185.0716-185.0809	87534708.58	119785772.96	69510418.47	136390328.11	115243131.74	132121538.22

3.10 Display barplot of isotopes

plotStackBar(outputSamplesArea)

TarMetCL

2021/3/12

1 Introduction

2 Targeted metabolite analysis

2.1 Preparation

2.2 Set targeted metabolite

2.3 Calculate targeted m/z value

2.4 Targeted EIC generation

2.5 Display peak detection results

2.6 Alignment

2.7 Display peak area of all samples

2.8 Display barplot of isotopes

3 Isotope tracer analysis

3.1 Preparation

3.2 Set targeted metabolite

3.3 Calculate m/z value

3.4 Targeted EIC generation

3.5 Display peak information

3.6 Set peak bounds manually

3.7 Calculate peak area

3.8 Alignment

3.9 Display peak area of all samples

3.10 Display barplot of isotopes